Volume
LIPIcs, Volume 277
12th International Conference on Geographic Information Science (GIScience 2023)
Event
GIScience 2023, September 12-15, 2023, Leeds, UKEditors
Publication Details
- published at: 2023-09-07
- Publisher: Schloss Dagstuhl – Leibniz-Zentrum für Informatik
- ISBN: 978-3-95977-288-4
- DBLP: db/conf/giscience/giscience2023
Access Numbers
- Detailed Access Statistics available here
-
Total Document Accesses (updated on a weekly basis):
0PDF Downloads
Documents
No documents found matching your filter selection.
Document
Complete Volume
LIPIcs, Volume 277, GIScience 2023, Complete Volume
Abstract
LIPIcs, Volume 277, GIScience 2023, Complete Volume
Cite as
12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 1-740, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@Proceedings{beecham_et_al:LIPIcs.GIScience.2023,
title = {{LIPIcs, Volume 277, GIScience 2023, Complete Volume}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {1--740},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023},
URN = {urn:nbn:de:0030-drops-188945},
doi = {10.4230/LIPIcs.GIScience.2023},
annote = {Keywords: LIPIcs, Volume 277, GIScience 2023, Complete Volume}
}
Document
Front Matter
Front Matter, Table of Contents, Preface, Conference Organization
Abstract
Front Matter, Table of Contents, Preface, Conference Organization
Cite as
12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 0:i-0:xxiv, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{beecham_et_al:LIPIcs.GIScience.2023.0,
author = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
title = {{Front Matter, Table of Contents, Preface, Conference Organization}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {0:i--0:xxiv},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.0},
URN = {urn:nbn:de:0030-drops-188950},
doi = {10.4230/LIPIcs.GIScience.2023.0},
annote = {Keywords: Front Matter, Table of Contents, Preface, Conference Organization}
}
Document
Do You Need Instructions Again? Predicting Wayfinding Instruction Demand
Abstract
The demand for instructions during wayfinding, defined as the frequency of requesting instructions for each decision point, can be considered as an important indicator of the internal cognitive processes during wayfinding. This demand can be a consequence of the mental state of feeling lost, being uncertain, mind wandering, having difficulty following the route, etc. Therefore, it can be of great importance for theoretical cognitive studies on human perception of the environment. From an application perspective, this demand can be used as a measure of the effectiveness of the navigation assistance system. It is therefore worthwhile to be able to predict this demand and also to know what factors trigger it. This paper takes a step in this direction by reporting a successful prediction of instruction demand (accuracy of 78.4%) in a real-world wayfinding experiment with 45 participants, and interpreting the environmental, user, instructional, and gaze-related features that caused it.
Cite as
Negar Alinaghi, Tiffany C. K. Kwok, Peter Kiefer, and Ioannis Giannopoulos. Do You Need Instructions Again? Predicting Wayfinding Instruction Demand. In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 1:1-1:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{alinaghi_et_al:LIPIcs.GIScience.2023.1,
author = {Alinaghi, Negar and Kwok, Tiffany C. K. and Kiefer, Peter and Giannopoulos, Ioannis},
title = {{Do You Need Instructions Again? Predicting Wayfinding Instruction Demand}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {1:1--1:16},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.1},
URN = {urn:nbn:de:0030-drops-188963},
doi = {10.4230/LIPIcs.GIScience.2023.1},
annote = {Keywords: Wayfinding, Navigation Instructions, Urban Computing, Gaze Analysis}
}
Document
Transitions in Dynamic Point Labeling
Abstract
The labeling of point features on a map is a well-studied topic. In a static setting, the goal is to find a non-overlapping label placement for (a subset of) point features. In a dynamic setting, the set of point features and their corresponding labels change, and the labeling has to adapt to such changes. To aid the user in tracking these changes, we can use morphs, here called transitions, to indicate how a labeling changes. Such transitions have not gained much attention yet, and we investigate different types of transitions for labelings of points, most notably consecutive transitions and simultaneous transitions. We give (tight) bounds on the number of overlaps that can occur during these transitions. When each label has a (non-negative) weight associated to it, and each overlap imposes a penalty proportional to the weight of the overlapping labels, we show that it is NP-complete to decide whether the penalty during a simultaneous transition has weight at most k. Finally, in a case study, we consider geotagged Twitter data on a map, by labeling points with rectangular labels showing tweets. We developed a prototype implementation to evaluate different transition styles in practice, measuring both number of overlaps and transition duration.
Cite as
Thomas Depian, Guangping Li, Martin Nöllenburg, and Jules Wulms. Transitions in Dynamic Point Labeling. In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 2:1-2:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{depian_et_al:LIPIcs.GIScience.2023.2,
author = {Depian, Thomas and Li, Guangping and N\"{o}llenburg, Martin and Wulms, Jules},
title = {{Transitions in Dynamic Point Labeling}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {2:1--2:19},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.2},
URN = {urn:nbn:de:0030-drops-188971},
doi = {10.4230/LIPIcs.GIScience.2023.2},
annote = {Keywords: Dynamic labels, Label overlaps, Morphs, NP-completeness, Case study}
}
Document
Reducing False Discoveries in Statistically-Significant Regional-Colocation Mining: A Summary of Results
Abstract
Given a set S of spatial feature types, its feature instances, a study area, and a neighbor relationship, the goal is to find pairs <a region (r_{g}), a subset C of S> such that C is a statistically significant regional-colocation pattern in r_{g}. This problem is important for applications in various domains including ecology, economics, and sociology. The problem is computationally challenging due to the exponential number of regional colocation patterns and candidate regions. Previously, we proposed a miner [Subhankar et. al, 2022] that finds statistically significant regional colocation patterns. However, the numerous simultaneous statistical inferences raise the risk of false discoveries (also known as the multiple comparisons problem) and carry a high computational cost. We propose a novel algorithm, namely, multiple comparisons regional colocation miner (MultComp-RCM) which uses a Bonferroni correction. Theoretical analysis, experimental evaluation, and case study results show that the proposed method reduces both the false discovery rate and computational cost.
Cite as
Subhankar Ghosh, Jayant Gupta, Arun Sharma, Shuai An, and Shashi Shekhar. Reducing False Discoveries in Statistically-Significant Regional-Colocation Mining: A Summary of Results. In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 3:1-3:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{ghosh_et_al:LIPIcs.GIScience.2023.3,
author = {Ghosh, Subhankar and Gupta, Jayant and Sharma, Arun and An, Shuai and Shekhar, Shashi},
title = {{Reducing False Discoveries in Statistically-Significant Regional-Colocation Mining: A Summary of Results}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {3:1--3:18},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.3},
URN = {urn:nbn:de:0030-drops-188985},
doi = {10.4230/LIPIcs.GIScience.2023.3},
annote = {Keywords: Colocation pattern, Participation index, Multiple comparisons problem, Spatial heterogeneity, Statistical significance}
}
Document
Genetic Programming for Computationally Efficient Land Use Allocation Optimization
Abstract
Land use allocation optimization is essential to identify ideal landscape compositions for the future. However, due to the solution encoding, standard land use allocation algorithms cannot cope with large land use allocation problems. Solutions are encoded as sequences of elements, in which each element represents a land unit or a group of land units. As a consequence, computation times increase with every additional land unit. We present an alternative solution encoding: functions describing a variable in space. Function encoding yields the potential to evolve solutions detached from individual land units and evolve fields representing the landscape as a single object. In this study, we use a genetic programming algorithm to evolve functions representing continuous fields, which we then map to nominal land use maps. We compare the scalability of the new approach with the scalability of two state-of-the-art algorithms with standard encoding. We perform the benchmark on one raster and one vector land use allocation problem with multiple objectives and constraints, with ten problem sizes each. The results prove that the run times increase exponentially with the problem size for standard encoding schemes, while the increase is linear with genetic programming. Genetic programming was up to 722 times faster than the benchmark algorithm. The improvement in computation time does not reduce the algorithm performance in finding optimal solutions; often, it even increases. We conclude that evolving functions enables more efficient land use allocation planning and yields much potential for other spatial optimization applications.
Cite as
Moritz J. Hildemann, Alan T. Murray, and Judith A. Verstegen. Genetic Programming for Computationally Efficient Land Use Allocation Optimization. In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 4:1-4:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{hildemann_et_al:LIPIcs.GIScience.2023.4,
author = {Hildemann, Moritz J. and Murray, Alan T. and Verstegen, Judith A.},
title = {{Genetic Programming for Computationally Efficient Land Use Allocation Optimization}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {4:1--4:15},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.4},
URN = {urn:nbn:de:0030-drops-188996},
doi = {10.4230/LIPIcs.GIScience.2023.4},
annote = {Keywords: Land use planning, Spatial optimization, Solution encoding, Computation time reduction}
}
Document
Visualizing Geophylogenies - Internal and External Labeling with Phylogenetic Tree Constraints
Abstract
A geophylogeny is a phylogenetic tree where each leaf (biological taxon) has an associated geographic location (site). To clearly visualize a geophylogeny, the tree is typically represented as a crossing-free drawing next to a map. The correspondence between the taxa and the sites is either shown with matching labels on the map (internal labeling) or with leaders that connect each site to the corresponding leaf of the tree (external labeling). In both cases, a good order of the leaves is paramount for understanding the association between sites and taxa. We define several quality measures for internal labeling and give an efficient algorithm for optimizing them. In contrast, minimizing the number of leader crossings in an external labeling is NP-hard. We show nonetheless that optimal solutions can be found in a matter of seconds on realistic instances using integer linear programming. Finally, we provide several efficient heuristic algorithms and experimentally show them to be near optimal on real-world and synthetic instances.
Cite as
Jonathan Klawitter, Felix Klesen, Joris Y. Scholl, Thomas C. van Dijk, and Alexander Zaft. Visualizing Geophylogenies - Internal and External Labeling with Phylogenetic Tree Constraints. In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 5:1-5:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{klawitter_et_al:LIPIcs.GIScience.2023.5,
author = {Klawitter, Jonathan and Klesen, Felix and Scholl, Joris Y. and van Dijk, Thomas C. and Zaft, Alexander},
title = {{Visualizing Geophylogenies - Internal and External Labeling with Phylogenetic Tree Constraints}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {5:1--5:16},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.5},
URN = {urn:nbn:de:0030-drops-189004},
doi = {10.4230/LIPIcs.GIScience.2023.5},
annote = {Keywords: geophylogeny, boundary labeling, external labeling, algorithms}
}
Document
Map Reproducibility in Geoscientific Publications: An Exploratory Study
Abstract
Reproducibility is a core element of the scientific method. In the Geosciences, the insights derived from geodata are frequently communicated through maps, and the computational methods to create these maps vary in their ease of reproduction. In this paper, we present the results from a study where we tried to reproduce the maps included in geoscientific publications. Following a systematic approach, we collected 27 candidate papers and in four cases, we were able to successfully reproduce the maps they contained. We report on the approach we applied, the issues we encountered and the insights we gained while attempting to reproduce the maps. In addition, we provide an initial set of criteria to assess the success of a map reproduction attempt. We also propose some guidelines for improving map reproducibility in geoscientific publications. Our work sheds a light on the current state of map reproducibility in geoscientific papers and can benefit researchers interested in publishing maps in a more reproducible way.
Cite as
Eftychia Koukouraki and Christian Kray. Map Reproducibility in Geoscientific Publications: An Exploratory Study. In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 6:1-6:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{koukouraki_et_al:LIPIcs.GIScience.2023.6,
author = {Koukouraki, Eftychia and Kray, Christian},
title = {{Map Reproducibility in Geoscientific Publications: An Exploratory Study}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {6:1--6:16},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.6},
URN = {urn:nbn:de:0030-drops-189015},
doi = {10.4230/LIPIcs.GIScience.2023.6},
annote = {Keywords: Reproducible Research, Reproduction Assessment, Map Making, Cartography}
}
Document
Semi-Supervised Learning from Street-View Images and OpenStreetMap for Automatic Building Height Estimation
Abstract
Accurate building height estimation is key to the automatic derivation of 3D city models from emerging big geospatial data, including Volunteered Geographical Information (VGI). However, an automatic solution for large-scale building height estimation based on low-cost VGI data is currently missing. The fast development of VGI data platforms, especially OpenStreetMap (OSM) and crowdsourced street-view images (SVI), offers a stimulating opportunity to fill this research gap. In this work, we propose a semi-supervised learning (SSL) method of automatically estimating building height from Mapillary SVI and OSM data to generate low-cost and open-source 3D city modeling in LoD1. The proposed method consists of three parts: first, we propose an SSL schema with the option of setting a different ratio of "pseudo label" during the supervised regression; second, we extract multi-level morphometric features from OSM data (i.e., buildings and streets) for the purposed of inferring building height; last, we design a building floor estimation workflow with a pre-trained facade object detection network to generate "pseudo label" from SVI and assign it to the corresponding OSM building footprint. In a case study, we validate the proposed SSL method in the city of Heidelberg, Germany and evaluate the model performance against the reference data of building heights. Based on three different regression models, namely Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN), the SSL method leads to a clear performance boosting in estimating building heights with a Mean Absolute Error (MAE) around 2.1 meters, which is competitive to state-of-the-art approaches. The preliminary result is promising and motivates our future work in scaling up the proposed method based on low-cost VGI data, with possibilities in even regions and areas with diverse data quality and availability.
Cite as
Hao Li, Zhendong Yuan, Gabriel Dax, Gefei Kong, Hongchao Fan, Alexander Zipf, and Martin Werner. Semi-Supervised Learning from Street-View Images and OpenStreetMap for Automatic Building Height Estimation. In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 7:1-7:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{li_et_al:LIPIcs.GIScience.2023.7,
author = {Li, Hao and Yuan, Zhendong and Dax, Gabriel and Kong, Gefei and Fan, Hongchao and Zipf, Alexander and Werner, Martin},
title = {{Semi-Supervised Learning from Street-View Images and OpenStreetMap for Automatic Building Height Estimation}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {7:1--7:15},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.7},
URN = {urn:nbn:de:0030-drops-189028},
doi = {10.4230/LIPIcs.GIScience.2023.7},
annote = {Keywords: OpenStreetMap, Street-view Images, VGI, GeoAI, 3D city model, Facade parsing}
}
Document
Towards a Multidimensional Interaction Framework for Promoting Public Engagement in Citizen Science Projects
Abstract
Citizen science (CS) projects are expanding into various fields and the number of CS applications is expanding. Despite this growth, engaging the public and sustaining their participation remains a challenge. Some studies have proposed that interacting with participants is an effective way to sustain their participation. This paper introduces a framework that outlines complementary levels of interaction including basic, incentivized, user-centered and action-oriented interactions. The interaction levels range from basic acknowledgments to instructions for taking action. The integration of these interactions within the spatial, temporal, and thematic dimensions is also discussed. The proposed framework is applied to a biodiversity CS project that involves different types of real-time feedback to participants based on the location, time, and image of the species observations. Location-based feedback is based on the species distribution models, and provides information on the probability of observing a certain species in a given location, as well as suggestions on the species to be observed in the participant’s vicinity. Overall, the multi-dimensional interaction framework provides CS practitioners with insights into the various ways they can maintain communication with participants, whether through real-time machine-generated interactions or interactions between the project team and participants.
Cite as
Maryam Lotfian, Jens Ingensand, and Christophe Claramunt. Towards a Multidimensional Interaction Framework for Promoting Public Engagement in Citizen Science Projects. In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 8:1-8:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{lotfian_et_al:LIPIcs.GIScience.2023.8,
author = {Lotfian, Maryam and Ingensand, Jens and Claramunt, Christophe},
title = {{Towards a Multidimensional Interaction Framework for Promoting Public Engagement in Citizen Science Projects}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {8:1--8:16},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.8},
URN = {urn:nbn:de:0030-drops-189030},
doi = {10.4230/LIPIcs.GIScience.2023.8},
annote = {Keywords: Citizen Science, Multidimensional Interaction, Participation, User-centered Feedback, Machine Learning, Biodiversity}
}
Document
Platial k-Anonymity: Improving Location Anonymity Through Temporal Popularity Signatures
Abstract
While it is increasingly necessary in today’s digital society, sharing personal location information comes at a cost. Sharing one’s precise place of interest, e.g., Compass Coffee, enables a range of location-based services, but substantially reduces the individual’s privacy. Methods have been developed to obfuscate and anonymize location data while still maintaining a degree of utility. One such approach, spatial k-anonymity, aims to ensure an individual’s level of anonymity by reporting their location as a set of k potential locations rather than their actual location alone. Larger values of k increase spatial anonymity while decreasing the utility of the location information. Typical examples of spatial k-anonymized datasets present elements as simple geographic points with no attributes or contextual information. In this work, we demonstrate that the addition of publicly available contextual data can significantly reduce the anonymity of a k-anonymized dataset. Through the analysis of place type temporal visitation patterns, hours of operation, and popularity values, one’s anonymity can be decreased by more than 50 percent. We propose a platial k-anonymity approach that leverages a combination of temporal popularity signatures and reports the amount that k must increase in order to maintain a certain level of anonymity. Finally, a method for reporting platial k-anonymous regions is presented and the implications of our methods are discussed.
Cite as
Grant McKenzie and Hongyu Zhang. Platial k-Anonymity: Improving Location Anonymity Through Temporal Popularity Signatures. In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 9:1-9:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{mckenzie_et_al:LIPIcs.GIScience.2023.9,
author = {McKenzie, Grant and Zhang, Hongyu},
title = {{Platial k-Anonymity: Improving Location Anonymity Through Temporal Popularity Signatures}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {9:1--9:15},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.9},
URN = {urn:nbn:de:0030-drops-189045},
doi = {10.4230/LIPIcs.GIScience.2023.9},
annote = {Keywords: location anonymity, location privacy, geoprivacy, place, temporal, geosocial}
}
Document
Data-Spatial Layouts for Grid Maps
Abstract
Grid maps are a well-known technique to visualize data associated with spatial regions. A grid map assigns each region to a tile in a grid (often orthogonal or hexagonal) and then represents the associated data values within this tile. Good grid maps represent the underlying geographic space well: regions that are geographically close are close in the grid map and vice versa.
Though Tobler’s law suggests that spatial proximity relates to data similarity, local variations may obscure clusters and patterns in the data. For example, there are often clear differences between urban centers and adjacent rural areas with respect to socio-economic indicators. To get a better view of the data distribution, we propose grid-map layouts that take data values into account and place regions with similar data into close proximity. In the limit, such a data layout is essentially a chart and loses all spatial meaning.
We present an algorithm to create hybrid layouts, allowing for trade-offs between data values and geographic space when assigning regions to tiles. Our algorithm also handles hierarchical grid maps and allows us to focus either on data or on geographic space on different levels of the hierarchy. Leveraging our algorithm we explore the design space of (hierarchical) grid maps with a hybrid layout and their semantics.
Cite as
Nathan van Beusekom, Wouter Meulemans, Bettina Speckmann, and Jo Wood. Data-Spatial Layouts for Grid Maps. In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 10:1-10:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{vanbeusekom_et_al:LIPIcs.GIScience.2023.10,
author = {van Beusekom, Nathan and Meulemans, Wouter and Speckmann, Bettina and Wood, Jo},
title = {{Data-Spatial Layouts for Grid Maps}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {10:1--10:17},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.10},
URN = {urn:nbn:de:0030-drops-189052},
doi = {10.4230/LIPIcs.GIScience.2023.10},
annote = {Keywords: Grid map, algorithms, trade-offs}
}
Document
Benchmarking Regression Models Under Spatial Heterogeneity
Abstract
Machine learning methods have recently found much application on spatial data, for example in weather forecasting, traffic prediction, and soil analysis. At the same time, methods from spatial statistics were developed over the past decades to explicitly account for spatial structuring in analytical and inference tasks. In the light of this duality of having both types of methods available, we explore the following question: Under what circumstances are local, spatially-explicit models preferable over machine learning models that do not incorporate spatial structure explicitly in their specification? Local models are typically used to capture spatial non-stationarity. Thus, we study the effect of strength and type of spatial heterogeneity, which may originate from non-stationarity of a process itself or from heterogeneous noise, on the performance of different linear and non-linear, local and global machine learning and regression models. The results suggest that it is necessary to assess the performance of linear local models on an independent hold-out dataset, since models may overfit under certain conditions. We further show that local models are advantageous in settings with small sample size and high degrees of spatial heterogeneity. Our findings allow deriving model selection criteria, which are validated in benchmarking experiments on five well-known spatial datasets.
Cite as
Nina Wiedemann, Henry Martin, and René Westerholt. Benchmarking Regression Models Under Spatial Heterogeneity. In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 11:1-11:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{wiedemann_et_al:LIPIcs.GIScience.2023.11,
author = {Wiedemann, Nina and Martin, Henry and Westerholt, Ren\'{e}},
title = {{Benchmarking Regression Models Under Spatial Heterogeneity}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {11:1--11:15},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.11},
URN = {urn:nbn:de:0030-drops-189064},
doi = {10.4230/LIPIcs.GIScience.2023.11},
annote = {Keywords: spatial machine learning, spatial non-stationarity, Geographically Weighted Regression, local models, geostatistics}
}
Document
Short Paper
Confidential, Decentralized Location-Based Data Services (Short Paper)
Abstract
There are many privacy risks when location data is collected and aggregated. We introduce the notion of using confidential smart contracts for building location-based decentralized applications that are privacy preserving. We describe a spatial library for smart contracts that run on Secret Network, a blockchain network that runs smart contracts in secure enclaves running in trusted execution environments. The library supports not only basic geometric operations but also cloaking and differential privacy mechanisms applied to spatial data stored in the contract.
Cite as
Benjamin Adams. Confidential, Decentralized Location-Based Data Services (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 12:1-12:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{adams:LIPIcs.GIScience.2023.12,
author = {Adams, Benjamin},
title = {{Confidential, Decentralized Location-Based Data Services}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {12:1--12:6},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.12},
URN = {urn:nbn:de:0030-drops-189078},
doi = {10.4230/LIPIcs.GIScience.2023.12},
annote = {Keywords: spatial data, privacy, smart contract, differential privacy}
}
Document
Short Paper
Towards an Inclusive Urban Environment: A Participatory Approach for Collecting Spatial Accessibility Data in Zurich (Short Paper)
Abstract
The unprecedented rate of urbanization, along with the increase in the aging and disabled populations, bring about an increasing demand for public services and an inclusive urban environment that allows easy access to those facilities. Spatial Accessibility is a measure to assess how inclusive a city is and how easily public facilities can be reached from a specific location through movement in physical space or built environment.
A detailed geodata source of accessibility features is needed for reliable spatial accessibility assessment, such as sidewalk width, surface type, and incline. However, such data are not readily available due to the huge implication costs. Remote crowdsourcing data collection using Street View Imagery, so-called 'virtual audits' have been introduced as a valid, cost-efficient tool for accessibility data enrichment at scales compared to conventional methods because it enables involving more participants, saving more time by avoiding field visits and covering a larger area.
Therefore, in our pilot project, ZuriACT: Zurich Accessible CiTy, with the help of digital tools that allow for virtual inspections and measurements of accessibility features, we want to contribute to collecting and enriching accessibility information in the city of Zurich embedded in a citizen science project that will have both scientific and social impacts.
With the help of additional accessibility data produced in this project, the issues of an inclusive urban environment can be demonstrated by mapping the potential spatial inequalities in access to public facilities for disabled or restricted people in terms of mobility. Thus, this project provides helpful insight into implementing policy interventions for overcoming accessibility biases to ensure equitable services, particularly for people with disabilities, and contributes to creating an inclusive and sustainable urban environment. It goes without saying that an inclusive city is beneficial and impacts the quality of life of not only the population groups mentioned above but also the society at large.
Cite as
Hoda Allahbakhshi. Towards an Inclusive Urban Environment: A Participatory Approach for Collecting Spatial Accessibility Data in Zurich (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 13:1-13:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{allahbakhshi:LIPIcs.GIScience.2023.13,
author = {Allahbakhshi, Hoda},
title = {{Towards an Inclusive Urban Environment: A Participatory Approach for Collecting Spatial Accessibility Data in Zurich}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {13:1--13:6},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.13},
URN = {urn:nbn:de:0030-drops-189086},
doi = {10.4230/LIPIcs.GIScience.2023.13},
annote = {Keywords: Spatial accessibility, virtual audits, digital tools, mobility disability, citizen science, inclusive city, Zurich}
}
Document
Short Paper
Development of a Semantic Segmentation Approach to Old-Map Comparison (Short Paper)
Abstract
This paper describes an innovative computational approach for comparing old maps. Maps older than 20 years remain a vast treasure of geographic information in many parts of the world with potential applications in many environmental and social analyses, e.g., establishing road construction over the past 80 years or identifying settlement growth since the middle ages. Semantic segmentation has developed into a viable computational method for analysing old maps from previous centuries. It allows for the discrete identification of elements, e.g., lakes, forests, and roads, from cartographic sources and their computational modelling. Semantic segmentation uses convolutional neural networks to extract elements. With this technique, we create a computational approach to compare old maps systematically and efficiently.
Cite as
Yves Annanias, Daniel Wiegreffe, Andreas Niekler, Marta Kuźma, and Francis Harvey. Development of a Semantic Segmentation Approach to Old-Map Comparison (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 14:1-14:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{annanias_et_al:LIPIcs.GIScience.2023.14,
author = {Annanias, Yves and Wiegreffe, Daniel and Niekler, Andreas and Ku\'{z}ma, Marta and Harvey, Francis},
title = {{Development of a Semantic Segmentation Approach to Old-Map Comparison}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {14:1--14:6},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.14},
URN = {urn:nbn:de:0030-drops-189099},
doi = {10.4230/LIPIcs.GIScience.2023.14},
annote = {Keywords: Geographic/Geospatial Visualization, Visual Knowledge Discovery, Cartographic Analysis}
}
Document
Short Paper
Why Is Greenwich so Common? Quantifying the Uniqueness of Multivariate Observations (Short Paper)
Abstract
The concept of uniqueness can play an important role when the assessment of an observation’s distinctiveness is essential. This article introduces a distance-based uniqueness measure that quantifies the relative rarity or commonness of a multi-variate observation within a dataset. Unique observations exhibit rare combinations of values, and not necessarily extreme values. Taking a cognitive psychological perspective, our measure defines uniqueness as the sum of distances between a target observation and all other observations. After presenting the measure u and its corresponding standardised version u_z, we propose a method to calculate a p value through a probability density function. We then demonstrate the measure’s behaviour in a case study on the uniqueness of Greater London boroughs, based on real-world socioeconomic variables. This initial investigation indicates that u can support exploratory data analysis.
Cite as
Andrea Ballatore and Stefano Cavazzi. Why Is Greenwich so Common? Quantifying the Uniqueness of Multivariate Observations (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 15:1-15:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{ballatore_et_al:LIPIcs.GIScience.2023.15,
author = {Ballatore, Andrea and Cavazzi, Stefano},
title = {{Why Is Greenwich so Common? Quantifying the Uniqueness of Multivariate Observations}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {15:1--15:6},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.15},
URN = {urn:nbn:de:0030-drops-189109},
doi = {10.4230/LIPIcs.GIScience.2023.15},
annote = {Keywords: uniqueness, distinctiveness, similarity, outlier detection, multivariate data}
}
Document
Short Paper
When Everything Is "Nearby": How Airbnb Listings in New York City Exaggerate Proximity (Short Paper)
Abstract
In recent years, the emergence and rapid growth of short-term rental (STR) markets has exerted considerable influence on real estate in most large cities across the world. Central location and transit access are two primary factors associated with the prevalence and expansion of STRs, including Airbnbs. Nevertheless, perhaps due to methodological challenges, no research has addressed how location and proximity are represented in the titles and descriptions of STRs. In this paper, we introduce a new methodological pipeline to extract spatial relations from text and show that expressions of distance in STR listings can indeed be quantified and measured against real-world distances. We then comparatively analyze Airbnb reviews (written by guests) and listings (written by hosts) from New York City in order to demonstrate systematically how listings exaggerate proximity compared to reviews. Moreover, we discover spatial patterns to these differences that warrant further investigation.
Cite as
Mikael Brunila, Priyanka Verma, and Grant McKenzie. When Everything Is "Nearby": How Airbnb Listings in New York City Exaggerate Proximity (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 16:1-16:8, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{brunila_et_al:LIPIcs.GIScience.2023.16,
author = {Brunila, Mikael and Verma, Priyanka and McKenzie, Grant},
title = {{When Everything Is "Nearby": How Airbnb Listings in New York City Exaggerate Proximity}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {16:1--16:8},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.16},
URN = {urn:nbn:de:0030-drops-189117},
doi = {10.4230/LIPIcs.GIScience.2023.16},
annote = {Keywords: spatial proximity, distance estimation, information extraction, named entity recognition, short-term rentals}
}
Document
Short Paper
Smarter Than Your Average Model - Bayesian Model Averaging as a Spatial Analysis Tool (Short Paper)
Abstract
Bayesian modelling averaging (BMA) allows the results of analysing competing data models to be combined, and the relative plausibility of the models to be assessed. Here, the potential to apply this approach to spatial statistical models is considered, using an example of spatially varying coefficient modelling applied to data from the 2016 UK referendum on leaving the EU.
Cite as
Chris Brunsdon, Paul Harris, and Alexis Comber. Smarter Than Your Average Model - Bayesian Model Averaging as a Spatial Analysis Tool (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 17:1-17:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{brunsdon_et_al:LIPIcs.GIScience.2023.17,
author = {Brunsdon, Chris and Harris, Paul and Comber, Alexis},
title = {{Smarter Than Your Average Model - Bayesian Model Averaging as a Spatial Analysis Tool}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {17:1--17:6},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.17},
URN = {urn:nbn:de:0030-drops-189123},
doi = {10.4230/LIPIcs.GIScience.2023.17},
annote = {Keywords: Bayesian, Varying coefficient regression, Spatial statistics}
}
Document
Short Paper
Anonymous Routing Using Minimum Capacity Clustering (Short Paper)
Abstract
We present a framework which allows one to use an online routing service and get live updates without revealing the sensitive starting and ending points of one’s route. For that, we obfuscate the starting and ending locations in minimum capacity clusters and reveal only the route between these clusters. We compare different anonymous clustering strategies on positions in the network with efficient approximations and analyse the impact of the anonymisation on the route. We experimentally evaluate the effect of the anonymisation scheme in real-world settings.
Cite as
Maike Buchin and Lukas Plätz. Anonymous Routing Using Minimum Capacity Clustering (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 18:1-18:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{buchin_et_al:LIPIcs.GIScience.2023.18,
author = {Buchin, Maike and Pl\"{a}tz, Lukas},
title = {{Anonymous Routing Using Minimum Capacity Clustering}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {18:1--18:6},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.18},
URN = {urn:nbn:de:0030-drops-189131},
doi = {10.4230/LIPIcs.GIScience.2023.18},
annote = {Keywords: Anonymity, approximation Algorithms, directed Networks, minimum capacity Clustering, Privacy}
}
Document
Short Paper
Achieving Least Relocation of Existing Facilities in Spatial Optimisation: A Bi-Objective Model (Short Paper)
Abstract
Spatial optimisation models have been widely used to support locational decision making of public service systems (e.g. hospitals, fire stations), such as selecting the optimal locations to maximise the coverage. These service systems are generally the product of long-term evolution, and there usually are existing facilities in the system. These existing facilities should not be neglected or relocated without careful consideration as they have financial or management implications. However, spatial optimisation models that account for the relocation or maintenance of existing facilities are understudied. In this study, we revisit a planning scenario where two objectives are adopted, including the minimum number of sites selected and the least relocation of existing facilities. We propose and discuss three different approaches that can achieve these two objectives. This model and the three approaches are applied to two case studies of optimising the retail stores in San Francisco and the large-scale COVID-19 vaccination network in England. The implications of this model and the efficiency of these approaches are discussed.
Cite as
Huanfa Chen and Rongbo Xu. Achieving Least Relocation of Existing Facilities in Spatial Optimisation: A Bi-Objective Model (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 19:1-19:5, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{chen_et_al:LIPIcs.GIScience.2023.19,
author = {Chen, Huanfa and Xu, Rongbo},
title = {{Achieving Least Relocation of Existing Facilities in Spatial Optimisation: A Bi-Objective Model}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {19:1--19:5},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.19},
URN = {urn:nbn:de:0030-drops-189144},
doi = {10.4230/LIPIcs.GIScience.2023.19},
annote = {Keywords: spatial optimisation, location set cover problem, multiple objective}
}
Document
Short Paper
Exploring Energy Deprivation Across Small Areas in England and Wales (Short Paper)
Abstract
Building on a growing field of research on vulnerability to energy poverty, this study focused on addressing the rising energy crisis by examining the issue of energy deprivation in local areas of England and Wales. We developed a classification for energy deprivation using a clustering method to group multiple indicators across various domains. By doing this, we identify spatial disparities of energy deprivation for people living in different neighbourhoods, aiming to provide valuable insights for governments, charities and stakeholders and inform policy making and intervention.
Cite as
Meixu Chen, Alex Singleton, and Caitlin Robinson. Exploring Energy Deprivation Across Small Areas in England and Wales (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 20:1-20:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{chen_et_al:LIPIcs.GIScience.2023.20,
author = {Chen, Meixu and Singleton, Alex and Robinson, Caitlin},
title = {{Exploring Energy Deprivation Across Small Areas in England and Wales}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {20:1--20:6},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.20},
URN = {urn:nbn:de:0030-drops-189159},
doi = {10.4230/LIPIcs.GIScience.2023.20},
annote = {Keywords: energy deprivation, spatial inequality, vulnerability, geodemographics}
}
Document
Short Paper
Using the Dynamic Microsimulation MINOS to Evidence the Effect of Energy Crisis Income Support Policy (Short Paper)
Abstract
Rates of anxiety and depression are increasing due to financial stress caused by energy pricing with over half of UK homes unable to afford comfortable heating. UK Government policies to address this energy crisis have been implemented with limited evidence and substantial criticism. This paper applies the dynamic microsimulation MINOS, which utilises longitudinal Understanding Society data, to evidence change in mental well-being under the Energy Price Cap Guarantee and Energy Bill Support Scheme Policies. Results demonstrate an overall improvement in Short Form 12 Mental Component Score (SF12-MCS) both on aggregate and over data zone spatial areas for the Glasgow City region compared with a baseline of no policy intervention. This is work in progress and discussion highlights potential future work in other energy policy areas, such as Net Zero.
Cite as
Robert Clay, Luke Archer, Alison Heppenstall, and Nik Lomax. Using the Dynamic Microsimulation MINOS to Evidence the Effect of Energy Crisis Income Support Policy (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 21:1-21:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{clay_et_al:LIPIcs.GIScience.2023.21,
author = {Clay, Robert and Archer, Luke and Heppenstall, Alison and Lomax, Nik},
title = {{Using the Dynamic Microsimulation MINOS to Evidence the Effect of Energy Crisis Income Support Policy}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {21:1--21:6},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.21},
URN = {urn:nbn:de:0030-drops-189160},
doi = {10.4230/LIPIcs.GIScience.2023.21},
annote = {Keywords: Dynamic Microsimulation, Mental Health, Energy Poverty}
}
Document
Short Paper
Multiscale Spatially and Temporally Varying Coefficient Modelling Using a Geographic and Temporal Gaussian Process GAM (GTGP-GAM) (Short Paper)
Abstract
The paper develops a novel approach to spatially and temporally varying coefficient (STVC) modelling, using Generalised Additive Models (GAMs) with Gaussian Process (GP) splines parameterised with location and time variables - a Geographic and Temporal Gaussian Process GAM (GTGP-GAM). This was applied to a Mongolian livestock case study and different forms of GTGP splines were evaluated in which space and time were combined or treated separately. A single 3-D spline with rescaled temporal and spatial attributes resulted in the best model under an assumption that for spatial and temporal processes interact a case studies with a sufficiently large spatial extent is needed. A fully tuned model was then created and the spline smoothing parameters were shown to indicate the degree of variation in covariate spatio-temporal interactions with the target variable.
Cite as
Alexis Comber, Paul Harris, and Chris Brunsdon. Multiscale Spatially and Temporally Varying Coefficient Modelling Using a Geographic and Temporal Gaussian Process GAM (GTGP-GAM) (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 22:1-22:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{comber_et_al:LIPIcs.GIScience.2023.22,
author = {Comber, Alexis and Harris, Paul and Brunsdon, Chris},
title = {{Multiscale Spatially and Temporally Varying Coefficient Modelling Using a Geographic and Temporal Gaussian Process GAM (GTGP-GAM)}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {22:1--22:6},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.22},
URN = {urn:nbn:de:0030-drops-189173},
doi = {10.4230/LIPIcs.GIScience.2023.22},
annote = {Keywords: Spatial Analysis, Spatiotemproal Analysis}
}
Document
Short Paper
Does Generalisation Matters in Pan-Scalar Maps? (Short Paper)
Abstract
Maps and their usage have widely evolved recently, to become more and more interactive, multi-scale and accessible. However, the design of maps did not change so much, leading to the following two problems: (1) in theory, it is not formalised how to create a good map in this context, (2) in practice, the most used maps are not good considering the quality criteria defined for the classical (static) maps. Therefore, it is necessary to question the usefulness of these principles in this new context. In this article, we focus on the role of cartographic generalisation in maps where one can easily zoom in and out to make information accessible. We draw up a list of hypotheses on the role of generalisation for pan-scalar maps, based on both a deductive approach (the role of map generalisation is deduced from a review of human-maps interactions), and an inductive approach (observation of maps with diverse qualities). Then, we discuss how these hypotheses might be experimentally verified.
Cite as
Azelle Courtial and Guillaume Touya. Does Generalisation Matters in Pan-Scalar Maps? (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 23:1-23:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{courtial_et_al:LIPIcs.GIScience.2023.23,
author = {Courtial, Azelle and Touya, Guillaume},
title = {{Does Generalisation Matters in Pan-Scalar Maps?}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {23:1--23:6},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.23},
URN = {urn:nbn:de:0030-drops-189181},
doi = {10.4230/LIPIcs.GIScience.2023.23},
annote = {Keywords: map generalisation, cartography, pan-scalar map, multi-scale map, spatial cognition}
}
Document
Short Paper
Understanding People’s Perceptions of Their Liveable Neighbourhoods: A Case Study of East Bristol (Short Paper)
Abstract
Liveable neighbourhoods are urban planning initiatives that aim to improve the quality of residential areas. In this paper, we focus on the East Bristol Liveable Neighbourhood (EBLN) to understand people’s perceptions of their neighbourhood’s urban reality. We analyse the opinions of citizens collected through the project, by examining their sentiments, the urban subjects they consider, and the language used to express their opinions. The findings of this study offer initial indications to inform urban planning processes and facilitate effective decision-making.
Cite as
Elisa Covato and Shelan Jeawak. Understanding People’s Perceptions of Their Liveable Neighbourhoods: A Case Study of East Bristol (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 24:1-24:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{covato_et_al:LIPIcs.GIScience.2023.24,
author = {Covato, Elisa and Jeawak, Shelan},
title = {{Understanding People’s Perceptions of Their Liveable Neighbourhoods: A Case Study of East Bristol}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {24:1--24:6},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.24},
URN = {urn:nbn:de:0030-drops-189194},
doi = {10.4230/LIPIcs.GIScience.2023.24},
annote = {Keywords: Urban analytics, liveable neighbourhoods, public participation geographic information system, citizen co-design, spatio-textual data, sentiment analysis, language analysis}
}
Document
Short Paper
Building Alternative Indices of Socioeconomic Status for Population Modeling in Data-Sparse Contexts (Short Paper)
Abstract
Population modeling requires clear definitions of socioeconomic status (SES) to ensure overall estimate accuracy and locate potentially underserved subpopulations. This presents a challenge as SES can be measured in myriad ways and for divergent purposes, and the data required to calculate these metrics may be lacking, particularly in low and middle income countries (LMICs). To support more refined SES measurement, we explore improvements upon the Demographic and Health Survey’s (DHS) Wealth Index (DHS-WI) using alternative characterizations of SES based on multiple correspondence analysis (MCA) and hierarchical clustering. We produce the MCA-derived metrics first on a full suite of household economic, demographic, and dwelling variables, then on a reduced set of occupant-only SES characteristics. We explore the utility of these metrics relative to DHS-WI based on their ability to 1) differentiate DHS household types and 2) identify mixtures of SES levels within DHS samples and mapped at high spatial resolution. We find that our full suite MCA yields more clearly defined SES segments and that our reduced MCA delineates occupant SES most clearly, suggesting potential pathways to improve upon the DHS-WI in future population modeling efforts for LMICs.
Cite as
Angela R. Cunningham, Joseph V. Tuccillo, and Tyler J. Frazier. Building Alternative Indices of Socioeconomic Status for Population Modeling in Data-Sparse Contexts (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 25:1-25:7, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{cunningham_et_al:LIPIcs.GIScience.2023.25,
author = {Cunningham, Angela R. and Tuccillo, Joseph V. and Frazier, Tyler J.},
title = {{Building Alternative Indices of Socioeconomic Status for Population Modeling in Data-Sparse Contexts}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {25:1--25:7},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.25},
URN = {urn:nbn:de:0030-drops-189204},
doi = {10.4230/LIPIcs.GIScience.2023.25},
annote = {Keywords: Demographic and Health Survey, multiple correspondence analysis, population modeling, socioeconomic status, spatial statistics}
}
Document
Short Paper
Uncertainty in Causal Neighborhood Effects: A Multi-Agent Simulation Approach (Short Paper)
Abstract
Interaction between individuals within an environment can result in complex patterns that a statistical analysis is unable to disentangle. The resulting social structure may pose important challenges for the identification of causal relations between variables using only observational data. In particular, the estimation of contextual or neighborhood effects will depend on the spatial configuration under study and the morphology of the areas used to define them. The relevant interpretation of estimates is hence put into question. I suggest adopting a Agent Based Modeling (ABM) approach to study the uncertainty of neighborhood effect estimations within complex spatial systems. An Approximate Bayesian Computing algorithm is used to quantify the uncertainty on the underlying processes that may lead to such estimations. An ABM model of spatial segregation is implemented to illustrate this method.
Cite as
Cécile de Bézenac. Uncertainty in Causal Neighborhood Effects: A Multi-Agent Simulation Approach (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 26:1-26:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{debezenac:LIPIcs.GIScience.2023.26,
author = {de B\'{e}zenac, C\'{e}cile},
title = {{Uncertainty in Causal Neighborhood Effects: A Multi-Agent Simulation Approach}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {26:1--26:6},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.26},
URN = {urn:nbn:de:0030-drops-189210},
doi = {10.4230/LIPIcs.GIScience.2023.26},
annote = {Keywords: Spatial causal inference, neighborhood effects, uncertainty, Agent Based Modeling, Pattern Oriented Modeling}
}
Document
Short Paper
Uncovering Spatiotemporal Patterns of Travel Flows Under Extreme Weather Events by Tensor Decomposition (Short Paper)
Abstract
Extreme weather events have caused dramatic damage to human society. Human mobility is one of the important aspects that are impacted significantly by extreme weather. Currently, focus on human mobility research during extreme weather is often limited to the transport infrastructure and emergency management perspectives, lacking a systematic understanding of the spatiotemporal patterns of human travel behavior. In this research, we examine the structural changes in human mobility under the severe rainstorm that occurred on July 20th, 2021 in Zhengzhou, Henan Province, China. Innovatively applying a tensor decomposition approach to analyzing spatiotemporal flows of human movements represented by the mobile phone big data, we extract the characteristic components of human travel behaviors from the spatial and temporal dimensions, which help discover and understand the latent spatiotemporal patterns hidden in human mobility data. This study provides a new methodological perspective and demonstrates that it can be useful for uncovering latent patterns of human mobility and identifying its structural changes during extreme weather events. This is of great importance to a better understanding of the behavioral side of human mobility and its response to external shocks and has significant implications for human-focused policies in urban risk mitigation and emergency response.
Cite as
Zhicheng Deng, Zhaoya Gong, and Pengjun Zhao. Uncovering Spatiotemporal Patterns of Travel Flows Under Extreme Weather Events by Tensor Decomposition (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 27:1-27:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{deng_et_al:LIPIcs.GIScience.2023.27,
author = {Deng, Zhicheng and Gong, Zhaoya and Zhao, Pengjun},
title = {{Uncovering Spatiotemporal Patterns of Travel Flows Under Extreme Weather Events by Tensor Decomposition}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {27:1--27:6},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.27},
URN = {urn:nbn:de:0030-drops-189228},
doi = {10.4230/LIPIcs.GIScience.2023.27},
annote = {Keywords: Urban travel behavior, Origin-Destination flows, Non-negative CP decomposition, Spatiotemporal analysis}
}
Document
Short Paper
GeoQAMap - Geographic Question Answering with Maps Leveraging LLM and Open Knowledge Base (Short Paper)
Abstract
GeoQA (Geographic Question Answering) is an emerging research field in GIScience, aimed at answering geographic questions in natural language. However, developing systems that seamlessly integrate structured geospatial data with unstructured natural language queries remains challenging. Recent advancements in Large Language Models (LLMs) have facilitated the application of natural language processing in various tasks. To achieve this goal, this study introduces GeoQAMap, a system that first translates natural language questions into SPARQL queries, then retrieves geospatial information from Wikidata, and finally generates interactive maps as visual answers. The system exhibits great potential for integration with other geospatial data sources such as OpenStreetMap and CityGML, enabling complicated geographic question answering involving further spatial operations.
Cite as
Yu Feng, Linfang Ding, and Guohui Xiao. GeoQAMap - Geographic Question Answering with Maps Leveraging LLM and Open Knowledge Base (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 28:1-28:7, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{feng_et_al:LIPIcs.GIScience.2023.28,
author = {Feng, Yu and Ding, Linfang and Xiao, Guohui},
title = {{GeoQAMap - Geographic Question Answering with Maps Leveraging LLM and Open Knowledge Base}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {28:1--28:7},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.28},
URN = {urn:nbn:de:0030-drops-189233},
doi = {10.4230/LIPIcs.GIScience.2023.28},
annote = {Keywords: Geographic Question Answering, Large Language Models, SPARQL, Knowledge Base, Wikidata}
}
Document
Short Paper
Understanding the Complex Behaviours of Electric Vehicle Drivers with Agent-Based Models in Glasgow (Short Paper)
Abstract
With the new policy aimed at advancing the phase-out date for the sale of new petrol and diesel cars and vans to 2030, the electric vehicle (EV) market share is expected to rise significantly in the coming years. This necessitates a deeper understanding of the driving and charging behaviours of EV drivers to accurately estimate future charging demand distribution and benefit for future infrastructure development. Traditional data-based approaches are limited in illustrating the granular spatiotemporal dynamics of individuals. Recent studies that use conventional vehicle trajectory data also have the sampling bias problem, despite their analyses being conducted at a finer resolution. Moreover, studies that use simulation approaches are often either based on limited behaviour rules for EV drivers or implemented in an artificial grid environment, showing limitations in reflecting real-world situations. To address the challenges, this work introduces an agent-based model (ABM) with complex behaviour rules for EV drivers, taking into account the drivers’ sensitivities to financial and time costs, as well as route deviation. By integrating the simulation model with the origin and destination information of drivers, this work can contribute to a better understanding of the behaviour patterns of EV drivers.
Cite as
Zixin Feng, Qunshan Zhao, and Alison Heppenstall. Understanding the Complex Behaviours of Electric Vehicle Drivers with Agent-Based Models in Glasgow (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 29:1-29:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{feng_et_al:LIPIcs.GIScience.2023.29,
author = {Feng, Zixin and Zhao, Qunshan and Heppenstall, Alison},
title = {{Understanding the Complex Behaviours of Electric Vehicle Drivers with Agent-Based Models in Glasgow}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {29:1--29:6},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.29},
URN = {urn:nbn:de:0030-drops-189243},
doi = {10.4230/LIPIcs.GIScience.2023.29},
annote = {Keywords: Electric vehicles, agent-based modelling, charging demand, route choices}
}
Document
Short Paper
Progress in Constructing an Open Map Generalization Data Set for Deep Learning (Short Paper)
Abstract
Recent pioneering works have shown the potential of a new deep-learning-backed paradigm for automated map generalization. However, this approach also puts a high demand on the availability of balanced and rich training sets. We present our design and progress of constructing an open training data set that can support relevant studies, collaborating with the Swiss Federal Office of Topography. The proposed data set will contain transitions of building and road generalization in Swiss maps at 1:25k, 1:50k, and 1:100k. By analyzing the generalization operators involved in these transitions, we also propose several challenges that can benefit from our proposed data set. Besides, we hope to also stimulate the production of further open data sets for deep-learning-backed map generalization.
Cite as
Cheng Fu, Zhiyong Zhou, Jan Winkler, Nicolas Beglinger, and Robert Weibel. Progress in Constructing an Open Map Generalization Data Set for Deep Learning (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 30:1-30:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{fu_et_al:LIPIcs.GIScience.2023.30,
author = {Fu, Cheng and Zhou, Zhiyong and Winkler, Jan and Beglinger, Nicolas and Weibel, Robert},
title = {{Progress in Constructing an Open Map Generalization Data Set for Deep Learning}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {30:1--30:6},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.30},
URN = {urn:nbn:de:0030-drops-189257},
doi = {10.4230/LIPIcs.GIScience.2023.30},
annote = {Keywords: open data, deep learning, map generalization}
}
Document
Short Paper
Project-Based Urban Dynamics: A Novel Method for Assessing Urban Sprawl (Short Paper)
Abstract
We present a new approach to categorizing different types of urban development, namely infilling, fringe, and leapfrogging, based on construction projects as the fundamental unit of analysis. We focus on the role of the leapfrogging projects as seeds for new developments, leading to urban sprawl extending beyond statutory plans. To examine this phenomenon, we analyze the 50-year growth of three major Israeli cities: Netanya, Haifa, and Safed and the 5-year dynamics of 66 cities in Israel that account for 68% of the country’s population. Our investigation utilizes extensive databases of Israeli development plans, along with high-resolution aerial photographs covering the investigated areas and time periods. These datasets were supplemented by detailed Israeli databases encompassing roads, buildings, and other infrastructure elements, compiled by the Israeli Mapping Centre for the year 2018. Our analysis reveals that although most construction projects in Israel adhere to land-use plans, urban sprawl in Israel remains highly unpredictable. Leapfrogging is specific in terms of both place and time, attracts additional development nearby, and forces the divergence from development plans. We conclude that urban modelers' view of urban dynamics being driven by common and systematic forces, is unrealistic. Instead, every city has its specific and self-enforcing development drivers that define its land-use dynamics. This explains the limited success of the Cellular Automata (CA) models in explaining and predicting urban dynamics.
Cite as
Nir Fulman, Yulia Grinblat, and Itzhak Benenson. Project-Based Urban Dynamics: A Novel Method for Assessing Urban Sprawl (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 31:1-31:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{fulman_et_al:LIPIcs.GIScience.2023.31,
author = {Fulman, Nir and Grinblat, Yulia and Benenson, Itzhak},
title = {{Project-Based Urban Dynamics: A Novel Method for Assessing Urban Sprawl}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {31:1--31:6},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.31},
URN = {urn:nbn:de:0030-drops-189267},
doi = {10.4230/LIPIcs.GIScience.2023.31},
annote = {Keywords: Urban sprawl, Leapfrogging, GIS analysis, Complex system}
}
Document
Short Paper
From Reproducible to Explainable GIScience (Short Paper)
Abstract
Communicating deep understanding between humans is key to the effective application and sharing of science, and this is critical in GIScience because much of what we do has practical implications in the modelling and governance of societal and environmental systems. Reproducible and explainable science is needed for public trust, for informed governance, for productivity and for global sustainability [Vicente-Saez et al., 2021]. This article summarises some of the more recent research on reproducibility from outside of GIScience, gives practical guidance to current best practice from a GIScience perspective, provides a clearer road-map towards reproducibility and adds in the additional step of explainable GIScience as our final goal.
Cite as
Mark Gahegan. From Reproducible to Explainable GIScience (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 32:1-32:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{gahegan:LIPIcs.GIScience.2023.32,
author = {Gahegan, Mark},
title = {{From Reproducible to Explainable GIScience}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {32:1--32:6},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.32},
URN = {urn:nbn:de:0030-drops-189279},
doi = {10.4230/LIPIcs.GIScience.2023.32},
annote = {Keywords: GIScience, Reproducible, Explainable, discoverable}
}
Document
Short Paper
Uncertainty Quantification in the Road-Level Traffic Risk Prediction by Spatial-Temporal Zero-Inflated Negative Binomial Graph Neural Network(STZINB-GNN) (Short Paper)
Abstract
Urban road-based risk prediction is a crucial yet challenging aspect of research in transportation safety. While most existing studies emphasize accurate prediction, they often overlook the importance of model uncertainty. In this paper, we introduce a novel Spatial-Temporal Zero-Inflated Negative Binomial Graph Neural Network (STZINB-GNN) for road-level traffic risk prediction, with a focus on uncertainty quantification. Our case study, conducted in the Lambeth borough of London, UK, demonstrates the superior performance of our approach in comparison to existing methods. Although the negative binomial distribution may not be the most suitable choice for handling real, non-binary risk levels, our work lays a solid foundation for future research exploring alternative distribution models or techniques. Ultimately, the STZINB-GNN contributes to enhanced transportation safety and data-driven decision-making in urban planning by providing a more accurate and reliable framework for road-level traffic risk prediction and uncertainty quantification.
Cite as
Xiaowei Gao, James Haworth, Dingyi Zhuang, Huanfa Chen, and Xinke Jiang. Uncertainty Quantification in the Road-Level Traffic Risk Prediction by Spatial-Temporal Zero-Inflated Negative Binomial Graph Neural Network(STZINB-GNN) (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 33:1-33:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{gao_et_al:LIPIcs.GIScience.2023.33,
author = {Gao, Xiaowei and Haworth, James and Zhuang, Dingyi and Chen, Huanfa and Jiang, Xinke},
title = {{Uncertainty Quantification in the Road-Level Traffic Risk Prediction by Spatial-Temporal Zero-Inflated Negative Binomial Graph Neural Network(STZINB-GNN)}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {33:1--33:6},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.33},
URN = {urn:nbn:de:0030-drops-189286},
doi = {10.4230/LIPIcs.GIScience.2023.33},
annote = {Keywords: Traffic Risk Prediction, Uncertainty Quantification, Zero-Inflated Issues, Road Safety}
}
Document
Short Paper
Simulating and Validating the Traffic of Blackwall Tunnel Using TfL Jam Cam Data and Simulation of Urban Mobility (SUMO) (Short Paper)
Abstract
Blackwall Tunnel is one of the most congested roadways in London. By simulating the tunnel and the connecting roads, information can be obtained about the traffic conditions and bottlenecks. In this paper, a model will be created using the Simulation of Urban Mobility (SUMO) tool and traffic flow data gathered from Transport for London (TfL) traffic cameras. The result from the simulation will be compared to the journey time data of Blackwall Tunnel in order to determine the accuracy of simulation.
Cite as
Chukun Gao. Simulating and Validating the Traffic of Blackwall Tunnel Using TfL Jam Cam Data and Simulation of Urban Mobility (SUMO) (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 34:1-34:8, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{gao:LIPIcs.GIScience.2023.34,
author = {Gao, Chukun},
title = {{Simulating and Validating the Traffic of Blackwall Tunnel Using TfL Jam Cam Data and Simulation of Urban Mobility (SUMO)}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {34:1--34:8},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.34},
URN = {urn:nbn:de:0030-drops-189299},
doi = {10.4230/LIPIcs.GIScience.2023.34},
annote = {Keywords: Traffic simulation, Validation, SUMO, Blackwall Tunnel}
}
Document
Short Paper
Building-Level Comparison of Microsoft and Google Open Building Footprints Datasets (Short Paper)
Abstract
Large-scale datasets of building footprints are a crucial source of information for a variety of efforts. In 2023, the general public benefits from open access to multiple sources of building footprints at the country scale or larger, such as those produced by Microsoft and Google. However, none of the available datasets have attained complete global coverage, and researchers and analysts may need to combine multiple sources to assemble a complete set of building footprints for their area of interest or choose between overlapping sources, requiring an understanding of the differences between different building sources. This paper presents a method to closely examine the quality of different building footprint sources by matching corresponding buildings across datasets, using building footprints in Ethiopia published by Microsoft and Google as an example set.
Cite as
Jack Joseph Gonzales. Building-Level Comparison of Microsoft and Google Open Building Footprints Datasets (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 35:1-35:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{gonzales:LIPIcs.GIScience.2023.35,
author = {Gonzales, Jack Joseph},
title = {{Building-Level Comparison of Microsoft and Google Open Building Footprints Datasets}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {35:1--35:6},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.35},
URN = {urn:nbn:de:0030-drops-189309},
doi = {10.4230/LIPIcs.GIScience.2023.35},
annote = {Keywords: Open data, Building footprints, Data comparison}
}
Document
Short Paper
Characterizing Urban Expansion Processes Using Dynamic Spatial Models – a European Application (Short Paper)
Abstract
Characterisation of the urban expansion processes using time series of binary urban/non-urban land cover data is complex due to the need to account for the initial configuration and the rate of urban expansion over the analysed period. Failure to account for these factors makes the interpretation of landscape metrics for compactness, fragmentation, or clumpiness problematic and the comparison between geographical areas and time periods contentious. This paper presents an approach for characterisation using spatio-dynamic modelling which is data-centred using a process based model, Bayesian optimization, cluster identification, and maximum likelihood classification. An application of the approach across 652 functional urban areas in Europe (1975-2014) demonstrates the consistency of the approach and its ability to identify spatial and temporal trends in urban expansion processes.
Cite as
Alex Hagen-Zanker, Jingyan Yu, Naratip Santitissadeekorn, and Susan Hughes. Characterizing Urban Expansion Processes Using Dynamic Spatial Models – a European Application (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 36:1-36:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{hagenzanker_et_al:LIPIcs.GIScience.2023.36,
author = {Hagen-Zanker, Alex and Yu, Jingyan and Santitissadeekorn, Naratip and Hughes, Susan},
title = {{Characterizing Urban Expansion Processes Using Dynamic Spatial Models – a European Application}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {36:1--36:6},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.36},
URN = {urn:nbn:de:0030-drops-189312},
doi = {10.4230/LIPIcs.GIScience.2023.36},
annote = {Keywords: Urban expansion, morphology, spatio-temporal dynamics, simulation, compactness}
}
Document
Short Paper
Understanding the Spatial Complexity in Landscape Narratives Through Qualitative Representation of Space (Short Paper)
Abstract
Narratives are the richest source of information about the human experience of place. They represent events and movement, both physical and conceptual, within time and space. Existing techniques in geographical text analysis usually incorporate named places with coordinate information. This is a serious limitation because many textual references to geography are ambiguous, non-specific, or relative. It is imperative but hard for a geographic information system to capture a text’s sense of place, an imprecise concept. This work aims to utilize qualitative spatial representation and natural language processing to allow representations of all three characteristics of place (location, locale, sense of place) as found in textual sources.
Cite as
Erum Haris, Anthony G. Cohn, and John G. Stell. Understanding the Spatial Complexity in Landscape Narratives Through Qualitative Representation of Space (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 37:1-37:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{haris_et_al:LIPIcs.GIScience.2023.37,
author = {Haris, Erum and Cohn, Anthony G. and Stell, John G.},
title = {{Understanding the Spatial Complexity in Landscape Narratives Through Qualitative Representation of Space}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {37:1--37:6},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.37},
URN = {urn:nbn:de:0030-drops-189323},
doi = {10.4230/LIPIcs.GIScience.2023.37},
annote = {Keywords: Narratives, Qualitative spatial representation, Natural language processing}
}
Document
Short Paper
Exascale Agent-Based Modelling for Policy Evaluation in Real-Time (ExAMPLER) (Short Paper)
Abstract
Exascale computing can potentially revolutionise the way in which we design and build agent-based models (ABM) through, for example, enabling scaling up, as well as robust calibration and validation. At present, there is no exascale computing operating with ABM (that we are aware of), but pockets of work using High Performance Computing (HPC). While exascale computing is expected to become more widely available towards the latter half of this decade, the ABM community is largely unaware of the requirements for exascale computing for agent-based modelling to support policy evaluation. This project will engage with the ABM community to understand what computing resources are currently used, what we need (both in terms of hardware and software) and to set out a roadmap by which to make it happen.
Cite as
Alison Heppenstall, J. Gary Polhill, Mike Batty, Matt Hare, Doug Salt, and Richard Milton. Exascale Agent-Based Modelling for Policy Evaluation in Real-Time (ExAMPLER) (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 38:1-38:5, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{heppenstall_et_al:LIPIcs.GIScience.2023.38,
author = {Heppenstall, Alison and Polhill, J. Gary and Batty, Mike and Hare, Matt and Salt, Doug and Milton, Richard},
title = {{Exascale Agent-Based Modelling for Policy Evaluation in Real-Time (ExAMPLER)}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {38:1--38:5},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.38},
URN = {urn:nbn:de:0030-drops-189334},
doi = {10.4230/LIPIcs.GIScience.2023.38},
annote = {Keywords: Exascale computing, Agent-Based Modelling, Policy evaluation}
}
Document
Short Paper
A Hierarchical and Geographically Weighted Regression Model and Its Backfitting Maximum Likelihood Estimator (Short Paper)
Abstract
Spatial heterogeneity is a typical and common form of spatial effect. Geographically weighted regression (GWR) and its extensions are important local modeling techniques for exploring spatial heterogeneity. However, when dealing with spatial data sampled at a micro-level but the geographical locations of them are only known at a higher level, GWR-based models encounter several problems, such as difficulty in establishing the bandwidth. Because data with this characteristic exhibit spatial hierarchical structures, such data can be suitably handled using hierarchical linear modeling (HLM). This model calibrates random effects for sample-level variables in each group to address spatial heterogeneity. However, it does not work when exploring spatial heterogeneity in some group-level variables when there is insufficient variance in each group. In this study, we therefore propose a hierarchical and geographically weighted regression (HGWR) model, together with a back-fitting maximum likelihood estimator, that can be applied to examine spatial heterogeneity in the regression relationships of data where observations nest into high-order groupings and share the same or very close coordinates within those groups. The HGWR model divides coefficients into three types: local fixed effects, global fixed effects, and random effects. Results of a simulation experiment show that HGWR distinguishes local fixed effects from others and also global effects from random effects. Spatial heterogeneity is reflected in the estimates of local fixed effects, along with the spatial hierarchical structure. Compared with GWR and HLM, HGWR produces estimates with the lowest deviations of coefficient estimates. Thus, the ability of HGWR to tackle both spatial and group-level heterogeneity simultaneously suggests its potential as a promising data modeling tool for handling the increasingly common occurrence where data, in secure settings for example, remove the specific geographic identifiers of individuals and release their locations only at a group level.
Cite as
Yigong Hu, Richard Harris, Richard Timmerman, and Binbin Lu. A Hierarchical and Geographically Weighted Regression Model and Its Backfitting Maximum Likelihood Estimator (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 39:1-39:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{hu_et_al:LIPIcs.GIScience.2023.39,
author = {Hu, Yigong and Harris, Richard and Timmerman, Richard and Lu, Binbin},
title = {{A Hierarchical and Geographically Weighted Regression Model and Its Backfitting Maximum Likelihood Estimator}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {39:1--39:6},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.39},
URN = {urn:nbn:de:0030-drops-189342},
doi = {10.4230/LIPIcs.GIScience.2023.39},
annote = {Keywords: spatial modelling, hierarchical data, spatial heterogeneity, geographically weighted regression}
}
Document
Short Paper
Introducing a General Framework for Locally Weighted Spatial Modelling Based on Density Regression (Short Paper)
Abstract
Traditional geographically weighted regression and its extensions are important methods in the analysis of spatial heterogeneity. However, they are based on distance metrics and kernel functions compressing differences in multidimensional coordinates into one-dimensional values, which rarely consider anisotropy and employ inconsistent definitions of distance in spatio-temporal data or spatial line data (for example). This article proposes a general framework for locally weighted spatial modelling to overcome the drawbacks of existing models using geographically weighted schemes. Underpinning it is a multi-dimensional weighting scheme based on density regression that can be applied to data in any space and is not limited to geographic distance.
Cite as
Yigong Hu, Binbin Lu, Richard Harris, and Richard Timmerman. Introducing a General Framework for Locally Weighted Spatial Modelling Based on Density Regression (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 40:1-40:7, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{hu_et_al:LIPIcs.GIScience.2023.40,
author = {Hu, Yigong and Lu, Binbin and Harris, Richard and Timmerman, Richard},
title = {{Introducing a General Framework for Locally Weighted Spatial Modelling Based on Density Regression}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {40:1--40:7},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.40},
URN = {urn:nbn:de:0030-drops-189354},
doi = {10.4230/LIPIcs.GIScience.2023.40},
annote = {Keywords: Spatial heterogeneity, Multidimensional space, Density regression, Spatial statistics}
}
Document
Short Paper
Understanding Place Identity with Generative AI (Short Paper)
Abstract
Researchers are constantly leveraging new forms of data to understand how people perceive the built environment and the collective place identity of cities. Latest advancements in generative artificial intelligence (AI) models have enabled the creation of realistic representations of real-world settings. In this study, we explore the potential of generative AI as the source of textual and visual information in capturing the place identity of cities assessed by filtered descriptions and images. We asked questions on the place identity of a set of 31 global cities to two generative AI models, ChatGPT and DALL·E2. Since generative AI has raised ethical concerns regarding its trustworthiness, we performed cross-validation to examine whether the results show similar patterns to real urban settings. In particular, we compared the outputs with Wikipedia data for text and images searched from Google for images. Our results indicate that generative AI models have the potential to capture the collective features of cities that can make them distinguishable. This study is among the first attempts to explore the capabilities of generative AI in understanding human perceptions of the built environment. It contributes to urban design literature by discussing future research opportunities and potential limitations.
Cite as
Kee Moon Jang, Junda Chen, Yuhao Kang, Junghwan Kim, Jinhyung Lee, and Fábio Duarte. Understanding Place Identity with Generative AI (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 41:1-41:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{jang_et_al:LIPIcs.GIScience.2023.41,
author = {Jang, Kee Moon and Chen, Junda and Kang, Yuhao and Kim, Junghwan and Lee, Jinhyung and Duarte, F\'{a}bio},
title = {{Understanding Place Identity with Generative AI}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {41:1--41:6},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.41},
URN = {urn:nbn:de:0030-drops-189363},
doi = {10.4230/LIPIcs.GIScience.2023.41},
annote = {Keywords: ChatGPT, DALL·E2, place identity, generative artificial intelligence, sense of place}
}
Document
Short Paper
An Integrated Uncertainty and Sensitivity Analysis for Spatial Multicriteria Models (Short Paper)
Abstract
This paper introduces an integrated Uncertainty and Sensitivity Analysis (US-A) approach for Spatial Multicriteria Models (SMM). The US-A approach evaluates uncertainty and sensitivity by considering both criteria values and weights, providing spatially distributed measures. A geodiversity assessment case study demonstrates the application of US-A, identifying influential inputs driving uncertainty in specific areas. The results highlight the importance of considering both criteria values and weights in analyzing model uncertainty. The paper contributes to the literature on spatially-explicit uncertainty and sensitivity analysis by providing a method for analyzing both categories of SMM inputs: evaluation criteria values and weights, and by presenting a novel form of visualizing their sensitivity measures with bivariate maps.
Cite as
Piotr Jankowski, Arika Ligmann-Zielińska, Zbigniew Zwoliński, and Alicja Najwer. An Integrated Uncertainty and Sensitivity Analysis for Spatial Multicriteria Models (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 42:1-42:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{jankowski_et_al:LIPIcs.GIScience.2023.42,
author = {Jankowski, Piotr and Ligmann-Zieli\'{n}ska, Arika and Zwoli\'{n}ski, Zbigniew and Najwer, Alicja},
title = {{An Integrated Uncertainty and Sensitivity Analysis for Spatial Multicriteria Models}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {42:1--42:6},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.42},
URN = {urn:nbn:de:0030-drops-189375},
doi = {10.4230/LIPIcs.GIScience.2023.42},
annote = {Keywords: model uncertainty, input factor sensitivity, geodiversity, spatial multicriteria models}
}
Document
Short Paper
Evaluating the Effectiveness of Large Language Models in Representing Textual Descriptions of Geometry and Spatial Relations (Short Paper)
Abstract
This research focuses on assessing the ability of large language models (LLMs) in representing geometries and their spatial relations. We utilize LLMs including GPT-2 and BERT to encode the well-known text (WKT) format of geometries and then feed their embeddings into classifiers and regressors to evaluate the effectiveness of the LLMs-generated embeddings for geometric attributes. The experiments demonstrate that while the LLMs-generated embeddings can preserve geometry types and capture some spatial relations (up to 73% accuracy), challenges remain in estimating numeric values and retrieving spatially related objects. This research highlights the need for improvement in terms of capturing the nuances and complexities of the underlying geospatial data and integrating domain knowledge to support various GeoAI applications using foundation models.
Cite as
Yuhan Ji and Song Gao. Evaluating the Effectiveness of Large Language Models in Representing Textual Descriptions of Geometry and Spatial Relations (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 43:1-43:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{ji_et_al:LIPIcs.GIScience.2023.43,
author = {Ji, Yuhan and Gao, Song},
title = {{Evaluating the Effectiveness of Large Language Models in Representing Textual Descriptions of Geometry and Spatial Relations}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {43:1--43:6},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.43},
URN = {urn:nbn:de:0030-drops-189381},
doi = {10.4230/LIPIcs.GIScience.2023.43},
annote = {Keywords: LLMs, foundation models, GeoAI}
}
Document
Short Paper
Framework for Motorcycle Risk Assessment Using Onboard Panoramic Camera (Short Paper)
Abstract
Traditional safety analysis methods based on historical crash data and simulation models have limitations in capturing real-world driving scenarios. In this experiment, panoramic videos recorded from a motorcyclist’s helmet in Bangkok, Thailand, were narrated using an image-to-text model and then put into a Large Language Model (LLM) to identify potential hazards and assess crash risks. The framework can assess static and moving objects with the potential for early warning and incident analysis. However, the limitations of the existing image-to-text model cause its inability to handle panoramic images effectively.
Cite as
Natchapon Jongwiriyanurak, Zichao Zeng, Meihui Wang, James Haworth, Garavig Tanaksaranond, and Jan Boehm. Framework for Motorcycle Risk Assessment Using Onboard Panoramic Camera (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 44:1-44:7, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{jongwiriyanurak_et_al:LIPIcs.GIScience.2023.44,
author = {Jongwiriyanurak, Natchapon and Zeng, Zichao and Wang, Meihui and Haworth, James and Tanaksaranond, Garavig and Boehm, Jan},
title = {{Framework for Motorcycle Risk Assessment Using Onboard Panoramic Camera}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {44:1--44:7},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.44},
URN = {urn:nbn:de:0030-drops-189394},
doi = {10.4230/LIPIcs.GIScience.2023.44},
annote = {Keywords: Traffic incident risk, Large Language Model, Vision-Language Model}
}
Document
Short Paper
National-Scale Spatiotemporal Variation in Driver Navigation Behaviour and Route Choice (Short Paper)
Abstract
Understanding human behaviour is an integral task in GIScience, facilitated by increasingly large and descriptive datasets on human activity. Large-scale trajectory data have been particularly useful in measuring behaviours in different contexts, and understanding the relationship between the built environment and people. Yet, to date, most of these studies have focused on urban or regional scale analyses, with less exploration of behavioural variation at larger spatial scales. Human navigation behaviour is inherently linked to variation in spatial structure, and a study of national variations could help to better understand this variability. In this paper, we analyse GPS data from over 1 million journeys by 50,000 connected cars across the UK. Some key statistics relating to route choice are computed, and their variations are explored over time and space. A k-mean clustering of the trips identifies different types of trips and shows that their distribution varies by time of day and across the country. The insights gained from the data highlight spatio-temporal variations in road navigation, which should be considered in transportation modelling and planning.
Cite as
Elliot Karikari, Manon Prédhumeau, Peter Baudains, and Ed Manley. National-Scale Spatiotemporal Variation in Driver Navigation Behaviour and Route Choice (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 45:1-45:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{karikari_et_al:LIPIcs.GIScience.2023.45,
author = {Karikari, Elliot and Pr\'{e}dhumeau, Manon and Baudains, Peter and Manley, Ed},
title = {{National-Scale Spatiotemporal Variation in Driver Navigation Behaviour and Route Choice}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {45:1--45:6},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.45},
URN = {urn:nbn:de:0030-drops-189404},
doi = {10.4230/LIPIcs.GIScience.2023.45},
annote = {Keywords: Connected Car, Geospatial big Data, Navigation Behaviour, Cluster Analysis}
}
Document
Short Paper
Status Poles and Status Zoning to Model Residential Land Prices: Status-Quality Trade off Theory (Short Paper)
Abstract
This study describes an approach for augmenting urban residential preference and hedonic house price models by incorporating Status-Quality Trade Off theory (SQTO). SQTO seeks explain the dynamic of urban structure using a multipolar, in which the location and strength of poles is driven by notions of residential status and dwelling quality. This paper presents in outline an approach for identifying status poles and for quantifying their effect on land and residential property prices. The results show how the incorporation of SQTO results in an enhanced understanding of variations in land / property process with increased spatial nuance. A number of future research areas are identified related to the status pole weights and the development of status pole index.
Cite as
Thuy Phuong Le, Alexis Comber, Binh Quoc Tran, Phe Huu Hoang, Huy Quang Man, Linh Xuan Nguyen, Tuan Le Pham, and Tu Ngoc Bui. Status Poles and Status Zoning to Model Residential Land Prices: Status-Quality Trade off Theory (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 46:1-46:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{le_et_al:LIPIcs.GIScience.2023.46,
author = {Le, Thuy Phuong and Comber, Alexis and Tran, Binh Quoc and Hoang, Phe Huu and Man, Huy Quang and Nguyen, Linh Xuan and Le Pham, Tuan and Bui, Tu Ngoc},
title = {{Status Poles and Status Zoning to Model Residential Land Prices: Status-Quality Trade off Theory}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {46:1--46:6},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.46},
URN = {urn:nbn:de:0030-drops-189415},
doi = {10.4230/LIPIcs.GIScience.2023.46},
annote = {Keywords: spatial theory, house prices}
}
Document
Short Paper
Investigating MAUP Effects on Census Data Using Approximately Equal-Population Aggregations (Short Paper)
Abstract
The modifiable areal unit problem (MAUP) can significantly impact the use of census data as different choices in aggregating geographic zones can lead to varying outcomes. Previous research studied the effects using random aggregations, which, however, may lead to the use of impractical and unrealistic zones that deviate from recommended census geography criteria (e.g., equal population). To address this issue, this study proposes the use of approximately equal-population aggregations (AEPAs) for exploring MAUP effects on various statistical properties of census data, including Moran coefficients, correlation coefficients, and regression statistics. A multistart and recombination algorithm (MSRA) is used to generate multiple sets of high-quality AEPAs for testing MAUP effects. The results of our computational experiments highlight the need for more well-defined census geographies and realistic alternative zones to fully understand MAUP effects on census data.
Cite as
Yue Lin and Ningchuan Xiao. Investigating MAUP Effects on Census Data Using Approximately Equal-Population Aggregations (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 47:1-47:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{lin_et_al:LIPIcs.GIScience.2023.47,
author = {Lin, Yue and Xiao, Ningchuan},
title = {{Investigating MAUP Effects on Census Data Using Approximately Equal-Population Aggregations}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {47:1--47:6},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.47},
URN = {urn:nbn:de:0030-drops-189428},
doi = {10.4230/LIPIcs.GIScience.2023.47},
annote = {Keywords: Census, heuristics, modifiable areal unit problem, spatial aggregation, spatial autocorrelation}
}
Document
Short Paper
Agent-Based Modelling and Disease: Demonstrating the Role of Human Remains in Epidemic Outbreaks (Short Paper)
Abstract
Hemorrhagic fever viruses present a high risk to humans, given their associated high fatality rates, extensive care requirements, and few relevant vaccines. One of the most famous such viruses is the Ebola virus, which first came to international attention during an outbreak in 1976. Another is Marburg virus, cases of which are being reported in Equatorial Guinea at the time of writing. Researchers and governments all over the world share a goal in seeking effective ways to reduce or prevent the influence or spreading of such diseases. This study introduces a prototype agent-based model to explore the epidemic infectious progression of a simulated fever virus. More specifically, this work seeks to recreate the role of human remains in the progression of such an epidemic, and to help gauge the influence of different environmental conditions on this dynamic.
Cite as
Huixin Liu and Sarah Wise. Agent-Based Modelling and Disease: Demonstrating the Role of Human Remains in Epidemic Outbreaks (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 48:1-48:7, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{liu_et_al:LIPIcs.GIScience.2023.48,
author = {Liu, Huixin and Wise, Sarah},
title = {{Agent-Based Modelling and Disease: Demonstrating the Role of Human Remains in Epidemic Outbreaks}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {48:1--48:7},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.48},
URN = {urn:nbn:de:0030-drops-189435},
doi = {10.4230/LIPIcs.GIScience.2023.48},
annote = {Keywords: Disease modelling, agent-based model, hemorrhagic fever virus, epidemiology, safe burial practices}
}
Document
Short Paper
How Does Travel Environment Affect Mood? A Study Using Geographic Ecological Momentary Assessment in the UK (Short Paper)
Abstract
Daily travel is a large part of life, and it is widely believed that our mood can be affected by the environment in which travel takes place. In this study, we investigate how environmental factors affect mood while performing daily travel activities using an app-based geographic ecological momentary assessment study. Our study (the WorkAndHome study) involved over 1000 participants tracked using a bespoke GPS mobile phone app in three cities (Birmingham, Leeds, and Brighton and Hove, UK) At the end of trips (i.e., when a stop in the GPS data was detected) we pushed a survey to participants asking them to score their current happiness and stress levels on a 7-point Likert scale. We combined individual GPS data with environmental data on green and blue spaces and weather conditions. We found that green and blue space availability and weather variables, such as daytime, apparent temperature, and visibility, significantly affect our happiness levels at the end of trips. While these weather factors were also significant predictors of stress level, availability of green and blue space was not. The results of this study provide fine-scale evidence from direct surveys about the associations between environment and weather and our moods when performing daily travel activities.
Cite as
Milad Malekzadeh, Darja Reuschke, and Jed A. Long. How Does Travel Environment Affect Mood? A Study Using Geographic Ecological Momentary Assessment in the UK (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 49:1-49:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{malekzadeh_et_al:LIPIcs.GIScience.2023.49,
author = {Malekzadeh, Milad and Reuschke, Darja and Long, Jed A.},
title = {{How Does Travel Environment Affect Mood? A Study Using Geographic Ecological Momentary Assessment in the UK}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {49:1--49:6},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.49},
URN = {urn:nbn:de:0030-drops-189441},
doi = {10.4230/LIPIcs.GIScience.2023.49},
annote = {Keywords: GEMA, GPS Tracking, Green and Blue Spaces}
}
Document
Short Paper
Calibration in a Data Sparse Environment: How Many Cases Did We Miss? (Short Paper)
Abstract
Reported case numbers in the COVID-19 pandemic are assumed in many countries to have underestimated the true prevalence of the disease. Deficits in reporting may have been particularly great in countries with limited testing capability and restrictive testing policies. Simultaneously, some models have been accused of over-reporting the scale of the pandemic. At a time when modeling consortia around the world are turning to the lessons learnt from pandemic modelling, we present an example of simulating testing as well as the spread of disease. In particular, we factor in the amount and nature of testing that was carried out in the first wave of the COVID-19 pandemic (March - September 2020), calibrating our spatial Agent Based Model (ABM) model to the reported case numbers in Zimbabwe.
Cite as
Robert Manning Smith, Sarah Wise, and Sophie Ayling. Calibration in a Data Sparse Environment: How Many Cases Did We Miss? (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 50:1-50:7, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{manningsmith_et_al:LIPIcs.GIScience.2023.50,
author = {Manning Smith, Robert and Wise, Sarah and Ayling, Sophie},
title = {{Calibration in a Data Sparse Environment: How Many Cases Did We Miss?}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {50:1--50:7},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.50},
URN = {urn:nbn:de:0030-drops-189452},
doi = {10.4230/LIPIcs.GIScience.2023.50},
annote = {Keywords: Agent Based Modelling, Infectious Disease Modelling, COVID-19, Zimbabwe, SARS-CoV-2, calibration}
}
Document
Short Paper
Geographic Analysis of Trade-Offs Between Amenity and Supply Effects in New Office Buildings (Short Paper)
Abstract
The supply of new office buildings in the neighborhood both positively and negatively affects rents. This study attempts to deepen the quantitative knowledge of this trade-off relationship and estimate the correlation between new supply and rent within a specific geographic area based on a hedonic model. Although the results exhibit biases, they indicate that supply effects become apparent after construction is completed, and that they vary geographically and are related to local market characteristics.
Cite as
Kazushi Matsuo, Morito Tsutsumi, and Toyokazu Imazeki. Geographic Analysis of Trade-Offs Between Amenity and Supply Effects in New Office Buildings (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 51:1-51:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{matsuo_et_al:LIPIcs.GIScience.2023.51,
author = {Matsuo, Kazushi and Tsutsumi, Morito and Imazeki, Toyokazu},
title = {{Geographic Analysis of Trade-Offs Between Amenity and Supply Effects in New Office Buildings}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {51:1--51:6},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.51},
URN = {urn:nbn:de:0030-drops-189460},
doi = {10.4230/LIPIcs.GIScience.2023.51},
annote = {Keywords: Office rent, new office building, amenity effect, supply effect}
}
Document
Short Paper
Impacts of Catchments Derived from Fine-Grained Mobility Data on Spatial Accessibility (Short Paper)
Abstract
Spatial accessibility is a powerful tool for understanding how access to important services and resources varies across space. While spatial accessibility methods traditionally rely on origin-destination matrices between centroids of administrative zones, recent work has examined creating polygonal catchments - areas within a travel-time threshold - from point-based fine-grained mobility data. In this paper, we investigate the difference between the convex hull and alpha shape algorithms for determining catchment areas and how this affects the results of spatial accessibility analyses. Our analysis shows that the choice of how we define a catchment produces differences in the measured accessibility which correlate with social vulnerability. These findings highlight the importance of evaluating and communicating minor methodological choices in spatial accessibility analyses.
Cite as
Alexander Michels, Jinwoo Park, Bo Li, Jeon-Young Kang, and Shaowen Wang. Impacts of Catchments Derived from Fine-Grained Mobility Data on Spatial Accessibility (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 52:1-52:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{michels_et_al:LIPIcs.GIScience.2023.52,
author = {Michels, Alexander and Park, Jinwoo and Li, Bo and Kang, Jeon-Young and Wang, Shaowen},
title = {{Impacts of Catchments Derived from Fine-Grained Mobility Data on Spatial Accessibility}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {52:1--52:6},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.52},
URN = {urn:nbn:de:0030-drops-189470},
doi = {10.4230/LIPIcs.GIScience.2023.52},
annote = {Keywords: Spatial accessibility, alpha shape, convex hull, cyberGIS, social vulnerability}
}
Document
Short Paper
Exploring the Potential of Machine and Deep Learning Models for OpenStreetMap Data Quality Assessment and Improvement (Short Paper)
Abstract
The OpenStreetMap (OSM) project is a widely-used crowdsourced geographic data platform that allows users to contribute, edit, and access geographic information. However, the quality of the data in OSM is often uncertain, and assessing the quality of OSM data is crucial for ensuring its reliability and usability. Recently, the use of machine and deep learning models has shown to be promising in assessing and improving the quality of OSM data. In this paper, we explore the current state-of-the-art machine learning models for OSM data quality assessment and improvement as an attempt to discuss and classify the underlying methods into different categories depending on (1) the associated learning paradigm (supervised or unsupervised learning-based methods), (2) the usage of extrinsic or intrinsic-based metrics (i.e., assessing OSM data by comparing it against authoritative external datasets or via computing some internal quality indicators), and (3) the use of traditional or deep learning-based models for predicting and evaluating OSM features. We then identify the main trends and challenges in this field and provide recommendations for future research aiming at improving the quality of OSM data in terms of completeness, accuracy, and consistency.
Cite as
Salim Miloudi and Bouhadjar Meguenni. Exploring the Potential of Machine and Deep Learning Models for OpenStreetMap Data Quality Assessment and Improvement (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 53:1-53:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{miloudi_et_al:LIPIcs.GIScience.2023.53,
author = {Miloudi, Salim and Meguenni, Bouhadjar},
title = {{Exploring the Potential of Machine and Deep Learning Models for OpenStreetMap Data Quality Assessment and Improvement}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {53:1--53:6},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.53},
URN = {urn:nbn:de:0030-drops-189486},
doi = {10.4230/LIPIcs.GIScience.2023.53},
annote = {Keywords: OpenStreetMap (OSM), Volunteered Geographic Information (VGI), Machine Learning (ML), Deep Learning (DL), Quality Assessment (QA), Building Footprint Detection, Semantic Segmentation}
}
Document
Short Paper
On the Cartographic Communication of Places (Short Paper)
Abstract
Maps are excellent as a medium for communicating spatial configurations at geographical scales. However, the communication of thematic qualities of geographical features is constrained by the traditionally assumed strict classification of features on the map and the strong focus on spatial representation. This is despite the fact that places are central aspects of everyday life that we use to structure our experiences and thus the need to include them in many maps. This paper explores how places can be communicated through the map medium. In particular, it addresses the question of the extent to which places are mediated or merely referenced, and the extent to which maps already communicate places through its inherent spatial and thematic aspects. This is followed by a discussion of how maps not only communicate but also shape places. In perspective, this contributes to a better and more targeted representation of places, especially through maps, but also advances our understanding of how places are conceptually entangled with spatial and thematic aspects.
Cite as
Franz-Benjamin Mocnik. On the Cartographic Communication of Places (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 54:1-54:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{mocnik:LIPIcs.GIScience.2023.54,
author = {Mocnik, Franz-Benjamin},
title = {{On the Cartographic Communication of Places}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {54:1--54:6},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.54},
URN = {urn:nbn:de:0030-drops-189499},
doi = {10.4230/LIPIcs.GIScience.2023.54},
annote = {Keywords: representation, reference, mediation, intentionality, conceptualization, maps}
}
Document
Short Paper
Resiliency: A Consensus Data Binning Method (Short Paper)
Abstract
Data binning, or data classification, involves grouping quantitative data points into bins (or classes) to represent spatial patterns and show variation in choropleth maps. There are many methods for binning data (e.g., natural breaks, quantile) that may make the same data appear very different on a map. Some of these methods may be more or less appropriate for certain types of data distributions and map purposes. Thus, when designing a map, novice users may be overwhelmed by the number of choices for binning methods and experts may find comparing results from different binning methods challenging. We present resiliency, a new data binning method that assigns areal units to their most agreed-upon, consensus bin as it persists across multiple chosen binning methods. We show how this "smart average" can effectively communicate spatial patterns that are agreed-upon across binning methods. We also measure the variety of bins a single areal unit can be placed in under different binning methods showing fuzziness and uncertainty on a map. We implement resiliency and other binning methods via an open-source JavaScript library, BinGuru.
Cite as
Arpit Narechania, Alex Endert, and Clio Andris. Resiliency: A Consensus Data Binning Method (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 55:1-55:7, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{narechania_et_al:LIPIcs.GIScience.2023.55,
author = {Narechania, Arpit and Endert, Alex and Andris, Clio},
title = {{Resiliency: A Consensus Data Binning Method}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {55:1--55:7},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.55},
URN = {urn:nbn:de:0030-drops-189509},
doi = {10.4230/LIPIcs.GIScience.2023.55},
annote = {Keywords: data binning, data classification, choropleth maps, geovisualization, geographic information systems, geographic information science, cartography}
}
Document
Short Paper
Counter-Intuitive Effect of Null Hypothesis on Moran’s I Tests Under Heterogenous Populations (Short Paper)
Abstract
We examine the effect of null hypothesis on spatial autocorrelation tests using Moran’s I statistic. There are two possible variable states that do not exhibit spatial autocorrelation. One is that they have the same average values in all small regions, and the other is that they are not the same, but their variations are spatially random. The second state is less restrictive than the first. Thus, it intuitively appears suitable for the null hypothesis of Moran’s I test. However, we found that it can make false discoveries more frequently than the nominal rate of the test when the first state is the true data generation process.
Cite as
Hayato Nishi and Ikuho Yamada. Counter-Intuitive Effect of Null Hypothesis on Moran’s I Tests Under Heterogenous Populations (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 56:1-56:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{nishi_et_al:LIPIcs.GIScience.2023.56,
author = {Nishi, Hayato and Yamada, Ikuho},
title = {{Counter-Intuitive Effect of Null Hypothesis on Moran’s I Tests Under Heterogenous Populations}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {56:1--56:6},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.56},
URN = {urn:nbn:de:0030-drops-189517},
doi = {10.4230/LIPIcs.GIScience.2023.56},
annote = {Keywords: Moran’s I statistic, spatial autocorrelation, spatial heterogeneity, false discovery, null hypothesis}
}
Document
Short Paper
A Data Fusion Framework for Exploring Mobility Around Disruptive Events (Short Paper)
Abstract
This paper proposes a data fusion framework that seeks to investigate joint mobility signals around wildfires in relation to geographic scale of analysis (level of spatial aggregation), as well as spatial and temporal extents (i.e. distance to the event and duration of the observation period). We highlight the usefulness of our framework using intra-urban mobility data from Mapbox and SafeGraph for two wildfires in California: Lake Fire (August-September 2020, Los Angeles County) and Silverado Fire (October-November 2020, Orange County). We identify two distinct patterns of mobility behavior: one associated with the wildfire event and another one - with the routine daily mobility of the nearby urban core. Using the combination of data fusion and tensor decomposition, the framework allows us to capture additional insights from the data, that were otherwise unavailable in raw mobility data.
Cite as
Evgeny Noi and Somayeh Dodge. A Data Fusion Framework for Exploring Mobility Around Disruptive Events (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 57:1-57:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{noi_et_al:LIPIcs.GIScience.2023.57,
author = {Noi, Evgeny and Dodge, Somayeh},
title = {{A Data Fusion Framework for Exploring Mobility Around Disruptive Events}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {57:1--57:6},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.57},
URN = {urn:nbn:de:0030-drops-189523},
doi = {10.4230/LIPIcs.GIScience.2023.57},
annote = {Keywords: geographic extent, geographic scale, tensor decomposition, spatio-temporal analysis}
}
Document
Short Paper
Finding Feasible Routes with Reinforcement Learning Using Macro-Level Traffic Measurements (Short Paper)
Abstract
The quest for identifying feasible routes holds immense significance in the realm of transportation, spanning a diverse range of applications, from logistics and emergency systems to taxis and public transport services. This research area offers multifaceted benefits, including optimising traffic management, maximising traffic flow, and reducing carbon emissions and fuel consumption. Extensive studies have been conducted to address this critical issue, with a primary focus on finding the shortest paths, while some of them incorporate various traffic conditions such as waiting times at traffic lights and traffic speeds on road segments. In this study, we direct our attention towards historical data sets that encapsulate individuals' route preferences, assuming they encompass all traffic conditions, real-time decisions and topological features. We acknowledge that the prevailing preferences during the recorded period serve as a guide for feasible routes. The study’s noteworthy contribution lies in our departure from analysing individual preferences and trajectory information, instead focusing solely on macro-level measurements of each road segment, such as traffic flow or traffic speed. These types of macro-level measurements are easier to collect compared to individual data sets. We propose an algorithm based on Q-learning, employing traffic measurements within a road network as positive attractive rewards for an agent. In short, observations from macro-level decisions will help us to determine optimal routes between any two points. Preliminary results demonstrate the agent’s ability to accurately identify the most feasible routes within a short training period.
Cite as
Mustafa Can Ozkan and Tao Cheng. Finding Feasible Routes with Reinforcement Learning Using Macro-Level Traffic Measurements (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 58:1-58:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{ozkan_et_al:LIPIcs.GIScience.2023.58,
author = {Ozkan, Mustafa Can and Cheng, Tao},
title = {{Finding Feasible Routes with Reinforcement Learning Using Macro-Level Traffic Measurements}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {58:1--58:6},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.58},
URN = {urn:nbn:de:0030-drops-189536},
doi = {10.4230/LIPIcs.GIScience.2023.58},
annote = {Keywords: routing, reinforcement learning, q-learning, data mining, macro-level patterns}
}
Document
Short Paper
Moran Eigenvectors-Based Spatial Heterogeneity Analysis for Compositional Data (Short Paper)
Abstract
Spatial analysis of data with compositional structure has gained increasing attention in recent years. However, the spatial heterogeneity of compositional data has not been widely discussed. This study developed a Moran eigenvectors-based spatial heterogeneity analysis framework to investigate the spatially varying relationships between the compositional dependent variable and real-value covariates. The proposed method was applied to municipal-level household income data in Tokyo, Japan in 2018.
Cite as
Zhan Peng and Ryo Inoue. Moran Eigenvectors-Based Spatial Heterogeneity Analysis for Compositional Data (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 59:1-59:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{peng_et_al:LIPIcs.GIScience.2023.59,
author = {Peng, Zhan and Inoue, Ryo},
title = {{Moran Eigenvectors-Based Spatial Heterogeneity Analysis for Compositional Data}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {59:1--59:6},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.59},
URN = {urn:nbn:de:0030-drops-189540},
doi = {10.4230/LIPIcs.GIScience.2023.59},
annote = {Keywords: Compositional data analysis, Spatial heterogeneity, Moran eigenvectors}
}
Document
Short Paper
Toward Causally Aware GIS: Events as Cornerstones (Short Paper)
Abstract
Over the last 50 years, Geographic Information Systems (GIS) have become a vital tool for decision-making. Yet, the increasing volume and complexity of geographical data pose challenges for real-time integration and analysis. To address these, we suggest a causally aware GIS that represents causal relationships. This system uses causality to analyze events and geographical impacts, aiming to offer a more comprehensive understanding of the geographic world. It integrates causality into design and operations, applying robust algorithms and visualization tools for scenario analysis. Unlike traditional GIS, our approach prioritizes an event-based model, emphasizing change as the core concept. This model moves beyond object-oriented models' limitations by considering events as primary entities. The proposed system adopts an event-oriented approach within a Spatio-Temporal Information System, with objects in space and time viewed as event components linked through processes. We introduce an innovative event-based ontology model that enriches GIS by focusing on modeling changes and their interconnections. Lastly, we suggest an IT implementation of this ontology to enhance GIS capabilities further.
Cite as
Nina Polous. Toward Causally Aware GIS: Events as Cornerstones (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 60:1-60:8, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{polous:LIPIcs.GIScience.2023.60,
author = {Polous, Nina},
title = {{Toward Causally Aware GIS: Events as Cornerstones}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {60:1--60:8},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.60},
URN = {urn:nbn:de:0030-drops-189559},
doi = {10.4230/LIPIcs.GIScience.2023.60},
annote = {Keywords: Causal Aware GIS, Events, Event-Oriented GIS, Causality}
}
Document
Short Paper
Mobility Vitality: Assessing Neighborhood Similarity Through Transportation Patterns In New York City (Short Paper)
Abstract
Though numerous studies have examined human mobility within an urban environment, few have explored the concept of urban vitality purely through the lens of urban transportation. Given the importance of different modes of transportation within a city, such analysis is necessary. In this short paper, we introduce the novel concept of mobility vitality by integrating human mobility and urban vitality, offering a multilayered framework to assess the degree of transportation and mobility within and between regions. The mobility patterns of three transportation modes, namely subway, taxicab, and bike-share, are first examined independently. These patterns are then aggregated to form the composite measure of static mobility vitality. Through this measure, we evaluate similarities between neighborhoods. Our results observed significant spatial differences in the travel patterns of three transportation modes on weekdays and weekends. Moreover, neighborhoods with high static mobility vitality have relatively similar mobility patterns. Ultimately, this approach aims to find neighborhoods with imbalanced transportation infrastructure or inadequate public.
Cite as
Dan Qiang and Grant McKenzie. Mobility Vitality: Assessing Neighborhood Similarity Through Transportation Patterns In New York City (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 61:1-61:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{qiang_et_al:LIPIcs.GIScience.2023.61,
author = {Qiang, Dan and McKenzie, Grant},
title = {{Mobility Vitality: Assessing Neighborhood Similarity Through Transportation Patterns In New York City}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {61:1--61:6},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.61},
URN = {urn:nbn:de:0030-drops-189566},
doi = {10.4230/LIPIcs.GIScience.2023.61},
annote = {Keywords: mobility vitality, mobility similarity, transportation, bike-sharing, taxi, subway, New York City}
}
Document
Short Paper
An Evaluation of the Impact of Ignition Location Uncertainty on Forest Fire Ignition Prediction Using Bayesian Logistic Regression (Short Paper)
Abstract
This study investigates the impact of location uncertainty on the predictive performance of Bayesian Logistic Regression (BLR) for forest fire ignition prediction in Austria. Historical forest fire ignitions are used to create a dataset for training models with the capability to assess the general forest fire ignition susceptibility. Each recorded fire ignition contains a timestamp, the estimated location of the ignition and a radius defining the area within which the unknown true location of the ignition point is located. As the values of the predictive features are calculated based on the assumed location, and not the unknown true location, the training data is biased due to input uncertainties. This study is set to assess the impact of input data uncertainty on the predictive performance of the model. For this we use a data binning approach that splits the input data into groups based on their location uncertainty and use them later for training multiple BLR models. The predictive performance of the models is then compared based on their accuracy, area under the receiver operating characteristic curve (AUC) scores and brier scores. The study revealed that higher location uncertainty leads to decreased accuracy and AUC score, accompanied by an increase in the brier score, while demonstrating that the BLR model trained on a smaller high-quality dataset outperforms the model trained on the full dataset, despite its smaller size. The study’s contribution is to provide insights into the practical implications of location uncertainty on the quality of forest fire susceptibility predictions, with potential implications for forest risk management and forest fire documentation.
Cite as
David Röbl, Rizwan Bulbul, Johannes Scholz, Mortimer M. Müller, and Harald Vacik. An Evaluation of the Impact of Ignition Location Uncertainty on Forest Fire Ignition Prediction Using Bayesian Logistic Regression (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 62:1-62:7, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{robl_et_al:LIPIcs.GIScience.2023.62,
author = {R\"{o}bl, David and Bulbul, Rizwan and Scholz, Johannes and M\"{u}ller, Mortimer M. and Vacik, Harald},
title = {{An Evaluation of the Impact of Ignition Location Uncertainty on Forest Fire Ignition Prediction Using Bayesian Logistic Regression}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {62:1--62:7},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.62},
URN = {urn:nbn:de:0030-drops-189576},
doi = {10.4230/LIPIcs.GIScience.2023.62},
annote = {Keywords: Forest Fire Prediction, Ignition Location Uncertainty, Bayesian Logistic Regression, Bayesian Inference, Probabilistic Programming}
}
Document
Short Paper
Calculating Shadows with U-Nets for Urban Environments (Short Paper)
Abstract
Shadow calculation is an important prerequisite for many urban and environmental analyses such as the assessment of solar energy potential. We propose a neural net approach that can be trained with 3D geographical information and predict the presence and depth of shadows. We adapt a U-Net algorithm traditionally used in biomedical image segmentation and train it on sections of Styria, Austria. Our two-step approach first predicts binary existence of shadows and then estimates the depth of shadows as well. Our results on the case study of Styria, Austria show that the proposed approach can predict in both models shadows with over 80% accuracy which is satisfactory for real-world applications, but still leaves room for improvement.
Cite as
Dominik Rothschedl, Franz Welscher, Franziska Hübl, Ivan Majic, Daniele Giannandrea, Matthias Wastian, Johannes Scholz, and Niki Popper. Calculating Shadows with U-Nets for Urban Environments (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 63:1-63:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{rothschedl_et_al:LIPIcs.GIScience.2023.63,
author = {Rothschedl, Dominik and Welscher, Franz and H\"{u}bl, Franziska and Majic, Ivan and Giannandrea, Daniele and Wastian, Matthias and Scholz, Johannes and Popper, Niki},
title = {{Calculating Shadows with U-Nets for Urban Environments}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {63:1--63:6},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.63},
URN = {urn:nbn:de:0030-drops-189581},
doi = {10.4230/LIPIcs.GIScience.2023.63},
annote = {Keywords: Neural Net, U-Net, Residual Net, Shadow Calculation}
}
Document
Short Paper
Beware the Rise of Models When They Are Wrong: A Look at Heat Vulnerability Modeling Through the Lens of Sensitivity (Short Paper)
Abstract
Extreme heat affects communities across the globe and is likely to increase as the climate changes; however, its consequences are not uniform. Geographically weighted regression is a useful modeling effort to understand the spatial linkage between various factors to heat-related casualty and supports decision-making in the spatial context. Still, as every complex spatial modeling approach, it is also bounded by uncertainty. Understanding model uncertainty and how this uncertainty is related to model input can be revealed by sensitivity analysis. In this study, we applied a spatial global sensitivity analysis to assess the model dynamics to address which input factors need to be prioritized in decision-making. A visual representation of the model’s sensitivity and the spatial pattern of factor influence is an important step toward establishing a robust confidence mechanism for understanding heat vulnerability and supporting policy-making.
Cite as
Seda Şalap-Ayça and Erica Akemi Goto. Beware the Rise of Models When They Are Wrong: A Look at Heat Vulnerability Modeling Through the Lens of Sensitivity (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 64:1-64:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{salapayca_et_al:LIPIcs.GIScience.2023.64,
author = {\c{S}alap-Ay\c{c}a, Seda and Goto, Erica Akemi},
title = {{Beware the Rise of Models When They Are Wrong: A Look at Heat Vulnerability Modeling Through the Lens of Sensitivity}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {64:1--64:6},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.64},
URN = {urn:nbn:de:0030-drops-189594},
doi = {10.4230/LIPIcs.GIScience.2023.64},
annote = {Keywords: heat vulnerability, uncertainty, sensitivity analysis}
}
Document
Short Paper
From Change Detection to Change Analytics: Decomposing Multi-Temporal Pixel Evolution Vectors (Short Paper)
Abstract
Change detection is a well-established process of detaining spatial and temporal changes of entities between two or more timesteps. Current advancements in digital map processing offer vast new sources of multitemporal geodata. As the temporal aspect gains complexity, the dismantling of detected changes on a pixel-based scale becomes a costly undertaking. In efforts to establish and preserve the evolution of detected changes in long time series, this paper presents a method that allows the decomposition of pixel evolution vectors into three dimensions of change, described as directed change, change variability, and change magnitude. The three dimensions of change compile to complex change analytics per individual pixels and offer a multi-faceted analysis of landscape changes on an ordinal scale. Finally, the integration of class confidence from learned uncertainty estimates illustrates the avenue to include uncertainty into the here presented change analytics, and the three dimensions of change are visualized in complex change maps.
Cite as
Victoria Scherelis, Patrick Laube, and Michael Doering. From Change Detection to Change Analytics: Decomposing Multi-Temporal Pixel Evolution Vectors (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 65:1-65:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{scherelis_et_al:LIPIcs.GIScience.2023.65,
author = {Scherelis, Victoria and Laube, Patrick and Doering, Michael},
title = {{From Change Detection to Change Analytics: Decomposing Multi-Temporal Pixel Evolution Vectors}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {65:1--65:6},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.65},
URN = {urn:nbn:de:0030-drops-189604},
doi = {10.4230/LIPIcs.GIScience.2023.65},
annote = {Keywords: Digital map processing, spatio-temporal modelling, land-use change}
}
Document
Short Paper
How to Count Travelers Without Tracking Them Between Locations (Short Paper)
Abstract
Understanding the movements of travelers is essential for sustainable city planning, and unique identifiers from wireless network access points or smart card check-ins provide the necessary information to count and track individuals as they move between locations. Nevertheless, it is challenging to deal with such uniquely identifying data in a way that does not violate the privacy of individuals. Even though several protection techniques have been proposed, the data they produce can often still be used to track down specific individuals when combined with other external information. To address this issue, we use a novel method based on encrypted Bloom filters. These probabilistic data structures are used to represent sets while preserving privacy under strong cryptographic guarantees. In our setup, encrypted Bloom filters offer statistical counts of travelers as the only accessible information. However, the probabilistic nature of Bloom filters may lead to undercounting or overcounting of travelers, affecting accuracy. We explain our privacy-preserving method and examine the accuracy of counting the number of travelers as they move between locations. To accomplish this, we used a simulated subway dataset. The results indicate that it is possible to achieve highly accurate counting while ensuring that data cannot be used to trace and identify an individual.
Cite as
Nadia Shafaeipour, Maarten van Steen, and Frank O. Ostermann. How to Count Travelers Without Tracking Them Between Locations (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 66:1-66:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{shafaeipour_et_al:LIPIcs.GIScience.2023.66,
author = {Shafaeipour, Nadia and van Steen, Maarten and Ostermann, Frank O.},
title = {{How to Count Travelers Without Tracking Them Between Locations}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {66:1--66:6},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.66},
URN = {urn:nbn:de:0030-drops-189610},
doi = {10.4230/LIPIcs.GIScience.2023.66},
annote = {Keywords: Privacy preservation, encrypted Bloom filters, traveler counting, subway networks}
}
Document
Short Paper
A Personalised Pedestrian Navigation System (Short Paper)
Abstract
Many existing navigation systems facilitate pedestrian routing but lack the provision of personalised route alternatives tailored to individual needs. Previous research suggests that pedestrians often prioritise factors such as safety or accessibility over the shortest possible route. This paper investigates ways to enhance existing pedestrian navigation systems and improve walking experiences by providing personalised routes based on walking preferences. This is achieved by defining a set of routing preferences and implementing a modified version of Dijkstra’s algorithm. The goal of this work is to promote walking by enhancing mobility, accessibility, comfort, and safety.
Cite as
Urmi Shah and Jia Wang. A Personalised Pedestrian Navigation System (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 67:1-67:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{shah_et_al:LIPIcs.GIScience.2023.67,
author = {Shah, Urmi and Wang, Jia},
title = {{A Personalised Pedestrian Navigation System}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {67:1--67:6},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.67},
URN = {urn:nbn:de:0030-drops-189628},
doi = {10.4230/LIPIcs.GIScience.2023.67},
annote = {Keywords: Pedestrian, Navigation, Walking, Preference, Graph}
}
Document
Short Paper
Estimating the Impact of a Flood Event on Property Value and Its Diminished Effect over Time (Short Paper)
Abstract
With the increase in natural disasters, flood events have become more frequent and severe calling for mortgage industries to take immediate steps to mitigate the financial risk posed by floods. This study looked more closely at the underlying effects of flood disasters on historical house prices as part of a climatic stress test. The discount applied on house prices due to a flood event was achieved by leveraging a causal inference approach supported by machine learning algorithms on repeat sales property and historic flood data. While the Average Treatment Effect (ATE) was employed to estimate the effect of a flood event on house prices in an area, the Conditional Average Treatment Effect (CATE) aided in overcoming the heterogeneous nature of the data by calculating the flood effect on property prices of each postcode. LightGBM as a base estimator of the causal model worked as an advantage to capture the nonlinear relationship between the features and the outcome variable and further allowed us to interpret the contribution of each feature towards the decay of these discounts using SHAP values.
Cite as
Nazia Ferdause Sodial, Oleksandr Galkin, and Aidan Slingsby. Estimating the Impact of a Flood Event on Property Value and Its Diminished Effect over Time (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 68:1-68:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{sodial_et_al:LIPIcs.GIScience.2023.68,
author = {Sodial, Nazia Ferdause and Galkin, Oleksandr and Slingsby, Aidan},
title = {{Estimating the Impact of a Flood Event on Property Value and Its Diminished Effect over Time}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {68:1--68:6},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.68},
URN = {urn:nbn:de:0030-drops-189638},
doi = {10.4230/LIPIcs.GIScience.2023.68},
annote = {Keywords: Flood, Causal Inference, Machine Learning, Property Analytics}
}
Document
Short Paper
Development and Operationalisation of Local Sustainability Indicators - A Global South Perspective on Data Challenges and Opportunities for GIScience (Short Paper)
Abstract
Evaluating and monitoring the sustainable development of nations and cities requires sets of indicators. Such indicator sets should measure equity, health, environmental, or governmental progress or recess - among other sustainability aspects. In 2015 the United Nations ratified 17 Sustainable Development Goals (SDG) assessed through 231 indicators. However, other - local - sets of indicators have been developed too. In this paper we review geodata challenges that emerged when we developed four sustainability indicator sets in Chile. Faced challenges include (geo)data availability and data representativeness, among others. We analyse how GIScience knowledge has contributed to indicator development and outline three priority research topics: (i) updating indicators based on automated processes, while respecting representativeness, (ii) tools for planning scenario generation, and (iii) methods for short- and long-term forecasting.
Cite as
Stefan Steiniger, Carolina Rojas, Ricardo Truffello, and Jonathan Barton. Development and Operationalisation of Local Sustainability Indicators - A Global South Perspective on Data Challenges and Opportunities for GIScience (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 69:1-69:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{steiniger_et_al:LIPIcs.GIScience.2023.69,
author = {Steiniger, Stefan and Rojas, Carolina and Truffello, Ricardo and Barton, Jonathan},
title = {{Development and Operationalisation of Local Sustainability Indicators - A Global South Perspective on Data Challenges and Opportunities for GIScience}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {69:1--69:6},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.69},
URN = {urn:nbn:de:0030-drops-189649},
doi = {10.4230/LIPIcs.GIScience.2023.69},
annote = {Keywords: geographic information, SDGs, indicators, sustainable development, Chile}
}
Document
Short Paper
Assessing Epidemic Spreading Potential with Encounter Network (Short Paper)
Abstract
Densely populated urban public transportation systems can provide inducive environments for transmitting viruses via close human contact or touching contaminated surfaces. In network analysis, Betweenness Centrality (BC) has been used as the primary metric to measure a node’s communication with others. This research extends from the concept of BC and develops new measures to assess the risk of transmitting disease through public transportation links. Three new concepts are introduced: source Total Betweenness centrality (TBC), target TBC, and Encounter Network. From a network node (source node), the set of shortest paths from that node to all other nodes composes a sub-graph (tree). The source TBC of this node is defined as the sum of BC of all edges of this tree. Similarly, using the shortest path tree consists of the set of the shortest paths from all nodes to the node as the destination, the target TBC of the node is defined as the sum of BC of all edges of this tree. Both TBC can be weighted by edge characteristics such as travel time or trip volume. Another new concept, Encounter Network, is constructed as the intersection between all source-target pairs of the public transportation network. We use the source TBC of a node to evaluate the relative risk of transmitting the disease from that node to other nodes. In contrast, the target TBC of a node can be used to assess the relative risk of being infected by a virus transmitted from other nodes to that node. A preliminary case study is conducted to illustrate the process and results.
Cite as
Behnam Tahmasbi, Farnoosh Roozkhosh, and X. Angela Yao. Assessing Epidemic Spreading Potential with Encounter Network (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 70:1-70:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{tahmasbi_et_al:LIPIcs.GIScience.2023.70,
author = {Tahmasbi, Behnam and Roozkhosh, Farnoosh and Yao, X. Angela},
title = {{Assessing Epidemic Spreading Potential with Encounter Network}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {70:1--70:6},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.70},
URN = {urn:nbn:de:0030-drops-189657},
doi = {10.4230/LIPIcs.GIScience.2023.70},
annote = {Keywords: Encounter Network, Total Betweenness Centrality, Complex Network, Epidemic spreading, Transmission risk, Public Transportation}
}
Document
Short Paper
Inferring the History of Spatial Diffusion Processes (Short Paper)
Abstract
When studying the spatial diffusion of a phenomenon, we often know its geographic distribution at one or more snapshots in time, while the complete history of the diffusion process is unknown. For example, we know when and where the first Indo-European languages arrived in South America and their current distribution. However, we do not know the history of how these languages spread, displacing the indigenous languages from their original habitat. We present a Bayesian model to interpolate the history of a diffusion process between two points in time with known geographical distributions. We apply the model to recover the spread of the Indo-European languages in South America and infer a posterior distribution of possible evolutionary histories of how they expanded their areas since the time of the first invasion by Europeans. Our model is more generally applicable to infer the evolutionary history of geographic diffusion phenomena from incomplete data.
Cite as
Takuya Takahashi, Geneviève Hannes, Nico Neureiter, and Peter Ranacher. Inferring the History of Spatial Diffusion Processes (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 71:1-71:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{takahashi_et_al:LIPIcs.GIScience.2023.71,
author = {Takahashi, Takuya and Hannes, Genevi\`{e}ve and Neureiter, Nico and Ranacher, Peter},
title = {{Inferring the History of Spatial Diffusion Processes}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {71:1--71:6},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.71},
URN = {urn:nbn:de:0030-drops-189662},
doi = {10.4230/LIPIcs.GIScience.2023.71},
annote = {Keywords: Bayesian inference, geographic diffusion, language evolution, Indo-European, colonisation of the Americas}
}
Document
Short Paper
Modelling Affordances as Emergent Phenomena (Short Paper)
Abstract
Affordances are an important basis for many human-environment interactions such as navigation or geo-design. In this short paper we present an approach to modelling affordances based on treating affordances as emergent phenomena in an agent-based simulation. We use the notion of an affordance schema to represent the setting in which the emergence of an affordance is made possible. We use a case study to show that (unexpected) affordances emerge during the course of the simulation. While the general approach is promising and may be used for other emergent phenomena such as landmarks, we also acknowledge and discuss the problems incurred during the modelling process. The paper closes with a reflection and some ideas for future work.
Cite as
Sabine Timpf and Franziska Klügl. Modelling Affordances as Emergent Phenomena (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 72:1-72:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{timpf_et_al:LIPIcs.GIScience.2023.72,
author = {Timpf, Sabine and Kl\"{u}gl, Franziska},
title = {{Modelling Affordances as Emergent Phenomena}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {72:1--72:6},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.72},
URN = {urn:nbn:de:0030-drops-189672},
doi = {10.4230/LIPIcs.GIScience.2023.72},
annote = {Keywords: agent-based modelling, cognitive engineering, spatial cognition, theory of modelling}
}
Document
Short Paper
The FogDetector: A User Survey to Measure Disorientation in Pan-Scalar Maps (Short Paper)
Abstract
When we navigate into interactive multi-scale maps that we call pan-scalar maps, it is usual to feel disoriented. This is partly due to the fact that map views do not always contain visual cues of the location of the past map views of the navigation. This paper presents an online study that seeks to understand and measure this disorientation occurring when zooming in or out of a pan-scalar map. An online study was designed and more than 150 participants finished the survey. The study shows a very small difference between the time to succeed in the memorising task after a zoom and a pan, but the difference is more significant when we compare zooming in with a large scale gap to panning. The study also shows that disorientation is not similar when zooming in and zooming out.
Cite as
Guillaume Touya and Justin Berli. The FogDetector: A User Survey to Measure Disorientation in Pan-Scalar Maps (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 73:1-73:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{touya_et_al:LIPIcs.GIScience.2023.73,
author = {Touya, Guillaume and Berli, Justin},
title = {{The FogDetector: A User Survey to Measure Disorientation in Pan-Scalar Maps}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {73:1--73:6},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.73},
URN = {urn:nbn:de:0030-drops-189686},
doi = {10.4230/LIPIcs.GIScience.2023.73},
annote = {Keywords: disorientation, zoom, pan, multi-scale map, desert fog, user survey}
}
Document
Short Paper
An Interpretable Index of Social Vulnerability to Environmental Hazards (Short Paper)
Abstract
Index-based measures of social vulnerability to environmental hazards are commonly modeled from composites of population-level risk factors. These models overlook individual context in communities' experiences of environmental hazards, producing metrics that may hinder spatial decision support for mitigating and responding to hazards. This paper introduces an interpretable, high-resolution model for generating an individual-oriented social vulnerability index (IOSVI) for the United States built on synthetic populations that couples individual and social determinants of vulnerability. The IOSVI combines an individual vulnerability index (IVI) that ranks individuals in an area’s synthetic population based on intersecting risk factors, with a social vulnerability index (SVI) based on the population’s cumulative distribution of IVI scores. Interpretability of the IOSVI procedure is demonstrated through examples of national, metropolitan, and neighborhood (census tract) level spatial variation in index scores and IVI themes, as well as an exploratory analysis examining risk factors affecting a specific sub-population (military veterans) in areas of high social and environmental vulnerability.
Cite as
Joseph V. Tuccillo. An Interpretable Index of Social Vulnerability to Environmental Hazards (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 74:1-74:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{tuccillo:LIPIcs.GIScience.2023.74,
author = {Tuccillo, Joseph V.},
title = {{An Interpretable Index of Social Vulnerability to Environmental Hazards}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {74:1--74:6},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.74},
URN = {urn:nbn:de:0030-drops-189699},
doi = {10.4230/LIPIcs.GIScience.2023.74},
annote = {Keywords: Social Vulnerability, Environmental Hazard, Synthetic Population, Census, Veteran}
}
Document
Short Paper
Power of GIS Mapping: ATLAS Flood Maps 2022 (Short Paper)
Abstract
In this paper, we are introducing an efficient method based on the GIS technology, to design data immediate and analysis-ready mapping from open GIS and remote sensing data, vector and raster data into a single visualization to facilitate fast and flexible mapping, also referred to as ATLAS maps. The Google Earth Engine approach is used to pre-process the satellite data, while ArcGIS software is to integrate all the data layers. Since the ArcGIS software is included as a default dependency in GIS and remote sensing data, the proposed method provides a cross-platform and single-technology solution for handling flood mapping. For now, we conducted flood analysis using the latest open data for Pakistan and Nigeria countries, then elaborated on the advantages of each data for flood mapping with respect to inundated areas, rainfall analysis, and affected populations, health, and education facilities. Given a wide range of tasks that can benefit from the method, future work will extend the methodology to heterogeneous geodata (vector and raster) to support seamless and make it automatic interfaces.
Cite as
Munazza Usmani, Hafiz Muhammad Tayyab Bhatti, Francesca Bovolo, and Maurizio Napolitano. Power of GIS Mapping: ATLAS Flood Maps 2022 (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 75:1-75:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{usmani_et_al:LIPIcs.GIScience.2023.75,
author = {Usmani, Munazza and Bhatti, Hafiz Muhammad Tayyab and Bovolo, Francesca and Napolitano, Maurizio},
title = {{Power of GIS Mapping: ATLAS Flood Maps 2022}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {75:1--75:6},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.75},
URN = {urn:nbn:de:0030-drops-189709},
doi = {10.4230/LIPIcs.GIScience.2023.75},
annote = {Keywords: GIS, Disaster Mapping, Open Data, Geospatial Technology, Remote Sensing}
}
Document
Short Paper
A Data-Driven Decision-Making Framework for Spatial Agent-Based Models of Infectious Disease Spread (Short Paper)
Abstract
Agent-based models (ABMs) are powerful tools used for better understanding, predicting, and responding to diseases. ABMs are well-suited to represent human health behaviors, a key driver of disease spread. However, many existing ABMs of infectious respiratory disease spread oversimplify or ignore behavioral aspects due to limited data and the variety of behavioral theories available. Therefore, this study aims to develop and implement a data-driven framework for agent decision-making related to health behaviors in geospatial ABMs of infectious disease spread. The agent decision-making framework uses a logistic regression model expressed in the form of odds ratios to calculate the probability of adopting a behavior. The framework is integrated into a geospatial ABM that simulates the spread of COVID-19 and mask usage among the student population at George Mason University in Fall 2021. The framework leverages odds ratios, which can be derived from surveys or open data, and can be modified to incorporate variables identified by behavioral theories. This advancement will offer the public and decision-makers greater insight into disease transmission, accurate predictions on disease outcomes, and preparation for future infectious disease outbreaks.
Cite as
Emma Von Hoene, Amira Roess, and Taylor Anderson. A Data-Driven Decision-Making Framework for Spatial Agent-Based Models of Infectious Disease Spread (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 76:1-76:7, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{vonhoene_et_al:LIPIcs.GIScience.2023.76,
author = {Von Hoene, Emma and Roess, Amira and Anderson, Taylor},
title = {{A Data-Driven Decision-Making Framework for Spatial Agent-Based Models of Infectious Disease Spread}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {76:1--76:7},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.76},
URN = {urn:nbn:de:0030-drops-189712},
doi = {10.4230/LIPIcs.GIScience.2023.76},
annote = {Keywords: Agent-based model, geographic information science, disease simulation, COVID-19, agent behavior, mask use}
}
Document
Short Paper
How to Improve Joint Suitability Mapping for Search Space Reduction? (Short Paper)
Abstract
Geoforensic analyses are used to identify the location history of objects or people of interest. An effective method for location history identification is to use joint probability or suitability of trace materials. Species distribution models have been used to derive joint suitability distributions using suitable biotic trace evidence such as pollen. One of the key objectives for such analyses is to effectively reduce potential search space and search effort for investigators. This research presents a novel framework for modeling the habitat suitability of pollen identified at the plant species-level to generate joint suitability maps. We provide major limitations and challenges faced by current geolocation analyses based on species distribution models, including opportunities to improve the joint suitability analyses for search space reduction. A conditional probability approach for geolocation identification is also demonstrated for possible future applications in real-world forensic cases.
Cite as
Haoyu Wang and Jennifer A. Miller. How to Improve Joint Suitability Mapping for Search Space Reduction? (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 77:1-77:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{wang_et_al:LIPIcs.GIScience.2023.77,
author = {Wang, Haoyu and Miller, Jennifer A.},
title = {{How to Improve Joint Suitability Mapping for Search Space Reduction?}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {77:1--77:6},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.77},
URN = {urn:nbn:de:0030-drops-189723},
doi = {10.4230/LIPIcs.GIScience.2023.77},
annote = {Keywords: forensic geolocation, species distribution modeling, conditional probability, search space reduction}
}
Document
Short Paper
Navigation in Complex Space: An Bayesian Nash Equilibrium-Informed Agent-Based Model (Short Paper)
Abstract
This study proposed an improved pedestrian evacuation ABM employing Bayesian Nash Equilibrium (BNE) to simulate more realistic and representative individual evacuating behaviours in complex scenarios. A set of vertical blockades with adjustable gate widths was introduced to establish a simulation space with narrow corridor and bottlenecks and to evaluate the influences of BNE on individual navigation in complex space. To better match with the evacuating behaviours in real-world scenarios, the decision-making criterion of BNE evacuees was improved to a multi-strategy combination, with 80% of evacuees taking the optimal strategy, 15% taking sub-optimal strategy, and 5% taking the third-best one. The preliminary results demonstrate a positive impact of BNE on individual navigation in complex space, showing a distinct decrease of evacuation time with increasing proportion of BNE evacuees. The non-monotonicity of the variations in evacuation time also indicates the dynamic adaptability of BNE in addressing immediate challenges (i.e. blockades and congestions), which identifies alternative and potential faster paths during evacuations. A detailed description of the proposed ABM and an analysis of relevant experimental results are provided in this paper. Several limitations are also identified.
Cite as
Yiyu Wang, Jiaqi Ge, and Alexis Comber. Navigation in Complex Space: An Bayesian Nash Equilibrium-Informed Agent-Based Model (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 78:1-78:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{wang_et_al:LIPIcs.GIScience.2023.78,
author = {Wang, Yiyu and Ge, Jiaqi and Comber, Alexis},
title = {{Navigation in Complex Space: An Bayesian Nash Equilibrium-Informed Agent-Based Model}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {78:1--78:6},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.78},
URN = {urn:nbn:de:0030-drops-189739},
doi = {10.4230/LIPIcs.GIScience.2023.78},
annote = {Keywords: Agent-based Modelling, Pedestrian Evacuation, Bayesian Nash Equilibrium, Individual Navigation, Complex Environment}
}
Document
Short Paper
Application of GIS in Public Health Practice: A Consortium’s Approach to Tackling Travel Delays in Obstetric Emergencies in Urban Areas (Short Paper)
Abstract
Geographic Information System (GIS) has become an effective and reliable tool for researchers, policymakers, and decision-makers to map health outcomes and inform targeted planning, evaluation, and monitoring. With the advent of big data-enabled GIS, researchers can now identify disparities and spatial inequalities in health at more granular levels, enabling them to provide more accurate and robust services and products for healthcare. This paper aims to showcase the progress of the On Tackling In-transit Delays for Mothers in Emergency (OnTIME) project, which is a unique collaborative effort between academia, policymakers, and industrial partners. The paper demonstrates how the limitations of traditional spatial accessibility models and data gaps have been overcome by combining GIS and big data to map the geographic accessibility and coverage of health facilities capable of providing emergency obstetric care (EmOC) in conurbations in Africa. The OnTIME project employs various GIS technologies and concepts, such as big spatial data, spatial databases, and public participation geographic information systems (PPGIS). We provide an overview of these concepts in relation to the OnTIME project to demonstrate the application of GIS in public health practice.
Cite as
Jia Wang, Itohan Osayande, Peter M. Macharia, Prestige Tatenda Makanga, Kerry L. M. Wong, Tope Olubodun, Uchenna Gwacham-Anisiobi, Olakunmi Ogunyemi, Abimbola Olaniran, Ibukun-Oluwa O. Abejirinde, Lenka Beňová, Bosede B. Afolabi, and Aduragbemi Banke-Thomas. Application of GIS in Public Health Practice: A Consortium’s Approach to Tackling Travel Delays in Obstetric Emergencies in Urban Areas (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 79:1-79:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{wang_et_al:LIPIcs.GIScience.2023.79,
author = {Wang, Jia and Osayande, Itohan and Macharia, Peter M. and Makanga, Prestige Tatenda and Wong, Kerry L. M. and Olubodun, Tope and Gwacham-Anisiobi, Uchenna and Ogunyemi, Olakunmi and Olaniran, Abimbola and Abejirinde, Ibukun-Oluwa O. and Be\v{n}ov\'{a}, Lenka and Afolabi, Bosede B. and Banke-Thomas, Aduragbemi},
title = {{Application of GIS in Public Health Practice: A Consortium’s Approach to Tackling Travel Delays in Obstetric Emergencies in Urban Areas}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {79:1--79:6},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.79},
URN = {urn:nbn:de:0030-drops-189748},
doi = {10.4230/LIPIcs.GIScience.2023.79},
annote = {Keywords: GIS, Public Health, Accessibility, OnTIME, EmOC, Public Participation GIS, Big Data, Google}
}
Document
Short Paper
The Ups and Downs of London High Streets Throughout COVID-19 Pandemic: Insights from Footfall-Based Clustering Analysis (Short Paper)
Abstract
As an important part of the economic and social fabric of urban areas, high streets were hit hard during the COVID-19 pandemic, resulting in massive closures of shops and plunge of footfall. To better understand how high streets respond to and recover from the pandemic, this paper examines the performance of London’s high streets, focusing on footfall-based clustering analysis. Applying time series clustering to longitudinal footfall data derived from a mobile phone GPS dataset spanning over two years, we identify distinct groups of high streets with similar footfall change patterns. By analysing the resulting clusters' footfall dynamics, composition and geographic distribution, we uncover the diverse responses of different high streets to the pandemic disruption. Furthermore, we explore the factors driving specific footfall change patterns by examining the number of local and nonlocal visitors. This research addresses gaps in the existing literature by presenting a holistic view of high street responses throughout the pandemic and providing in-depth analysis of footfall change patterns and underlying causes. The implications and insights can inform strategies for the revitalisation and redevelopment of high streets in the post-pandemic era.
Cite as
Xinglei Wang, Xianghui Zhang, and Tao Cheng. The Ups and Downs of London High Streets Throughout COVID-19 Pandemic: Insights from Footfall-Based Clustering Analysis (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 80:1-80:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{wang_et_al:LIPIcs.GIScience.2023.80,
author = {Wang, Xinglei and Zhang, Xianghui and Cheng, Tao},
title = {{The Ups and Downs of London High Streets Throughout COVID-19 Pandemic: Insights from Footfall-Based Clustering Analysis}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {80:1--80:6},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.80},
URN = {urn:nbn:de:0030-drops-189754},
doi = {10.4230/LIPIcs.GIScience.2023.80},
annote = {Keywords: High street, performance, footfall, clustering analysis, COVID-19}
}
Document
Short Paper
Agent-Based Modeling of Consumer Choice by Utilizing Crowdsourced Data and Deep Learning (Short Paper)
Abstract
People’s opinions are one of the defining factors that turn spaces into meaningful places. Online platforms such as Yelp allow users to publish their reviews on businesses. To understand reviewers' opinion formation processes and the emergent patterns of published opinions, we utilize natural language processing (NLP) techniques especially that of aspect-based sentiment analysis methods (a deep learning approach) on a geographically explicit Yelp dataset to extract and categorize reviewers' opinion aspects on places within urban areas. Such data is then used as a basis to inform an agent-based model, where consumers' (i.e., agents') choices are based on their characteristics and preferences. The results show the emergent patterns of reviewers' opinions and the influence of these opinions on others. As such this work demonstrates how using deep learning techniques on geospatial data can help advance our understanding of place and cities more generally.
Cite as
Boyu Wang and Andrew Crooks. Agent-Based Modeling of Consumer Choice by Utilizing Crowdsourced Data and Deep Learning (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 81:1-81:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{wang_et_al:LIPIcs.GIScience.2023.81,
author = {Wang, Boyu and Crooks, Andrew},
title = {{Agent-Based Modeling of Consumer Choice by Utilizing Crowdsourced Data and Deep Learning}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {81:1--81:6},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.81},
URN = {urn:nbn:de:0030-drops-189769},
doi = {10.4230/LIPIcs.GIScience.2023.81},
annote = {Keywords: aspect-category sentiment analysis, consumer choice, agent-based modeling, online restaurant reviews}
}
Document
Short Paper
Harnessing the Sunlight on Facades - an Approach for Determining Vertical Photovoltaic Potential (Short Paper)
Abstract
The paper deals with the calculation of the photovoltaic potential of vertical structures. Photovoltaic systems are a core technology for producing renewable energy. As roughly 50% of the population on planet Earth lives in urban environments, the production of renewable energy in urban contexts is of particular interest. As several papers have elaborated on the photovoltaic potential of roofs, this paper focuses on vertical structures. Hence, we present a methodology to extract facades suitable for photovoltaic installation, calculate their southness and percentage of shaded areas. The approach is successfully tested, based on a dataset located in the city of Graz, Styria (Austria). The results show the wall structures of each building, the respective shadow depth, and their score based on a multi-criteria analysis that represents the suitability for the installation of a photovoltaic system.
Cite as
Franz Welscher, Ivan Majic, Franziska Hübl, Rizwan Bulbul, and Johannes Scholz. Harnessing the Sunlight on Facades - an Approach for Determining Vertical Photovoltaic Potential (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 82:1-82:7, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{welscher_et_al:LIPIcs.GIScience.2023.82,
author = {Welscher, Franz and Majic, Ivan and H\"{u}bl, Franziska and Bulbul, Rizwan and Scholz, Johannes},
title = {{Harnessing the Sunlight on Facades - an Approach for Determining Vertical Photovoltaic Potential}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {82:1--82:7},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.82},
URN = {urn:nbn:de:0030-drops-189777},
doi = {10.4230/LIPIcs.GIScience.2023.82},
annote = {Keywords: Vertical Photovoltaics, Facades, Southness, Multi-Criteria-Analysis, Shadow}
}
Document
Short Paper
Betweenness Centrality in Spatial Networks: A Spatially Normalised Approach (Short Paper)
Abstract
Centrality metrics are essential to network analysis. They reveal important morphological properties of networks, indicating e.g. node or edge importance. Applications are manifold, ranging from biology to transport planning. However, while being commonly applied in spatial contexts such as urban analytics, the implications of the spatial configuration of network elements on these metrics are widely neglected. As a consequence, a systematic bias is introduced into spatial network analyses. When applied to real-world problems, unintended side effects and wrong conclusions might be the result. In this paper, we assess the impact of node density on betweenness centrality. Furthermore, we propose a method for computing spatially normalised betweenness centrality. We apply it to a theoretical case as well as real-world transport networks. Results show that spatial normalisation mitigates the prevalent bias of node density.
Cite as
Christian Werner and Martin Loidl. Betweenness Centrality in Spatial Networks: A Spatially Normalised Approach (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 83:1-83:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{werner_et_al:LIPIcs.GIScience.2023.83,
author = {Werner, Christian and Loidl, Martin},
title = {{Betweenness Centrality in Spatial Networks: A Spatially Normalised Approach}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {83:1--83:6},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.83},
URN = {urn:nbn:de:0030-drops-189781},
doi = {10.4230/LIPIcs.GIScience.2023.83},
annote = {Keywords: spatial network analysis, edge betweenness centrality, flow estimation, SIBC, spatial interaction, spatial centrality, urban analytics}
}
Document
Short Paper
Predicting visit frequencies to new places (Short Paper)
Abstract
Human mobility exhibits power-law distributed visitation patterns; i.e., a few locations are visited frequently and many locations only once. Current research focuses on the important locations of users or on recommending new places based on collective behaviour, neglecting the existence of scarcely visited locations. However, assessing whether a user will return to a location in the future is highly relevant for personalized location-based services. Therefore, we propose a new problem formulation aimed at predicting the future visit frequency to a new location, focusing on the previous mobility behaviour of a single user. Our preliminary results demonstrate that visit frequency prediction is a difficult task, but sophisticated learning models can detect insightful patterns in the historic mobility indicative of future visit frequency. We believe these models can uncover valuable insights into the spatial factors that drive individual mobility behaviour.
Cite as
Nina Wiedemann, Ye Hong, and Martin Raubal. Predicting visit frequencies to new places (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 84:1-84:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{wiedemann_et_al:LIPIcs.GIScience.2023.84,
author = {Wiedemann, Nina and Hong, Ye and Raubal, Martin},
title = {{Predicting visit frequencies to new places}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {84:1--84:6},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.84},
URN = {urn:nbn:de:0030-drops-189794},
doi = {10.4230/LIPIcs.GIScience.2023.84},
annote = {Keywords: Human mobility, Visitation patterns, Place recommendation, Next location prediction}
}
Document
Short Paper
Waffle Homes: Utilizing Aerial Imagery of Unfinished Buildings to Determine Average Room Size (Short Paper)
Abstract
A primary function of the Population Density Tables Project (PDT) at Oak Ridge National Laboratory is to produce residential population densities per 1000 sq. ft. for each country and their associated first-level administrative units. This is accomplished by utilizing the average size of different types of dwelling areas (urban, rural, single-family, multi-family, etc.) and the average household size provided by a country’s Census or statistical bureau records. This data is available for the majority of Europe, North America, and large swathes of Asia, but is less easily found in Africa and South America. In these regions, Censuses generally report dwelling area by number of rooms, which poses the challenging question of how we can translate number of rooms to dwelling size when no dwelling size areas are available with which to compare. Using sub-meter resolution satellite imagery of Accra, Ghana, this challenge can be tackled using imagery of roofless buildings currently under construction that show the interior floor plan of the dwelling. A sample of buildings from the different neighborhoods of Accra can be digitized to provide an estimate and range of average room sizes of dwellings. This average room size can then be translated to a total dwelling area using the "number of rooms occupied by a household" variable from the Ghanaian Census. This intermediate step between average dwelling size and number of rooms occupied, fills the missing link that prevents PDT from continually producing new population densities for countries where dwelling size is unavailable through any official means.
Cite as
Carson Woody and Tyler Frazier. Waffle Homes: Utilizing Aerial Imagery of Unfinished Buildings to Determine Average Room Size (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 85:1-85:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{woody_et_al:LIPIcs.GIScience.2023.85,
author = {Woody, Carson and Frazier, Tyler},
title = {{Waffle Homes: Utilizing Aerial Imagery of Unfinished Buildings to Determine Average Room Size}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {85:1--85:6},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.85},
URN = {urn:nbn:de:0030-drops-189804},
doi = {10.4230/LIPIcs.GIScience.2023.85},
annote = {Keywords: Urban Analytics, Aerial Imagery, Satellite Imagery, Population Density, Human Geography, Africa, Residential Dwellings}
}
Document
Short Paper
A Comparison of Global and Local Statistical and Machine Learning Techniques in Estimating Flash Flood Susceptibility (Short Paper)
Abstract
Flash floods, as a type of devastating natural disasters, can cause significant damage to infrastructure, agriculture, and people’s livelihoods. Mapping flash flood susceptibility has long been an effective measure to help with the development of flash flood risk reduction and management strategies. Recent studies have shown that machine learning (ML) techniques perform better than traditional statistical and process-based models in estimating flash flood susceptibility. However, a major limitation of standard ML models is that they ignore the local geographic context where flash floods occur. To address this limitation, we developed a local Geographically Weighted Random Forest (GWRF) model and compared its performance against other global and local statistical and ML alternatives using an empirical flash floods model of Jiangxi Province, China.
Cite as
Jing Yao, Ziqi Li, Xiaoxiang Zhang, Changjun Liu, and Liliang Ren. A Comparison of Global and Local Statistical and Machine Learning Techniques in Estimating Flash Flood Susceptibility (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 86:1-86:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{yao_et_al:LIPIcs.GIScience.2023.86,
author = {Yao, Jing and Li, Ziqi and Zhang, Xiaoxiang and Liu, Changjun and Ren, Liliang},
title = {{A Comparison of Global and Local Statistical and Machine Learning Techniques in Estimating Flash Flood Susceptibility}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {86:1--86:6},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.86},
URN = {urn:nbn:de:0030-drops-189815},
doi = {10.4230/LIPIcs.GIScience.2023.86},
annote = {Keywords: Machine Learning, Spatial Statistics, Flash floods, Susceptibility}
}
Document
Short Paper
Understand the Geography of Financial Precarity in England and Wales (Short Paper)
Abstract
Financial precarity is a growing and pressing issue in many countries, which refers to a precarious existence which lacks job security, predictability, and psychological or material welfare. Its negative effects can be observed in cognitive functioning, emotional stability and social inclusion. Financial precarity has been proved to be impacted by multifaceted factors ranging from poor quality, unpredictable work, unmanaged debt, insecure asset wealth and insufficient money and resource. However, the geographical variation of financial precarity and the embedded social-spatial inequalities remain understudied. This paper addresses this research gap by introducing a new geodemographic classification of financial precarity, which is developed from a series of small area measurements covering employment, income, asset, liability and lifestyle characteristics of neighbourhoods. The research is conducted within the spatial extent of England and Wales.
Cite as
Zi Ye and Alex Singleton. Understand the Geography of Financial Precarity in England and Wales (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 87:1-87:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{ye_et_al:LIPIcs.GIScience.2023.87,
author = {Ye, Zi and Singleton, Alex},
title = {{Understand the Geography of Financial Precarity in England and Wales}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {87:1--87:6},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.87},
URN = {urn:nbn:de:0030-drops-189828},
doi = {10.4230/LIPIcs.GIScience.2023.87},
annote = {Keywords: Financial precarity, Geodemographic classification, Household finance, Financial Wellbeing}
}
Document
Short Paper
Understanding Active Travel Networks Using GPS Data from an Outdoor Mapping App (Short Paper)
Abstract
To support a shift to active travel there is a vital need for better data to understand active travel networks: their extent, attributes and current utilisation. Using a big dataset of volunteered geographic information from an outdoor mapping smartphone app, a methodology has been developed to analyse recorded routes to identify missing links in a routable street and path network and to visualise the relative importance of different links of the active travel network. This methodology has then been used to analyse the network for a case study area around Winchester, UK, with new pathways equivalent to 8% of the existing network dataset identified. The automated method developed can be readily applied to other locations and the outputs used to augment existing network datasets and to inform the planning and development of active travel infrastructure.
Cite as
Marcus A. Young. Understanding Active Travel Networks Using GPS Data from an Outdoor Mapping App (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 88:1-88:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{young:LIPIcs.GIScience.2023.88,
author = {Young, Marcus A.},
title = {{Understanding Active Travel Networks Using GPS Data from an Outdoor Mapping App}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {88:1--88:6},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.88},
URN = {urn:nbn:de:0030-drops-189830},
doi = {10.4230/LIPIcs.GIScience.2023.88},
annote = {Keywords: active travel, map construction, GPS, volunteered geographic information}
}
Document
Short Paper
Geography and the Brain’s Spatial System (Short Paper)
Abstract
Extensive research in spatial cognition and mobility has advanced our knowledge about the effects of geographic settings on human behaviors. This study, however, takes an alternative perspective to examine how the brain’s spatial system may mediate the geographic effects on spatial behaviors. Our previous research using data from OpenStreetMap, SafeGraph POIs, and human participants from the National Alzheimer’s Coordinating Center (NACC) resulted in a model with 83.33% prediction accuracy from geographic settings to the zonal categorization of the cognitive state based on NACC participants. A follow-up study showed that the complexity of a geographic setting has a direct effect on cortical thickness in the brain’s spatial cell system. In this study, we leverage findings from the two studies and interrogate the geographic settings to discern environmental correlates to zonal cognitive categorization. We conclude with thoughts on the implications of brain-inspired GIScience.
Cite as
May Yuan and Kristen Kennedy. Geography and the Brain’s Spatial System (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 89:1-89:7, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{yuan_et_al:LIPIcs.GIScience.2023.89,
author = {Yuan, May and Kennedy, Kristen},
title = {{Geography and the Brain’s Spatial System}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {89:1--89:7},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.89},
URN = {urn:nbn:de:0030-drops-189847},
doi = {10.4230/LIPIcs.GIScience.2023.89},
annote = {Keywords: Brain, geographic complexity, mild cognitive impairment, Alzheimer’s Disease}
}
Document
Short Paper
Visual Methods for Representing Flow Space with Vector Fields (Short Paper)
Abstract
The issue of human mobility has been a focal point of research among numerous scholars in the field of geography for decades. Among them, the visualization of origin-destination (OD) data is an important branch of geographic flow studies. In this paper, we vectorize and represent immigration flows using OD flow data of U.S. immigrants in the year 2000, constructing an immigration space. Through data validation, it is confirmed that the vector field satisfies the Gauss’s theorem and is irrotational, demonstrating that the field can be derived from a potential and that the field is uniquely determined by the potential. Scalar potential fields are inferred from the vector field, providing a more intuitive and convenient description of the underlying flow patterns in geographical interaction matrices. Additionally, this paper employs potential fields and applies a density-equalizing areal cartogram to reconstruct the global representation of functional space, constructing cartogram maps based on potential magnitudes.
Cite as
Han Zhang, Zhaoya Gong, and Jean-Claude Thill. Visual Methods for Representing Flow Space with Vector Fields (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 90:1-90:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{zhang_et_al:LIPIcs.GIScience.2023.90,
author = {Zhang, Han and Gong, Zhaoya and Thill, Jean-Claude},
title = {{Visual Methods for Representing Flow Space with Vector Fields}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {90:1--90:6},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.90},
URN = {urn:nbn:de:0030-drops-189852},
doi = {10.4230/LIPIcs.GIScience.2023.90},
annote = {Keywords: interstate migration, vector field, areal cartogram, geographic visualization}
}
Document
Short Paper
Causal Effects Under Spatial Confounding and Interference (Short Paper)
Abstract
Spatial causal inference is an emerging field of research with wide ranging areas of applications. As a key methodological challenge, spatial confounding and spatial interference can compromise the performance of standard statistical inference methods. In the current literature, there is a lack of appreciation of the connections between spatial confounding and interference. This could potentially lead to overspecialized silos of research. Therefore, we need further research to bridge such gaps theoretically, and to find creative solutions for complex spatial causal inference problems. This short paper offers a brief demonstration: It discusses the connections between spatial confounding and interference. An illustrative simulation study shows how commonly used approaches compare across four test scenarios. The simulation study is discussed with an emphasis on the promising performance of counterfactual prediction based inference methods.
Cite as
Jing Zhang. Causal Effects Under Spatial Confounding and Interference (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 91:1-91:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{zhang:LIPIcs.GIScience.2023.91,
author = {Zhang, Jing},
title = {{Causal Effects Under Spatial Confounding and Interference}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {91:1--91:6},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.91},
URN = {urn:nbn:de:0030-drops-189865},
doi = {10.4230/LIPIcs.GIScience.2023.91},
annote = {Keywords: Spatial causal inference, confounding, interference, counterfactual}
}
Document
Short Paper
Unlocking the Power of Mobile Phone Application Data to Accelerate Transport Decarbonisation (Short Paper)
Abstract
Decarbonising transport is crucial in addressing climate change and achieving the Net Zero target. However, limitations arising from traditional data sources and methods obstruct the provision of individual travel information with comprehensive travel modes, high spatiotemporal granularity and updating frequency for achieving transport decarbonisation. Mobile phone application data, an essentially new form of data, can provide valuable travel information after effective mining and assist in progress monitoring, policy evaluation, and system optimisation in transport decarbonisation. This paper proposes a standardised methodology to unlock the power of mobile phone application data for supporting transport decarbonisation. Three typical cases are employed to demonstrate the capabilities of the generated individual multimodal dataset, including monitoring Londoners’ 20-minute active travel target, transport GHGs emissions and their contributors, and evaluating small-scale transport interventions. The paper also discusses the limitations of mobile phone application data, such as issues surrounding data privacy and regulation.
Cite as
Xianghui Zhang and Tao Cheng. Unlocking the Power of Mobile Phone Application Data to Accelerate Transport Decarbonisation (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 92:1-92:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{zhang_et_al:LIPIcs.GIScience.2023.92,
author = {Zhang, Xianghui and Cheng, Tao},
title = {{Unlocking the Power of Mobile Phone Application Data to Accelerate Transport Decarbonisation}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {92:1--92:6},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.92},
URN = {urn:nbn:de:0030-drops-189873},
doi = {10.4230/LIPIcs.GIScience.2023.92},
annote = {Keywords: Transport decarbonisation, Mobile phone application data, Application, London}
}
Document
Short Paper
The Ethics of AI-Generated Maps: DALL·E 2 and AI’s Implications for Cartography (Short Paper)
Abstract
The rapid advancement of artificial intelligence (AI) such as the emergence of large language models ChatGPT and DALL·E 2 has brought both opportunities for improving productivity and raised ethical concerns. This paper investigates the ethics of using artificial intelligence (AI) in cartography, with a particular focus on the generation of maps using DALL·E 2. To accomplish this, we first created an open-sourced dataset that includes synthetic (AI-generated) and real-world (human-designed) maps at multiple scales with a variety of settings. We subsequently examined four potential ethical concerns that may arise from the characteristics of DALL·E 2 generated maps, namely inaccuracies, misleading information, unanticipated features, and irreproducibility. We then developed a deep learning-based model to identify those AI-generated maps. Our research emphasizes the importance of ethical considerations in the development and use of AI techniques in cartography, contributing to the growing body of work on trustworthy maps. We aim to raise public awareness of the potential risks associated with AI-generated maps and support the development of ethical guidelines for their future use.
Cite as
Qianheng Zhang, Yuhao Kang, and Robert Roth. The Ethics of AI-Generated Maps: DALL·E 2 and AI’s Implications for Cartography (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 93:1-93:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{zhang_et_al:LIPIcs.GIScience.2023.93,
author = {Zhang, Qianheng and Kang, Yuhao and Roth, Robert},
title = {{The Ethics of AI-Generated Maps: DALL·E 2 and AI’s Implications for Cartography}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {93:1--93:6},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.93},
URN = {urn:nbn:de:0030-drops-189886},
doi = {10.4230/LIPIcs.GIScience.2023.93},
annote = {Keywords: Ethics, GeoAI, DALL-E, Cartography}
}
Document
Short Paper
Digital Injustice: A Case Study of Land Use Classification Using Multisource Data in Nairobi, Kenya (Short Paper)
Abstract
The utilisation of big data has emerged as a critical instrument for land use classification and decision-making processes due to its high spatiotemporal accuracy and ability to diminish manual data collection. However, the reliability and feasibility of big data are still controversial, the most important of which is whether it can represent the whole population with justice. The present study incorporates multiple data sources to facilitate land use classification while proving the existence of data bias caused digital injustice. Using Nairobi, Kenya, as a case study and employing a random forest classifier as a benchmark, this research combines satellite imagery, night-time light images, building footprint, Twitter posts, and street view images. The findings of the land use classification also disclose the presence of data bias resulting from the inadequate coverage of social media and street view data, potentially contributing to injustice in big data-informed decision-making. Strategies to mitigate such digital injustice situations are briefly discussed here, and more in-depth exploration remains for future work.
Cite as
Wenlan Zhang, Chen Zhong, and Faith Taylor. Digital Injustice: A Case Study of Land Use Classification Using Multisource Data in Nairobi, Kenya (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 94:1-94:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{zhang_et_al:LIPIcs.GIScience.2023.94,
author = {Zhang, Wenlan and Zhong, Chen and Taylor, Faith},
title = {{Digital Injustice: A Case Study of Land Use Classification Using Multisource Data in Nairobi, Kenya}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {94:1--94:6},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.94},
URN = {urn:nbn:de:0030-drops-189899},
doi = {10.4230/LIPIcs.GIScience.2023.94},
annote = {Keywords: Data bias, Digital injustice, Multi-source sensor data, Land use classification, Random forest classifier}
}
Document
Short Paper
Exploring Map App Usage Behaviour Through Touchscreen Interactions (Short Paper)
Abstract
Mobile map apps are rapidly changing the way we live by providing a broad range of services such as mapping, travel support, public transport, and trip-booking. Despite their widespread use, understanding how people use these apps in their everyday lives is still a challenge. In order to design context-aware mobile map apps, it is important to understand mobile map app usage behaviour. In this study, we employed a novel approach of recording touchscreen interactions (taps) on mobile map apps and combined them with users' distances from their homes to capture everyday map app usage. We analysed data from 30 participants recorded between February 2021 and March 2022 and applied two different data-driven analysis techniques to evaluate map apps usage. Our results reveal two distinct tapping signatures: a "home behaviour", characterised by high interactions with map-related apps close to home, and a "travel behaviour", defined by lower interactions scattered over a range of distances. Our findings have important implications for future work in this field and demonstrate the potential of our new approach for understanding mobile map app usage behaviour.
Cite as
Donatella Zingaro, Mona Bartling, and Tumasch Reichenbacher. Exploring Map App Usage Behaviour Through Touchscreen Interactions (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 95:1-95:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
Copy BibTex To Clipboard
@InProceedings{zingaro_et_al:LIPIcs.GIScience.2023.95,
author = {Zingaro, Donatella and Bartling, Mona and Reichenbacher, Tumasch},
title = {{Exploring Map App Usage Behaviour Through Touchscreen Interactions}},
booktitle = {12th International Conference on Geographic Information Science (GIScience 2023)},
pages = {95:1--95:6},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-288-4},
ISSN = {1868-8969},
year = {2023},
volume = {277},
editor = {Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.95},
URN = {urn:nbn:de:0030-drops-189906},
doi = {10.4230/LIPIcs.GIScience.2023.95},
annote = {Keywords: mobile maps, tappigraphy, cluster analysis, archetypal analysis, user-context, map-app usage}
}