99 Search Results for "Wise, Sarah"


Volume

LIPIcs, Volume 277

12th International Conference on Geographic Information Science (GIScience 2023)

GIScience 2023, September 12-15, 2023, Leeds, UK

Editors: Roger Beecham, Jed A. Long, Dianna Smith, Qunshan Zhao, and Sarah Wise

Document
Position
Grounding Stream Reasoning Research

Authors: Pieter Bonte, Jean-Paul Calbimonte, Daniel de Leng, Daniele Dell'Aglio, Emanuele Della Valle, Thomas Eiter, Federico Giannini, Fredrik Heintz, Konstantin Schekotihin, Danh Le-Phuoc, Alessandra Mileo, Patrik Schneider, Riccardo Tommasini, Jacopo Urbani, and Giacomo Ziffer

Published in: TGDK, Volume 2, Issue 1 (2024): Special Issue on Trends in Graph Data and Knowledge - Part 2. Transactions on Graph Data and Knowledge, Volume 2, Issue 1


Abstract
In the last decade, there has been a growing interest in applying AI technologies to implement complex data analytics over data streams. To this end, researchers in various fields have been organising a yearly event called the "Stream Reasoning Workshop" to share perspectives, challenges, and experiences around this topic. In this paper, the previous organisers of the workshops and other community members provide a summary of the main research results that have been discussed during the first six editions of the event. These results can be categorised into four main research areas: The first is concerned with the technological challenges related to handling large data streams. The second area aims at adapting and extending existing semantic technologies to data streams. The third and fourth areas focus on how to implement reasoning techniques, either considering deductive or inductive techniques, to extract new and valuable knowledge from the data in the stream. This summary is written not only to provide a crystallisation of the field, but also to point out distinctive traits of the stream reasoning community. Moreover, it also provides a foundation for future research by enumerating a list of use cases and open challenges, to stimulate others to join this exciting research area.

Cite as

Pieter Bonte, Jean-Paul Calbimonte, Daniel de Leng, Daniele Dell'Aglio, Emanuele Della Valle, Thomas Eiter, Federico Giannini, Fredrik Heintz, Konstantin Schekotihin, Danh Le-Phuoc, Alessandra Mileo, Patrik Schneider, Riccardo Tommasini, Jacopo Urbani, and Giacomo Ziffer. Grounding Stream Reasoning Research. In Special Issue on Trends in Graph Data and Knowledge - Part 2. Transactions on Graph Data and Knowledge (TGDK), Volume 2, Issue 1, pp. 2:1-2:47, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)


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@Article{bonte_et_al:TGDK.2.1.2,
  author =	{Bonte, Pieter and Calbimonte, Jean-Paul and de Leng, Daniel and Dell'Aglio, Daniele and Della Valle, Emanuele and Eiter, Thomas and Giannini, Federico and Heintz, Fredrik and Schekotihin, Konstantin and Le-Phuoc, Danh and Mileo, Alessandra and Schneider, Patrik and Tommasini, Riccardo and Urbani, Jacopo and Ziffer, Giacomo},
  title =	{{Grounding Stream Reasoning Research}},
  journal =	{Transactions on Graph Data and Knowledge},
  pages =	{2:1--2:47},
  ISSN =	{2942-7517},
  year =	{2024},
  volume =	{2},
  number =	{1},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/TGDK.2.1.2},
  URN =		{urn:nbn:de:0030-drops-198597},
  doi =		{10.4230/TGDK.2.1.2},
  annote =	{Keywords: Stream Reasoning, Stream Processing, RDF streams, Streaming Linked Data, Continuous query processing, Temporal Logics, High-performance computing, Databases}
}
Document
Complete Volume
LIPIcs, Volume 277, GIScience 2023, Complete Volume

Authors: Roger Beecham, Jed A. Long, Dianna Smith, Qunshan Zhao, and Sarah Wise

Published in: LIPIcs, Volume 277, 12th International Conference on Geographic Information Science (GIScience 2023)


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)


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@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

Authors: Roger Beecham, Jed A. Long, Dianna Smith, Qunshan Zhao, and Sarah Wise

Published in: LIPIcs, Volume 277, 12th International Conference on Geographic Information Science (GIScience 2023)


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)


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@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

Authors: Negar Alinaghi, Tiffany C. K. Kwok, Peter Kiefer, and Ioannis Giannopoulos

Published in: LIPIcs, Volume 277, 12th International Conference on Geographic Information Science (GIScience 2023)


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)


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@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

Authors: Thomas Depian, Guangping Li, Martin Nöllenburg, and Jules Wulms

Published in: LIPIcs, Volume 277, 12th International Conference on Geographic Information Science (GIScience 2023)


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)


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@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

Authors: Subhankar Ghosh, Jayant Gupta, Arun Sharma, Shuai An, and Shashi Shekhar

Published in: LIPIcs, Volume 277, 12th International Conference on Geographic Information Science (GIScience 2023)


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)


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@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

Authors: Moritz J. Hildemann, Alan T. Murray, and Judith A. Verstegen

Published in: LIPIcs, Volume 277, 12th International Conference on Geographic Information Science (GIScience 2023)


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)


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@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

Authors: Jonathan Klawitter, Felix Klesen, Joris Y. Scholl, Thomas C. van Dijk, and Alexander Zaft

Published in: LIPIcs, Volume 277, 12th International Conference on Geographic Information Science (GIScience 2023)


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)


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@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

Authors: Eftychia Koukouraki and Christian Kray

Published in: LIPIcs, Volume 277, 12th International Conference on Geographic Information Science (GIScience 2023)


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)


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@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

Authors: Hao Li, Zhendong Yuan, Gabriel Dax, Gefei Kong, Hongchao Fan, Alexander Zipf, and Martin Werner

Published in: LIPIcs, Volume 277, 12th International Conference on Geographic Information Science (GIScience 2023)


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)


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@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

Authors: Maryam Lotfian, Jens Ingensand, and Christophe Claramunt

Published in: LIPIcs, Volume 277, 12th International Conference on Geographic Information Science (GIScience 2023)


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)


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@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

Authors: Grant McKenzie and Hongyu Zhang

Published in: LIPIcs, Volume 277, 12th International Conference on Geographic Information Science (GIScience 2023)


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)


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@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

Authors: Nathan van Beusekom, Wouter Meulemans, Bettina Speckmann, and Jo Wood

Published in: LIPIcs, Volume 277, 12th International Conference on Geographic Information Science (GIScience 2023)


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)


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@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

Authors: Nina Wiedemann, Henry Martin, and René Westerholt

Published in: LIPIcs, Volume 277, 12th International Conference on Geographic Information Science (GIScience 2023)


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)


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@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}
}
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