Volume
LIPIcs, Volume 177
11th International Conference on Geographic Information Science (GIScience 2021) - Part I
Event
GIScience 2021, September 27-30, 2021, Poznań, PolandEditors
Publication Details
- published at: 2020-09-25
- Publisher: Schloss Dagstuhl – Leibniz-Zentrum für Informatik
- ISBN: 978-3-95977-166-5
- DBLP: db/conf/giscience/giscience2021-1
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Document
Complete Volume
LIPIcs, Volume 177, GIScience 2021, Complete Volume
Abstract
LIPIcs, Volume 177, GIScience 2021, Complete Volume
Cite as
11th International Conference on Geographic Information Science (GIScience 2021) - Part I. Leibniz International Proceedings in Informatics (LIPIcs), Volume 177, pp. 1-284, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)
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@Proceedings{janowicz_et_al:LIPIcs.GIScience.2021.I,
title = {{LIPIcs, Volume 177, GIScience 2021, Complete Volume}},
booktitle = {11th International Conference on Geographic Information Science (GIScience 2021) - Part I},
pages = {1--284},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-166-5},
ISSN = {1868-8969},
year = {2020},
volume = {177},
editor = {Janowicz, Krzysztof and Verstegen, Judith A.},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2021.I},
URN = {urn:nbn:de:0030-drops-130344},
doi = {10.4230/LIPIcs.GIScience.2021.I},
annote = {Keywords: LIPIcs, Volume 177, GIScience 2021, Complete Volume}
}
Document
Front Matter
Front Matter, Table of Contents, Preface, Conference Organization
Abstract
Front Matter, Table of Contents, Preface, Conference Organization
Cite as
11th International Conference on Geographic Information Science (GIScience 2021) - Part I. Leibniz International Proceedings in Informatics (LIPIcs), Volume 177, pp. 0:i-0:xii, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)
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@InProceedings{janowicz_et_al:LIPIcs.GIScience.2021.I.0,
author = {Janowicz, Krzysztof and Verstegen, Judith A.},
title = {{Front Matter, Table of Contents, Preface, Conference Organization}},
booktitle = {11th International Conference on Geographic Information Science (GIScience 2021) - Part I},
pages = {0:i--0:xii},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-166-5},
ISSN = {1868-8969},
year = {2020},
volume = {177},
editor = {Janowicz, Krzysztof and Verstegen, Judith A.},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2021.I.0},
URN = {urn:nbn:de:0030-drops-130359},
doi = {10.4230/LIPIcs.GIScience.2021.I.0},
annote = {Keywords: Front Matter, Table of Contents, Preface, Conference Organization}
}
Document
Using Georeferenced Twitter Data to Estimate Pedestrian Traffic in an Urban Road Network
Abstract
Since existing methods to estimate the pedestrian activity in an urban area are data-intensive, we ask the question whether just georeferenced Twitter data can be a viable proxy for inferring pedestrian activity. Walking is often the mode of the last leg reaching an activity location, from where, presumably, the tweets originate. This study analyses this question in three steps. First, we use correlation analysis to assess whether georeferenced Twitter data can be used as a viable proxy for inferring pedestrian activity. Then we adopt standard regression analysis to estimate pedestrian traffic at existing pedestrian sensor locations using georeferenced tweets alone. Thirdly, exploiting the results above, we estimate the hourly pedestrian traffic counts at every segment of the study area network for every hour of every day of the week. Results show a fair correlation between tweets and pedestrian counts, in contrast to counts of other modes of travelling. Thus, this method contributes a non-data-intensive approach for estimating pedestrian activity. Since Twitter is an omnipresent, publicly available data source, this study transcends the boundaries of geographic transferability and scalability, unlike its more traditional counterparts.
Cite as
Debjit Bhowmick, Stephan Winter, and Mark Stevenson. Using Georeferenced Twitter Data to Estimate Pedestrian Traffic in an Urban Road Network. In 11th International Conference on Geographic Information Science (GIScience 2021) - Part I. Leibniz International Proceedings in Informatics (LIPIcs), Volume 177, pp. 1:1-1:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)
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@InProceedings{bhowmick_et_al:LIPIcs.GIScience.2021.I.1,
author = {Bhowmick, Debjit and Winter, Stephan and Stevenson, Mark},
title = {{Using Georeferenced Twitter Data to Estimate Pedestrian Traffic in an Urban Road Network}},
booktitle = {11th International Conference on Geographic Information Science (GIScience 2021) - Part I},
pages = {1:1--1:15},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-166-5},
ISSN = {1868-8969},
year = {2020},
volume = {177},
editor = {Janowicz, Krzysztof and Verstegen, Judith A.},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2021.I.1},
URN = {urn:nbn:de:0030-drops-130367},
doi = {10.4230/LIPIcs.GIScience.2021.I.1},
annote = {Keywords: Twitter, pedestrian traffic, location-based, regression analysis, correlation analysis}
}
Document
Estimation of Moran’s I in the Context of Uncertain Mobile Sensor Measurements
Abstract
Measures of spatial autocorrelation like Moran’s I do not take into account information about the reliability of observations. In a context of mobile sensors, however, this is an important aspect to consider. Mobile sensors record data asynchronously and capture different contexts, which leads to considerable heterogeneity. In this paper we propose two different ways to integrate the reliability of observations with Moran’s I. These proposals are tested in the light of two case studies, one based on real temperatures and movement data and the other using synthetic data. The results show that the way reliability information is incorporated into the Moran’s I estimates has a strong impact on how the measure responds to volatile available information. It is shown that absolute reliability information is much less powerful in addressing the problem of differing contexts than relative concepts that give more weight to more reliable observations, regardless of the general degree of uncertainty. The results presented are seen as an important stimulus for the discourse on spatial autocorrelation measures in the light of uncertainties.
Cite as
Dominik Bucher, Henry Martin, David Jonietz, Martin Raubal, and René Westerholt. Estimation of Moran’s I in the Context of Uncertain Mobile Sensor Measurements. In 11th International Conference on Geographic Information Science (GIScience 2021) - Part I. Leibniz International Proceedings in Informatics (LIPIcs), Volume 177, pp. 2:1-2:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)
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@InProceedings{bucher_et_al:LIPIcs.GIScience.2021.I.2,
author = {Bucher, Dominik and Martin, Henry and Jonietz, David and Raubal, Martin and Westerholt, Ren\'{e}},
title = {{Estimation of Moran’s I in the Context of Uncertain Mobile Sensor Measurements}},
booktitle = {11th International Conference on Geographic Information Science (GIScience 2021) - Part I},
pages = {2:1--2:15},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-166-5},
ISSN = {1868-8969},
year = {2020},
volume = {177},
editor = {Janowicz, Krzysztof and Verstegen, Judith A.},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2021.I.2},
URN = {urn:nbn:de:0030-drops-130375},
doi = {10.4230/LIPIcs.GIScience.2021.I.2},
annote = {Keywords: mobile sensors, Moran’s I, uncertainty, probabilistic forecasting}
}
Document
Generalizing Deep Models for Overhead Image Segmentation Through Getis-Ord Gi* Pooling
Abstract
That most deep learning models are purely data driven is both a strength and a weakness. Given sufficient training data, the optimal model for a particular problem can be learned. However, this is usually not the case and so instead the model is either learned from scratch from a limited amount of training data or pre-trained on a different problem and then fine-tuned. Both of these situations are potentially suboptimal and limit the generalizability of the model. Inspired by this, we investigate methods to inform or guide deep learning models for geospatial image analysis to increase their performance when a limited amount of training data is available or when they are applied to scenarios other than which they were trained on. In particular, we exploit the fact that there are certain fundamental rules as to how things are distributed on the surface of the Earth and these rules do not vary substantially between locations. Based on this, we develop a novel feature pooling method for convolutional neural networks using Getis-Ord Gi* analysis from geostatistics. Experimental results show our proposed pooling function has significantly better generalization performance compared to a standard data-driven approach when applied to overhead image segmentation.
Cite as
Xueqing Deng, Yuxin Tian, and Shawn Newsam. Generalizing Deep Models for Overhead Image Segmentation Through Getis-Ord Gi* Pooling. In 11th International Conference on Geographic Information Science (GIScience 2021) - Part I. Leibniz International Proceedings in Informatics (LIPIcs), Volume 177, pp. 3:1-3:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)
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@InProceedings{deng_et_al:LIPIcs.GIScience.2021.I.3,
author = {Deng, Xueqing and Tian, Yuxin and Newsam, Shawn},
title = {{Generalizing Deep Models for Overhead Image Segmentation Through Getis-Ord Gi* Pooling}},
booktitle = {11th International Conference on Geographic Information Science (GIScience 2021) - Part I},
pages = {3:1--3:14},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-166-5},
ISSN = {1868-8969},
year = {2020},
volume = {177},
editor = {Janowicz, Krzysztof and Verstegen, Judith A.},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2021.I.3},
URN = {urn:nbn:de:0030-drops-130387},
doi = {10.4230/LIPIcs.GIScience.2021.I.3},
annote = {Keywords: Remote sensing, convolutional neural networks, pooling function, semantic segmentation, generalization}
}
Document
Serverless GEO Labels for the Semantic Sensor Web
Abstract
With the increasing amount of sensor data available online, it is becoming more difficult for users to identify useful datasets. Semantic Web technologies can improve such discovery via meaningful ontologies, but the decision of whether a dataset is suitable remains with the users. Users can be aided in this process through the GEO label, which provides a visual summary of the standardised metadata. However, the GEO label is not yet available for the Semantic Sensor Web. This work presents novel rules for deriving the information for the GEO label’s multiple facets, such as user feedback or quality information, based on the Semantic Sensor Network Ontology and related ontologies. Thereby, this work enhances an existing implementation of the GEO label API to generate labels for resources of the Semantic Sensor Web. Further, the prototype is deployed to serverless cloud infrastructures. We find that serverless GEO label generation is capable of handling two evaluation scenarios for concurrent users and burst generation. Nonetheless, more real-world semantic sensor descriptions, an analysis of requirements for GEO label facets specific to the Semantic Sensor Web, and an integration into large-scale discovery platforms are needed.
Cite as
Anika Graupner and Daniel Nüst. Serverless GEO Labels for the Semantic Sensor Web. In 11th International Conference on Geographic Information Science (GIScience 2021) - Part I. Leibniz International Proceedings in Informatics (LIPIcs), Volume 177, pp. 4:1-4:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)
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@InProceedings{graupner_et_al:LIPIcs.GIScience.2021.I.4,
author = {Graupner, Anika and N\"{u}st, Daniel},
title = {{Serverless GEO Labels for the Semantic Sensor Web}},
booktitle = {11th International Conference on Geographic Information Science (GIScience 2021) - Part I},
pages = {4:1--4:14},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-166-5},
ISSN = {1868-8969},
year = {2020},
volume = {177},
editor = {Janowicz, Krzysztof and Verstegen, Judith A.},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2021.I.4},
URN = {urn:nbn:de:0030-drops-130392},
doi = {10.4230/LIPIcs.GIScience.2021.I.4},
annote = {Keywords: GEO label, geospatial metadata, data discovery, Semantic Sensor Web, serverless}
}
Document
Search Facets and Ranking in Geospatial Dataset Search
Abstract
This study surveys the state of search on open geospatial data portals. We seek to understand 1) what users are able to control when searching for geospatial data, 2) how these portals process and interpret a user’s query, and 3) if and how user query reformulations alter search results. We find that most users initiate a search using a text input and several pre-created facets (such as a filter for tags or format). Some portals supply a map-view of data or topic explorers. To process and interpret queries, most portals use a vertical full-text search engine like Apache Solr to query data from a content-management system like CKAN. When processing queries, most portals initially filter results and then rank the remaining results using a common keyword frequency relevance metric (e.g., TF-IDF). Some portals use query expansion. We identify and discuss several recurring usability constraints across portals. For example, users are typically only given text lists to interact with search results. Furthermore, ranking is rarely extended beyond syntactic comparison of keyword similarity. We discuss several avenues for improving search for geospatial data including alternative interfaces and query processing pipelines.
Cite as
Thomas Hervey, Sara Lafia, and Werner Kuhn. Search Facets and Ranking in Geospatial Dataset Search. In 11th International Conference on Geographic Information Science (GIScience 2021) - Part I. Leibniz International Proceedings in Informatics (LIPIcs), Volume 177, pp. 5:1-5:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)
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@InProceedings{hervey_et_al:LIPIcs.GIScience.2021.I.5,
author = {Hervey, Thomas and Lafia, Sara and Kuhn, Werner},
title = {{Search Facets and Ranking in Geospatial Dataset Search}},
booktitle = {11th International Conference on Geographic Information Science (GIScience 2021) - Part I},
pages = {5:1--5:15},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-166-5},
ISSN = {1868-8969},
year = {2020},
volume = {177},
editor = {Janowicz, Krzysztof and Verstegen, Judith A.},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2021.I.5},
URN = {urn:nbn:de:0030-drops-130405},
doi = {10.4230/LIPIcs.GIScience.2021.I.5},
annote = {Keywords: search, portal, discovery, GIR, facet, relevance, ranking}
}
Document
How Do People Describe Locations During a Natural Disaster: An Analysis of Tweets from Hurricane Harvey
Abstract
Social media platforms, such as Twitter, have been increasingly used by people during natural disasters to share information and request for help. Hurricane Harvey was a category 4 hurricane that devastated Houston, Texas, USA in August 2017 and caused catastrophic flooding in the Houston metropolitan area. Hurricane Harvey also witnessed the widespread use of social media by the general public in response to this major disaster, and geographic locations are key information pieces described in many of the social media messages. A geoparsing system, or a geoparser, can be utilized to automatically extract and locate the described locations, which can help first responders reach the people in need. While a number of geoparsers have already been developed, it is unclear how effective they are in recognizing and geo-locating the locations described by people during natural disasters. To fill this gap, this work seeks to understand how people describe locations during a natural disaster by analyzing a sample of tweets posted during Hurricane Harvey. We then identify the limitations of existing geoparsers in processing these tweets, and discuss possible approaches to overcoming these limitations.
Cite as
Yingjie Hu and Jimin Wang. How Do People Describe Locations During a Natural Disaster: An Analysis of Tweets from Hurricane Harvey. In 11th International Conference on Geographic Information Science (GIScience 2021) - Part I. Leibniz International Proceedings in Informatics (LIPIcs), Volume 177, pp. 6:1-6:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)
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@InProceedings{hu_et_al:LIPIcs.GIScience.2021.I.6,
author = {Hu, Yingjie and Wang, Jimin},
title = {{How Do People Describe Locations During a Natural Disaster: An Analysis of Tweets from Hurricane Harvey}},
booktitle = {11th International Conference on Geographic Information Science (GIScience 2021) - Part I},
pages = {6:1--6:16},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-166-5},
ISSN = {1868-8969},
year = {2020},
volume = {177},
editor = {Janowicz, Krzysztof and Verstegen, Judith A.},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2021.I.6},
URN = {urn:nbn:de:0030-drops-130410},
doi = {10.4230/LIPIcs.GIScience.2021.I.6},
annote = {Keywords: Geoparsing, geographic informational retrieval, social media, tweet analysis, disaster response}
}
Document
Introducing Diversion Graph for Real-Time Spatial Data Analysis with Location Based Social Networks
Abstract
Neighbourhood graphs are useful for inferring the travel network between locations posted in the Location Based Social Networks (LBSNs). Existing neighbourhood graphs, such as the Stepping Stone Graph lack the ability to process a high volume of LBSN data in real time. We propose a neighbourhood graph named Diversion Graph, which uses an efficient edge filtering method from the Delaunay triangulation mechanism for fast processing of LBSN data. This mechanism enables Diversion Graph to achieve a similar accuracy level as Stepping Stone Graph for inferring travel networks, but with a reduction of the execution time of over 90%. Using LBSN data collected from Twitter and Flickr, we show that Diversion Graph is suitable for travel network processing in real time.
Cite as
Sameera Kannangara, Hairuo Xie, Egemen Tanin, Aaron Harwood, and Shanika Karunasekera. Introducing Diversion Graph for Real-Time Spatial Data Analysis with Location Based Social Networks. In 11th International Conference on Geographic Information Science (GIScience 2021) - Part I. Leibniz International Proceedings in Informatics (LIPIcs), Volume 177, pp. 7:1-7:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)
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@InProceedings{kannangara_et_al:LIPIcs.GIScience.2021.I.7,
author = {Kannangara, Sameera and Xie, Hairuo and Tanin, Egemen and Harwood, Aaron and Karunasekera, Shanika},
title = {{Introducing Diversion Graph for Real-Time Spatial Data Analysis with Location Based Social Networks}},
booktitle = {11th International Conference on Geographic Information Science (GIScience 2021) - Part I},
pages = {7:1--7:15},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-166-5},
ISSN = {1868-8969},
year = {2020},
volume = {177},
editor = {Janowicz, Krzysztof and Verstegen, Judith A.},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2021.I.7},
URN = {urn:nbn:de:0030-drops-130428},
doi = {10.4230/LIPIcs.GIScience.2021.I.7},
annote = {Keywords: moving objects, shortest path, graphs}
}
Document
Not Arbitrary, Systematic! Average-Based Route Selection for Navigation Experiments
Abstract
While studies on human wayfinding have seen increasing interest, the criteria for the choice of the routes used in these studies have usually not received particular attention. This paper presents a methodological framework which aims at filling this gap. Based on a thorough literature review on route choice criteria, we present an approach that supports wayfinding researchers in finding a route whose characteristics are as similar as possible to the population of all considered routes with a predefined length in a particular area. We provide evidence for the viability of our approach by means of both, synthetic and real-world data. The proposed method allows wayfinding researchers to justify their route choice decisions, and it enhances replicability of studies on human wayfinding. Furthermore, it allows to find similar routes in different geographical areas.
Cite as
Bartosz Mazurkiewicz, Markus Kattenbeck, Peter Kiefer, and Ioannis Giannopoulos. Not Arbitrary, Systematic! Average-Based Route Selection for Navigation Experiments. In 11th International Conference on Geographic Information Science (GIScience 2021) - Part I. Leibniz International Proceedings in Informatics (LIPIcs), Volume 177, pp. 8:1-8:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)
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@InProceedings{mazurkiewicz_et_al:LIPIcs.GIScience.2021.I.8,
author = {Mazurkiewicz, Bartosz and Kattenbeck, Markus and Kiefer, Peter and Giannopoulos, Ioannis},
title = {{Not Arbitrary, Systematic! Average-Based Route Selection for Navigation Experiments}},
booktitle = {11th International Conference on Geographic Information Science (GIScience 2021) - Part I},
pages = {8:1--8:16},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-166-5},
ISSN = {1868-8969},
year = {2020},
volume = {177},
editor = {Janowicz, Krzysztof and Verstegen, Judith A.},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2021.I.8},
URN = {urn:nbn:de:0030-drops-130437},
doi = {10.4230/LIPIcs.GIScience.2021.I.8},
annote = {Keywords: Route Selection, Route Features, Human Wayfinding, Navigation, Experiments, Replicability}
}
Document
Traffic Congestion Aware Route Assignment
Abstract
Traffic congestion emerges when traffic load exceeds the available capacity of roads. It is challenging to prevent traffic congestion in current transportation systems where vehicles tend to follow the shortest/fastest path to their destinations without considering the potential congestions caused by the concentration of vehicles. With connected autonomous vehicles, the new generation of traffic management systems can optimize traffic by coordinating the routes of all vehicles. As the connected autonomous vehicles can adhere to the routes assigned to them, the traffic management system can predict the change of traffic flow with a high level of accuracy. Based on the accurate traffic prediction and traffic congestion models, routes can be allocated in such a way that helps mitigating traffic congestions effectively. In this regard, we propose a new route assignment algorithm for the era of connected autonomous vehicles. Results show that our algorithm outperforms several baseline methods for traffic congestion mitigation.
Cite as
Sadegh Motallebi, Hairuo Xie, Egemen Tanin, and Kotagiri Ramamohanarao. Traffic Congestion Aware Route Assignment. In 11th International Conference on Geographic Information Science (GIScience 2021) - Part I. Leibniz International Proceedings in Informatics (LIPIcs), Volume 177, pp. 9:1-9:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)
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@InProceedings{motallebi_et_al:LIPIcs.GIScience.2021.I.9,
author = {Motallebi, Sadegh and Xie, Hairuo and Tanin, Egemen and Ramamohanarao, Kotagiri},
title = {{Traffic Congestion Aware Route Assignment}},
booktitle = {11th International Conference on Geographic Information Science (GIScience 2021) - Part I},
pages = {9:1--9:15},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-166-5},
ISSN = {1868-8969},
year = {2020},
volume = {177},
editor = {Janowicz, Krzysztof and Verstegen, Judith A.},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2021.I.9},
URN = {urn:nbn:de:0030-drops-130443},
doi = {10.4230/LIPIcs.GIScience.2021.I.9},
annote = {Keywords: Road Network, Traffic Congestion, Route Assignment, Shortest Path}
}
Document
Estimating Hourly Population Distribution Patterns at High Spatiotemporal Resolution in Urban Areas Using Geo-Tagged Tweets and Dasymetric Mapping
Abstract
This paper introduces a spatiotemporal analysis framework for estimating hourly changing population distribution patterns in urban areas using geo-tagged tweets (the messages containing users’ geospatial locations), land use data, and dasymetric maps. We collected geo-tagged social media (tweets) within the County of San Diego during one year (2015) by using Twitter’s Streaming Application Programming Interfaces (APIs). A semi-manual Twitter content verification procedure for data cleaning was applied first to separate tweets created by humans from non-human users (bots). The next step was to calculate the number of unique Twitter users every hour within census blocks. The final step was to estimate the actual population by transforming the numbers of unique Twitter users in each census block into estimated population densities with spatial and temporal factors using dasymetric maps. The temporal factor was estimated based on hourly changes of Twitter messages within San Diego County, CA. The spatial factor was estimated by using the dasymetric method with land use maps and 2010 census data. Comparing to census data, our methods can provide better estimated population in airports, shopping malls, sports stadiums, zoo and parks, and business areas during the day time.
Cite as
Jaehee Park, Hao Zhang, Su Yeon Han, Atsushi Nara, and Ming-Hsiang Tsou. Estimating Hourly Population Distribution Patterns at High Spatiotemporal Resolution in Urban Areas Using Geo-Tagged Tweets and Dasymetric Mapping. In 11th International Conference on Geographic Information Science (GIScience 2021) - Part I. Leibniz International Proceedings in Informatics (LIPIcs), Volume 177, pp. 10:1-10:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)
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@InProceedings{park_et_al:LIPIcs.GIScience.2021.I.10,
author = {Park, Jaehee and Zhang, Hao and Han, Su Yeon and Nara, Atsushi and Tsou, Ming-Hsiang},
title = {{Estimating Hourly Population Distribution Patterns at High Spatiotemporal Resolution in Urban Areas Using Geo-Tagged Tweets and Dasymetric Mapping}},
booktitle = {11th International Conference on Geographic Information Science (GIScience 2021) - Part I},
pages = {10:1--10:16},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-166-5},
ISSN = {1868-8969},
year = {2020},
volume = {177},
editor = {Janowicz, Krzysztof and Verstegen, Judith A.},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2021.I.10},
URN = {urn:nbn:de:0030-drops-130456},
doi = {10.4230/LIPIcs.GIScience.2021.I.10},
annote = {Keywords: Population Estimation, Twitter, Social Media, Dasymetric Map, Spatiotemporal}
}
Document
Multiple Resource Network Voronoi Diagram
Abstract
Given a spatial network and a set of service center nodes from k different resource types, a Multiple Resource-Network Voronoi Diagram (MRNVD) partitions the spatial network into a set of Service Areas that can minimize the total cycle distances of graph-nodes to allotted k service center nodes with different resource types. The MRNVD problem is important for critical societal applications such as assigning essential survival supplies (e.g., food, water, gas, and medical assistance) to residents impacted by man-made or natural disasters. The MRNVD problem is NP-hard; it is computationally challenging due to the large size of the transportation network. Previous work is limited to a single or two different types of service centers, but cannot be generalized to deal with k different resource types. We propose a novel approach for MRNVD that can efficiently identify the best routes to obtain the k different resources. Experiments and a case study using real-world datasets demonstrate that the proposed approach creates MRNVD and significantly reduces the computational cost.
Cite as
Ahmad Qutbuddin and KwangSoo Yang. Multiple Resource Network Voronoi Diagram. In 11th International Conference on Geographic Information Science (GIScience 2021) - Part I. Leibniz International Proceedings in Informatics (LIPIcs), Volume 177, pp. 11:1-11:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)
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@InProceedings{qutbuddin_et_al:LIPIcs.GIScience.2021.I.11,
author = {Qutbuddin, Ahmad and Yang, KwangSoo},
title = {{Multiple Resource Network Voronoi Diagram}},
booktitle = {11th International Conference on Geographic Information Science (GIScience 2021) - Part I},
pages = {11:1--11:16},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-166-5},
ISSN = {1868-8969},
year = {2020},
volume = {177},
editor = {Janowicz, Krzysztof and Verstegen, Judith A.},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2021.I.11},
URN = {urn:nbn:de:0030-drops-130463},
doi = {10.4230/LIPIcs.GIScience.2021.I.11},
annote = {Keywords: Network Voronoi Diagram, Resource Allocation, Route Optimization}
}
Document
LSTM-TrajGAN: A Deep Learning Approach to Trajectory Privacy Protection
Abstract
The prevalence of location-based services contributes to the explosive growth of individual-level trajectory data and raises public concerns about privacy issues. In this research, we propose a novel LSTM-TrajGAN approach, which is an end-to-end deep learning model to generate privacy-preserving synthetic trajectory data for data sharing and publication. We design a loss metric function TrajLoss to measure the trajectory similarity losses for model training and optimization. The model is evaluated on the trajectory-user-linking task on a real-world semantic trajectory dataset. Compared with other common geomasking methods, our model can better prevent users from being re-identified, and it also preserves essential spatial, temporal, and thematic characteristics of the real trajectory data. The model better balances the effectiveness of trajectory privacy protection and the utility for spatial and temporal analyses, which offers new insights into the GeoAI-powered privacy protection.
Cite as
Jinmeng Rao, Song Gao, Yuhao Kang, and Qunying Huang. LSTM-TrajGAN: A Deep Learning Approach to Trajectory Privacy Protection. In 11th International Conference on Geographic Information Science (GIScience 2021) - Part I. Leibniz International Proceedings in Informatics (LIPIcs), Volume 177, pp. 12:1-12:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)
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@InProceedings{rao_et_al:LIPIcs.GIScience.2021.I.12,
author = {Rao, Jinmeng and Gao, Song and Kang, Yuhao and Huang, Qunying},
title = {{LSTM-TrajGAN: A Deep Learning Approach to Trajectory Privacy Protection}},
booktitle = {11th International Conference on Geographic Information Science (GIScience 2021) - Part I},
pages = {12:1--12:17},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-166-5},
ISSN = {1868-8969},
year = {2020},
volume = {177},
editor = {Janowicz, Krzysztof and Verstegen, Judith A.},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2021.I.12},
URN = {urn:nbn:de:0030-drops-130471},
doi = {10.4230/LIPIcs.GIScience.2021.I.12},
annote = {Keywords: GeoAI, Deep Learning, Trajectory Privacy, Generative Adversarial Networks}
}
Document
Analyzing Trajectory Gaps for Possible Rendezvous: A Summary of Results
Abstract
Given trajectory data with gaps, we investigate methods to identify possible rendezvous regions. Societal applications include improving maritime safety and regulations. The challenges come from two aspects. If trajectory data are not available around the rendezvous then either linear or shortest-path interpolation may fail to detect the possible rendezvous. Furthermore, the problem is computationally expensive due to the large number of gaps and associated trajectories. In this paper, we first use the plane sweep algorithm as a baseline. Then we propose a new filtering framework using the concept of a space-time grid. Experimental results and case study on real-world maritime trajectory data show that the proposed approach substantially improves the Area Pruning Efficiency over the baseline technique.
Cite as
Arun Sharma, Xun Tang, Jayant Gupta, Majid Farhadloo, and Shashi Shekhar. Analyzing Trajectory Gaps for Possible Rendezvous: A Summary of Results. In 11th International Conference on Geographic Information Science (GIScience 2021) - Part I. Leibniz International Proceedings in Informatics (LIPIcs), Volume 177, pp. 13:1-13:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)
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@InProceedings{sharma_et_al:LIPIcs.GIScience.2021.I.13,
author = {Sharma, Arun and Tang, Xun and Gupta, Jayant and Farhadloo, Majid and Shekhar, Shashi},
title = {{Analyzing Trajectory Gaps for Possible Rendezvous: A Summary of Results}},
booktitle = {11th International Conference on Geographic Information Science (GIScience 2021) - Part I},
pages = {13:1--13:16},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-166-5},
ISSN = {1868-8969},
year = {2020},
volume = {177},
editor = {Janowicz, Krzysztof and Verstegen, Judith A.},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2021.I.13},
URN = {urn:nbn:de:0030-drops-130482},
doi = {10.4230/LIPIcs.GIScience.2021.I.13},
annote = {Keywords: Spatial data mining, trajectory mining, time geography}
}
Document
You Are Not Alone: Path Search Models, Traffic, and Social Costs
Abstract
Existing cognitively motivated path search models ignore that we are hardly ever alone when navigating through an environment. They neither account for traffic nor for the social costs that being routed through certain areas may incur. In this paper, we analyse the effects of "not being alone" on different path search models, in particular on fastest paths and least complex paths. We find a significant effect of aiming to avoid traffic on social costs, but interestingly only minor effects on path complexity when minimizing either traffic load or social costs. Further, we find that ignoring traffic in path search leads to significantly increased average traffic load for all tested models. We also present results of a combined model that accounts for complexity, traffic, and social costs at the same time. Overall, this research provides important insights into the behavior of path search models when optimizing for different aspects, and explores some ways of mitigating unwanted effects.
Cite as
Fateme Teimouri and Kai-Florian Richter. You Are Not Alone: Path Search Models, Traffic, and Social Costs. In 11th International Conference on Geographic Information Science (GIScience 2021) - Part I. Leibniz International Proceedings in Informatics (LIPIcs), Volume 177, pp. 14:1-14:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)
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@InProceedings{teimouri_et_al:LIPIcs.GIScience.2021.I.14,
author = {Teimouri, Fateme and Richter, Kai-Florian},
title = {{You Are Not Alone: Path Search Models, Traffic, and Social Costs}},
booktitle = {11th International Conference on Geographic Information Science (GIScience 2021) - Part I},
pages = {14:1--14:16},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-166-5},
ISSN = {1868-8969},
year = {2020},
volume = {177},
editor = {Janowicz, Krzysztof and Verstegen, Judith A.},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2021.I.14},
URN = {urn:nbn:de:0030-drops-130496},
doi = {10.4230/LIPIcs.GIScience.2021.I.14},
annote = {Keywords: wayfinding, navigation complexity, spatial cognition, social costs}
}
Document
Enhancing Usability Evaluation of Web-Based Geographic Information Systems (WebGIS) with Visual Analytics
Abstract
Many websites nowadays incorporate geospatial data that users interact with, for example, to filter search results or compare alternatives. These web-based geographic information systems (WebGIS) pose new challenges for usability evaluations as both the interaction with classic interface elements and with map-based visualizations have to be analyzed to understand user behavior. This paper proposes a new scalable approach that applies visual analytics to logged interaction data with WebGIS, which facilitates the interactive exploration and analysis of user behavior. In order to evaluate our approach, we implemented it as a toolkit that can be easily integrated into existing WebGIS. We then deployed the toolkit in a user study (N=60) with a realistic WebGIS and analyzed users' interaction in a second study with usability experts (N=7). Our results indicate that the proposed approach is practically feasible, easy to integrate into existing systems, and facilitates insights into the usability of WebGIS.
Cite as
René Unrau and Christian Kray. Enhancing Usability Evaluation of Web-Based Geographic Information Systems (WebGIS) with Visual Analytics. In 11th International Conference on Geographic Information Science (GIScience 2021) - Part I. Leibniz International Proceedings in Informatics (LIPIcs), Volume 177, pp. 15:1-15:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)
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@InProceedings{unrau_et_al:LIPIcs.GIScience.2021.I.15,
author = {Unrau, Ren\'{e} and Kray, Christian},
title = {{Enhancing Usability Evaluation of Web-Based Geographic Information Systems (WebGIS) with Visual Analytics}},
booktitle = {11th International Conference on Geographic Information Science (GIScience 2021) - Part I},
pages = {15:1--15:16},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-166-5},
ISSN = {1868-8969},
year = {2020},
volume = {177},
editor = {Janowicz, Krzysztof and Verstegen, Judith A.},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2021.I.15},
URN = {urn:nbn:de:0030-drops-130509},
doi = {10.4230/LIPIcs.GIScience.2021.I.15},
annote = {Keywords: map interaction, usability evaluation, visual analytics}
}
Document
Volume from Outlines on Terrains
Abstract
Outlines (closed loops) delineate areas of interest on terrains, such as regions with a heightened risk of landslides. For various analysis tasks it is necessary to define and compute a volume of earth (soil) based on such an outline, capturing, for example, the possible volume of a landslide in a high-risk region. In this paper we discuss several options to define meaningful 2D surfaces induced by a 1D outline, which allow us to compute such volumes. We experimentally compare the proposed surface options for two applications: similarity of paths on terrains and landslide susceptibility analysis.
Cite as
Marc van Kreveld, Tim Ophelders, Willem Sonke, Bettina Speckmann, and Kevin Verbeek. Volume from Outlines on Terrains. In 11th International Conference on Geographic Information Science (GIScience 2021) - Part I. Leibniz International Proceedings in Informatics (LIPIcs), Volume 177, pp. 16:1-16:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)
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@InProceedings{vankreveld_et_al:LIPIcs.GIScience.2021.I.16,
author = {van Kreveld, Marc and Ophelders, Tim and Sonke, Willem and Speckmann, Bettina and Verbeek, Kevin},
title = {{Volume from Outlines on Terrains}},
booktitle = {11th International Conference on Geographic Information Science (GIScience 2021) - Part I},
pages = {16:1--16:15},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-166-5},
ISSN = {1868-8969},
year = {2020},
volume = {177},
editor = {Janowicz, Krzysztof and Verstegen, Judith A.},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2021.I.16},
URN = {urn:nbn:de:0030-drops-130512},
doi = {10.4230/LIPIcs.GIScience.2021.I.16},
annote = {Keywords: Terrain model, similarity, volume, computation}
}
Document
Traffic Prediction Framework for OpenStreetMap Using Deep Learning Based Complex Event Processing and Open Traffic Cameras
Abstract
Displaying near-real-time traffic information is a useful feature of digital navigation maps. However, most commercial providers rely on privacy-compromising measures such as deriving location information from cellphones to estimate traffic. The lack of an open-source traffic estimation method using open data platforms is a bottleneck for building sophisticated navigation services on top of OpenStreetMap (OSM). We propose a deep learning-based Complex Event Processing (CEP) method that relies on publicly available video camera streams for traffic estimation. The proposed framework performs near-real-time object detection and objects property extraction across camera clusters in parallel to derive multiple measures related to traffic with the results visualized on OpenStreetMap. The estimation of object properties (e.g. vehicle speed, count, direction) provides multidimensional data that can be leveraged to create metrics and visualization for congestion beyond commonly used density-based measures. Our approach couples both flow and count measures during interpolation by considering each vehicle as a sample point and their speed as weight. We demonstrate multidimensional traffic metrics (e.g. flow rate, congestion estimation) over OSM by processing 22 traffic cameras from London streets. The system achieves a near-real-time performance of 1.42 seconds median latency and an average F-score of 0.80.
Cite as
Piyush Yadav, Dipto Sarkar, Dhaval Salwala, and Edward Curry. Traffic Prediction Framework for OpenStreetMap Using Deep Learning Based Complex Event Processing and Open Traffic Cameras. In 11th International Conference on Geographic Information Science (GIScience 2021) - Part I. Leibniz International Proceedings in Informatics (LIPIcs), Volume 177, pp. 17:1-17:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)
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@InProceedings{yadav_et_al:LIPIcs.GIScience.2021.I.17,
author = {Yadav, Piyush and Sarkar, Dipto and Salwala, Dhaval and Curry, Edward},
title = {{Traffic Prediction Framework for OpenStreetMap Using Deep Learning Based Complex Event Processing and Open Traffic Cameras}},
booktitle = {11th International Conference on Geographic Information Science (GIScience 2021) - Part I},
pages = {17:1--17:17},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-166-5},
ISSN = {1868-8969},
year = {2020},
volume = {177},
editor = {Janowicz, Krzysztof and Verstegen, Judith A.},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2021.I.17},
URN = {urn:nbn:de:0030-drops-130523},
doi = {10.4230/LIPIcs.GIScience.2021.I.17},
annote = {Keywords: Traffic Estimation, OpenStreetMap, Complex Event Processing, Traffic Cameras, Video Processing, Deep Learning}
}