98 Search Results for "Sester, Monika"

Volume

LIPIcs, Volume 114

10th International Conference on Geographic Information Science (GIScience 2018)

GIScience 2018, August 28-31, 2018, Melbourne, Australia

Editors: Stephan Winter, Amy Griffin, and Monika Sester

Document
DOI: 10.4230/DagRep.12.4.26
Urban Mobility Analytics (Dagstuhl Seminar 22162)

Authors: David Jonietz, Monika Sester, Kathleen Stewart, Stephan Winter, Martin Tomko, and Yanan Xin

Published in: Dagstuhl Reports, Volume 12, Issue 4 (2022)

Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 22162 "Urban Mobility Analytics". The seminar brought together researchers from academia and industry who work in complementary ways on urban mobility analytics. The seminar especially aimed at bringing together ideas and approaches from deep learning research, which is requiring large datasets, and reproducible research, which is requiring access to data.
Cite as

David Jonietz, Monika Sester, Kathleen Stewart, Stephan Winter, Martin Tomko, and Yanan Xin. Urban Mobility Analytics (Dagstuhl Seminar 22162). In Dagstuhl Reports, Volume 12, Issue 4, pp. 26-53, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@Article{jonietz_et_al:DagRep.12.4.26,
  author =	{Jonietz, David and Sester, Monika and Stewart, Kathleen and Winter, Stephan and Tomko, Martin and Xin, Yanan},
  title =	{{Urban Mobility Analytics (Dagstuhl Seminar 22162)}},
  pages =	{26--53},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2022},
  volume =	{12},
  number =	{4},
  editor =	{Jonietz, David and Sester, Monika and Stewart, Kathleen and Winter, Stephan and Tomko, Martin and Xin, Yanan},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagRep.12.4.26},
  URN =		{urn:nbn:de:0030-drops-172792},
  doi =		{10.4230/DagRep.12.4.26},
  annote =	{Keywords: data analytics, Deep learning, Reproducible research, urban mobility}
}
Document
Short Paper
DOI: 10.4230/LIPIcs.COSIT.2022.27
Improving Pedestrians Traffic Priority via Grouping and Virtual Lanes in Shared Spaces (Short Paper)

Authors: Yao Li, Vinu Kamalasanan, Mariana Batista, and Monika Sester

Published in: LIPIcs, Volume 240, 15th International Conference on Spatial Information Theory (COSIT 2022)

Abstract
The shared space design is applied in urban streets to support barrier-free movement and integrate traffic participants (such as pedestrians, cyclists and vehicles) into a common road space. Regardless of the low-speed environment, sharing space with motor vehicles can make vulnerable road users feel uneasy. Yet, walking in groups increases their confidence as well as influence the yielding behavior of drivers. Therefore, we propose an innovative approach to support the crossing of pedestrians via grouping and project the virtual lanes in shared spaces. This paper presents the important components of the crowd steering system, discusses the enablers and gaps in the current approach, and illustrates the proposed idea with concept diagrams.
Cite as

Yao Li, Vinu Kamalasanan, Mariana Batista, and Monika Sester. Improving Pedestrians Traffic Priority via Grouping and Virtual Lanes in Shared Spaces (Short Paper). In 15th International Conference on Spatial Information Theory (COSIT 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 240, pp. 27:1-27:8, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{li_et_al:LIPIcs.COSIT.2022.27,
  author =	{Li, Yao and Kamalasanan, Vinu and Batista, Mariana and Sester, Monika},
  title =	{{Improving Pedestrians Traffic Priority via Grouping and Virtual Lanes in Shared Spaces}},
  booktitle =	{15th International Conference on Spatial Information Theory (COSIT 2022)},
  pages =	{27:1--27:8},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-257-0},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{240},
  editor =	{Ishikawa, Toru and Fabrikant, Sara Irina and Winter, Stephan},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.COSIT.2022.27},
  URN =		{urn:nbn:de:0030-drops-169125},
  doi =		{10.4230/LIPIcs.COSIT.2022.27},
  annote =	{Keywords: shared space, urban traffic system, augmented reality, pedestrian grouping}
}
Document
Complete Volume
DOI: 10.4230/LIPIcs.GIScience.2018
LIPIcs, Volume 114, GIScience'18, Complete Volume

Authors: Stephan Winter, Amy Griffin, and Monika Sester

Published in: LIPIcs, Volume 114, 10th International Conference on Geographic Information Science (GIScience 2018)

Abstract
LIPIcs, Volume 114, GIScience'18, Complete Volume
Cite as

10th International Conference on Geographic Information Science (GIScience 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 114, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@Proceedings{winter_et_al:LIPIcs.GIScience.2018,
  title =	{{LIPIcs, Volume 114, GIScience'18, Complete Volume}},
  booktitle =	{10th International Conference on Geographic Information Science (GIScience 2018)},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-083-5},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{114},
  editor =	{Winter, Stephan and Griffin, Amy and Sester, Monika},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2018},
  URN =		{urn:nbn:de:0030-drops-97424},
  doi =		{10.4230/LIPIcs.GIScience.2018},
  annote =	{Keywords: Information systems, Location based services, Geographic information systems, Personalization}
}
Document
Front Matter
DOI: 10.4230/LIPIcs.GISCIENCE.2018.0
Front Matter, Table of Contents, Preface, Conference Organization

Authors: Stephan Winter, Amy Griffin, and Monika Sester

Published in: LIPIcs, Volume 114, 10th International Conference on Geographic Information Science (GIScience 2018)

Abstract
Front Matter, Table of Contents, Preface, Conference Organization
Cite as

10th International Conference on Geographic Information Science (GIScience 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 114, pp. 0:i-0:xvi, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@InProceedings{winter_et_al:LIPIcs.GISCIENCE.2018.0,
  author =	{Winter, Stephan and Griffin, Amy and Sester, Monika},
  title =	{{Front Matter, Table of Contents, Preface, Conference Organization}},
  booktitle =	{10th International Conference on Geographic Information Science (GIScience 2018)},
  pages =	{0:i--0:xvi},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-083-5},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{114},
  editor =	{Winter, Stephan and Griffin, Amy and Sester, Monika},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.GISCIENCE.2018.0},
  URN =		{urn:nbn:de:0030-drops-93282},
  doi =		{10.4230/LIPIcs.GISCIENCE.2018.0},
  annote =	{Keywords: Front Matter, Table of Contents, Preface, Conference Organization}
}
Document
DOI: 10.4230/LIPIcs.GISCIENCE.2018.1
Early Detection of Herding Behaviour during Emergency Evacuations

Authors: David Amores, Maria Vasardani, and Egemen Tanin

Published in: LIPIcs, Volume 114, 10th International Conference on Geographic Information Science (GIScience 2018)

Abstract
Social scientists have observed a number of irrational behaviours during emergency evacuations, caused by a range of possible cognitive biases. One such behaviour is herding - people following and trusting others to guide them, when they do not know where the nearest exit is. This behaviour may lead to safety under a knowledgeable leader, but can also lead to dead-ends. We present a method for the automatic early detection of herding behaviour to avoid suboptimal evacuations. The method comprises three steps: (i) people clusters identification during evacuation, (ii) collection of clusters' spatio-temporal information to extract features for describing cluster behaviour, and (iii) unsupervised learning classification of clusters' behaviour into 'benign' or 'harmful' herding. Results using a set of different detection scores show accuracies higher than baselines in identifying harmful behaviour; thus, laying the ground for timely irrational behaviour detection to increase the performance of emergency evacuation systems.
Cite as

David Amores, Maria Vasardani, and Egemen Tanin. Early Detection of Herding Behaviour during Emergency Evacuations. In 10th International Conference on Geographic Information Science (GIScience 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 114, pp. 1:1-1:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@InProceedings{amores_et_al:LIPIcs.GISCIENCE.2018.1,
  author =	{Amores, David and Vasardani, Maria and Tanin, Egemen},
  title =	{{Early Detection of Herding Behaviour during Emergency Evacuations}},
  booktitle =	{10th International Conference on Geographic Information Science (GIScience 2018)},
  pages =	{1:1--1:15},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-083-5},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{114},
  editor =	{Winter, Stephan and Griffin, Amy and Sester, Monika},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.GISCIENCE.2018.1},
  URN =		{urn:nbn:de:0030-drops-93293},
  doi =		{10.4230/LIPIcs.GISCIENCE.2018.1},
  annote =	{Keywords: spatio-temporal data, emergency evacuations, herding behaviour}
}
Document
DOI: 10.4230/LIPIcs.GISCIENCE.2018.2
What Makes Spatial Data Big? A Discussion on How to Partition Spatial Data

Authors: Alberto Belussi, Damiano Carra, Sara Migliorini, Mauro Negri, and Giuseppe Pelagatti

Published in: LIPIcs, Volume 114, 10th International Conference on Geographic Information Science (GIScience 2018)

Abstract
The amount of available spatial data has significantly increased in the last years so that traditional analysis tools have become inappropriate to effectively manage them. Therefore, many attempts have been made in order to define extensions of existing MapReduce tools, such as Hadoop or Spark, with spatial capabilities in terms of data types and algorithms. Such extensions are mainly based on the partitioning techniques implemented for textual data where the dimension is given in terms of the number of occupied bytes. However, spatial data are characterized by other features which describe their dimension, such as the number of vertices or the MBR size of geometries, which greatly affect the performance of operations, like the spatial join, during data analysis. The result is that the use of traditional partitioning techniques prevents to completely exploit the benefit of the parallel execution provided by a MapReduce environment. This paper extensively analyses the problem considering the spatial join operation as use case, performing both a theoretical and an experimental analysis for it. Moreover, it provides a solution based on a different partitioning technique, which splits complex or extensive geometries. Finally, we validate the proposed solution by means of some experiments on synthetic and real datasets.
Cite as

Alberto Belussi, Damiano Carra, Sara Migliorini, Mauro Negri, and Giuseppe Pelagatti. What Makes Spatial Data Big? A Discussion on How to Partition Spatial Data. In 10th International Conference on Geographic Information Science (GIScience 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 114, pp. 2:1-2:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@InProceedings{belussi_et_al:LIPIcs.GISCIENCE.2018.2,
  author =	{Belussi, Alberto and Carra, Damiano and Migliorini, Sara and Negri, Mauro and Pelagatti, Giuseppe},
  title =	{{What Makes Spatial Data Big? A Discussion on How to Partition Spatial Data}},
  booktitle =	{10th International Conference on Geographic Information Science (GIScience 2018)},
  pages =	{2:1--2:15},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-083-5},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{114},
  editor =	{Winter, Stephan and Griffin, Amy and Sester, Monika},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.GISCIENCE.2018.2},
  URN =		{urn:nbn:de:0030-drops-93306},
  doi =		{10.4230/LIPIcs.GISCIENCE.2018.2},
  annote =	{Keywords: Spatial join, SpatialHadoop, MapReduce, partitioning, big data}
}
Document
DOI: 10.4230/LIPIcs.GISCIENCE.2018.3
Intersections of Our World

Authors: Paolo Fogliaroni, Dominik Bucher, Nikola Jankovic, and Ioannis Giannopoulos

Published in: LIPIcs, Volume 114, 10th International Conference on Geographic Information Science (GIScience 2018)

Abstract
There are several situations where the type of a street intersections can become very important, especially in the case of navigation studies. The types of intersections affect the route complexity and this has to be accounted for, e.g., already during the experimental design phase of a navigation study. In this work we introduce a formal definition for intersection types and present a framework that allows for extracting information about the intersections of our planet. We present a case study that demonstrates the importance and necessity of being able to extract this information.
Cite as

Paolo Fogliaroni, Dominik Bucher, Nikola Jankovic, and Ioannis Giannopoulos. Intersections of Our World. In 10th International Conference on Geographic Information Science (GIScience 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 114, pp. 3:1-3:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@InProceedings{fogliaroni_et_al:LIPIcs.GISCIENCE.2018.3,
  author =	{Fogliaroni, Paolo and Bucher, Dominik and Jankovic, Nikola and Giannopoulos, Ioannis},
  title =	{{Intersections of Our World}},
  booktitle =	{10th International Conference on Geographic Information Science (GIScience 2018)},
  pages =	{3:1--3:15},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-083-5},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{114},
  editor =	{Winter, Stephan and Griffin, Amy and Sester, Monika},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.GISCIENCE.2018.3},
  URN =		{urn:nbn:de:0030-drops-93310},
  doi =		{10.4230/LIPIcs.GISCIENCE.2018.3},
  annote =	{Keywords: intersection types, navigation, experimental design}
}
Document
DOI: 10.4230/LIPIcs.GISCIENCE.2018.4
Considerations of Graphical Proximity and Geographical Nearness

Authors: Francis Harvey

Published in: LIPIcs, Volume 114, 10th International Conference on Geographic Information Science (GIScience 2018)

Abstract
"Near things are more similar than more distant things" states Tobler's first law of geography. This seems obvious and is part to much cognitive research into the perception of the environment. The statement's validity for assessments of geographical nearness purely from map symbols has yet to be ascertained. This paper considers this issue through a theoretical framework grounded in Gestalt concepts, behavioral ecological psychology and information psychology. It sets out to consider how influential experience or training may be on the association of graphical proximity with geographical nearness. A pilot study presents some initial findings. The findings regarding the influence of experience or training are ambiguous, but point to the rapid acquisition of affordances in the survey instruments as another factor for future research.
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Francis Harvey. Considerations of Graphical Proximity and Geographical Nearness. In 10th International Conference on Geographic Information Science (GIScience 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 114, pp. 4:1-4:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@InProceedings{harvey:LIPIcs.GISCIENCE.2018.4,
  author =	{Harvey, Francis},
  title =	{{Considerations of Graphical Proximity and Geographical Nearness}},
  booktitle =	{10th International Conference on Geographic Information Science (GIScience 2018)},
  pages =	{4:1--4:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-083-5},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{114},
  editor =	{Winter, Stephan and Griffin, Amy and Sester, Monika},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.GISCIENCE.2018.4},
  URN =		{urn:nbn:de:0030-drops-93322},
  doi =		{10.4230/LIPIcs.GISCIENCE.2018.4},
  annote =	{Keywords: proximity, nearness, perception, cognition}
}
Document
DOI: 10.4230/LIPIcs.GISCIENCE.2018.5
An Empirical Study on the Names of Points of Interest and Their Changes with Geographic Distance

Authors: Yingjie Hu and Krzysztof Janowicz

Published in: LIPIcs, Volume 114, 10th International Conference on Geographic Information Science (GIScience 2018)

Abstract
While Points Of Interest (POIs), such as restaurants, hotels, and barber shops, are part of urban areas irrespective of their specific locations, the names of these POIs often reveal valuable information related to local culture, landmarks, influential families, figures, events, and so on. Place names have long been studied by geographers, e.g., to understand their origins and relations to family names. However, there is a lack of large-scale empirical studies that examine the localness of place names and their changes with geographic distance. In addition to enhancing our understanding of the coherence of geographic regions, such empirical studies are also significant for geographic information retrieval where they can inform computational models and improve the accuracy of place name disambiguation. In this work, we conduct an empirical study based on 112,071 POIs in seven US metropolitan areas extracted from an open Yelp dataset. We propose to adopt term frequency and inverse document frequency in geographic contexts to identify local terms used in POI names and to analyze their usages across different POI types. Our results show an uneven usage of local terms across POI types, which is highly consistent among different geographic regions. We also examine the decaying effect of POI name similarity with the increase of distance among POIs. While our analysis focuses on urban POI names, the presented methods can be generalized to other place types as well, such as mountain peaks and streets.
Cite as

Yingjie Hu and Krzysztof Janowicz. An Empirical Study on the Names of Points of Interest and Their Changes with Geographic Distance. In 10th International Conference on Geographic Information Science (GIScience 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 114, pp. 5:1-5:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@InProceedings{hu_et_al:LIPIcs.GISCIENCE.2018.5,
  author =	{Hu, Yingjie and Janowicz, Krzysztof},
  title =	{{An Empirical Study on the Names of Points of Interest and Their Changes with Geographic Distance}},
  booktitle =	{10th International Conference on Geographic Information Science (GIScience 2018)},
  pages =	{5:1--5:15},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-083-5},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{114},
  editor =	{Winter, Stephan and Griffin, Amy and Sester, Monika},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.GISCIENCE.2018.5},
  URN =		{urn:nbn:de:0030-drops-93337},
  doi =		{10.4230/LIPIcs.GISCIENCE.2018.5},
  annote =	{Keywords: Place names, points of interest, geographic information retrieval, semantic similarity, geospatial semantics}
}
Document
DOI: 10.4230/LIPIcs.GISCIENCE.2018.6
Outlier Detection and Comparison of Origin-Destination Flows Using Data Depth

Authors: Myeong-Hun Jeong, Junjun Yin, and Shaowen Wang

Published in: LIPIcs, Volume 114, 10th International Conference on Geographic Information Science (GIScience 2018)

Abstract
Advances in location-aware technology have resulted in massive trajectory data. Origin-destination (OD) trajectories provide rich information on urban flow and transport demand. This study describes a new method for detecting OD flows outliers and conducting hypothesis testing between two OD flow datasets in terms of the variations of spatial extent, that is, spread. The proposed method is based on data depth, which measures the centrality and outlyingness of a point with respect to a given dataset in R^d. Based on the center-outward ordering property, the proposed method analyzes the underlying characteristics of OD flows, such as location, outlyingness, and spread. The ability of the method to detect OD anomalies is compared with that of the Mahalanobis distance approach, and an F-test is used to verify the difference in scale. Empirical evaluation has demonstrated that our method effectively identifies OD flows outliers in an interactive way. Furthermore, the method can provide new perspectives such as spatial extent by considering the overall structure of data when comparing two different OD flows in terms of scale.
Cite as

Myeong-Hun Jeong, Junjun Yin, and Shaowen Wang. Outlier Detection and Comparison of Origin-Destination Flows Using Data Depth. In 10th International Conference on Geographic Information Science (GIScience 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 114, pp. 6:1-6:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@InProceedings{jeong_et_al:LIPIcs.GISCIENCE.2018.6,
  author =	{Jeong, Myeong-Hun and Yin, Junjun and Wang, Shaowen},
  title =	{{Outlier Detection and Comparison of Origin-Destination Flows Using Data Depth}},
  booktitle =	{10th International Conference on Geographic Information Science (GIScience 2018)},
  pages =	{6:1--6:14},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-083-5},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{114},
  editor =	{Winter, Stephan and Griffin, Amy and Sester, Monika},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.GISCIENCE.2018.6},
  URN =		{urn:nbn:de:0030-drops-93341},
  doi =		{10.4230/LIPIcs.GISCIENCE.2018.6},
  annote =	{Keywords: Movement Analysis, Trajectory Data Mining, Data Depth, Outlier Detection}
}
Document
DOI: 10.4230/LIPIcs.GISCIENCE.2018.7
Is Salience Robust? A Heterogeneity Analysis of Survey Ratings

Authors: Markus Kattenbeck, Eva Nuhn, and Sabine Timpf

Published in: LIPIcs, Volume 114, 10th International Conference on Geographic Information Science (GIScience 2018)

Abstract
Differing weights for salience subdimensions (e.g. visual or structural salience) have been suggested since the early days of salience models in GIScience. Up until now, however, it remains unclear whether weights found in studies are robust across environments, objects and observers. In this study we examine the robustness of a survey-based salience model. Based on ratings of N_{o}=720 objects by N_{p}=250 different participants collected in-situ in two different European cities (Regensburg and Augsburg) we conduct a heterogeneity analysis taking into account environment and sense of direction stratified by gender. We find, first, empirical evidence that our model is invariant across environments, i.e. the strength of the relationships between the subdimensions of salience does not differ significantly. The structural model coefficients found can, hence, be used to calculate values for overall salience across different environments. Second, we provide empirical evidence that invariance of our measurement model is partly not given with respect to both, gender and sense of direction. These compositional invariance problems are a strong indicator for personal aspects playing an important role.
Cite as

Markus Kattenbeck, Eva Nuhn, and Sabine Timpf. Is Salience Robust? A Heterogeneity Analysis of Survey Ratings. In 10th International Conference on Geographic Information Science (GIScience 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 114, pp. 7:1-7:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@InProceedings{kattenbeck_et_al:LIPIcs.GISCIENCE.2018.7,
  author =	{Kattenbeck, Markus and Nuhn, Eva and Timpf, Sabine},
  title =	{{Is Salience Robust? A Heterogeneity Analysis of Survey Ratings}},
  booktitle =	{10th International Conference on Geographic Information Science (GIScience 2018)},
  pages =	{7:1--7:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-083-5},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{114},
  editor =	{Winter, Stephan and Griffin, Amy and Sester, Monika},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.GISCIENCE.2018.7},
  URN =		{urn:nbn:de:0030-drops-93353},
  doi =		{10.4230/LIPIcs.GISCIENCE.2018.7},
  annote =	{Keywords: Salience Model, Measurement Invariance, Heterogeneity Analysis, PLS Path Modeling, Structural Equation Models}
}
Document
DOI: 10.4230/LIPIcs.GISCIENCE.2018.8
Labeling Points of Interest in Dynamic Maps using Disk Labels

Authors: Filip Krumpe

Published in: LIPIcs, Volume 114, 10th International Conference on Geographic Information Science (GIScience 2018)

Abstract
Dynamic maps which support panning, rotating and zooming are available on every smartphone today. To label geographic features on these maps such that the user is presented with a consistent map view even on map interaction is a challenge. We are presenting a map labeling scheme, which allows to label maps at an interactive speed. For any possible map rotation the computed labeling remains free of intersections between labels. It is not required to remove labels from the map view to ensure this. The labeling scheme supports map panning and continuous zooming. During zooming a label appears and disappears only once. When zooming out of the map a label disappears only if it may overlap an equally or more important label in an arbitrary map rotation. This guarantees that more important labels are preferred to less important labels on small scale maps. We are presenting some extensions to the labeling that could be used for more sophisticated labeling features such as area labels turning into point labels at smaller map scales. The proposed labeling scheme relies on a preprocessing phase. In this phase for each label the map scale where it is removed from the map view is computed. During the phase of map presentation the precomputed label set must only be filtered, what can be done very fast. We are presenting some hints that allow to efficiently compute the labeling in the preprocessing phase. Using these a labeling of about 11 million labels can be computed in less than 20 minutes. We are also presenting a datastructure to efficiently filter the precomputed label set in the interaction phase.
Cite as

Filip Krumpe. Labeling Points of Interest in Dynamic Maps using Disk Labels. In 10th International Conference on Geographic Information Science (GIScience 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 114, pp. 8:1-8:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@InProceedings{krumpe:LIPIcs.GISCIENCE.2018.8,
  author =	{Krumpe, Filip},
  title =	{{Labeling Points of Interest in Dynamic Maps using Disk Labels}},
  booktitle =	{10th International Conference on Geographic Information Science (GIScience 2018)},
  pages =	{8:1--8:14},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-083-5},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{114},
  editor =	{Winter, Stephan and Griffin, Amy and Sester, Monika},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.GISCIENCE.2018.8},
  URN =		{urn:nbn:de:0030-drops-93369},
  doi =		{10.4230/LIPIcs.GISCIENCE.2018.8},
  annote =	{Keywords: Map labeling, dynamic maps, label consistency, real-time, sorting/searching}
}
Document
DOI: 10.4230/LIPIcs.GISCIENCE.2018.9
Improving Discovery of Open Civic Data

Authors: Sara Lafia, Andrew Turner, and Werner Kuhn

Published in: LIPIcs, Volume 114, 10th International Conference on Geographic Information Science (GIScience 2018)

Abstract
We describe a method and system design for improved data discovery in an integrated network of open geospatial data that supports collaborative policy development between governments and local constituents. Metadata about civic data (such as thematic categories, user-generated tags, geo-references, or attribute schemata) primarily rely on technical vocabularies that reflect scientific or organizational hierarchies. By contrast, public consumers of data often search for information using colloquial terminology that does not align with official metadata vocabularies. For example, citizens searching for data about bicycle collisions in an area are unlikely to use the search terms with which organizations like Departments of Transportation describe relevant data. Users may also search with broad terms, such as "traffic safety", and will then not discover data tagged with narrower official terms, such as "vehicular crash". This mismatch raises the question of how to bridge the users' ways of talking and searching with the language of technical metadata. In similar situations, it has been beneficial to augment official metadata with semantic annotations that expand the discoverability and relevance recommendations of data, supporting more inclusive access. Adopting this strategy, we develop a method for automated semantic annotation, which aggregates similar thematic and geographic information. A novelty of our approach is the development and application of a crosscutting base vocabulary that supports the description of geospatial themes. The resulting annotation method is integrated into a novel open access collaboration platform (Esri's ArcGIS Hub) that supports public dissemination of civic data and is in use by thousands of government agencies. Our semantic annotation method improves data discovery for users across organizational repositories and has the potential to facilitate the coordination of community and organizational work, improving the transparency and efficacy of government policies.
Cite as

Sara Lafia, Andrew Turner, and Werner Kuhn. Improving Discovery of Open Civic Data. In 10th International Conference on Geographic Information Science (GIScience 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 114, pp. 9:1-9:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@InProceedings{lafia_et_al:LIPIcs.GISCIENCE.2018.9,
  author =	{Lafia, Sara and Turner, Andrew and Kuhn, Werner},
  title =	{{Improving Discovery of Open Civic Data}},
  booktitle =	{10th International Conference on Geographic Information Science (GIScience 2018)},
  pages =	{9:1--9:15},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-083-5},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{114},
  editor =	{Winter, Stephan and Griffin, Amy and Sester, Monika},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.GISCIENCE.2018.9},
  URN =		{urn:nbn:de:0030-drops-93376},
  doi =		{10.4230/LIPIcs.GISCIENCE.2018.9},
  annote =	{Keywords: data discovery, metadata, query expansion, interoperability}
}
Document
DOI: 10.4230/LIPIcs.GISCIENCE.2018.10
Local Co-location Pattern Detection: A Summary of Results

Authors: Yan Li and Shashi Shekhar

Published in: LIPIcs, Volume 114, 10th International Conference on Geographic Information Science (GIScience 2018)

Abstract
Given a set of spatial objects of different features (e.g., mall, hospital) and a spatial relation (e.g., geographic proximity), the problem of local co-location pattern detection (LCPD) pairs co-location patterns and localities such that the co-location patterns tend to exist inside the paired localities. A co-location pattern is a set of spatial features, the objects of which are often related to each other. Local co-location patterns are common in many fields, such as public security, and public health. For example, assault crimes and drunk driving events co-locate near bars. The problem is computationally challenging because of the exponential number of potential co-location patterns and candidate localities. The related work applies data-unaware or clustering heuristics to partition the study area, which results in incomplete enumeration of possible localities. In this study, we formally defined the LCPD problem where the candidate locality was defined using minimum orthogonal bounding rectangles (MOBRs). Then, we proposed a Quadruplet & Grid Filter-Refine (QGFR) algorithm that leveraged an MOBR enumeration lemma, and a novel upper bound on the participation index to efficiently prune the search space. The experimental evaluation showed that the QGFR algorithm reduced the computation cost substantially. One case study using the North American Atlas-Hydrography and U.S. Major City Datasets was conducted to discover local co-location patterns which would be missed if the entire dataset was analyzed or methods proposed by the related work were applied.
Cite as

Yan Li and Shashi Shekhar. Local Co-location Pattern Detection: A Summary of Results. In 10th International Conference on Geographic Information Science (GIScience 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 114, pp. 10:1-10:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@InProceedings{li_et_al:LIPIcs.GISCIENCE.2018.10,
  author =	{Li, Yan and Shekhar, Shashi},
  title =	{{Local Co-location Pattern Detection: A Summary of Results}},
  booktitle =	{10th International Conference on Geographic Information Science (GIScience 2018)},
  pages =	{10:1--10:15},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-083-5},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{114},
  editor =	{Winter, Stephan and Griffin, Amy and Sester, Monika},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.GISCIENCE.2018.10},
  URN =		{urn:nbn:de:0030-drops-93387},
  doi =		{10.4230/LIPIcs.GISCIENCE.2018.10},
  annote =	{Keywords: Co-location pattern, Participation index, Spatial heterogeneity}
}
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