117 Search Results for "Winter, Stephan"

Volume

LIPIcs, Volume 240

15th International Conference on Spatial Information Theory (COSIT 2022)

COSIT 2022, September 5-9, 2022, Kobe, Japan

Editors: Toru Ishikawa, Sara Irina Fabrikant, and Stephan Winter

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
Complete Volume
DOI: 10.4230/LIPIcs.COSIT.2022
LIPIcs, Volume 240, COSIT 2022, Complete Volume

Authors: Toru Ishikawa, Sara Irina Fabrikant, and Stephan Winter

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

Abstract
LIPIcs, Volume 240, COSIT 2022, Complete Volume
Cite as

15th International Conference on Spatial Information Theory (COSIT 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 240, pp. 1-316, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@Proceedings{ishikawa_et_al:LIPIcs.COSIT.2022,
  title =	{{LIPIcs, Volume 240, COSIT 2022, Complete Volume}},
  booktitle =	{15th International Conference on Spatial Information Theory (COSIT 2022)},
  pages =	{1--316},
  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},
  URN =		{urn:nbn:de:0030-drops-168842},
  doi =		{10.4230/LIPIcs.COSIT.2022},
  annote =	{Keywords: LIPIcs, Volume 240, COSIT 2022, Complete Volume}
}
Document
Front Matter
DOI: 10.4230/LIPIcs.COSIT.2022.0
Front Matter, Table of Contents, Preface, Conference Organization

Authors: Toru Ishikawa, Sara Irina Fabrikant, and Stephan Winter

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

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

15th International Conference on Spatial Information Theory (COSIT 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 240, pp. 0:i-0:x, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{ishikawa_et_al:LIPIcs.COSIT.2022.0,
  author =	{Ishikawa, Toru and Fabrikant, Sara Irina and Winter, Stephan},
  title =	{{Front Matter, Table of Contents, Preface, Conference Organization}},
  booktitle =	{15th International Conference on Spatial Information Theory (COSIT 2022)},
  pages =	{0:i--0:x},
  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.2022.0},
  URN =		{urn:nbn:de:0030-drops-168854},
  doi =		{10.4230/LIPIcs.COSIT.2022.0},
  annote =	{Keywords: Front Matter, Table of Contents, Preface, Conference Organization}
}
Document
DOI: 10.4230/LIPIcs.COSIT.2022.1
What Do You Mean You're in Trafalgar Square? Comparing Distance Thresholds for Geospatial Prepositions

Authors: Niloofar Aflaki, Kristin Stock, Christopher B. Jones, Hans Guesgen, Jeremy Morley, and Yukio Fukuzawa

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

Abstract
Natural language location descriptions frequently describe object locations relative to other objects (the house near the river). Geospatial prepositions (e.g.near) are a key element of these descriptions, and the distances associated with proximity, adjacency and topological prepositions are thought to depend on the context of a specific scene. When referring to the context, we include consideration of properties of the relatum such as its feature type, size and associated image schema. In this paper, we extract spatial descriptions from the Google search engine for nine prepositions across three locations, compare their acceptance thresholds (the distances at which different prepositions are acceptable), and study variations in different contexts using cumulative graphs and scatter plots. Our results show that adjacency prepositions next to and adjacent to are used for a large range of distances, in contrast to beside; and that topological prepositions in, at and on can all be used to indicate proximity as well as containment and collocation. We also found that reference object image schema influences the selection of geospatial prepositions such as near and in.
Cite as

Niloofar Aflaki, Kristin Stock, Christopher B. Jones, Hans Guesgen, Jeremy Morley, and Yukio Fukuzawa. What Do You Mean You're in Trafalgar Square? Comparing Distance Thresholds for Geospatial Prepositions. In 15th International Conference on Spatial Information Theory (COSIT 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 240, pp. 1:1-1:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{aflaki_et_al:LIPIcs.COSIT.2022.1,
  author =	{Aflaki, Niloofar and Stock, Kristin and Jones, Christopher B. and Guesgen, Hans and Morley, Jeremy and Fukuzawa, Yukio},
  title =	{{What Do You Mean You're in Trafalgar Square? Comparing Distance Thresholds for Geospatial Prepositions}},
  booktitle =	{15th International Conference on Spatial Information Theory (COSIT 2022)},
  pages =	{1:1--1:14},
  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.2022.1},
  URN =		{urn:nbn:de:0030-drops-168865},
  doi =		{10.4230/LIPIcs.COSIT.2022.1},
  annote =	{Keywords: contextual factors, spatial descriptions, acceptance model, spatial template, applicability model, geospatial prepositions}
}
Document
DOI: 10.4230/LIPIcs.COSIT.2022.2
I Can Tell by Your Eyes! Continuous Gaze-Based Turn-Activity Prediction Reveals Spatial Familiarity

Authors: Negar Alinaghi, Markus Kattenbeck, and Ioannis Giannopoulos

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

Abstract
Spatial familiarity plays an essential role in the wayfinding decision-making process. Recent findings in wayfinding activity recognition domain suggest that wayfinders' turning behavior at junctions is strongly influenced by their spatial familiarity. By continuously monitoring wayfinders' turning behavior as reflected in their eye movements during the decision-making period (i.e., immediately after an instruction is received until reaching the corresponding junction for which the instruction was given), we provide evidence that familiar and unfamiliar wayfinders can be distinguished. By applying a pre-trained XGBoost turning activity classifier on gaze data collected in a real-world wayfinding task with 33 participants, our results suggest that familiar and unfamiliar wayfinders show different onset and intensity of turning behavior. These variations are not only present between the two classes -familiar vs. unfamiliar- but also within each class. The differences in turning-behavior within each class may stem from multiple sources, including different levels of familiarity with the environment.
Cite as

Negar Alinaghi, Markus Kattenbeck, and Ioannis Giannopoulos. I Can Tell by Your Eyes! Continuous Gaze-Based Turn-Activity Prediction Reveals Spatial Familiarity. In 15th International Conference on Spatial Information Theory (COSIT 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 240, pp. 2:1-2:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{alinaghi_et_al:LIPIcs.COSIT.2022.2,
  author =	{Alinaghi, Negar and Kattenbeck, Markus and Giannopoulos, Ioannis},
  title =	{{I Can Tell by Your Eyes! Continuous Gaze-Based Turn-Activity Prediction Reveals Spatial Familiarity}},
  booktitle =	{15th International Conference on Spatial Information Theory (COSIT 2022)},
  pages =	{2:1--2:13},
  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.2022.2},
  URN =		{urn:nbn:de:0030-drops-168872},
  doi =		{10.4230/LIPIcs.COSIT.2022.2},
  annote =	{Keywords: Spatial Familiarity, Gaze-based Activity Recognition, Wayfinding, Machine Learning}
}
Document
DOI: 10.4230/LIPIcs.COSIT.2022.3
Automatically Discovering Conceptual Neighborhoods Using Machine Learning Methods

Authors: Ling Cai, Krzysztof Janowicz, and Rui Zhu

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

Abstract
Qualitative spatio-temporal reasoning (QSTR) plays a key role in spatial cognition and artificial intelligence (AI) research. In the past, research and applications of QSTR have often taken place in the context of declarative forms of knowledge representation. For instance, conceptual neighborhoods (CN) and composition tables (CT) of relations are introduced explicitly and utilized for spatial/temporal reasoning. Orthogonal to this line of study, we focus on bottom-up machine learning (ML) approaches to investigate QSTR. More specifically, we are interested in questions of whether similarities between qualitative relations can be learned from data purely based on ML models, and, if so, how these models differ from the ones studied by traditional approaches. To achieve this, we propose a graph-based approach to examine the similarity of relations by analyzing trained ML models. Using various experiments on synthetic data, we demonstrate that the relationships discovered by ML models are well-aligned with CN structures introduced in the (theoretical) literature, for both spatial and temporal reasoning. Noticeably, even with significantly limited qualitative information for training, ML models are still able to automatically construct neighborhood structures. Moreover, patterns of asymmetric similarities between relations are disclosed using such a data-driven approach. To the best of our knowledge, our work is the first to automatically discover CNs without any domain knowledge. Our results can be applied to discovering CNs of any set of jointly exhaustive and pairwise disjoint (JEPD) relations.
Cite as

Ling Cai, Krzysztof Janowicz, and Rui Zhu. Automatically Discovering Conceptual Neighborhoods Using Machine Learning Methods. In 15th International Conference on Spatial Information Theory (COSIT 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 240, pp. 3:1-3:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{cai_et_al:LIPIcs.COSIT.2022.3,
  author =	{Cai, Ling and Janowicz, Krzysztof and Zhu, Rui},
  title =	{{Automatically Discovering Conceptual Neighborhoods Using Machine Learning Methods}},
  booktitle =	{15th International Conference on Spatial Information Theory (COSIT 2022)},
  pages =	{3:1--3:14},
  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.2022.3},
  URN =		{urn:nbn:de:0030-drops-168884},
  doi =		{10.4230/LIPIcs.COSIT.2022.3},
  annote =	{Keywords: Qualitative Spatial Reasoning, Qualitative Temporal Reasoning, Conceptual Neighborhood, Machine Learning, Knowledge Discovery}
}
Document
DOI: 10.4230/LIPIcs.COSIT.2022.4
Predicting Distance and Direction from Text Locality Descriptions for Biological Specimen Collections

Authors: Ruoxuan Liao, Pragyan P. Das, Christopher B. Jones, Niloofar Aflaki, and Kristin Stock

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

Abstract
A considerable proportion of records that describe biological specimens (flora, soil, invertebrates), and especially those that were collected decades ago, are not attached to corresponding geographical coordinates, but rather have their location described only through textual descriptions (e.g. North Canterbury, Selwyn River near bridge on Springston-Leeston Rd). Without geographical coordinates, millions of records stored in museum collections around the world cannot be mapped. We present a method for predicting the distance and direction associated with human language location descriptions which focuses on the interpretation of geospatial prepositions and the way in which they modify the location represented by an associated reference place name (e.g. near the Manawatu River). We study eight distance-oriented prepositions and eight direction-oriented prepositions and use machine learning regression to predict distance or direction, relative to the reference place name, from a collection of training data. The results show that, compared with a simple baseline, our model improved distance predictions by up to 60% and direction predictions by up to 31%.
Cite as

Ruoxuan Liao, Pragyan P. Das, Christopher B. Jones, Niloofar Aflaki, and Kristin Stock. Predicting Distance and Direction from Text Locality Descriptions for Biological Specimen Collections. In 15th International Conference on Spatial Information Theory (COSIT 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 240, pp. 4:1-4:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{liao_et_al:LIPIcs.COSIT.2022.4,
  author =	{Liao, Ruoxuan and Das, Pragyan P. and Jones, Christopher B. and Aflaki, Niloofar and Stock, Kristin},
  title =	{{Predicting Distance and Direction from Text Locality Descriptions for Biological Specimen Collections}},
  booktitle =	{15th International Conference on Spatial Information Theory (COSIT 2022)},
  pages =	{4:1--4:15},
  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.2022.4},
  URN =		{urn:nbn:de:0030-drops-168892},
  doi =		{10.4230/LIPIcs.COSIT.2022.4},
  annote =	{Keywords: geospatial prepositions, biological specimen collections, georeferencing, natural language processing, locative expressions, locality descriptions, geoparsing, geocoding, geographic information retrieval, regression, machine learning}
}
Document
DOI: 10.4230/LIPIcs.COSIT.2022.5
An Incremental Algorithm for Handling Qualitative Spatio-Temporal Information

Authors: Zhiguo Long, Qiyuan Hu, Hua Meng, and Michael Sioutis

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

Abstract
In this paper, we present an online (incremental) algorithm for checking the satisfiability of qualitative spatio-temporal data, with direct implications to other fundamental knowledge representation and reasoning problems for such data, like the problems of deductive closure and redundancy removal. In particular, qualitative data come in the form of human-like, symbolic, descriptions such as "region x contains or overlaps region y", which are abundant in the Web of Data. Our approach is also able to maintain, to some extent, any sparse graph structure that may be inherent in the data, i.e., it acts parsimoniously and only tries to infer new information when needed for soundness and completeness. To this end, we complement our practical algorithm with certain theoretical results to assert its correctness and efficiency. A subsequent evaluation with publicly available large-scale real-world and random datasets against the state of the art, shows the interest and promise of our method.
Cite as

Zhiguo Long, Qiyuan Hu, Hua Meng, and Michael Sioutis. An Incremental Algorithm for Handling Qualitative Spatio-Temporal Information. In 15th International Conference on Spatial Information Theory (COSIT 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 240, pp. 5:1-5:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{long_et_al:LIPIcs.COSIT.2022.5,
  author =	{Long, Zhiguo and Hu, Qiyuan and Meng, Hua and Sioutis, Michael},
  title =	{{An Incremental Algorithm for Handling Qualitative Spatio-Temporal Information}},
  booktitle =	{15th International Conference on Spatial Information Theory (COSIT 2022)},
  pages =	{5:1--5:13},
  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.2022.5},
  URN =		{urn:nbn:de:0030-drops-168907},
  doi =		{10.4230/LIPIcs.COSIT.2022.5},
  annote =	{Keywords: Online algorithm, qualitative data, spatio-temporal reasoning, satisfiability checking, knowledge representation and reasoning}
}
Document
DOI: 10.4230/LIPIcs.COSIT.2022.6
Rethinking Route Choices! On the Importance of Route Selection in Wayfinding Experiments

Authors: Bartosz Mazurkiewicz, Markus Kattenbeck, and Ioannis Giannopoulos

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

Abstract
Route selection for a wayfinding experiment is not a trivial task and is often made in an undocumented way. Only recently (2021), a systematic, reproducible and score-based approach for route selection for wayfinding experiments was published. However, it is still unclear how robust study results are across all potential routes in a particular experimental area. An important share of routes might lead to different conclusions than most routes. This share would distort and/or invert the study outcome. If so, the question of selecting routes that are unlikely to distort the results of our wayfinding experiments remains unanswered. In order to answer these questions, an agent-based simulation study with four different sample sizes (N = 15, 25, 50, 3000 agents) comparing Turn-by-Turn and Free Choice Navigation approaches (between-subject design) regarding their arrival rates on more than 11000 routes in the city center of Vienna, Austria, was run. The results of our study indicate that with decreasing sample size, there is an increase in the share of routes which lead to contradictory results regarding the arrival rate, i.e., the results become less robust. Therefore, based on simulation results, we present an approach for selecting suitable routes even for small-scale in-situ studies.
Cite as

Bartosz Mazurkiewicz, Markus Kattenbeck, and Ioannis Giannopoulos. Rethinking Route Choices! On the Importance of Route Selection in Wayfinding Experiments. In 15th International Conference on Spatial Information Theory (COSIT 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 240, pp. 6:1-6:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{mazurkiewicz_et_al:LIPIcs.COSIT.2022.6,
  author =	{Mazurkiewicz, Bartosz and Kattenbeck, Markus and Giannopoulos, Ioannis},
  title =	{{Rethinking Route Choices! On the Importance of Route Selection in Wayfinding Experiments}},
  booktitle =	{15th International Conference on Spatial Information Theory (COSIT 2022)},
  pages =	{6:1--6:13},
  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.2022.6},
  URN =		{urn:nbn:de:0030-drops-168916},
  doi =		{10.4230/LIPIcs.COSIT.2022.6},
  annote =	{Keywords: Route Selection, Route Features, Human Wayfinding, Navigation, Experiments, Experimental Design}
}
Document
DOI: 10.4230/LIPIcs.COSIT.2022.7
Empirical Evidence for Concepts of Spatial Information as Cognitive Means for Interpreting and Using Maps

Authors: Enkhbold Nyamsuren, Eric J. Top, Haiqi Xu, Niels Steenbergen, and Simon Scheider

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

Abstract
Due to the increasing prevalence and relevance of geo-spatial data in the age of data science, Geographic Information Systems are enjoying wider interdisciplinary adoption by communities outside of GIScience. However, properly interpreting and analysing geo-spatial information is not a trivial task due to knowledge barriers. There is a need for a trans-disciplinary framework for sharing specialized geographical knowledge and expertise to overcome these barriers. The core concepts of spatial information were proposed as such a conceptual framework. These concepts, such as object and field, were proposed as cognitive lenses that can simplify understanding of and guide the processing of spatial information. However, there is a distinct lack of empirical evidence for the existence of such concepts in the human mind or whether such concepts can be indeed useful. In this study, we have explored for such empirical evidence using behavioral experiments with human participants. The experiment adopted a contrast model to investigate whether the participants can semantically distinguish between the object and field core concepts visualized as maps. The statistically significant positive results offer evidence supporting the existence of the two concepts or cognitive concepts closely resembling them. This gives credibility to the core concepts of spatial information as tools for sharing, teaching, or even automating the process of geographical information processing.
Cite as

Enkhbold Nyamsuren, Eric J. Top, Haiqi Xu, Niels Steenbergen, and Simon Scheider. Empirical Evidence for Concepts of Spatial Information as Cognitive Means for Interpreting and Using Maps. In 15th International Conference on Spatial Information Theory (COSIT 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 240, pp. 7:1-7:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{nyamsuren_et_al:LIPIcs.COSIT.2022.7,
  author =	{Nyamsuren, Enkhbold and Top, Eric J. and Xu, Haiqi and Steenbergen, Niels and Scheider, Simon},
  title =	{{Empirical Evidence for Concepts of Spatial Information as Cognitive Means for Interpreting and Using Maps}},
  booktitle =	{15th International Conference on Spatial Information Theory (COSIT 2022)},
  pages =	{7:1--7:14},
  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.2022.7},
  URN =		{urn:nbn:de:0030-drops-168926},
  doi =		{10.4230/LIPIcs.COSIT.2022.7},
  annote =	{Keywords: core concepts, cognition, map interpretation, spatial analysis}
}
Document
DOI: 10.4230/LIPIcs.COSIT.2022.8
Generalized, Inaccurate, Incomplete: How to Comprehensively Analyze Sketch Maps Beyond Their Metric Correctness

Authors: Angela Schwering, Jakub Krukar, Charu Manivannan, Malumbo Chipofya, and Sahib Jan

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

Abstract
Sketch mapping is a method to investigate a person’s spatial perception and knowledge about the surrounding environment. While cartographic maps can be easily evaluated with respect to the represented features, map scale, and spatial accuracy, there still does not exist a comprehensive method to evaluate sketch maps. This paper aims to overcome this gap and proposes a sketch map analysis method that allows for analyzing the completeness, generalization and (qualitative) spatial accuracy of the sketched information in a three-step process. After describing the method, we illustrate how our computer-supported method performs in a use case with three sketch maps. Our approach may assist researchers in geography, psychology, and education to evaluate spatial knowledge in a systematic way independent of specific research questions and experimental scenarios.
Cite as

Angela Schwering, Jakub Krukar, Charu Manivannan, Malumbo Chipofya, and Sahib Jan. Generalized, Inaccurate, Incomplete: How to Comprehensively Analyze Sketch Maps Beyond Their Metric Correctness. In 15th International Conference on Spatial Information Theory (COSIT 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 240, pp. 8:1-8:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{schwering_et_al:LIPIcs.COSIT.2022.8,
  author =	{Schwering, Angela and Krukar, Jakub and Manivannan, Charu and Chipofya, Malumbo and Jan, Sahib},
  title =	{{Generalized, Inaccurate, Incomplete: How to Comprehensively Analyze Sketch Maps Beyond Their Metric Correctness}},
  booktitle =	{15th International Conference on Spatial Information Theory (COSIT 2022)},
  pages =	{8:1--8:15},
  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.2022.8},
  URN =		{urn:nbn:de:0030-drops-168936},
  doi =		{10.4230/LIPIcs.COSIT.2022.8},
  annote =	{Keywords: sketch map analysis, spatial knowledge evaluation, cognitive map}
}
Document
DOI: 10.4230/LIPIcs.COSIT.2022.9
Perceptions of Qualitative Spatial Arrangements of Three Objects

Authors: Ningran Xu, Ivan Majic, and Martin Tomko

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

Abstract
Cognitive grounding of formal models of qualitative spatial relations is important to bridge between spatial data and human perceptions of spatial arrangements. Here, we report on an experimental verification of the cognitive alignment of the recently proposed Ray Intersection Model (RIM) capturing qualitative relationships between three spatial objects, and human perceptions of spatial arrangements through a grouping task. Further, we explore arrangements with an object positioned "between" two other objects. We show that RIM has sufficient expressive power and aligns well with human perceptions of ternary spatial relationships.
Cite as

Ningran Xu, Ivan Majic, and Martin Tomko. Perceptions of Qualitative Spatial Arrangements of Three Objects. In 15th International Conference on Spatial Information Theory (COSIT 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 240, pp. 9:1-9:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{xu_et_al:LIPIcs.COSIT.2022.9,
  author =	{Xu, Ningran and Majic, Ivan and Tomko, Martin},
  title =	{{Perceptions of Qualitative Spatial Arrangements of Three Objects}},
  booktitle =	{15th International Conference on Spatial Information Theory (COSIT 2022)},
  pages =	{9:1--9:14},
  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.2022.9},
  URN =		{urn:nbn:de:0030-drops-168948},
  doi =		{10.4230/LIPIcs.COSIT.2022.9},
  annote =	{Keywords: Spatial Perception, Qualitative Spatial Relationships, Betweenness, Evaluation, Ternary Relationships}
}
Document
Vision Paper
DOI: 10.4230/LIPIcs.COSIT.2022.10
Are Psychological Variables Relevant to Evaluating Geoinformatics Applications? The Case of Landmarks (Vision Paper)

Authors: Jakub Krukar and Angela Schwering

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

Abstract
Interdisciplinary integration of spatial cognition and spatial computation promises to create better spatial technology based on findings from cognitive psychology experiments. Using the example of psychological studies and computational modelling of landmarks, this paper argues that core evaluation criteria of both disciplines are not well aligned with the goal of evaluating landmark-enhanced navigation support systems that support users in everyday wayfinding. The paper raises two points. First, it reviews evaluation criteria used in the interdisciplinary field of landmark research. It is argued that when to consider the role of landmark-enhanced navigation support systems in everyday life of their users, different evaluation criteria are needed. If strictly-psychological or strictly-computational criteria continue being prioritised by the community, we risk undervaluing significant technological contributions. Second, it proposes one such potential criterion: testing whether the cognitive task has changed due to equipping users with the new technology. This goal might be achieved at the expense of criteria typical to strictly-psychological studies (such as spatial memory of landmarks along the travelled route) or strictly-computational studies (such as efficiency and accuracy of a landmark-selection algorithm). Thus, promoting and implementing alternative evaluation criteria comes with methodological risks. In order to mitigate them we propose a process based on pre-registration of "postdiction" studies and hope to stimulate a further debate on a consensus-based approach in the community.
Cite as

Jakub Krukar and Angela Schwering. Are Psychological Variables Relevant to Evaluating Geoinformatics Applications? The Case of Landmarks (Vision Paper). In 15th International Conference on Spatial Information Theory (COSIT 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 240, pp. 10:1-10:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{krukar_et_al:LIPIcs.COSIT.2022.10,
  author =	{Krukar, Jakub and Schwering, Angela},
  title =	{{Are Psychological Variables Relevant to Evaluating Geoinformatics Applications? The Case of Landmarks (Vision Paper)}},
  booktitle =	{15th International Conference on Spatial Information Theory (COSIT 2022)},
  pages =	{10:1--10:13},
  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.2022.10},
  URN =		{urn:nbn:de:0030-drops-168956},
  doi =		{10.4230/LIPIcs.COSIT.2022.10},
  annote =	{Keywords: wayfinding, navigation support systems, cognitive geoengineering, landmarks}
}
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