124 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
Invited Talk
DOI: 10.4230/LIPIcs.IPEC.2025.1
A Brief History of Parameterized Algorithms for Block-Structured Integer Programs (Invited Talk)

Authors: Martin Koutecký

Published in: LIPIcs, Volume 358, 20th International Symposium on Parameterized and Exact Computation (IPEC 2025)

Abstract
Integer Programming (IP) is a fundamental but computationally hard problem. Still, certain efficiently solvable subclasses have been identified over time, most notably totally unimodular IPs in the 1950s, and fixed-dimension IPs in the 1980s. Starting around the year 2000, a stream of research has identified block-structured IPs as yet another tractable subclass. In this paper, we give a brief and incomplete review of this history, with a focus on several of the author’s contributions.
Cite as

Martin Koutecký. A Brief History of Parameterized Algorithms for Block-Structured Integer Programs (Invited Talk). In 20th International Symposium on Parameterized and Exact Computation (IPEC 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 358, pp. 1:1-1:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{koutecky:LIPIcs.IPEC.2025.1,
  author =	{Kouteck\'{y}, Martin},
  title =	{{A Brief History of Parameterized Algorithms for Block-Structured Integer Programs}},
  booktitle =	{20th International Symposium on Parameterized and Exact Computation (IPEC 2025)},
  pages =	{1:1--1:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-407-9},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{358},
  editor =	{Agrawal, Akanksha and van Leeuwen, Erik Jan},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.IPEC.2025.1},
  URN =		{urn:nbn:de:0030-drops-251338},
  doi =		{10.4230/LIPIcs.IPEC.2025.1},
  annote =	{Keywords: Integer Programming, Parameterized Algorithm, Graver Basis, Treedepth, n-fold, tree-fold, 2-stage stochastic, multistage stochastic, Mixed-Integer Programming}
}
Document
Short Paper
DOI: 10.4230/LIPIcs.TIME.2025.17
Visit Probability in Space-Time Prisms for Moving Object Data (Short Paper)

Authors: Arthur Jansen and Bart Kuijpers

Published in: LIPIcs, Volume 355, 32nd International Symposium on Temporal Representation and Reasoning (TIME 2025)

Abstract
Space-time prisms have been extensively studied as a model to describe the uncertainty of the spatio-temporal location of a moving object in between measured space-time locations. In many applications, the desire has been expressed to provide an internal structure to these prisms, that includes what has been called "visit probability". Although several proposals have been studied in the past decades, a precise definition of this concept has been missing. The contribution of this paper is to provide such a specification by means of a formal framework for visit probability. Once this concept is established, we are able to derive on which parts of a prism, visit probability can be seen to give rise to a probability space.
Cite as

Arthur Jansen and Bart Kuijpers. Visit Probability in Space-Time Prisms for Moving Object Data (Short Paper). In 32nd International Symposium on Temporal Representation and Reasoning (TIME 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 355, pp. 17:1-17:4, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{jansen_et_al:LIPIcs.TIME.2025.17,
  author =	{Jansen, Arthur and Kuijpers, Bart},
  title =	{{Visit Probability in Space-Time Prisms for Moving Object Data}},
  booktitle =	{32nd International Symposium on Temporal Representation and Reasoning (TIME 2025)},
  pages =	{17:1--17:4},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-401-7},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{355},
  editor =	{Vidal, Thierry and Wa{\l}\k{e}ga, Przemys{\l}aw Andrzej},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.TIME.2025.17},
  URN =		{urn:nbn:de:0030-drops-244633},
  doi =		{10.4230/LIPIcs.TIME.2025.17},
  annote =	{Keywords: Spatio-temporal databases, moving object databases, space-time prisms, probability spaces}
}
Artifact
Software
DOI: 10.4230/artifacts.24214
hamzeiehsan/leisure-walking-analysis

Authors: Ehsan Hamzei, Thi Minh Hoai Bui, Martin Tomko, and Stephan Winter

Abstract
Cite as

Ehsan Hamzei, Thi Minh Hoai Bui, Martin Tomko, Stephan Winter. hamzeiehsan/leisure-walking-analysis (Software, Source code). Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@misc{dagstuhl-artifact-24214,
   title = {{hamzeiehsan/leisure-walking-analysis}}, 
   author = {Hamzei, Ehsan and Bui, Thi Minh Hoai and Tomko, Martin and Winter, Stephan},
   note = {Software, swhId: \href{https://archive.softwareheritage.org/swh:1:dir:cddb6d133e212246c2e458e4ea46f1358cd27927;origin=https://github.com/hamzeiehsan/leisure-walking-analysis;visit=swh:1:snp:24de26e43def854424cacc232f9289821b17290a;anchor=swh:1:rev:22c33b5a1a32915a600817db808f716df265b70b}{\texttt{swh:1:dir:cddb6d133e212246c2e458e4ea46f1358cd27927}} (visited on 2025-08-15)},
   url = {https://github.com/hamzeiehsan/leisure-walking-analysis},
   doi = {10.4230/artifacts.24214},
}
Document
DOI: 10.4230/LIPIcs.GIScience.2025.5
Analysis of Points of Interests Recommended for Leisure Walk Descriptions

Authors: Ehsan Hamzei, Thi Minh Hoai Bui, Martin Tomko, and Stephan Winter

Published in: LIPIcs, Volume 346, 13th International Conference on Geographic Information Science (GIScience 2025)

Abstract
Leisure walking is a physical activity where locomotion through a natural or even urban environment is the goal in itself, e.g., in pursuit of health and wellbeing. In contrast to destination-oriented walks that are focused on navigation efficiency (i.e., shortest or simplest walk from source to destination), leisure walks emphasize experiencing the environment, engaging in activities, and discovering places that may be off route, or intermediate destinations en-route, summarily called points of interest (POIs). POIs are key for recommending leisure walks, yet a detailed analysis of POIs in the context of leisure walking is missing in the literature. This study extracts and annotates POIs of leisure walking recommendations available in WalkingMaps.com.au, creating an annotated dataset to address this research gap and provide a first analysis of leisure walking descriptions. We classify POIs using the verbal description provided in the dataset, match them with data available in OpenStreetMap (OSM), and compare the POIs with nearby alternatives in OSM. Our analysis reveals thematic and spatial patterns in POI selection, offering a machine learning approach to model POI choices for leisure walks. We further evaluate the availability of rich data in OSM for future automated leisure walking recommendation. This study contributes to automated systems for recommending leisure walks, tailoring suggestions based on available information in the spatial open data, and presents an annotated dataset to facilitate future research in this field.
Cite as

Ehsan Hamzei, Thi Minh Hoai Bui, Martin Tomko, and Stephan Winter. Analysis of Points of Interests Recommended for Leisure Walk Descriptions. In 13th International Conference on Geographic Information Science (GIScience 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 346, pp. 5:1-5:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{hamzei_et_al:LIPIcs.GIScience.2025.5,
  author =	{Hamzei, Ehsan and Bui, Thi Minh Hoai and Tomko, Martin and Winter, Stephan},
  title =	{{Analysis of Points of Interests Recommended for Leisure Walk Descriptions}},
  booktitle =	{13th International Conference on Geographic Information Science (GIScience 2025)},
  pages =	{5:1--5:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-378-2},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{346},
  editor =	{Sila-Nowicka, Katarzyna and Moore, Antoni and O'Sullivan, David and Adams, Benjamin and Gahegan, Mark},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2025.5},
  URN =		{urn:nbn:de:0030-drops-238341},
  doi =		{10.4230/LIPIcs.GIScience.2025.5},
  annote =	{Keywords: leisure walks, points of interest, places, platial information}
}
Document
DOI: 10.4230/LIPIcs.GIScience.2025.12
Large Multi-Modal Model Cartographic Map Comprehension for Textual Locality Georeferencing

Authors: Kalana Wijegunarathna, Kristin Stock, and Christopher B. Jones

Published in: LIPIcs, Volume 346, 13th International Conference on Geographic Information Science (GIScience 2025)

Abstract
Millions of biological sample records collected in the last few centuries archived in natural history collections are un-georeferenced. Georeferencing complex locality descriptions associated with these collection samples is a highly labour-intensive task collection agencies struggle with. None of the existing automated methods exploit maps that are an essential tool for georeferencing complex relations. We present preliminary experiments and results of a novel method that exploits multi-modal capabilities of recent Large Multi-Modal Models (LMM). This method enables the model to visually contextualize spatial relations it reads in the locality description. We use a grid-based approach to adapt these auto-regressive models for this task in a zero-shot setting. Our experiments conducted on a small manually annotated dataset show impressive results for our approach (∼1 km Average distance error) compared to uni-modal georeferencing with Large Language Models and existing georeferencing tools. The paper also discusses the findings of the experiments in light of an LMM’s ability to comprehend fine-grained maps. Motivated by these results, a practical framework is proposed to integrate this method into a georeferencing workflow.
Cite as

Kalana Wijegunarathna, Kristin Stock, and Christopher B. Jones. Large Multi-Modal Model Cartographic Map Comprehension for Textual Locality Georeferencing. In 13th International Conference on Geographic Information Science (GIScience 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 346, pp. 12:1-12:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{wijegunarathna_et_al:LIPIcs.GIScience.2025.12,
  author =	{Wijegunarathna, Kalana and Stock, Kristin and Jones, Christopher B.},
  title =	{{Large Multi-Modal Model Cartographic Map Comprehension for Textual Locality Georeferencing}},
  booktitle =	{13th International Conference on Geographic Information Science (GIScience 2025)},
  pages =	{12:1--12:19},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-378-2},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{346},
  editor =	{Sila-Nowicka, Katarzyna and Moore, Antoni and O'Sullivan, David and Adams, Benjamin and Gahegan, Mark},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2025.12},
  URN =		{urn:nbn:de:0030-drops-238412},
  doi =		{10.4230/LIPIcs.GIScience.2025.12},
  annote =	{Keywords: Large Multi-Modal Models, Large Language Models, LLM, Georeferencing, Natural History collections}
}
Document
Vision
DOI: 10.4230/TGDK.1.1.8
Machine Learning and Knowledge Graphs: Existing Gaps and Future Research Challenges

Authors: Claudia d'Amato, Louis Mahon, Pierre Monnin, and Giorgos Stamou

Published in: TGDK, Volume 1, Issue 1 (2023): Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge, Volume 1, Issue 1

Abstract
The graph model is nowadays largely adopted to model a wide range of knowledge and data, spanning from social networks to knowledge graphs (KGs), representing a successful paradigm of how symbolic and transparent AI can scale on the World Wide Web. However, due to their unprecedented volume, they are generally tackled by Machine Learning (ML) and mostly numeric based methods such as graph embedding models (KGE) and deep neural networks (DNNs). The latter methods have been proved lately very efficient, leading the current AI spring. In this vision paper, we introduce some of the main existing methods for combining KGs and ML, divided into two categories: those using ML to improve KGs, and those using KGs to improve results on ML tasks. From this introduction, we highlight research gaps and perspectives that we deem promising and currently under-explored for the involved research communities, spanning from KG support for LLM prompting, integration of KG semantics in ML models to symbol-based methods, interpretability of ML models, and the need for improved benchmark datasets. In our opinion, such perspectives are stepping stones in an ultimate view of KGs as central assets for neuro-symbolic and explainable AI.
Cite as

Claudia d'Amato, Louis Mahon, Pierre Monnin, and Giorgos Stamou. Machine Learning and Knowledge Graphs: Existing Gaps and Future Research Challenges. In Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge (TGDK), Volume 1, Issue 1, pp. 8:1-8:35, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@Article{damato_et_al:TGDK.1.1.8,
  author =	{d'Amato, Claudia and Mahon, Louis and Monnin, Pierre and Stamou, Giorgos},
  title =	{{Machine Learning and Knowledge Graphs: Existing Gaps and Future Research Challenges}},
  journal =	{Transactions on Graph Data and Knowledge},
  pages =	{8:1--8:35},
  year =	{2023},
  volume =	{1},
  number =	{1},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/TGDK.1.1.8},
  URN =		{urn:nbn:de:0030-drops-194824},
  doi =		{10.4230/TGDK.1.1.8},
  annote =	{Keywords: Graph-based Learning, Knowledge Graph Embeddings, Large Language Models, Explainable AI, Knowledge Graph Completion \& Curation}
}
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.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.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.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.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.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.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.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}
}
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