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Documents authored by Xin, Yanan

Document
DOI: 10.4230/DagRep.14.5.25
Causal Inference for Spatial Data Analytics (Dagstuhl Seminar 24202)

Authors: Martin Tomko, Yanan Xin, and Jonas Wahl

Published in: Dagstuhl Reports, Volume 14, Issue 5 (2024)

Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 24202 "Causal Inference for Spatial Data Analytics", taking place at Schloss Dagstuhl between May 12superscript{th} and 17superscript{th}, 2024. The ability to identify causal relationships in spatial data is increasingly important for designing effective policy interventions in environmental science, epidemiology, urban planning, and traffic management. Current spatial data analytic methods rely mainly on descriptive and predictive methods that lack explicit causal models. Spatial causal inference, i.e. causal inference with spatial information offers a promising tool to address this challenge by extending causal inference methodologies to spatial domains. However, this translation is challenging due to spatial effects that might violate fundamental assumptions of causal inference. Spatial causal inference is therefore still in its infancy, and there is a pressing need to accelerate its theoretical development and support its adoption with a well-grounded methodological toolset. To facilitate the necessary interdisciplinary exchange of ideas we convened the first Dagstuhl Seminar on Causal Inference for Spatial Data Analytics.
Cite as

Martin Tomko, Yanan Xin, and Jonas Wahl. Causal Inference for Spatial Data Analytics (Dagstuhl Seminar 24202). In Dagstuhl Reports, Volume 14, Issue 5, pp. 25-57, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@Article{tomko_et_al:DagRep.14.5.25,
  author =	{Tomko, Martin and Xin, Yanan and Wahl, Jonas},
  title =	{{Causal Inference for Spatial Data Analytics (Dagstuhl Seminar 24202)}},
  pages =	{25--57},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2024},
  volume =	{14},
  number =	{5},
  editor =	{Tomko, Martin and Xin, Yanan and Wahl, Jonas},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.14.5.25},
  URN =		{urn:nbn:de:0030-drops-222668},
  doi =		{10.4230/DagRep.14.5.25},
  annote =	{Keywords: Spatial Causal Analysis, Spatial Causal Inference, Spatial Causal Discovery, Spatial Analysis, Spatial Data, Dagstuhl Seminar}
}
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
DOI: 10.4230/LIPIcs.GIScience.2021.II.4
A Clustering-Based Framework for Individual Travel Behaviour Change Detection

Authors: Ye Hong, Yanan Xin, Henry Martin, Dominik Bucher, and Martin Raubal

Published in: LIPIcs, Volume 208, 11th International Conference on Geographic Information Science (GIScience 2021) - Part II

Abstract
The emergence of passively and continuously recorded movement data offers new opportunities to study the long-term change of individual travel behaviour from data-driven perspectives. This study proposes a clustering-based framework to identify travel behaviour patterns and detect potential change periods on the individual level. First, we extract important trips that depict individual characteristic movement. Then, considering trip mode, trip distance, and trip duration as travel behaviour dimensions, we measure the similarities of trips and group them into clusters using hierarchical clustering. The trip clusters represent dimensions of travel behaviours, and the change of their relative proportions over time reflect the development of travel preferences. We use two different methods to detect changes in travel behaviour patterns: the Herfindahl-Hirschman index-based method and the sliding window-based method. The framework is tested using data from a large-scale longitudinal GPS tracking data study in which participants had access to a Mobility-as-a-Service (MaaS) offer. The methods successfully identify significant travel behaviour changes for users. Moreover, we analyse the impact of the MaaS offer on individual travel behaviours with the obtained change information. The proposed framework for behaviour change detection provides valuable insights for travel demand management and evaluating people’s reactions to sustainable mobility options.
Cite as

Ye Hong, Yanan Xin, Henry Martin, Dominik Bucher, and Martin Raubal. A Clustering-Based Framework for Individual Travel Behaviour Change Detection. In 11th International Conference on Geographic Information Science (GIScience 2021) - Part II. Leibniz International Proceedings in Informatics (LIPIcs), Volume 208, pp. 4:1-4:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{hong_et_al:LIPIcs.GIScience.2021.II.4,
  author =	{Hong, Ye and Xin, Yanan and Martin, Henry and Bucher, Dominik and Raubal, Martin},
  title =	{{A Clustering-Based Framework for Individual Travel Behaviour Change Detection}},
  booktitle =	{11th International Conference on Geographic Information Science (GIScience 2021) - Part II},
  pages =	{4:1--4:15},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-208-2},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{208},
  editor =	{Janowicz, Krzysztof and Verstegen, Judith A.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2021.II.4},
  URN =		{urn:nbn:de:0030-drops-147635},
  doi =		{10.4230/LIPIcs.GIScience.2021.II.4},
  annote =	{Keywords: Human mobility, Travel behaviour, Change detection, Trip clustering}
}
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