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Documents authored by Dodge, Somayeh

Document
DOI: 10.4230/LIPIcs.GIScience.2025.9
Identifying Resilient Communities in Road Networks: A Path-Based Embedding Approach

Authors: Christopher Wagner, Somayeh Dodge, and Danial Alizadeh

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

Abstract
Effective resilience analysis of road networks is fundamental to building sustainable and disaster prepared cities. Identifying which road segments share similar vulnerabilities is important for pinpointing high-risk areas within the network and implementing measures to safeguard them against future disruptions. Graph-based community detection can be applied to group together areas of the network sharing similar structural vulnerabilities. However, current graph-based community detection methods either struggle with integrating node features during partitioning or do not account for the path-based dependencies in road networks. This paper introduces the Path-based Community Embedding (PCE) model, an approach that leverages path-based embeddings to overcome these limitations. PCE combines the strengths of graph attention networks and Long Short-Term Memory models (LSTMs) to learn representations that incorporate both local neighborhood information and long-range path dependencies. Our results on the Santa Barbara road network show that PCE improves community detection performance for resilience analysis, thus offering a powerful tool for urban planners and transportation engineers to preemptively identify vulnerabilities in road networks.
Cite as

Christopher Wagner, Somayeh Dodge, and Danial Alizadeh. Identifying Resilient Communities in Road Networks: A Path-Based Embedding Approach. In 13th International Conference on Geographic Information Science (GIScience 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 346, pp. 9:1-9:10, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{wagner_et_al:LIPIcs.GIScience.2025.9,
  author =	{Wagner, Christopher and Dodge, Somayeh and Alizadeh, Danial},
  title =	{{Identifying Resilient Communities in Road Networks: A Path-Based Embedding Approach}},
  booktitle =	{13th International Conference on Geographic Information Science (GIScience 2025)},
  pages =	{9:1--9:10},
  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.2025.9},
  URN =		{urn:nbn:de:0030-drops-238380},
  doi =		{10.4230/LIPIcs.GIScience.2025.9},
  annote =	{Keywords: road networks, resilience analysis, machine learning, graph neural networks}
}
Document
Short Paper
DOI: 10.4230/LIPIcs.GIScience.2023.57
A Data Fusion Framework for Exploring Mobility Around Disruptive Events (Short Paper)

Authors: Evgeny Noi and Somayeh Dodge

Published in: LIPIcs, Volume 277, 12th International Conference on Geographic Information Science (GIScience 2023)

Abstract
This paper proposes a data fusion framework that seeks to investigate joint mobility signals around wildfires in relation to geographic scale of analysis (level of spatial aggregation), as well as spatial and temporal extents (i.e. distance to the event and duration of the observation period). We highlight the usefulness of our framework using intra-urban mobility data from Mapbox and SafeGraph for two wildfires in California: Lake Fire (August-September 2020, Los Angeles County) and Silverado Fire (October-November 2020, Orange County). We identify two distinct patterns of mobility behavior: one associated with the wildfire event and another one - with the routine daily mobility of the nearby urban core. Using the combination of data fusion and tensor decomposition, the framework allows us to capture additional insights from the data, that were otherwise unavailable in raw mobility data.
Cite as

Evgeny Noi and Somayeh Dodge. A Data Fusion Framework for Exploring Mobility Around Disruptive Events (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 57:1-57:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{noi_et_al:LIPIcs.GIScience.2023.57,
  author =	{Noi, Evgeny and Dodge, Somayeh},
  title =	{{A Data Fusion Framework for Exploring Mobility Around Disruptive Events}},
  booktitle =	{12th International Conference on Geographic Information Science (GIScience 2023)},
  pages =	{57:1--57:6},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-288-4},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{277},
  editor =	{Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.57},
  URN =		{urn:nbn:de:0030-drops-189523},
  doi =		{10.4230/LIPIcs.GIScience.2023.57},
  annote =	{Keywords: geographic extent, geographic scale, tensor decomposition, spatio-temporal analysis}
}
Document
DOI: 10.4230/DagRep.7.7.54
From Observations to Prediction of Movement (Dagstuhl Seminar 17282)

Authors: Mark Birkin, Somayeh Dodge, Brittany Terese Fasy, and Richard Philip Mann

Published in: Dagstuhl Reports, Volume 7, Issue 7 (2018)

Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 17282 "From Observations to Prediction of Movement". This seminar brought together researchers from Animal Behaviour, GIS, Computational Geometry, Data Science and other fields to exchange insights from these diverse fields. Presentations focused both on outstanding practical questions, as well as on fundamental mathematical and computational tools.
Cite as

Mark Birkin, Somayeh Dodge, Brittany Terese Fasy, and Richard Philip Mann. From Observations to Prediction of Movement (Dagstuhl Seminar 17282). In Dagstuhl Reports, Volume 7, Issue 7, pp. 54-71, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@Article{birkin_et_al:DagRep.7.7.54,
  author =	{Birkin, Mark and Dodge, Somayeh and Fasy, Brittany Terese and Mann, Richard Philip},
  title =	{{From Observations to Prediction of Movement (Dagstuhl Seminar 17282)}},
  pages =	{54--71},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2018},
  volume =	{7},
  number =	{7},
  editor =	{Birkin, Mark and Dodge, Somayeh and Fasy, Brittany Terese and Mann, Richard Philip},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.7.7.54},
  URN =		{urn:nbn:de:0030-drops-84235},
  doi =		{10.4230/DagRep.7.7.54},
  annote =	{Keywords: trajectory analysis, computational geometry and topology, GIS, animal movement, prediction, home range}
}
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