License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.GIScience.2023.57
URN: urn:nbn:de:0030-drops-189523
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2023/18952/
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Noi, Evgeny ; Dodge, Somayeh

A Data Fusion Framework for Exploring Mobility Around Disruptive Events (Short Paper)

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LIPIcs-GIScience-2023-57.pdf (2 MB)

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.

BibTeX - Entry

@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/opus/volltexte/2023/18952},
  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}
}

Keywords: geographic extent, geographic scale, tensor decomposition, spatio-temporal analysis
Collection: 12th International Conference on Geographic Information Science (GIScience 2023)
Issue Date: 2023
Date of publication: 07.09.2023

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