License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.GIScience.2021.I.10
URN: urn:nbn:de:0030-drops-130456
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2020/13045/
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Park, Jaehee ; Zhang, Hao ; Han, Su Yeon ; Nara, Atsushi ; Tsou, Ming-Hsiang

Estimating Hourly Population Distribution Patterns at High Spatiotemporal Resolution in Urban Areas Using Geo-Tagged Tweets and Dasymetric Mapping

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LIPIcs-GIScience-2021-I-10.pdf (5 MB)

Abstract

This paper introduces a spatiotemporal analysis framework for estimating hourly changing population distribution patterns in urban areas using geo-tagged tweets (the messages containing users’ geospatial locations), land use data, and dasymetric maps. We collected geo-tagged social media (tweets) within the County of San Diego during one year (2015) by using Twitter’s Streaming Application Programming Interfaces (APIs). A semi-manual Twitter content verification procedure for data cleaning was applied first to separate tweets created by humans from non-human users (bots). The next step was to calculate the number of unique Twitter users every hour within census blocks. The final step was to estimate the actual population by transforming the numbers of unique Twitter users in each census block into estimated population densities with spatial and temporal factors using dasymetric maps. The temporal factor was estimated based on hourly changes of Twitter messages within San Diego County, CA. The spatial factor was estimated by using the dasymetric method with land use maps and 2010 census data. Comparing to census data, our methods can provide better estimated population in airports, shopping malls, sports stadiums, zoo and parks, and business areas during the day time.

BibTeX - Entry

@InProceedings{park_et_al:LIPIcs:2020:13045,
  author =	{Jaehee Park and Hao Zhang and Su Yeon Han and Atsushi Nara and Ming-Hsiang Tsou},
  title =	{{Estimating Hourly Population Distribution Patterns at High Spatiotemporal Resolution in Urban Areas Using Geo-Tagged Tweets and Dasymetric Mapping}},
  booktitle =	{11th International Conference on Geographic Information Science (GIScience 2021) - Part I},
  pages =	{10:1--10:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-166-5},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{177},
  editor =	{Krzysztof Janowicz and Judith A. Verstegen},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2020/13045},
  URN =		{urn:nbn:de:0030-drops-130456},
  doi =		{10.4230/LIPIcs.GIScience.2021.I.10},
  annote =	{Keywords: Population Estimation, Twitter, Social Media, Dasymetric Map, Spatiotemporal}
}

Keywords: Population Estimation, Twitter, Social Media, Dasymetric Map, Spatiotemporal
Collection: 11th International Conference on Geographic Information Science (GIScience 2021) - Part I
Issue Date: 2020
Date of publication: 25.09.2020

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