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

Authors Jaehee Park, Hao Zhang, Su Yeon Han, Atsushi Nara , Ming-Hsiang Tsou

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Author Details

Jaehee Park
  • Department of Geography, San Diego State University, CA, USA
Hao Zhang
  • HDMA center, San Diego State University, CA, USA
Su Yeon Han
  • Department of Geography, San Diego State University, CA, USA
Atsushi Nara
  • Department of Geography, San Diego State University, CA, USA
Ming-Hsiang Tsou
  • Department of Geography, San Diego State University, CA, USA

Funding

This material is partially based upon work supported by the National Science Foundation under Grant No. 1416509 and Grant No. 1634641.Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the National Science Foundation.

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Jaehee Park, Hao Zhang, Su Yeon Han, Atsushi Nara, and Ming-Hsiang Tsou. Estimating Hourly Population Distribution Patterns at High Spatiotemporal Resolution in Urban Areas Using Geo-Tagged Tweets and Dasymetric Mapping. In 11th International Conference on Geographic Information Science (GIScience 2021) - Part I. Leibniz International Proceedings in Informatics (LIPIcs), Volume 177, pp. 10:1-10:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020) https://doi.org/10.4230/LIPIcs.GIScience.2021.I.10

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.

Subject Classification

ACM Subject Classification
  • Human-centered computing → Social media
Keywords
  • Population Estimation
  • Twitter
  • Social Media
  • Dasymetric Map
  • Spatiotemporal

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