Document Open Access Logo

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

PDF
Thumbnail PDF

File

LIPIcs.GIScience.2021.I.10.pdf
  • Filesize: 4.72 MB
  • 16 pages

Document Identifiers

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.

Cite AsGet BibTex

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

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads
    0
    Metadata Views

References

  1. Rein Ahas, Anto Aasa, Y Yuan, Martin Raubal, Zbigniew Smoreda, Yu Liu, Cezary Ziemlicki, Margus Tiru, and Matthew Zook. Everyday space-time geographies: using mobile phone-based sensor data to monitor urban activity in harbin, paris, and tallinn. International Journal of Geographical Information Science, 29(11):2017-2039, 2015. Google Scholar
  2. Li An, Ming-Hsiang Tsou, Stephen ES Crook, Yongwan Chun, Brian Spitzberg, J Mark Gawron, and Dipak K Gupta. Space-time analysis: Concepts, quantitative methods, and future directions. Annals of the Association of American Geographers, 105(5):891-914, 2015. Google Scholar
  3. Albert-Laszlo Barabasi. The origin of bursts and heavy tails in human dynamics. Nature, 435(7039):207-211, 2005. Google Scholar
  4. Linus Bengtsson, Xin Lu, Anna Thorson, Richard Garfield, and Johan Von Schreeb. Improved response to disasters and outbreaks by tracking population movements with mobile phone network data: a post-earthquake geospatial study in haiti. PLoS medicine, 8(8), 2011. Google Scholar
  5. Budhendra Bhaduri, Edward Bright, Phillip Coleman, and Marie L Urban. Landscan usa: a high-resolution geospatial and temporal modeling approach for population distribution and dynamics. GeoJournal, 69(1-2):103-117, 2007. Google Scholar
  6. Christoph Bregler. Learning and recognizing human dynamics in video sequences. In Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 568-574. IEEE, 1997. Google Scholar
  7. Zi Chu, Indra Widjaja, and Haining Wang. Detecting social spam campaigns on twitter. In International Conference on Applied Cryptography and Network Security, pages 455-472. Springer, 2012. Google Scholar
  8. Pierre Deville, Catherine Linard, Samuel Martin, Marius Gilbert, Forrest R Stevens, Andrea E Gaughan, Vincent D Blondel, and Andrew J Tatem. Dynamic population mapping using mobile phone data. Proceedings of the National Academy of Sciences, 111(45):15888-15893, 2014. Google Scholar
  9. Pinliang Dong, Sathya Ramesh, and Anjeev Nepali. Evaluation of small-area population estimation using lidar, landsat tm and parcel data. International Journal of Remote Sensing, 31(21):5571-5586, 2010. Google Scholar
  10. Maeve Duggan, Nicole B Ellison, Cliff Lampe, Amanda Lenhart, and Mary Madden. Social media update 2014. Pew research center, 19, 2015. Google Scholar
  11. Jim Edwards. Leaked twitter api data shows the number of tweets is in serious decline. Business Insider, February 2016. URL: http://www.businessinsider.com/tweets-on-twitter-is-in-serious-decline-2016-2.
  12. Cory L Eicher and Cynthia A Brewer. Dasymetric mapping and areal interpolation: Implementation and evaluation. Cartography and Geographic Information Science, 28(2):125-138, 2001. Google Scholar
  13. Su Yeon Han, Ming-Hsiang Tsou, and Keith C Clarke. Do global cities enable global views? using twitter to quantify the level of geographical awareness of us cities. PloS one, 10(7), 2015. Google Scholar
  14. Su Yeon Han, Ming-Hsiang Tsou, and Keith C Clarke. Revisiting the death of geography in the era of big data: the friction of distance in cyberspace and real space. International Journal of Digital Earth, 11(5):451-469, 2018. Google Scholar
  15. Su Yeon Han, Ming-Hsiang Tsou, Elijah Knaap, Sergio Rey, and Guofeng Cao. How do cities flow in an emergency? tracing human mobility patterns during a natural disaster with big data and geospatial data science. Urban Science, 3(2):51, 2019. Google Scholar
  16. Yusuke Hara and Masao Kuwahara. Traffic monitoring immediately after a major natural disaster as revealed by probe data-a case in ishinomaki after the great east japan earthquake. Transportation research part A: policy and practice, 75:1-15, 2015. Google Scholar
  17. James B Holt, CP Lo, and Thomas W Hodler. Dasymetric estimation of population density and areal interpolation of census data. Cartography and Geographic Information Science, 31(2):103-121, 2004. Google Scholar
  18. Elias Issa, Ming-Hsiang Tsou, Atsushi Nara, and Brian Spitzberg. Understanding the spatio-temporal characteristics of twitter data with geotagged and non-geotagged content: two case studies with the topic of flu and ted (movie). Annals of GIS, 23(3):219-235, 2017. Google Scholar
  19. Bin Jiang, Ding Ma, Junjun Yin, and Mats Sandberg. Spatial distribution of city tweets and their densities. Geographical Analysis, 48(3):337-351, 2016. Google Scholar
  20. Brian McKenzie, William Koerber, Alison Fields, Megan Benetsky, and Melanie Rapino. Commuter-adjusted population estimates: Acs 2006-10. Washington, DC: Journey to Work and Migration Statistics Branch, US Census Bureau, 2010. Google Scholar
  21. Jeremy Mennis and Torrin Hultgren. Intelligent dasymetric mapping and its application to areal interpolation. Cartography and Geographic Information Science, 33(3):179-194, 2006. Google Scholar
  22. Anna C Nagel, Ming-Hsiang Tsou, Brian H Spitzberg, Li An, J Mark Gawron, Dipak K Gupta, Jiue-An Yang, Su Han, K Michael Peddecord, Suzanne Lindsay, et al. The complex relationship of realspace events and messages in cyberspace: case study of influenza and pertussis using tweets. Journal of medical Internet research, 15(10):e237, 2013. Google Scholar
  23. Atsushi Nara, Xianfeng Yang, Sahar Ghanipoor Machiani, and Ming-Hsiang Tsou. An integrated evacuation decision support system framework with social perception analysis and dynamic population estimation. International journal of disaster risk reduction, 25:190-201, 2017. Google Scholar
  24. Tao Pei, Stanislav Sobolevsky, Carlo Ratti, Shih-Lung Shaw, Ting Li, and Chenghu Zhou. A new insight into land use classification based on aggregated mobile phone data. International Journal of Geographical Information Science, 28(9):1988-2007, 2014. Google Scholar
  25. Ming-Hsiang Tsou. Research challenges and opportunities in mapping social media and big data. Cartography and Geographic Information Science, 42(sup1):70-74, 2015. Google Scholar
  26. Ming-Hsiang Tsou, Ick-Hoi Kim, Sarah Wandersee, Daniel Lusher, Li An, Brian Spitzberg, Dipak Gupta, Jean Mark Gawron, Jennifer Smith, Jiue-An Yang, et al. Mapping ideas from cyberspace to realspace: visualizing the spatial context of keywords from web page search results. International Journal of Digital Earth, 7(4):316-335, 2014. Google Scholar
  27. Ming-Hsiang Tsou and Michael Leitner. Visualization of social media: seeing a mirage or a message?, 2013. Google Scholar
  28. Jessica JunLin Wang and Sameer Singh. Video analysis of human dynamics—a survey. Real-time imaging, 9(5):321-346, 2003. Google Scholar
  29. John K Wright. A method of mapping densities of population: With cape cod as an example. Geographical Review, 26(1):103-110, 1936. Google Scholar
  30. Sarita Yardi, Daniel Romero, Grant Schoenebeck, et al. Detecting spam in a twitter network. First Monday, 15(1), 2010. Google Scholar
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail
© 2023-2024 Schloss Dagstuhl – LZI GmbH Imprint Privacy Contact