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Documents authored by Tsou, Ming-Hsiang

Document
DOI: 10.4230/LIPIcs.GIScience.2021.I.10
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, and Ming-Hsiang Tsou

Published in: LIPIcs, Volume 177, 11th International Conference on Geographic Information Science (GIScience 2021) - Part I (2020)

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.
Cite as

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)

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@InProceedings{park_et_al:LIPIcs.GIScience.2021.I.10,
  author =	{Park, Jaehee and Zhang, Hao and Han, Su Yeon and Nara, Atsushi and Tsou, Ming-Hsiang},
  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 =	{Janowicz, Krzysztof and Verstegen, Judith A.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2021.I.10},
  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}
}
Document
Short Paper
DOI: 10.4230/LIPIcs.GISCIENCE.2018.70
Assessing Neighborhood Conditions using Geographic Object-Based Image Analysis and Spatial Analysis (Short Paper)

Authors: Chi-Feng Yen, Ming-Hsiang Tsou, and Chris Allen

Published in: LIPIcs, Volume 114, 10th International Conference on Geographic Information Science (GIScience 2018)

Abstract
Traditionally, understanding urban neighborhood conditions heavily relies on time-consuming and labor-intensive surveying. As the growing development of computer vision and GIScience technology, neighborhood conditions assessment can be more cost-effective and time-efficient. This study utilized Google Earth Engine (GEE) to acquire 1m aerial imagery from the National Agriculture Image Program (NAIP). The features within two main categories: (i) aesthetics and (ii) street morphology that have been selected to reflect neighborhood socio-economic (SE) and demographic (DG) conditions were subsequently extracted through geographic object-based image analysis (GEOBIA) routine. Finally, coefficient analysis was performed to validate the relationship between selected SE indicators, generated via spatial analysis, as well as actual SE and DG data within region of interests (ROIs). We hope this pilot study can be leveraged to perform cost- and time- effective neighborhood conditions assessment in support of community data assessment on both demographics and health issues.
Cite as

Chi-Feng Yen, Ming-Hsiang Tsou, and Chris Allen. Assessing Neighborhood Conditions using Geographic Object-Based Image Analysis and Spatial Analysis (Short Paper). In 10th International Conference on Geographic Information Science (GIScience 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 114, pp. 70:1-70:7, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@InProceedings{yen_et_al:LIPIcs.GISCIENCE.2018.70,
  author =	{Yen, Chi-Feng and Tsou, Ming-Hsiang and Allen, Chris},
  title =	{{Assessing Neighborhood Conditions using Geographic Object-Based Image Analysis and Spatial Analysis}},
  booktitle =	{10th International Conference on Geographic Information Science (GIScience 2018)},
  pages =	{70:1--70:7},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-083-5},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{114},
  editor =	{Winter, Stephan and Griffin, Amy and Sester, Monika},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GISCIENCE.2018.70},
  URN =		{urn:nbn:de:0030-drops-93983},
  doi =		{10.4230/LIPIcs.GISCIENCE.2018.70},
  annote =	{Keywords: neighborhood conditions assessment, geographic object-based image analysis, spatial analysis}
}
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