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Modeling and Visualizing Regular Human Mobility Patterns with Uncertainty: An Example Using Twitter Data

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  • Qunying Huang
  • David W. S. Wong

Abstract

Traditional space–time paths show the spatiotemporal trajectories of individuals in one to several days. Based on data for such short periods, these space–time paths might not be able to show regular activity patterns, which are pertinent to various types of planning and policy analysis. Travel data gathered for longer periods might capture regular activity patterns, but footprints captured by these data also include irregular activities, introducing noises or uncertainty. Our objective is to determine the representative spatiotemporal trajectories of individuals, accounting for stochastic disturbances and spatiotemporal variability, but using activity data with longer duration. Therefore, we explore using Twitter data, which have relatively low and irregular spatial and temporal resolutions. This article introduces a methodology to construct individual representative space–time paths using various aggregation and spatiotemporal clustering techniques. To depict and visualize spatiotemporal trajectories with uncertain information, we propose space–time cones of variable sizes to reflect the spatial precision of the paths and use colors on the cones to represent the confidence level. To illustrate the proposed methodology, we use the geo-tagged tweets for an extended period. Our analysis indicates that the representative space–time path reasonably describes an individual's regular activity patterns. As visual elements, cones and cone colors effectively show the varying geographical precision along the path and changing certainty levels across different path segments, respectively.

Suggested Citation

  • Qunying Huang & David W. S. Wong, 2015. "Modeling and Visualizing Regular Human Mobility Patterns with Uncertainty: An Example Using Twitter Data," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 105(6), pages 1179-1197, November.
  • Handle: RePEc:taf:raagxx:v:105:y:2015:i:6:p:1179-1197
    DOI: 10.1080/00045608.2015.1081120
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    Cited by:

    1. Arias-Molinares, Daniela & Romanillos, Gustavo & García-Palomares, Juan Carlos & Gutiérrez, Javier, 2021. "Exploring the spatio-temporal dynamics of moped-style scooter sharing services in urban areas," Journal of Transport Geography, Elsevier, vol. 96(C).
    2. Jennifer Candipan & Nolan Edward Phillips & Robert J Sampson & Mario Small, 2021. "From residence to movement: The nature of racial segregation in everyday urban mobility," Urban Studies, Urban Studies Journal Limited, vol. 58(15), pages 3095-3117, November.
    3. Sebastian Rauch, 2022. "Analysing Long Term Spatial Mobility Patterns of Individuals and Large Groups Using 3D‐GIS: A Sport Geographic Approach," Tijdschrift voor Economische en Sociale Geografie, Royal Dutch Geographical Society KNAG, vol. 113(3), pages 257-272, July.
    4. Bin Tian & Bin Meng & Juan Wang & Guoqing Zhi & Zhenyu Qi & Siyu Chen & Jian Liu, 2022. "Spatio-Temporal Patterns of Fitness Behavior in Beijing Based on Social Media Data," Sustainability, MDPI, vol. 14(7), pages 1-18, March.
    5. Shahabi, Cyrus & Kim, Seon Ho, 2023. "Evaluating Accessibility of Los Angeles Metropolitan Area Using Data-Driven Time-Dependent Reachability Analysis," Institute of Transportation Studies, Working Paper Series qt7pm429tk, Institute of Transportation Studies, UC Davis.
    6. María Henar Salas-Olmedo & Carolina Rojas Quezada, 2017. "The use of public spaces in a medium-sized city: from Twitter data to mobility patterns," Journal of Maps, Taylor & Francis Journals, vol. 13(1), pages 40-45, January.
    7. Shuran Li & Chengwei Wang & Liying Rong & Shiqi Zhou & Zhiqiang Wu, 2024. "Understanding How People Perceive and Interact with Public Space through Social Media Big Data: A Case Study of Xiamen, China," Land, MDPI, vol. 13(9), pages 1-28, September.

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