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Research on Population Spatiotemporal Aggregation Characteristics of a Small City: A Case Study on Shehong County Based on Baidu Heat Maps

Author

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  • Deyi Feng

    (Department of Urban Planning, School of Architecture and Urban Planning, Chongqing University, Chongqing 400030, China
    Institute of Architecture and Planning, Guiyang Urban Planning and Design Institute, Guiyang 550001, China)

  • Lingli Tu

    (Department of Urban Planning, School of Architecture and Urban Planning, Chongqing University, Chongqing 400030, China)

  • Zhongwei Sun

    (Department of Urban Planning, School of Architecture and Urban Planning, Chongqing University, Chongqing 400030, China)

Abstract

Baidu heat maps can be used to explore the pattern of individual citizens conducting their activities and their agglomeration effects at the city scale. To investigate the spatiotemporal pattern of population aggregation and its relationship with land parcel attributes in small cities, we collected Baidu heat map data for a weekday and a weekend day in Shehong County and used Getis–Ord general G and the raster overlay methods to analyze population aggregation spatiotemporal characteristics. Chi-squared and Pearson correlation tests were used to analyze the correlation between population aggregation and land parcel attributes against three types of land parcel divisions: land use parcels, road network blocks, and grids. The results showed that, (1) for most hours of the workday, the degree of population aggregation was greater than on the weekend, and the fluctuation magnitude on the workday was higher as well. (2) On the weekday, people clustered and dispersed faster than on the weekend. (3) On the weekday and weekend, the spatial position of people aggregation was highly overlapping. (4) The correlation between the degree of population aggregation and the type of parcel was not significant. (5) Regarding different parcel unit sizes, the correlations between population aggregation degree and point of interest (POI) density, floor area ratio, and building density were significant and positively correlated, and the correlation coefficients increased as the grid size increased.

Suggested Citation

  • Deyi Feng & Lingli Tu & Zhongwei Sun, 2019. "Research on Population Spatiotemporal Aggregation Characteristics of a Small City: A Case Study on Shehong County Based on Baidu Heat Maps," Sustainability, MDPI, vol. 11(22), pages 1-19, November.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:22:p:6276-:d:284898
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    1. Marta C. González & César A. Hidalgo & Albert-László Barabási, 2009. "Understanding individual human mobility patterns," Nature, Nature, vol. 458(7235), pages 238-238, March.
    2. Lie Ma & Dandan Li & Xiaobo Tao & Haifeng Dong & Bei He & Xiaosu Ye, 2017. "Inequality, Bi-Polarization and Mobility of Urban Infrastructure Investment in China’s Urban System," Sustainability, MDPI, vol. 9(9), pages 1-19, September.
    3. Jonathan Reades & Francesco Calabrese & Carlo Ratti, 2009. "Eigenplaces: Analysing Cities Using the Space–Time Structure of the Mobile Phone Network," Environment and Planning B, , vol. 36(5), pages 824-836, October.
    4. Jizhe Zhou & Quanhua Hou & Wentao Dong, 2019. "Spatial Characteristics of Population Activities in Suburban Villages Based on Cellphone Signaling Analysis," Sustainability, MDPI, vol. 11(7), pages 1-19, April.
    5. Mi-Kyeong Kim & Sangpil Kim & Hong-Gyoo Sohn, 2018. "Relationship between Spatio-Temporal Travel Patterns Derived from Smart-Card Data and Local Environmental Characteristics of Seoul, Korea," Sustainability, MDPI, vol. 10(3), pages 1-18, March.
    6. Camille Roth & Soong Moon Kang & Michael Batty & Marc Barthélemy, 2011. "Structure of Urban Movements: Polycentric Activity and Entangled Hierarchical Flows," PLOS ONE, Public Library of Science, vol. 6(1), pages 1-8, January.
    7. William H. Alfonso Piña & Clara Inés Pardo Martínez, 2016. "Development and Urban Sustainability: An Analysis of Efficiency Using Data Envelopment Analysis," Sustainability, MDPI, vol. 8(2), pages 1-15, February.
    8. Higinio Mora & Raquel Pérez-delHoyo & José F. Paredes-Pérez & Rafael A. Mollá-Sirvent, 2018. "Analysis of Social Networking Service Data for Smart Urban Planning," Sustainability, MDPI, vol. 10(12), pages 1-19, December.
    9. Rajiv D. Banker & Richard C. Morey, 1986. "The Use of Categorical Variables in Data Envelopment Analysis," Management Science, INFORMS, vol. 32(12), pages 1613-1627, December.
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