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Generating and Visualizing Spatially Disaggregated Synthetic Population Using a Web-Based Geospatial Service

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  • Jian Liu

    (Chongqing University, Chongqing 400044, China
    Chongqing Geomatics and Remote Sensing Center, Chongqing 401147, China
    University of Chinese Academy of Sciences (UCAS Chongqing), Chongqing 400714, China)

  • Xiaosu Ma

    (School of Architecture, Southeast University, Nanjing 210096, China)

  • Yi Zhu

    (School of Public Economics and Administration, Shanghai University of Finance and Economics, Shanghai 200433, China)

  • Jing Li

    (Chongqing Geomatics and Remote Sensing Center, Chongqing 401147, China)

  • Zong He

    (Chongqing Geomatics and Remote Sensing Center, Chongqing 401147, China)

  • Sheng Ye

    (University of Chinese Academy of Sciences (UCAS Chongqing), Chongqing 400714, China)

Abstract

Geographically fine-grained population information is critical for various urban planning and management tasks. This is especially the case for the Chinese cities that are undergoing rapid development and transformation. However, detailed population data are rarely available in comprehensive and timely means. Therefore, appropriate approaches are needed to estimate populations from available data sets in a systematic way to support the continuous demand from urban analytics and planning. Population synthesis approaches such as Iterative Proportional Fitting (IPF) were developed to combine microdata samples with marginal statistics about population characteristics at aggregated spatial levels in order to expand the microdata sample into a complete synthetic population. This paper presents the framework for and the implementation of a geospatial platform for supporting the generation and exploration of spatially detailed urban synthetic populations. The platform provides analytical and visualization tools to support the quick generation of a full urban population with critical attributes based on the latest data available. The case of the synthetic population of Chongqing is used to illustrate the population information and types of visualization that are facilitated.

Suggested Citation

  • Jian Liu & Xiaosu Ma & Yi Zhu & Jing Li & Zong He & Sheng Ye, 2021. "Generating and Visualizing Spatially Disaggregated Synthetic Population Using a Web-Based Geospatial Service," Sustainability, MDPI, vol. 13(3), pages 1-16, February.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:3:p:1587-:d:492034
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    References listed on IDEAS

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