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

Jian Liu, Xiaosu Ma, Yi Zhu, Jing Li, Zong He and Sheng Ye
Additional contact information
Jian Liu: Chongqing University, Chongqing 400044, 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

Sustainability, 2021, vol. 13, issue 3, 1-16

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.

Keywords: distributed modeling; geospatial services; visualization; population synthesis; service-oriented architecture (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/2071-1050/13/3/1587/pdf (application/pdf)
https://www.mdpi.com/2071-1050/13/3/1587/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:13:y:2021:i:3:p:1587-:d:492034

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().