A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Generating and Visualizing Spatially Disaggregated Synthetic Population Using a Web-Based Geospatial Service
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.
Generating and Visualizing Spatially Disaggregated Synthetic Population Using a Web-Based Geospatial Service
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.
Generating and Visualizing Spatially Disaggregated Synthetic Population Using a Web-Based Geospatial Service
2021
Article (Journal)
Electronic Resource
Unknown
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