Mapping Fine-Scale Urban Spatial Population Distribution Based on High-Resolution Stereo Pair Images, Points of Interest, and Land Cover Data

Min Xu,Peng Jia,Chunxiang Cao

doi:10.3390/rs12040608

Abstract

Fine-scale population distribution is increasingly becoming a research hotspot owing to its high demand in many applied fields. It is of great significance in urban emergency response, disaster assessment, resource allocation, urban planning, market research, and transportation route design. This study employed land cover, building address, and housing price data, and high-resolution stereo pair remote sensing images to simulate fine-scale urban population distribution. We firstly extracted the residential zones on the basis of land cover and Google Earth data, combined them with building information including address and price. Then, we employed the stereo pair analysis method to obtain the building height on the basis of ZY3-02 high-resolution satellite data and transform the building height into building floors. After that, we built a sophisticated, high spatial resolution model of population density. Finally, we evaluated the accuracy of the model using the survey data from 12 communities in the study area. Results demonstrated that the proposed model for spatial fine-scale urban population products yielded more accurate small-area population estimation relative to high-resolution gridded population surface (HGPS). The approach proposed in this study holds potential to improve the precision and automation of high-resolution population estimation.

Highlights

Population distribution data on a fine scale are of great significance in many areas including public health, emergency evacuation, disaster management, resource allocation, urban planning, market research, and transportation route design [1]
This study explores the frontier by presenting an approach to modeling urban nighttime population distribution at the building level with both the footprint and height of buildings considered as ancillary data
On the basis of the object-oriented classification and stereo pair photogrammetry, our method is more effective to urban areas where the residential buildings are more regular in size, shape, distribution and high-rise

Summary

Introduction

Population distribution data on a fine scale are of great significance in many areas including public health, emergency evacuation, disaster management, resource allocation, urban planning, market research, and transportation route design [1]. Such data was once only presented as choropleth maps, where the population numbers, normally derived from demographic surveys (e.g., census data), were aggregated over enumeration units represented by irregular polygons (e.g., census blocks). There is an urgent need for fine-scale population products which accurately describe spatial distribution of the population to make well informed and immediate decisions [7,10]

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Conclusion