Method and system for estimating population density in urban built-up areas by cooperation with spatial big data

Abstract

The invention relates to a method and a system for estimating population density in urban built-up areas coordinated by spatial big data. The method comprises the following steps: obtaining a buildingrange based on an object-oriented classification method by utilizing high-resolution optical remote sensing data; dividing urban blocks through a spatial topology algorithm by utilizing spatial big data of an urban fine road network, and dividing the urban blocks into residential areas and non-residential areas by combination with spatial big data of points of interest; obtaining building heightby utilizing remote sensing optical three-dimensional imaging data; constructing a population density estimation model by utilizing a multiple regression analysis method according to the building height and the building range and by combination with population statistical data; and inputting the building range and the building height as input variables into the population density estimation model,performing calculation to obtain population density, and then, assigning the population density to regular grid space corresponding to urban built-up areas by utilizing a spatial statistical analysismethod. The method and the system can precisely and accurately realize estimation of the population density in the built-up urban areas, are more suitable for requirements of urban planning, disasterprevention and reduction and business decision for fine population data, and are beneficial to further popularization and application.

Description

technical field [0001] The invention relates to the technical field of urban remote sensing and urban demography, in particular to a method and system for estimating the population density of urban built-up areas with spatial big data collaboration. Background technique [0002] Population density distribution is the spatial manifestation of population process, and urban population density distribution and simulation are important contents in the study of urban spatial structure and suburbanization. Scholars at home and abroad have used mathematical models to reveal the general law of urban population density distribution based on the characteristics of "circle-type" cities. These studies analyze the spatial distribution of population density based on statistical data, assuming that the population is continuously and evenly distributed within the census area, but the actual population distribution is not continuous. Internationally, many institutions are committed to the re...

AI Technical Summary

Problems solved by technology

[0005] Aiming at the problem that traditional population spatialization products based on land use / land cover data and optimized parameters cannot accurately describe the actual spatial distribution characteristics of population in urban built-up areas, the present invention provides a method for estimating the population density of urban built-up areas with spatial big data collaboration, Based on the range and height of buildings extracted from high-resolution optical remote sensing data and remote sensing optical stereoscopic imaging data, the population density estimation model is collaboratively constructed using road network and spatial big data of interest points, and the fine population density estimation of urban built-up areas is realized.

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