Population space partition fitting method based on multi-source data

Abstract

The invention provides a population space partition fitting method based on multi-source data, and aims to solve the problems that the spatial resolution is low and the spatial resolution of population distribution cannot be depicted finely when administrative units such as streets, towns and villages are used as division boundaries for statistical population data. The invention provides a population space partition fitting method based on multi-source data. In order to improve the precision of population spatialization and comprehensively consider the heterogeneity of space, various high-precision novel data such as Esri10m land coverage, NPP / VIIRS-like night light, Gade POI and Qipu village and town population are selected, population influence factors of each district and county are counted to serve as model independent variables, population spatialization is performed on a 1km grid through a geographically weighted regression model, and the population spatialization accuracy is improved. Performing precision evaluation on the result; and counting counties which do not meet the precision requirement, and carrying out secondary zoning and spatialization again. A population space partition fitting method is adopted, the overall precision of a research area is obviously improved, and reference can be provided for population spatialization research.

Description

technical field [0001] The invention relates to the field of population spatialization, in particular to a population space partition fitting method based on multi-source data. Background technique [0002] Human beings are operators in the development of real society, and accurate statistics on population numbers and population types play a certain reference role in formulating specific development policies in various regions, including medical care, industry, agriculture, and transportation. The traditional population data mainly comes from the national population census, and the data obtained is the most authoritative, accurate and comprehensive. However, the population census is carried out once every ten years, with a long cycle and slow data update, which cannot be continuously and dynamically surveyed, and the cost is very high. It is divided into administrative regions. Population data, which is a statistical unit, can no longer meet the requirements of high-quality ...

AI Technical Summary

Problems solved by technology

[0004] In view of this, the purpose of the present invention is to provide a population spatial partition fitting method based on multi-source data, which is used to solve the problem of poor spatial resolution caused by mainly using administrative units such as streets, towns, and villages as division boundaries in the existing population spatialization. It is too low to accurately describe the spatial differentiation of population distribution and other issues, thus providing an effective reference for population spatialization research.

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