Random forest model-based population data spatialization method

Abstract

The invention discloses a random forest model-based population data spatialization method. According to the method, population distribution-related variable factors such as surface coverage data and lamp light data are selected; the population distribution-related variable factors are pre-processed, and the pre-processed population distribution-related variable factors are inputted into a random forest model; the relationship between population density and the variable factors, and the importance of the variable factors are determined through using the random forest model; the population density of each grid is obtained through inversion based on the relationship; and an estimation result is corrected through regional density charting, so that a gridded population distribution result can be obtained. With the method adopted, the precision of population data spatialization can be further improved, and the importance of the variable factors are interpreted.

Description

technical field [0001] The present invention relates to the theoretical field of population data spatialization, and more specifically, to a population data spatialization method based on a random forest model; Accurate and quick access to population information required in policies related to ecological protection. [0002] technical background [0003] As the most important factor in productivity, population agglomeration will not only produce agglomeration effects, but also increase the degree of land intensive use while reducing per capita living costs. However, if the growth of population exceeds the load capacity of land in a certain area, it will damage the environment. And the virtuous circle of ecology, and finally harm human beings themselves. Moreover, with the increasing population density, while facing traditional threats such as fires, earthquakes, typhoons, and floods, cities have also brought new problems to urban management, such as traffic congestion, exces...

AI Technical Summary

Problems solved by technology

[0004] The currently widely used population data is usually based on the administrative division as a unit, and the typical demographic data obtained through census, sampling statistics, etc. have the following three deficiencies in practical applications: first, the time resolution is low, The national census is held every 10 years, and the data update cycle is long, so it is difficult to accurately reveal the status of the population.

Second, the spatial resolution is low, and the population data obtained with the administrative area as the unit is evenly distributed within the administrative unit, which cannot reflect the spatial distribution characteristics of the population data; third, it is not conducive to multi-source data fusion and comprehensive spatial analysis. There is a spatial mismatch between statistical data and physical geographic units, which limits the application of demographic data in multidisciplinary fields

[0005] With the continuous advancement of science and technology, especially the rapid development of earth information science in recent years, information extraction from remote sensing images can provide information on the spatial distribution and changes of a large number of variable factors. Rapid, great achievements have been made, but there are still deficiencies such as low accuracy, slow model operation speed, and poor explanatory power of variable factors

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