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Population spatial distribution prediction method and system based on POIs data

A technology of spatial distribution and prediction method, which is applied in the direction of prediction, data processing application, complex mathematical operations, etc., and can solve problems such as the stagnation of research results, the difficulty of realizing the spatialization of population at a fine spatial scale, and the difficulty of reflecting the characteristics of population distribution at a fine scale. , to achieve spatial precision improvement, fine-scale spatial population prediction, and high precision effects

Inactive Publication Date: 2019-11-15
SOUTH CHINA AGRI UNIV
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Problems solved by technology

However, the spatial resolution of nighttime light data is 500m or 1km, which makes the research results stay at large and medium scales, and it is difficult to realize population spatialization research at fine spatial scales.
[0006] Therefore, it is difficult to reflect the fine-scale population distribution characteristics in the results of population spatialization research using remote sensing data (land use data, night light data, etc.)

Method used

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  • Population spatial distribution prediction method and system based on POIs data
  • Population spatial distribution prediction method and system based on POIs data
  • Population spatial distribution prediction method and system based on POIs data

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[0042] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided for more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0043] refer to figure 1 As shown, the population spatial distribution prediction method based on POIs data provided by the embodiment of the present invention includes:

[0044] S11. Dividing the area to be predicted into q grids, each grid being an area with a preset area;

[0045] S12. Count the number of POIs in q grids;

[0046] S13. Input the number of POIs into the preset BPNN model as an input variable;

[0047] S14. Out...

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Abstract

The invention discloses a population spatial distribution prediction method and system based on POIs data, and the method comprises the steps: dividing a to-be-predicted region into q grids, and enabling each grid to be a region with a preset area; counting the number of the POIs in the q grids; taking the number of the POIs as an input variable, and inputting the input variable into a preset BPNNmodel; and outputting a population distribution prediction result in each grid. Compared with population spatialization research using night light and land utilization data as auxiliary data, the spatial precision of the population spatialization research using POIs is greatly improved. Rapid and high-spatial-resolution population density spatial prediction can be realized and spatial distribution prediction of population is realized by utilizing a machine learning BPNN model, and the prediction result is relatively high in precision. POIs are introduced to serve as single auxiliary data, spatial distribution prediction of population is achieved through a BPNN model, and fine-scale population spatialization prediction can be achieved.

Description

technical field [0001] The invention relates to the field of Internet big data, in particular to a method and system for predicting population spatial distribution based on POIs data. Background technique [0002] Mastering population information can provide scientific support for regional sustainable development research and planning. The spatialized population can better approach the actual spatial distribution of the population, break the boundaries of traditional administrative regions, and realize the integration of information such as population, resources and environment. Therefore, the spatialization of population has become an important research hotspot. [0003] At present, population spatialization mainly uses auxiliary variables related to population distribution for regional interpolation, and then guides population redistribution. Often, the quality and appropriateness of the auxiliary variables used will affect the accuracy and fine-scale of the results on th...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06F17/18
CPCG06F17/18G06Q10/04
Inventor 胡月明赵鑫宋英强林子聪刘轶伦朱阿兴
Owner SOUTH CHINA AGRI UNIV
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