A method for identifying urban function areas based on WeChat data and interest point data

Abstract

The invention provides a method for identifying urban function areas based on WeChat data and interest point data. The method comprises the steps of extracting a people flow matrix, an out-people flow matrix and an in-people flow matrix of each base station from the WeChat data, extracting an interest point type distribution matrix of each base station from the interest point data, and performing normalization processing on the four matrixes based on a base station area element; then performing outlier deleting operations and performing urban space division, data extracting and normalization treatment on the base stations with outliers deleted to generate a new matrix, and then performing clustering analysis on the generated new matrix; finally performing space distribution area overlap ratio calculating on a clustering result and determining functions of urban areas according to overlap ratio calculating results. The method can identify the functions of each urban area accurately, better achieve functional partitioning and characteristic analysis of urban areas, provide technical support for urban structure layout and exploitation and utilization of land, and provide a realistic basis for reasonable utilization of urban space.

Description

technical field [0001] The invention relates to the field of big data analysis, in particular to a method for identifying urban functional areas based on WeChat data and point-of-interest data. Background technique [0002] With the rapid development of the economy, a series of urban problems have come one after another, especially for some provincial capital cities or metropolises, the urban problems are particularly serious. "Urban disease" is the result of urbanization in developing countries, manifested in traffic congestion, housing shortage, insufficient water supply, energy shortage, environmental degradation, employment difficulties, etc., which has caused a burden to the city and even restricted the development of the city. It is also easy to cause physical and mental diseases of citizens. [0003] In recent years, some experts and scholars have used various heterogeneous big data for "urban computing" to solve the problems caused by urbanization. Urban computing ...

AI Technical Summary

Problems solved by technology

Floating car data includes the usage data of urban taxis, buses, and subways. These data rely on the city’s road network information, and can only locate human activities at the point of getting on and off the car, but cannot locate people in the car. Where did you come from before getting in the car, and where did you go after getting off the car?

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