An urban mesoscopic layer real demographic method based on mobile phone signaling

Abstract

The invention provides an urban mesoscopic layer real demographic method based on mobile phone signaling. The method is used for counting relevant indexes of real population in a city through mobile phone signaling data, collected by mobile phone base stations, of users, and is characterized by comprising the following steps that S1, importing the mobile phone signaling data collected by the mobile phone base stations, and extracting individual travel chains of the users; S2, performing user type identification on the user according to the individual travel chain, and performing statistics onthe real population data on the scale of the mobile phone base station; Step S3, establishing a conversion relationship between the mobile phone base station and the urban space unit of the city; AndS4, carrying out conversion calculation on the actual population indexes of the urban space units according to the actual population data of the mobile phone base stations and the conversion relationship.

Description

technical field [0001] The invention belongs to the field of urban planning and urban population management, and in particular relates to a method for counting real population at the mesoscopic level of a city based on mobile phone signaling. Background technique [0002] Obtaining the total size and attribute information of urban population has always been the basic work in the fields of urban planning and urban population management. At present, there are two population concepts commonly used at the macro level of cities: one is the "resident population" in the decennial census data, that is, the population who has lived in the local area for more than half a year; the other is the population that has lived in the local area for less than 6 months The "short-term resident population" is usually held in the hands of the Municipal Public Security Bureau or the sub-district offices. At the microscopic level of the city, the population that actually appears in the local space...

AI Technical Summary

Problems solved by technology

[0003] However, the data used by existing statistical methods is difficult to take into account the breadth of spatial sampling and the granularity of temporal sampling

Among them, the census is conducted once every 10 years, and each census consumes huge labor costs; it is very difficult to obtain the registered population data of the Municipal Public Security Bureau and the sub-district offices, and it is difficult to integrate them in a unified manner; the data of crowd flow observation and questionnaire interviews are limited to the micro level of the city and difficult to cover entire city area

This defect makes it difficult for existing methods to obtain information on urban population changes in a short period

[0004] Therefore, existing statistical methods do not take into account the impact of urban residents’ daily activities on urban population distribution, an

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