Method for processing mobile phone signaling data by using kernel function to identify employment place

Abstract

The invention discloses a method for processing mobile phone signaling data by using a kernel function to identify an employment place, and belongs to the field of urban employment place research. According to the method, the method comprises the steps of: using a Thiessen polygon formed by a mobile phone base station as a to-be-identified unit; performing data statistics on the occurrence time point of the access activity of any to-be-identified unit in any working day; judging the user behavior by comparing the base station position information recorded by each piece of mobile phone signaling data of the user with the base station position information recorded before and after; and then counting the time node of the access behavior of each to-be-identified unit, analyzing the peak law caused by the time node of the access behavior of each to-be-identified unit by using a kernel function estimation method, and jointly judging whether the to-be-identified unit is an employment place and the type of the to-be-identified unit according to the occurrence frequency and time interval of the peak. Compared with the prior art, the method can conveniently and accurately identify the employment place of the urban population, and provides powerful technical support for effective analysis and utilization of the urban space.

Description

technical field [0001] The invention relates to the research field of urban employment places, in particular to a method for identifying employment places by using kernel functions to process mobile phone signaling data. Background technique [0002] The place of employment is a fixed spatial range formed by the participation of urban residents in production activities. In terms of time, the time for participating in production activities in the place of employment is regular, usually showing the characteristics of many people during the day and few people at night; A space frequently used by production activities. Therefore, according to the above characteristics, the place of employment discussed in this study refers to the geographical location where urban residents participate in production activities. [0003] The traditional way of identifying the place of employment mainly depends on the regularity of the user's daytime data, that is, to judge whether it is the place...

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Problems solved by technology

[0004] However, the biggest problem with the above-mentioned identification method is that the identification time mainly depends on the mobile base station of the user in a single period (daytime), and it is likely that night shifts, or even two shifts and three shifts (such as specific enterprises such as hospitals and factories) Unit) the actual place of employment is ignored, and it is even misjudged as the user’s place of residence

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