Position point of interest clustering method and related device

Abstract

Disclosed are a method for clustering position points of interest and a related device. The method for clustering position points of interest comprises: acquiring a positioning point set of a user within a pre-determined time period; according to the positioning point set, generating a residing point set, wherein each residing point in the residing point set represents a hot zone, and the hot zone satisfies the following condition: the geographic position distance between any two positioning points in the hot zone is less than the larger positioning accuracy of the positioning accuracies of the two positioning points; and the maximum value of the time interval between two positioning points in the hot zone is greater than a pre-set time threshold value; calculating the credibility of each residing point in the residing point set; according to the credibility of each residing point in the residing point set, screening out credible residing points from the residing point set; and clustering the credible residing points of which the densities are adjacent into a position point of interest. The technical solution provided in the present invention can effectively increase the reliability and reference value of a POI.

Description

technical field [0001] The present invention relates to the technical field of geographic information processing, and in particular to a location interest point clustering method and a related device. Background technique [0002] A POI (Point Of Interest) refers to a location area where a user frequently stays for a long time, such as a home, an office, a supermarket frequented, and other areas that are important to the user. [0003] Using the Wi-Fi network of mobile phones and other terminals, Global Positioning System (GPS, Global Positioning System), and base station identification number (ID, Identity) positioning functions, etc., the trajectory information of users' daily activities can be obtained, and these trajectory information is obtained by a large number of positioning systems. Deviated positioning coordinate points, research on how to extract the user's POI from these trajectory information is of great value for context awareness and location-based service (LB...

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Problems solved by technology

[0005] However, the above method extracts the user's residence point through the space-time dimension, which can only represent a single visit of the user, and cannot represent the POI location that is important to the user, and only refers to the user's historical location data when mining POIs , the reliability and reference value of the mined POIs are low

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