Abstract
As an emerging spatial trajectory data, mobile terminal location data can be widely used to analyze the behavior characteristics and interests of individuals or groups in smart cities, transportation planning and other civil fields. It can also be used to track suspects in anti-terrorism security and public opinion management. Aiming at the problem that it is difficult to determine suitable input parameters of clustering caused by different subscriber location data size and distribution difference, an improved density peak clustering algorithm is proposed and the performance of the improved algorithm is verified on the UCI data set. Firstly the important location is identified by the proposed algorithm, and the personal location is further inferred by the algorithm based on the subscriber's schedule and maximum cluster. Then, the algorithm adopts Google's inverse geocoding technology to obtain the semantic names corresponding to the coordinate points, and introduces the natural language processing technology to achieve word frequency statistics and keyword extraction. The simulation results based on the Geolife data set show that the algorithm is feasible for identifying important locations and inferring personal locations.
Highlights
In recent years, with the rapid development of mobile communication technologies and the increasingly powerful functions of smart mobile terminal networks, smart terminal devices such as mobile phones and tablet computers have gradually surpassed personal computers and become the most widely used information devices for people
Important location identification refers to the region where the user has a long stay and a high frequency of visits from the historical location data of the target user[1]
The personal location inference is based on the identification of important locations, further analysis of the extracted areas, inferring the user's home address, workplace or entertainment, etc, to discover the user's activity patterns, establish their behavior patterns[2], and speculate user's hobbies, even health and income levels, etc
Summary
With the rapid development of mobile communication technologies and the increasingly powerful functions of smart mobile terminal networks, smart terminal devices such as mobile phones and tablet computers have gradually surpassed personal computers and become the most widely used information devices for people. Important location identification refers to the region where the user has a long stay and a high frequency of visits from the historical location data of the target user[1]. The personal location inference is based on the identification of important locations, further analysis of the extracted areas, inferring the user's home address, workplace or entertainment, etc, to discover the user's activity patterns, establish their behavior patterns[2], and speculate user's hobbies, even health and income levels, etc. The user information obtained through mining can recommend personalized interest points for users at the commercial level, and can track suspicious targets at the security level. Montoliu et al first extracted the stay points using a time-based clustering algorithm, extracted the stay regions using grid-based clustering, and output them as important locations[4]. Attention is paid to the information contained in the semantic level
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