Group activity data collection method and system based on multisource space-time trajectory data

Abstract

The invention discloses a group activity data collection method and system based on multisource space-time trajectory data. The method comprises the steps that a background obtains and preprocesses original mobile terminal signal data and original social software signature data and generates to-be-processed signal data and to-be-processed signature data in consistent with specific formats; the background obtains activity point trajectory data from the to-be-processed signal data, establishes and learns prior information of group activity rules, obtains activity point trajectory data, and obtains activity location data; the background marks activity point trajectory semantic information by employing a Bayesian model and generates an activity space-time trajectory chain according to the activity point trajectory data, the prior information of group activity rules and the activity location data. According to the method and the system, individual activities are deduced by employing the Bayesian model; the influence of the activity type of a former moment on the activity type of a latter moment in the space-time activity trajectory is taken into consideration; and accurate, fast and efficient extraction and collection of wide-range and massive group activities are realized.

Description

technical field [0001] The invention relates to the technical field of data processing, in particular to a group activity data collection method and system based on multi-source spatiotemporal trajectory data. Background technique [0002] Traditional activity collection methods rely on activity logs or activity surveys, with small sample sizes, long collection times, and time-consuming and labor-intensive efforts. The explosion of spatio-temporal trajectory data provides a new method for the collection of large-scale group activities. Research on spatio-temporal data analysis mainly focuses on the identification of individual activities in real space, especially travel activities, and lacks the extraction of basic attribute information of activities. It is necessary to develop a group activity extraction method that integrates multi-source spatiotemporal trajectory data to lay a data foundation for urban scientific research based on massive activities. Spatio-temporal tra...

AI Technical Summary

Problems solved by technology

Spatio-temporal trajectory data (such as mobile phone signaling data, vehicle GPS data, social check-in data, etc.) contains rich time information and location information, but the semantic information is relatively lacking, and the temporal and spatial resolutions are different, so it cannot directly provide group activity information

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