Multi-Scale Extraction of Regular Activity Patterns in Spatio-Temporal Events Databases: A Study Using Geolocated Tweets from Central Mexico

Thursday Sep 29   03:30 PM to 05:30 PM (2 hours)
social MediaUrban ActivityGeographic Data Mining
This paper proposes a new technique for the extraction of regular activity patterns at different scales, mined from the micro-blogging platform Twitter. The approach is based on the recursive application of the DBSCAN clustering algorithm to the geolocated Twitter feed. The proposed technique includes a novel way to obtain averaged regular activity zones based on the rasterization and aggregation of the Concave Hull of the clusters identified at each resolution level. The proposed technique uses only the spatio-temporal characteristics of the geolocated Twitter feed and it does not depend on the messages, therefore it can be extended to work with different spatio-temporal event sources such as mobile telephone records. An experiment was carried out to demonstrate the effectiveness of the technique in the extraction of known activity patterns in the Mexico City Metropolitan Area.

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