Anomaly behaviour detection based on the meta-Morisita index for large scale spatio-temporal data set

In this paper, we propose a framework for processing and analysing large-scale spatio-temporal data that uses a battery of machine learning methods based on a meta-data representation of point patterns. Existing spatio-temporal analysis methods do not include a specific mechanism for analysing meta-data (point pattern information). In this work, we extend a spatial point pattern analysis method (the Morisita index) with meta-data analysis, which includes anomaly behaviour detection and unsupervised learning to support spatio-temporal data analysis and demonstrate its practical use. The resulting framework is robust and has the capability to detect anomalies among large-scale spatio-temporal data using meta-data based on point pattern analysis. It returns visualized reports to end users.