Big spatio‐temporal data mining for emergency management information systems

Abstract

Nowadays, spatio-temporal data analytics plays an important role in different applications based on geographic information system (GIS). One of such applications is emergency management information systems (EMISs) which process a huge amount of spatio-temporal data that can be considered as Big Data (BD). Effective EMIS data analysis will allow authorities to improve the management of emergency events, including those related to traffic problems (traffic accidents, traffic jams, etc.). However, the stack of modern technologies dealing with BD does not provide natively supported functions for spatio-temporal data analysis, so they have to be developed and implemented for EMIS. In this study, the authors developed and evaluated several algorithms and a framework for big spatio-temporal data mining in the Russian EMIS GLONASS + 112112. They suggested improved spatio-temporal co-location patterns mining technique based on DBSCAN, FPGrowth, and natural language processing (NLP). They detected spatio-temporal outliers and spatial autocorrelation. Finally, they evaluated the scalability and time performance of algorithms. They found that using NLP and aggregation of an emergency dataset with weather to enhance the number of emergency categories could significantly increase the quality of association rules. They discovered valuable association rules related to traffic accidents in the dedicated area of Kazan city.