INTELLIGENT ANALYSIS OF LARGE SPATIAL-TEMPORAL DATA FOR EMERGENCY SERVICES
Abstract
Recently, rescue services have shown great interest in the use of geographic information systems (GIS) to improve the efficiency of monitoring emergency events and rapid response. The efficiency of using such systems can be enhanced by applying spatial-temporal data mining techniques to provide decision support and extracting potentially useful knowledge that could help effectively detect emergencies in real time and prevent some incidents. Spatio-temporal data play a large role in various fields of science, such as geography, ecology, health, safety. Today, population growth and increased social activity has a significant impact on the urban environment and leads to emergencies such as fires, crimes, threats of terrorist acts, and road accidents. In this regard, government agencies need new solutions to effective respond and monitor emergencies. However, the storage and processing of large amounts of spatio-temporal data is currently still a serious problem faced by services related to emergency response. As part of this work, a number of approaches to the analysis of spatial data were considered: the analysis of spatial patterns, the detection of space-time anomalies and the analysis of spatial autocorrelation.
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