Multi-disciplinary ontological geo-analytical incident modeling

Cultural patterns and representations of crime areas are important in geo-analytics. We discuss a multi-disciplinary ontology that provides an analytical, architectural framework to apply a broad spectrum of analytical capabilities to changes the world faces on a daily basis to determine an optimal allocation of resources for continuous enrichment of Foundation GEOINT content. All of these changes such as food shortages, weather, environmental hazards, traffic congestion, economic crises, political unrest, un-employment, and specifically crime, pose a constant struggle in societies to maintain status quo. What has always been difficult to predict is change to status quo, or predicting regions at risk or undergoing stress. We propose taking knowledge of a location and society, and marrying that with modern analytical capabilities, including linear game theory, to pit forces of maintaining status quo against forces within society that desire to force a drastic change. The result is a probabilistic output of potential outcomes that can be further analyzed to predict most likely and most dangerous outcomes. Our solution is based on analytical environment to support ingestion of many data sources and integration of analytical algorithms such as feature extraction, crowd source analysis, open source data mining, trends, pattern analysis and linear game theory optimization. Our framework consists of a hierarchy of data, space, time and knowledge entities. We exploit such crime statistics to predict crime activity based on past observations. We also show simulations based on minimum mean squared error (mmse) and pseudo estimators of crime activity.