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Geospatial predictive modeling is conceptually rooted in the principle that the occurrences of events being modeled are limited in distribution. Occurrences of events are neither uniform nor random in distribution – there are spatial environment factors (infrastructure, sociocultural, topographic, etc.) that constrain and influence where the locations of events occur. Geospatial predictive modeling attempts to describe those constraints and influences by spatially correlating occurrences of historical geospatial locations with environmental factors that represent those constraints and influences. Geospatial predictive modeling is a process for analyzing events through a geographic filter in order to make statements of likelihood for event occurrence or emergence.〔 *Gary P. Beauvais, Douglas A. Keinath, Pilar Hernandez, Larry Master, Rob Thurston. ''(Element Distribution Modeling: A Primer (Version 2) )'', Natureserve, Arlington, Virginia, June 1, 2006, last referenced December 29, 2009〕 〔 *Donald Brown, Jason Dalton, and Heidi Hoyle. ''(Spatial forecast methods for terrorist events in urban environments )'', In Proceedings of the Second NSF/NIJ Symposium on Intelligence and Security Informatics, Lecture Notes in Computer Science, pages 426–435, Tucson, Arizona, Springer-Verlag Heidelberg, June 2004.〕 == Predictive models == There are two broad types of geospatial predictive models: deductive and inductive. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Geospatial predictive modeling」の詳細全文を読む スポンサード リンク
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