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{{morefootnotes|date=June 2009}}
'''Geospatial predictive modeling''' is conceptually rooted in the principle that the occurrences of
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<ref>Donald Brown, Jason Dalton, and Heidi Hoyle (2004) ''[http://www.springerlink.com/content/9k5tqr6xtb1br393/ Spatial Forecast Methods for Terrorist Events in Urban Environments]''</ref>
▲== Predictive Models ==
There are two broad types of geospatial predictive models: deductive and inductive.
[[Image:Signature_Analyst_Assessment_of_DC.jpg|thumb|right|250px|Crime Forecast of Washington DC. Red and orange colors indicate areas of high risk. The risk assessment was generated using an inductive predictive modeling tool called Signature Analyst. Signature Analyst is used to analyze past events and predict where subsequent events are most likely to occur.]]
=== Deductive
The deductive method relies on qualitative data or a subject matter expert (SME) to describe
the relationship between event occurrences and factors that describe the environment. As a
result, the deductive process generally will rely on more subjective information. The means
that the modeler could potentially be limiting the model by only inputting a
An example of a deductive model is as follows:
* Between 100 and 700 meters from airports.
* In the grassland land cover category.
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limited by the depth of qualitative data inputs to the model.
=== Inductive
The inductive method relies on the empirically-calculated spatial relationship between
historical or known event occurrence locations and factors that make up the environment
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==See also==
* [[Predictive
* [[Predictive modelling
* [[Suitability
==References==
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