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#REDIRECT [[Geospatial analysis]]
== 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.
=== Deductive method ===
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. This means
that the modeler could potentially be limiting the model by only inputting a number of factors that the human brain can comprehend.
An example of a deductive model is as follows:
Sets of events are typically found …
* Between 100 and 700 meters from airports.
* In the grassland land cover category.
* At elevations between 1000 and 1500 meters.
In this deductive model, high suitability locations for the set of events are constrained and
influenced by non-empirically calculated spatial ranges for airports, land cover, and elevation: lower
suitability areas would be everywhere else. The accuracy and detail of the deductive model is
limited by the depth of qualitative data inputs to the model.
=== Inductive method ===
The inductive method relies on the empirically-calculated spatial relationship between
historical or known event occurrence locations and factors that make up the environment
(infrastructure, socio-culture, topographic, etc.). Each event occurrence is plotted in
geographic space and a quantitative relationship is defined between the event occurrence
and the factors that make up the environment. The advantage of this method is that software
can be developed to empirically discover – harnessing the speed of computers, which is
crucial when hundreds of factors are involved – both known and unknown correlations
between factors and events. Those quantitative relationship values are then processed by a
statistical function to find spatial patterns that define high and low suitability areas for event
occurrence.
==See also==
* [[Predictive Analysis]]
* [[Predictive modelling]]
* [[Suitability analysis]]
==References==
{{reflist}}
[[Category:Geographic information systems]]
[[Category:Spatial analysis]]
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