Spatial Data Processing
Not every spatial query results in a map
Executive Summary
Problem
Businesses need to incorporate spatial information (i.e. addresses, proximity to resources, etc) into their business analyses, but they are not necessarily looking for a visual map, but rather a quantifiable number they can then use in other calculations.
Solution
Spatial data processing uses location information that can be processed in a spatially-enabled database or statistics program, to create a set of quantifiable numbers that can then be used in other analyses.
Spatial Data Processing Sub-Categories
When most people think of GIS and Location Technology, they think of data displayed in maps. However, spatial data processing utilizes the tools and technologies of GIS without the necessary production of a map. Spatial data processing utilizes large volumes of geodata to answer business questions and solve critical problems. The end result can be numeric, a code, a list of products or of course, a map.
Spatial data processing can be very resource intensive and complex, since it usually involves the analysis of large amounts of spatial and non-spatial data from various diverse applications, and can additionally incorporate the dimension of time.
There are many times when you don't need a visual representation of data and only care about how location impacts some other decision process. For example: cellular phone service patterns in time, length, distance, and location of phone calls can be mined can be used to dynamically allocate bandwidth for better service and price policy planning for maximum profit; patterns in cancer incidence and mortality to identify at risk populations; or what products to stock in a supermarket based on the demographics of the neighborhood and location relative to competitors.
Farallon has implemented advanced spatial data processing in the insurance and underwriting industries to help better set premium levels based upon risk. Firms can analyze a broad range of location-based information that can be statistically analyzed to determine whether specific spatial relationships between risk factors and the location of policy holders correlate with levels of risk.