Geographic Determination - geographicdeterminationlibrary - 2024.00

Geographic Determination Library Reference for Windows and z/OS

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GeoStan Geocoding Suite
GeoStan Geocoding Suite > Geographic Determination Library
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GeoStan Geocoding Suite
Geographic Determination Library Reference for Windows and z/OS
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Calculations made in the initial geocoding process may have produced an answer that is almost, but not totally, accurate. GDL eliminates any uncertainty by providing a confidence factor and associating it with the geocode so that additional rules may be applied. This confidence factor can be described as either a distance value (between two points and/or lines) or as a percentage representing the amount of overlap between two polygons.

Geographic determination describes both the spatial relationship between two entities and the degree of confidence in that description. High confidence determinations, based on any level of geocode (address, ZIP+4, ZIP+2, or ZIP Code) are critical for many geographically sensitive business decisions.

High-level Overview

To further understand how a GDL application works, this section provides a high-level overview of a GDL application that only performs polygon comparison operations (see Figure 1.) First, the application attempts to geocode an address using GeoStan. If the address was successfully geocoded, GDL is invoked to create a geo-variance surface. Next, GDL determines the quality level of the geocode (address, ZIP+4, ZIP+2, or ZIP)

and creates the appropriate geo-variance surface. To test the variance surface, GDL calls are made to open a spatial file containing a polygon for comparison. GDL then returns the percentage overlap between the two areas.

The application then uses this value in conjunction with business rules to make the final determination. In this example, a 100 percent overlap would be understood to be a high confidence match. Anything less results in a low confidence match. The business rules used to evaluate the GDL results are unique to every organization and are based on the way you do business. Most important is that very fine levels of automated decision making can be implemented with GDL, especially when distance values are used.

For example, the business rules used in the example above could be modified to create new medium level confidence for address level polygons that overlapped between 99 and 75 percent. In this new situation, further distance calculations could be performed by GDL to determine how far inside the target area the geocode actually resides. The net result of this processing is that fewer expensive manual determinations need to be performed, saving your organization both time and money.

The following flow chart depicts one typical process that GDL might address.

Figure 1. Flowchart depicting the determination process