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A Spatial Decision Support System (SDSS) for Electoral Districting

What is a SDSS?

A computational system for generating “interesting alternative sets of electoral districts”
Designed to control the properties of each set of districts generated by the model
Users specify their “optimal criteria” (defined below) and the SDSS then generates a new set of electoral districts
Users can add spatial constraints–for example, the boundaries of electoral districts must fall within provincial or state boundaries; electoral districts must not cross natural features such as rivers and lakes or mountains.

"Compactness" vs. "Equality"

The distinguishing feature of this SDSS is that it permits the user to control the search for electoral districts that maximize geographic compactness, or equality of populations, or the "best" compromise between the two.
The SDSS generates successive maps of the electoral districts along the “solution frontier” represented by the following diagram - the non-inferior set of alternatives.
Thus, for this demonstration, we generate an alternative set of districts that seeks to maximize for “compactness” with the smallest possible deviation from the goal of achieving “equality” or the same number of voters per district. Conversely, we generate an alternative set of districts that seeks to maximize for “equality” with the smallest deviation from “compactness.”
The “best” trade-off between these two criteria is represented by the green frontier line in the following diagram.

Maximum compactness with spatially uniform distribution of voters.

Note: There is no trade-off between equality and compactness.

Compactness is sacrificed to achieve equity with a spatially non-uniform distribution of voters (Rushton,1972).

Note: Darker areas have higher densities of population, therefore the constituency areas are smaller. Lower density areas have larger constituency areas, but less regular shapes and more sinuous boundaries.