The viability of the genetic algorithm geodemographic modelling in the case of Korean DIF zoning

Abstract

Development impact fees (DIF) are used for the provision of public infrastructure services to adequately serve new developments. Korean Ministry of Land, Transport, and Maritime Affairs introduced the DIF zoning in 2008 and, like its US counterpart, it requires Korean localities to designate specific districts called ‘DIF zones’ based on the local population growth rate. This study examines the genetic algorithms as a method for DIF zoning-related geodemographic modelling using the Korean National Geographic Information Systems as property-level ancillary data. A borough of Hwasung City is taken as the case area since the city internally collected population data by ward-level enumeration areas for DIF zoning in 2008. The gridded population map is built from this source enumeration area population data to select the training dataset. The functional form of the genetic algorithm model has been formulated to have a hierarchical weighting system in which categorical weights of variable groups and individual weights of subordinate variables are sought bilaterally. The model is run by a carefully pretested set of reproductive plan parameters and the consequences are compared with the conventional regression models. It is found that the genetic algorithm solutions are quite comparable to the ones obtained by the regression methods, and it seemed, in this regard, worth adopting the two approaches simultaneously in a complementary manner to take unique advantages of each other either in facilitating the analysis processes or in obtaining more promising outputs for the DIF zoning as well as other geodemographic applications.