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008			221128b |||||||| |||| 00| 0 eng d
040			_aMSU _cMSU _erda
100			_aChoei, Nae-Young _eauthor
245			_aThe viability of the genetic algorithm geodemographic modelling in the case of Korean DIF zoning _ccreated by Nae-Young Choei
264			_bTaylor & Francis _c2012
336			_2rdacontent _atext _btxt
337			_2rdamedia _aunmediated _bn
338			_2rdacarrier _avolume _bnc
440			_vVolume , number ,
520			_aDevelopment 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.
650			_agenetic algorithms
650			_adevelopment impact fee
650			_aancillary data
856			_uhttps://doi.org/10.1080/13658816.2011.626781
942			_2lcc _cJA
999			_c160618 _d160618