Time and costs are often the most critical constraints in implementing a development impact fee (DIF) for local infrastructure installation planning in South Korea. For this reason, drafting quality plan alternatives and calculating precise DIFs for improvement remain challenging. This study proposes an application of a procedural modeling method using CityEngine as an alternative to traditional methods, which rely on AutoCAD. A virtual low-density suburban development project in Jeju, South Korea was used to compare the workability of the two methods. The findings suggest that procedural modeling outperforms the other approach by significantly reducing the number of steps and commands required in the planning process. This paper also argues that procedural modeling provides real-time 2- and 3-dimensional modeling and design evaluation and allows for a more efficient assessment of plan quality and calculation of DIF. We also argue for the need to diffuse procedural modeling to better support local planning practices.

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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.

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