Multi-stage and multi-objective allocation procedures of urban parks using location decision support system (UPLDSS)

Recreation has an important role in human life. Specifically, the open space around residential areas such as playgrounds or neighborhood parks influences people's daily lives implicitly or explicitly. In spite of such contributions, however, the literature directly related to the provision of parks is quite rare. Moreover, planning around size standards for park resources, as is current practice, is inadequate because it does not address how the spatial distribution of recreational resources should be performed.The purpose of this study is that the incorporation of location-allocation in a Spatial Decision Support System (SDSS) can address the methodological short-comings of current practice in open space planning, especially urban park allocation, and that location-allocation models alone are insufficient because they do not function well in ill-structured problems. Regarding the location allocation of parks, two factors--user behavior, and possible policies involving equity and efficiency--are the main concerns. This study focuses on the second factor to address how and where recreational resources should be located. To incorporate these issues in the location allocation of parks, it is suggested that SDSSs are useful tools because the location-allocation of parks, as an ill-structured problem, needs to handle the large amount of data and requires many computational tasks. SDSSs also help to solve these planning problems through decision makers' choices in standards, goals, weights, and other model parameters. SDSSs are necessary in the appropriate open space planning procedure, because open space planning problems are mostly ill-structured as well as other planning problems.UPLDSS has three modules: database module, analytical module, and representation module. The UPLDSS is implemented, examined, and discussed using archetypes of urban regions--small, but non-trivial datasets. Then, UPLDSS tools was demonstrated on a real case site, Portland, Oregon. Among the various types of urban park systems, a neighborhood park system was implemented. The results of archetype showed that behaviors of models were clearly demonstrated in various types of archetype, responding to incorporating multi-issues and to multi-decision stages. Also, the UPLDSS was successfully applied to the real case site.In conclusion, implications of UPLDSS showed that it helps planners to try out different scenarios, construct alternatives, and evaluate them regarding various open space planning objectives (e.g., equity and efficiency) when it is, specially, difficult to anticipate the benefit of pursuing planning polices. Also, this study showed that current practice (e.g., planning around size standards for park resources) is sometimes inadequate in terms of other planning objectives (e.g., equity or efficiency) through UPLDSS even though the adequacy measure was met the criteria given by National Recreation and Park Association (NRPA).U of I OnlyETDs are only available to UIUC Users without author permissio