An empirical comparative study for urban regeneration: measuring the effectiveness of DSS and GIS approaches
||Alberto Calzada, , Jun Liu, Hui Wang, and Anil Kashyap
||An empirical comparative study for urban regeneration: measuring the effectiveness of DSS and GIS approaches
||19th Annual European Real Estate Society Conference in Edinburgh, Scotland
||Urban regeneration (UR) projects encompass complex decision-making processes that usually comprise a great amount of information collected from numerous data sources which may be uncertain, inconsistent or incomplete. Many stakeholders and other actors provide subjective judgments that need to be considered throughout the decision process. To support the decision-making procedure while handling the large and complex quantitative data along with qualitative information, a belief rule-base inference methodology (RIMER) has been proposed to be used in this research. The initial finding of the research based on RIMER shows promising results in terms of flexibility, accuracy, and applicability based on some case studies relevant to urban regeneration decision making problems. Furthermore, most factors involved in regeneration projects (i.e. indicators or alternatives) are geographically referenced, making spatial component a key input in the decision making process. Although there is a substantial body of literature regarding the combination of Geographic Information Systems (GIS) and Decision Support Systems (DSS) to tackle spatial decision problems, there is still a lack of empirical and comparative studies able to measure in real terms the results and effects when using both GIS and DSS together against the use of DSS or GIS technologies alone. Therefore, this research proposes to include a spatial analysis along with the RIMER approach for comprehensive analysis of input indicators. To demonstrate so, this paper presents a comparative study developed using real data of the Greater Belfast Area (GBA). First, an approach of the UR decision problem from a RIMER-based DSS point of view shows in numerical terms the benefits of using spatial analysis for a further adjustment of DSSs. Then, an analysis is executed from an entirely GIS perspective, based on the recently proposed Geographically Weighted Regression (GWR) model. To finish off, the empirical comparative study of both approaches is then conducted. The promising results retrieved in this empirical study indicate that RIMER-based DSS can provide a well-established base to implement further research in combination with different GIS methods to effectively handle the UR decision problem from an IT perspective, compared with GWR model in terms of flexibility, interpretation, accuracy, and applicability.
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||PhD Session 1B
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