An Empirical Comparative Study for Urban Regeneration: Measuring the Effectiveness of DSS and GIS Approaches

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. 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. This paper utilises, a belief rule-base inference methodology (RIMER) to support the decision-making procedure while handling the large and complex quantitative data along with qualitative information. This research paper considers a spatial analysis along with the RIMER approach for comprehensive analysis of input indicators. 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. This empirical study indicate that RIMERbased 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. Keywordsdecision-making; urban regeneration; GIS; spatial analysis; uncertainty; belief rule-base; GWR; decision support system; spatial decision making