Urban regeneration (DR) projects involve a crucial decision-making process that contains a great amount of quantitative and qualitative data including socio-economic processes, policies, expert judgments, stakeholders' opinions, etc. A number of authorities and research studies have used different decision support techniques including Geographic Information Systems (GIS) to approach urban planning decision problems. However, how to handle the uncertainty and incompleteness of information related with many aspects of the UR decision problem is still a challenge issue to be solved. A belief rule-base inference methodology (RIMER) has been recently proposed to handle the uncertainty and incompleteness and incorporate both qualitative and quantitative data within the human decision making procedure. This paper presents an application of the extended RIMER (called RIMER+) to address UR decision problem, where the detailed sensitivity analysis of RIMER+ performance for predicting deprivation measures of the Greater Belfast Region is given by varying the uncertainty and incompleteness levels of the inputs of the system. These case studies are based on real practical data of the Greater Belfast Region in UK. The results demonstrate the positive performance of the RIMER+ method to provide valid and supportive evaluation results, and at the same time to measure the incompleteness and uncertainty range as a reflection of reality as additional support information to help decision making. These positive results indicate that RIMER+ can provide a well-established base to implement further research with combination with GIS to tackle the UR decision problem.