Urban planning and urban design involve collaboration of diverse participants with multiple agendas and multiple criteria. The participants typically use multiple representations of spatial data to derive inferences and insights about the planning problems, leading to a shared decision-making process. To support such multidisciplinary work, this paper proposes a new computational approach and technique for translation between multiple representations of spatial data. This approach is designed to supportdesign decision-making in the interrelated interests of design participants. Prototype implementation and evaluation are conducted to test and validate the proposals.