A GIS-based multicriteria spatial decision support system for planning urban infrastructures

a r t i c l e i n f o The planning of urban infrastructures has important spatial implications. The evaluation of alternative courses of action in this setting requires the explicit consideration of multiple criteria as they have important social, economic, and environmental effects. This paper presents a decision support system aimed at offering the users (e.g., government or municipal agencies) a flexible and user-friendly environment to provide decision aid in urban infrastructure planning. The visualization of available alternatives on maps provides a value-added for decision support processes in urban infrastructure evaluation problems. The development of this system has been motivated by a real world urban case study. Urban areas are constantly changing as they expand into new territory or existing areas are redeveloped. Infrastructure investments are key components for successfully implementing these changes. However, the planning and analysis of such investments is complex. Typically, they are strategic and long-term in nature as they involve large capital expenditures designed for many years of service. The evaluation of these investments requires the explicit consideration of multiple, conflicting and incommensurate criteria as they have important social, economic, and environmental effects that impact various stakeholders differently. These decisions also have important spatial implications as many of their costs and benefits are distributed spatially. Consequently, they have differing impacts on several stakeholders depending upon the type and location of the investments, and upon the citizen groups affected. The appraisal of alternative urban plans involves the analytical and systemic determination of all the factors influencing the multidimen-sional value of courses of action, supported in clear principles and using well-defined criteria. The process of evaluation should be scientifically sound, and its adequate application depends not just on the quality and amount of the information gathered but also on the features offered to the user. In order to analyze decision problems, the need to integrate spatial data with algorithmic techniques has been recognized and gave rise to a research stream in the context of decision support systems (DSS) related to the so-called Spatial Decision Support Systems (SDSS). As mentioned by Maniezzo et al. [12], these systems are concerned with how to integrate spatially referenced information in a decision making environment in order to positively affect the performance of decision makers, showing how spatially integrated DSS can be used to bridge the gap between policy makers and complex computerized models. In the context of urban and …