This chapter describes Multi-Criteria Evaluation (MCE) from the perspective of spatial decision support methodology adopted for geospatial problems and GIS applications. It highlights that MCE is essentially a systematic way of comparing pros and cons of choice alternatives, often using weighted criteria to generate a measure of a relative strength of each alternative vis-à-vis other alternatives. The chapter emphasizes the everyday use of MCE in geographic information science and technology (GIS&T), and starts from introducing the MCE principles followed by examples of MCE implementation in GIS. Theoretical considerations of geospatial MCE are discussed by focusing on issues of space and scale as well as spatio-temporal representation in MCE. The chapter concludes with an overview of recent trends in geospatial MCE including the adoption of behavioral theories explaining spatial choice preferences, data-driven approaches leveraging large data sets and machine learning techniques to derive MCE model parameters, and development of methods for addressing uncertainty in parameters, with applications in urban land use, renewable energy planning, and geomarketing.
Linear programming is a set of methods for finding optimal solutions to mathematical models composed of a set of linear functions. Many spatial location problems can be structured as linear programs. However, even modest-sized problem instances can be very difficult to solve due to the combinatorial complexity of the problems and the associated computational expense that they incur. Geographic Information Systems software does not typically incorporate formal linear programming functionality, and instead commonly uses heuristic solution procedures to generate near-optimal solutions quickly. There is growing interest in integrating the spatial analytic tools incorporated in Geographic Information Systems with the solution power of linear programming software to generate guaranteed optimal solutions to spatial location problems.