In recent years, the explosion of geographic data has led to a burgeoning interest in the application of ‘spatial decision support systems’, in which large volumes are mobilised in order to inform decision-making processes

In recent years, the explosion of geographic data has led to a burgeoning interest in the application of ‘spatial decision support systems’, in which large volumes of data are mobilised in order to inform decision-making processes. Examples of SDSS might include McDonalds making decisions about the location of new restaurants in Thailand, Brazil, or Egypt using information about local populations, fast food consumption patterns and competitive outlets in Bangkok, Sao Paulo or Cairo. Or the Department of Education using catchment area demographics to determine appropriate schools performance benchmarks, flexed by the mix of characteristics in the local population. Or maybe Deutsche Telekom wants to define the best schedule of tariffs to apply for telephone calls in a variety of local or regional European markets. Further discussion and background relating to both ‘public service’ and commercial applications of SDSS is provided by Geertman and Stillwell (2002), and by Clarke and Madden (2001).