Analytics and Spatial Information Technology: Retrospect and Prospects

Abstract

Decision makers increasingly rely upon extracting relevant information from large repositories of data in order to make strategic decisions. The information is often used to test hypotheses or discover insights or intelligence (Chan et al. 2009). The support for strategic decisions is usually based upon data collected from internal operations, supplemented by relevant external information, and insights gained from a model of the real world. We logically associate all the tools, techniques, and processes associated with deriving intelligence from the core data as “Analytics.” We include many diverse techniques within the broad term analytics, including statistics, predictive models, visualization systems, etc. The important point is that all these techniques may play a role in knowledge discovery and decision-making (Davenport and Harris 2007). Simply put, analytics is “the science of analysis.” A more practical definition would be how an individual or enterprise arrives at sound decisions based on existing data. For the purpose of this book, the field of analytics includes the use of mathematical models, building upon statistics, probability, simulation, optimization, decision analysis, and most notably, spatial analysis. Analytics closely resembles data mining, but tends to be based on modeling, sometime involving extensive computation.

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