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Table of Contents | Background & Objective | Contributors
Spatially Integrated Social Science: Chapter 5
< Chapter 4 - Chapter 6 >
Too Much of the Wrong Kind of Data: Implications for the Practice of Micro-Scale Spatial Modeling
David O'Sullivan
Abstract
There has been considerable recent excitement in the spatial modeling community about the potential for micro-scale simulation methodologies such as cellular automata (CA) and agent-based models (ABM). An important issue, frequently overlooked in the enthusiasm for these techniques, is their voracious appetite for detailed data. Although detailed geo-referenced digital data are now routinely available, they are often not suitable for use in models built at the micro-scale of individual households, buildings and tracts. Instead, data are available in aggregated and anonymized geographical forms. It is argued that this is an issue with practical implications for contemporary spatial modeling, which also raises questions about the purpose of such models. It is further suggested that the issues raised bring into question the traditional cycle of pattern-matching and recalibration as a methodology for evaluation of the success of such models. The discussion of these issues is conducted with reference to recent applied work by the author on pedestrian behavior and gentrification.
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