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Table of Contents | Background
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Spatially Integrated Social Science: Chapter 5
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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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