Categorical models for spatial data uncertainty

Sarah L. Hession, Ashton M. Shortridge and Nathan M. Torbick
Department of Geography
Michigan State University
East Lansing, MI 48824, USA
Tel.: + 001 517 355 4649; Fax: + 001 517 432 1671
hessions@msu.edu; ashton@msu.edu; torbick@msu.edu

Abstract
Considerable disparity exists between the current state of the art for categorical spatial data error modeling and the current state of the practice for reporting categorical data quality. On one hand, the general Monte Carlo simulation-based error propagation framework is a fixture in spatial data error handling; researchers have identified potentially powerful approaches to characterizing categorical data  error so that its effects on  application uncertainty may be assessed. On the other hand, standard data quality assessments for categorical data are 'spatially unaware,' fail to provide critical information for error propagation models, and neglect the fitness for use paradigm underlying the longstanding rationale for accuracy metadata. Many error assessments rely on area-averaged indicators of map error that do not reflect spatial variability, such as the confusion matrix. How might this gulf between state of the art and state of the practice be bridged? In the present work we lay the foundation for such an edifice: we contrast several categorical error models proposed in the literature in terms of input parameters and performance for a heterogeneous land cover dataset. Familiar methods such as the confusion matrix are considered for their utility in developing error propagation models, as well as theoretically-based, spatially explicit methods like indicator simulation that are not commonly employed in applied research. We develop a comparative matrix to summarize different model requirements, characteristics, and performance, and utilize available secondary data sources where possible to develop improved inputs for the analysis of uncertainty propagation.

Keywords: categorical data, land cover data, indicator kriging, indicator simulation, uncertainty propagation

In: Caetano, M. and Painho, M. (eds). Proceedings of the 7th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences, 5 – 7 July 2006, Lisboa, Instituto Geográfico Português

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