Locally accurate prediction standard errors with spatially varying regression coefficient models A comparison of techniques

Paul Harris, Stewart Fotheringham, Chris Brunsdon

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This study assesses the prediction and prediction uncertainty performance of models that cater for both: (i) a nonstationary relationship between the response and a contextual variable and (ii) a nonstationary residual variance (or variogram), at point locations for a single realisation spatial process. Here the crucial aspect of the model specification is allowing the residual variance (or variogram) to vary across space. Without this, the estimated prediction standard errors are only likely to be accurate in a global (or overall) sense and not the desired, local sense. Locally-accurate prediction standard errors, allow locally-relevant prediction confidence intervals and/or locally-relevant estimates of risk (e.g. the risk of exceeding some critical threshold) which is not only valuable to researchers who attempt to model spatial processes, but also to policy makers who need to plan and manage the outcomes of spatial processes at different spatial scales.

Original languageEnglish (US)
Title of host publicationAccuracy 2010 - Proceedings of the 9th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences
PublisherInternational Spatial Accuracy Research Association (ISARA)
Pages137-140
Number of pages4
StatePublished - 2010
Externally publishedYes
Event9th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences, Accuracy 2010 - Leicester, United Kingdom
Duration: Jul 20 2010Jul 23 2010

Other

Other9th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences, Accuracy 2010
CountryUnited Kingdom
CityLeicester
Period7/20/107/23/10

Keywords

  • Bayesian prediction models
  • Georaphically weighted regression
  • Heteroskedastic
  • Moving window kriging

ASJC Scopus subject areas

  • Environmental Science(all)

Fingerprint Dive into the research topics of 'Locally accurate prediction standard errors with spatially varying regression coefficient models A comparison of techniques'. Together they form a unique fingerprint.

  • Cite this

    Harris, P., Fotheringham, S., & Brunsdon, C. (2010). Locally accurate prediction standard errors with spatially varying regression coefficient models A comparison of techniques. In Accuracy 2010 - Proceedings of the 9th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences (pp. 137-140). International Spatial Accuracy Research Association (ISARA).