Estimating uncertainty in spatial microsimulation approaches to small area estimation: A new approach to solving an old problem. (May 2017)

• Record Type: Journal Article
• Authors: Whitworth, A.; Carter, E.; Ballas, D.; Moon, G.
• Is Part Of: Computers, environment and urban systems. Volume 63(2017)
• Page Start: 50
• Page End: 57
• Publication Date: 2017-05
• Subjects: Small area estimation -- Spatial microsimulation -- Iterative proportional fitting -- Credible intervals -- Confidence intervals -- Variance estimation; City planning -- Data processing -- Periodicals; Regional planning -- Data processing -- Periodicals; 303.4834
• DOI: 10.1016/j.compenvurbsys.2016.06.004
• Languages: English
• ISSNs: 0198-9715

Abstract

Abstract: A wide range of user groups from policy makers to media commentators demand ever more spatially detailed information yet the desired data are often not available at fine spatial scales. Increasingly, small area estimation (SAE) techniques are called upon to fill in these informational gaps by downscaling survey outcome variables of interest based on the relationships seen with key covariate data. In the process SAE techniques both rely extensively on small area Census data to enable their estimation and offer potential future substitute data sources in the event of Census data becoming unavailable. Whilst statistical approaches to SAE routinely incorporate intervals of uncertainty around central point estimates in order to indicate their likely accuracy, the continued absence of such intervals from spatial microsimulation SAE approaches severely limits their utility and arguably represents their key methodological weakness. The present article presents an innovative approach to resolving this key methodological gap based on the estimation of variance of the between-area error term from a multilevel regression specification of the constraint selection for iterative proportional fitting (IPF). The performance of the estimated credible intervals are validated against known Census data at the target small area and show an extremely high level of performance. As well as offering an innovative solution to this long-standing methodological problem, it is hoped more broadly that the research will stimulate the spatial microsimulation community to adopt and build on these foundations so that we can collectively move to a position where intervals of uncertainty are delivered routinely around spatial microsimulation small area point estimates. Highlights: Small area estimation is a powerful tool to gain new spatial detail from national surveys. Spatial microsimulation approaches cannot calculate uncertainty around estimates. This key weakness is well recognised and undermines the method's utility to users. We outline an innovative technique to derive and test such credible intervals. The approach performs well and is an important first step in resolving this dilemma