Spatial Misalignment Models for Small Area Estimation: A Simulation Study

Trevisani, Matilde; Gelfand, Alan E.

doi:10.1007/978-3-642-35588-2_25

Cited by 11 publications

(10 citation statements)

References 8 publications

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“…Several methods have extended the Fay-Herriot model to handle two or more spatially defined areas. Some are based on subareal models that borrow strength from both areas and subareas to obtain more efficient estimators (Torabi and Rao, 2014;Trevisani and Gelfand, 2013). In these models, a subarea must be fully nested within a single area.…”

Section: Problem Identification and Reviewmentioning

confidence: 99%

“…1). To handle spatial mismatching between source areas and target areas of auxiliary information Trevisani and Gelfand (2013) proposed a socalled atom-based linking model for each intersection of the source and target areas, which then can be used to make predictions to the target areas. Again it is not simple to aggregate all the survey variables to the postcode level and then to link them with corresponding variables at the SA2 level.…”

Section: Problem Identification and Reviewmentioning

confidence: 99%

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A Bayesian Spatial Categorical Model for Prediction to Overlapping Geographical Areas in Sample Surveys

Bakar

Biddle

Kokic

et al. 2019

Journal of the Royal Statistical Society Series A: Statistics in Society

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Summary Motivated by the Australian National University poll, we consider a situation where survey data have been collected from respondents for several categorical variables and a primary geographic classification, e.g. postcode. Here, a common and important problem is to obtain estimates for a second target geography that overlaps with the primary geography but has not been collected from the respondents. We examine this problem when areal level census information is available for both geographic classifications. Such a situation is challenging from a small area estimation perspective for several reasons: there is a misalignment between the census and survey information as well as the geographical classifications; the geographic areas are potentially small and so prediction can be difficult because of the sparse or spatially missing data issue; and there is the possibility of non‐stationary spatial dependence. To address these problems we develop a Bayesian model using latent processes, underpinned by a non‐stationary spatial basis that combines Moran's I and multiresolution basis functions with a small but representative set of knots. The study results based on simulated data demonstrate that the model can be highly effective and gives more accurate estimates for areas defined by the target geography than several existing models. The model also performs well for the Australian National University poll data to predict on a second geographic classification: statistical area level 2.

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Section: Problem Identification and Reviewmentioning

confidence: 99%

Section: Problem Identification and Reviewmentioning

confidence: 99%

A Bayesian Spatial Categorical Model for Prediction to Overlapping Geographical Areas in Sample Surveys

Bakar

Biddle

Kokic

et al. 2019

Journal of the Royal Statistical Society Series A: Statistics in Society

View full text Add to dashboard Cite

show abstract

“…The areal units in this intersection partition are sometimes referred to as the "atom" (Trevisani and Gelfand, 2013). Our goal is to construct spatial factor analysis models that relate the observed variables across A and B to a common set of spatially correlated latent factors across S. In other words, we want to write both Y (A j ) and Z(B k ) in terms of latent factors…”

Section: The General Multi-scale Joint Spatial Factor Analysis Modelmentioning

confidence: 99%

“…While there are limited instances of spatial misalignment addressed specifically in the context of spatial factor analysis, the topic of spatial misalignment in general has received a great deal of attention (Gotway and Young, 2002;Trevisani and Gelfand, 2013). Some of the general techniques for handling Factor analysis with spatially misaligned data 471 misalignment can be trivially extended to the spatial factor analysis setting.…”

Section: Introductionmentioning

confidence: 99%

A joint spatial factor analysis model to accommodate data from misaligned areal units with application to Louisiana social vulnerability

et al. 2018

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With the threat of climate change looming, the public health community has an interest in identifying communities at the highest risk of devastation based not only on geographic features but also on social characteristics. Indices of community social vulnerability can be created by applying a spatial factor analysis to a set of relevant social variables measured for each community; however, current spatial factor analysis methodology is ill-equipped to handle spatially misaligned data. We introduce a joint spatial factor analysis model that can accommodate spatial data from two distinct partitions of a geographic space and identify a common set of latent factors underlying them. By defining the latent factors over the intersection of the two partitions, the model minimizes loss of information. Using simulated data constructed to mimic the spatial structure of our real data, we confirm the reliability of the model and demonstrate its superiority over competing ad hoc methods for dealing with misaligned data in spatial factor analysis. Finally, we construct an index of community social vulnerability for each census tract in Louisiana, a state prone to environmental disasters, which could be exacerbated by climate change, by applying the joint spatial factor analysis model to a set of misaligned social indicator data from the state. To demonstrate the utility of this index, we integrate it with Louisiana flood insurance claims data to identify communities that may be at particularly high risk during natural disasters, based on both social and geographic features.

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“…Following the HB way of thinking, we independently proposed a Normal-Poisson-logNormal model arguing that this unmatched form could be more appropriate for taking explicitly into account the nature of the variable of interest [9] [10]. An application of this model, originally extended to enable the use of multiple data sources possibly misaligned with small areas, is in [11]. Moreover, we suggested a Gamma-Poisson-logNormal model, that introduces a nonnormal sampling error stage, and advocated a natural extension of the several above specifications by letting sampling variances be stochastically determined rather than fixed to design estimates as is the general practice [12].…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Hierarchical Bayesian Models for Small Area Estimation of Counts

Trevisani¹,

Torelli²

2017

OJS

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Small area estimation (SAE) tackles the problem of providing reliable estimates for small areas, i.e., subsets of the population for which sample information is not sufficient to warrant the use of a direct estimator. Hierarchical Bayesian approach to SAE problems offers several advantages over traditional SAE models including the ability of appropriately accounting for the type of surveyed variable. In this paper, a number of model specifications for estimating small area counts are discussed and their relative merits are illustrated. We conducted a simulation study by reproducing in a simplified form the Italian Labour Force Survey and taking the Local Labor Markets as target areas. Simulated data were generated by assuming population characteristics of interest as well as survey sampling design as known. In one set of experiments, numbers of employment/unemployment from census data were utilized, in others population characteristics were varied. Results show persistent model failures for some standard Fay-Herriot specifications and for generalized linear Poisson models with (log-)normal sampling stage, whilst either unmatched or nonnormal sampling stage models get the best performance in terms of bias, accuracy and reliability. Though, the study also found that any model noticeably improves on its performance by letting sampling variances be stochastically determined rather than assumed as known as is the general practice. Moreover, we address the issue of model determination to point out limits and possible deceptions of commonly used criteria for model selection and checking in SAE context

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Spatial Misalignment Models for Small Area Estimation: A Simulation Study

Cited by 11 publications

References 8 publications

A Bayesian Spatial Categorical Model for Prediction to Overlapping Geographical Areas in Sample Surveys

A Bayesian Spatial Categorical Model for Prediction to Overlapping Geographical Areas in Sample Surveys

A joint spatial factor analysis model to accommodate data from misaligned areal units with application to Louisiana social vulnerability

A Comparison of Hierarchical Bayesian Models for Small Area Estimation of Counts

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