Uncovering a positive and negative spatial autocorrelation mixture pattern: a spatial analysis of breast cancer incidences in Broward County, Florida, 2000–2010

Lan Hu (), Yongwan Chun () and Daniel A. Griffith ()
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Lan Hu: University of Texas at Dallas
Yongwan Chun: University of Texas at Dallas
Daniel A. Griffith: University of Texas at Dallas

Journal of Geographical Systems, 2020, vol. 22, issue 3, No 1, 308 pages

Abstract: Abstract Spatial cancer data analyses frequently utilize regression techniques to investigate associations between cancer incidences and potential covariates. Model specification, a process of formulating an appropriate model, is a well-recognized task in the literature. It involves a distributional assumption for a dependent variable, a proper set of predictor variables (i.e., covariates), and a functional form of the model, among other things. For example, one of the assumptions of a conventional statistical model is independence of model residuals, an assumption that can be easily violated when spatial autocorrelation is present in observations. A failure to account for spatial structure can result in unreliable estimation results. Furthermore, the difficulty of describing georeferenced data may increase with the presence of a positive and negative spatial autocorrelation mixture, because most current model specifications cannot successfully explain a mixture of spatial processes with a single spatial autocorrelation parameter. Particularly, properly accounting for a spatial autocorrelation mixture is challenging. This paper empirically investigates and uncovers a possible spatial autocorrelation mixture pattern in breast cancer incidences in Broward County, Florida, during 2000–2010, employing different model specifications. The analysis results show that Moran eigenvector spatial filtering provides a flexible method to examine such a mixture.

Keywords: Spatial autocorrelation; Moran eigenvector spatial filtering; Breast cancer; Poisson regression; Negative binomial regression (search for similar items in EconPapers)
JEL-codes: Z (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s10109-020-00323-5

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