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

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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.

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Acknowledgements

We thank the “anonymous” reviewers and editor who provided insight and comments that greatly assisted the research.

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  1. School of Economic, Political and Policy Sciences, University of Texas at Dallas, 800 West Campbell Road, Richardson, TX, 75080, USA

    Lan Hu, Yongwan Chun & Daniel A. Griffith

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  1. Lan Hu
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Correspondence to Lan Hu.

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Hu, L., Chun, Y. & Griffith, D.A. Uncovering a positive and negative spatial autocorrelation mixture pattern: a spatial analysis of breast cancer incidences in Broward County, Florida, 2000–2010. J Geogr Syst 22, 291–308 (2020). https://doi.org/10.1007/s10109-020-00323-5

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