Name
Title | ||
|---|---|---|
Uncovering a positive and negative spatial autocorrelation mixture pattern: a spatial analysis of breast cancer incidences in Broward County, Florida, 2000-2010. |
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/s10109-020-00323-5 | Journal of Geographical Systems |
Keywords | DocType | Volume |
Spatial autocorrelation, Moran eigenvector spatial filtering, Breast cancer, Poisson regression, Negative binomial regression, Z | Journal | 22 |
Issue | ISSN | Citations |
3 | 1435-5930 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lan Hu | 1 | 0 | 0.34 |
| Yongwan Chun | 2 | 21 | 6.25 |
| Daniel A. Griffith | 3 | 91 | 23.76 |