Name
Abstract | ||
|---|---|---|
. The Moran Coefficient spatial autocorrelation index can be decomposed into orthogonal map pattern components. This decomposition
relates it directly to standard linear regression, in which corresponding eigenvectors can be used as predictors. This paper
reports comparative results between these linear regressions and their auto-Gaussian counterparts for the following georeferenced
data sets: Columbus (Ohio) crime, Ottawa-Hull median family income, Toronto population density, southwest Ohio unemployment,
Syracuse pediatric lead poisoning, and Glasgow standard mortality rates, and a small remotely sensed image of the High Peak
district. This methodology is extended to auto-logistic and auto-Poisson situations, with selected data analyses including
percentage of urban population across Puerto Rico, and the frequency of SIDs cases across North Carolina. These data analytic
results suggest that this approach to georeferenced data analysis offers considerable promise. |
Year | DOI | Venue |
|---|---|---|
2000 | 10.1007/PL00011451 | Journal of Geographical Systems |
Keywords | Field | DocType |
Key words: Eigenfunction,spatial autocorrelation,spatial autoregression,geographic weights matrix,georeferenced data,JEL classification: C49,C13,R15 | Spatial analysis,Population,Data set,Regression analysis,Georeference,Statistics,Geography,Cartography,Eigenvalues and eigenvectors,Linear regression,Autocorrelation | Journal |
Volume | Issue | Citations |
2 | 2 | 18 |
PageRank | References | Authors |
5.00 | 0 | 1 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniel A. Griffith | 1 | 91 | 23.76 |