Mapping the results of local statistics
Using geographically weighted regression
Datos Bibliográficos
| ID | 2636664 |
|---|---|
| Autores | Stephen A Matthews (0000-0002-1645-4854, Max Planck Institute for Demographic Research, autor de correspondencia), Stephen Matthews (0000-0001-7683-8051), Tse-Chuan Yang (0000-0002-4271-4584, Max Planck Institute for Demographic Research) |
| Año | 2012 |
| Volumen | 26 |
| Páginas | 151-166 |
| Fecha de publicación | 2012-03-02 |
| Peer Reviewed | Sí |
| Open Access | Sí |
| Tipo | ARTICLE |
| Revista | Demographic Research (JOURNAL) |
| Identificadores de la revista | ISSN: 1435-9871 • E-ISSN: 2363-7064 |
| Editorial | Max Planck Institute for Demographic Research (PUBLISHER • DE) |
| DOI | 10.4054/demres.2012.26.6 |
| PMID | 25578024 |
| OpenAlex | W2155008024 |
| Idioma | EN |
| Citas recibidas | 46 |
| Referencias citadas | 44 |
The application of geographically weighted regression (GWR) - a local spatial statistical technique used to test for spatial nonstationarity - has grown rapidly in the social, health and demographic sciences. GWR is a useful exploratory analytical tool that generates a set of location-specific parameter estimates which can be mapped and analysed to provide information on spatial nonstationarity in relationships between predictors and the outcome variable. A major challenge to GWR users, however, is how best to map these parameter estimates. This paper introduces a simple mapping technique that combines local parameter estimates and local t-values on one map. The resultant map can facilitate the exploration and interpretation of nonstationarity
Cartography · Data mining · Data set · Econometrics · Exploratory data analysis · Geographically Weighted Regression · Geography · Regression · Spatial analysis · Statistics · Computer Science · Mathematics · Regional Economics and Spatial Analysis · Spatial and Panel Data Analysis · Urban Transport and Accessibility
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| Obras citantes distintas | 46 |
|---|---|
| Citas por año | 3,29 |
| Intervalo de citas | 2012 - 2026 (15) |
| Velocidad de citación | current |
| Altamente citado | No |
| Tipos de cita | Neutras: 44 |