Mapping the results of local statistics: Using geographically weighted regression
BACKGROUND 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-sp...
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| Format: | Article |
| Language: | English |
| Published: |
Max Planck Institute for Demographic Research
2012-03-01
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| Series: | Demographic Research |
| Online Access: | http://www.demographic-research.org/volumes/vol26/6/ |
| _version_ | 1811310844502343680 |
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| author | Stephen A. Matthews Tse-Chuan Yang |
| author_facet | Stephen A. Matthews Tse-Chuan Yang |
| author_sort | Stephen A. Matthews |
| collection | DOAJ |
| description | BACKGROUND 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 the relationships between predictors and the outcome variable. OBJECTIVE A major challenge to users of GWR methods is how best to present and synthesize the large number of mappable results, specifically the local parameter parameter estimates and local t-values, generated from local GWR models. We offer an elegant solution. METHODS This paper introduces a mapping technique to simultaneously display local parameter estimates and local t-values on one map based on the use of data selection and transparency techniques. We integrate GWR software and GIS software package (ArcGIS) and adapt earlier work in cartography on bivariate mapping. We compare traditional mapping strategies (i.e., side-by-side comparison and isoline overlay maps) with our method using an illustration focusing on US county infant mortality data. CONCLUSIONS The resultant map design is more elegant than methods used to date. This type of map presentation can facilitate the exploration and interpretation of nonstationarity, focusing map reader attention on the areas of primary interest. |
| first_indexed | 2024-04-13T10:07:25Z |
| format | Article |
| id | doaj.art-530a7e49f6c94b479217956cda2a764f |
| institution | Directory Open Access Journal |
| issn | 1435-9871 |
| language | English |
| last_indexed | 2024-04-13T10:07:25Z |
| publishDate | 2012-03-01 |
| publisher | Max Planck Institute for Demographic Research |
| record_format | Article |
| series | Demographic Research |
| spelling | doaj.art-530a7e49f6c94b479217956cda2a764f2022-12-22T02:51:03ZengMax Planck Institute for Demographic ResearchDemographic Research1435-98712012-03-01266Mapping the results of local statistics: Using geographically weighted regressionStephen A. MatthewsTse-Chuan YangBACKGROUND 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 the relationships between predictors and the outcome variable. OBJECTIVE A major challenge to users of GWR methods is how best to present and synthesize the large number of mappable results, specifically the local parameter parameter estimates and local t-values, generated from local GWR models. We offer an elegant solution. METHODS This paper introduces a mapping technique to simultaneously display local parameter estimates and local t-values on one map based on the use of data selection and transparency techniques. We integrate GWR software and GIS software package (ArcGIS) and adapt earlier work in cartography on bivariate mapping. We compare traditional mapping strategies (i.e., side-by-side comparison and isoline overlay maps) with our method using an illustration focusing on US county infant mortality data. CONCLUSIONS The resultant map design is more elegant than methods used to date. This type of map presentation can facilitate the exploration and interpretation of nonstationarity, focusing map reader attention on the areas of primary interest.http://www.demographic-research.org/volumes/vol26/6/ |
| spellingShingle | Stephen A. Matthews Tse-Chuan Yang Mapping the results of local statistics: Using geographically weighted regression Demographic Research |
| title | Mapping the results of local statistics: Using geographically weighted regression |
| title_full | Mapping the results of local statistics: Using geographically weighted regression |
| title_fullStr | Mapping the results of local statistics: Using geographically weighted regression |
| title_full_unstemmed | Mapping the results of local statistics: Using geographically weighted regression |
| title_short | Mapping the results of local statistics: Using geographically weighted regression |
| title_sort | mapping the results of local statistics using geographically weighted regression |
| url | http://www.demographic-research.org/volumes/vol26/6/ |
| work_keys_str_mv | AT stephenamatthews mappingtheresultsoflocalstatisticsusinggeographicallyweightedregression AT tsechuanyang mappingtheresultsoflocalstatisticsusinggeographicallyweightedregression |