An Introduction to Macro-Level Spatial Nonstationarity: A Geographically Weighted Regression Analysis of Diabetes and Poverty
Type II diabetes is a growing health problem in the United States. Understanding geographic variation in diabetes prevalence will inform where resources for management and prevention should be allocated. Investigations of the correlates of diabetes prevalence have largely ignored how spatial nonstat...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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University of Bucharest
2012-11-01
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| Series: | Human Geographies: Journal of Studies and Research in Human Geography |
| Subjects: | |
| Online Access: | http://humangeographies.org.ro/articles/62/6_2_12_1_siordia.pdf |
| _version_ | 1823994246221266944 |
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| author | Carlos Siordia Joseph Saenz Sarah E. Tom |
| author_facet | Carlos Siordia Joseph Saenz Sarah E. Tom |
| author_sort | Carlos Siordia |
| collection | DOAJ |
| description | Type II diabetes is a growing health problem in the United States. Understanding geographic variation in diabetes prevalence will inform where resources for management and prevention should be allocated. Investigations of the correlates of diabetes prevalence have largely ignored how spatial nonstationarity might play a role in the macro-level distribution of diabetes. This paper introduces the reader to the concept of spatial nonstationarity—variance in statistical relationships as a function of geographical location. Since spatial nonstationarity means different predictors can have varying effects on model outcomes, we make use of a geographically weighed regression to calculate correlates of diabetes as a function of geographic location. By doing so, we demonstrate an exploratory example in which the diabetes-poverty macro-level statistical relationship varies as a function of location. In particular, we provide evidence that when predicting macro-level diabetes prevalence, poverty is not always positively associated with diabetes. |
| first_indexed | 2024-12-18T16:31:48Z |
| format | Article |
| id | doaj.art-a62400157fd14f9e8fe3b7c4f3efa44f |
| institution | Directory Open Access Journal |
| issn | 1843-6587 2067-2284 |
| language | English |
| last_indexed | 2024-12-18T16:31:48Z |
| publishDate | 2012-11-01 |
| publisher | University of Bucharest |
| record_format | Article |
| series | Human Geographies: Journal of Studies and Research in Human Geography |
| spelling | doaj.art-a62400157fd14f9e8fe3b7c4f3efa44f2022-12-21T21:01:27ZengUniversity of BucharestHuman Geographies: Journal of Studies and Research in Human Geography1843-65872067-22842012-11-016251310.5719/hgeo.2012.62.5 An Introduction to Macro-Level Spatial Nonstationarity: A Geographically Weighted Regression Analysis of Diabetes and PovertyCarlos Siordia0 Joseph Saenz1Sarah E. Tom2Division of Sociomedical Sciences, Department of Preventive Medicine and Community Health, University of Texas Medical Branch, Galveston, Texas, USADivision of Sociomedical Sciences, Department of Preventive Medicine and Community Health, University of Texas Medical Branch, Galveston, Texas, USADivision of Sociomedical Sciences, Department of Preventive Medicine and Community Health, University of Texas Medical Branch, Galveston, Texas, USAType II diabetes is a growing health problem in the United States. Understanding geographic variation in diabetes prevalence will inform where resources for management and prevention should be allocated. Investigations of the correlates of diabetes prevalence have largely ignored how spatial nonstationarity might play a role in the macro-level distribution of diabetes. This paper introduces the reader to the concept of spatial nonstationarity—variance in statistical relationships as a function of geographical location. Since spatial nonstationarity means different predictors can have varying effects on model outcomes, we make use of a geographically weighed regression to calculate correlates of diabetes as a function of geographic location. By doing so, we demonstrate an exploratory example in which the diabetes-poverty macro-level statistical relationship varies as a function of location. In particular, we provide evidence that when predicting macro-level diabetes prevalence, poverty is not always positively associated with diabetes.http://humangeographies.org.ro/articles/62/6_2_12_1_siordia.pdfDiabetesGISGWRPovertySpatial DemographySpatial Nonstationarity |
| spellingShingle | Carlos Siordia Joseph Saenz Sarah E. Tom An Introduction to Macro-Level Spatial Nonstationarity: A Geographically Weighted Regression Analysis of Diabetes and Poverty Human Geographies: Journal of Studies and Research in Human Geography Diabetes GIS GWR Poverty Spatial Demography Spatial Nonstationarity |
| title | An Introduction to Macro-Level Spatial Nonstationarity: A Geographically Weighted Regression Analysis of Diabetes and Poverty |
| title_full | An Introduction to Macro-Level Spatial Nonstationarity: A Geographically Weighted Regression Analysis of Diabetes and Poverty |
| title_fullStr | An Introduction to Macro-Level Spatial Nonstationarity: A Geographically Weighted Regression Analysis of Diabetes and Poverty |
| title_full_unstemmed | An Introduction to Macro-Level Spatial Nonstationarity: A Geographically Weighted Regression Analysis of Diabetes and Poverty |
| title_short | An Introduction to Macro-Level Spatial Nonstationarity: A Geographically Weighted Regression Analysis of Diabetes and Poverty |
| title_sort | introduction to macro level spatial nonstationarity a geographically weighted regression analysis of diabetes and poverty |
| topic | Diabetes GIS GWR Poverty Spatial Demography Spatial Nonstationarity |
| url | http://humangeographies.org.ro/articles/62/6_2_12_1_siordia.pdf |
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