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ISPRS Int. J. Geo-Inf. 2014, 3(4), 1198-1210; doi:10.3390/ijgi3041198

Impacts of Scale on Geographic Analysis of Health Data: An Example of Obesity Prevalence

1
Department of Geography, Kent State University, Kent, OH 44242, USA
2
College of Environment and Planning, Henan University, Kaifeng 475001, China
3
Department of Geography and Geographic Information Science, George Mason University, Fairfax, VA 22030, USA
4
Department of Epidemiology and Biostatistics, Kent State University, Kent, OH 44242, USA
5
Family Medicine Research Center, Summa Health System, Akron, OH 44309, USA
6
Department of Geography, University of Hong Kong, Hong Kong
*
Author to whom correspondence should be addressed.
Received: 23 June 2014 / Revised: 24 September 2014 / Accepted: 28 September 2014 / Published: 24 October 2014
(This article belongs to the Special Issue Remote Sensing and Geospatial Technologies in Public Health)
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Abstract

The prevalence of obesity has increased dramatically in recent decades. It is an important public health issue as it causes many other chronic health conditions, such as hypertension, cardiovascular diseases, and type II diabetics. Obesity affects life expectancy and even the quality of lives. Eventually, it increases social costs in many ways due to increasing costs of health care and workplace absenteeism. Using the spatial patterns of obesity prevalence as an example; we show how different geographic units can reveal different degrees of detail in results of analysis. We used both census tracts and census block groups as units of geographic analysis. In addition; to reveal how different geographic scales may impact on the analytic results; we applied geographically weighted regression to model the relationships between obesity rates (dependent variable) and three independent variables; including education attainment; unemployment rates; and median family income. Though not including an exhaustive list of explanatory variables; this regression model provides an example for revealing the impacts of geographic scales on analysis of health data. With obesity data based on reported heights and weights on driver’s licenses in Summit County, Ohio, we demonstrated that geographically weighted regression reveals varying spatial trends between dependent and independent variables that conventional regression models such as ordinary least squares regression cannot. Most importantly, analyses carried out with different geographic scales do show very different results. With these findings, we suggest that, while possible, smaller geographic units be used to allow better understanding of the studies phenomena. View Full-Text
Keywords: obesity prevalence; geographic scales; geographically weighted regression obesity prevalence; geographic scales; geographically weighted regression
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Lee, J.; Alnasrallah, M.; Wong, D.; Beaird, H.; Logue, E. Impacts of Scale on Geographic Analysis of Health Data: An Example of Obesity Prevalence. ISPRS Int. J. Geo-Inf. 2014, 3, 1198-1210.

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