https://doi.org/10.4081/gh.2010.205
Identifying geographical variations in poverty-obesity relationships: empirical evidence from Taiwan
Submitted: 22 December 2014
Accepted: 22 December 2014
Published: 1 May 2010
Accepted: 22 December 2014
Abstract Views: 3356
PDF: 1353
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Authors
How to Cite
Wen, T.-H., Chen, D.-R., & Tsai, M.-J. (2010). Identifying geographical variations in poverty-obesity relationships: empirical evidence from Taiwan. Geospatial Health, 4(2), 257–265. https://doi.org/10.4081/gh.2010.205
PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.
Similar Articles
- Tzu-Yi Yang, Yu-Tai Yang, Ssu-Han Chen, Yu-Ting Lan, Chia-Jui Peng, Dynamic effect of economic growth on the persistence of suicide rates , Geospatial Health: Vol. 18 No. 1 (2023)
- Akihiko Michimi, Michael C. Wimberly, The food environment and adult obesity in US metropolitan areas , Geospatial Health: Vol. 10 No. 2 (2015)
- André Oliveira, Antònio J.R. Cabral, Jorge M. Mendes, Maria R.O. Martins, Pedro Cabral, Spatiotemporal analysis of the relationship between socioeconomic factors and stroke in the Portuguese mainland population under 65 years old , Geospatial Health: Vol. 10 No. 2 (2015)
- Lung-Chang Chien, Xiao Li, Amanda Staudt, Physical inactivity displays a mediator role in the association of diabetes and poverty: A spatiotemporal analysis , Geospatial Health: Vol. 12 No. 2 (2017)
- Xiao Li, Amanda Staudt, Lung-Chang Chien, Identifying counties vulnerable to diabetes from obesity prevalence in the United States: a spatiotemporal analysis , Geospatial Health: Vol. 11 No. 3 (2016)
- Abdulkader Murad, Fazlay Faruque, Ammar Naji, Alok Tiwari, Mansour Helmi, Ammar Dahlan, Modelling geographical heterogeneity of diabetes prevalence and socio-economic and built environment determinants in Saudi City - Jeddah , Geospatial Health: Vol. 17 No. 1 (2022)
- Kyungsoo Han, Sejin Park, Jürgen Symanzik, Sookhee Choi, Jeongyong Ahn, Trends in obesity at the national and local level among South Korean adolescents , Geospatial Health: Vol. 11 No. 2 (2016)
- Addisu Jember Zeleke, Rossella Miglio, Pierpaolo Palumbo, Paolo Tubertini, Lorenzo Chiari, Bologna MODELS4COVID Study Group of the University of Bologna and the National Institute for Nuclear Physics (INFN), Spatiotemporal heterogeneity of SARS-CoV-2 diffusion at the city level using geographically weighted Poisson regression model: The case of Bologna, Italy , Geospatial Health: Vol. 17 No. 2 (2022)
- Kiara M. Rivera, Abolfazl Mollalo, Spatial analysis and modelling of depression relative to social vulnerability index across the United States , Geospatial Health: Vol. 17 No. 2 (2022)
- Marcela Martínez Bascuñán, Carolina Rojas Quezada, Geographically weighted regression for modelling the accessibility to the public hospital network in Concepción Metropolitan Area, Chile , Geospatial Health: Vol. 11 No. 3 (2016)
You may also start an advanced similarity search for this article.
