Proximity effects in obesity rates in the US: A Spatial Markov Chains approach
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DOI: 10.1016/j.socscimed.2018.11.013
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Cited by:
- Yabo Zhao & Dixiang Xie & Xiwen Zhang & Shifa Ma, 2021. "Integrating Spatial Markov Chains and Geographically Weighted Regression-Based Cellular Automata to Simulate Urban Agglomeration Growth: A Case Study of the Guangdong–Hong Kong–Macao Greater Bay Area," Land, MDPI, vol. 10(6), pages 1-19, June.
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- Zhongxun Zhang & Kaifang Shi & Zhiyong Zhu & Lu Tang & Kangchuan Su & Qingyuan Yang, 2022. "Spatiotemporal Evolution and Influencing Factors of the Rural Natural Capital Utilization Efficiency: A Case Study of Chongqing, China," Land, MDPI, vol. 11(5), pages 1-29, May.
- Lan Yao & Zhenning Yu & Mengya Wu & Jiachen Ning & Tiangui Lv, 2020. "The Spatiotemporal Evolution and Trend Prediction of Ecological Wellbeing Performance in China," Land, MDPI, vol. 10(1), pages 1-17, December.
- Christopoulos, Konstantinos & Eleftheriou, Konstantinos, 2020. "The fiscal impact of health care expenditure: Evidence from the OECD countries," Economic Analysis and Policy, Elsevier, vol. 67(C), pages 195-202.
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Keywords
Obesity rates; Obesity epidemics; Proximity effects; Spatial Markov chain; Ergodic distribution;All these keywords.
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