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Relationships between urban green land cover and human health at different spatial resolutions

  • Wei-Lun TsaiEmail author
  • Yu-Fai Leung
  • Melissa R. McHale
  • Myron F. Floyd
  • Brian J. Reich
Article

Abstract

Relationships between landscape patterns and ecological processes can vary with changing resolution. Many studies in ecosystem services and human health rely on spatial-dependent data, yet the effects of changes in spatial resolution on the linkages between landscape and human health are underexplored. This study seeks to address the research gap by exploring the relationships of green land cover and pattern metrics at 1 m, 10 m, and 30 m with life expectancy in the City of Baltimore, Maryland, USA. Spearman’s rho correlation and stepwise and hierarchical regression models were applied. Results showed that the effects of resolution change did not emerge for percent green land cover but were evident in other pattern metrics. Multivariate relationships showed that metrics at 1 m explained the most variability of the relationships between green land cover and life expectancy after controlling for potential confounding factors (adjusted R2 = 0.776, and 0.752 at 10 m and 0.747 at 30 m). Edge density of coarse vegetation was significantly associated with life expectancy at 1 m (adjusted odds ratio [AOR] = 1.012, 95%CI = 1.004–1.024, p < 0.01) and 10 m (AOR = 1.018, 95%CI = 1.009–1.027, p < 0.001) but not at 30 m. Euclidean distance of fine vegetation had a strong positive association with greater life expectancy at 1 m (AOR = 2.067, 95%CI = 1.185–4.072, p < 0.05) but not at 10 m and 30 m. These findings underscore the importance of acknowledging the effects of resolution on the interpretation of landscape-human health relationships and the need for caution when results are used in planning and management decisions.

Keywords

MAUP Landscape metrics Life expectancy 

Notes

Acknowledgements

We would like to thank the National Urban and Community Forestry Advisory Council and U.S. Forest Service for their support of this research. We would also like to express our gratitude to the research team at the Baltimore Ecosystem Study, Long-Term Ecological Research, and specifically thank Morgan Grove and Jarlath O’Neil-Dunn for their contribution and providing access to the land cover data. We also thank Baltimore City Department of Health for their release of Neighborhood Health Profile.

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Authors and Affiliations

  1. 1.Department of Parks, Recreation and Tourism ManagementNorth Carolina State UniversityRaleighUSA
  2. 2.Center for Geospatial AnalyticsNorth Carolina State UniversityRaleighUSA
  3. 3.Department of Ecosystem Science and SustainabilityColorado State UniversityFort CollinsUSA
  4. 4.Department of StatisticsNorth Carolina State UniversityRaleighUSA

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