Measuring high-density built environment for public health research: Uncertainty with respect to data, indicator design and spatial scale

  • Guibo Sun Healthy High Density Cities Lab, Faculty of Architecture, University of Hong Kong, Hong Kong.
  • Chris Webster Healthy High Density Cities Lab, Faculty of Architecture, University of Hong Kong, Hong Kong.
  • Michael Y. Ni School of Public Health, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong.
  • Xiaohu Zhang | xiaohu@smart.mit.edu Singapore-MIT Alliance for Research and Technology, Singapore.

Abstract

Uncertainty with respect to built environment (BE) data collection, measure conceptualization and spatial scales is evident in urban health research, but most findings are from relatively lowdensity contexts. We selected Hong Kong, an iconic high-density city, as the study area as limited research has been conducted on uncertainty in such areas. We used geocoded home addresses (n=5732) from a large population-based cohort in Hong Kong to extract BE measures for the participants’ place of residence based on an internationally recognized BE framework. Variability of the measures was mapped and Spearman’s rank correlation calculated to assess how well the relationships among indicators are preserved across variables and spatial scales. We found extreme variations and uncertainties for the 180 measures collected using comprehensive data and advanced geographic information systems modelling techniques. We highlight the implications of methodological selection and spatial scales of the measures. The results suggest that more robust information regarding urban health research in high-density city would emerge if greater consideration were given to BE data, design methods and spatial scales of the BE measures.

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Published
2018-05-07
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Original Articles
Keywords:
High-density, Built environment, Spatial scale, Uncertainty, GIS, Hong Kong.
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How to Cite
Sun, G., Webster, C., Ni, M. Y., & Zhang, X. (2018). Measuring high-density built environment for public health research: Uncertainty with respect to data, indicator design and spatial scale. Geospatial Health, 13(1). https://doi.org/10.4081/gh.2018.653