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Implications for spatial non-stationarity and the neighborhood effect averaging problem (NEAP) in green inequality research: evidence from three states in the USA

Author

Listed:
  • Sophiya Gyanwali

    (Arizona State University)

  • Shashank Karki

    (Virginia Tech)

  • Kee Moon Jang

    (Massachusetts Institute of Technology)

  • Tom Crawford

    (Virginia Tech)

  • Mengxi Zhang

    (Virginia Tech Carilion School of Medicine)

  • Junghwan Kim

    (Virginia Tech)

Abstract

Recent studies on green space exposure have argued that overlooking human mobility could lead to erroneous exposure estimates and their associated inequality. However, these studies are limited as they focused on single cities and did not investigate multiple cities, which could exhibit variations in people’s mobility patterns and the spatial distribution of green spaces. Moreover, previous studies focused mainly on large-sized cities while overlooking other areas, such as small-sized cities and rural neighborhoods. In other words, it remains unclear the potential spatial non-stationarity issues in estimating green space exposure inequality. To fill these significant research gaps, we utilized commute data of 31,862 people from Virginia, West Virginia, and Kentucky. The deep learning technique was used to extract green spaces from street-view images to estimate people’s home-based and mobility-based green exposure levels. The results showed that the overall inequality in exposure levels reduced when people’s mobility was considered compared to the inequality based on home-based exposure levels, implying the neighborhood effect averaging problem (NEAP). Correlation coefficients between individual exposure levels and their social vulnerability indices demonstrated mixed and complex patterns regarding neighborhood type and size, demonstrating the presence of spatial non-stationarity. Our results underscore the crucial role of mobility in exposure assessments and the spatial non-stationarity issue when evaluating exposure inequalities. The results imply that local-specific studies are urgently needed to develop local policies to alleviate inequality in exposure precisely.

Suggested Citation

  • Sophiya Gyanwali & Shashank Karki & Kee Moon Jang & Tom Crawford & Mengxi Zhang & Junghwan Kim, 2025. "Implications for spatial non-stationarity and the neighborhood effect averaging problem (NEAP) in green inequality research: evidence from three states in the USA," Journal of Geographical Systems, Springer, vol. 27(1), pages 7-30, January.
    • Handle: RePEc:kap:jgeosy:v:27:y:2025:i:1:d:10.1007_s10109-024-00448-x
    DOI: 10.1007/s10109-024-00448-x
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    More about this item

    Keywords

    Exposure; Green space; Inequality; Spatial non-stationarity; Street-view images;
    All these keywords.

    JEL classification:

    • I14 - Health, Education, and Welfare - - Health - - - Health and Inequality
    • R14 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Land Use Patterns
    • R40 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - General

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