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Neighborhood Greenery as a Predictor of Outdoor Crimes between Low and High Income Neighborhoods

Author

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  • Young-Jae Kim

    (Department of Forest Resources and Landscape Architecture, Yeungnam University, Gyeongbuk 38541, Korea)

  • Eun Jung Kim

    (Department of Urban Planning, Keimyung University, Daegu 42601, Korea)

Abstract

Neighborhood greenery contributes to improving mental, emotional, and physical health and may help to promote neighborhood safety. Several studies have reported positive effects of neighborhood greenery on the improvement of outdoor safety, but little is known about whether the relationship between green vegetation and outdoor safety varies with the income status of neighborhoods. The purpose of this study is to examine neighborhood greenery as a predictor of outdoor crime rates between low and high-income neighborhoods while controlling for the sociodemographic conditions of the neighborhoods. This study used 2010 census block group data and objectively measured natural environment data derived from GIS in Austin, Texas. Comparison t-tests and ordinal least square regressions were conducted as statistical analyses. The t-tests showed that low-income neighborhoods were more socioeconomically disadvantaged and had less greenery than high-income neighborhoods. The final regression models showed that neighborhood greenery had a negative relationship with outdoor crimes for low-income neighborhoods but a positive relationship with crimes for high-income neighborhoods. The results suggest that different strategies may be needed in dealing with neighborhood safety according to neighborhood-level income.

Suggested Citation

  • Young-Jae Kim & Eun Jung Kim, 2020. "Neighborhood Greenery as a Predictor of Outdoor Crimes between Low and High Income Neighborhoods," IJERPH, MDPI, vol. 17(5), pages 1-13, February.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:5:p:1470-:d:324810
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    References listed on IDEAS

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    Cited by:

    1. Md Amiruzzaman & Ye Zhao & Stefanie Amiruzzaman & Aryn C. Karpinski & Tsung Heng Wu, 2023. "An AI-based framework for studying visual diversity of urban neighborhoods and its relationship with socio-demographic variables," Journal of Computational Social Science, Springer, vol. 6(1), pages 315-337, April.
    2. Xueying Wu & Yi Lu & Jingjing Wang & Bin Jiang, 2023. "Built Environment in Urban Space Affect Protests: A Cross-Sectional Study in Hong Kong," Sustainability, MDPI, vol. 15(17), pages 1-18, August.

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