Environmental justice and park quality in an intermountain west gateway community: assessing the spatial autocorrelation

• Published in: Landscape ecology 2019-10, Vol.34 (10), p.2323-2335
• Main Authors: Chen, Shuolei, Sleipness, Ole Russell, Christensen, Keith M., Feldon, David, Xu, Yannan
• Format: Article
• Language: English
• Subjects: Autocorrelation; Biomedical and Life Sciences; Density; Dependence; Ecology; Employment; Environmental indicators; Environmental justice; Environmental Management; Environmental research; Indicators; Landscape Ecology; Landscape/Regional and Urban Planning; Life Sciences; Nature Conservation; Poverty; Programming languages; Quality; Quantitative research; Regression analysis; Regression models; Research Article; Social factors; Socioeconomic data; Socioeconomic factors; Socioeconomics; Spatial data; Statistical analysis; Statistics; Sustainable Development
• ISSN: 0921-2973; 1572-9761
• DOI: 10.1007/s10980-019-00891-y

Context Research on environmental justice issues, particularly unequal park distribution and quality, has found that communities’ minority density and socioeconomic status (SES) are often correlated with disparate park qualities. However, most studies of spatial relationships between park quality and socioeconomic factors employ simple statistical analyses, which do not account for potential spatial autocorrelations and their effects on validity. Objectives This study determines whether the distribution of park quality is spatially autocorrelated and assesses the associations among multiple indicators of environmental justice and both separate park features and overall park quality. Methods This study evaluates spatial relationships between park quality and multiple environmental justice indicators in Cache County, Utah following the spatial regression process conducted in R programming language. Both overall park quality and separate feature qualities were audited by the PARK (Parks, Activity, and Recreation among Kids) tool. Environmental justice indicators included minority density, poverty, unemployment, low-education, renter rate, and yard size. Results Results illustrate a spatial autocorrelation existing in park quality distribution, detecting the dependence of the variable for quantitative research. They also show significant correlations between park quality and environmental justice indicators. Conclusions The study’s spatial regression model is a model for analyzing the spatial data and avoids the autocorrelation which is overlooked by the normal statistical approaches. Also, variances of park quality can be accounted for by different environmental justice indicators, such as minority density, poverty, and yard size. This disclosure of disparate public resource quality treatment among different groups of individuals could inspire policy makers and city planners to correct these disparities.