Environmental justice and park quality in an intermountain west gateway community: assessing the spatial autocorrelation
- 59 Downloads
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.
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.
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 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.
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.
KeywordsSpatial analysis and assessment Spatial regression Socioeconomic factor Minority Landscape design Urban planning Parks and recreation
- Anselin L (2004) Exploring spatial data with GeoDaTM: a workbook. Urbana 51(61801):309Google Scholar
- Anselin L, Bera A (1998) Spatial dependence in linear regression models with an introduction to spatial econometrics. Handbook of Applied Economic Statistics, Marcel DekkerGoogle Scholar
- Babey SH, Wolstein J, Krumholz S, Robertson B, Diamant AL (2013) Physical activity, park access and park use among California adolescents. UCLA Cent Health Policy Res 2013:1–8Google Scholar
- Cliff A, Ord J (1973) Spatial autocorrelation. Pion, LondonGoogle Scholar
- Howe J, McMahon ET, Propst L (2012) Balancing nature and commerce in gateway communities. Island Press, Washington D.CGoogle Scholar
- Hughey SM, Walsemann KM, Child S, Powers A, Reed JA, Kaczynski AT (2016) Using an environmental justice approach to examine the relationships between park availability and quality indicators, neighborhood disadvantage, and racial/ethnic composition. Landsc Urban Plan 148:159–169CrossRefGoogle Scholar
- Hurst N (2016) Racist history, lack of park-going culture among reasons for African Americans under-representation at National, State Parks: researchers say park systems should engage schools to bring children to parks at an early age. University of Missouri News Bureau, ColumbiaGoogle Scholar
- Louv R (2008) Last child in the woods: Saving our children from nature-deficit disorder. Algonquin books, Chapel HillGoogle Scholar
- Mowen AJ (2010) Parks, playgrounds and active living. Robert Wood Johnson Foundation, San DiegoGoogle Scholar
- United States Census Bureau (2014) American Community Survey. http://www.census.gov/acs/www/
- United States Census Bureau (2015) American Fact Finder. https://factfinder.census.gov/faces/nav/jsf/pages/index.xhtml#none
- United States Census Bureau (2018) QuickFacts Cache County, Utah. https://www.census.gov/quickfacts/fact/table/cachecountyutah/LND110210