Space, race, and poverty: Spatial inequalities in walkable neighborhood amenities?

Duncan, Dustin T.; Aldstadt, Jared; Whalen, John; White, Kellee; Castro, Márcia C.; Williams, David R.

doi:10.4054/demres.2012.26.17

Cited by 63 publications

(59 citation statements)

References 95 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The census tract was our definition of a neighborhood. [34][35][36][37][38] Census tracts have approximately 4,000 people and are designated as homogenous spatial units "with respect to population characteristics, economic status, and living conditions." 39 In this study, we used 2010 census tract boundaries.…”

Section: Geographic Regionmentioning

confidence: 99%

The Geography of Recreational Open Space: Influence of Neighborhood Racial Composition and Neighborhood Poverty

et al. 2012

Self Cite

View full text Add to dashboard Cite

The geography of recreational open space might be inequitable in terms of minority neighborhood racial/ethnic composition and neighborhood poverty, perhaps due in part to residential segregation. This study evaluated the association between minority neighborhood racial/ethnic composition, neighborhood poverty, and recreational open space in Boston, Massachusetts (US). Across Boston census tracts, we computed percent non-Hispanic Black, percent Hispanic, and percent families in poverty as well as recreational open space density. We evaluated spatial autocorrelation in study variables and in the ordinary least squares (OLS) regression residuals via the Global Moran's I. We then computed Spearman correlations between the census tract socio-demographic characteristics and recreational open space density, including correlations adjusted for spatial autocorrelation. After this, we computed OLS regressions or spatial regressions as appropriate. Significant positive spatial autocorrelation was found for neighborhood sociodemographic characteristics (all pvalue00.001). We found marginally significant positive spatial autocorrelation in recreational open space (Global Moran's I00.082; pvalue00.053). However, we found no spatial autocorrelation in the OLS regression residuals, which indicated that spatial models were not appropriate. There was a negative correlation between census tract percent non-Hispanic Black and recreational open space density (r S 0−0.22; conventional pvalue00.005; spatially adjusted p value00.019) as well as a negative correlation between predominantly non-Hispanic Black census tracts (960 % non-Hispanic Black in a census tract) and recreational open space density (r S 0−0.23; conventional pvalue00.003; spatially adjusted p value00.007). In bivariate and multivariate OLS models, percent non-Hispanic Black in a census tract and predominantly Black census tracts were associated with decreased density of recreational open space (pvalueG0.001). Consistent with several previous studies in other geographic locales, we found that Black neighborhoods in Boston were less likely to have recreational open spaces, indicating the need for policy interventions promoting equitable access. Such interventions may contribute to reductions and disparities in obesity.

show abstract

Section: Geographic Regionmentioning

confidence: 99%

The Geography of Recreational Open Space: Influence of Neighborhood Racial Composition and Neighborhood Poverty

et al. 2012

Self Cite

View full text Add to dashboard Cite

show abstract

“…More recently, scholars have expanded their research to include analyses of unequal proximity to social amenities such as playgrounds and recreational facilities (Cradock et al, 2005), banks (Squires & O'Connor, 1998), and restaurants, grocery stores, coffee houses, movie theaters, and clothing stores (Duncan et al, 2012). In particular, researchers have examined the proximity of different racial and economic groups to green space such as urban parks, street trees, greenways, and tree canopy cover.…”

mentioning

confidence: 99%

Are There Relationships Among Racial Segregation, Economic Isolation, and Proximity to Green Space?

Saporito¹,

Casey²

2015

HER

View full text Add to dashboard Cite

Prior research has investigated differences in exposure to green space between racial and economic groups across the United States. Results have generally shown that people of color and lower-income groups tend to live in neighborhoods with less green space than their more-advantaged counterparts. Our research extends existing studies by measuring the association between racial and economic differences in exposure to green space and racial or economic segregation across neighborhoods within cities. Findings show that cities with lower levels of racial segregation exhibit smaller racial disparities in exposure to green space. By contrast, in cities with greater racial segregation, white people tend to live in neighborhoods with more green space than do people of color. This difference is more pronounced between white and Hispanic people. Finally, there is a strong association between segregation among different income groups and differences in exposure to green space between lower-and higher-income people. Consistent with much of the literature on racial and economic segregation-and studies of environment (in)justice-we find that lower-income people and members of minority groups live in neighborhoods with much less vegetation than their wealthier, white counterparts and these differences are exacerbated in racially and economically segregated cities.

show abstract

“…We then compared some fixed distance bands for the neighbor matrix above and below 2 Km and found that 1.7 Km resulted in the best R-squared models and also in best consistency among spatial dependence tests for all models. The results of the Lagrange Multiplier tests indicated that we needed to include a spatial lag of the endogenous variable in order to account for spatial autocorrelation in the statistical regressions, and we feel justified to use a spatial lag model since it has been established in the literature that poverty is a phenomena that can spill over into adjacent regions (Duncan et al, 2012;Holt, 2007;Okwi et al, 2007;Orford, 2004;Sowunmi et al, 2012;Voss et al, 2006). Table 5 shows the spatially adjusted regressions of Slum index as a function of remote sensing derived variables for all previous model specifications except for M4, which was removed from this analysis due to the serious presence of multicollinearity.…”

Section: Resultsmentioning

confidence: 99%

Quantifying Slumness with Remote Sensing Data

et al. 2013

View full text Add to dashboard Cite

The presence of slums in a city is an indicator of poverty and its proper delimitation is a matter of interest for researchers and policy makers. Socio-economic data from surveys and censuses are the primary source of information to identify and quantify slumness within a city or a town. One problem of using survey data for quantifying slumness is that this type of data is usually collected every ten years and is an expensive and time consuming process. Based on the premise that the physical appearance of an urban settlement is a reflection of the society that created it and on the assumption that people living in urban areas with similar physical housing conditions will have similar social and demographic characteristics (Jain, 2008;Taubenböck et al., 2009b); this paper uses data from Medellin City, Colombia, to estimate slum index using solely remote sensing data from an orthorectified, pan-sharpened, natural color Quickbird scene. For Medellin city, the percentage of clay roofs cover and the mean swimming pool density at the analytical region level can explain up to 59% of the variability in the slum index. Structure and texture measures are useful to characterize the differences in the homogeneity of the spatial pattern of the urban layout and they improve the explanatory power of the statistical models when taken into account. When no other information is used, they can explain up to 30% of the variability of the slum index. The results of this research are encouraging and many researchers, urban planners and policy makers could benefit from this rapid and low cost approach to characterize the intra-urban variations of slumness in cities with sparse data or no data at all.

show abstract

Space, race, and poverty: Spatial inequalities in walkable neighborhood amenities?

Cited by 63 publications

References 95 publications

The Geography of Recreational Open Space: Influence of Neighborhood Racial Composition and Neighborhood Poverty

The Geography of Recreational Open Space: Influence of Neighborhood Racial Composition and Neighborhood Poverty

Are There Relationships Among Racial Segregation, Economic Isolation, and Proximity to Green Space?

Quantifying Slumness with Remote Sensing Data

Contact Info

Product

Resources

About