Community garden data were collected in collaboration with Gateway Greening, a local community organization focused on gardening and urban agriculture. Additional garden data were retrieved through chain-referral sampling methods. Census Block Group data were collected to identify contextual factors thought to influence community garden incubation. ArcMap 10.5.1 was used to map community garden locations. Standard deviational ellipses were drawn to analyze community garden distribution and summarize spatial characteristics. Spatial autocorrelation (Moran’s I) and hotspot analysis (Getis-Ord Gi*) were conducted to analyze patterns of community garden locations in St. Louis City. Logistic regression models were specified to examine predictors of community garden locations. Model diagnostics including checks for heteroscedasticity and multicollinearity were conducted to test model assumptions. Predicted probabilities were assessed and indicated that 15% of the sample (n=150) were outside the range of the linear relationship between the covariates and the dependent variable (between .2 to .8). Therefore, average marginal effects were used to interpret results.
Results: There were 130 community gardens in the St. Louis City and St. Louis County area, with more than 70% of gardens located in St. Louis City. Spatial autocorrelation indicated a clustered pattern of community gardens in the city (z =3.73), and community garden hotspots are located in 14 block groups. Logistic regression results indicate that neighborhoods with higher percentages of African American/Black residents, higher percentages of residents with a bachelor’s degree or higher, and higher housing vacancies increase the predicted probability of having a community garden, while higher percentages of home ownership and increases in the median year of structures built decrease the predicted probability of having a community garden.
Implications: The results of this study show that neighborhood factors such as race/ethnic composition, educational attainment, and housing vacancy are associated with community garden incubation. The study also finds that community gardens in St. Louis display a clustering pattern. By identifying patterns in community garden distribution and exploring community-level factors that influence garden incubation, practitioners may be better equipped to target resources effectively to address community concerns and achieve program goals. Future research is needed to explore distinguishing features of gardens that comprise clusters compared to gardens that are spatially isolated.