Jun Yan, Chao Guo, and Laurie E. Paarlberg
The geographic distribution of nonprofit antipoverty organizations has important implications for economic development, social services, public health, and policy efforts. With counts of antipoverty nonprofits at the census tract level in Greater Hartford, Connecticut, we examine whether these organizations are located in areas with high levels of poverty with a spatial zero-inflated-Poisson model. Covariates that measure need, resources, urban structure, and demographic characteristics are incorporated into both the zero-inflation component and the Poisson component of the model. Variation not explained by the covariates is captured by the combination of a spatial random effect and an unstructured random effect. Statistical inferences are done within the Bayesian framework. Model comparison with the conditional predictive ordinate suggests that the random effects and the zero-inflation are both important components in fitting the data. All three need measures—proportion of people below the poverty line, unemployment rate, and rental occupancy—are found to have significantly positive effect on the mean of the count, providing evidence that antipoverty nonprofits tend to locate where they are needed. The dataset and R/OpenBUGS code are available in supplementary materials online.
KEY WORDS: Intrinsic conditional autoregressive; Spatial random effect; Zero-inflated Poisson.