Table 2.

Association between neighborhood demographics and log of tobacco retailer density in Boston: spatial lag model estimation

Note: In the spatial lag model, which was estimated via maximum likelihood, there was evidence of remaining spatial autocorrelation and heteroskedasticity in some models. However, findings overall did not change when more advanced spatial models were implemented, including a combination spatial model where spatial effects were accounted for, including a spatial lag of the dependent variable and a spatial lag of the error term (sometimes referred to as spatial autoregressive model with autoregressive disturbances [SARAR]) and a two-stage least-squares model for the spatial lag model with heteroskedasticity and autocorrelation consistent SEs. The spatial lag model estimated via maximum likelihood is presented because it is the most parsimonious, but appropriate, spatial model. For instance, the Akaike Information Criterion value is lower in the maximum likelihood spatial lag model compared with the SARAR model.

^aMultivariate models are controlled for population density and other neighborhood sociodemographics.

SE = standard error