Increasingly, social work researchers are interested in examining how "place" and "location" contribute to social problems. Yet, often these researchers do not use the specialized spatial statistical techniques developed to handle the analytic issues faced when conducting ecological analyses. This article explains the importance of these techniques when analyzing spatial data, describes appropriate spatial statistical techniques, provides an illustration of how using inappropriate statistical techniques with spatial data can produce biased estimates of statistical tests, and discusses challenges to conducting spatial analysis. The study involved analyzing data for 941 census tracts for structural factors related to child maltreatment using traditional ordinary least squares (OLS) regression and generalized linear squares (GLS) spatial regression. When using OLS, results showed that immigrant populations/child care burden was negatively related to rates of child maltreatment but not related when using GLS, which controls for correlations between these spatial units. Relying on OLS regression techniques to create interventions to reduce child maltreatment in spatial areas could result in developing ineffective strategies that fail to reduce maltreatment.