This study provides a measurable model of the concept of place vulnerability for HIV/AIDS that incorporates both community and structural level effects using data provided at the ZIP code level from the Texas Department of State Health Services. Sociological literature on the effects of place on health has been growing but falls short of providing an operational definition of the effects of place on health. This dissertation looks to the literature in medical/health geography to supplement sociology’s understanding of the effects of place on health, to the end of providing a measurable model. Prior research that has recognized the complexity of the effects of place still have forced data into one scale and emphasized individual-level outcomes. A multilevel model allows for keeping the associated spatial unit data, without aggregating or parsing it out for convenience of model fit. The place vulnerability model proposed examines how exposure, capacity and potentiality variables all influence an area’s HIV/AIDS count. To capture the effects of place vulnerability at multiple levels, this dissertation research uses a multilevel zero-inflated poisson (MLZIP) model to examine how factors measured at the ZIP code and county both affect HIV/AIDS counts per ZIP code as an outcome. Furthermore, empirical Bayes estimates are mapped to display how well the model fits across the state of Texas. Limitations of this research include the need to incorporate time, more specific predictors, and individual level factors. The methodology developed permits a more thorough understanding of place effects on the spatial variation of HIV/AIDS.