This study investigates the relationship between urban built environment and traffic crash occurrences in San Diego County by employing a multi-level Poisson regression model. This model is intended to address the so-called 'Modifiable Areal Unit Problem' by properly capturing the spatial correlations that might exist among crash occurrences within or across zones specified for data aggregation. A preliminary inspection based on a geographic information system reveals that the crash occurrences of Traffic Analysis Zones (TAZs) tend to show similar spatial pattern if they belong to the same city. This observation motivated us to incorporate hierarchical random components into a Poisson regression model to complement its systematic components (i.e. explanatory variables) related to socio-demographic characteristics, travel characteristics, or urban built environment. This approach turns out to improve the model's explanatory power in assessing the relationship between urban built environment and traffic crash occurrences while controlling other variables. A key finding from this study is that crash occurrences of individual TAZs tend to decrease with the expansion of their urban portion.