This study evaluates the nuanced history, trends, and clustering behavior of car break-ins, also known as “smash-and-grabs,” within San Francisco in 2023. Northeast San Francisco is selected as the region of interest due to the high density of tourist hot-spots and the large volume of car smash-and-grab clustering within the area. Point process analysis is leveraged to create spatial and spatial-temporal models that predict the location and intensity of clustering for future car smash-and-grabs. Results indicate Inhomogeneous Poisson models yield the most promising fitted trends when compared against real data. Future work is recommended to rule out any self-exciting behavior of car smash-and-grab events and to explore additional models using data on street traffic, law enforcement involvement and repeat-offenders.