The extent to which urban tree cover influences crime is in debate in the literature. This research took advantage of geocoded crime point data and high resolution tree canopy data to address this question in Baltimore City and County, MD, an area that includes a significant urban?rural gradient. Using ordinary least squares and spatially adjusted regression and controlling for numerous potential confounders, we found that there is a strong inverse relationship between tree canopy and our index of robbery, burglary, theft and shooting. The more conservative spatially adjusted model indicated that a 10% increase in tree canopy was associated with a roughly 12% decrease in crime. When we broke down tree cover by public and private ownership for the spatial model, we found that the inverse relationship continued in both contexts, but the magnitude was 40% greater for public than for private lands. We also used geographically weighted regression to identify spatial non-stationarity in this relationship, which we found for trees in general and trees on private land, but not for trees on public land. Geographic plots of pseudo-t statistics indicated that while there was a negative relationship between crime and trees in the vast majority of block groups of the study area, there were a few patches where the opposite relationship was true, particularly in a part of Baltimore City where there is an extensive interface between industrial and residential properties. It is possible that in this area a significant proportion of trees is growing in abandoned lands between these two land uses.