  Scale-adjusted metrics (SAMs) are a significant achievement of the urban
scaling hypothesis. SAMs remove the inherent biases of per capita measures
computed in the absence of isometric allometries. However, this approach is
limited to urban areas, while a large portion of the world's population still
lives outside cities and rural areas dominate land use worldwide. Here, we
extend the concept of SAMs to population density scale-adjusted metrics (DSAMs)
to reveal relationships among different types of crime and property metrics.
Our approach allows all human environments to be considered, avoids problems in
the definition of urban areas, and accounts for the heterogeneity of population
distributions within urban regions. By combining DSAMs, cross-correlation, and
complex network analysis, we find that crime and property types have intricate
and hierarchically organized relationships leading to some striking
conclusions. Drugs and burglary had uncorrelated DSAMs and, to the extent
property transaction values are indicators of affluence, twelve out of fourteen
crime metrics showed no evidence of specifically targeting affluence. Burglary
and robbery were the most connected in our network analysis and the modular
structures suggest an alternative to "zero-tolerance" policies by unveiling the
crime and/or property types most likely to affect each other.
