Unveiling relationships between crime and property in England and Wales via density scale-adjusted metrics and network tools

Abstract

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.

Nursing Research Literature Production in Terms of the Scope of Country and Health Determinants: A Bibliometric Study

“…That might be due to the fact that per capita indicators represent normalized variables, eliminating the population size as a factor in the analysis. The normalization is comparable to scale‐adjusted metrics (Alves, Mendes, Lenzi, & Ribeiro, ), which were better predicators in different urban, environmental, and demographics analyses (Oliveira, Ribeiro, Bastos‐Filho, & Menezes, ; Ribeiro, Hanley, & Lewis, ).…”

Rural–urban scaling of age, mortality, crime and property reveals a loss of expected self-similar behaviour

“…The success of DSAMs and related methodologies 2,8,10,11 makes clear that any set of scaling laws provides an incomplete picture of both rural and urban landscapes. Although they may appear to be, the residuals are not randomly distributed around the scaling law whatever the model.…”

“…In the data considered here, this issue is occasionally severe. Although progress has been made on these problems we note the following: (1) The analysis of scale adjusted metrics 2,8,10 assumes that the power law fits are an incomplete explanation of the data. Specifically, the approach 2,8,10 assumes the residuals around a power law fit contain explainable variance and are not random relative to other residuals.…”

Urban scaling, geography, centrality: Relation with local government structures

“…Also population cutoff used to create a set of cities will influence the measured scaling [18]. Interestingly, the measured scaling itself may suggest the existence of different power-law regimes related to population size [40].…”