ABSTRACT
A key piece of information we explore in environmental criminology is the “exact” location of crimes, typically in the form of street address in which a crime is recorded as having occurred. A key goal of the analysis of this type of mapped point data is to detect patterns, in particular to detect areas where crime locations appear as clustered and reflecting an increased likelihood of occurrence. In this chapter we will introduce techniques that are used as an alternative visualisations for point patterns and that are based on the mathematics of density estimation. They are used to produce isarithmic maps, these are the kind of maps you often see in the weather reports displaying temperature. In crime analysis they are often referred to as hotspot maps. What we are doing is creating an interpolated surface from discrete data points. Before we get to the detail of how we produce these maps we will briefly and very generally introduce the field of spatial point pattern analysis. The analysis of discrete locations are a subfield of spatial statistics. In this chapter we will introduce an R package called spatstat, that was developed for spatial point pattern analysis and modelling.
