The Hotspot: An Exploration into the Mapping of Contaminated Urban Space

Abstract

In public discourse and in law enforcement, policing crime and drugs in urban space is frequently framed in spatial terms, for example as policing certain areas and places that are deemed to be “crime prone”, “crime and drug infested” or indeed “criminal spaces”. This is especially true of crime that is discursively connected to public urban space and “the street”, like crime associated with urban drug cultures. Often, these urban places are described as hotspots. In their book GIS and Crime Mapping, Spencer Chainey and Jerry Ratcliffe state that “One of the first geographical questions that is asked of crime data is ‘where are the hotspots?’” (Chainey and Radcliffe 2005, 145). Criminologist Lawrence Sherman defined hotspots in 1995 as “small places in which the occurrence of crime is so frequent that it is highly predictable” (Sherman 1995, 36). Thus, as a place of frequent criminal activities, as well as a place where future crime and deviance are to be expected with some degree of certainty, the hotspot is an area of concern for police, planners, and citizens.

As a spatial means of representing and thinking about crime (and other threats), hotspots are frequently visualized through maps. This includes drug mapping, especially by law enforcement agencies and media. In this rather methodological contribution, we propose to examine hotspot maps as a prominent and popular cartographic genre that is often used not only to represent “drugs and crime” in urban space but to link them together discursively. We are especially interested in a type of map that not only maps occurrences and frequencies of reported crime but also makes claims about the “predictability” of future crime that Sherman mentions in his definition of the hotspot. It is a type of map and visualization that in GIS is often called a heatmap. Our primary goal here is less to deconstruct or criticize problematic real-world maps than it is to understand the methods and assumptions behind those maps. As these maps rely on the computing power of GIS, we want to take a closer look at the algorithms and technological blackboxes that make these maps. To illustrate our point about the assumptions and idiosyncrasies of software we use three different software tools to map a set of crime data. One key goal of this article is to show how technology, ideology, and governmentality are closely entangled with each other and how they find their way into drug mapping. Looking behind the map can reveal these liaisons in order to understand them and formulate a critical response. We will therefore construct a number of experimental maps using real-world data.