Nick Malleson, Alison Heppenstall, and Andrew Crooks
Since the earliest geographical explorations of criminal phenomena, scientists have come to the realization that crime occurrences can often be best explained by analysis at local scales. For example, the works of Guerry and Quetelet—which are often credited as being the first spatial studies of crime—analyzed data that had been aggregated to regions approximately similar to US states. The next major seminal work on spatial crime patterns was from the Chicago School in the 20th century and increased the spatial resolution of analysis to the census tract (an American administrative area that is designed to contain approximately 4,000 individual inhabitants). With the availability of higher-quality spatial data, as well as improvements in the computing infrastructure (particularly with respect to spatial analysis and mapping), more recent empirical spatial criminology work can operate at even higher resolutions; the “crime at places” literature regularly highlights the importance of analyzing crime at the street segment or at even finer scales. These empirical realizations—that crime patterns vary substantially at micro places—are well grounded in the core environmental criminology theories of routine activity theory, the geometric theory of crime, and the rational choice perspective. Each theory focuses on the individual-level nature of crime, the behavior and motivations of individual people, and the importance of the immediate surroundings. For example, routine activities theory stipulates that a crime is possible when an offender and a potential victim meet at the same time and place in the absence of a capable guardian. The geometric theory of crime suggests that individuals build up an awareness of their surroundings as they undertake their routine activities, and it is where these areas overlap with crime opportunities that crimes are most likely to occur. Finally, the rational choice perspective suggests that the decision to commit a crime is partially a cost-benefit analysis of the risks and rewards. To properly understand or model these three decisions it is important to capture the motivations, awareness, rationality, immediate surroundings, etc., of the individual and include a highly disaggregate representation of space (i.e. “micro-places”). Unfortunately one of the most common methods for modeling crime, regression, is somewhat poorly suited capturing these dynamics. As with most traditional modeling approaches, regression models represent the underlying system through mathematical aggregations. The resulting models are therefore well suited to systems that behave in a linear fashion (e.g., where a change in model input leads to a predictable change in the model output) and where low-level heterogeneity is not important (i.e., we can assume that everyone in a particular group of people will behave in the same way). However, as alluded to earlier, the crime system does not necessarily meet these assumptions. To really understand the dynamics of crime patterns, and to be able to properly represent the underlying theories, it is necessary to represent the behavior of the individual system components (i.e. people) directly. For this reason, many scientists from a variety of different disciplines are turning to individual-level modeling techniques such as agent-based modeling.
Using Naturalistic Observation to Develop Crime-Control Policies in Nighttime Entertainment Districts
For the last 20 years, research based on the idea that opportunities for crime are related to specific times and places has informed crime-control policies in nighttime entertainment districts. In order to examine crime in these areas, many studies have relied on large data sets that associate city- and neighborhood-level factors with crime and delinquency. These studies have helped us understand the importance of environmental and situational factors, as well as the impact of changes in legislation and regulations to control alcohol availability (e.g., reducing the density of alcohol outlets and trading hours) and the implementation of interventions in licensed premises to reduce intoxication and disorder. However, when informing crime-control policies, the use of alternative methods to examine entertainment districts, such as naturalistic observations, can be vital. Because nighttime entertainment districts are extremely complex environments, observation is useful to examine and identify situational factors and local dynamics that increase or decrease the opportunities for crime in specific places. Observational methods can be particularly useful to understand the context in which criminal behavior and aggressive incidents occur, the interplay of situational risk factors specific to a public drinking environment, and the social and cultural factors (e.g., the relationship between police, staff, and customers) that can facilitate or challenge the implementation of crime-control strategies in these multifaceted contexts.
Naturalistic observation is a data-collection method that involves accessing the field to systematically record and describe features of the space, people’s characteristics and patterns of movement, individual behaviors, and exchanges between actors in natural settings. It can be used in both quantitative and qualitative designs, although in different ways. In entertainment districts, researchers have used this method to understand crimes that are underreported and underregistered, such as sexual harassment, and to study patrons’ behaviors in licensed premises and surrounding streets, as well as staff management practices and control strategies. While they have some limitations, such as the fact that information is filtered by what observers see and how they interpret events, observation methods can uniquely contribute to the development of crime-control policies in entertainment districts by focusing on specific situational and cultural factors relating to violence and crime at a local level, as well as suggesting differentiated responses to the types of incidents that take place in these settings.