Future applications of big data in environmental criminology

Introduction

Big data are increasingly becoming a topic in research in both the physical and the social sciences. Though its definition has been a moving target with increasingly powerfill computers, big data is generally defined as extremely large or complex (i.e., multiple dimensions) data sets that need to be analysed using methods outside of the traditional data-processing applications commonly used. These data have significantly increased the possibilities for analysis of new topics but also presenting new opportunities to re-visit only questions in new ways.

Big data are increasingly being used in criminology and, specifically, environmental criminology. Environmental criminology is primarily concerned with the analysis of crime through a spatial and temporal lens. Topics can range from crime prevention to policing to theoretical testing, but spatial and temporal methods are at the heart of much of this research. These added dimensions of space and time alone could turn ‘small data’ into big data, but there is also a trend of environmental criminology using smaller and smaller units of analysis that leads to a gr eater number of observations in the data sets environmental criminologists use (Weisburd, Brtiinsma, & Bemasco, 2009).

In this chapter we discuss some of the potential uses of big data and its associated computational methods for the future of environmental criminology. There are aspects of research in which big data has already been used. We discuss some of this research, but also new directions. We conclude with a brief discussion of the considerations that must be taken into account with the use of this new data source in future research.

Making better small data with big data

Though big data is often used an analysed as a whole, we would also argue that pieces of big data can be used to make the small data we use in criminology better. For example, some research within environmental criminology has used satellite data, social media data, and mobile phone data to measure where people are in order to better identify risky places (Andresen, 2011; Malleson & Andresen, 2015a, 2015b, 2016). Because such data may better represent where people are th ough the geo-referencing of social media and mobile phone networks, they can be usefill in identifying where people are, on average (rather than census data), or where they are on particular days of the week, or times within a given day. Given that there are hundreds of millions of tweets every day alone, specialised statistical and computing techniques are necessary to be able to analyse these data. However, most often, researchers in environmental criminology are interested in particular places. These places could be the size of cities, but also the size of a str eet segment (Andresen et al.. 2017a, 2017b; Hodgkinson, Andresen, & Farrell, 2016).

Within these contexts of cartographically larger places, social media or mobile phone information can be obtained to better identify where people are and when they are there. This leads to the better identification of risk for different places and those places at different times of the day. This is what has been done, but the potential for such analyses has only begun to be utilised. There are a number of confidentiality and ethical implications for these types of analyses (to be discussed further later), but the potential for understanding crime patterns (spatially and temporally, among others) is tremendous with the availability of big data.

For example, knowing the age and gender of people, how long they spend at particular places, and their spending habits can all be instructive for understanding why offending and victimisation occurs here and there, now and not then. These data can be identified from larger big data data sets for smaller areas and then aggregated to protect confidentiality. Such data could then be used in traditional (statistical) analyses to better measure theoretical constructs, testing theory' in ways would simply could not do before. But the presence and availability of big data should not only be used to revisit old methods in new ways but open the door for new types of analyses and pushing the field of environmental criminology forward.

Big data, data mining, and machine learning in environmental criminology'

One of the ways in which environmental criminology may move forward with big data is through the use of modem computing techniques such as data mining and machine learning. Generally speaking, data mining involves the identification of patterns in big data that are not feasible, or extremely costly in terms of time and/or money; machine learning is one method within data mining that uses algorithms and statistical modelling without explicit instructions. In other words, these techniques rely less on theory guiding what we think should be in the model than on inference itself. As such, all the data available can be used to test theoretical models and policy implementation, rather than a relatively small set of data our theories tell us should matter.

For example, studies that use census data for predictor variables rarely use more than 10 to 20 predictor variables when there are hundreds of such variables available through most censuses. Data mining can be used for inferential testing of all these variables and their interactions that is simply not tractable using conventional techniques. Moreover, with the availability of crime and police data at relatively fine spatial resolutions for many police agencies across many countries (sometimes available over many years), data mining techniques can be used to analyse crime patterns at multiple scales (spatially and temporally), across multiple crime types, across multiple policing services, using predictors that have not been able to be incorporated before because of data, software, and user limitations. The implications of such data and methods are significant for the testing of our theories across many different contexts with similarly defined data and the evaluation of larger scale policies that may work in some places but not others.

Another potential with the use of big data and data mining is the testing of theories that have simply not been possible in the past. For example, the theoretical construct of the geometry of crime requires activity patterns for offenders and non-offenders, alike to be able to test the predictions of this theory. These activity patterns need to be for both deviant and non-deviant activities and measured overtime in order to know where and when people travel to their activity nodes and the pathways between them. These data could be identified through mobile phone activity, for example, and then cross checked with victimisation and offending data. Hypotheses and propositions that have never been fully tested in the past can be tested using big data and its corresponding methods.