Abstract
New forms of data are now widely used in social sciences, and much debate surrounds their ideal application to the study of crime problems. Limitations associated with this data, including the subjective bias in reporting are often a point of this debate. In this article, we argue that by re-conceptualizing such data and focusing on their mode of production of crowdsourcing, this bias can be understood as a reflection of people’s subjective experiences with their environments. To illustrate, we apply the theoretical framework of signal crimes to empirical analysis of crowdsourced data from an online problem reporting website. We show how this approach facilitates new insight into people’s experiences and discuss implications for advancing research on perception of crime and place.
Highlights
In the era when over 6.2 exabytes of global mobile data traffic is generated each month (Cisco 2016) it is inevitable that open source ‘big data’ plays an increasingly important role in the advancement of research in the social sciences (Preis et al 2013)
There are plenty of data sets being produced as a result of people’s online activities that show promise in offering new lines of enquiry in social science, in particular concepts related to crime and disorder which we review below
We suggested that the bias of what is reported on FMS means that these data represent not an econometric measure of disorder in a neighbourhood, but issues that people subjectively evaluate as problematic, in line with the signal crimes framework
Summary
In the era when over 6.2 exabytes of global mobile data traffic is generated each month (Cisco 2016) it is inevitable that open source ‘big data’ plays an increasingly important role in the advancement of research in the social sciences (Preis et al 2013). There are plenty of data sets being produced as a result of people’s online activities that show promise in offering new lines of enquiry in social science, in particular concepts related to crime and disorder which we review below These studies conceptualize such data as an econometric measures of crime and disorder issues, and, we argue, miss an important quality of such information. CROWDSOURCING SUBJECTIVE PERCEPTIONS OF NEIGHBOURHOOD DISORDER crowdsourced data collection techniques provides a filter of what communities deem subjectively important We illustrate this by applying signal crimes framework to conceptualize data collected from online problem-reporting website. By considering the subjectivity and bias present in the data generation process when conceptualizing the meaning behind such data, we gain novel insight into people’s experiences with disorder This approach is further transferable to other areas of research on people’s subjective perceptions about their environments
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