Crowdsourcing Subjective Perceptions of Neighbourhood Disorder: Interpreting Bias in Open Data

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.