Crowdsourcing subjective perceptions of neighbourhood disorder: interpreting bias in Open Data

Réka Solymosi

18 Apr 2018, 2:45 p.m.
Auditorium 2

Crowdsourced, open data is often seen as inherently problematic for researchers, primarily because of the inevitable subjective biases that occur when contributors are self-selected.

Réka uses data from mySociety’s street fault reporting service FixMyStreet to reframe those biases as a useful route into understanding people’s subjective experiences within their environments.

There are plenty of data sets now 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. However, these studies conceptualise such data as an econometric measures of crime and disorder issues, and, Réka argues, miss an important quality of such information. She reconceptualises the use of crowdsourced data in representing theoretical concepts in criminology, by focusing on the subjective bias inherent in their mode of production.

She proposes that a strength of crowdsourced information is that it can represent a measure of what matters to a community, suggesting that the underlying bias in what gets reported through such crowdsourced data collection techniques actually provides a filter of what communities deem subjectively important.

By considering the subjectivity and bias present in the data generation process when conceptualising 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.