Goldmine or minefield? The methodological challenges associated with the analysis of the FixMyStreet neighbourhood problems dataset

We report the results of a ‘big data’ research project based on the analysis of 1.1 million user-generated neighbourhood fault reports (on such things as abandoned cars, fly-tipping, or broken streetlights). Using data provided by FixMyStreet.com from the United Kingdom we explore the relationship between neighbourhood service needs and the socio-economic composition of areas. We draw upon the literature on neighbourhood environmental quality to show that significantly fewer reports are submitted from the most deprived neighbourhoods, compared to more affluent areas. Previous research has shown that this can lead to better local service provision in wealthier areas, in contrast to poorer areas, and that the reasons behind it are often related to residents of wealthier areas being more ‘pushy’ (i.e. the ‘sharp elbows’ thesis). The results demonstrate the potential of using large geospatial datasets in a socio-economic context. For example, this work may be particularly useful to environmental policy officers at the local level in terms of identifying levels of need, or areas of over-reporting. However, our results also demonstrate that we need to be careful if we are to avoid pitfalls of interpretation.