Utilizing geo-referenced imagery for systematic social observation of neighborhood disorder

Research methods in social science take advantage from broader trends such as digitalization and increasing computational power, however, this is an evolving explorative search. The main purpose of this article is to describe the methodological innovations in the collection and processing of geo-referenced imagery for the observation of neighborhood disorder. In this narrative review, attention is paid to advances in both the data sources and the data processing methods used. Neighborhood disorder is traditionally measured by means of survey methods and (systematic) (social) observations, but these methods have specific shortcomings, such as respectively the subjective measurement that does not deliver a valid measure of actual prevalence of disorderly phenomena and the intensive use of resources in terms of time and money. This has repercussions for (the interpretation of) the results based on these data. Today, scholars have innovative data sources and cutting-edge data processing methods at their disposal that can meet (some of) these shortcomings, but which have not yet been fully explored. In this article, the evolutions in the use of geo-referenced imagery for the observation of neighborhood disorder from the last 25 years are described with a focus on the empirical opportunities, and the methodological challenges and prospects. We conclude by outlining the road ahead: promising avenues for future research to exploit the full potential of 'big primary data'.

Automated summary

Research methods in social science are constantly evolving, taking advantage of trends like digitalization and increasing computational power. This article focuses on methodological innovations in the collection and processing of geo-referenced imagery for observing neighborhood disorder, highlighting both the opportunities and challenges of new data sources and processing methods. Scholars have the opportunity to explore the full potential of these innovative methods in future research.