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Social scientists increasingly use video data, but large-scale analysis of its content is often constrained by scarce manual coding resources. Upscaling may be possible with the application of automated coding procedures, which are being developed in the field of computer vision. Here, we introduce computer vision to social scientists, review the state-of-the-art in relevant subfields, and provide a working example of how computer vision can be applied in empirical sociological work. Our application involves defining a ground truth by human coders, developing an algorithm for automated coding, testing the performance of the algorithm against the ground truth, and running the algorithm on a large-scale dataset of CCTV images. The working example concerns monitoring social distancing behavior in public space over more than a year of the COVID-19 pandemic. Finally, we discuss prospects for the use of computer vision in empirical social science research and address technical and ethical challenges.

Epidemiological evidence and recommendations from the World Health Organization suggest that close face-to-face interactions pose a particular coronavirus transmission risk. The real-life prevalence and nature of such high-risk contacts are understudied, however. Here, we video-observed high-risk contacts in outdoor public places in Amsterdam, the Netherlands, during the COVID-19 pandemic. We found that high-risk contacts were relatively uncommon: Of the 7,813 individuals observed, only 20 (0.26%) displayed high-risk contacts. Further, we qualitatively examined the 20 high-risk contacts identified and found that they occurred disproportionally between affiliated persons engaged in affiliative behaviors. We discuss the potential public health implications of the relatively low incident rate of high-risk contacts.

To mitigate the spread of the COVID-19 virus, the Dutch government has implemented several rules and regulations during the pandemic. Compliance with these rules and regulations is crucial for its effectiveness. In the current article, the authors give an overview of research findings from three different studies looking at compliance with the COVID-19 mitigating measures in the Netherlands. In these studies, both manual and computer-based video-analysis is used to give insight in the behavior of people on the streets of Amsterdam. Study 1 monitors compliance with the social distancing directive and stay-at-home advice, showing that people keep less distance when it is crowded on the street. Study 2 focusses on compliance with mandatory mask-wearing and shows that mask-wearing increases with the implementation of the mandatory mask areas, but crowding does not decrease. Finally, Study 3 looks at compliance of citizens during the curfew and shows that streets became far less crowded after 9 p.m. during curfew nights.

Om verspreiding van het coronavirus te eperken, roept de regering burgers op om zo veel mogelijk thuis te blijven en minstens 1,5 meter afstand te houden van anderen. In hoeverre leven mensen in Amsterdam deze richtlijnen na? Hoe druk is het in de openbare ruimte van de stad en houden mensen deze afstand van elkaar? Op basis van camerabeelden in de openbare ruimte van Amsterdam geven wij maandelijks een overzicht van hoe druk het is op straat, in hoeverre burgers 1,5 meter afstand houden van elkaar, en hoe dit zich ontwikkelt in de loop van de dag en over een periode van een aantal weken. In deze eerste factsheet worden bevindingen gepresenteerd van de tweede golf in september tot eind november.