Surveilling Surveillance: Estimating the Prevalence of Surveillance Cameras with Street View Data

TL;DR

This study estimates the prevalence and distribution of surveillance cameras in major cities worldwide using computer vision and human verification on street view images, revealing patterns related to land use and demographics.

Contribution

It introduces a novel method combining computer vision, human verification, and statistical analysis to systematically estimate camera prevalence from street view data.

Findings

Cameras range from 0.2 to 0.9 per km across cities.

Cameras are concentrated in commercial and industrial zones.

Higher camera density in neighborhoods with more non-white residents.

Abstract

The use of video surveillance in public spaces -- both by government agencies and by private citizens -- has attracted considerable attention in recent years, particularly in light of rapid advances in face-recognition technology. But it has been difficult to systematically measure the prevalence and placement of cameras, hampering efforts to assess the implications of surveillance on privacy and public safety. Here, we combine computer vision, human verification, and statistical analysis to estimate the spatial distribution of surveillance cameras. Specifically, we build a camera detection model and apply it to 1.6 million street view images sampled from 10 large U.S. cities and 6 other major cities around the world, with positive model detections verified by human experts. After adjusting for the estimated recall of our model, and accounting for the spatial coverage of our sampled images, we are able to estimate the density of surveillance cameras visible from the road. Across the 16 cities we consider, the estimated number of surveillance cameras per linear kilometer ranges from 0.2 (in Los Angeles) to 0.9 (in Seoul). In a detailed analysis of the 10 U.S. cities, we find that cameras are concentrated in commercial, industrial, and mixed zones, and in neighborhoods with higher shares of non-white residents -- a pattern that persists even after adjusting for land use. These results help inform ongoing discussions on the use of surveillance technology, including its potential disparate impacts on communities of color.