Surveillance Project V4

Methodology & Data

Our project is primarily based upon the Los Angeles County subsection of the Atlas of Surveillance dataset. Atlas of Surveillance uses "a combination of crowdsourcing, data journalism and public records reporting" to compile their repository. Although the dataset provided a significant base, we identified gaps that required data mining. Specifically, we have added coordinates for law enforcement agencies and vendors using the Python geopy library to locate them using GIS tools, and economic data when profiling vendors to give a sense of the profits they are garnering.

We chose to include the category “unknown” rather than deleting these from the dataset to mark this silence, foregrounding that we do not always know who is watching us nor who is profiting from it. All data, including our altered dataset, is housed on GitHub for transparency.

Critiques & Gaps

Information in the original dataset relies on what is publicly available online. Like all datasets, it is shaped by what is missing, which in this case is likely informed by omissions and obfuscations of the public record by law enforcement agencies themselves. Location data displays headquarters of specific agencies rather than the full area of coverage. Were we to have that information, a heatmap might allow visitors to better understand where surveillance technologies are concentrated to make clearer the disproportionate targeting of minoritized communities.

Meta-Surveillance & Theory

This site incorporates a meta-critique via the custom counter at the top of the page. As Wendy Chun notes, "Online, one is not simply a spectator-citizen-commodity owner. Even when 'just viewing' or 'lurking,' one actively sends and receives data (all spectators are still visible—the degree of their visibility, or more properly their traceability, is the issue)." By turning ourselves and site visitors into the surveillants, we hope to raise questions about how being subjected to constant surveillance can naturalize carceral ideologies.

Our theoretical framework draws heavily on Helen Nissenbaum’s context integrity—examining how privacy is violated not just by exposure but by inappropriate information flows—and Critical Data Studies to map the spatial and transactional relationships of control.

Environmental Scan & Contribution

Data-driven projects on surveillance technology such as Surveillance Watch and Feminist Smart Cities have offered an insightful approach to our project. Surveillance Watch aims to reveal which countries surveillance entities, or companies, are collecting data from, and who funds this data extraction. Although they provide detailed information about companies and funders, the project provides a big picture, global perspective.

Our project intervenes by providing a closer look into not only the surveillance of LA County, but also what cities – both across the nation and internationally – the vendors are headquartered.

The Team