Follow the Data & Money: Mapping NYC's Unregulated Surveillance Economy to Expand Local Governance Solutions | NYU Tandon School of Engineering

Follow the Data & Money: Mapping NYC's Unregulated Surveillance Economy to Expand Local Governance Solutions

Transportation & Infrastructure,
Urban


Project Sponsor:

 


Abstract

NYC is a key site of technological innovation—including an expansive surveillance economy. Much of this technology is acquired by City agencies through contracts with private corporations. By following data and money trails, this project illuminates the scale of the unregulated surveillance economy and how NYC’s procurement system—an antiquated and opaque web—deeply impacts urban life. Specifically, we explore how technology procurement expands discriminatory policing power and undermines community-centered governance. Combined with qualitative research, our data analysis illustrates how the surveillance economy exacerbates racism and inequality. We are developing recommendations for community-based governance mechanisms to make procurement more accountable to all residents.


Project Description & Overview

Technological innovations play a critical role in urban infrastructure in cities such as New York, which are key sites for tech markets and innovation. Smart City projects and other tech-driven solutions to urban management are proliferating and often have substantial impacts on urban communities. Yet many of the decisions to purchase surveillance tech are made behind closed doors and through obscured purchasing processes that offer little to no opportunity for community input or understanding. This is a pressing concern for urban governance as many of these technological initiatives have substantial harmful impacts—including enhancing discriminatory policing practices, exacerbating inequality through displacement or unequal access to resources. 

This project helps address the significant gaps in understanding the breadth, depth, and consequences of surveillance tech purchasing in NYC. Alongside qualitative research, this project will begin to demystify obscured tech procurement processes towards increased organizing, advocacy and reform initiatives. Drawing from publicly available data, and data from Freedom of Information Laws (FOIL) requests, this project centers two data-driven research products—money mapping that traces NYC’s spending on surveillance tech, and one that maps hidden surveillance spending by New York City. 

We aim to identify tangible mechanisms for community input before significant surveillance purchases are made and build with advocates around best practices for procurement of technology by the City. This research will be conducted as a starting point for understanding the contours of this little understood but very significant urban process and towards community engagement and organizing for more equitable and transparent procurement processes.


Datasets

Research Question 1: Mapping spending on surveillance technology

Our primary data set will be drawn from the spending and contract records made publicly available on CheckBook NYC, from 2010-2022 (the records only go back to 2010). We will filter by:

  • Specific agencies, focusing on NYPD and NYC Department of Corrections, but also including the NYC Department of Citywide Administrative Services, Office of Emergency Management, and others that hold interagency contracts or are responsible for acquiring data-driven or “smart-city” technologies
  • Budget codes we have previously identified as relating to surveillance and data-driven technologies
  • Specific vendors that sell surveillance tools and related technologies

This data will be supplemented by review of:

  • City agency budgeting documents, from 2010-present
  • City Record Online, to cross-check information about specific contracts
  • Review of contracts for surveillance and data-driven technology released by advocates through FOIL requests 
  • Promotional materials from companies and industry associations about their existing contracts

We are separately submitting FOIL requests to the Comptroller and other City agencies for a more comprehensive mapping of these contracts, including an explanation of budget codes and overview of spending, and will include these in the data set if the production is relevant.

Research Question 2: Mapping hidden spending

Our dataset will primarily be drawn from NYC spending records from CheckBook NYC that use the anonymized “N/A (Privacy/Security)” vendor code, approximately 4,700,000 records from 2010 to present. We will analyze this by agency, year, budgeting code, and other categories to identify trends around anonymized vendor spending. This will include an analysis of whether or not use of the “N/A (Privacy/Security)” code increased after the implementation of the POST Act and the subsequent termination of the “Special Expense Fund (SPEX).”

We will supplement this data with a review of SPEX spending records, released by advocates including the Legal Aid Society and STOP. We will also follow up with the Comptroller’s Office for additional data on this spending, which we will include if relevant.


Competencies

Our team is open to collaborating with students who have a variety of data analysis/data science skills. In particular, we are looking to partner with students who have these skills: 

  • Financial analysis
  • Public policy analysis 
  • Data visualization
  • Data science and data analysis (more generally) and with data sets containing financial and vendor information

Learning Outcomes & Deliverables

We anticipate the capstone project having several deliverables, including but not limited to: 

  • Interactive data visualization tool/dashboard with data analysis outcomes.
  • Primer/explainer or a “how to” toolkit for advocacy partners in other jurisdictions who wish to take on a similar data analysis project. 
  • Academic/technical paper explaining research methodology, implications, and outcomes.

Results:

Follow the Data & Money: Mapping NYC’s Unregulated Surveillance Economy to Expand Local Governance Solutions


Students

Xueying Xiao, Shanshan Xie, Vivian Zhao, and Chaofan Zheng