Exploring NYC 311 Through Data Justice | Critical Data Literacies for Social Policy

Goals

01

Understand how digital data systems like 311 shape homelessness policy — and what that means for social workers.

02

Ground our discussion in key concepts from the literature, then explore several ways to interface with NYC 311 data and examine how homelessness is measured and represented within it.

03

Explore additional tools for thinking about spatial justice and the relationship between space, data, and homelessness policy.

Warm-Up Discussion

Before diving in, consider these questions — on your own or with a group:

What are some policies you are thinking about for your projects?
How do you get information for these policies?
What roles do digital technologies and public databases play in your projects? In your work?
What tools do you use to collect and analyze information in your practice?
What have you learned so far about how homelessness is measured, represented, and understood through information?

What Is Data?

The word "data" is the Latin plural of datum. Here's how Merriam-Webster defines it:

1.

Factual information (such as measurements or statistics) used as a basis for reasoning, discussion, or calculation.

"the data is plentiful and easily available" — H. A. Gleason, Jr.

2.

Information in digital form that can be transmitted or processed.

3.

Information output by a sensing device or organ that includes both useful and irrelevant or redundant information and must be processed to be meaningful.

Source: Merriam-Webster Dictionary

What Does This Have to Do with Social Policy?

Policy as "scientific" and evidence-informed, shaped by expertise.

Using information about social issues to plan and make decisions.

Allocating resources according to identified needs and trends.

Building (and justifying) an evidence base.

Performance measurement for services, contractors, and providers.

Interacting with communities through sensors and data collection methods.

Data-Driven Policymaking

Data-driven policy making uses ICTs to capture the benefits of new data sources, and to support collaboration with relevant stakeholders and citizens. In the literature on evidence-based policy making, three types of evidence are considered relevant: "systematic ('scientific') research, program management experience ('practice'), and political judgement."

Data-driven policy making acknowledges the importance of these types of evidence, but can be distinguished from evidence-based policy making, since it is mainly concerned with the inclusion of big and open data sources into policy making as well as with co-creation of policy by involving citizens. Data-driven policy making is not only expected to result in better policies, but also aims to create legitimacy. Involvement of citizens in a data-driven policy making process is especially important since public data and statistics are increasingly met by citizens' distrust.

van Veenstra, A. F., & Kotterink, B. (2017). Data-driven policy making: The policy lab approach. International conference on electronic participation, pp. 100–111. Cham: Springer. Google Scholar

Data Justice

What is the relationship between data (science) and social justice?

Datafication

The process of transforming social action and human life into quantified data.

Representation

Who and what gets counted — and how categories are constructed.

Fairness & Bias

How data systems encode and reproduce existing social inequities.

Social Sorting

Using data to classify, rank, and categorize people and populations.

Digital Surveillance

The monitoring of individuals and communities through digital systems and infrastructure.

Defining Data Justice

Data justice is an approach that redresses ways of collecting and disseminating data that have invisibilized and harmed historically marginalized communities. For decades, if not centuries, data has been weaponized against BIPOC communities, in particular, to reinforce oppressive systems that result in divestment and often inappropriate and harmful policies.

Data justice aims to capture forms of knowledge and lived experiences that are community-centered and community-driven to counter the systemic erasure and harm perpetrated on BIPOC communities via oppressive data practices. The fundamental premises of data justice are that data should:

Make visible community-driven needs, challenges, and strengths
Be representative of community
Treat data in ways that promote community self-determination

University of California Berkeley Libraries. (2024). Research Justice: Data Justice. UC Berkeley Library Guides. guides.lib.berkeley.edu

Data Justice & Social Work

"We argue here that individual social workers and the social work field are responsible to apprehend and to protest advancing technological mechanisms of disenfranchisement. Although some social work educators are committed to infusing computational and data science tools into social work training and curricula, professional research, practice and training must attend not simply to the acquisition of skills but also to the application of new technological tools and currencies to protect or erode human rights."

Goldkind, L., Wolf, L., & LaMendola, W. (2021). Data justice: Social work and a more just future. Journal of Community Practice, 29(3), 237–256. Google Scholar

Data Justice & Critical Data Literacy

"Data literacy is a term that is in relatively common use and usually defined as the knowledge and skills citizens need to participate in the digital world. However, in the context of data justice, the capabilities required are not simply those enabling participation — instead citizens need to be able to understand and reflect critically upon the implications of datafication. As Sander (2020a) has argued, 'Datafied societies need informed public debate about the implications of data science technologies.'"

Ballantyne, N. (2023). Advancing data justice. Social work in an online world: A guide to digital practice, 87–116. Google Scholar

How Does Data Relate to Homelessness?

Federal Policy Timeline

1987

McKinney-Vento Act

Requires "unduplicated counts" of homeless populations. Beginnings of estimates like the HOPE Count.

2009

HEARTH Act

Renews McKinney-Vento, establishes the Continuum of Care (CoC) model. Targeted housing outreach requires more data collection.

2010s

Data-Driven Rapid Rehousing

Introduction of data-driven rapid rehousing strategies.

2020s

COVID-Era Changes

COVID-era changes intensify data collection requirements.

2025

Housing First Under Threat

Attempts to dismantle Housing First policies at the federal level.

New York State & City

Right to Shelter

Managed through shelter population counts.

Supportive Housing

Development informed by shelter counts, community needs, data on units & affordability.

Street Homelessness as Data

Street homelessness, encampments, and "quality of life" issues related to homelessness all become data.

Daily Reporting Systems

CompStat (NYPD) and HOME-STAT (DHS/DSS) both report daily on street homelessness and related measures.

Citizen Reporting

Citizens use tools like 311 to report concerns — which feed directly into policy and enforcement responses.

NYC 311: Data-Driven Policymaking & Mediated Service Infrastructure

Before we dive in — if you've used 311 before: What for? How did you use it? Did you follow up on it? Does your job make or require 311 calls? Does your job respond to 311 calls?

A Brief Timeline of NYC 311

1996

Baltimore leads the way

First use of 311 — designed to divert "non-emergency calls" away from 911.

2003

NYC Launch

Launched under the Bloomberg administration.

2008

Photos & Videos

Integration of photos and videos into calls/requests.

2009

Social Media

NYC 311 joins Skype and Twitter.

2010

Open311 API

Open311 Application Program Interface established as an open technical standard.

2011

Text Messaging

Text messaging capabilities added to NYC 311.

2018

24/7 Social Media & Chat

Social media and chat channels go 24/7.

Background reading: Quintata, J. (2019). The evolution of 311 and government customer service. GovLoop. govloop.com

Advantages & Limitations of 311

Advantages

Feedback mechanism for public concerns
Data to track and collect information about problems, develop responses
Prioritizes and diverts resources, including staffing
"Smart" city integrating citizen sensing
Transparency and accountability for complaint resolution

Limitations

May not address actual problems, only visible symptoms
Bias in data in terms of what is captured and represented
Prioritization/sorting of public resources based on complaints — who has access and means?
Could distract from less visible problems and issues

Abazajian, K. (2022). We Need To Talk about 311 Data. CivicSource. civicsource.info

311 As Infrastructure

The 311 data infrastructure is built on the 'installed base' of the 311 reporting infrastructure and thus inherits its limitations (Star 1999). Not everyone that experiences a problem in the city reports it to 311. New Yorkers do not report issues to 311 when they do not know that the service exists, when they do not have access to the infrastructure to report to 311, or when they don't want to take the time to bother with inputting the necessary information into the service.

Perhaps most notably, 311 does not accept calls about NYC Housing Authority (NYCHA) properties, which comprise the largest public housing system in the country. As a result, 311 does not record any housing complaint data about some of the lowest-income areas in the city. 311 data only count issues where and when they have been reported to 311, not where and when issues have actually occurred.

Poirier, L. (2021). Data (-) based ambivalence regarding NYC 311 data infrastructure. Cultural Studies, 35(4-5), 968–995. Google Scholar

Hands-On Activities: Exploring NYC 311

We will explore four layers of 311 data interfaces. As you work through each activity, keep these questions in mind:

What is visible?

What is missing?

What patterns emerge?

Who is the data about?

What actions are triggered by these reports?

Activity 1

NYC Open Data Portal

opendata.cityofnewyork.us →

1

Visit the NYC Open Data Portal. Explore the interface. What do you notice?

2

Search "homeless". What do you see?

3

Search "311 Service Requests" and open the dataset "311 Service Requests from 2010 to Present." Examine it. What do you see?

4

Open the Data Dictionary attachment. What do you see? Look at the complaint types under the Department of Homeless Services.

Note: The Data Dictionary lists the majority of NYC agencies and the unique complaint types, complaint details, and location types present in the 311 dataset. You can use it to identify new types of complaints captured by 311 — and to understand what categories are used to describe people experiencing homelessness.

Activity 2

311 Portal: A View Into Homelessness

portal.311.nyc.gov →

1

Click "Look Up Service Requests", then scroll to "See Service Requests on a Map."

2

In Location, type "Silberman" and select "HUNTER COLL SCHOOL OF SOCIAL WORK." Set the range to 2000 feet.

3

In Date, set the "From" field to 1/1/2025.

4

In Problem Area, select "Homeless." In Problem, select "Homeless Person Assistance."

5

Explore the map. Look around at the service requests. What do you see? What don't you see?

Slide 29: Step-by-step instructions for searching the 311 portal map near Hunter College School of Social Work, with a screenshot of the portal's map interface showing purple dots representing Homeless Person Assistance service requests clustered across East Harlem and surrounding neighborhoods. The search is filtered by location (Silberman / Hunter College School of Social Work, 2000 ft radius), date from 1/1/2025, Problem Area: Homeless, Problem: Homeless Person Assistance. — Slide 29 — The 311 portal map filtered for "Homeless Person Assistance" service requests near Hunter College School of Social Work (East Harlem, 2000 ft, from Jan 2025). Each dot represents an individual service request. · portal.311.nyc.gov

Activity 3

311 Portal: Quality of Life

portal.311.nyc.gov →

1

Return to the map. Use the same location (Hunter College School of Social Work, 2000 feet) and date range (from 1/1/2025).

2

In Problem Area, select "Quality of Life."

3

In Problem, select one that you think might relate to homelessness. The available options include: Bike/Roller/Skate Chronic, Disorderly Youth, Drinking, Drug Activity, Encampment, Graffiti, Illegal Fireworks, Non-Emergency Police Matter, Panhandling, Posting Advertisement, Squeegee, Urinating in Public.

4

Explore the map. How does this view compare to the "Homeless" category? What is similar? What is different? What does it mean that these categories exist separately?

Activity 4

311 Dashboards: Resolution & Monitoring

Resolution Satisfaction Dashboard

nyc.gov/311reporting →

When a Service Request is closed by a City Agency, a survey is sent to the person who filed it measuring how satisfied they are with how the agency handled it.

What does satisfaction look like across different problem types? Across boroughs? What might a low score reveal about how agencies respond to homelessness-related calls?

311 Monitoring Tool

osc.ny.gov →

Created by the NY State Comptroller's Office. Draws from the NYC 311 dataset to include neighborhood-level insights on monthly requests by complaint type.

What patterns do you notice at the neighborhood level? Which neighborhoods generate the most homelessness-related requests — and what might explain that?

Bonus

HOME-STAT Daily Dashboard

nyc.gov/homestat →

→

HOME-STAT (Homeless Outreach & Mobile Engagement Street Action Teams) reports daily on 311 requests for outreach assistance for people who may be homeless and living on the streets or in other public places. Note that multiple requests could be made about one person.

→

How does this specialized dashboard differ from the general 311 portal? What does it reveal — and what does it hide? Who is the intended audience for this data?

311 Reflections

As a social worker, how does your work show up in these reports? In these maps?
Where is your agency visible in these systems? Where is it invisible?
What stories are told by the way the data is structured, or displayed on the maps?

Critical Analysis: Complaints, Enforcement & Infrastructure

311 & NYPD Quality of Life Division

The NYPD and the Mayor's Office announced a citywide public safety initiative focused on addressing everyday issues that impact New Yorkers' sense of safety and well-being. The Quality of Life Division unites specially-trained officers — including neighborhood coordination officers, youth coordination officers, and traffic safety officers — into a citywide effort to tackle persistent quality-of-life concerns.

Non-emergency 311 concerns, such as noise complaints, illegal parking, homelessness-related issues, outdoor drug use, and aggressive panhandling have risen steadily across the five boroughs over the last six years and are the focus of this division.

City Council Hearing: Q-Stat System

In March 2025, the NYC City Council Committee on Public Safety held a hearing on quality of life enforcement and the creation of Q-Stat — a new tracking system linking 311-based quality of life complaints to enforcement responses.

Watch the hearing at citymeetings.nyc →

Complaint-Oriented Policing

"[C]omplaint-oriented policing exposes new means of exclusion and fractures of citizenship. Widening the analysis of the policing of marginality beyond the police and politicians to encompass the residents and businesses who directly instigate the policing of the poor exposes the inherent yet underappreciated tension between the insecurity of the housed and insecurity of the unhoused."

"Those with access to private property who feel threatened by those without it are able to call on the police to remove them, which in turn directly increases the insecurity of the unhoused, whose survival is disrupted by criminalization." (p. 795)

Herring, C. (2019). Complaint-oriented policing: Regulating homelessness in public space. American Sociological Review, 84(5), 769–800. Google Scholar

More on 311 & Open Data

Discovering Open Data

Monthly free, online introductory classes on NYC Open Data and 311 — open to anyone.

Learn NYC Open Data →

NYC 311 ChatGPT Open Data Tutor

An educational tool created by Nathan Storey (NYC OTI) for exploring 311 data with AI assistance.

Try the Data Tutor → Read the Medium post →

We Need to Talk About 311 Data

A critical overview of what 311 data can and cannot tell us about cities and the people who live in them.

Read on CivicSource →

Spatial Justice

Going further: How does space relate to data justice and homelessness policy?

What is the relationship between space and social justice? What does zoning have to do with this? Who has a right to shape and use space? How does a data justice lens enhance or complicate this?

Who has a right to exist, and where?

Who polices and controls space? Consider the distinction between public and private spaces — and the in-betweens, like Privately Owned Public Spaces (POPs).

Tools for Spatial Analysis

Privately Owned Public Spaces (POPs) Map

Maps spaces that are privately owned but required to be publicly accessible. Who can occupy them — and on whose terms? What rules govern who can stay?

Explore POPs Map (NYC OpenData) →

NYC Zoning & Land Use Map (ZoLa)

Research zoning regulations, discover new proposals for your neighborhood, and learn where City Planning initiatives are happening throughout the city.

Explore ZoLa →

City of Yes Zoning Reform

NYC's zoning reform initiative. Maps show where the City of Yes could add housing and remove parking requirements across the five boroughs.

Bloomberg mapping feature →

Zoned Out! Race, Displacement, and City Planning in New York City

Tom Angotti & Sylvia Morse (Eds.), Revised Edition — A critical examination of how zoning has been used as a tool of race and class segregation in NYC.

Reflections

Return to these questions after working through the activities:

What are some policies you are thinking about for your projects?
How do you get information for these policies?
What roles do digital technologies and public databases play in your projects? In your work?
What tools do you use to collect and analyze information in your practice?
What have you learned about how homelessness is measured, represented, and understood through information?

Resources & Further Reading

These books influence the thinking behind this lesson. All links go to Bookshop.org, which supports local bookstores.

The Value of Homelessness

Craig Willse

Managing Surplus Life in the United States

The Data Revolution

Rob Kitchin

A Critical Analysis of Big Data, Open Data & Data Infrastructures

Automating Inequality

Virginia Eubanks

How High-Tech Tools Profile, Police, and Punish the Poor

Uneven Innovation

Jennifer Clark

The Work of Smart Cities

The Innovation Complex

Sharon Zukin

Cities, Tech, and the New Economy

Techno-Fixers

Sean F. Johnston

Origins and Implications of Technological Faith

Data Feminism

Catherine D'Ignazio & Lauren F. Klein

Objectivity

Lorraine Daston & Peter Galison

Zone Books