The Case for a Data-Driven Approach to Tackle Homelessness

Cities across the globe are finding innovative ways to apply smart technologies to help improve the lives of their citizens and communities. At the Smart Cities New York conference this week, public sector and business leaders will gather to share pain points and best practices. One of those shared struggles is homelessness. While our national economy has flourished since the economic downturn of the late 2000s, many of our country’s most vulnerable citizens have been left behind. To the everyday urban citizen, the problem of homelessness may seem never-ending and never-changing. However, it is changing, and in most places it is for the worse.

Homelessness has become an ever-increasing issue with which cities and communities across the United States must grapple. In 2017, 553,000 Americans were homeless, with one out of every five of them living in New York City or Los Angeles. Today, homelessness in New York City and Los Angeles has reached crisis levels. New York has 78 percent more people sleeping in shelters than 10 years ago; Los Angeles saw their numbers rise 20 percent in 2017. A multitude of factors, such as a rise in housing prices and rent as well as wage stagnation, have led to an increase in the number of people who cannot afford to live in urban areas. Properly addressing the root causes and attempting to assist those affected by this complex issue means organizations need to better understand the communities that are being impacted, as well as the extent of the problem.

I was born and raised in Brooklyn. I have seen and engaged in New York City’s homelessness problem at the grassroots and local neighborhood level. When I became a White House Fellow in 2012, I learned a great deal about applying data-driven policy initiatives. I learned that data-driven policies should facilitate efficient execution of a city’s day-to-day business, drive quantitative analysis of a city’s operations, identify ways to use emerging urban datasets in order to construct a ‘digital twin’; and promote transparency during data use and building algorithms have on people’s daily lives.

In 2014, as the Chief Analytics Officer for NYC, I began to build practical solutions to complex city problems at the operational and policy level. I know the role that data, analytics, and algorithms should play in providing support to citywide efforts.

New York City Mayor Bill de Blasio recently unveiled an ambitious plan to place 90 new homeless shelters in and around New York City. The plan, as proposed, is designed to reduce the homeless population by 2,500 people over the next five years, a mere five percent of the current population. In a city with more than 8.5 million people, helping only 2,500 people doesn’t seem like a great impact. I believe we owe our homeless men, women, and children in New York City more than a one percent reduction per year solution.

What’s missing from the mayor’s plan is an urban analytics approach that methodically addresses the many factors of this complex problem. Urban analytics empowers us to convert layers of data on poverty, unemployment, and affordable housing into action. Homelessness is a complex issue, that requires a holistic view of many types of data. Using mapping techniques, data can be showcased and correlations found, such as:

  • Where are different types of homelessness-related requests happening?

  • How available are human services to those most at risk of becoming homeless?

  • And, very important, what disparities might exist between availability of services and areas where residents are most at risk?

We can also look at physical or mental health, substance abuse, family and relationship breakdown, and domestic abuse to make certain we provide a safety net for these underlying issues before individuals find themselves on the streets. Urban analytics empowers us to look at the city overall, at each neighborhood, and at each individual case.

For example, are the locations of food stamp centers, where hungry homeless persons might find assistance, and the locations of panhandling reports, correlated? That’s a tough question. But if we look at this information on a map, create 10-minute walk-times around food stamp centers and pair it with aggregated data about panhandling reports, we can explore this question more deeply.

Through the analysis in the image, we can see there may be a positive correlation between distance from food stamp centers and increased reports of panhandling.

Urban analytics can detect, diagnose, and monitor problems in the same way an MRI scan helps doctors. Through visualization of data, it provides a common language to center efforts around a common goal. It underwrites the capacity to micro target solutions for specific problems or individuals, to provide precision on decisions where low investments can yield high impacts.

On the New York City analytics team, we thought of ourselves in terms used by the military special forces. We were the highly trained individuals summoned to fix specific citywide problems.

Here is how this looked using a real situation from my time as the Director of the New York City Mayor’s Office of Data Analytics (MODA). Playing a key part in the NYC Mayor’s effort to combat homelessness and low-income housing issues within the city, the NYC Commission on Human Rights (CHR) began moving towards a more proactive strategy for identifying landlords in any of the five boroughs who may be discriminating against potential renters based on their source of income. In the past, the CHR served mostly as a mechanism to receive reports of this alleged conduct. Their process was reliant on people reporting a possible violation, which would only get reported after the alleged action took place.

CHR was looking to be more proactive, so they reached out to MODA to partner with them and implement a data-driven strategy for income discrimination enforcement, which Jessica McKenzie reported on for Civicist last year. The city problem was “how do we proactively drive down housing-based income discrimination?” The translated analytics problem was “how do we use citywide data to predict where a landlord may likely refuse a renter an opportunity to rent an apartment based on their use of a housing voucher?” This was broken down to even more granular analytics questions after more investigation, such as:

  • How do we geospatially define a NYC neighborhood?

  • What data can we use to test the crime/schools/housing stock hypothesis?

  • How can we group buildings into ownership portfolios?

  • What do we know about each building/tax lot in NYC?

Essentially the plan was to use location intelligence to identify neighborhoods that would be targeted for inspection. In this instance, we looked at Neighborhood Tabulation Areas (NTA)as defined by the NYC Department of City Planning data. We then looked to characterize a neighborhood for likelihood of issues with discrimination. In short, we were trying to characterize neighborhoods where renters would be happy to live, that had low income housing, but surprisingly low or no instances of voucher usage.

For this, once we identified all of the NTAs across the city, five variables describing each NTA were used: population, mean Student Achievement Score, NYPD Felony Crime per Capita, total rental housing stock, and total count of Housing Choice Vouchers (HCVs) from the federal government. Once that was complete, we began to build ownership portfolios. Building a portfolio in this case was just a way of grouping buildings (or rather lots, which is the ownership structure here in NYC) based on common variables.

The CHR had “testers” that posed as potential candidates for housing and would take the location of target neighborhoods and test whether a landlord would commit income discrimination. The result was CHR filing a record-high 120 income discrimination complaints against landlords.

We were an asymmetric force that worked to find the root cause and distill a problem down to a course of action. We built data-driven solutions with mechanisms to continuously fine tune our model and approach. We employed open data so that our work would be transparent, accountable, and replicable.

Central to the mayor’s current proposal is the question of where to place homeless shelters to have the greatest impact. With urban analytics, you can run simulations and see, depending on location, how each of the 90 shelters would affect the overall problem. Diving a little deeper, you can optimize the placement of the 90 shelters and know in advance what resources will be required at each location.

Urban analytics provides a framework to look at homelessness in a new light. It underpins decision making on individual initiatives, and provides the glue to integrate insights from each initiative for overall understanding.

Homelessness cannot be solved without the capable leadership of a city, input and involvement from the people in our communities, and the insight and experiences of the homeless families and children who are looking to us for a solution. Like the best of technologies, urban analytics provides a force multiplier in the effort to gain ground on the still-mushrooming problem of homelessness in New York City and beyond.

Amen Ra Mashariki is a fellow at Harvard University’s Ash Center for Democratic Governance and Innovation and the urban analytics lead at Esri.