harnessing the power of data, for good

Time For Homes uses data to do good.

Time For Homes uses data to do good.

In keeping with the main values and goals of Time For Homes, we created the Data for Good program to use and gather data related to Homelessness and its many associated issues.

We use the mathematics and data science that companies use to pad their bottom line, to solve the problems of the world. Though humans, and our issues, are infinitely more complex than supply chains or other issues that can be distilled into profit it is possible to quantify much of our existence. In the end, the program operates under a simple philosophy: “One cannot solve a problem if they do not know how the problem works.”

It seems rather basic, but the available data out there is just insufficient. Thus we are quantifying how homelessness works. We are quantifying the causes of how people end up experiencing homelessness and what helps them regain housing security. We aren’t interested in the straightforward, pat answers such as lack of income or lack of available programs. We want to explore things like did climate change produce a bad

storm that caused someone to get behind on bills—and similar correlations. Identifying these enables us to find answers where they weren’t expected.

There are many aspects of the advancing sciences and mathematics available to us, allowing the discover of heretofore unknown complications. We are applying these to homelessness and poverty.

Numbers in New York

As a digestible example, please explore this map we created that shows levels of homelessness in New York State as of 2019 per HUD’s (flawed) annual point in time count.


Analysis is the major overarching goal of the Data for Good program. Taking the gathered data, we apply many analysis techniques to find patterns and correlations that can help inform policy and eventually enable us to highlight trends and predict future states of the world.

The main goal of this analysis is to make sure that we know what is going on and why it happens in the way it does. We want to know about things that can cause, or fix, homelessness. We want to know about people who are more at risk, and we want to be able to point to exactly why. We want to be able to help NPOs and the government fix the patchwork solution that is currently in place.

We want to actually figure out how to solve the problem, not simply treat the symptoms.

Explore some of the techniques we use in our analysis efforts:

This analysis technique is all about extrapolating from incomplete data to make a more complete picture. It starts with the idea that we know something about how the data is collected and we have that collected data so we can use those two pieces of information to build a comprehensive view. Usually, this technique is applied to things like CT scans or MRIs; only recently has compressed sensing been used in the field of data science in a use case such as ours–we are on the leading edge. The key difficulty lies in the mathematical quantification of the CT/MRI sensing technology compared to the data collection methods in human surveys like those relating to homelessness.

Another key analysis method we use is multilevel methods. Multilevel methods are meant to handle non-independent samples and stitch them together into one more complete image of what is happening. There is promising work being done on police brutality and the murder of civilians that leads us to be hopeful this method can be used to produce better estimates on the homeless problem.

This method works hand in hand with each other method, building on patterns found by fixing the data with the above methods, and bringing in the data science below to make more abstract models. The main purpose of this modeling is to understand some concept or area well enough to quantify it mathematically and then interpolate and extrapolate new information. If we can predict what happens, we can use modeling to test policies and events. This would be very useful in confirming what we know to be true or to help try and fix things till we find something that works.

Disease Modeling

One suggested method of solving homelessness is to treat it like a disease, something that you can prescribe treatment to, and it seems to work. With that thought, we think it is possible to model homelessness as a disease using the already existing techniques to make a model that can help us understand how homelessness works on a population.

Stochastic Modeling

Stochastic Modeling is the technique of modeling with built-in unknowns. Things like weather and finances are some key examples of the power of stochastic modeling. In a world where we don’t know exactly what might happen, stochastic modeling tries to build that element of unknown into the model itself.

This is perhaps the widest net method. It includes things as simple as simply creating visualizations that make data more readable, to using machine learning and data mining to create a word mining AI to predict possible spikes in service demands using keyword searches. This aspect of the program is perhaps the most nebulous because it is also the most flexible and includes many small techniques whose veracity has yet to be determined in the context of our problem. That said, there are many well-established data science applications and we hope to bend as many of them to uses similar to the example above to help us understand what exactly homelessness is.