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Homelessness Analysis

How We Conducted This Analysis

We conducted this analysis using data from the following sources.

    Homeless Population by Region:

  • Federal grant award data from
  • Point-in-Time (PIT) estimates of people experiencing homelessness from the Department of Housing and Urban Development's (HUD's) 2017 Point-in-Time (PIT) Data

    Comparing and Clustering Continuum of Care Areas:

  • Point-in-Time (PIT) estimates of people experiencing homelessness from the Department of Housing and Urban Development's (HUD's) 2017 Point-in-Time (PIT) Data
  • Federal grant award data from
  • Area , Population , Median Gross Rent , and median income by quintile data from the Department of the Census
  • Property crime data from the FBI Uniform Crime Report
  • Data on drug use, alcohol abuse, and mental health from HHS's National Survey on Drug Use and Health (NSDUH)
  • Selecting programs

    To accurately determine the amount the federal government spends addressing homelessness, we reviewed federal program descriptions using the CFDA, looking for any mention of keywords related to homelessness.

    We developed a ranking system to assess whether individuals experiencing homelessness would directly benefit from these programs, and categorized these programs by the type of service they provide. (Check out our Github page if you would like to learn more about the development of our ranking and categorization systems.)

    For example, the Homeless Veterans Reintegration Program provides services to homeless veterans looking for jobs, which we classified as employment related. We presented these programs to the U.S. Interagency Council on Homelessness, who recommended that we remove several programs where the preponderance of funds went to beneficiaries other than individuals experiencing homelessness.

    Identifying grants data and linking to continuum of care area.

    We used this dataset to identify all grant spending from for the programs we determined to have a direct relation to homelessness. As part of our analysis, we identified two categories of grant awards in data that are relevant to our work:

    • Grant awards for a Continuum of Care area (an administrative region recognized by HUD) or were awarded to organizations within its geographic area;
    • Grant awards that didn't have specific location data; and
    • Grant awards that supported organizations across a state.

    To create our visualization, we mapped funding amounts for the programs that fell within a Continuum of Care area using mapping files provided by HUD. The data used in this story was updated as of January 2018.

    Linking additional data to Continuum of Care areas

    Area and population:

    Most continuum of care areas are based on the geographic boundaries of one or more counties. Where that is true for counties, the data for counties in the Continuum of Care areas were added together to get the total for the Continuum of Care area. In instances where boundaries were determined by cities or cities and counties, the data for city was subtracted from the surrounding county and the county/city data was allocated to the correct Continuum of Care area.

    Income and Rent:

    The lowest geographic area for income was county. For this reason both counties and cities were linked to the data for their county. Using the population data described above, we used a weighted average based on me median lowest quintile income of the geographic area that make up each Continuum of Care area based on the population of each area.


    Crime rates are provided on a state and agency level. In cases where a continuum of care is based upon only one county, the crime rate for the county's largest city was used for the Continuum of Care area. For areas based upon a city, the city rate was used. For Continuum of Care areas based on more than one county, the state rate was used.

    Mental Health, illicit drug use, and alcohol dependence or abuse:

    The data for these metrics are based upon geographic areas created by HHS. Like Continuum of Care areas, these areas are largely based upon counties. We linked these geographic areas to counties and created a weighted average for each Continuum of Care area based upon population.

    This data was linked to the HUD PIT Count and Housing Inventory Count as well as the total funding data.

    Creating clusters

    To find the optimal number of clusters to target for this analysis we used a method that looks at the percentage of variance explained as a function of the number of clusters, the optimal value comes when the marginal gain for adding more clusters begins to level off. We chose five clusters as our target.

    We tested several unsupervised clustering algorithms, the method we employed was Fuzzy C Means.

    The clusters were then further segmented by population density in an attempt to manage the size of the final clusters, but also to visualize areas with the approximately similar behavior and conditions for comparison.

    Notes on the data


    Measured in square miles of land area (not including water area); data from 2010


    Estimated 2016 data

    Median Rent:

    Estimated median gross rent per month


    2016 Annual income estimates by quintile. We used the lowest quintile.


    2014 Property crime rate per 100,000 people

    Drug Use:

    Illicit drug use in the past month among individuals 12 or over, percentages. Data from 2014.

    Illicit Drugs include, marijuana/hashish, cocaine (including crack), heroin, hallucinogens, inhalants, or prescription-type psychotherapeutics used nonmedically, including data from original methamphetamine questions but not including new methamphetamine items added in 2005 and 2006

    Alcohol Dependence:

    Alcohol dependence or abuse in the past year among individuals age 12 or older, percentages. Data from 2014.

    Alcohol Dependence or abuse is based on definitions found in the 4th edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV).

    Metal Illness:

    Serious mental illness in the past year among adults aged 18 or older, percentages. Data from 2014.

    Serious mental illness (SMI) is defined as having a diagnosable mental, behavioral, or emotional disorder, other than a developmental or substance use disorder, as assessed by the Mental Health Surveillance Study (MHSS) Structured Clinical Interview for the Diagnostic and Statistical Manual of Mental Disorders-Fourth Edition-Research Version-Axis I Disorders (MHSS-SCID), which is based on the 4th edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV). SMI included individuals with diagnoses resulting in serious functional impairment. For details, see Section B in the "2011-2012 National Survey on Drug Use and Health: Guide to State Tables and Summary of Small Area Estimation Methodology" at

    Notes on the map:

    • Since the shapefile used in the above visualization was created, several CoC's have merged with the balance of state CoC. These CoC's are: IN-500, LA-504, MA-518, and ME-502. We edited the shape file so that the hover over and color for these CoC's reflect the new information but the boundaries for the old CoC are still included in the map.