Nonpublic Personal Information (NPI)

Gramm-Leach-Bliley Act (GLBA), 15 U.S.C. § 6801-6809 (2002). Available at:

(4)Nonpublic personal information
(A)The term “nonpublic personal information” means personally identifiable financial information—
(i)provided by a consumer to a financial institution;
(ii)resulting from any transaction with the consumer or any service performed for the consumer; or
(iii)otherwise obtained by the financial institution.
(B)Such term does not include publicly available information, as such term is defined by the regulations prescribed under section 6804 of this title.
(C)Notwithstanding subparagraph (B), such term—
(i)shall include any list, description, or other grouping of consumers (and publicly available information pertaining to them) that is derived using any nonpublic personal information other than publicly available information; but
(ii)shall not include any list, description, or other grouping of consumers (and publicly available information pertaining to them) that is derived without using any nonpublic personal information.

(GLBA, 15 U.S.C. § 6809(4)(B))


Personally Identifiable Financial Information (PIFI)

PIFI is defined in Securities and Exchange Commission (SEC), Final Rule: Privacy of Consumer Financial Information (Regulation S-P) 17 CFR Part 248 (2000). Available at:

Both the GLBA and the regulations define NPI[5] in terms of PIFI.
The GLBA does not define PIFI but the FTC regulations define the term to mean any information:
(i) A consumer provides to you [the financial institution] to obtain a financial product or service from you;
(ii) About a consumer resulting from any transaction involving a financial product or service between you and a consumer; or
(iii) You otherwise obtain about a consumer in connection with providing a financial product or service to that consumer.

Automating the Forced Removal of Children in Poverty

Quote 1

Where the line is drawn between the routine conditions of poverty and child neglect is particularly vexing. Many struggles common among poor families are officially defined as child maltreatment, including not having enough food, having inadequate or unsafe housing, lacking medical care, or leaving a child alone while you work. Unhoused families face particularly difficult challenges holding on to their children, as the very condition of being homeless is judged neglectful.

Quote 2:

The AFST sees the use of public services as a risk to children. A quarter of the predictive variables in the AFST are direct measures of poverty: they track use of means-tested programs such as TANF, Supplemental Security Income, SNAP, and county medical assistance. Another quarter measure interaction with juvenile probation and CYF itself, systems that are disproportionately focused on poor and working-class communities, especially communities of color. The juvenile justice system struggles with many of the same racial and class inequities as the adult criminal justice system. A family’s interaction with CYF is highly dependent on social class: professional middle-class families have more privacy, interact with fewer mandated reporters, and enjoy more cultural approval of their parenting than poor or working-class families.

Quote 3:

We might call this poverty profiling. Like racial profiling, poverty profiling targets individuals for extra scrutiny based not on their behavior but rather on a personal characteristic: living in poverty. Because the model confuses parenting while poor with poor parenting, the AFST views parents who reach out to public programs as risks to their children.

Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor by Virginia Eubanks

First They Came for the Poor

…one day in early 2000, I sat talking to a young mother on welfare about her experiences with technology. When our conversation turned to EBT cards, Dorothy Allen said, “They’re great. Except [Social Services] uses them as a tracking device.” I must have looked shocked, because she explained that her caseworker routinely looked at her purchase records. Poor women are the test subjects for surveillance technology, Dorothy told me. Then she added, “You should pay attention to what happens to us. You’re next.”

Dorothy’s insight was prescient. The kind of invasive electronic scrutiny she described has become commonplace across the class spectrum today.

Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor by Virginia Eubanks

A Feedback Loop of Injustice

Marginalized groups face higher levels of data collection when they access public benefits, walk through highly policed neighborhoods, enter the health-care system, or cross national borders. That data acts to reinforce their marginality when it is used to target them for suspicion and extra scrutiny. Those groups seen as undeserving are singled out for punitive public policy and more intense surveillance, and the cycle begins again. It is a kind of collective red-flagging, a feedback loop of injustice.

Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor by Virginia Eubanks

Data Analysis: Who Are The Homeless (2016)?

Trying to place ‘most of the homeless’ into different categories of deserving and undeserving poor is a common element in virtually every conversation or debate about homelessness, poverty and poverty survivors. The numbers are often sliced, diced and presented in a dozen different ways, making comparative analysis and logical conclusions difficult (at best).

This is my attempt to collect current data (available freely through the internet) and present the numbers in a reasonably easy-to-understand manner.

The primary question being answered: Who are the homeless?

Data Differences

I’ve included a list of links to key sources of data on Homelessness at the end of this post. All of these resources are academically respected and frequently cited in articles and other forms of research. Unfortunately, the data presented often contains inconsistencies that must be identified and addressed before completing a truly effective analysis or a clear presentation of high-level data. These inconsistencies do not negate the quality of the data or the effectiveness of the research, they are simply the natural outcome of a data-collection survey that (literally) examines millions of people.

For the purposes of this post, I have decided to focus on presenting the data contained within a single data source: HUD Exchange.

Step 1: Analysis of Data Collection Techniques

It’s important to begin the analysis by getting an understanding of the methods used during collection. An examination of the HUD Point in Time (PIT) Count Implementation Tools provides the following key details:

Data Collection Personnel consist of average people (volunteers), professionals in the ‘Helping Services’ (e.g.: homeless shelter workers), formerly homeless people and currently homeless people. According to the Tips For Including People Experiencing Homelessness (PDF) reference sheet, formerly and currently homeless are employed as Subject Matter Experts (SME) and may or may not be paid for their assistance.

Pre-Selected Data Categories outlined in the PIT Count Planning Worksheet (PDF) are detailed, extensive and specific. The data that collection personnel are expecting to find and, therefore, seeking out is clearly defined. The data outlined in the Sub-population Crosswalk (PDF) survey instrument is limited and specific. The data collection personnel are expecting to find and, therefore, seeking out is both clearly defined and restrictive, presenting the possibility of missed data points (e.g.: people not included in the count because they are not ‘real’ homeless) or inflated/inaccurate data points (e.g.: placing people in non-applicable categories for the sole purpose of including the data somewhere). The data points contained within the Sub-population Crosswalk (PDF) survey instrument are as follows:

  • Chronically Homeless Individuals or Families (based on family head of household)
  • Veteran
  • Adults with a Serious Mental Illness
  • Adults with a Substance Use Disorder
  • Adults with HIV/AIDS
  • Victims of Domestic Violence

Database Affected Data Points are a possibility due to the nature of the data contained with the Homeless Management Information System (HMIS), as outlined in the Sheltered PIT Count and HMIS Data Element Crosswalk (PDF) guide. Based on the categories and subcategories of data contained within the database, the expectations surrounding the situations of all homeless are clearly defined. This presents the possibility of inaccurate or inflated data points resulting from workers trying to find a way to enter data into the database.

Flexibility of Toolkit in Data Collection suggests that more extensive and (potentially) accurate data is being collected than may (or may not) be found within the databases. The Point In Time (PIT) Survey Tools (HTML) include forms that specifically address situations where the individuals conducting the survey are unable to talk to the individuals being counted and the answers include ‘unsure’ or ‘unknown.’ In other words, a data point may consist of a family that is found sleeping outside, but the collector is unable to access the location or communicate with the individuals in question (e.g.: does not want to wake them up, cannot speak their language, etc.) so it is not possible to verify whether the family is truly homeless or dealing with some other situation.

Inherent Data Collection Problems are centered around evaluating the entirety of a poverty survivors situation through distant observation or a single face-to-face interaction. Accurately identifying an age or race can be extremely difficult under these circumstances. Correctly evaluating mental health and assessing whether or not an individuation meets the ‘chronically homeless’ definition are nearly impossible.

Inherent Data Collection Strengths are in the total count of human bodies. The PIT count provides a total number of people who are living on the street or in shelters during a specific period of time. While exact ages are difficult to pinpoint, total numbers of individuals falling within per-defined age ranges are reasonably reliable. Total numbers of family groups and children, teens, adults and the elderly who are trying to survive the streets alone are also reasonably reliable. Therefore, the strength is in the reasonable reliability of the high-level total counts.

Step 2: General Examination of Raw Data

Positive: The HUD data is clear and easy to decipher. It provides total counts for high-level categories, divided by state and geographic region.

Negative: The revisions tab lists changes to historic data that have occurred since 2007 (earliest available data). The changes listed are significant. However, the changes are also limited to select portions of historic data and do not indicate that equally significant changes will be made throughout all bodies of data.

Step 3: Data Analysis

Based on my analysis of the data collection methods, I focused on the strongest data points available.

Percentages: All percentages are a comparison to the total number of homeless people in the United States during the 2016 PIT count. Because the totals change, depending on the data being presented, the specific totals used to generate the percentages are included at the top of each chart followed by 100%.

Children and Youth: The data provided by HUD does not provide a high-level total count of all children and/or youth included in the PIT count. There are several subcategories focusing on children and youth and I have summed these categories to create a rough total, but I suspect this number represents BOTH overlap in data categories and a a significantly deflated total. Without knowing the total number of children and youth that are included in the total number of people ‘In Families’ it’s impossible to calculate the total number of children and youth.

Total Homeless, 2016
Count Percentage
Total Homeless, 2016 549,928 100.00%
Sheltered Homeless, 2016 373,571 67.93%
Unsheltered Homeless, 2016 176,357 32.07%


Sheltered Homeless, 2016
Count Percentage
Sheltered Homeless, 2016 373,571 100.00%
Sheltered Homeless Individuals, 2016 198,008 53.00%
Sheltered Homeless People in Families, 2016 175,563 47.00%


Unsheltered Homeless, 2016
Count Percentage
Unsheltered Homeless, 2016 176,357 100.00%
Unsheltered Homeless Individuals, 2016 157,204 89.14%
Unsheltered Homeless People in Families, 2016 19,153 10.86%


Families Vs Individuals, 2016
Count Percentage
Total Homeless, 2016 549,928 100.00%
Homeless Individuals, 2016 355,212 64.59%
Homeless People in Families, 2016 194,716 35.41%


Homeless Subcategories, 2016
Count Percentage
Total Homeless, 2016 549,928 100.00%
Total Subcategories 248,419 45.17%
Total Youth and Children 104,474 19.00%
Homeless Unaccompanied Youth (Under 25), 2016 35,686 6.49%
Homeless Unaccompanied Children (Under 18), 2016 3,824 0.70%
Homeless Unaccompanied Young Adults (Age 18-24), 2016 31,862 5.79%
Parenting Youth (Under 25), 2016 9,892 1.80%
Parenting Youth Under 18, 2016 92 0.02%
Parenting Youth Age 18-24, 2016 9,800 1.78%
Children of Parenting Youth, 2016 13,318 2.42%
Homeless Veterans, 2016 39,471 7.18%
Chronically Homeless, 2016 86,132 15.66%

Data Sources

Data Provided by Homeless Services Providers (smaller scale)