Table of contents
- Story
- Slides
- Slide 1 Data Science for Homelessness Data: QlikView, Tableau, & TIBCO Spotfire
- Slide 2 GartnerBI analyst JoshParenteau: BI MagicQ: "buyers are buying ease-of-use: Tableau, QlikView & TIBCO Spotfire are easy to use"
- Slide 3 Averages Hide Information: Analysis of the US Homeless Population
- Slide 4 Homelessness Data Sources
- Slide 5 UK Government Homelessness Statistics: Statutory Homelessness
- Slide 6 Homeless Analytics Initiative: Dashboard
- Slide 7 Homelessness Analytics Initiative Methodology: Data.HUD.gov
- Slide 8 Homelessness Analytics Initiative Methodology: Data.HUD.gov Data Sets
- Slide 9 Highlights from the 2014 Point-in-Time Count of People Experiencing Homelessness in Fairfax Virginia County
- Slide 10 Data Science for Homeless Data: MindTouch Knowledge Base
- Slide 11 Data Science for Homeless Data: Excel Knowledge Base
- Slide 12 Data Science for Homeless Data: Spotfire Cover Page
- Slide 13 Data Science for Homeless Data: Spotfire United Kingdom Homelessness
- Slide 14 Data Science for Homeless Data: Spotfire Homelessness Analytics Initiative
- Slide 15 Data Science for Homeless Data: Spotfire Fairfax Virginia County
- Slide 16 Some Conclusions and Recommendations
- Spotfire Dashboard
- Slides
- Slide 1 Data Science for Homeless Data: Tableau
- Slide 2 Meet Tableau 9.0
- Slide 3 Features
- Slide 4 Specifications
- Slide 5 Try It Free
- Slide 6 Download Instructions
- Slide 7 Other Information
- Slide 8 Connect
- Slide 9 Connect to Excel HomelessData
- Slide 10 Preview Data Source
- Slide 11 Manage Metadata
- Slide 12 Blank Worksheet
- Slide 13 Visualization Worksheet
- Slide 14 Welcome to Tableau Desktop
- Slide 15 Tableau Training & Tutorials
- Slide 16 Visual Gallery
- Slide 17 Seattle Real Estate
- Slide 18 Super Store
- Slide 19 Regional
- Slide 20 World Indicators
- Research Notes
- Homelessness Data Sources
- UK Government Homelessness Statistics
- Homeless Analytics Initiative
- Homelessness Analytics Initiative Methodology
- Methods and Data Sources Overview
- Levels of Geography in the Homelessness Analytics Initiative
- Data Sources
- 50th Percentile Rent Estimates
- American Community Survey (ACS)
- Behavioral Risk Factor Surveillance System (BRFSS)
- Community Health Status Indicators (CHSI)
- County Health Rankings
- Decennial Census
- Department of Veterans Affairs (VA) Homeless Program Data
- Fair Market Rents (FMRs)
- FBI Uniform Crime Reports (UCR)
- Housing Inventory Chart
- National Association of State Budget Officers’ State Expenditure Report
- National Survey on Drug Use and Health (NSDUH)
- Point-In-Time (PIT) Estimates of Homelessness
- Picture of Subsidized Households Dataset
- Supplemental Nutrition Assistance Program (SNAP) Data
- Social Security Administration (SSA) Annual Statistical Supplement
- Veterans Benefit Administration (VBA) Compensation and Pension by County Dataset
- Complete List of Indicators Included in the Homelessness Analytics Initiative
- Procedures Used to Transform County Level Data Sources Into CoC Level Indicators
- Procedures Used to Calculate Rates of Homelessness and Other Indicators From Multiple Sources
- Procedures Used to Create the Forecasting Tool
- Homelessness Analytics Initiative Methodology
- Fairfax Virginia County
- Averages Hide Information: Analysis of the US Homeless Population
- PART 1 Point-in-Time Estimates of Homelessness
- Acknowledgements
- Key Findings
- Definition of Terms
- Continuums of Care (CoC)
- Chronically Homeless People in Families
- Chronically Homeless Individuals
- Emergency Shelter
- Other Permanent Housing
- Rapid Rehousing
- Permanent Supportive Housing
- People in Families
- Point-in-Time Counts
- Safe Havens
- Sheltered Homeless People
- Transitional Housing Program
- Unaccompanied Children
- Unaccompanied Youth
- Unsheltered Homeless People
- Progress on the Federal Strategic Plan to Prevent and End Homelessness
- About this Report
- SECTION 1
- SECTION 2
- SECTION 3
- SECTION 4
- SECTION 5
- SECTION 6
- SECTION 7
- NEXT
- Story
- Slides
- Slide 1 Data Science for Homelessness Data: QlikView, Tableau, & TIBCO Spotfire
- Slide 2 GartnerBI analyst JoshParenteau: BI MagicQ: "buyers are buying ease-of-use: Tableau, QlikView & TIBCO Spotfire are easy to use"
- Slide 3 Averages Hide Information: Analysis of the US Homeless Population
- Slide 4 Homelessness Data Sources
- Slide 5 UK Government Homelessness Statistics: Statutory Homelessness
- Slide 6 Homeless Analytics Initiative: Dashboard
- Slide 7 Homelessness Analytics Initiative Methodology: Data.HUD.gov
- Slide 8 Homelessness Analytics Initiative Methodology: Data.HUD.gov Data Sets
- Slide 9 Highlights from the 2014 Point-in-Time Count of People Experiencing Homelessness in Fairfax Virginia County
- Slide 10 Data Science for Homeless Data: MindTouch Knowledge Base
- Slide 11 Data Science for Homeless Data: Excel Knowledge Base
- Slide 12 Data Science for Homeless Data: Spotfire Cover Page
- Slide 13 Data Science for Homeless Data: Spotfire United Kingdom Homelessness
- Slide 14 Data Science for Homeless Data: Spotfire Homelessness Analytics Initiative
- Slide 15 Data Science for Homeless Data: Spotfire Fairfax Virginia County
- Slide 16 Some Conclusions and Recommendations
- Spotfire Dashboard
- Slides
- Slide 1 Data Science for Homeless Data: Tableau
- Slide 2 Meet Tableau 9.0
- Slide 3 Features
- Slide 4 Specifications
- Slide 5 Try It Free
- Slide 6 Download Instructions
- Slide 7 Other Information
- Slide 8 Connect
- Slide 9 Connect to Excel HomelessData
- Slide 10 Preview Data Source
- Slide 11 Manage Metadata
- Slide 12 Blank Worksheet
- Slide 13 Visualization Worksheet
- Slide 14 Welcome to Tableau Desktop
- Slide 15 Tableau Training & Tutorials
- Slide 16 Visual Gallery
- Slide 17 Seattle Real Estate
- Slide 18 Super Store
- Slide 19 Regional
- Slide 20 World Indicators
- Research Notes
- Homelessness Data Sources
- UK Government Homelessness Statistics
- Homeless Analytics Initiative
- Homelessness Analytics Initiative Methodology
- Methods and Data Sources Overview
- Levels of Geography in the Homelessness Analytics Initiative
- Data Sources
- 50th Percentile Rent Estimates
- American Community Survey (ACS)
- Behavioral Risk Factor Surveillance System (BRFSS)
- Community Health Status Indicators (CHSI)
- County Health Rankings
- Decennial Census
- Department of Veterans Affairs (VA) Homeless Program Data
- Fair Market Rents (FMRs)
- FBI Uniform Crime Reports (UCR)
- Housing Inventory Chart
- National Association of State Budget Officers’ State Expenditure Report
- National Survey on Drug Use and Health (NSDUH)
- Point-In-Time (PIT) Estimates of Homelessness
- Picture of Subsidized Households Dataset
- Supplemental Nutrition Assistance Program (SNAP) Data
- Social Security Administration (SSA) Annual Statistical Supplement
- Veterans Benefit Administration (VBA) Compensation and Pension by County Dataset
- Complete List of Indicators Included in the Homelessness Analytics Initiative
- Procedures Used to Transform County Level Data Sources Into CoC Level Indicators
- Procedures Used to Calculate Rates of Homelessness and Other Indicators From Multiple Sources
- Procedures Used to Create the Forecasting Tool
- Homelessness Analytics Initiative Methodology
- Fairfax Virginia County
- Averages Hide Information: Analysis of the US Homeless Population
- PART 1 Point-in-Time Estimates of Homelessness
- Acknowledgements
- Key Findings
- Definition of Terms
- Continuums of Care (CoC)
- Chronically Homeless People in Families
- Chronically Homeless Individuals
- Emergency Shelter
- Other Permanent Housing
- Rapid Rehousing
- Permanent Supportive Housing
- People in Families
- Point-in-Time Counts
- Safe Havens
- Sheltered Homeless People
- Transitional Housing Program
- Unaccompanied Children
- Unaccompanied Youth
- Unsheltered Homeless People
- Progress on the Federal Strategic Plan to Prevent and End Homelessness
- About this Report
- SECTION 1
- SECTION 2
- SECTION 3
- SECTION 4
- SECTION 5
- SECTION 6
- SECTION 7
- NEXT
Story
Data Science for Homelessness Data: QlikView, Tableau, & TIBCO Spotfire
The recent Gartner BI Bakeoff: See Winner of the BI Bake Off, The BI Bake Off Comes to Gartner, and Gartner Business Intelligence & Analytics Summit, led me to do a Proposed Meetup June 1st: Data Science for Homelessness Data with Spotfire, and invite QlikView and Tableau to participate.
The agenda is:
- 6:30 p.m. Welcome and Introduction (New Tutorial and Mentoring) Slides (PPT) See Slides below
- 6:45 p.m. QlikView, Monica McEwen, Federal Account Manager
- 7:15 p.m. Brief Member Introductions
- 7:30 p.m. Tableau, Gerard Valerio, DATAvangelist Slides (PPT) See Slides below
- 8:00 p.m. Spotfire, Michael OConnell, Chief Data Scientist
- 8:30 p.m. Open Discussion
- 8:45 p.m. Networking
- 9:00 p.m. Depart
Please note that I did quick tutorial slides below for Tableau since their representative had a last minute scheduling problem and we will hopefully have them at a future date since other BI Platforms have expressed interest as well.
MORE TO FOLLOW
Slides
Slide 1 Data Science for Homelessness Data: QlikView, Tableau, & TIBCO Spotfire
http://semanticommunity.info
http://www.meetup.com/Federal-Big-Data-Working-Group/
http://www.meetup.com/Virginia-Big-Data-Meetup/
http://www.meetup.com/Northern-Virginia-Semantic-Web-Meetup/
http://semanticommunity.info/Data_Sc...g_Group_Meetup
Slide 2 GartnerBI analyst JoshParenteau: BI MagicQ: "buyers are buying ease-of-use: Tableau, QlikView & TIBCO Spotfire are easy to use"
Slide 3 Averages Hide Information: Analysis of the US Homeless Population
http://www.tibco.com/blog/author/michael-oconnell/
http://www.tibco.com/blog/?p=15773
Slide 5 UK Government Homelessness Statistics: Statutory Homelessness
Slide 8 Homelessness Analytics Initiative Methodology: Data.HUD.gov Data Sets
Slide 9 Highlights from the 2014 Point-in-Time Count of People Experiencing Homelessness in Fairfax Virginia County
Slide 10 Data Science for Homeless Data: MindTouch Knowledge Base
Slide 11 Data Science for Homeless Data: Excel Knowledge Base
Spotfire Dashboard
For Internet Explorer Users and Those Wanting Full Screen Display Use: Web Player Get Spotfire for iPad App
Slides
Research Notes
Winner of the BI Bake Off
by Cindi Howson | April 21, 2015 | 5 Comments
The winner of Gartner’s first BI Bake Off was …. it was a tie!
Or more accurately, the winner depended on whom you asked and what their requirements were. This is, of course, as it should be. I mean, it’s like having Rita and I sample chocolate cake and fruit tart and asking us to pick the winner. I will choose chocolate, every time. Rita, the fruit tart. Meanwhile, Kurt wouldn’t choose either because he’s a mint chocolate chip ice cream man!
With the BI Bake Off, our goal is to have you think about your requirements as we help reveal the strengths and weaknesses of each product. Visual data discovery tools all fit in the same BI category (just as cakes and pies are desserts), but their differences in architecture, analytic power, data connectivity, use of visual perception and ease of use all vary substantially.
There were some amazing discoveries with the data and business question. We asked the vendors to use the same sample data on shelters, homelessness and population trends, facilitated by Posiba, an organization that helps charities leverage data. The data was somewhat messy, a norm for anyone dealing with external data, acquired companies, and so on.
But it’s a departure from BI tools that access nice, cleansed data in a central data warehouse. State and county information was stored in one column that needed splitting, counties combined facilities over time, and years were in multiple tabs on spreadsheets. This kind of messy data is why we are seeing a rise in start ups for self-service data preparation tools as well as so many data discovery vendors baking this into their products (check out the Market Guide for Self-Service Data Preparation and Best Practices in deploying).
The SAS dashboard showed the differences in number of beds in shelters versus homelessness. California has the biggest lack of shelters, but it has shown modest improvement. Michigan meanwhile seems to have closed the gap. I wonder if this is at all related to differences in climate in each state?
TIBCO, meanwhile, focused on Massachusetts where there are more beds than homeless people. At first blush, that’s the good news! But it seems the beds are not located where the homeless people are. Just south of Boston, there are more homeless people than beds. TIBCO also used their location intelligence to show where some can be supported through Veterans Administration facilities (more on their blog here.) My Note: See below
In the last demo topic, we let the vendors choose a cool innovation to showcase, and Tableau chose to publish live to the cloud so that anyone can explore homelessness in their own state, here. As a New Jersey girl, I was disheartened to see the shifts in homelessness by county following hurricane Sandy where homes and shelters were damaged or destroyed, and more people needed temporary housing. Union County, for example, has one of the highest portions of homeless children.
Answering the business question is job one, and technology enables that. Sometimes we have to dig under the covers to see which tools provide the right fit and how they work. Qlik focused on governance, and you can read more on their blog here.
For this initial bake-off, we selected panelists based on number of inquiries but many more vendors participated by using the same sample data and demonstrating capabilities at their booths. MicroStrategy posted a video of what’s coming in version 10. (Note to vendors: feel free to post your link below if you also participated.)
While we had some technical difficulties during the bake off with the video (HDMI and 4 panelists seemed a stretch!), attendee feedback was overwhelmingly positive. So I hope this track will become a mainstay at future Gartner BI summits. Beyond the bake off, if you are looking for analyst opinion on the strengths and weaknesses of many more BI vendors, I see that our new “Critical Capabilities for BI and Analytics Platforms” is in final edit. So watch Twitter or create an alert on Gartner.com for when it publishes! I’m hoping any day now!
The BI Bake Off Comes to Gartner
by Cindi Howson | March 11, 2015 | 1 Comment
Yep, a real live BI bake off among four arch competitors: Qlik, SAS, Tableau, and TIBCO! It’s a first for the Gartner BI Summit, and I hope you will be able to join us March 29, 1 p.m., in Las Vegas.
I’ve been facilitating bake offs for customers and at events such as TDWI and DAMA for several years now. It is a high-action, visual way to get a side-by-side comparison of leading visual data discovery products. The bake off is somewhat terrifying – for vendors and for me – but it’s great for BI buyers. I mean, really, would you rather wade through more paper-based RFP responses? I didn’t think so!
In the bake-off, vendors adhere to scripted demo topics that we’ve selected to best reveal each product’s strengths and weaknesses. And here is a new twist I love: we’ve asked vendors to use a consistent sample data set to allow for clearer comparisons. We’ve partnered with Posiba, an organization that uses data to support charities. For this bake off, we will be using data on homeless shelters, at risk individuals, and Census data. So it’s an educational event as well as a way to use BI for a greater good.
We are limited on time so could only invite four vendors to the main session, but a number of vendors in the exhibit hall will also be participating via scripted demos and the sample data set at their booths.
Live polling allows attendees to vote by use case and criterion on which product best meets your needs. As the facilitator, I’ll let you know where Gartner agrees on particular strengths, or where the demo might have looked better than what we our research finds.
This first bake off will focus on visual data discovery that’s driving much of the current BI buying, confusion abounds, and we’ve seen rapid innovation. If this first bake off goes well, I hope we will be able to add future sessions on particular focus areas. I look forward to seeing you there, and if you can’t attend in person, watch the tweet stream #GartnerBI.
Cindi Howson
Research VP
1 years at Gartner
25 years IT Industry
Cindi Howson is a Research Vice President at Gartner, where she focuses on business intelligence (BI) and analytics. Her work includes writing about market trends, vendors and best practices and advising organizations on these subjects. Read Full Bio
Gartner Business Intelligence & Analytics Summit
Source: http://www.gartner.com/technology/su...acks/day-2.jsp
1:00 PM - 2:45 PM
BI Bake Off: Visual Data Discovery with Qlik, SAS, Tableau and Tibco
Rita L. Sallam , Cindi Howson
BI Bake Off: Visual Data Discovery with Qlik, SAS, Tableau and Tibco
29 March 2015 1:00 PM to 2:45 PM
Visual data discovery tools are the hottest growing market segment in BI. Business users love them for the visual appeal and rapid implementation time, but central IT teams worry about yet another tool to manage and support. What’s changed to make visual discovery tools more important, when is it most suitable, and what are key features to look for when evaluating solutions? The BI Bake Off gives you a hype-free environment to see four vendors in action. Scripted demos focus on the most important requirements and differentiators. Check the website for participating vendors. You will learn:
- Industry trends driving the adoption of visual discovery
- Features to consider when evaluating tools
- Examples of four vendor products with scripted demos
- How to maximize your own scripted demos
Speakers
Speakers: Rita L. Sallam, Research VP, and Cindi Howson, Research VP
Proposed Meetup June 1st: Data Science for Homelessness Data with Spotfire
Impressive thank you: https://twitter.com/moc_tib
I will try to emulate this.
From: Michael O'Connell [mailto:moconnel@tibco.com]
Sent: Monday, March 30, 2015 9:00 AM
To: Brand Niemann
Cc: Rick Schrader
Subject: Re: Proposed Meetup June 1st: Data Science for Homelessness Data with Spotfire
Hi Brand
That could work
See our analysis for Gartner event on my Twitter feed
@moc_tib
Regards
Michael
+1-919-7401560
@moc_tib
On Mar 30, 2015, at 1:57 AM, Brand Niemann <bniemann@cox.net> wrote:
How about our doing a Federal Big Data Working Group Meetup June 1st on Data Science for Homelessness Data with Spotfire?
From: Brand Niemann [mailto:bniemann@cox.net]
Sent: Tuesday, March 24, 2015 7:58 AM
To: 'moconnel@tibco.com'; Rick Schrader (rschrade@tibco.com)
Subject: Homelessness Data for Spotfire Data Science
Hello, Just thought you both should know that you have made requests for suggestions for Spotfire analysis of homeless data (Michael) and participation in a Census Webinar on April 11th (Rich).
Below is the initial search for data sets and the Linkedin dialogue. I can develop this into a Meetup as well.
I like your TIBCO framework on Turning Data Into Value: Integration, Analytics, and Event processing and am using it for our April 20th Meetup on President's Chief Data Scientist and EPA Big Data Analytics: http://www.meetup.com/Federal-Big-Data-Working-Group/events/220799665/
http://semanticommunity.info/Data_Science/Data_Science_for_EPA_Big_Data_Analytics
Best regards, Brand
Statistics: https://www.gov.uk/government/collections/homelessness-statistics
Dashboard: http://homelessnessanalytics.org/
I have worked with them: http://www.fairfaxcounty.gov/homeless/
More to be found
Google Searches related to homelessness data:
homelessness information
homelessness statistics
homelessness conclusion
poverty data
hunger data
crime rate data
homelessness data collection
homelessness data exchange
Recent Dialogue:
Michael O'Connel
Chief Data Scientist at TIBCO Software Inc.
Hi Brand
I'm working on a spatial analysis of homeless people and wondered if u had any suggestions re Spotfire and or R spatial / temporal analysis
I hope this finds you well !
Michael
Michael, I am well thank you.
Do you have a data set that I could look at?
Brand
Brand
thx for email
I have a (confidential) Spotfire 7 analysis file
Do you have Spotfire 7?
Do you have 30 min tomorrow to discuss?
thanks
Michael
Do you have Spotfire 7? Using Spotfire 2.01 Cloud
Do you have 30 min tomorrow to discuss? Yes, late p.m.
- Cindi Howson
- Research VP
Homelessness Data Sources
UK Government Homelessness Statistics
https://www.gov.uk/government/collec...ess-statistics
From:
- First published:
- 13 September 2013
- Last updated:
- 26 March 2015 , see all updates
This collection brings together all documents relating to homelessness and rough sleeping statistics.
This collection contains statistics on statutory homelessness, rough sleeping and homelessness prevention and relief.
Local authorities compiling this data or other interested parties may wish to see the notes and definitions for homelessness which also includes the latest P1E quarterly form and guidance notes.
Guidance for local authorities about recording cases where homelessness is prevented or relieved can be found in P1E guidance: homelessness prevention and relief.
Copies of previously published statutory homelessness quarterly statistical releases are available on request. Please contact homelessnessstats@communities.gsi.gov.uk to obtain copies.
Statutory homelessness
-
Statutory homelessness in England: October to December 2014
- 26 March 2015
- Statistics - national statistics
-
Statutory homelessness in England: July to September 2014
- 11 December 2014
- Statistics - national statistics
-
Statutory homelessness in England: April to June 2014
- 25 September 2014
- Statistics - national statistics
-
Statutory homelessness in England: January to March 2014
- 31 July 2014
- Statistics - national statistics
Homelessness prevention and relief
-
Homelessness prevention and relief: England 2013 to 2014
- 24 July 2014
- Statistics
-
Homelessness prevention and relief: England 2012 to 2013
- 15 August 2013
- Statistics
-
Homelessness prevention and relief: England 2011 to 2012
- 16 August 2012
- Statistics
-
Homelessness prevention and relief: England 2010 to 2011
- 18 August 2011
- Statistics
-
Homelessness prevention and relief: England 2009 to 2010
- 26 August 2010
- Statistics
-
Homelessness prevention and relief: England 2008 to 2009
- 26 November 2009
- Statistics
-
Homelessness prevention and relief: guidance on section E10 of P1E form
- 14 December 2009
- Guidance
Rough sleeping
-
Evaluating the extent of rough sleeping: local authority form and guidance
- 1 September 2014
- Guidance
-
Rough sleeping in England: autumn 2014
- 26 February 2015
- Statistics
-
Rough sleeping in England: autumn 2013
- 25 February 2014
- Statistics
-
Rough sleeping in England: autumn 2012
- 6 February 2013
- Statistics
-
Rough sleeping in England: autumn 2011
- 23 February 2012
- Statistics
-
Rough sleeping in England: autumn 2010
- 17 February 2011
- Statistics
-
Rough sleeping in England: total street count and estimates 2010
- 23 July 2010
- Statistics
Live tables
-
- 26 March 2015
- Statistical data set
-
Families in bed and breakfast accommodation for more than 6 weeks
- 4 June 2013
- Statistical data set
- 26 March 2015 9:31am
- Added Statutory homelessness in England: October to December 2014.
- 26 February 2015 9:30am
- Added Rough sleeping in England: autumn 2014.
- 11 December 2014 9:30am
- Added Statutory homelessness in England: July to September 2014.
- 25 September 2014 9:30am
- Added Statutory homelessness in England: April to June 2014.
- 24 July 2014 9:30am
- Added Homelessness prevention and relief: England 2013 to 2014.
- 19 June 2014 9:30am
- Added Statutory homelessness in England: January to March 2014.
- 6 March 2014 9:32am
- Added Statutory homelessness in England: October to December 2013
- 25 February 2014 9:31am
- Added Rough sleeping in England: autumn 2013.
- 5 December 2013 9:30am
- Added Statutory homelessness in England: July to September 2013
- 13 September 2013 3:29pm
- First published.
Services and information
- Benefits
- Births, deaths, marriages and care
- Business and self-employed
- Citizenship and living in the UK
- Crime, justice and the law
- Disabled people
- Driving and transport
- Education and learning
- Employing people
- Environment and countryside
- Housing and local services
- Money and tax
- Passports, travel and living abroad
- Visas and immigration
- Working, jobs and pensions
Departments and policy
Support links
- Help
- Cookies
- Contact
- Cymraeg
- Built by the Government Digital Service
All content is available under the Open Government Licence v3.0, except where otherwise stated. © Crown copyright
Statutory homelessness in England: October to December 2014
Source: https://www.gov.uk/government/statis...-december-2014

- From:
- First published:
- 26 March 2015
- Part of:
- Applies to:
- England
Data on households found to be homeless by local authorities under homelessness legislation.
Document
Homeless Analytics Initiative
Source: http://homelessnessanalytics.org/
The Homelessness Analytics Initiative (HAI)—a collaboration between the U.S. Department of Veterans Affairs (VA) and the U.S. Department of Housing and Urban Development (HUD)—is intended to provide users with access to national, state, and local information about homelessness among the general population, homelessness among Veterans, risk and protective factors for homelessness, services and resources. The aim of the HAI is to empower communities, organizations and individuals with critical information on trends in homelessness, factors related to homelessness, and services in place to prevent and intervene in situations of homelessness. Additionally, the HAI will enable the VA and HUD to plan and allocate resources, and effectively coordinate efforts to address homelessness, by linking and leveraging data held by these two federal partners, and by creating links with other national-, state, and community-level data sources.
Download Initiative Methodology (.pdf)
Charts, Graphs, and Tables
Source: http://homelessnessanalytics.org/map/
Chart trends in homelessness over time, explore relationships between social indicators and homelessness, and download tables of data.
My Note: Downloaded CSV
Homelessness Analytics Initiative Methodology
Source: http://homelessnessanalytics.org/sta...dology2013.pdf (PDF and Word)
Last Updated: 5/3/2013
Methods and Data Sources Overview
The Homelessness Analytics Initiative (HAI) synthesizes information from an array of federal government and other data sources. Indicators included in HAI are measured at the state, county or Continuum of Care (CoC) level, although the geography level(s) for which specific indicators are available varies. In addition, the locations of U.S. Department of Veterans Affairs (VA) Medical Centers, and other VA health care clinics/providers are represented as points in the Homelessness Analytics Application, and some limited information about each of these facilities is available in the HAI.
Most of the data indicators included in the HAI are publicly available from their respective sources. However, some indicators were calculated using data from one or more data sources. This process was used primarily to create CoC level measures of demographic, health, behavioral health, economic and housing market conditions from county level data sources; and to calculate rates of homelessness.
The remainder of this document provides comprehensive details about the methodology and data sources used to create the HAI including:
- An explanation of the levels of geography represented in the Homelessness Analytics Application
- Descriptions of the various data sources from which HAI indicators were obtained
- A complete list of indicators included in the HAI
- A description of procedures used to transform county level data sources into CoC level indicators
- A description of procedures used to calculate rates of homelessness and other indicators from multiple sources
- A description of the procedures used to create the HAI’s forecasting tool
Levels of Geography in the Homelessness Analytics Initiative
Indicators included in the HAI are available at one or more of the following levels of geography:
- Continuum of Care (CoC)
- County
- State
- VA Medical Centers and other VA facilities (point level data)
The level of geography at which specific indicators included in the HAI are available varies. This is largely because the various data sources used to create the HAI collect and report counts of homelessness and other economic, housing and social indicators at varying levels of geography, which do not always align with one another. However, as the CoC is the smallest geographic unit at which annual point-in-time counts of the number of persons experiencing homelessness are available, efforts were made to include as many indicators as possible at that geographic level.
Unlike geographies such as counties and states, whose boundaries are effectively permanent, the universe of CoCs and their particular boundaries can change slightly from year to year as some CoCs merge with one another, some disband, and others are created. 1 The HAI includes the universe of CoCs that were in existence in 2012, and therefore provides data for all years only for the 2012 CoCs.
In addition to the indicators available at the CoC, county and state level, the locations of U.S. Department of Veterans Affairs Medical Centers, and other VA health care clinics/providers are represented as points in the HAI’s map interface. Users can access information about these facilities by selecting a location of interest in the map interface.
Planned future updates of the HAI will expand the number of indicators that are available at the CoC, county and state level and will also add other geographies (e.g. Census tracts or block groups). In addition, future updates will enhance the scope of information available about VA Medical Centers/clinics.
Data Sources
The data sources used to select indicators included in the HAI are described below. Where possible, links are provided to each data source.
50th Percentile Rent Estimates
The U.S. Department of Housing and Urban Development (HUD) estimates 50th percentile rents, which are defined as the dollar amount of gross rents at the 50th percentile of the rent distribution (i.e. the median rent) for housing units of varying size, on an annual basis using data from the Census Bureau and telephone surveys. 50thpercentile rent estimate data are used in the HAI to obtain rent level indicators and are available at: http://www.huduser.org/portal/datasets/50per.html
American Community Survey (ACS)
The U.S. Census Bureau’s American Community Survey (ACS) is a population-based survey that collects information on demographic, social economic and housing characteristics from a representative sample of American households. In contrast to the Decennial Census, which is conducted once every 10 years, the ACS is conducted on annual basis. The HAI primarily uses the ACS 5-year estimates, which, unlike the 1- year and 3-year estimates, are available for every county in the United States, to obtain demographic, economic and housing related indicators. The 1-year estimates were used in calculating rates of homelessness at the state level. Note that the year of availability denoted in the HAI for indicators that were obtained from the ACS is the last year of the period from which the estimates were derived (e.g. 2009 for the 2005-2009 5-year estimates). The ACS data are available at: http://factfinder2.census.gov
Behavioral Risk Factor Surveillance System (BRFSS)
The Behavioral Risk Factor Surveillance System (BRFSS) is a telephone-based survey administered by the Centers for Disease Control (CDD) that collects uniform, state-level data on preventative health practices and risk behaviors that are linked to chronic and infectious diseases as well as injuries. BRFSS data are used in the HAI for select indicators of population health and are available at: http://www.cdc.gov/brfss/.
Community Health Status Indicators (CHSI)
The Community Health Status Indicators (CHSI) Report collates county-level indicators of public health. It is published by the U.S. Department of Health and Human Services (HHS) and uses a variety of Federal data sources, as well as other sources, which have been vetted by HHS. The life expectancy indicator included in the HAI is from the CHSI Report. The CHSI Report can be accessed at: http://www.communityhealth.hhs.gov
County Health Rankings
The County Health Rankings is a collaboration between the Robert Wood Johnson Foundation and the University of Wisconsin Population Health Institute. The County Health Rankings uses a scientific approach to rank counties within states with respect to health outcomes and health factors. The County Health Rankings also provide a wide array of county-level health and socioeconomic related measures and indicators, which are collected from a number of public and private sources. For the HAI, the County Health Rankings are used to obtain select public health and economic indicators. The County Health Rankings are available at: http://www.countyhealthrankings.org
Decennial Census
Beginning with the 1990 Census, The U.S. Census Bureau has enumerated persons in emergency and transitional shelters as part of its Decennial Census. Although not intended as an official count of the entire homeless population, The Census provides national level age and gender stratified counts of persons enumerated at emergency shelters and transitional housing programs. For the HAI, County level age and gender stratified counts of persons in emergency shelter and transitional housing were obtained from the Census Bureau via a special tabulation request. Additional information about Census enumeration of persons in emergency shelter and transitional housing, including national level estimates, are available at: http://www.census.gov/prod/cen2010/reports/c2010sr-02.pdf
Department of Veterans Affairs (VA) Homeless Program Data
The U.S. Department of Veterans Affairs (VA) operates and funds a number of residential homeless assistance programs for veterans experiencing homelessness. Data from the Compensated Work Therapy/ Transitional Residence (CWT/TR), Domiciliary Care for Homeless Veterans (DHCV), Grant and Per Diem (GPD), Health Care for Homeless Veterans (HCHV), HUD-VA Supportive Housing (HUD-VASH), and Supportive Service for Veteran Families (SSVF) programs, were used to obtain information about the number of beds/units in each of these program types.
Fair Market Rents (FMRs)
The U.S. Department of Housing and Urban Development (HUD) estimates fair market rents (FMRs), which are defined as the dollar amount of gross rents at the 40th percentile of the rent distribution for housing units of varying size, on an annual basis using data from the Census Bureau and telephone surveys. FMRs are used to determine payment amounts for various housing assistance programs. FMR data are used in the HAI to obtain rent level indicators and are available at: http://www.huduser.org/portal/datasets/fmr.html
FBI Uniform Crime Reports (UCR)
The Federal Bureau of Investigation’s Uniform Crime Reports (UCR) provide are official measures of crime in the United States. They provide an array of crime statistics and are based on reports from state, county and local law enforcement agencies. UCR data is used in the HAI for select crime indicators. UCR data are available at: http://www.fbi.gov/about-us/cjis/ucr/ucr
Housing Inventory Chart
Each Continuum of Care (CoC) provides a Housing Inventory Chart (HIC) to HUD on an annual basis. The HIC reports the results of a point-in-time count of the inventory of all beds and residential units dedicated to serve persons who meet HUD’s homeless definition. The inventory count is required to take place during a single night in the last 10 days of January (the same night as the point-in-time count of homelessness). The HIC provides the number of residential beds within each community, stratified by target population and bed/unit type (i.e. emergency shelter, transitional housing, permanent supportive housing, rapid re-housing, safe haven). Housing Inventory data are available at: http://www.hudhre.info
National Association of State Budget Officers’ State Expenditure Report
The National Association of State Budget Officers is the professional organization for state budget and finance officers. Their annual State Expenditure Report provides statistics of state spending in a number of domains including education, public assistance, corrections, Medicald, and transportation. Select measures of social program spending included in the HAI are obtained from the Expenditure Report. The data are available at: http://www.nasbo.org/publications-data/state-expenditure-report/
National Survey on Drug Use and Health (NSDUH)
The National Survey on Drug Use and Health (NSDUH), which is sponsored by the Substance Abuse and Mental Health Services Administration (SAMHSA), is an annual national survey of a random sample of 70,000 persons ages 12 and older. The NSDUH provides state-level data on the use of tobacco, alcohol and illicit drugs and mental health status. The HAI uses NSDUH data for select behavioral health indicators. NSDUH data are available at: https://nsduhweb.rti.org/
Point-In-Time (PIT) Estimates of Homelessness
The U.S. Department of Housing and Urban Development (HUD) publishes point-in- time (PIT) estimates of homelessness on an annual basis. The PIT estimates are available at the Continnuum of Care (CoC) level. CoCs are required to report PIT counts to HUD as part of their annual applications for federal funding for homeless assistance programs. The counts must take place during a single night in the last 10 days of January, and must enumerate certain sub-groups of the homeless population (e.g. individuals, families, veterans, persons experiencing chronic homelessness). HUD requires that CoCs conduct counts of sheltered homeless people each year and counts of unsheltered homeless people in odd-numbered years. However, many CoCs undertake both sheltered and unsheltered counts on annual basis. The PIT estimates are available at: http://www.hudhre.info
Picture of Subsidized Households Dataset
The Picture of Subsidized Households is a U.S. Department of Housing and Urban Development Dataset that provides data on the number of low rent and Section 8 Housing Choice Voucher Program units in PHAs administered by HUD. It is used in HAI to provide indicators of the availability of public and subsidized housing. The PHA Inventory dataset can be accessed at: http://www.huduser.org/portal/picture2008/index.html
Supplemental Nutrition Assistance Program (SNAP) Data
The U.S. Department of Agriculture’s Food and Nutrition Service Program provides national and state level data about the Supplemental Nutrition Assistance Program (SNAP), the new name for the federal Food Stamp Program. SNAP data are used by the HAI for information about the amount of the average monthly SNAP benefit provided to SNAP recipients. SNAP data can be accessed at: http://www.fns.usda.gov/pd/snapmain.htm
Social Security Administration (SSA) Annual Statistical Supplement
The Social Security Administration (SSA) publishes an Annual Statistical Supplement that provides a wide array of data on expenditures, enrollment, and utilization of SSA administered programs. The HAI uses the Annual Statistical Supplement for select social safety net indicators. The data are available at: http://www.ssa.gov/policy/docs/statcomps/supplement/
Veterans Benefit Administration (VBA) Compensation and Pension by County Dataset
The Department of Veterans Affair’s Veterans Benefit Administration (VBA) Compensation and Pension by County dataset provides counts of the number of veterans receiving disability compensation or pension payments from the VA for each county in the United States. The HAI uses this dataset to construct a measure of the proportion of veterans receiving such benefits. The VBA Compensation and Pension dataset is available at:
Complete List of Indicators Included in the Homelessness Analytics Initiative
The table below provides a complete list of the indicators that are available in the HAI—with data sources, level(s) of geography and years of availability for each indicator. This table is also available for download in an Excel spreadsheet on the HAI website. Planned future updates to the HAI will expand the number of available metrics and levels of geography.
List of Data Indicators Included in the VA-HUD Homelessness Analytics Initiative. Last Update Date: 5/3/13
Indicator | Data Source | Years of Data | CoC Level | County Level | State Level |
Homeless Count and Rate Variables | |||||
Number of Persons with HIV/AIDS-Sheltered | PIT Estimates of Homelessness | 2006-2012 | X |
| X |
Number of Persons with HIV/AIDS-Unsheltered | PIT Estimates of Homelessness | 2006-2012 | X |
| X |
Number of Persons with HIV/AIDS-Total | PIT Estimates of Homelessness | 2006-2012 | X |
| X |
Number of Chronically Homeless-Sheltered | PIT Estimates of Homelessness | 2006-2012 | X |
| X |
Number of Chronically Homeless-Unsheltered | PIT Estimates of Homelessness | 2006-2012 | X |
| X |
Number of Chronically Homeless-Total | PIT Estimates of Homelessness | 2006-2012 | X |
| X |
Number of Persons with Chronic Substance Abuse- Sheltered | PIT Estimates of Homelessness | 2006-2012 | X |
| X |
Number of Persons with Chronic Substance Abuse- Unsheltered | PIT Estimates of Homelessness | 2006-2012 | X |
| X |
Number of Persons with Chronic Substance Abuse-Total | PIT Estimates of Homelessness | 2006-2012 | X |
| X |
Number of households with dependent children in emergency shelter | PIT Estimates of Homelessness | 2006-2012 | X |
| X |
Number of households with dependent children in transitional housing | PIT Estimates of Homelessness | 2006-2012 | X |
| X |
Number of households with dependent children that are unsheltered | PIT Estimates of Homelessness | 2006-2012 | X |
| X |
Number of individual households (i.e. households without children or households with only children) in emergency shelter | PIT Estimates of Homelessness | 2007-2012 | X |
| X |
Number of individual households (i.e. households without children or households with only children) in transitional housing |
PIT Estimates of Homelessness |
2007-2012 |
X |
|
X |
Number of individual households (i.e. households without children or households with only children) that are unsheltered |
PIT Estimates of Homelessness |
2007-2012 |
X |
|
X |
Number of individuals (i.e.without dependent children or only children) in emergency shelter | PIT Estimates of Homelessness | 2006-2012 | X |
| X |
Number of individuals (i.e.without dependent children or only children) in transitional housing | PIT Estimates of Homelessness | 2006-2012 | X |
| X |
Number of individuals (i.e.without dependent children or only children) that are unsheltered | PIT Estimates of Homelessness | 2006-2012 | X |
| X |
Number of persons in households with dependent children in emergency shelter | PIT Estimates of Homelessness | 2006-2012 | X |
| X |
Number of persons in households with dependent children in transitional housing | PIT Estimates of Homelessness | 2006-2012 | X |
| X |
Number of persons in households with dependent children that are unsheltered | PIT Estimates of Homelessness | 2006-2012 | X |
| X |
Total Sheltered Persons Count | PIT Estimates of Homelessness | 2006-2012 | X |
| X |
Number of households with dependent children that are sheltered (in emergency shelter or transitional housing) | PIT Estimates of Homelessness | 2006-2012 | X |
| X |
Number of persons in households with dependent children that are sheltered (in emergency shelter or transitional housing) |
PIT Estimates of Homelessness |
2006-2012 |
X |
|
X |
Number of persons with severe mental illness-Sheltered | PIT Estimates of Homelessness | 2006-2012 | X |
| X |
Number of persons with severe mental illness- Unsheltered | PIT Estimates of Homelessness | 2006-2012 | X |
| X |
Number of persons with severe mental illness-Total | PIT Estimates of Homelessness | 2006-2012 | X |
| X |
Number of Homeless Persons-Total (Sheltered & Unsheltered) | PIT Estimates of Homelessness | 2006-2012 | X |
| X |
Number Households with dependent children-Total (Sheltered & Unsheltered) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Number of Persons in households with dependent children -Total (Sheltered & Unsheltered) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Number of individual households (i.e. households without children or households with only children)-Total (Sheltered & Unsheltered) | PIT Estimates of Homelessness, American Community Survey |
2006-2012 |
X |
|
X |
Number of individuals (i.e.without dependent children or only children)-Total (Sheltered & Unsheltered) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Total Unsheltered Persons Count | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Number of Victims of Domestic Violence-Sheltered | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Number of Victims of Domestic Violence-Unsheltered | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Number of Victims of Domestic Violence-Total | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Number of Veteran-Sheltered | PIT Estimates of Homelessness, American Community Survey | 2009-2012 | X |
| X |
Number of Veterans-Unsheltered | PIT Estimates of Homelessness, American Community Survey | 2009-2012 | X |
| X |
Number of Veterans-Total | PIT Estimates of Homelessness, American Community Survey | 2009-2012 | X |
| X |
Number of Unaccompanied Youth-Sheltered | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Number of Unaccompanied Youth-Unsheltered | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Number of Unaccompanied Youth-Total | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Persons with HIV/AIDS-Sheltered (rate per 10,000 people) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Persons with HIV/AIDS-Unsheltered (rate per 10,000 people) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Persons with HIV/AIDS-Total (rate per 10,000 people) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Chronically Homeless-Sheltered (rate per 10,000 people) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Chronically Homeless-Unsheltered (rate per 10,000 people) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Chronically Homeless-Total (rate per 10,000 people) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Persons with Chronic Substance Abuse-Sheltered (rate per 10,000 people) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Persons with Chronic Substance Abuse-Unsheltered (rate per 10,000 people) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Persons with Chronic Substance Abuse-Total (rate per 10,000 people | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Family Households-Emergency Shelter (rate per 10,000 people) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Family Households-Transitional Housing (rate per 10,000 people) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Family Households-Unsheltered (rate per 10,000 people) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Individual Households-Emergency Shelter (rate per 10,000 people) | PIT Estimates of Homelessness, American Community Survey | 2007-2012 | X |
| X |
Individual Households-Transitional Housing (rate per 10,000 people) | PIT Estimates of Homelessness, American Community Survey | 2007-2012 | X |
| X |
Individual Households-Unsheltered (rate per 10,000 people) | PIT Estimates of Homelessness, American Community Survey | 2007-2012 | X |
| X |
Individuals-Emergency Shelter (rate per 10,000 people) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Individuals-Transitional Housing (rate per 10,000 people) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Individuals-Unsheltered (rate per 10,000 people) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Persons in Families-Emergency Shelter (rate per 10,000 people) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Persons in Families-Transitional Housing (rate per 10,000 people) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Persons in Families-Unsheltered (rate per 10,000 people) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Total Sheltered Persons (rate per 10,000 people) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Family Households-Sheltered (ES & TH) (rate per 10,000 people) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Persons in Families-Sheltered (ES & TH) (rate per 10,000 people) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Persons with Severe Mental Illness-Sheltered (rate per 10,000 people) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Persons with Severe Mental Illness-Unsheltered (rate per 10,000 people) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Persons with Severe Mental Illness-Total (rate per 10,000 people) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Total Homeless Persons (rate per 10,000 people) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Family Households-Total (rate per 10,000 people) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Persons in Families-Total (rate per 10,000 people) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Individual Households-Total (rate per 10,000 people) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Individuals-Total (rate per 10,000 people) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Total Unsheltered Persons (rate per 10,000 people) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Victims of Domestic Violence-Sheltered (rate per 10,000 people) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Victims of Domestic Violence-Unsheltered (rate per 10,000 people) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Victims of Domestic Violence-Total (rate per 10,000 people) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Veterans-Sheltered (rate per 10,000 veterans) | PIT Estimates of Homelessness, American Community Survey | 2009-2012 | X |
| X |
Veterans-Unsheltered (rate per 10,000 veterans) | PIT Estimates of Homelessness, American Community Survey | 2009-2012 | X |
| X |
Veterans-Total (rate per 10,000 veterans) | PIT Estimates of Homelessness, American Community Survey | 2009-2012 | X |
| X |
Unaccompanied Youth-Sheltered (rate per 10,000 people) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Unaccompanied Youth-Unsheltered (rate per 10,000 people) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Unaccompanied Youth-Total (rate per 10,000 people) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Persons with HIV/AIDS-Sheltered (rate per 10,000 persons in poverty) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Persons with HIV/AIDS-Unsheltered (rate per 10,000 persons in poverty) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Persons with HIV/AIDS-Total (rate per 10,000 persons in poverty) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Chronically Homeless-Sheltered (rate per 10,000 persons in poverty) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Chronically Homeless-Unsheltered (rate per 10,000 persons in poverty) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Chronically Homeless-Total (rate per 10,000 persons in poverty) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Persons with Chronic Substance Abuse-Sheltered (rate per 10,000 persons in poverty) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Persons with Chronic Substance Abuse-Unsheltered (rate per 10,000 persons in poverty) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Persons with Chronic Substance Abuse-Total (rate per 10,000 persons in poverty) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Family Households-Emergency Shelter (rate per 10,000 persons in poverty) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Family Households-Transitional Housing (rate per 10,000 persons in poverty) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Family Households-Unsheltered (rate per 10,000 persons in poverty) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Individual Households-Emergency Shelter (rate per 10,000 persons in poverty) | PIT Estimates of Homelessness, American Community Survey | 2007-2012 | X |
| X |
Individual Households-Transitional Housing (rate per 10,000 persons in poverty) | PIT Estimates of Homelessness, American Community Survey | 2007-2012 | X |
| X |
Individual Households-Unsheltered (rate per 10,000 persons in poverty) | PIT Estimates of Homelessness, American Community Survey | 2007-2012 | X |
| X |
Individuals-Emergency Shelter (rate per 10,000 persons in poverty) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Individuals-Transitional Housing (rate per 10,000 persons in poverty) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Individuals-Unsheltered (rate per 10,000 persons in poverty) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Persons in Families-Emergency Shelter (rate per 10,000 persons in poverty) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Persons in Families-Transitional Housing (rate per 10,000 persons in poverty) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Persons in Families-Unsheltered (rate per 10,000 persons in poverty) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Total Sheltered Persons (rate per 10,000 persons in poverty) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Family Households-Sheltered (ES & TH) (rate per 10,000 persons in poverty) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Persons in Families-Sheltered (ES & TH) (rate per 10,000 persons in poverty) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Persons with Severe Mental Illness-Sheltered (rate per 10,000 persons in poverty) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Persons with Severe Mental Illness-Unsheltered (rate per 10,000 persons in poverty) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Persons with Severe Mental Illness-Total (rate per 10,000 persons in poverty) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Total Homeless Persons (rate per 10,000 persons in poverty) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Family Households-Total (rate per 10,000 persons in poverty) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Persons in Families-Total (rate per 10,000 persons in poverty) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Individual Households-Total (rate per 10,000 persons in poverty) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Individuals-Total (rate per 10,000 persons in poverty) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Total Unsheltered Persons (rate per 10,000 persons in poverty) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Victims of Domestic Violence-Sheltered (rate per 10,000 persons in poverty) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Victims of Domestic Violence-Unsheltered (rate per 10,000 persons in poverty) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Victims of Domestic Violence-Total (rate per 10,000 persons in poverty) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Unaccompanied Youth-Sheltered (rate per 10,000 persons in poverty) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Unaccompanied Youth-Unsheltered (rate per 10,000 persons in poverty) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Unaccompanied Youth-Total (rate per 10,000 persons in poverty) | PIT Estimates of Homelessness, American Community Survey | 2006-2012 | X |
| X |
Housing Inventory Variables | |||||
Number of VA CWT/TR Beds | VA Homeless Program Data | 2012 | X |
| X |
Number of Domiciliary Care for Homeless Veterans Beds | VA Homeless Program Data | 2012 | X |
| X |
Number of VA Grant and Per Diem Beds | VA Homeless Program Data | 2012 | X |
| X |
VA Supportive Services for Veterans Families (SSVF) Grant Totals ($) | VA Homeless Program Data | 2012 |
|
| X |
Number of Emergency Shelter Beds for Families | Housing Inventory Chart | 2006-2012 | X |
| X |
Number of Emergency Shelter Beds for Individuals | Housing Inventory Chart | 2006-2012 | X |
| X |
Number of Permanent Housing Units Reserved for Chronically Homeless Individuals | Housing Inventory Chart | 2006-2012 | X |
| X |
Number of Permanent Housing Units for Families | Housing Inventory Chart | 2006-2012 | X |
| X |
Number of Permanent Housing Units for Individuals | Housing Inventory Chart | 2006-2012 | X |
| X |
Number of Transitional Housing Beds for Families | Housing Inventory Chart | 2006-2012 | X |
| X |
Number of Transitional Housing Beds for Individuals | Housing Inventory Chart | 2006-2012 | X |
| X |
Number of Safe Haven Beds for Individuals | Housing Inventory Chart | 2006-2012 | X |
| X |
Number of Safe Haven Beds for Families | Housing Inventory Chart | 2006-2012 | X |
| X |
Number of HUD-VASH vouchers allocated in year | VA Homeless Program Data | 2008-2012 | X |
| X |
Total number of HUD-VASH vouchers | VA Homeless Program Data | 2012 | X |
| X |
Community Demographic, Health, and Behavioral Health Variables | |||||
# Motor vehicle thefts per 100,000 people | FBI Uniform Crime Reports | 2009 |
|
| X |
Total population 18-65 years | American Community Survey | 2009 | X |
|
|
Total population <18 years | American Community Survey | 2009 | X |
|
|
Total population 65+ years | American Community Survey | 2009 | X |
|
|
% Total population that is Asian | American Community Survey | 2009 | X |
|
|
% Total population that is of black race alone | American Community Survey | 2009-2010 | X |
|
|
% of Adult population that are Veterans | American Community Survey | 2009 | X |
|
|
% Total population that is Hispanic/Latino | American Community Survey | 2009-2010 | X |
|
|
% Total population that is Hawaiian and other Pacific Islander | American Community Survey | 2009 | X |
|
|
% Total population that is American Indian or Alaska Native | American Community Survey | 2009 | X |
|
|
% Total population that is some other race alone | American Community Survey | 2009 | X |
|
|
% of occupied units occupied by householder living alone | American Community Survey | 2009-2010 | X |
|
|
% Total population that is two or more races | American Community Survey | 2009 | X |
|
|
% Total population that is of white race alone, not Hispanic | American Community Survey | 2009 | X |
|
|
Total population | American Community Survey | 2006-2011 | X (2009 only) |
| X |
Total veterans in civilian population | American Community Survey | 2006-2011 | X (2009 only) |
| X |
Average life expectancy (in years) | Community Health Status Indicators | 2009 | X |
|
|
% Adults >=1 drinks last 30 days | Behavioral Risk Factor Surveillance System | 2009 |
|
| X |
Number of 18+ year old illicit drug users in past month per 100,000 people | National Survey on Drug Use and Health | 2008 |
|
| X |
% of population with fair or poor health | County Health Rankings | 2009 | X |
|
|
Gini coefficient of income inequality (household) | County Health Rankings | 2007 | X |
|
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Number of Age-adjusted deaths due to homicide per 100,000 people | Community Health Status Indicators | 2009 | X |
|
|
County designated as a health professional shortage area | Community Health Status Indicators | 2009 | X |
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Number of Liquor stores per 10,000 people | County Health Rankings | 2006 | X |
|
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% of population who are Medicaid beneficiaries | Community Health Status Indicators | 2009 | X |
|
|
% of births to unmarried women | Community Health Status Indicators | 2009 | X |
|
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Number Primary care providers per 100,000 people | County Health Rankings | 2006 | X |
|
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% of 18-65 year olds without health insurance | County Health Rankings | 2005 | X |
|
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% Adults with vigorous activity in last week | Behavioral Risk Factor Surveillance System | 2009 |
|
| X |
Economic and Housing Condition Variables | |||||
Median household income (2009 dollars) | American Community Survey | 2009 | X |
|
|
Median property value--owner-occupied housing units (2009 dollars) | American Community Survey | 2009 | X |
|
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% Population with income below 50% of poverty threshold in past 12 months | American Community Survey | 2009 | X |
|
|
% of the population at or below poverty threshold | American Community Survey | 2009 | X |
|
|
Total number of persons with income at or below poverty threshold | American Community Survey | 2006-2011 | X (2009 only) |
| X |
Unemployment rate among civilians in labor force | American Community Survey | 2009 | X |
|
|
Fair Market Rent: efficiency unit | Fair Market Rents | 2008-2011 | X |
|
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Fair Market Rent: one-bedroom | Fair Market Rents | 2008-2011 | X |
|
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Fair Market Rent: two-bedroom | Fair Market Rents | 2008-2011 | X |
|
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Fair Market Rent: three-bedroom | Fair Market Rents | 2008-2011 | X |
|
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Fair Market Rent: four-bedroom | Fair Market Rents | 2008-2011 | X |
|
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Median rent: efficiency | 50th Percentile Rent Estimates | 2008-2011 | X |
|
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Median rent: 1-bedroom | 50th Percentile Rent Estimates | 2008-2011 | X |
|
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Median rent: 2-bedroom | 50th Percentile Rent Estimates | 2008-2011 | X |
|
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Median rent: 3-bedroom | 50th Percentile Rent Estimates | 2008-2011 | X |
|
|
Median rent: 4-bedroom | 50th Percentile Rent Estimates | 2008-2011 | X |
|
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% Occupied housing units that are overcrowded (i.e., more than 1 person/room | American Community Survey | 2009 | X |
|
|
% Occupied housing units lacking complete plumbing facilities | American Community Survey | 2009 | X |
|
|
% Occupied housing units that are renter-occupied | American Community Survey | 2009-2010 | X |
|
|
% Occupied housing units that are owner occupied | American Community Survey | 2009 | X |
|
|
% Occupied housing units with gross rent 30% or more of income | American Community Survey | 2009-2010 | X |
|
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% Occupied housing units with gross mortgage costs 30% or more of income | American Community Survey | 2009 | X |
|
|
% Housing units that are vacant | American Community Survey | 2009 | X |
|
|
Total housing units | American Community Survey | 2009 | X |
|
|
Safety Net Variables | |||||
Average monthly Food Stamp benefit, for state | Supplemental Nutrition Asssistance Program Data | 2009 |
|
| X |
Average monthly state supplement to SSI payment | Social Security Administration Annual Statistical Supplement | 2009 |
|
| X |
% of households in poverty that received food stamps in the past 12 months | American Community Survey | 2009 | X |
|
|
Public Assistance expenditures as percent of total state spending | National Association of State Budget Officers’ State Expenditure Report | 2009 |
|
| X |
% of households in poverty that received public assistance income in the past 12 months | American Community Survey | 2009 | X |
|
|
% of households in poverty that received SSI income in the past 12 months | American Community Survey | 2009 | X |
|
|
Per capita Expenditures on TANF cash assistance from state general fund | National Association of State Budget Officers’ State Expenditure Report | 2009 |
|
| X |
Per capita Medicaid Expenditures | National Association of State Budget Officers’ State Expenditure Report | 2009 |
|
| X |
Medicaid expenditures as % of total state spending | National Association of State Budget Officers’ State Expenditure Report | 2009 |
|
| X |
Ratio of Total Public Housing units and Section 8 vouchers to households in poverty | Picture of Subsidized Households; American Community Survey | 2008 | X |
|
|
% veterans receiving either VA compensation or VA pension payments | VBA Compensation and Pension By County Dataset, American Community Survey |
2008 |
X |
|
|
Age Distribution of Sheltered Homeless Population | |||||
Number of homeless males ages 18-21 | Decennial Census | 2010 |
| X | X |
Number of homeless males ages 22-24 | Decennial Census | 2010 |
| X | X |
Number of homeless males ages 25-27 | Decennial Census | 2010 |
| X | X |
Number of homeless males ages 28-30 | Decennial Census | 2010 |
| X | X |
Number of homeless males ages 31-33 | Decennial Census | 2010 |
| X | X |
Number of homeless males ages 34-36 | Decennial Census | 2010 |
| X | X |
Number of homeless males ages 37-39 | Decennial Census | 2010 |
| X | X |
Number of homeless males ages 40-42 | Decennial Census | 2010 |
| X | X |
Number of homeless males ages 43-45 | Decennial Census | 2010 |
| X | X |
Number of homeless males ages 46-48 | Decennial Census | 2010 |
| X | X |
Number of homeless males ages 49-51 | Decennial Census | 2010 |
| X | X |
Number of homeless males ages 52-54 | Decennial Census | 2010 |
| X | X |
Number of homeless males ages 55-57 | Decennial Census | 2010 |
| X | X |
Number of homeless males ages 58-59 | Decennial Census | 2010 |
| X | X |
Number of homeless males ages 60-61 | Decennial Census | 2010 |
| X | X |
Number of homeless males ages 62-64 | Decennial Census | 2010 |
| X | X |
Number of homeless males ages 65-74 | Decennial Census | 2010 |
| X | X |
Number of homeless males ages 75+ | Decennial Census | 2010 |
| X | X |
Number of homeless males total | Decennial Census | 2010 |
| X | X |
Percent of total homeless male population between ages 18-21 | Decennial Census | 2010 |
| X | X |
Percent of total homeless male population between ages 22-24 | Decennial Census | 2010 |
| X | X |
Percent of total homeless male population between ages 25-27 | Decennial Census | 2010 |
| X | X |
Percent of total homeless male population between ages 28-30 | Decennial Census | 2010 |
| X | X |
Percent of total homeless male population between ages 31-33 | Decennial Census | 2010 |
| X | X |
Percent of total homeless male population between ages 34-36 | Decennial Census | 2010 |
| X | X |
Percent of total homeless male population between ages 37-39 | Decennial Census | 2010 |
| X | X |
Percent of total homeless male population between ages 40-42 | Decennial Census | 2010 |
| X | X |
Percent of total homeless male population between ages 43-45 | Decennial Census | 2010 |
| X | X |
Percent of total homeless male population between ages 46-48 | Decennial Census | 2010 |
| X | X |
Percent of total homeless male population between ages 49-51 | Decennial Census | 2010 |
| X | X |
Percent of total homeless male population between ages 52-54 | Decennial Census | 2010 |
| X | X |
Percent of total homeless male population between ages 55-57 | Decennial Census | 2010 |
| X | X |
Percent of total homeless male population between ages 58-59 | Decennial Census | 2010 |
| X | X |
Percent of total homeless male population between ages 60-61 | Decennial Census | 2010 |
| X | X |
Percent of total homeless male population between ages 62-64 | Decennial Census | 2010 |
| X | X |
Percent of total homeless male population between ages 65-74 | Decennial Census | 2010 |
| X | X |
Percent of total homeless male population between ages 75+ | Decennial Census | 2010 |
| X | X |
Percent of homeless male population, total | Decennial Census | 2010 |
| X | X |
Procedures Used to Transform County Level Data Sources Into CoC Level Indicators
Continuums of Care (CoCs) are geographic units at which providers of homelessness assistance share federal resources and work collaboratively to develop a strategic plan to address homelessness within their jurisdiction. CoCs vary in size and composition and can be comprised of single cities, individual counties, several counties, or entire states. CoCs are also the smallest geographic unit at which the official point-in-time counts of the homeless population are collected and reported by the Department of Housing and Urban Development (HUD). These CoC level counts are then aggregated to provide state, and national estimates of the size of the overall homeless population and homeless sub-populations.
As CoCs constitute geographies that often have irregular boundaries, CoC-level indicators of demographic, health, economic, housing and safety net characteristics are virtually non-existent in other data sources. Therefore, the HAI team used county level data to construct the CoC-level measures of demographic, health, behavioral health, economic housing and safety net characteristics that are included in the HAI. County level data sources were transformed into CoC level indicators using a two-step process described below (No transformation was required for PIT estimates of homelessness and housing inventory variables, which were available at the CoC level).
Step 1: Matching CoC and County Boundaries
The HAI team used Geographic Information Systems (GIS) software and spatial matching procedures to link all counties with their appropriate CoC. To complete the matches, we superimposed county centroids (i.e., points representing the geographic center of counties) on a map of CoC boundaries. This revealed three types of possible relationships between county and CoC boundaries:
- The boundary for a single CoC and a single county was identical;
- A single CoC may was comprised of an aggregation of two or more counties; and
- Multiple CoCs fell within a single county.
Step 2: Statistical Adjustment
After appropriately matching CoCs and counties, the HAI team statistically adjusted the CoCs that fit the second and third types of relationships described above to complete the construction of CoC-level variables from county measures (no adjustments were necessary for the CoCs that met the criteria for the first type of relationship). In the case of the second type of relationship, the HAI team constructed CoC-level variables from county measures by taking either the sum or a population-weighted average of the county measures from all of the counties within a given CoC.
For the third type of relationship, where possible, measures were obtained at the sub- county level to match the exact boundaries of the multiple boundaries of CoCs that were nested within a single county. For the most part, this entailed obtaining measures at the
city or town level, and taking the sum or population weighted average of these measures when a CoC boundary included more than one town or city. However, there were certain measures that were not available at geographies smaller than the county. As a result, for such measures, in instances where there are multiple CoCs within a single county, all CoCs in that county were assigned the county level value, and should be interpreted cautiously. The indicators included in the HAI that were not available at the sub-county level are listed below:
- Average life expectancy (in years)
- % of population with fair or poor health
- Gini coefficient of income inequality (household)
- Number of Age-adjusted deaths due to homicide per 100,000 people
- County designated as a health professional shortage area
- Number of Liquor stores per 10,000 people
- % of population who are Medicaid beneficiaries
- % of births to unmarried women
- Number Primary care providers per 100,000 people
- % of 18-65 year olds without health insurance
- % veterans receiving either VA compensation or VA pension payments
Procedures Used to Calculate Rates of Homelessness and Other Indicators From Multiple Sources
Raw counts of the number or persons experiencing homelessness do not account for population size, and therefore have a number of limitations as metrics for understanding the extent of homelessness in a given community. They also make it difficult to compare the severity of the problem of homelessness across communities with different population sizes. Therefore, for each homelessness indicator, the HAI includes both a raw, unadjusted count and a rate per 10,000 persons and per 10,000 persons in poverty. For the indicators of veteran homelessness, the rate is calculated per 10,000 members of the veteran population. Calculating these rate indicators required combining the HUD point-in-time estimates of homelessness (used in the numerator) and American Community Survey data on size of the overall, poverty and veteran populations (used in the denominator). Note that in calculating these rate variables at the CoC level, the 2005-2009 5-Year ACS estimates were used in the denominator for all years for which homeless count data were available. However, in calculating rates of homelessness at the state level, the 1-year ACS estimates from the corresponding year were used in the denominator, with the exception of 2012, where the 2011 ACS state populations were used because 2012 estimates were not yet available.
A small number of other indicators included in the Homelessness Analytics Application were constructed in a similar fashion, using data from one source in the numerator and another source (usually the American Community Survey) in the denominator. For indicators that were created using the procedures described above, all data sources used in their calculation are noted in the complete list of data indicators.
Procedures Used to Create the Forecasting Tool
Overview
The forecasting tool in the HAI is based on a series of statistical models that were estimated to examine the relationship between eight homeless outcome variables and clusters of variables in three primary domains of interest: 1) demographic, behavioral, and public health; 2) economic; and 3) safety net. Separate models were estimated for each of the eight outcome variables and each of the three predictor clusters. The results of these 24 statistical models provided “weights” (i.e. regression parameter estimates) for each community level indicator. These weights provide an estimate of how much the homeless outcome variable is predicted to change given a one-unit change (either an increase or decrease) in a particular community level indicator. In turn, these weights are used in the forecasting tool to allow users to view the expected impact on homelessness of changes in one or more community level indicator. More detailed information about the procedures used to estimate these models is provided below.
Homeless outcome variables
We estimated separate models for the following homeless outcome variables, which were constructed using the 2009 PIT counts were used in constructing:
- Veterans-Total (rate per 10,000 veterans)
- Individuals-Total (rate per 10,000 people)
- Single Adults-Total (rate per 10,000 adults)
- Single Adults-Total (rate per 10,000 adults in poverty)
- Family Households-Total (rate per 10,000 family households)
- Family Households-Total (rate per 10,000 family households in poverty)
- Total Unsheltered Persons (rate per 10,000 people)
- Total Unsheltered Persons (rate per 10,000 people in poverty)
In estimating these models, we applied a natural logarithmic transformation to each outcome variable due to their highly skewed nature.
Community predictors
In developing the HAI, we collected a large number of indicators at the county or state level from the sources described above pertaining to the three primary domains of interest (i.e. demographic, behavioral, and public health; economic; and safety net).
Given the large number of predictors that were initially collected in each of these domains, we conducted initial variable screening procedures using univariable linear mixed-effects models. Only those variables that were considered to be modifiable, non- redundant with other predictors (r < .80), and had a p-value < .20 from univariable models were included in the multivariable models. Variables were removed from multivariable models if they were not statistically significant in any of the models.
The final set of predictors are presented below:
Demographic, Behavioral, and Public health | Economic | Safety Net |
% Adult heavy drinkers (men >=2 drinks/day, women >=1 drink/day) last 30 days | Unemployment rate among civilians in labor force | % of households in poverty that received SSI income in the past 12 months |
Number of 18+ year old illicit drug users in past month per 100,000 people |
Median rent: 2-bedroom | Ratio of Total Public Housing units and Section 8 vouchers (HCV)to households in poverty |
Number of Liquor stores per 10,000 people | % Occupied housing units that are overcrowded (i.e., more than 1 person/room) |
Expenditures on Medicaid as percent of total state spending |
% of births to unmarried women | % Housing units with a mortgage having owner costs 30% or more of income | Per capita Expenditures on TANF cash assistance from state general fund |
Number of Age-adjusted deaths due to homicide per 100,000 people | Median property value, owner- occupied housing units |
|
# Motor vehicle thefts per 100,000 people | % Occupied housing units with gross rent 30% or more of income |
|
County designated as a health professional shortage area | % Occupied housing units that are renter-occupied |
|
| % Occupied housing units lacking complete plumbing facilities |
|
Analysis approach
After identifying the final set of predictors using the procedures described above, the HAI team estimated a final set of multivariable regression models. Because CoCs are nested within states, data from CoCs located within the same state are not considered to be independent from one another and this clustering violates the basic assumption of independence in ordinary least squares (OLS) regression. Therefore, in estimating the models used for the forecasting tool, the HAI team employed a linear mixed-effects modeling approach (i.e., multilevel modeling) with random intercepts for U.S. states. In doing so, the HAI team stratified CoCs by metropolitan and non-metropolitan status based on the U.S. Department of Agriculture’s rural-urban continuum codes and conducted analyses separately for each stratum. In turn, the forecasting tool is based on the results of the models estimated for the metropolitan CoCs.
Model Results
The results for the models that were estimated for each outcome variable (which were all log-transformed) and in each domain are provided below. The unstandardized regression coefficients are shown for each variable, and these unstandardized coefficients served as the weights in developing the forecasting tool. Given that the outcome variables are log-transformed, the regression coefficients can be interpreted as follows: the outcome variable changes by 100*(coefficient) percent for a one unit increase in the predictor variable while all other variable in the model are held constant. For example, a one unit increase in the number of age adjusted deaths due to homicides per 100,00 people is associated with roughly a 4.8% increase in the number of total homeless veterans per 10,000 veterans. It is important to note that not all of the predictor variables were found to be statistically significant at the p<.05 level in every model, but that the all variables (regardless of their level of significance) were used in developing the forecasting tool.
Demographic, Behavioral, and Public health | ||||||||
|
Veterans-Total (rate per 10,000 veterans) |
Individuals-Total (rate per 10,000 people) |
Single Adults-Total (rate per 10,000 adults) | Single Adults-Total(rate per 10,000 adults in poverty) | Family Households-Total (rate per 10,000 family households) | Family Households-Total (rate per 10,000 family households in poverty) | Total Unsheltered Persons(rate per 10,000 people) | Total Unsheltered Persons(rate per 10,000 people in poverty) |
Intercept | 0.9126000 | 0.8971000 | 2.7697883 | 4.8690000 | 0.3956000 | 3.6150000 | -10.7400000 | -7.5920000 |
% Adult heavy drinkers (men >=2 drinks/day, women >=1 drink/day) last 30 days |
0.0506100 |
0.0467300 |
0.0052460 |
0.0576100 |
0.08874* |
0.1868* |
-0.0944600 |
-0.0395400 |
Number of 18+ year old illicit drug users in past month per 100,000 people |
0.0000638 |
0.0001181* |
0.0001145* |
0.00009086* |
0.0001073* |
0.00008636* |
0.0001837* |
0.0001601* |
Number of Liquor stores per 10,000 people |
0.0417300 |
0.0227200 |
-0.0583607 |
-0.0730600 |
0.1216* |
0.1959* |
-0.0853400 |
-0.0620900 |
% of births to unmarried women | 0.01553* | 0.01304* | 0.014694* | -0.0070360 | 0.01077* | -0.02779* | 0.0129600 | -0.01853* |
Number of Age- adjusted deaths due to homicide per |
0.04822* |
0.0159100 |
0.0162470 |
0.03076* |
0.0010420 |
0.0056130 |
0.0280200 |
0.0322000 |
100,000 people |
|
|
|
|
|
|
|
|
# Motor vehicle thefts per 100,000 people | 0.0013360 | 0.001332* | 0.0017172* | 0.002049* | 0.0005580 | 0.0004287 | 0.004048* | 0.003961* |
County designated as a health professional shortage area |
-0.6439* |
-0.2834* |
-0.3000746* |
-0.5232* |
-0.1812000 |
-0.3074* |
-0.0127900 |
-0.1186000 |
R2 | 0.37 | 0.41 | 0.54 | 0.49 | 0.18 | 0.40 | 0.53 | 0.50 |
Economic | ||||||||
Intercept | -0.1150 | 0.7152 | 2.2180 | 4.3700 | 0.1134 | 3.2580 | -10.7600 | -7.5340 |
Unemployment rate among civilians in labor force |
0.0250 |
0.0413 |
0.0437 |
0.0368 |
0.0334 |
-0.0553 |
0.1510 |
0.09387* |
Median rent: 2- bedroom | 0.0004 | -0.0002 | -0.0003 | 0.0002 | -0.0004 | -0.0001 | 0.0004406* | 0.0009 |
% Occupied housing units that are overcrowded (i.e., more than 1 person/room) |
-0.0315 |
-0.05386* |
0.0540 |
0.06891* |
-0.07353* |
-0.1271* |
0.0096 |
-0.0159 |
% Housing units with a mortgage having owner costs 30% or more of income |
0.0092 |
0.0006 |
-0.0017 |
0.0064 |
-0.0034 |
-0.0022 |
0.0151 |
0.0215 |
Median property value, owner- occupied housing units |
0.0000 |
0.000001361* |
0.0000 |
0.000001688* |
0.0000 |
0.00000303* |
0.0000 |
0.0000 |
% Occupied housing units with gross rent 30% or more of income |
-0.0026 |
0.0153* |
0.02731* |
0.0053 |
0.02624* |
0.0236 |
-0.0110 |
-0.0263 |
% Occupied housing units that are renter- occupied |
0.06469* |
0.03624* |
0.02474* |
0.0100 |
0.02966* |
0.01098* |
0.0238 |
0.0029 |
% Occupied housing units lacking complete plumbing facilities |
-0.0201 |
0.0911 |
-0.0022 |
-0.1425 |
0.2119* |
0.1285 |
0.04147* |
-0.0402 |
R2 | 0.47 | 0.51 | 0.56 | 0.49 | 0.34 | 0.45 | 0.57 | 0.55 |
Safety Net | ||||||||
Intercept | 3.16778 | 3.140567 | 5.159641 | 6.1368787 | 2.35404 | 4.298514 | -7.61221 | -5.906792 |
% of households in poverty that received SSI income in the past 12 months |
-0.030252* |
-0.015461* |
-0.014532* |
0.0028219 |
-0.01397* |
-0.006696 |
-0.007682 |
0.008591 |
Ratio of Total Public Housing units and Section 8 vouchers (HCV)to households in poverty |
0.03514653* |
0.01987729* |
0.01781122* |
0.025452442* |
0.0140782* |
0.01755728* |
0.01780478* |
0.01942534* |
Expenditures on Medicaid as percent of total state spending |
-0.026796 |
-0.016955 |
-0.030768* |
-0.0359124* |
-0.005866 |
-0.007914 |
-0.041473 |
-0.045644 |
Per capita Expenditures on TANF cash assistance from state general fund |
0.011297* |
0.006362 |
0.004158 |
0.0006448 |
0.009964* |
0.013446 |
-0.003239 |
-0.003554 |
R2 | 0.37 | 0.43 | 0.4 | 0.5 | 0.26 | 0.36 | 0.55 | 0.53 |
*=Statistically significant at the p<.05 level
Using Model Results to Generate Forecasted Values
We used the weights obtained from the regression models to generated forecasted values for each of the outcome variables using the formula below:
Yforecast=Yobservedx (1+!X) Where:
- Yforecast is the forecasted value for a particular homeless outcome variable for a given CoC
- Yobserved is the observed value for a particular homeless outcome variable for a given CoC (based on 2012 PIT counts)
- ! represents the coefficients for the full set of community level predictors within a given domain (i.e. Demographic, Behavioral, and Public health; economic; safety net)
- X indicates the unit change in each predictor, relative to a starting value of 0 (i.e. no change)
In effect, the tool works by calculating the cumulative percent by which the outcome variable would be expected to change given increases or decreases in the full set of predictor variables, and then multiplying this by the observed value. As an example of how the forecasting tool works in practice, consider the example of a CoC with an observed 40 homeless veterans per 10,000 veterans in 2012. Assuming one unit increases in the each of the safety net predictors, the forecasted value for the number of veterans per 10,000 veterans would be calculated as follows:
Yforecast=40 x (1+(1 x -0.03 +1 x 0.04+1 x -0.03+1 x 0.01))
Yforecast=40 x (1+ -0.01)
Yforecast=39.6
Fairfax Virginia County
http://www.fairfaxcounty.gov/homeless/
My Note: Excellent Slides: http://www.fairfaxcounty.gov/homeles...1-snapshot.pdf
Data from the Community Partnership to Prevent and End Homelessness
Source: http://www.fairfaxcounty.gov/homeless/data.htm
We are always working towards our goal to end homelessness as we know it in the Fairfax-Falls Church community.
Ending Homelessness Community Snapshot
Source: http://www.fairfaxcounty.gov/homeles...unitysnapshot/
The Fairfax-Falls Church Community Snapshot provides communitywide data to:
- Reflect the outcomes of the community's 10-Year Plan to Prevent and End Homelessness.
- Highlight the community's collective successes and challenges.
- Depict the tremendous need that exists in the community.
Archive
2011 Ending Homelessness Community Snapshot
Source: http://www.fairfaxcounty.gov/homeles...snapshot/2011/
The Fairfax-Falls Church Community Snapshot provides communitywide data to:
- Reflect the outcomes of the community's 10-Year Plan to Prevent and End Homelessness.
- Highlight the community's collective successes and challenges.
- Depict the tremendous need that exists in the community.
2011 Community Snapshot
Key Highlights
- Of the 2,982 people who were literally homeless, 714 secured permanent housing.
- The number of people in the Fairafax-Falls Church community who became homeless for the first time dropped 16% in the last year.
- Collective efforts are now focusing on the creation of 2,650 new affordable housing units.
Complete 2011 Fairfax-Falls Church Community Snapshot (all links )
- Introduction
- Ending Homelessness
- Progress toward 10-Year Plan goals and overview of homelessness.
- Bringing People Home
- Helping our most vulnerable neighbors obtain housing.
- Preventing Homelessness
- Community's collective efforts to keep people in their homes.
- Unsheltered Outreach
- Winter seasonal programs, healthcare for the homeless and PATH outreach.
- Keeping Families Together
- Shelter and housing programs for individuals and families, survivors of domestic violence and unaccompanied youth.
- Community Partnership
- Businesses, nonprofits, faith-based communities and government agencies working together.
- Building Momentum
- Looking ahead and getting involved.
- Acknowledgments
Related Links
Highlights from the 2014 Point-in-Time Count of People Experiencing Homelessness
Source: http://www.fairfaxcounty.gov/homeles...e/pit-2014.htm
On the night of January 29, 2014 there were 1,225 people who were literally homeless in the Fairfax-Falls Church community. This represents a 9% reduction from the number counted in January 2013, or 125 less people. These results demonstrate the continuing decline in homelessness as evidenced in the chart below. The total decrease in the homeless population from 2008 to 2014 is 33%. Adoption of housing first and rapid rehousing models, heightened prevention efforts, prioritizing housing for the longest and most vulnerable homeless through the 100,000 Homes campaign, additional VASH vouchers, dedication of new housing options to the chronically homeless, and the opening of Mondloch Place have assisted in this significant reduction. The results would be even more substantial if additional housing options were available. The reduction in homelessness will not continue at the same pace in the future without significant increases in the availability of affordable housing.
- During the past year, the count of people experiencing homelessness in the Fairfax-Falls Church Community declined by nine percent (125 people) from the number counted in January 2013.
- Persons in families decreased by seven percent (52 people) compared to 2013.
- The number of single adults decreased by 12 percent (73 adults) compared to 2013.
- Single individuals represent 43 percent of the total number of persons counted.
- 55 percent of single individuals who were homeless suffered from serious mental illness and/or substance abuse, a slight decrease from 57 percent in 2013, and many had chronic health problems and/or physical disabilities.
- 196 adults were chronically homeless individuals. This is a reduction of 19 percent from 2013.
- 73 percent of the homeless individuals were male, the same percentage as in past years.
- 24 percent of single individuals who were homeless were employed, a slight increase from 22 percent in 2013.
- 8 percent of the single adults were reported as veterans, compared to nine percent in 2013.
- 66 adults were counted as unsheltered in 2014, marking 38 less than during the 2013 count. Unsheltered individuals comprised 12 percent of total single adults compared to 17 percent in the 2013 count.
- People in families accounted for 57 percent of all persons counted.
- 33 percent of all persons who were homeless were children under the age of 18, the same percentage as the last two years.
- 78 percent of the adults in homeless families are female. The number of adult men in families increased from 18 percent in 2013 to 22 percent in 2014.
- 59 percent of adults in families that were homeless were employed. In 2013, 58 percent of adults in families were employed.
- 33 percent of all persons in families were homeless due to domestic violence, an increase from 27 percent in 2013.
- 62 percent of the homeless people in families were in a transitional housing program. 38 percent were being provided emergency shelter.
- 19 fewer families were homeless in 2014 compared to 2013, with 52 fewer people, including 45 fewer children and 7 fewer adults.
The Point-in-Time count was conducted on January 29, 2014 in coordination with the entire Metro DC region. The annual count is conducted consistent with the guidelines from the U.S. Department of Housing and Urban Development, and covers people who are literally homeless – those who are in shelters, in time-limited transitional housing programs, or unsheltered and living on the street. Conducting the enumeration requires extensive efforts by a wide range of community partners, involving dozens of staff and volunteers from public and private nonprofit organizations that work with people who are homeless in the Fairfax-Falls Church community.
My Note: See below for these Tables
Characteristics of Single Individuals ![]() | Characteristics of Persons in Families![]() |
View Archived Point-in-Time Counts.
Characteristics for Single Individuals
Source: http://www.fairfaxcounty.gov/homeles...ime-tables.htm
Total number of single individuals: 530
Characteristic | Number | Percent |
Age: 18-24 | 48 | 9% |
25-34 | 82 | 15% |
35-54 | 252 | 48% |
55 and over | 148 | 28% |
Gender: Male | 385 | 73% |
Female | 144 | 27% |
Transgendered | 1 | 0% |
Ethnicity: Hispanic | 84 | 16% |
Race: White | 260 | 49% |
Black | 227 | 43% |
Asian/Indian/Pacific Islander/Multi-racial | 43 | 8% |
Serious mental illness, substance abuse or both | 294 | 55% |
Chronic health problems | 98 | 18% |
Physical disability | 72 | 14% |
Homeless due to domestic violence | 22 | 4% |
Current or prior history of domestic violence | 52 | 10% |
Limited English Proficiency | 74 | 14% |
Homeless from an institution | 60 | 11% |
Formerly in foster care | 22 | 4% |
Veteran of U.S. military service | 45 | 8% |
Chronic homeless | 196 | 37% |
Unsheltered | 66 | 12% |
Employed | 126 | 24% |
With income from any source | 296 | 56% |
Characteristics for Persons in Families
Source: http://www.fairfaxcounty.gov/homeles...les.htm#family
Number of families: 211 - Total number of persons in families: 695
Adults in families: 288 - Children in families: 407
Characteristic | Number | Percent |
Age: Under 6 | 178 | 25% |
6-11 | 127 | 18% |
12-17 | 102 | 15% |
18-24 | 61 | 9% |
25-34 | 102 | 15% |
35-54 | 116 | 17% |
55 and over | 9 | 1% |
Gender: Adults, male | 63 | 9% |
Gender: Adults, female | 225 | 32% |
Gender: Children, male | 220 | 32% |
Gender: Children, female | 187 | 27% |
Ethnicity: Hispanic | 128 | 18% |
Race: White | 294 | 42% |
Black | 347 | 50% |
Asian/Indian/Pacific Islander/Multi-racial | 54 | 8% |
Homeless due to domestic violence | 231 | 33% |
Current or prior history of domestic violence | 291 | 42% |
For Adults: Limited English proficiency | 78 | 27% |
Chronic health problems | 17 | 6% |
Serious mental illness, substance abuse or both | 39 | 14% |
Physical disability | 9 | 3% |
Veteran of U.S. military service | 6 | 2% |
Formerly in foster care | 8 | 3% |
No identified subpopulation | 90 | 31% |
Employed | 171 | 59% |
With income from any source | 229 | 80% |
Averages Hide Information: Analysis of the US Homeless Population
Source: http://www.tibco.com/blog/?p=15773
29 March 2015 by Michael O'Connell in Analytics - No Comments
Michael OConnell Tweets My Note: See https://twitter.com/moc_tib/media
Introduction
The Department of Housing and Urban Development (HUD) collects data on homelessness from the US and releases two annual reports to Congress called the Annual Homelessness Assessment Report (AHAR), Parts 1 and 2. Part 1 contains information from the annual Point-in-Time Counts (PIT) conducted by communities nationwide on a single night in January. Part 2 includes information obtained from homeless shelters throughout the course of an calendar year—the Homeless Inventory Count (HIC).
Raw data is available online at http://data.hud.gov. We obtained PIT and HIC data for 2007-2013 as part of a bake-off with Qlikview, Tableau, and SAS at the annual Gartner BI conference in Las Vegas. The HIC and PIT data are yearly measures across 473 spatial regions in the US—CoC’s (Continuums of Care). Estimates of homeless veterans are included beginning in 2011. HUD partners with the VA on the Veterans Homelessness Prevention Demonstration Program.
Our analysis shows that from 2007 to 2013 homeless rates and bed utilization have dropped across the US. In 2013, the average bed utilization across the US was approximately 84%. However, there are still many states and regions that have high homeless rates and high bed utilization.
There are 13 states with utilization greater than 100%. See Figure 1 below:
Figure 1 2007 to 2013 Homeless Rates and Bed Utilization
Even in states with low utilization overall, there are regions within these states with utilization above 100%. For example, the downtown Boston area (CoC 500) has 64% bed utilization in 2013, but the region immediately to the southeast (CoC 520) has experienced utilization above 100% for five of the past seven years. See Figure 2 below:
Figure 2 Boston Area Continuums of Care (CoC) Areas
Our analysis shows the hot spots of homelessness and high bed utilization—and shows how Spotfire can identify these hotspots on a rolling basis via scheduled analysis and reporting. We also show how the homeless can be routed from one region to another in order to provide services (e.g. at shelters with available beds and at facilities such as VA Hospitals for our homeless veterans). See Figure 3 below:
Figure 3 Hot Spots of Homelessness and High-bed Utilization
This pattern indicates some homeless migration to warmer regions and can depend on prevailing climate. See Figure 4 for contour analysis of temperature and precipitation. Redder indicates warmer temperature and circle size shows amount of precipitation:
Figure 4 Contour Analysis of Temperature and Precipitation
The TIBCO Fast Data platform can trigger these analyses automatically, and via notifications and alerts, can help the homeless population obtain shelters and services from existing capacity in the system.
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