Table of contents
  1. Story
  2. Slides
    1. Slide 1 Data Science for Homelessness Data: QlikView, Tableau, & TIBCO Spotfire
    2. Slide 2 GartnerBI analyst JoshParenteau: BI MagicQ: "buyers are buying ease-of-use: Tableau, QlikView & TIBCO Spotfire are easy to use"
    3. Slide 3 Averages Hide Information: Analysis of the US Homeless Population
    4. Slide 4 Homelessness Data Sources
    5. Slide 5 UK Government Homelessness Statistics: Statutory Homelessness
    6. Slide 6 Homeless Analytics Initiative: Dashboard
    7. Slide 7 Homelessness Analytics Initiative Methodology: Data.HUD.gov
    8. Slide 8 Homelessness Analytics Initiative Methodology: Data.HUD.gov Data Sets
    9. Slide 9 Highlights from the 2014 Point-in-Time Count of People Experiencing Homelessness in Fairfax Virginia County
    10. Slide 10 Data Science for Homeless Data: MindTouch Knowledge Base
    11. Slide 11 Data Science for Homeless Data: Excel Knowledge Base
    12. Slide 12 Data Science for Homeless Data: Spotfire Cover Page
    13. Slide 13 Data Science for Homeless Data: Spotfire United Kingdom Homelessness
    14. Slide 14 Data Science for Homeless Data: Spotfire Homelessness Analytics Initiative
    15. Slide 15 Data Science for Homeless Data: Spotfire Fairfax Virginia County
    16. Slide 16 Some Conclusions and Recommendations
  3. Spotfire Dashboard
  4. Slides
    1. Slide 1 Data Science for Homeless Data: Tableau
    2. Slide 2 Meet Tableau 9.0
    3. Slide 3 Features
    4. Slide 4 Specifications
    5. Slide 5 Try It Free
    6. Slide 6 Download Instructions
    7. Slide 7 Other Information
    8. Slide 8 Connect
    9. Slide 9 Connect to Excel HomelessData
    10. Slide 10 Preview Data Source
    11. Slide 11 Manage Metadata
    12. Slide 12 Blank Worksheet
    13. Slide 13 Visualization Worksheet
    14. Slide 14 Welcome to Tableau Desktop
    15. Slide 15 Tableau Training & Tutorials
    16. Slide 16 Visual Gallery
    17. Slide 17 Seattle Real Estate
    18. Slide 18 Super Store
    19. Slide 19 Regional
    20. Slide 20 World Indicators
  5. Research Notes
    1. Winner of the BI Bake Off
    2. The BI Bake Off Comes to Gartner
    3. Gartner Business Intelligence & Analytics Summit
    4. Proposed Meetup June 1st: Data Science for Homelessness Data with Spotfire
  6. Homelessness Data Sources
    1. UK Government Homelessness Statistics
      1. Statutory homelessness
      2. Homelessness prevention and relief
      3. Rough sleeping
      4. Live tables
      5. Services and information
      6. Departments and policy
      7. Support links
      8. Statutory homelessness in England: October to December 2014
        1. Document
        2. Detail
        3. Live tables
        4. Detailed local authority level responses
        5. Detailed local authority level responses: prevention and relief
        6. Discontinued tables
    2. Homeless Analytics Initiative
      1. Homelessness Analytics Initiative Methodology
        1. Methods and Data Sources Overview
        2. Levels of Geography in the Homelessness Analytics Initiative
        3. Data Sources
          1. 50th Percentile Rent Estimates
          2. American Community Survey (ACS)
          3. Behavioral Risk Factor Surveillance System (BRFSS)
          4. Community Health Status Indicators (CHSI)
          5. County Health Rankings
          6. Decennial Census
          7. Department of Veterans Affairs (VA) Homeless Program Data
          8. Fair Market Rents (FMRs)
          9. FBI Uniform Crime Reports (UCR)
          10. Housing Inventory Chart
          11. National Association of State Budget Officers’ State Expenditure Report
          12. National Survey on Drug Use and Health (NSDUH)
          13. Point-In-Time (PIT) Estimates of Homelessness
          14. Picture of Subsidized Households Dataset
          15. Supplemental Nutrition Assistance Program (SNAP) Data
          16. Social Security Administration (SSA) Annual Statistical Supplement
          17. Veterans Benefit Administration (VBA) Compensation and Pension by County Dataset
        4. Complete List of Indicators Included in the Homelessness Analytics Initiative
          1. Homeless Count and Rate Variables
          2. Housing Inventory Variables
          3. Community Demographic, Health, and Behavioral Health Variables
          4. Economic and Housing Condition Variables
          5. Safety Net Variables
          6. Age Distribution of Sheltered Homeless Population
        5. Procedures Used to Transform County Level Data Sources Into CoC Level Indicators
          1. Step 1: Matching CoC and County Boundaries
          2. Step 2: Statistical Adjustment
        6. Procedures Used to Calculate Rates of Homelessness and Other Indicators From Multiple Sources
        7. Procedures Used to Create the Forecasting Tool
          1. Overview
          2. Homeless outcome variables
          3. Community predictors
          4. Analysis approach
          5. Model Results
          6. Demographic, Behavioral, and Public health
          7. Economic
          8. Safety Net
          9. Using Model Results to Generate Forecasted Values
    3. Fairfax Virginia County
      1. Data from the Community Partnership to Prevent and End Homelessness
        1. Ending Homelessness Community Snapshot
          1. 2011 Ending Homelessness Community Snapshot
        2. Highlights from the 2014 Point-in-Time Count of People Experiencing Homelessness
          1. Characteristics for Single Individuals
          2. Characteristics for Persons in Families
  7. Averages Hide Information: Analysis of the US Homeless Population
    1. Introduction
    2. Figure 1 2007 to 2013 Homeless Rates and Bed Utilization
    3. Figure 2 Boston Area Continuums of Care (CoC) Areas
    4. Figure 3 Hot Spots of Homelessness and High-bed Utilization
    5. Figure 4 Contour Analysis of Temperature and Precipitation
    6. Spotfire Dashboard
    7. Michael O'Connell
  8. PART 1 Point-in-Time Estimates of Homelessness
    1. Acknowledgements
    2. Key Findings
      1. All Homeless People
      2. Homelessness by Household Type
      3. Homelessness among Subpopulations
      4. Percent of Homeless People by Household Type
    3. Definition of Terms
      1. Continuums of Care (CoC)
      2. Chronically Homeless People in Families
      3. Chronically Homeless Individuals
      4. Emergency Shelter
      5. Other Permanent Housing
      6. Rapid Rehousing
      7. Permanent Supportive Housing
      8. People in Families
      9. Point-in-Time Counts
      10. Safe Havens
      11. Sheltered Homeless People
      12. Transitional Housing Program
      13. Unaccompanied Children
      14. Unaccompanied Youth
      15. Unsheltered Homeless People
    4. Progress on the Federal Strategic Plan to Prevent and End Homelessness
      1. GOAL: Finish the job of ending chronic homelessness by 2015
      2. GOAL: Prevent and end homelessness among Veterans by 2015
      3. GOAL: Prevent and end homelessness for families, youth, and children by 2020
      4. GOAL: Set a path to ending all types of homelessness
    5. About this Report
    6. SECTION 1
      1. Homelessness in the United States
      2. National Estimates
      3. State Estimates
      4. Estimates by CoC
    7. SECTION 2
      1. Homeless Individuals
      2. National Estimates
      3. State Estimates
      4. Estimates by CoC
    8. SECTION 3
      1. Homeless Families
      2. National Estimates
      3. State Estimates
      4. Estimates by CoC
    9. SECTION 4
      1. Unaccompanied Homeless Children and Youth
      2. National Estimates
      3. State Estimates
      4. Estimates by CoC
    10. SECTION 5
      1. Homeless Veterans
      2. National Estimates
      3. State Estimates
      4. Estimates by CoC
    11. SECTION 6
      1. Chronically Homeless People
      2. National Estimates
      3. State Estimates
      4. Estimates by CoC
    12. SECTION 7
      1. National Inventory of Beds
  9. NEXT

Data Science for Homeless Data

Last modified
Table of contents
  1. Story
  2. Slides
    1. Slide 1 Data Science for Homelessness Data: QlikView, Tableau, & TIBCO Spotfire
    2. Slide 2 GartnerBI analyst JoshParenteau: BI MagicQ: "buyers are buying ease-of-use: Tableau, QlikView & TIBCO Spotfire are easy to use"
    3. Slide 3 Averages Hide Information: Analysis of the US Homeless Population
    4. Slide 4 Homelessness Data Sources
    5. Slide 5 UK Government Homelessness Statistics: Statutory Homelessness
    6. Slide 6 Homeless Analytics Initiative: Dashboard
    7. Slide 7 Homelessness Analytics Initiative Methodology: Data.HUD.gov
    8. Slide 8 Homelessness Analytics Initiative Methodology: Data.HUD.gov Data Sets
    9. Slide 9 Highlights from the 2014 Point-in-Time Count of People Experiencing Homelessness in Fairfax Virginia County
    10. Slide 10 Data Science for Homeless Data: MindTouch Knowledge Base
    11. Slide 11 Data Science for Homeless Data: Excel Knowledge Base
    12. Slide 12 Data Science for Homeless Data: Spotfire Cover Page
    13. Slide 13 Data Science for Homeless Data: Spotfire United Kingdom Homelessness
    14. Slide 14 Data Science for Homeless Data: Spotfire Homelessness Analytics Initiative
    15. Slide 15 Data Science for Homeless Data: Spotfire Fairfax Virginia County
    16. Slide 16 Some Conclusions and Recommendations
  3. Spotfire Dashboard
  4. Slides
    1. Slide 1 Data Science for Homeless Data: Tableau
    2. Slide 2 Meet Tableau 9.0
    3. Slide 3 Features
    4. Slide 4 Specifications
    5. Slide 5 Try It Free
    6. Slide 6 Download Instructions
    7. Slide 7 Other Information
    8. Slide 8 Connect
    9. Slide 9 Connect to Excel HomelessData
    10. Slide 10 Preview Data Source
    11. Slide 11 Manage Metadata
    12. Slide 12 Blank Worksheet
    13. Slide 13 Visualization Worksheet
    14. Slide 14 Welcome to Tableau Desktop
    15. Slide 15 Tableau Training & Tutorials
    16. Slide 16 Visual Gallery
    17. Slide 17 Seattle Real Estate
    18. Slide 18 Super Store
    19. Slide 19 Regional
    20. Slide 20 World Indicators
  5. Research Notes
    1. Winner of the BI Bake Off
    2. The BI Bake Off Comes to Gartner
    3. Gartner Business Intelligence & Analytics Summit
    4. Proposed Meetup June 1st: Data Science for Homelessness Data with Spotfire
  6. Homelessness Data Sources
    1. UK Government Homelessness Statistics
      1. Statutory homelessness
      2. Homelessness prevention and relief
      3. Rough sleeping
      4. Live tables
      5. Services and information
      6. Departments and policy
      7. Support links
      8. Statutory homelessness in England: October to December 2014
        1. Document
        2. Detail
        3. Live tables
        4. Detailed local authority level responses
        5. Detailed local authority level responses: prevention and relief
        6. Discontinued tables
    2. Homeless Analytics Initiative
      1. Homelessness Analytics Initiative Methodology
        1. Methods and Data Sources Overview
        2. Levels of Geography in the Homelessness Analytics Initiative
        3. Data Sources
          1. 50th Percentile Rent Estimates
          2. American Community Survey (ACS)
          3. Behavioral Risk Factor Surveillance System (BRFSS)
          4. Community Health Status Indicators (CHSI)
          5. County Health Rankings
          6. Decennial Census
          7. Department of Veterans Affairs (VA) Homeless Program Data
          8. Fair Market Rents (FMRs)
          9. FBI Uniform Crime Reports (UCR)
          10. Housing Inventory Chart
          11. National Association of State Budget Officers’ State Expenditure Report
          12. National Survey on Drug Use and Health (NSDUH)
          13. Point-In-Time (PIT) Estimates of Homelessness
          14. Picture of Subsidized Households Dataset
          15. Supplemental Nutrition Assistance Program (SNAP) Data
          16. Social Security Administration (SSA) Annual Statistical Supplement
          17. Veterans Benefit Administration (VBA) Compensation and Pension by County Dataset
        4. Complete List of Indicators Included in the Homelessness Analytics Initiative
          1. Homeless Count and Rate Variables
          2. Housing Inventory Variables
          3. Community Demographic, Health, and Behavioral Health Variables
          4. Economic and Housing Condition Variables
          5. Safety Net Variables
          6. Age Distribution of Sheltered Homeless Population
        5. Procedures Used to Transform County Level Data Sources Into CoC Level Indicators
          1. Step 1: Matching CoC and County Boundaries
          2. Step 2: Statistical Adjustment
        6. Procedures Used to Calculate Rates of Homelessness and Other Indicators From Multiple Sources
        7. Procedures Used to Create the Forecasting Tool
          1. Overview
          2. Homeless outcome variables
          3. Community predictors
          4. Analysis approach
          5. Model Results
          6. Demographic, Behavioral, and Public health
          7. Economic
          8. Safety Net
          9. Using Model Results to Generate Forecasted Values
    3. Fairfax Virginia County
      1. Data from the Community Partnership to Prevent and End Homelessness
        1. Ending Homelessness Community Snapshot
          1. 2011 Ending Homelessness Community Snapshot
        2. Highlights from the 2014 Point-in-Time Count of People Experiencing Homelessness
          1. Characteristics for Single Individuals
          2. Characteristics for Persons in Families
  7. Averages Hide Information: Analysis of the US Homeless Population
    1. Introduction
    2. Figure 1 2007 to 2013 Homeless Rates and Bed Utilization
    3. Figure 2 Boston Area Continuums of Care (CoC) Areas
    4. Figure 3 Hot Spots of Homelessness and High-bed Utilization
    5. Figure 4 Contour Analysis of Temperature and Precipitation
    6. Spotfire Dashboard
    7. Michael O'Connell
  8. PART 1 Point-in-Time Estimates of Homelessness
    1. Acknowledgements
    2. Key Findings
      1. All Homeless People
      2. Homelessness by Household Type
      3. Homelessness among Subpopulations
      4. Percent of Homeless People by Household Type
    3. Definition of Terms
      1. Continuums of Care (CoC)
      2. Chronically Homeless People in Families
      3. Chronically Homeless Individuals
      4. Emergency Shelter
      5. Other Permanent Housing
      6. Rapid Rehousing
      7. Permanent Supportive Housing
      8. People in Families
      9. Point-in-Time Counts
      10. Safe Havens
      11. Sheltered Homeless People
      12. Transitional Housing Program
      13. Unaccompanied Children
      14. Unaccompanied Youth
      15. Unsheltered Homeless People
    4. Progress on the Federal Strategic Plan to Prevent and End Homelessness
      1. GOAL: Finish the job of ending chronic homelessness by 2015
      2. GOAL: Prevent and end homelessness among Veterans by 2015
      3. GOAL: Prevent and end homelessness for families, youth, and children by 2020
      4. GOAL: Set a path to ending all types of homelessness
    5. About this Report
    6. SECTION 1
      1. Homelessness in the United States
      2. National Estimates
      3. State Estimates
      4. Estimates by CoC
    7. SECTION 2
      1. Homeless Individuals
      2. National Estimates
      3. State Estimates
      4. Estimates by CoC
    8. SECTION 3
      1. Homeless Families
      2. National Estimates
      3. State Estimates
      4. Estimates by CoC
    9. SECTION 4
      1. Unaccompanied Homeless Children and Youth
      2. National Estimates
      3. State Estimates
      4. Estimates by CoC
    10. SECTION 5
      1. Homeless Veterans
      2. National Estimates
      3. State Estimates
      4. Estimates by CoC
    11. SECTION 6
      1. Chronically Homeless People
      2. National Estimates
      3. State Estimates
      4. Estimates by CoC
    12. SECTION 7
      1. National Inventory of Beds
  9. NEXT

  1. Story
  2. Slides
    1. Slide 1 Data Science for Homelessness Data: QlikView, Tableau, & TIBCO Spotfire
    2. Slide 2 GartnerBI analyst JoshParenteau: BI MagicQ: "buyers are buying ease-of-use: Tableau, QlikView & TIBCO Spotfire are easy to use"
    3. Slide 3 Averages Hide Information: Analysis of the US Homeless Population
    4. Slide 4 Homelessness Data Sources
    5. Slide 5 UK Government Homelessness Statistics: Statutory Homelessness
    6. Slide 6 Homeless Analytics Initiative: Dashboard
    7. Slide 7 Homelessness Analytics Initiative Methodology: Data.HUD.gov
    8. Slide 8 Homelessness Analytics Initiative Methodology: Data.HUD.gov Data Sets
    9. Slide 9 Highlights from the 2014 Point-in-Time Count of People Experiencing Homelessness in Fairfax Virginia County
    10. Slide 10 Data Science for Homeless Data: MindTouch Knowledge Base
    11. Slide 11 Data Science for Homeless Data: Excel Knowledge Base
    12. Slide 12 Data Science for Homeless Data: Spotfire Cover Page
    13. Slide 13 Data Science for Homeless Data: Spotfire United Kingdom Homelessness
    14. Slide 14 Data Science for Homeless Data: Spotfire Homelessness Analytics Initiative
    15. Slide 15 Data Science for Homeless Data: Spotfire Fairfax Virginia County
    16. Slide 16 Some Conclusions and Recommendations
  3. Spotfire Dashboard
  4. Slides
    1. Slide 1 Data Science for Homeless Data: Tableau
    2. Slide 2 Meet Tableau 9.0
    3. Slide 3 Features
    4. Slide 4 Specifications
    5. Slide 5 Try It Free
    6. Slide 6 Download Instructions
    7. Slide 7 Other Information
    8. Slide 8 Connect
    9. Slide 9 Connect to Excel HomelessData
    10. Slide 10 Preview Data Source
    11. Slide 11 Manage Metadata
    12. Slide 12 Blank Worksheet
    13. Slide 13 Visualization Worksheet
    14. Slide 14 Welcome to Tableau Desktop
    15. Slide 15 Tableau Training & Tutorials
    16. Slide 16 Visual Gallery
    17. Slide 17 Seattle Real Estate
    18. Slide 18 Super Store
    19. Slide 19 Regional
    20. Slide 20 World Indicators
  5. Research Notes
    1. Winner of the BI Bake Off
    2. The BI Bake Off Comes to Gartner
    3. Gartner Business Intelligence & Analytics Summit
    4. Proposed Meetup June 1st: Data Science for Homelessness Data with Spotfire
  6. Homelessness Data Sources
    1. UK Government Homelessness Statistics
      1. Statutory homelessness
      2. Homelessness prevention and relief
      3. Rough sleeping
      4. Live tables
      5. Services and information
      6. Departments and policy
      7. Support links
      8. Statutory homelessness in England: October to December 2014
        1. Document
        2. Detail
        3. Live tables
        4. Detailed local authority level responses
        5. Detailed local authority level responses: prevention and relief
        6. Discontinued tables
    2. Homeless Analytics Initiative
      1. Homelessness Analytics Initiative Methodology
        1. Methods and Data Sources Overview
        2. Levels of Geography in the Homelessness Analytics Initiative
        3. Data Sources
          1. 50th Percentile Rent Estimates
          2. American Community Survey (ACS)
          3. Behavioral Risk Factor Surveillance System (BRFSS)
          4. Community Health Status Indicators (CHSI)
          5. County Health Rankings
          6. Decennial Census
          7. Department of Veterans Affairs (VA) Homeless Program Data
          8. Fair Market Rents (FMRs)
          9. FBI Uniform Crime Reports (UCR)
          10. Housing Inventory Chart
          11. National Association of State Budget Officers’ State Expenditure Report
          12. National Survey on Drug Use and Health (NSDUH)
          13. Point-In-Time (PIT) Estimates of Homelessness
          14. Picture of Subsidized Households Dataset
          15. Supplemental Nutrition Assistance Program (SNAP) Data
          16. Social Security Administration (SSA) Annual Statistical Supplement
          17. Veterans Benefit Administration (VBA) Compensation and Pension by County Dataset
        4. Complete List of Indicators Included in the Homelessness Analytics Initiative
          1. Homeless Count and Rate Variables
          2. Housing Inventory Variables
          3. Community Demographic, Health, and Behavioral Health Variables
          4. Economic and Housing Condition Variables
          5. Safety Net Variables
          6. Age Distribution of Sheltered Homeless Population
        5. Procedures Used to Transform County Level Data Sources Into CoC Level Indicators
          1. Step 1: Matching CoC and County Boundaries
          2. Step 2: Statistical Adjustment
        6. Procedures Used to Calculate Rates of Homelessness and Other Indicators From Multiple Sources
        7. Procedures Used to Create the Forecasting Tool
          1. Overview
          2. Homeless outcome variables
          3. Community predictors
          4. Analysis approach
          5. Model Results
          6. Demographic, Behavioral, and Public health
          7. Economic
          8. Safety Net
          9. Using Model Results to Generate Forecasted Values
    3. Fairfax Virginia County
      1. Data from the Community Partnership to Prevent and End Homelessness
        1. Ending Homelessness Community Snapshot
          1. 2011 Ending Homelessness Community Snapshot
        2. Highlights from the 2014 Point-in-Time Count of People Experiencing Homelessness
          1. Characteristics for Single Individuals
          2. Characteristics for Persons in Families
  7. Averages Hide Information: Analysis of the US Homeless Population
    1. Introduction
    2. Figure 1 2007 to 2013 Homeless Rates and Bed Utilization
    3. Figure 2 Boston Area Continuums of Care (CoC) Areas
    4. Figure 3 Hot Spots of Homelessness and High-bed Utilization
    5. Figure 4 Contour Analysis of Temperature and Precipitation
    6. Spotfire Dashboard
    7. Michael O'Connell
  8. PART 1 Point-in-Time Estimates of Homelessness
    1. Acknowledgements
    2. Key Findings
      1. All Homeless People
      2. Homelessness by Household Type
      3. Homelessness among Subpopulations
      4. Percent of Homeless People by Household Type
    3. Definition of Terms
      1. Continuums of Care (CoC)
      2. Chronically Homeless People in Families
      3. Chronically Homeless Individuals
      4. Emergency Shelter
      5. Other Permanent Housing
      6. Rapid Rehousing
      7. Permanent Supportive Housing
      8. People in Families
      9. Point-in-Time Counts
      10. Safe Havens
      11. Sheltered Homeless People
      12. Transitional Housing Program
      13. Unaccompanied Children
      14. Unaccompanied Youth
      15. Unsheltered Homeless People
    4. Progress on the Federal Strategic Plan to Prevent and End Homelessness
      1. GOAL: Finish the job of ending chronic homelessness by 2015
      2. GOAL: Prevent and end homelessness among Veterans by 2015
      3. GOAL: Prevent and end homelessness for families, youth, and children by 2020
      4. GOAL: Set a path to ending all types of homelessness
    5. About this Report
    6. SECTION 1
      1. Homelessness in the United States
      2. National Estimates
      3. State Estimates
      4. Estimates by CoC
    7. SECTION 2
      1. Homeless Individuals
      2. National Estimates
      3. State Estimates
      4. Estimates by CoC
    8. SECTION 3
      1. Homeless Families
      2. National Estimates
      3. State Estimates
      4. Estimates by CoC
    9. SECTION 4
      1. Unaccompanied Homeless Children and Youth
      2. National Estimates
      3. State Estimates
      4. Estimates by CoC
    10. SECTION 5
      1. Homeless Veterans
      2. National Estimates
      3. State Estimates
      4. Estimates by CoC
    11. SECTION 6
      1. Chronically Homeless People
      2. National Estimates
      3. State Estimates
      4. Estimates by CoC
    12. SECTION 7
      1. National Inventory of Beds
  9. NEXT

Story

Data Science for Homelessness Data: QlikView, Tableau, & TIBCO Spotfire

The recent Gartner BI Bakeoff: See Winner of the BI Bake OffThe 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. TableauGerard Valerio, DATAvangelist Slides (PPT) See Slides below
  • 8:00 p.m.​ SpotfireMichael 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

Slides

Slide 2 GartnerBI analyst JoshParenteau: BI MagicQ: "buyers are buying ease-of-use: Tableau, QlikView & TIBCO Spotfire are easy to use"

https://twitter.com/moc_tib

BrandNiemann06012015Slide2.PNG

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

BrandNiemann06012015Slide3.PNG

Slide 4 Homelessness Data Sources

BrandNiemann06012015Slide4.PNG

Slide 5 UK Government Homelessness Statistics: Statutory Homelessness

https://www.gov.uk/government/statis...-december-2014

BrandNiemann06012015Slide5.PNG

Slide 6 Homeless Analytics Initiative: Dashboard

http://homelessnessanalytics.org/map/

BrandNiemann06012015Slide6.PNG

Slide 7 Homelessness Analytics Initiative Methodology: Data.HUD.gov

http://data.hud.gov/

BrandNiemann06012015Slide7.PNG

Slide 8 Homelessness Analytics Initiative Methodology: Data.HUD.gov Data Sets

http://data.hud.gov/data_sets.html

BrandNiemann06012015Slide8.PNG

Slide 9 Highlights from the 2014 Point-in-Time Count of People Experiencing Homelessness in Fairfax Virginia County

http://www.fairfaxcounty.gov/homeles...e/pit-2014.htm

BrandNiemann06012015Slide9.PNG

Slide 10 Data Science for Homeless Data: MindTouch Knowledge Base

http://semanticommunity.info/Data_Sc..._Homeless_Data

BrandNiemann06012015Slide10.PNG

Slide 11 Data Science for Homeless Data: Excel Knowledge Base

http://semanticommunity.info/%40api/...?origin=mt-web

BrandNiemann06012015Slide11.PNG

Slide 12 Data Science for Homeless Data: Spotfire Cover Page

Web Player

BrandNiemann06012015Slide12.PNG

Slide 13 Data Science for Homeless Data: Spotfire United Kingdom Homelessness

Web Player

BrandNiemann06012015Slide13.PNG

Slide 14 Data Science for Homeless Data: Spotfire Homelessness Analytics Initiative

Web Player

BrandNiemann06012015Slide14.PNG

Slide 15 Data Science for Homeless Data: Spotfire Fairfax Virginia County

Web Player

BrandNiemann06012015Slide15.PNG

Slide 16 Some Conclusions and Recommendations

BrandNiemann06012015Slide16.PNG

Spotfire Dashboard

For Internet Explorer Users and Those Wanting Full Screen Display Use: Web Player Get Spotfire for iPad App

Error: Embedded data could not be displayed. Use Google Chrome

Slides

Slides

Slide 1 Data Science for Homeless Data: Tableau

http://semanticommunity.info

BrandNiemann05302015Slide1.PNG

Slide 2 Meet Tableau 9.0

http://www.tableau.com

BrandNiemann05302015Slide2.PNG

Slide 3 Features

BrandNiemann05302015Slide3.PNG

Slide 4 Specifications

BrandNiemann05302015Slide4.PNG

Slide 5 Try It Free

BrandNiemann05302015Slide5.PNG

Slide 6 Download Instructions

BrandNiemann05302015Slide6.PNG

Slide 7 Other Information

BrandNiemann05302015Slide7.PNG

Slide 8 Connect

BrandNiemann05302015Slide8.PNG

Slide 9 Connect to Excel HomelessData

BrandNiemann05302015Slide9.PNG

Slide 10 Preview Data Source

BrandNiemann05302015Slide10.PNG

Slide 11 Manage Metadata

BrandNiemann05302015Slide11.PNG

Slide 12 Blank Worksheet

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Slide 13 Visualization Worksheet

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Slide 14 Welcome to Tableau Desktop

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Slide 15 Tableau Training & Tutorials

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Slide 16 Visual Gallery

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Slide 17 Seattle Real Estate

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Slide 18 Super Store

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Slide 19 Regional

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Slide 20 World Indicators

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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.

Shelters Data Prep 

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).

 

SAS Visual Analytics BI Bake Off

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

TIBCO Spotfire BI Bake Off

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

moconnell@tibco.com

@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

  1. Statutory homelessness in England: October to December 2014

    • 26 March 2015
    • Statistics - national statistics
  2. Statutory homelessness in England: July to September 2014

    • 11 December 2014
    • Statistics - national statistics
  3. Statutory homelessness in England: April to June 2014

    • 25 September 2014
    • Statistics - national statistics
  4. Statutory homelessness in England: January to March 2014

    • 31 July 2014
    • Statistics - national statistics

Live tables

  1. Live tables on homelessness

    • 26 March 2015
    • Statistical data set
  2. 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.
From:
Department for Communities and Local Government
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Support links

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

Statutory homelessness in England: October to December 2014

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Detail

This release provides information on the decisions taken by local authorities on homelessness applications and households accepted as owed a main homelessness duty by local authorities.

The live tables provide the latest, most useful or most popular data, presented by type and other variables, including by geographical area or as a time series.

Live tables

Figures for individual local authorities are given in tables 784, 784a, 792 and 793.

Table 770: decisions taken by local authorities under the Housing Act 1996 on applications from eligible households, England My Note: I downloaded this Excel Table

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Table 770a: decisions taken by local authorities on eligible households owed the reapplication duty under section 195a of the Localism Act 2011, England 2011 to 2014

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Table 771: households accepted by local authorities as owed a main homelessness duty by ethnicity, England 1998 to 2014

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Table 773: households accepted by local authorities as owed a main homelessness duty by priority need category, England 1998 to 2014

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Table 774: households accepted by local authorities as owed a main homelessness duty by reason for loss of last settled home, England 1998 to 2014

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Table 775: households in temporary accommodation by type of accommodation, and cases where duty owed but no accommodation has been secured at the end of each quarter, England, London and Rest of England 1998 to 2014

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Table 777: immediate outcome of decision by local authority to accept household as unintentionally homeless, eligible and in priority need

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Table 778: households leaving temporary accommodation (or no longer recorded 'duty owed, no accommodation secured'), by outcome, England 1998 to 2014

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Table 779: households leaving temporary accommodation (or no longer recorded as 'duty owed, no accommodation secured') during each quarter, by length of time since acceptance, England and London, 1998 to 2014

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Table 780: homeless households in priority need accepted by local authorities by household type, England 2006 to 2014

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Table 781: homeless households in priority need accepted by local authorities by age of applicant, England 2006 to 2014

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Table 782: household types in temporary accommodation, England 2006 to 2014

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Table 784: local authorities' action under the homelessness provisions of the Housing Acts, financial year 2013 to 2014

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Table 784a: local authorities' action under the homelessness provisions of the Housing Acts: quarterly data for financial year 2014 to 2015

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Table 785: local authority assistance to foreign nationals under homelessness provisions of the Housing Act 1996 - decisions on applications for assistance, England

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Table 786: local authority assistance to foreign nationals under homelessness provisions of the Housing Act 1996 - reason for eligibily of accepted households, England

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Table 787: outcome of homelessness prevention and relief, England, 2009-10 to 2013-14

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Table 788: type of homelessness prevention and relief, England, 2009-10 to 2013-14

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Table 789: cases assisted to obtain alternative accommodation broken down by prevention and relief, England, 2009-10 to 2013-14

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Table 792: total reported cases of homelessness prevention and relief by outcome and local authority, 2009-10 to 2013-14

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Table 793: families with children in bed and breakfast accommodation for more than 6 weeks excluding those pending review, by local authority, quarterly data from 2012 Q4

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Detailed local authority level responses

Detailed local authority level homelessness figures: October to December 2014

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Detailed local authority level homelessness figures: July to September 2014

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Detailed local authority level homelessness figures: April to June 2014

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Detailed local authority level homelessness figures: January to March 2014

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Detailed local authority level homelessness figures: October to December 2013

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Detailed local authority level homelessness figures: July to September 2013

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Detailed local authority level homelessness figures: April to June 2013

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Detailed local authority level homelessness figures: January to March 2013

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Detailed local authority level homelessness figures: October to December 2012

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Detailed local authority level homelessness figures: July to September 2012

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Detailed local authority level homelessness figures: April to June 2012

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Detailed local authority level responses: prevention and relief

Detailed local authority level homelessness prevention and relief figures: 2013 to 2014

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Detailed local authority level homelessness prevention and relief figures: 2012 to 2013

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Discontinued tables

Tables 772, 776, 783, 790 and 791 have been discontinued and are no longer being updated. They have been frozen following the decision made that regional totals should not be published in DCLG statistics with effect from 1 October 2012.

Table 772: homeless households accepted by local authorities, by region (final version)

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Table 776: homeless households in temporary accommodation at the end of each quarter, by region (final version)

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Table 783: homeless households in temporary accommodation at the end of each quarter, by type of accommodation and region (final version)

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Table 790: outcome of homelessness prevention and relief by region, England, 2009-10 to 2011-12 (final version)

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Table 791: total cases of homelessness prevention and relief by type and region, England, 2009-10 to 2011-12 (final version)

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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/

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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.   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:

https://explore.data.gov/Social-Insu...unty/xx6t-m2j9

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

 

 

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

 

 

Number of Liquor stores per 10,000 people

County Health Rankings

2006

X

 

 

% of population who are Medicaid beneficiaries

Community Health Status Indicators

2009

X

 

 

% of births to unmarried women

Community Health Status Indicators

2009

X

 

 

Number Primary care providers per 100,000 people

County Health Rankings

2006

X

 

 

% of 18-65 year olds without health insurance

County Health Rankings

2005

X

 

 

% 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

 

 

% 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

 

 

Fair Market Rent: one-bedroom

Fair Market Rents

2008-2011

X

 

 

Fair Market Rent: two-bedroom

Fair Market Rents

2008-2011

X

 

 

Fair Market Rent: three-bedroom

Fair Market Rents

2008-2011

X

 

 

Fair Market Rent: four-bedroom

Fair Market Rents

2008-2011

X

 

 

Median rent: efficiency

50th Percentile Rent Estimates

2008-2011

X

 

 

Median rent: 1-bedroom

50th Percentile Rent Estimates

2008-2011

X

 

 

Median rent: 2-bedroom

50th Percentile Rent Estimates

2008-2011

X

 

 

Median rent: 3-bedroom

50th Percentile Rent Estimates

2008-2011

X

 

 

Median rent: 4-bedroom

50th Percentile Rent Estimates

2008-2011

X

 

 

% 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

 

 

% 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:

  1. The boundary for a single CoC and a single county was identical;
  2. A single CoC may was comprised of an aggregation of two or more counties; and
  3. 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.

2011 Community Snapshot

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 )

  1. Introduction
  2. Ending Homelessness
    • Progress toward 10-Year Plan goals and overview of homelessness.
  3. Bringing People Home
    • Helping our most vulnerable neighbors obtain housing.
  4. Preventing Homelessness
    • Community's collective efforts to keep people in their homes. 
  5. Unsheltered Outreach
    • Winter seasonal programs, healthcare for the homeless and PATH outreach.
  6. Keeping Families Together
    • Shelter and housing programs for individuals and families, survivors of domestic violence and unaccompanied youth.
  7. Community Partnership
    • Businesses, nonprofits, faith-based communities and government agencies working together.
  8. Building Momentum
    • Looking ahead and getting involved.
  9. 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.

2014 Point-in-Time Data

 

  • 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 

2014 Individual Characteristics for Point in Time Survey
 
Characteristics of Persons in Families

2014 Family Characteristics for Point in Time Survey

 

 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  in Analytics - No Comments

Michael OConnell Tweets My Note: See https://twitter.com/moc_tib/media

 

homless

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

figure 1

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

Figure 2

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

Figure 3

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

Figure 4

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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