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Homelessness Data Discussion

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First Annual Canadian Homelessness Data Sharing Initiative
Calgary Homeless Foundation and The School of Public Policy at the University of Calgary
May 4, 2016, Officer’s Mess – Fort Calgary, Calgary, Alberta

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Homelessness Data Discussion

  1. 1. Homelessness Data Discussion First Annual Canadian Homelessness Data Sharing Initiative Calgary Homeless Foundation and The School of Public Policy at the University of Calgary May 4, 2016, Officer’s Mess – Fort Calgary, Calgary, Alberta Dr Tracey P. Lauriault, School of Journalism and Communication, Carleton University & Programmable City Project, Maynooth University
  2. 2. Theoretical Framework
  3. 3. Data Studies Vision  Unpack the complex assemblages that produce, circulate, share/sell and utilise data in diverse ways  Chart the diverse work they do and their consequences for how the world is known, governed and lived-in  Survey the wider landscape of data assemblages and how they interact to form intersecting data products, services and markets and shape policy and regulation Rob Kitchin and Tracey P. Lauriault, Forthcoming, Toward a Critical Data Studies: Charting and Unpacking Data Assemblages and their Work, in J. Eckert,, A. Shears & J. Thatcher, Geoweb and Big Data, University of Nebraska Press , Pre-Print http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2474112
  4. 4. How is the city translated into software and data? Programmable City Project Translation: City into Code/Data Transduction: Code/Data Reshape City THE CITYSOFTWARE/DATA Discourses, Practices, Knowledge, Models Mediation, Augmentation, Facilitation, Regulation How do software and data reshape the city? Rob Kitchin, 2013
  5. 5. Socio-technological data assemblage Material Platform (infrastructure – hardware) Code Platform (operating system) Code/algorithms (software) Data(base) Interface Reception/Operation (user/usage) Systems of thought Forms of knowledge Finance Political economies Governmentalities & legalities Organisations and institutions Subjectivities and communities Marketplace System/process performs a task Context frames the system/task Digital socio-technical assemblage HCI, remediation studies Critical code studies Software studies Critical data studies New media studies game studies Critical Social Science Science Technology Studies Platform studies Places Practices Flowline/Lifecycle Surveillance studies Rob Kitchin, 2013
  6. 6. Knowledge Production Tracey P. Lauriault, 2012, Data, Infrastructures and Geographical Imaginations. Ph.D. Thesis, Carleton University, Ottawa, http://curve.carleton.ca/theses/27431 Making up Spaces and People – Modified Ian Hacking Dynamic Nominalism Framework
  7. 7. Data Person  Data Double (Virilio, 2000)  Digital doppelgänger (Robinson, 2008)  Data Ghost (Sports analytics)  Data Trails / Traces / Shadows / Footprints  Data (statistical) Person (Dunne & Dunne, 2014)  Dataveillance (Clarke, 1988)
  8. 8. Case Study
  9. 9. Homeless case study scope Object of Study: A. Dublin Ireland:  Pathway Accommodation and Support System (PASS)  Dublin Street Count  Central Statistics Office (CSO) national census enumeration of the homeless. B. Boston, MA, USA:  Homelessness Data Exchange (HDX) Housing and Urban Development (HUD) Housing Inventory Count (HIC)  Boston Health Commission Annual Street/Point- in-Time (PIT) Count of Homelessness  US Census Bureau National Survey of Homeless Assistance Providers and Clients (NSHAPC) C. Ottawa, ON, Canada:  National Homelessness Information System (HIFIS)  Ottawa Street Count  Statistics Canada national census enumeration of the homeless.  Federation of Canadian Municipalities (FCM) Municipal Data Collection Tool (MDCT) indicators on Homelessness Funding  Programmable City Project  P.I. Prof. Rob Kitchin  NIRSA, Maynooth University  European Research Council Advanced Investigator Award  ERC-2012-AdG-323636-SOFTCITY
  10. 10. http://www.homel essdublin.ie/pass http://www.dublincit y.ie/official-street- count-figures-rough- sleeping-winter-2014- across-dublin-region http://www.cso.ie /en/census/censu s2011reports/ho melesspersonsinirel andaspecialcensus 2011report/ The making of homeless people
  11. 11. Dublin Homeless Action Plan  The Cross–Department Team, under the aegis of the Department of the Environment and Local Government, was established under the auspices of the Cabinet Committee on Social Inclusion.  The Departments of Finance,  Health and Children,  Social, Community and Family Affairs, Justice,  Equality and Law Reform,  Education and Science, Tourism, Sport and Recreation as well as FÁS  Probation and Welfare Service
  12. 12. Dublin Regional Homelessness Executive
  13. 13. Data dissemination http://www.environ.ie/en/DevelopmentHousing/Housing/SpecialNeeds/HomelessPeople/
  14. 14. Homeless case study outputs A. 3 site specific city case studies for comparative analysis  3 CS reports with accompanying data, information and literature including:  3 national homeless shelter intake software systems  3 city specific point in time street counts  3 national statistical agency censuses which enumerated people who are homeless  Interview recordings and transcripts from key informants  Repository of related grey literature  B. Data Assemblages  Data assemblage for each intake data system, street count and homeless census  Comparative analysis of these data assemblages  C. Construction of homeless people and homelessness  Application of the modified Ian Hacking framework to the making up of homeless people and spaces  3 homelessness data classification genealogies  Comparative analysis of genealogies  D. Academic Papers
  15. 15. Acknowledgements The research for these studies is funded by a European Research Council Advanced Investigator award ERC-2012-AdG-323636-SOFTCITY. I would like to express my gratitude Dublin City Council, and all the people interviewed as part of this study.
  16. 16. Atlas of the Risk of Homelessness
  17. 17. Pilot Atlas of the Risk of Homelessness • Funded by: – Data Development Projects on Homelessness Program, Homelessness Knowledge Development Program, Homelessness Partnering Secretariat of Human Resources and Social Development Canada (HRSDC) • Partnership: – Federation of Canadian Municipalities (FCM) Quality of Life Reporting System (QOLRS) (24 cities) and the Geomatics and Cartographic Research Centre • 2 cities and 1 metropolitan area: – City of Calgary – City of Toronto – Communauté métropolitaine de Montréal • Geomatics and Cartographic Research Centre Research Team: (https://gcrc.carleton.ca/confluence/display/GCRCWEB/Pilot+Atlas+of+the+Risk+of+Homelessness): – Research Leader: Tracey P. Lauriault (Tracey.Lauriault@NUIM.ie) – Cartographer: Dr. Sebastien Cacquard, – Geomatician: Christine Homuth – Primary Investigator: Dr. D. R. Fraser Taylor – Thanks to: Glenn Brauen, Amos Hayes and Jean-Pierre Fiset
  18. 18. Federation of Canadian Municipalities Quality of Life Reporting System (QoLRS)
  19. 19. Introduction to the Pilot Atlas of the Risk of Homelessness
  20. 20. City Indicators Across Time
  21. 21. City of Toronto 50%+, Housing Starts & Vacancy Rates
  22. 22. City of Calgary: LICO & 30% of Income Spent on Rent
  23. 23. City of Calgary: LICO & 30% of Income Spent on Rent
  24. 24. Grand Montréal: Logements sociaux et populations ayant des difficultés financières pour se loger
  25. 25. Aging Social Housing Stock by Neighbourhood: Toronto
  26. 26. Data Issues • Statistics Canada Geographies change • Health districts, wards, neighbourhoods and StatCan boundaries differ • Formats differ • The cost of StatCan special tabulations are cost prohibitive • Restrictive access to some datasets – HIFIS • CMHC data is very expensive • Licenses are restrictive • City data are the richest  The stories we can tell about Canada's social-policital-economy is impeded with due to data cost and access issues
  27. 27. Municipal Data Collection Tool
  28. 28. Municipal Data Collection Tool
  29. 29. Community Data Program
  30. 30. Community Data Program
  31. 31. Data Negotiation
  32. 32. Questions  What are the big data issues that need to be addressed?  How can we work together?  Access to HIFIS data – a strategy?  Broader analytics? Do we need a broader team of analysts?  Standards?  Portal – data & research?  How do we get the data to change policy?  Open Data?
  33. 33. Data cultures Research Data GovData GeoData Physical Sciences Public Sector Data Access to Data Open Data Social Sciences 2005 VGI Crowdsource Citizen Science Scientists, Cultural Institutions E-Government, CTOs AdminData

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