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

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. Theoretical Framework

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. 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. 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. 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. 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. Case Study

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. 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. 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. Dublin Regional Homelessness Executive

13. Data
dissemination
http://www.environ.ie/en/DevelopmentHousing/Housing/SpecialNeeds/HomelessPeople/

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. 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. Atlas of the Risk of Homelessness

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. Federation of
Canadian
Municipalities
Quality of Life
Reporting
System (QoLRS)

19. Introduction to the Pilot Atlas of the
Risk of Homelessness

20. City Indicators Across Time

21. City of Toronto
50%+, Housing Starts & Vacancy Rates

22. City of Calgary: LICO & 30% of
Income Spent on Rent

23. City of Calgary: LICO & 30% of Income
Spent on Rent

24. Grand Montréal: Logements sociaux et populations
ayant des difficultés financières pour se loger

25. Aging Social Housing Stock by
Neighbourhood: Toronto

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. Municipal Data Collection Tool

28. Municipal Data Collection Tool

29. Community Data Program

30. Community Data Program

31. Data Negotiation

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. 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