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
Introduction to Mongo DB-open-‐source, high-‐performance, document-‐orient...
Homelessness Data Discussion
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
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)
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
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
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.
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
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
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?