Conference of Irish Geographies 2018
The Earth as Our Home
Automating Homelessness May 12, 2018
The research for these studies is funded by a European Research Council Advanced Investigator award ERC-2012-AdG-323636-SOFTCITY.
1. Conference of Irish Geographies 2018
The Earth as Our Home
Automating Homelessness
May 12, 2018
Dr. Tracey P. Lauriault
Assistant Professor of Critical Media and Big Data
School of Journalism and Communication
Carleton University, Ottawa, ON, Canada
Tracey.Lauriault@Carleton.ca
ORCID: orcid.org/0000-0003-1847-2738
2. TOC
1. Critical Data Studies
2. Theoretical Framework
3. Methodology
4. Intake System Case Studies
5. Next Steps
6. Acknowledgements
Dr Tracey P. Lauriault, School of Journalism and Communication, Carleton University
3. 1. Critical Data Studies
Dr Tracey P. Lauriault, School of Journalism and Communication, Carleton University
4. Research and thinking that applies critical social
theory to data to explore the ways in which:
Data are more than the unique arrangement of objective
and politically neutral facts
&
It is understood that data do not exist independently of
ideas, techniques, technologies, systems, people and
contexts regardless of them being presented in that way
1.1 Critical Data Studies
Tracey P. Lauriault, 2012, Data, Infrastructures and Geographical Imaginations. Ph.D. Thesis,
Carleton University, Ottawa, http://curve.carleton.ca/theses/27431
5. 1.2 Framing Data
1. Technically
2. Ethically
3. Politically &
economically
4. Spatial/Temporal
5. Philosophically
6. Technological
Citizenship + Engaged
Scholarship
Rob Kitchin, 2014, The Data Revolution, Sage.
Andrew Feenberg, 2011, Agency and Citizenship in a Technological Society, https://www.sfu.ca/~andrewf/copen5-1.pdf
6. 1.3 Critical 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.
Kitchin and Lauriault, 2015
8. 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
Media studies
Critical Social Science
Science Technology Studies
Platform studies Places
Practices
Flowline/Lifecycle
Surveillance Studies
Critical data studies
2.1 Socio-Technological Assemblage (Kitchin 2014)
Algorithm Studies
AI / Machine Learning
Game Studies
9. 1. Classification
2. Object of Study
3. Institutions4. Knowledge
5. Experts
Looping Effect
a. Counting b. Quantifying
c. Creating Norms d. Correlation
e. Taking Action f. Scientification
g. Normalization
h. Bureaucracy i. Resistance
Engines of Discovery
Derived Engines
2.2 Dynamic Nominalism
Modified Dynamic Nominalism Hacking (Lauriault 2012)
10. 2.3 Reassembling the Data Person / Subject
from Intake Systems
• 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)
11. 20101990 1995 2000 20051985 2015
Data
Liberation
Initiative (DLI)
Geogratis Data
Portal
GeoBase
Canadian
Internet
Public Policy
Clinic
Maps Data and Government
Information Services
(MADGIC) Carleton U
GeoConnections
GeoGratis
Census Data Consortium
Canadian Association of
Research Libraries
(CARL)
Atlas of Canada
Online (1st)
CeoNet Discovery
Portal
Research Data
Network
How'd they Vote
CivicAccess.ca
Campaign for
Open
Government
(FIPA)
Canadian
Association of
Public Data
Users
Datalibre.ca
VisibleGovernment.ca
I Believe in Open Campaign
Change Camps Start
Nanaimo BC
Toronto
Open Data Portals
Edmonton
Mississauga launches open data
Citizen Factory
B.C.'s Climate Change Data Catalogue
Open Parliament
DatadotGC.ca
Ottawa
Ottawa, Prince George, Medicine Hat
Data.gc.ca
Global TV
Hansard in XML
Langley
Let the Data Flow
GovCamp
Fed. Expenses
Montreal Ouvert
Fed.Gov. Travel and
Hospitality
Expenses
London
Hamilton
Windsor
Open Data Hackfest
Aid Agency
Proactive.ca
DataBC
Hacking
Health
14 Cities
Quebec
Ontario
OGP
3 Cities
Alberta
G8
Community Data Program
FCM Quality of Life Reporting
System
Geographic and
Numeric Information
System (GANIS)
Int. Open
Data
Charter
ODX/PSD
CODS
VancouverG4+1
GO Open
Data
Census
E4D
First Nations
Information
Governance
OCAP
2.4 Genealogy
14. 3. Methodology
Dr Tracey P. Lauriault, School of Journalism and Communication, Carleton University
15. 3.1 Ethnography of data collection practices
and digital objects
• Semi-Structured & Transcribed Interviews with:
• Developers
• Creators
• Policy advisors and analysts
• Users – Agencies
• Dublin
• Grey literature in Boston and Ottawa
• Engaged research
• Participation in Rough Sleeper / Street Counts / Point in Time Counts
• Ireland CSO Census Working Group on Homelessness
• Member of the Dublin Regional Homelessness Initiative
• Attend 2 Calgary Homeless Foundation Homelessness Research Symposium
Dr Tracey P. Lauriault, School of Journalism and Communication, Carleton University
16. 3.2 Modified Walkthrough
• Downloading & using the
software
• Observing users
• Call for Tender
• Specification Manuals
• Training Manuals
• Examining data models
• Screen captures of interfaces
Dr Tracey P. Lauriault, School of Journalism and Communication, Carleton University
DOI: 10.1177/1461444816675438DOI: 10.1080/1369118X.2016.1168471
DOI: 10.1177/1461444815589702
17. 3.3 Grey Literature
• Policies, directives, strategies
• Laws, regulation
• Performance indicators, metrics
• Reports
• Community literature
• Funding mechanisms
Dr Tracey P. Lauriault, School of Journalism and Communication, Carleton University
18. 4. Intake System: 3 Case Studies
Dr Tracey P. Lauriault, School of Journalism and Communication, Carleton University
19. 4.1 Programmable City
Translation:
City into code & data
Transduction:
Code & data reshape city
Understanding the
city
(Knowledge)
How are digital data materially &
discursively supported &
processed about cities & their
citizens?
How does software drive public
policy development &
implementation?
Managing
the city
(Governance)
How are discourses & practices of
city governance translated into code?
How is software used to regulate &
govern city life?
Working
in the city
(Production)
How is the geography & political
economy of software production
organised?
How does software alter the form
& nature of work?
Living
in the city
(Social Politics)
How is software discursively
produced & legitimated by vested
interests?
How does software transform the
spatiality & spatial behaviour of
individuals?
Creating the
smart city
Dublin Dashboard
Rob Kitchin, National University of Ireland, Maynooth
20. 4.2 Making up Homelessness
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
21. 4.3 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
22. 4.4 3 Homelessness Intake Systems
Dr Tracey P. Lauriault, School of Journalism and Communication, Carleton University
Ottawa, Canada
Homeless Individuals and Families
Information System (HIFIS)
Dublin, Republic of Ireland
Pathway Accommodation and
Support System (PASS)
Boston, US, Homelessness
Management Information
Systems (HMIS)
23. 4.5 Intake System Objectives
• HIFIS
• Comprehensive and
comparative data collection
• ESDC will grant a licence in
return for non-identifiable
personal information related
to the Service Provider and
its clientele ("Personal
Information").
• PASS
• Provision of 'real-time'
information in terms of
homeless presentation and
bed occupancy
• Dublin local authorities,
Health Service Executive
and all homeless services
• Improve service delivery
• Monitor the delivery of services
• Coordinate services
• Planning and development of
services
Dr Tracey P. Lauriault, School of Journalism and Communication, Carleton University
• HUD HMIS
Coordinated housing and
services funding for homeless
families and individuals with:
• Outreach,
• Intake and assessment,
• Emergency shelter,
• transitional housing and
• permanent supportive housing.
24. 4.6 Intake System Interface
Dr Tracey P. Lauriault, School of Journalism and Communication, Carleton University
25. 4.7 Intake System Governance
• HIFIS
• 1999
• 1st Developed by Canada
Housing Mortgage
Corporation (CHMC)
• 2004? Ongoing dev. by
Homelessness Secretariat,
Employment Social
Development Canada ( ESDC
Fed)
• Community Coordinators
• Deployed in independent
shelters and other service
points
• +/- 500 service points
• Not complete coverage
• PASS
• 2013
• Developed & Coordinated by
Dublin Region Homeless
Executive (DRHE)
• In collab. w/Office of the Data
Protection Commissioner
(ODPC)
• Regional PASS Coordinator
• Deployed in all service points
in Ireland that receive state
funding
• Multiple agencies, including
local authorities and Voluntary
Organizations
• Complete Coverage
Dr Tracey P. Lauriault, School of Journalism and Communication, Carleton University
• HUD HMIS
• 2004
• US Dept. of Housing & Urban
Development (HUD), w/ the
Dept. of Health and Human
Services (HHS), Dept. of
Veterans Affairs (VA), of the
Data Standards
• Neighborhood Development
Supportive Housing Division,
w/ direction from Continuum
of Care Board the Boston
Continuum of Care Leadership
Council
• Coverage?
26. 4.8 Intake System Design
• HIFIS
• In collaboration with service
providers
• National consensus Bldg.
• National User Group
• Joint Application Design
• In house developers
• HIFIS 4 web based
• PASS
• w/Service Providers
• Open Sky
• Cloud based MS BI
Platform
• Cloud based
• Proprietary
• Intake and case management
Dr Tracey P. Lauriault, School of Journalism and Communication, Carleton University
• HUD HMIS
• Social Solutions’ Efforts to
Outcomes (ETO®) HMIS
• Platform, SaaS
w/TouchPoint CMS
• Cloud based
• Open Path Warehouse
developed by GreenRiver
• Intake Social Solutions Inc.
27. 4.9 Intake System Policy & Regulation
• HIFIS
• PIPEDA
• Data Provision Agreements
• Homelessness Partnering
Strategy (HPS)
• National Homeless
Information System (NHIS)
• PASS
• Data Protection Acts 1988 &
2003 and fulfils the role of
certified Data Controller.
• Health Act, 1953
• Childcare Act, 1991,
• Housing Act 1988
• Housing (Miscellaneous
Provisions) Act 2009
Dr Tracey P. Lauriault, School of Journalism and Communication, Carleton University
• HUD HMIS
• Housing First
• US Dept. of HUD awards
Homeless Assistance
Program funding through
Continuum of Care (CoC)
28. 4.10 Intake System Data Output & Reporting
• HIFIS • PASS
Dr Tracey P. Lauriault, School of Journalism and Communication, Carleton University
• HUD HMIS
29. 4.11 Data Model Open Path Wharehouse Boston
Dr Tracey P. Lauriault, School of Journalism and Communication, Carleton University
30. 5. Next Steps
Dr Tracey P. Lauriault, School of Journalism and Communication, Carleton University
31. 5.1 Next Steps
• Installation and trial
• Data fields, flow line & model
• Data - Decision making processes
• Mapping governance model to the data fields
• Licencing
• Comparative analysis
• What does a person who experiences homelessness look like in
data? And do the data make a difference? Do the data affect
them?
Dr Tracey P. Lauriault, School of Journalism and Communication, Carleton University
33. 6.1 Acknowledgements
The research for these studies is funded by a European
Research Council Advanced Investigator award ERC-2012-
AdG-323636-SOFTCITY.
http://progcity.maynoothuniversity.ie/
I would like to express my gratitude to Officials at Dublin
City Council, Officials at Employment and Social
Development Canada, and Boston Foundation & Metropolitan
Area Planning Council (MAPC) and all those who shared their
knowledge and time.
Co-functioning heterogeneous elements of a large complex socio-technological system – these elements are loosely coupled.
They contend that data do not exist independently from the context within which they were created, and the systems and processes that produce them. The Prime2 Data model and platform is no exception. In order to study data in their ‘habitat’ and ‘ecosystem’, Kitchin (2014) offers a socio-technological assemblage approach to guide the empirical analysis of data (See also Kitchin & Lauriault 2014). The assemblage can be conceptualized as a constellation of co-functioning, loosely-coupled heterogeneous elements, and it is these elements that guide data collection. Here, the assemblage is both a tool for research as well as a theoretical framing of data (Anderson et. al 2012).
Furthermore, data modelling requires a particular form of logical abstract thinking, in the case of the OSi and 1Spatial those that were involved in the modelling exercise were very senior, experienced and renowned spatial data experts, all formally trained in spatial database design and maintenance as well as spatial analysis at the enterprise level. The design and testing of a model is very labour intensive, re-cursive, and incredibly expensive. At the OSi, this work was not done in house, thus requiring the enactment of a procurement process to cover this major expenditure, and because of this, and because the model is key, it is a high stakes tendering process.
For example, infrastructure is not simply hardware and software it is the systems of thought that led to its creation including how object oriented modeling came to be and how that model materializes into code and algorithms which reformulated the entire data production flowline and its association with not only the equipment used by surveyors, but the entire database stack.
It is only by looking at the model and how it came to be through database specifications and requirements, the observation of data production on site in real time and in communication with database designers and mangers, that attributes of an infrastructure’s assemblage can be observed in their state of play.
The process of modelling is situated in the domain of object oriented programming, the semantic web, GIScience, modelling software, taxonomies, the burgeoning database and GIS industry, modelling schemas, mathematics, consulting firms, and offshore data re-engineering companies.
Ian Hacking, deconstructed classification systems, primary in the health sciences, to understand how these in turn produce knowledge about the work these do in the world, especially when classifications become understood as being the ‘real thing’ (1986, 1991).
Hacking suggests that there are two interrelated processes at work within a data assemblage which both produce and legitimate a class, and those processes shape how that class does work in the world.
In addition, he observed that nominal classes are not firm constructs. He calls this dynamic nominalism, wherein there is an interaction between data classifications and what they represent that leads to mutual changes in the things classified and how classifications are understood across time and space.
In the case of the Prime2 data model, Hacking’s approach illustrates how ‘real-world’ objects and their attributes, and the things those objects represent, stay the same or change between the old Prime system and the new Prime2 system in terms of how Dublin is captured and represented. Hacking calls the first part of this process (2002,2007) ‘the looping effect’.
The looping effect concerns how data are classified and organised; in other words, how a data ontology or model comes into existence and how that can reshape that which has been classified.
This is where the systems of classification work to reshape spaces and places in the image of a data ontology; for example, when ‘sites’ such as Phoenix Park become a prestigious city treasure that are acted upon in such a way by citizens. In Prime2, Phoenix Park as an object is a site with a geometry derived from the sum of topologically related objects such as vegetation, buildings (including the President’s residence, Dublin Zoo, military training grounds and the OSi offices), ways, and other sites.
Image Source: http://www.nesta.org.uk/sites/default/files/digital_person_1.jpg
Data Double - Virilio, Paul. Open Sky. Julie Rose, Trans. New York: Verso, 2000.. p. 40
Digital doppelgänger - Robinson, Sandra 2008. The Doppelganger Effect: Spaces, Traces, and Databases and the Multiples of Cyberspace
Ghost players - http://grantland.com/the-triangle/the-sportvu-follow-up-answering-the-most-common-questions-and-more-ghost-players/
Dataveillance - Clarke, Roger. 1988. Information technology and dataveillance. Communications of the ACM 31(5): 498-512.
Statistical Person – Dunne & Dunne, 2014, Person Activity Register - a statistical register of persons, Central Statistics Organization Administrative Data Seminar, http://www.cso.ie/en/media/csoie/newsevents/documents/administrativedataseminar/4.10Person_Activity_Register_Dublin_Castle_20th_Feb_2014.pptx
Today I will be discussing a methodological approach for critically examining a data model.
The central question examines is how is a city translated into code and data, and how does that code and data transduce and reshape the city with the objective of trying to understand the techno-political processes by which a city is modeled / translated into a database?
What does that database model look like?
In what ways does that model transduce space and reshape the city?
Is the relationship between model and city recursive and can the city database eventually learn about itself from itself and simulate the city (Beaudrillard, 1981)?
What would be included and what would be left out of the database in order to avoid the similitude problem of Lewis Carrol’s map of the city at the scale of a ‘mile of a mile’ (Carroll 1893), or where cartography is so perfect that a map includes each house, mountain or tree represented by just that, the houses, mountains and trees as Borges’ satirically wrote in the Exactitude of Science (1946). Who decides?
The central objective of the programmable city project is to unpack“how software and data make a difference to contemporary urbanism”, by analyzing the city with “respect to four key urban practices - understanding, managing, working, and living in the city”.