Data Day 3.0 Tuesday, 29 March 2016, 9:30 to 10:45, Carleton University http://carleton.ca/cuids/cu-events/data-day-3-0-2/

1. Ongoing Research in Data Studies
Data Day 3.0
Tuesday, 29 March 2016, 9:30 to 10:45, Carleton University
Dr Tracey P. Lauriault, School of Journalism and Communication, Carleton University & Programmable City Project, Maynooth University

2. 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

3. 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

4. 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

5. 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

6. 3 Case Studies
1. Ontologizing the city
2. The making of homeless people
3. Open data

7. 1. Ontologizing the City

8. Ontologizing the City - From Old School National
Cartographic Based Classification toward a Rules Based
Real-World Object Oriented National Database
Object of Study
 Data assemblage of OSi PRIME2
 Examine how ‘real’ things are understood in
the new object oriented data model
 Assess if these change how space is
modelled and then acted upon
Time frame
 Jan. 2015-2018
Data Management and Ethics
 ERC
 Maynooth University
 SSHRC Tri-Council
Case Study Outputs
 Case study report
 Data assemblage
 Tracing the production of space
 Genealogy from class to object
 Academic publications
Funding
 Programmable City Project
 P.I. Prof. Rob Kitchin
 NIRSA, Maynooth University
 European Research Council Advanced
Investigator Award
 ERC-2012-AdG-323636-SOFTCITY

9. Data Collection
Attend OSi & 1Spatial Road shows and public speaking events
 One day coordinated field trip & group interviews at OSi Sligo
(survey data capture unit)
 Examine the Prime & Prime2 flow lines
 Real-time survey and data update of a building
 1.5 months as an embedded researcher, OSi in Phoenix Park
 One-on-one interviews with key actors (Transcribed audio recordings):
 Group interview
 Document Collection
 Collection of objects across time for Dublin

10. Prime 2 Models and Concepts
Skin of the earth ‘real world’ object modelling
5 skin of the earth objects
 Ways
 Water
 Vegetation
 Artificial
 Exposed
 Z-Layer
 Superimposed Objects
 Segmentation &
Connectivity
 GDF1 GDF2 centrelines
 Sites & Locals
 Boundaries
 Links objects using
persistent ID’s
 Form & Function
object classification
 3D data storage
(CityGML LOD2)
 Grouped

11. Seamless, topologically consistent blanket of polygons covers
the entire surface of Ireland w/no holes or gaps

12. Cassini, 6”, 1st ed. Circa?
Heuston station across time
Cassini 6”, 1943-44
Cassini 25”, 1st ed, Circa?
Cassini25”, 1936
Heuston Station, Prime2 MapGenie
Heuston Station, Prime2 SOE

13. Evolution
 Institutionally
 Colonial surveyor
 Military Mapping
Organization
 Civil Service National
Mapping Organization
(NMO)
 State Body NMO
 Will become a NMO
w/in Tailte Éireann
 Technologically
 Data collection
 Techniques
 Scale
 Geometry
 Skill
 Technologies
 Dissemination
 Scope
 Colonial mapping
 National mapping
 Post Colonial mapping
 OSi/OSNI/OSGB
 EU / Inspire / NSDI
 Global

14. Etymology

15. Relationality

16. Genealogy of a model

17. Genealogy of a data model

18. Models are also actors
 Models shape
 how the world is viewed
 the world of work
 tools & techniques
 the structure of an organization
 how organizations interconnect with others
 Models augment space
 Models are socially constructed
by people

19. 2. Making of Homeless People

20. 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

21. 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

22. 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

23. 3. Open data

24. Open data case study
Object of Study
A. Dublin, Republic of Ireland (NI)
B. Ottawa, ON & Montreal, QC, Canada
 National, County Level Public Sector Data Portals
 Academic Portals
 Public, Academic and Private Sector Partnership
Portals/Initiatives
 Open access to data initiatives
 The private sector data dissemination on behalf & w/ the
public sector.
 Research projects
 Civil Society Initiatives
 Initiatives that have shaped debates on openness &
transparency
 Indicators & evaluation
 Consultants/developers
 Other actors
 Tools & Instruments
Case Study Outputs:
A. 2 site specific city case studies
B. Data Assemblages & Landscape
C. Open Data discourse Analysis
D. Academic Papers
Funding
 Programmable City Project
 P.I. Prof. Rob Kitchin
 NIRSA, Maynooth University
 European Research Council Advanced
Investigator Award
 ERC-2012-AdG-323636-SOFTCITY

25. Open Licences

26. Open Source/Formats/Specs

27. Open Infrastructure

28. Open Data Definitions (sample)
 1959 Antarctic Treaty
 1992 - UNCED – Agenda 21 Chapter 40,
Information for Decision Making
 1996 Global Map
 2002 – UNCED – Ageday 21 + 10 Down To Earth
 2005 - Open Knowledge Foundation (OKNF) - 11
Principles (Licence specific)
 2007 GEOSS - Data Sharing Principles for the
Global Earth Observing System of Systems
 2007 - US Open Government Working Group - 8
principles of Open Government Data
 2007 Science Commons Protocol for Implementing
Open Access Data
 2007 Sunlight Foundation - 10 Principles for
Opening Up Government Informatio
 2007 OECD, Principles and Guidelines for Access
to Research Data from Public Funding
 2008 OECD, Recommendations on Public Sector
Information
 2009 W3C - Publishing Open Government Data
 2010 Tim Berners-Lee 5 Star of Open Data
 2010 Panton Principles for Open Data in Science
 2010 Ontario Information Privacy Commissioner -
7 Principles
 2013 Open Economics Principles
 US Association of Computing Machinery (USACM)
– Recommendations on Open Government
 American Library Association (ALA) – Access to
Government Information Principles

29. 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

30. Communication & Media Studies

31. Core courses
 COMM 5225 (0.5) Critical Data Studies
 DATA 5000: Introduction to Data Science
Electives
 COMM 5203 (0.5) Communication,
Technology & Society
 COMM 5224 (0.5) Internet Infrastructure &
Materialities
 COMM 5221 (0.5) Science & the Making of
Knowledge

32. 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 to Ordnance
Survey Ireland (OSi), Dublin City Council and the
Open Data Community in Ireland for generously
sharing their knowledge and time.