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Automating Homelessness
Dr. Tracey P. Lauriault's presentation at the 2018 Conference of Irish Geographies discusses critical data studies, focusing on the intersection of data infrastructure and its socio-political implications in addressing homelessness. It explores the methodologies and frameworks utilized in analyzing intake systems through case studies in Dublin, Boston, and Ottawa. The research aims to understand the governance and impact of data on homeless individuals, informed by participant engagement and institutional collaborations.
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Automating Homelessness
- 1. Conference of IrishGeographies 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 DataStudies 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 DataStudies Dr Tracey P. Lauriault, School of Journalism and Communication, Carleton University
- 4. Research and thinkingthat 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 DataStudies 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
- 7. 2. Theoretical Framework DrTracey P. Lauriault, School of Journalism and Communication, Carleton University
- 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. Objectof 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 theData 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 200020051985 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
- 12. 1. Inventory 2. Assessment Use& Implications 3. Governance Citizen & Private Sector Engagement 4. Accountability Transparency, Rights, Auditing 5. Mobilization Plain Language 2.5 Un-blackboxing technology
- 13. 2.6 Social-shaping qualitiesof data (Rob Kitchin, 2012)
- 14. 3. Methodology Dr TraceyP. Lauriault, School of Journalism and Communication, Carleton University
- 15. 3.1 Ethnography ofdata 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: Cityinto 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 upHomelessness 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 casestudy 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 HomelessnessIntake 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 SystemObjectives • 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 SystemInterface Dr Tracey P. Lauriault, School of Journalism and Communication, Carleton University
- 25. 4.7 Intake SystemGovernance • 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 SystemDesign • 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 SystemPolicy & 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 SystemData Output & Reporting • HIFIS • PASS Dr Tracey P. Lauriault, School of Journalism and Communication, Carleton University • HUD HMIS
- 29. 4.11 Data ModelOpen Path Wharehouse Boston Dr Tracey P. Lauriault, School of Journalism and Communication, Carleton University
- 30. 5. Next Steps DrTracey 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
- 32. 6. Acknowledgements Dr TraceyP. Lauriault, School of Journalism and Communication, Carleton University
- 33. 6.1 Acknowledgements The researchfor 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.
- 34. Thank you!@TraceyLauriault Dr TraceyP. Lauriault, School of Journalism and Communication, Carleton University
Editor's Notes
- #9 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.
- #10 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.
- #11 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
- #14 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?
- #17 http://journals.sagepub.com/doi/full/10.1177/1461444816675438 https://doi.org/10.1080/1369118X.2016.1168471 http://journals.sagepub.com/doi/abs/10.1177/1461444815589702
- #20 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”.
- #21 Socio technological approach