COMS4407 Communication and Critical Data Studies - Week 1 Slides

http://doi.org/10.22215/tplauriault.courses.2018.coms4407
COMS4407
Week 1:
Introduction - What are data?
Critical Data Studies
September 6, 2018
Class Schedule: Thursdays, 14:30 - 15:30
Location: RH3112
Instructor: Dr. Tracey P. Lauriault
E-mail: Tracey.Lauriault@Carleton.ca
Office: 4110b River Building
Office Hours: Thursdays 9-noon, Friday Afternoon by apt.
ORCID:0000-0003-1847-2738
CU IR: https://ir.library.carleton.ca/ppl/8

Week 1: Agenda
Introductions
Events
CuLearn
Course Outline
Assessment
Readings
Partnership with the City of Ottawa
In-Class Group Database Activity
Assignment 1

Events
https://carleton.ca/sjc/cu-
events/10th-annual-attallah-lecture-
featuring-will-straw/
When: Thursday, Sept. 13th, 2018
Time: 6:30 pm — 9:00 pm
Location: Richcraft Hall, 2220
https://carleton.ca/cuids/events/
seminars/

CuLearn

13 Weeks – 36 Hours
Weeks Date Guests Assignments
Week 1 – Introduction Sept. 6
Week 2 – Conceptualizing Data Sept. 13 Assignment 1: Description
Week 3 – Indicators, Control rms. & Dashboards Sept. 20
Week 4 – Open Data, Trans., Account. & Part. Sept. 27 City of Ottawa
Week 5 – The Characteristics of Big Data Oct. 4 Dashboard: Part 1 Sketch
Week 6 – In-Class Work on Dashboard Report Oct. 11 Dashboard: Part 2 TOC
Week 7 – Enablers and Rationale for Big Data Oct. 18 Assignment 3: Indicators
Study Break
Week 8 – Open Government Nov. 1 Dr Mary Francoli
Week 9 – Data Science, Analytics & Smart Cities Nov. 8
Week 10 – Data Politics, Activism & Cultures Nov. 15 Dashboard: Part 3 Draft Rep.
Week 11 – Data Brokers Nov. 22 Assignment 2: Data Trail
Week 12 – Ethics & The Environment Nov. 30
Week 13 – Assemblage, Methods & Review Dec. 6
TBD
Dashboard: Part 4 FINAL
Dashboard: Part 5 Pres.

City of Ottawa, Community & Social Services Department
Dashboard Recommendation Report
 Students write a formal report that is a cross between a technical, recommendation, comparative and research report. A
formal report is a very specific business writing style and format. In the library you will find some business writing guides and
in these you will find templates and instructions on formal report writing. Design the structure of your report accordingly.
Part 1: Prototype Sketch (Due Week 5 @ noon Oct.4) (5%)
 Sketch by hand or digitally what you think the key components of your dashboard might be. Include a landing page, some
navigation instructions, and maybe a flow, network or tree diagram to illustrate how themes, data and indicators are related.
This is a prototype it need not be pretty. You can take a picture of your sketch and embed it in a document.
Part 2: DRAFT table of Contents, with list of figures and tables, list the indicators and data you will include in your dashboard,
and references (Due Week 6 @ end of class Oct.11) (5%)
 The week 6 class is set aside for you to work in class or go to the library and use the reference materials. In this draft, also
include techniques to represent your data and indicators and the justification for doing so. Be sure to include all the
components of a formal report as discussed in the reference guides and point to any techniques you find useful from the data
visualization material. It is perfectly acceptable to take a picture or a scan of an item in a book (reference it of course) and
include it as an example.
Part 3: Draft Report for Peer Review (Due Week 10 @ noon Nov. 15) (5%)
 This week you submit a draft of your report, a classmate will be assigned to review your report according to a checklist and
they will have one week to send this to you. The marks are for the reviewer.
Part 4: Submit your final report (Due Week 13 @ noon Dec. 6) (15%)
 Submit your final report. Also email to the City of Ottawa and cc Tracey. Contact information to follow.
Part 5: Presentation to City of Ottawa Staff (Date TBD) (10%)
 You can use Power Point, Sway, or Keynote. It should follow the same structure as your report and it should be no more than
6 minutes long.

Assignment 1 - Data Description
Look for any dataset
related to housing,
shelter or
homelessness from
anywhere, and if
possible download
those data.
Take into
consideration the in-
class dataset
exercise and
describe the
dataset, where you
found it, the steps
you took to
download it,
formats, licences
etc.
You are also asked
to write a brief
report about this
dataset.
The following is a list of ideas to help you do so, but do not limit yourselves to these:
 Who produced these data and for what purpose?
 How are the variables defined?
 Dates?
 Geographic extent?
 What are the methodological strengths and limitations of this dataset?
 What is not being measured?
 Could these data be used to inform public policy?
 Is there a fee to access these data?
 What rights do you have to use these data?
 If you were to use them would you include any cautionary notes?
 Do you trust these data and if so why?
 Find a news article that refers to these data and consider whether the article
accurately reported the issue.
 This is descriptive precise writing, this is not an essay, it is a report.

Assignment 2 – Follow Your Personal Data Trail
Part 1: Order your credit score either from TransUnion, Equifax
or your bank or request to see the data collected about you
from any one of the loyalty services you use such as AirMiles,
Shoppers Optimum, Aveda points, etc.
Part 2: In 2 pages, discuss the process of ordering these data,
the policies related to the protection of these data, are they
sold to any third parties, and without disclosing any personal
information report some of the things you discovered.

Assignment 3 - Indicators
 Choose any city, housing or homelessness data or your choice. It can be from any
country, at any scale, from a trans-national organization such as the OECD, the
World Bank or the UN, Etc.
 Your job will be to evaluate these data according to the Open Data Index
definition and scoring methodology (http://index.okfn.org/methodology/).
 The Open Knowledge Foundation evaluates 15 dataset types according to 11
criteria. Read the methodological guide carefully. If you go to the Download
Page (http://index.okfn.org/download/) you will find useful CSV files to help you,
especially the Datasets.csv and the Questions.csv files.
 Be sure to explore the Index’s website to see how different Places are reported,
The Dataset Overview and read the Insights.
 Report your assessment in a table, describe the overall results and how you came
to conclude the openness of your dataset.
 Finally, critically discuss the Index as a system. Does the Index assess any housing
data? If you were to recommend a housing dataset for the Index to evaluate what
would it be and why? Remember to consider international comparability, and
challenges. What do you think about this way of evaluating and reviewing data,
the nature of the question, how data are framed, is this process objective and fair?
What would you improve? What was missing?

Readings
 Kitchin, R. (2014), The Data Revolution. Sage
(Bookstore and on reserve).
 Papers & Reports are available from CuLearn
and ARES.
Resources & Datasets for in-class group
exercise:
 You do not need read these before class.
 You will however want to have copies of these on
your electronic devices as we will do in-class
exercises that relate to these.
 Being familiar with them is a good thing though!

Submission of Assignments
Updates, course information and slides will be
posted on CuLearn.
Submit all assignments to CuLearn,
 write in 12 pt font,
 use 1.5 line spacing and 2.55 cm margins,
 apply Harvard, APA or Chicago citation style,
 number the pages.
Document Header: COMS4407A, Critical Data Studies, Submitted to Dr. Tracey P.
Lauriault, Assignment # and name, dd/mm/yyyy, Margaret Frazer, 01001001
01000100
File Name: FraserMargaret_COMS4407_Assignmen#.doc

Why do we count
things?

2006
Counting makes things visible
2011

Quantifying things provides information

http://euclid.psych.yorku.ca/SCS/Gallery
/images/dan/quetelet-binomial.jpg
https://archive.org/stream/lathoriedelhom00halbuoft#page/n5/mode/2up
Counting & quantifying reveal the norm

http://www.calculator.net/bmi-calculator.html
Correlating things shows relationships

When things are known actions can be taken
Obesity was considered a moral defect, biology
research/science and the political economy of demographics
and locales have been shown as factors associated with it, it
has now become a social issue
Homosexuals were deviants and Genetics/science
demonstrated a biological predisposition
Poor air quality is associated w/traffic congestion, transit and
car pooling are remedial planning actions, and the index tells
us when it is safe for different types of physical activities

http://www.oireachtas.ie/documents/bills28/bills/2015/515/b515d.pdf
http://www.refcom.ie/en/
Bureaucracy acts upon known things

Classification and counting is resisted by
those counted

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
New media studies
Game studies
Critical Social Science
Science Technology Studies
Platform studies
Places
Practices
Flowline/Lifecycle
Surveillance Studies
Critical data studies
Algorithm Studies
Socio-Technological Assemblage
Modified by Lauriault from Kitchin, 2014, The Data Revolution, Sage.

Dynamic Nominalism
Modified from Ian Hacking’s Dynamic Nominalism
Tracey P. Lauriault, 2012, Data, Infrastructures and Geographical Imaginations. Ph.D. Thesis,
Carleton University, Ottawa, http://curve.carleton.ca/theses/27431

Social-shaping qualities of data

Discussion Datasets

Data Sets for Discussion
Anti-Eviction Mapping
Project: https://www.antievictionmap.com/
Inside Airbnb http://insideairbnb.com/about.html
How Airbnb hid the facts in New York
City http://insideairbnb.com/how-airbnb-hid-the-facts-in-
nyc/
Atlas of the Risk of
Homelessness http://legacy.gcrc.carleton.ca/homelessness/intr
o/intro.xml.html

Data Search Exercise

In Class Data Search Exercise
Open Government Portal https://open.canada.ca/data
Statistics Canada 2016
Census http://www12.statcan.gc.ca/census-recensement/index-
eng
<ODESI> https://search2.odesi.ca/

Indicator Exercise

In Class Indicator Exercise
Shelter Capacity Report
2016 https://www.canada.ca/en/employment-social-
development/programs/communities/homelessness/publicatio
ns-bulletins/shelter-capacity-2016.html
Calgary Homeless Foundation Key Performance
Indicators http://calgaryhomeless.com/content/uploads/2017-
09-08-KPI-Performance-Goals-Update-v2.pdf

For Week 2

Week 2 Readings
Readings
 Chapter 1, The Data Revolution
(26 pages).
 Government of Canada (2018)
Canada’s National Housing
Strategy
https://www.placetocallhome.ca
/pdfs/Canada-National-
Housing-Strategy.pdf
Resources
 Government of Canada
Housing First Strategy Website:
https://www.canada.ca/en/empl
oyment-social-
development/programs/comm
unities/homelessness/housing-
first.html
 Homeless Individuals and
Families Information System
(HIFIS)
https://www.canada.ca/en/empl
oyment-social-
development/programs/comm
unities/homelessness/nhis/hifis.
html

Dashboard

COMS4407 Communication and Critical Data Studies - Week 1 Slides

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