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COMS4407 Communication and Critical Data Studies - Week 1 Slides

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COMS 4407
Fall 2018
COMMUNICATION and
CRITICAL DATA STUDIES
Communication and Media Studies
School of Journalism and Communication
Sept. 6, 2018 – Dec. 06, 2018
Thursdays, 14:30 - 17:30, RH3112
Dr. Tracey P. Lauriault
Tracey.Lauriault@Carleton.ca
http://orcid.org/0000-0003-1847-2738
4110b River Building
Office Hours:
Thursdays 9:00 - 12:00
Fridays: By Apt. After 13:00

Publié dans : Données & analyses
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COMS4407 Communication and Critical Data Studies - Week 1 Slides

  1. 1. 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
  2. 2. Week 1: Agenda Introductions Events CuLearn Course Outline Assessment Readings Partnership with the City of Ottawa In-Class Group Database Activity Assignment 1 http://doi.org/10.22215/tplauriault.courses.2018.coms4407
  3. 3. Events http://doi.org/10.22215/tplauriault.courses.2018.coms4407 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/
  4. 4. CuLearn http://doi.org/10.22215/tplauriault.courses.2018.coms4407
  5. 5. 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. http://doi.org/10.22215/tplauriault.courses.2018.coms4407
  6. 6. 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. http://doi.org/10.22215/tplauriault.courses.2018.coms4407
  7. 7. http://doi.org/10.22215/tplauriault.courses.2018.coms4407
  8. 8. 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. http://doi.org/10.22215/tplauriault.courses.2018.coms4407
  9. 9. 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. http://doi.org/10.22215/tplauriault.courses.2018.coms4407
  10. 10. 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? http://doi.org/10.22215/tplauriault.courses.2018.coms4407
  11. 11. Readings http://doi.org/10.22215/tplauriault.courses.2018.coms4407  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!
  12. 12. 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. http://doi.org/10.22215/tplauriault.courses.2018.coms4407 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
  13. 13. Why do we count things? http://doi.org/10.22215/tplauriault.courses.2018.coms4407
  14. 14. 2006 Counting makes things visible 2011 http://doi.org/10.22215/tplauriault.courses.2018.coms4407
  15. 15. Quantifying things provides information http://doi.org/10.22215/tplauriault.courses.2018.coms4407
  16. 16. 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://doi.org/10.22215/tplauriault.courses.2018.coms4407
  17. 17. http://www.calculator.net/bmi-calculator.html Correlating things shows relationships http://doi.org/10.22215/tplauriault.courses.2018.coms4407
  18. 18. 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://doi.org/10.22215/tplauriault.courses.2018.coms4407
  19. 19. http://www.oireachtas.ie/documents/bills28/bills/2015/515/b515d.pdf http://www.refcom.ie/en/ Bureaucracy acts upon known things http://doi.org/10.22215/tplauriault.courses.2018.coms4407
  20. 20. Classification and counting is resisted by those counted http://doi.org/10.22215/tplauriault.courses.2018.coms4407
  21. 21. 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.
  22. 22. 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
  23. 23. Social-shaping qualities of data
  24. 24. http://doi.org/10.22215/tplauriault.courses.2018.coms4407 Discussion Datasets
  25. 25. 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 http://doi.org/10.22215/tplauriault.courses.2018.coms4407
  26. 26. http://doi.org/10.22215/tplauriault.courses.2018.coms4407
  27. 27. http://doi.org/10.22215/tplauriault.courses.2018.coms4407
  28. 28. http://doi.org/10.22215/tplauriault.courses.2018.coms4407
  29. 29. http://doi.org/10.22215/tplauriault.courses.2018.coms4407
  30. 30. http://doi.org/10.22215/tplauriault.courses.2018.coms4407 Data Search Exercise
  31. 31. 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/ http://doi.org/10.22215/tplauriault.courses.2018.coms4407
  32. 32. http://doi.org/10.22215/tplauriault.courses.2018.coms4407 Indicator Exercise
  33. 33. 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 http://doi.org/10.22215/tplauriault.courses.2018.coms4407
  34. 34. http://doi.org/10.22215/tplauriault.courses.2018.coms4407
  35. 35. http://doi.org/10.22215/tplauriault.courses.2018.coms4407 For Week 2
  36. 36. 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 http://doi.org/10.22215/tplauriault.courses.2018.coms4407
  37. 37. 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. http://doi.org/10.22215/tplauriault.courses.2018.coms4407
  38. 38. Dashboard http://doi.org/10.22215/tplauriault.courses.2018.coms4407

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