Communication Studies, Carleton University
Class Schedule: Fridays, 8:30 - 11:25
Location: Residence Commons 372
Instructor: Dr. Tracey P. Lauriault
Office: 4110b Richcraft Hall
Office Hours: Thursdays 9:00-12:00, Fridays by appt. after 13:00
E-mail: Tracey.Lauriault@Carleton.ca (include COMS2200 in the subject line)
| Course Objectives and Description |
|---|
| Assignments |
| Readings & Schedule |
The collection of data about us starts just before we are born, it continues when we go to school, enter the labour force and when we register and use any kind of service from an institution or a platform. Data are also produced as we go about our daily activities –shopping, banking, making phone calls, ordering a taxi, posting to social media, navigating the city, voting, exercising, and filing taxes. Data are also collected as we do exceptional things such as crossing borders, checking into hospital, if we commit crimes, and when we die. We are datafied, we leave behind data shadows or trails and living a dataless life is nearly impossible and sometimes puts you on a watch list.
The course covers a wide range of topics, themes and concepts such as data infrastructures, smart cities, the internet of things (IoT), surveillance and sousveillance, algorithmic governance, predictive policing, the quantified self, location-based services, data collection devices, geodemographics and open data among many others. In this course we examine how data shape actions and the material outcomes of data.
The objectives of this course are to:
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Distinguish big data from small data, and to recognize data types;
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Conceptualize data as part of socio-technological and political processes, as a form of discourse and as media;
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Recognize the interconnections between data, their infrastructures, collection and dissemination technologies, software, platforms and how data are produced and used;
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Identify data politics;
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Understand the construction of facts and the framing of the truth.
| Assignment | Due Date | Weight |
|---|---|---|
| In class Participation and Attendance | Throughout the term | 5% |
| Assignment 1: Data Description (2-3 pages) | Week 2, Sept.14 | 10% |
| Assignment 2: Remote Sensing & Human Rights (2-3 pages) | Week 6, Oct.12 | 10% |
| Assignment 3: Critical Review of a Data-based News Article (2 pages) | Week 7, Oct.19 | 5% |
| Assignment 4: Follow Your Facebook Data Trail (2 pages) | Week 10, Nov.16 | 10% |
| Part 1: Get your Facebook Data | ||
| Part 2: Write a 2-page Data Trail Reflection Article | ||
| Final Project: Open Data Infographic | Throughout the term | 35% |
| Part 1: Forum | Weekly | 10% |
| Part 2: Peer Review | Week 8, Nov.2 | 5% |
| Part 3: Write Up (2 pages) | Week 12, Nov.30 | 5% |
| Part 4: Final Infographic | Week 12, Nov.30 | 15% |
| Final Exam | Exam Period, Dec.9-21 | 25% |
| Total | 100% |
You will produce an informative, relevant, accurate, purposeful, fun, and creative infographic about open data at the City of Ottawa. It can be about:
- how open data came to be,
- any dataset in the open portal,
- the open data licence, policy or directive,
- open data applications, contests,
- open government,
- key performance indicators,
- mapping, or
- crowdsourcing projects at the City.
We will look at many examples in-class and do exercises to get you ready. You can discuss a process, findings in the data, an issue that uses any City data, compare things, show a dataset flowline, tell a story with a dataset, unpack the pieces of a dataset, discuss data found in a report, etc.. It can be digital or it can be done by hand.
- See the COMS2200 resources in the Map section of the Library and the eBooks listed in ARES.
- See curated resources for you here.
- See CULearn Description Infographic Project.
To ensure your success you will have a small activity every week that helps you build up to the final project and these will be posted in the CULearn Class Forum.
The final exam will include a combination of multiple choice and short answer questions about concepts, topics, and issues, and an essay question. Final exams are held between December 9th and 21st.
- Submit to cuLearn
- Format: .doc, .docx, .rtf (NOT .pdf or .Pages)
- Use 12 pt font, 1.5 line spacing, 1-inch margins and indent paragraphs
- Include page numbers
- Citation style: Chicago, Harvard, APA
- Include a document header as follows:
COMS2200 Big Data and Society, Submitted to: Dr. Tracey P. Lauriault, Assignment #, dd/mm/yyyy, Hans Rosling, 010101010
- File naming convention:
RoslingHans_COMS2200_Assignment#
Late Policy: Submit assignments on time.
| Week | Theme |
|---|---|
| Week 1 (Sept.7) | Introduction - What are Data? |
| Week 2 (Sept.14) | Crowdsourcing & Digital Humanitarianism |
| Week 3 (Sept.21) | Open Data |
| Week 4 (Sept.28) | Moving, Locating and Sensing You |
| Week 5 (Oct.5) | Counting You |
| Week 6 (Oct.12) | Social Media |
| Week 7 (Oct.19) | Sorting You |
| Fall Break (Oct.22-26) | No Classes |
| Week 8 (Nov.2) | Identifying You |
| Week 9 (Nov.9) | Watching You |
| Week 10 (Nov.16) | Big Data You |
| Week 11 (Nov.23) | Data Brokers |
| Week 11 (Nov.30) | Remembering You |
| Week 12 (Dec.7) | Critical Data Studies and Review |
WWelcome to the class! We will get to know each other, go over the course outline, assessment, responsibilities, learning objectives and discuss a provocative short essay. You will be exposed to what a data assemblage with a sports analytics example. You will meet your TAs & there will be an in-class datasets activity. Students will learn to critically read a dataset, describe it and discuss the social shaping qualities of data.
- Bell, Genevieve, 2015, The Secret Life of Big Data, Chapter 2 in Boellstorff, T. and Maurer, B. Eds. Data, Now Bigger and Better, Paradigm Press, (pp.7-26)
Assignment 1 - Data Description (Due @ 8:00AM Sept. 14) (10 %) 2-3 pages:
Look for any dataset that interests you and download the data. In 2-3 two pages, report the download process, where you found these data (e.g. a portal, news blog, data library, etc.), and describe the dataset. Explain your interest in this dataset, what might you use the data for? Be sure to provide a full citation of the dataset. You are welcome to use screen captures and they will not go against your page count! The following is a list of ideas to help you write this short report, but do not limit yourselves to these: Who produced the data and for what purpose? How are the variables defined? Dates? Format? Geographic extent? Was there a manual or definitions? Any limitations with these data? Were they free to access? What rights do you have to use these data? Do you trust these data and if so why? This is a short report.
INFOGRAPHIC A – (Due Week 2 Sept. 14)
Go to the MacOdrum Library MAP section on the 1st Floor. Peruse the COMS2200/COMS4407 2018 display on top of the map cabinets. Examine the books & maps and pick 3 that you find interesting. Cite (use proper citation) these 3 resources in the CULearn Forum and explain how you think this will help you with your infographic project. Share any interesting or useful tips. You can include a picture if you want but you do not have to (note that if you do, all images require captions).
This week we will learn about user generated content (UGC), citizen science, crowdsourcing & volunteered geographic information (VGI). We will also discuss divergent views on data humanitarianism. In this lecture we will also examine crowdsourcing in action.
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Meier, Patrick. 2015. The Rise of Digital Humanitarianism in Digital Humanitarians: How Big Data is Changing the Face of Humanitarian Response, CRC Press.
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Resor, Elizabeth (2015) The Neo‐Humanitarians: Assessing the Credibility of Organized Volunteer Crisis Mappers, Policy & Internet, 8(1): 34-54
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1 Vignette: Williams, Sarah; Beijing Air Tracks: Tracking Data for Good, 32-39 in Offenhuber, Dietmar and Schechtner, Katja (2013) Accountability Technologies: Tools for asking hard questions, Vienna: Ambra.
INFOGRAPHIC B – (Due Week 3 Sept. 21)
Find 2 infographics in the library, online or anywhere else about any topic. The infographics can be about concepts, processes, a paper, a story, and should include data, etc. Cite and share an image of these in the CULearn Forum. In a few words, explain why you selected these, how you found them, why you think they are good, discuss if there is room for improvement? What kind of visualization techniques did they use? What would you do differently?
This week, students will learn about open data policies, practices and processes. For some open data will make the world a better place and democratize institution while others think this promise is a myth and we will discuss both. We will have a guest speaker from the City of Ottawa, who is the client for your Infographic project. Darrell Bridge is the Senior Data Analytics Strategist, Open Data Lead, City of Ottawa, Service Innovation & Performance Department (SIPD). Students will learn about open data in the real world and will be exposed to indicator and open data projects.
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Kitchin, Rob, (2014) Open and Linked Data, chapter 3 in the Data Revolution, Sage.
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Marijn Janssen , Yannis Charalabidis & Anneke Zuiderwijk (2012) Benefits, Adoption Barriers and Myths of Open Data and Open Government, 29 (4): 258-268 European Research on Electronic Citizen Participation and Engagement in Public Policy Making
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Neff, Gina and Nafus, Dawn (2016), An introduction to Self-Tracking in Self-Tracking, MIT Essential Knowledge Series, Cambridge MA., MIT Press. 1-35.
| Open Data Videos Resources | Open Data Resources |
|---|---|
| Open Data City of Edmonton | International Open Data Charter |
| City of Ottawa Smart Cities Challenge | Open Data Barometer |
| City of Ottawa Open Data Resources | Open Data Index |
| City of Ottawa – Open Data Council Report (May 12, 2010) | Open North |
| Municipal Freedom of Information and Protection of Privacy Act | Powered by Data |
| City of Ottawa Accountability & Transparency Policy | Open Data Institute |
| City of Ottawa Smart City 2.0 |
INFOGRAPHIC C – (Due Week 4 Sept. 28)
Go back to the infographics display in the library and/or scroll through the eBooks in ARES. Select 2 resources that include a section on visualizing data in maps. Cite those 2 resources in the forum and share a couple of useful tips, include ideas or examples that were striking to you. Test drive some of the infographic apps listed here You will have to create a free account. You can also use Google Draw, Google Spreadsheets, power point or any other graphical or mapmaking tool of your choice. In the CULearn Forum, let us know which tools you tried, if you might use it/them for your infographic project and list its strengths and limitations.
In this class student learn about sensors and the work they do in augmenting places, transportation, and mobility. Students will explore drones, satellites, GPS, LIDAR point clouds, Geiger counters and location-based services (LBS) data. The study of sensors will be framed in the context of smart cities, IoT, autonomous cars, transportation, disasters and human rights.
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Greengard, Samuel (2015) The Internet Changes Everything, The Internet of Things, Cambridge: MIT Press. 1-26.
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Milner, Greg (2016) The Whisper from Space, Chapter 1 in Pinpoint: How GPS is Changing the World, Granta Press.
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Pentland, Alex (2014) Sensing Cities: How Mobile Sensing is Creating a Nervous System for Cities, Enabling Them to Become More Healthy, Safe and Efficient, chapter 8 in Social Physics, The Penguin Press.
3 Vignettes in Offenhuber, Dietmar and Schechtner, Katja (eds.) (2012) Inscribing a Square: Urban Data as Public Space, New York: SpringerWien.
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IGiegie, Safecast, 48-49,
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Safecast, 50-53,
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On The Collection of Human Mobility Data, 54-57
Alternate link here
Pirokka, Michalis; Ellis, Erle C.; and Del Trecidi, Peter (2015) Personal Remote Sensing: Computer Vision Landscaped in New Geographies 07, Geographies of Information, Harvard University.
US Government Accountability Office (2013) IN-CAR LOCATION-BASED SERVICES Companies Are Taking Steps to Protect Privacy, but Some Risks May Not Be Clear to Consumers, Report GAO-14-81
Go to the American Association for the Advancement of Science (AAAS) website and select one of any of the EO human rights case studies or one of their human rights reports and documents.
Explain how Earth Observation (EO) technologies were used to document a crisis or a human rights issue. Include how the issues were reported, which organizations were involved, how were data accessed, data sources, and your reflections on this type of analysis and reporting. Also consider why you think the organizations involved chose this to do this type of research and reporting.
In the CULearn Forum state your infographic topic, include a thesis question, suggest a target audience, create a catchy title to hook your viewers, outline your main argument in bullet points, and list a/some potential City of Ottawa datasets.
In this lecture students learn about some of the infrastructures and institutions dedicated to counting them. They will discover why they matter to the nation and government administrations. They will begin to see the social shaping qualities of data and maps, and how they are part of social, technical and geographical imaginaries. The in-class discussion will focus on the Census of Canada, the cancellation of the long-form in 2010 and its reinstatement in 2016. Concepts such as governmentality, biopolitics, nation building, will be discussed. Can you imagine and guide the nation without data?
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Anderson, Benedict. (1991). Census, Map, Museum in Imagined Communities: Reflections on the Origin and Spread of Nationalism. Revised Edition, Verso, New York. 163-186.
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Thompson, Debra (2010) The Politics of the Census: Lessons from Abroad, Canadian Public Policy, 36(3): 377-382.
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Video- MTV Decoded: Are Hispanic people White? And what’s the difference between Hispanic and Latinx? Alternate link here.
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Graham, Mark (2015) Information Geographies and Geographies of Information, in in New Geographies 07, Geographies of Information, Harvard University.
Assignment 3 - Critical review of a data-based news article (Due Week 7 @ 8AM Oct. 19) (10%) 2 pages:
Select any article from the New York Times UpShot or the UK Guardian Data Blog. In 2 pages, critically review this article and discuss how data were used to support the story. Be sure to introduce the story, the data used, and how they were used. Could you access the data related to the article? What value did the data add to the story? Would the story have been as convincing without data?
Sketch a rough draft of your infographic or use some sticky notes on a sheet of paper/table/wall to sort out your ideas or draw something in PowerPoint. Take a picture of the sketch and/or post it notes or include the document you created in PPT in the CULearn Forum, etc. You can also start trying any of the infographic tools listed here or another tool of your choice and post an image of what you created.
In this class, we will be joined by guest lecturer Dr. Merlyna Lim.
- Lim, M. (2018). Roots, Routes, Routers: Communications and Media of Contemporary Social Movements. Journalism & Communication Monographs Series, 20(2): 92-136.
This week students learn about the power of classification systems and segmentation techniques. Students will discover that classified things are socially constructed, that there may not be ‘natural kinds’ of things yet things get sorted nonetheless, and once sorted it is hard to imagine those things in any other way. This will also include an examination of unique identifiers (UIDs), algorithms and methods by which data about you is collected to situate and put you in your place.
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Rudder, Christian (2014) The Confounding Factor Dataclysm: Love, Sex, Race, and Identity –What Our Online Lives Tell Us About Our Offline Selves, Vintage Canada, 107-123.
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Turow, Joseph. (2006). Chapter 6, The Customized Store in Niche Envy: Marketing Discrimination in the Digital Age. MIT Press. 125 – 147.
- Do you match the profile? - Environics Analytics PRIZM5
- Smith, Michael D. (2016) Moneyball, Chapter 9 in Streaming, Sharing, Stealing: Big Data and the Future of Entertainment, MIT Press.
Create a DRAFT infographic and upload an image of it to the CULearn Forum. In the Forum indicate the tool/s you used to create it. 2 peer reviewers will be assigned to review this DRAFT infographic.
Unique identifiers (UID) are a big data enabling technology. These include biometrics, RFID, QRCodes, facial recognition software, barcodes, social-insurance-numbers, postal codes, credit card numbers and etc. UIDs not only identify you, and help you find things, they also link you to any number of other datasets, objects and locations including markets and into any number of platforms.
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Mayer-schonberger, Viktor and Cukier, Kenneth (2013) Datafication, Chapter 5 in Big Data: A Revolution That Will Transform How We Live, Work and Think. London: John Murray Publishers. 73-97.
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Kragh-Furto, Mette; Mckenzie, Adrian; Mort, Maggie and Roberts, Celia (2016) Do Biosensors Biomedicalize? Sites of Negotiations in DNA-Based Biosensing Data Practices, Chapter 1 in Nafus, Dawn (ed.) Quantified: Biosensing Technologies in Everyday Life, MIT Press.
Peer Reviewers review the 2 infographics assigned to you according to the Infographic rubric. You can mark up a document in pen and take a picture of it to send to your class mate, or you can edit directly in the document. Be constructive, honest, direct, and provide useful suggestions. Submit your peer review document to the CULearn Assignment Area and email each review to each of your class mates.
State institutions are often engaged in surveillance activities. The Canadian Border Service is one such institution, and airports are a unique assemblage of surveillance technologies, data collection and systems. Sometimes surveillance is to reduce harm, detect fraud and cheating, and other times it is to monitor behaviour in order to either serve you better or to market to you. This week students will also discover concepts such as dataveillance, sousveillance, counterveillance, and surveillance.
Schneier, Bruce (2015) Data and Goliath: The Hidden Battles to Collect Your Data and Control Your World, New York: Norton and Company.
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Chapter 2: Data as Surveillance, 20-32
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Chapter 3: Analyzing Our Data, 33-45
Mann, S. and J. Ferenbok. 2013. New Media and the Power Politics of Sousveillance in a Surveillance Dominated World. Surveillance & Society 11(1/2): 18-34.
Part 1: Read this Wired Article and download your personal Facebook data.
Part 2: In 2 pages, discuss what you found? Any surprises? Will you change your settings? Does this change how you will use social media?
In this lecture students explore what drives government and the private sector to embrace big data and its practices. Student study how statistical agencies might use big data for national reporting and we will discuss the implications of doing so. Student will learn to distinguish big data from small data and the characteristics of big data.
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Siegel, Eric. (2013) With Power Comes Responsibility: Hewlett-Packard, Target and the Police Deduce Your Secrets Predictive Analytics, in The Power to Predict Who will Click, Buy, Lie or Die, New Jersey: John Wiley and Sons.
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Tetlock, Philip E. (2015) Superquants Chapter 6 in Superforecasting: The Art and Science of Prediction, McCleland & Stewart.
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Weigend, Andreas (2017) Seeing the Controls: Transparency for the People: What can you demand to see about your data? Chapter 5 in Data for the People: How to make our post-privacy economy work for you, Basic Books.
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Kitchin, R. and McArdle, G. (2016) What makes Big Data, Big Data? Exploring the ontological characteristics of 26 datasets, Big Data and Society.
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Pasquale, Frank (2015) Toward an Intelligible Society, Chapter 6 in The Black Box Society: The Secret Algorithms That Control Money and Information, Harvard University Press.
Submit an image of the revised DRAFT of your infographic to the CULearn Forum. Be sure to include some notes about what you modified based on the peer review. Include a few words about what the peer review process taught you.
Big data are alive and well in data brokerage firms. This week we will examine the political economy of data by examining how data get bundled up and re-sold and how data about you and I get bought and sold and in the hands of corporations with whom we have no relationship. We will also discuss your personal data trail.
Goodman, Marc (2015) Future Crimes: Everything is Connected, Everyone is Vulnerable, and What We Can Do About it. Double Day Canada.
- Chapter 4: You’re not the customer you’re the product
- Chapter 5: The Surveillance Economy
Francke, G. (2017, July 27). Data Driven Marketing and the GDPR: The Data Brokers Conundrum. Retrieved January 2018, from Privacy and Security Blog
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60 Minutes, The end of privacy "The Data Brokers: Selling your personal information"
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C-Span: (2018) Cambridge Analytica Whistle-blower on Facebook Data and Election Interference
- Canadian Internet Public Policy Interest Clinic (2016) On the Data Trail: How detailed information about you gets into the hands of organizations with whom you have no relationship
- Cracked Labs, (2017) Corporate Surveillance In Every Day Life
This week students learn about the data collected by researchers, how those data are paid for and what happens to them once collected. Students will learn about archives and why these remain critical infrastructures in the 21st century big data era. Student will study data as cultural artefacts, and as historical markers of key social, cultural and political events. Student will also be exposed to the construction of data, who controls remembering and data sovereignty.
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Halilovich, Hariz, 2014, Reclaiming erased lives: archives, records and memories in post-war Bosnia and the Bosnian diaspora, Archival Science, 14:231–247,
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Video: Gwen Phillips, (2017) Data Power 2017 Keynote: Indigenous Data Sovereignty and Reconciliation, Carleton University, Ottawa
- National Centre for Truth and Reconciliation
- Global Resources Human Rights Archives and Documentation Program
INFOGRAPHIC K – Submit FINAL infographic & text to CuLearn Assignment, also submit the final infographic to twitter with the title of your infographic, and the tags @ottawacity @ottawaville #COMS2200 #OpenData #YOW
We made it! It is the last week of class. We will assemble what we have learned, we examine what critical data studies means. We will also conduct a thorough review in preparation for the exam.
- Kitchin, Rob and Lauriault, Tracey P. (2014) Towards Critical Data Studies: Charting and Unpacking Data Assemblages and Their Work. The Programmable City Working Paper 2; pre-print version of chapter to be published in Eckert, J., Shears, A. and Thatcher, J. (eds) Geoweb and Big Data. University of Nebraska Press.
COMS2200 Syllabus by Tracey P. Lauriault is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Based on a work at https://github.com/TraceyLauriault/COMS2200_Fall2018.