Communication Studies, Carleton University
Class Schedule: Tuesdays, 18:00 - 21:00
Location: Tory Building 360
Instructor: Dr. Tracey P. Lauriault
Office: 4110b Richcraft Hall
Office Hours: Wednesdays 10-13:00
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. Data about us are collected when we go to school, enter the labour force, often with the help of unique IDs such as SIN#s. Data are also produced as we go about our daily activities – when we shop, bank, make phone calls, order a taxi, use social media, navigate the city, vote, exercise, and file taxes. Data are also collected as we do exceptional things such as cross international borders, check into hospital, when we commit crimes, at death and even afterward depending on what happens to our bodies, how our families and friends commemorate and remember us and if our actions are archived. We are datafied, and living a dataless life is nearly impossible as is ‘living below the radar’ since we each have a data double that leaves behind data shadows.
In this course we examine what data generated by and about us do, how they are used, who owns them, and if and when linked, filtered through algorithms, search engines or visualized into maps, how they shape actions and material outcomes.
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, social physics, algorithmic governance, predictive policing, the quantified self, location based services, data collection devices and geodemographics among many others.
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 | Weight |
|---|---|
| Participation | 5% |
| Final Infographic Project | 35% |
| Assignment 1: Describing a Dataset | 10% |
| Assignment 2: Crowdsourcing in Open Street Map | 20% |
| Assignment 3: Remote Sensing and Human Rights | 10% |
| Final Exam | 20% |
| Total | 100% |
The course is structured around in class activities, forum, participation and meaningful discussion about the readings.
You will produce an informative, relevant, accurate, purposeful, fun, and creative infographic on a topic related to Big Data and Society. We will look at many examples in-class and do exercises to get you ready. You can discuss a process, phenomenon, findings in the data, compare things, show a dataset flowline, tell a story with a dataset, unpack the pieces of a dataset, abstract a scientific paper into an infographic, and you can even map out the OSM assignment etc. It can be digital or it can be done by hand.
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See the COMS2200 resources in the Map section of the Library and the eBooks listed in ARES.
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See curated resources for you here.
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See CULearn Description Infographic Project.
To ensure your success you will have a small activity to do every week that helps you build up to the final project. These are to be posted on the CULearn Class Forum.
During the 2nd half of the Week 2 class we will go through what you need to do for this assignment. We will have 2 guest speakers join us and they will walk us through the process. See the Crowdsourcing Grid on CULearn.
Submit text files to CULearn, write in 12 pt. font, use 1.5 line spacing and 2.55 cm margins, apply Harvard, APA or Chicago citation style, and number the pages. Use the following header and file naming protocol Header:
Big Data and Society COMS2200A 2017, Submitted to Dr. Tracey P. Lauriault, My Favourite Big Data Assignment, 17/09/2017, Grace Hopper, Student ID: 01010101
Name your files as follows
HopperGrace_COMS2200A_MyFavourite.doc
Exams will be held between December 10th – 22th and will include a combination of multiple choice questions, short answer questions about concepts, topics, and issues, and 1 essay question. Exams cannot be deferred or rescheduled to accommodate travel plans.
Late Policy: Submit assignments on time.
| Week | Theme |
|---|---|
| Week 1 (Sept.12) | Introduction |
| Week 2 (Sept.19) | Crowdsourcing & Digital Humanitarianism |
| Week 3 (Sept.26) | The Quantified You |
| Week 4 (Oct.3) | Counting You |
| Week 5 (Oct.10) | Sorting You |
| Week 6 (Oct.17) | Moving, Locating and Sensing You |
| Fall Break (Oct.23-27) | No Classes |
| Week 7 (Oct.31) | Identifying You |
| Week 8 (Nov.7) | Watching You |
| Week 9 (Nov.14) | Big Data You |
| Week 10 (Nov.27) | Remembering You |
| Week 11 (Dec.5) | Assembling You |
| Week 12 (Dec.9) | Review |
Welcome to the class! We will get to know each other, go over the course outline, class deliverables, learning objectives and discuss a provocative short essay. There will be an in-class group based guided datasets exercise (see the datasets below). 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: Describing a Dataset (Due Week 3 @ 5PM Sept. 26) (10 %) 2 pages:
Read the description on CULearn. We will practice this in class.
INFOGRAPHIC A – (due Sept. 19)
Go to the MacOdrum Library MADGIC section on the 1st Floor. Peruse the COMS2200 Display. Examine the books & maps and pick 3 that you find interesting. Cite those 3 books in the Week1 Forum, include the name of a chapter or section from each of those books that you think will help you with your infographic project and in a few sentences explain why. Any interesting and useful tips? Any useful examples?
This week we will learn about user generated content (UGC), citizen science, crowdsourcing & volunteered geographic information (VGI). In this lecture we will learn about digital humanitarians and citizenship. Students will learn about data contributors, be they ‘amateurs’ or ‘expert professionals’, the disruption of formal data producing institutions, the OSM platform, and how to add to contribute data to OSM.
We will also have 2 guests:
- Denis Carriere, a digital humanitarian who will discuss his work in Nepal
- Els Aelvoet, a Geomatics expert. Denis and Els will also guide us through the crowdsourcing assignment.
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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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- 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 Sept. 26)
Find 2 infographics. The infographics can be about concepts, processes, a paper, a story, and should include data, etc. Cite and share these in the class forum. In a few words, explain why you selected these, how you found them, why you think they are good, discuss if there room for improvement? What would you do differently or how would you improve them?
Crowdsourcing 1 – (due Week 3 Sept. 26)
You have created you OSM, Mapillary and GitHub accounts and contributed your user names to CULearn.
In this lecture students learn about the quantified self and how the sensors they wear and/or carry in their pockets and/or are embedded in their bodies collect data about them. Students will examine an array of self-tracking data collection devices, why people quantify themselves and how their knowledge and understanding of themselves changes as a result. Students will also learn about personal story telling with data, the process of lifelogging, how the data one collects about oneself are shared with third parties with or without one’s consent. The following concepts will be discussed: quantified self, dataism, data shadows, data doubles, data doppleganger, cyborgs, biosensing, data privacy, data rights, data ownership and terms of use.
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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.
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Video: Shannon Conners- A Lifetime of Personal Data
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Video: Peter Torelli- 20 Years of Memories Tucked Away in Personal Finance Data
INFOGRAPHIC C – (Due Oct. 3)
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 GoogleDraw and Google Spreadsheets, or any other tool of your choice. In the CULearn Forum, let us know which tools you think you might use for your infographic exercise and note strengths and limitations.
Crowdsourcing 2 – (due Week 4 Oct. 3)
You have taken 20 photos with the Mapillary app and you have contributed the 20 Mapillary photo keys to CULearn.
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 with an emphasis on its cancellation of the long-form census in 2010 and its reinstatement in 2016. Concepts such as governmentality, biopolitics, nation building, will be discussed and indicators will be introduced.
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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.
INFOGRAPHIC D – (Due Week 5 Oct. 10)
In the CULearn Forum state your infographic topic, include a thesis question you aim to answer with your infographic, suggest a target audience, create a catchy title to hook your viewers, outline your main argument in bullet points, and list potential datasets.
Crowdsourcing – (due Week 5 Oct. 10)
You will have searched for the suitable OSM TAGs (aka metadata) to match your 20 photos and you will have contributed those tags along with the photos keys in CULearn.
This week students learn about the power of classification systems and segmentation techniques. Students will discover that there may not be ‘natural kinds’ of things but things do get sorted out, and once sorted it is hard to imagine those things in any other way. This will also include an examination of unique identifiers and methods by which data about you is collected in order 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.
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Environics Analytics PRIZM5
INFOGRAPHIC E – (Due Week 6 Oct. 17)
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 and post to the forum or include the document you created in PPT, and etc. You can also start trying any of the infographic tools listed here or another tool of your choice and post an image file.
Crowdsourcing – (due Week 6 Oct. 17)
You have linked your photos with keys and metadata to OSM. Share a screen capture of your map to CULearn that includes your 20 photo points. Have one screen capture with your point and your metadata showing on the map. Be sure to include your OSM User name in the forum post.
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 the visualization of 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.
- Greengard, Samuel (2015) The Internet Changes Everything, The Internet of Things, Cambridge: MIT Press. 1-26.
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
Remote Sensing Assignment (Due Week 9 @ 5PM Nov. 14) (10 %) 2 pages
Read the description on CULearn. In-Class we will look at the American Association for the Advancement of Science (AAAS) website to warm you up.
INFOGRAPHIC F – (due Week 7 Oct. 31)
Create a DRAFT infographic and upload an image of it to the CULearn FORUM. In the FORUM the tool/s you used to create it. 2 peer reviewers will be assigned to review this DRAFT infographic.
Unique identifiers 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. Unique IDs not only identify you, but they link you to any number of other datasets and locations.
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Mayer-Schonberger, Viktor and Cukier, Kenneth (2013) Datafication in Big Data: A Revolution That Will Transform How We Live, Work and Think. London: John Murray Publishers. 73-97.
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Goodman, Marc (2015) You’re not the customer, You’re the product in Future Crimes: Everything is Connected, Everyone is Vulnerable, and What We Can Do About it. Double Day Canada, 44-64. Alternate link here.
INFOGRAPHIC G – (due Week 8)
Peer Reviewers, review the 2 infographics assigned to you and using the criteria sheet provided. 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. Post your peer review document o the CULearn.
Crowdsourcing 5 – (due Week 8)
You have submitted your 2 page reflection document to CULearn. In this document you considered the 3 platforms, the process of capturing data all the way to the posting of your final photo in OSM, you will discuss challenge, insights, things you found enjoyable, provide suggestions on how to improve the process, and etc.. You will express what you learned. End your reflection by discussing this form of databased citizenship.
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 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
INFOGRAPHIC H
Refine your Infographic based on peer review.
In this lecture, students explore what drives government and the private sector to embrace big data and its practices. Students study how statistical agencies might use big data for national reporting and we will discuss the implications of doing so. Students will learn to distinguish big data from small data and the characteristics of big data.
- Siegel, Eric. (2013) The Ensemble Effect: Netflix, Crowdsourcing and Supercharging Prediction.Predictive Analytics: The Power to Predict Who will Click, Buy, Lie or Die, New Jersey: John Wiley and Sons. 133-150.
INFOGRAPHIC I – (due Week 10 Nov. 21)
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.
Up to this points students have primarily examined data collected by corporations, the government, or crowdsourced data. 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.
Halilovich, Hariz, 2014, Reclaiming erased lives: archives, records and memories in post-war Bosnia and the Bosnian diaspora, Archival Science, 14:231–247,
INFOGRAPHIC J
Polish your infographic and ask for help on the forum if you need it.
This week, students examine an approach to the study big data called an assemblage. Students will learn to think about data as discourse and as a socio-technological assemblage. This will include defining what big data are, and brief overview of some of the enablers of big data. This framework will be illustrated with a sports analytics example.
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.
SUBMIT INFOGRAPHIC – along with the text.
It all comes together. In-class we will examine the knowledge gained in the past 11 weeks and we will use the assemblage framework to guide our understanding of big data and society. We will also prepare for the final exam.
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_Fall2017.