Every week you will participate in a discussion session with your peers to discuss assigned readings. This session will be moderated by a TA in a group of 10-20 students each. The papers that you will read for each week will come along with a set of questions that will guide your reading and subsequent discussions. These papers address some of the topics discussed that week in lectures, therefore you are strongly encouraged to participate. The concepts covered in these sessions will also shape your ideas for the Final Group Project.

List of Discussions

  1. Week 2: Planning Healthy Cities
  2. Week 4: Smart Cities, Big Data and Urban policy
  3. Week 5: Making Space for All
  4. Week 6: Spatialities of Gender and Energy Poverty
  5. Week 7: Participation in Civic Design
  6. Week 8: Ethics of Smart Cities

Guidelines for Reading

The objective of these reading discussions is,

  • to explore how articles are written
  • how data analysis is conducted and reported
  • how to critically analyse literature
  • learn to frame your own arguments using evidence
  • practice your analytical skills for the final project
Your final project will benefit from these discussions. You can skip complex models and math equations introduced in these discussion papers but the bigger picture of why some models are preferred over other, or how to report data limitations and shortcomings of your work is important for the final assessment of this course. If you skip readings and discussions, you will fall behind in project work which amounts to 50% of your grade.

A short guide for reading these papers is mentioned below,

What are the different parts of a paper?

  • The Abstract: the abstract presents a brief summary and will immediately provide insights into understanding if the research relates to your research question and topic.
  • The Conclusion: briefly scanning the conclusion may also give you insights into whether reading this publication will be useful for your research.
  • The Bibliography: within every discipline there are certain key authors that emerge. Have all the cited works been published by men in prestigious institutions? What does that mean for careers of other people?
  • The Authors: take note of who authored the paper - you can ask questions like, has this person been cited a lot, which Department do they belong to, what gender do they identify with, whose ideas are being promoted?
  • The Journal: different journals are ranked differently and hold different kinds of research from varying disciplines.

Scan the paper, to quickly extract:

  • Where it is positioned within an existing body of literature? You will generally find this in the Literature overview and perhaps in the Introduction. Is your paper responding to a set of policies? Is it responding to an existing set of theories?
  • Methodology and Methods - which methodology was employed, is the study qualitative, quantitative? Which particular methods were used? Is the methodology/methods innovative or is the approach fairly standard and rigorous?
  • Key findings - what are the results of the paper?
  • Implications - what implications do these results have for the theory / an existing set of policies?

Note some other important points,

  • Think about why your instructor assigned this reading. What subject will this article prepare you to discuss? How does this article fit into the main questions or topics of the course?
  • Use the discussion questions as reading guides for the paper. (They will be posted a week before)
  • Before you read in detail, skim the paper all the way through. Identify the organization of the document and the central ideas or arguments. The abstract, introduction, section headings and conclusion can provide information about the purpose and main point(s) about the paper.
  • Using your first impression of the paper and the reading guides, you should read the main text purposefully, and decide where to place more attention. If you don’t know critical terms and concepts, look them up in a dictionary, textbook, on the Internet or post on the appropriate discussion forum on Brightspace.
  • If a complex model is explained, how would you simplify it or address the complexity differently? You may also choose to ignore the inner workings if it doesn’t pertain to the course material and try to identify the use and application of that complex model.
  • Did the reading clarify answers to the main point(s) of the article and raise other questions?
  • The authors may not draw all possible conclusions of their analysis or provide all arguments to support it. You must be a critical reader! An author’s view may not be in line with your own. Always keep in mind that reading academic writing means you’re participating in a conversation. What do you learn from it if/how the reading sparked your thoughts is what you should reflect on.
  • After reading the paper, try to summarize it in one paragraph. It should be your summary and not the abstract. Always use five questions as your guiding hand to write summaries.
    • What do we know about the subject/problem?
    • What is it that we do not know?
    • How are we going to address it?
    • What do we find with our method?
    • Why is it relevant for research and policy?
  • Besides, challenge the parts of the article which do not present a persuasive argument. What are, in your opinion, the strong points of the paper? If you were supposed to be a co-author, how would you make it a better article? If you were allowed to ask a question from the authors, what would you ask?

Week 2 - Planning Healthy Cities

City Planning and Population Health


  1. How do the authors describe the relationship between city planning and population health?
  2. What are the 8 urban and transport planning and design (a.k.a. city planning) intervention points the the paper talks about? How do these policy interventions work on a regional level as opposed to a local level?
  3. What are the risk exposures people face and how does city planning relate to mitigating each exposure?
  4. What is the health implication difference between vulnerable (low income, minorities, elders, etc.) and the non-vulnerable population with regards to the risk exposures discussed in the paper? Could you try to bring insight with your own experiences in life?
  5. Do you think it is possible to translate the evidence given in the context of high-income countries into appropriate policies for low-middle income countries (LMIC)? What are some challenges in LMICs? What are some differences that the authors highlight that must be accounted for?
  6. The authors highlight the fact that “mobilising and supporting community engagement and action is also critical.” In this sense, what can be done to democratize decision making with regards to city planning that prioritizes walking, cycling and public transportation?
  7. How do you think that data science can be benefit the monitoring and regulation of risk exposure indicators that drive policymaking? Could we address the limitations of the paper perhaps by utilizing more data? Or perhaps can data science aid in the policy translation process from high income countries to low-middle income countries?

Week 3 - No Discussion

Week 4 - Data, Analytics, and Policy

Smart cities, big data and urban policy: Towards urban analytics for the long run


  1. How do the authors generally define/introduce urban analytics and how do urban analytics generally relate to smart city development?
  2. The authors highlight three specific ways in which smart cities have been conceptualised in the literature, what are these conceptualisations and how does each conceptualisation change the role of what urban analytics does? The authors suggest that urban analytics are more than merely a method, but a set of practices, that are carried out in specific academic, social and political contexts. Why do you think that is?
  3. The authors suggest that in order to understand the causes behind mobility patterns, there needs to be inferences made beyond simply the insights gained from the analysis of big mobility data, for example, from smart cards. They suggest that these external inferences need to increase over time, why would they say that and what kind of external inferences are they referring to? How does this differ from a purely computational approach?
  4. The authors formulate various propositions on the value of urban analytics for strategic policy and planning - why is a theoretical grounding and contextualisation of the data important alongside the computational analysis? How is there a potential conflict between the short term insights real time data can provide and long term processes within cities?
  5. The authors conclude with the identification of a number of research needs, can you present one of these needs and explain your opinion/interpretation of it.

Week 5 - Making Space for All

Making Space in Geographical Analysis


  1. According to the authors, what are the challenges of population homogeneity in research, and why is it important to be aware of ‘gaps’ in data? How can geographic data and spatial analysis tackle this issue?
  2. How have the authors defined activity spaces, and what are the challenges associated with current research methods that address them?
  3. Why are the current methods of evaluating accessibility inadequate, and what do the authors delineate as a solution to address it?
  4. What are ‘social capital’ and ‘geographic social capital’? According to the authors, why is it essential to include time-use surveys of social interactions in geographic-social capital studies?
  5. The authors state, “when knowledge is created using data that excludes key populations, it may do more harm than good”. What are some examples you can think of that support this statement?
  6. According to the authors, which research areas have the potential to generate novel insights using inclusive geographical analysis? Which of these do you think has the most potential, and can you think of additional research areas?
  7. The authors identify three challenges to increasing representativeness and inclusiveness in research. What are these, and how do you think these changes can help address the issues identified in the paper?

Week 6 - Spatialities of Gender and Energy Poverty

Energy poverty and gender in England: A spatial perspective


  1. What societal problem are the authors looking into and in what manner are the authors using spatial analysis as a tool to aid in their research?
  2. What are the gender-sensitive indicators and how are they used by the authors to quantify the research?
  3. The authors make use of Moran’s I Cluster, what other spatial analysis methods from the course could be used to analyze the data presented in this paper?
  4. The authors mention that the second stage of the analysis is to overcome some of the limitations from the first stage. The authors also mention limitations with the second stage of the analysis, what are those limitations?
  5. Why is it not possible to draw final conclusions on spatialities of gendered energy vulnerability based on the results presented in this paper?

Week 7 - Participation in Civic Design

A visit to the smart-city-in-progress at Sidewalk Toronto prompts questions about what it means to “participate” in civic design. - by Prof. Shannon Mattern

[Link to article]

  1. What are the concerns surrounding the redevelopment of the 12-acre Quayside site? What controversies came up and why do you think people have these concerns?
  2. What is the risk for governments of mandating public participation and outsourcing it to consultanting companies for redevelopment projects? What other risks has the author specified that can impede public participation? Can you think of any more ways that data is being used to skew results in favor of big tech behemoths?
  3. Do you think the parameters entered in CommonLife are enough? Would you think adding more parameters to the models that create such images would make the tool less useful or more useful? What do you think does the author think about the utility of such a tool as CommonLife?
  4. Does public participation and democratization of such decision making processes require commercial apps as CommonLife, etc.? What could be the alternative? Do citizens need to feed the datasets that are not collected based on consent in order to be able to live in a society that they presumably wish for themselves?
  5. How have the smart-city operators responded to skeptical citizens demanding transparency and accountability from urban tech companies? How can they deflect attention from privacy, justice and governance?
  6. How does the author warn communities who are considering smart-city projects? What are the main points they highlight? Can you give a definition of mapwashing with respect to the article?
  7. According to the author, how do they summarize “what really matters” regarding public participation and the democratization of decision making in the context of comprehensive digital mapping of the public realm? After reading the article do you feel like more data helps public participation and creates voluntary, open, transparent discussion? Why or why not? What do you think would have “really mattered” to you as a participant?

Week 8 - Ethics of Smart Cities


  1. What are the risks of only letting big data influence your decision-making as a policy maker? How can these risks be reduced?
  2. What are the two current epistemological positions on urban science? Explain what they entail and your stance on them.
  3. What are the main ethical issues discussed in the paper? Which issue do you think needs the most attention? Are there other justice or social concerns not mentioned in the paper?
  4. What is ‘data determinism’? Are there cases in which data determinism is warranted?
  5. What three dimensions are named to re-imagine and re-cast smart cities and urban science?
  6. The paper states that “Researchers need to consider the ethical implications of their work with respect to privacy harms, notice and consent, and the uses to which their research is being deployed” (p.12) and “In addition, professional bodies should review their ethical standards in the light of big data and revise accordingly” (p.12). Do you think these conclusions are easy to guide but difficult to deploy? Why or why not?
  7. There are multiple stakeholders (e.g. researchers, citizens, professional bodies, government) that are involved in urban data science. Who do you think should take the lead in safeguarding the ethical collection and use of urban data?