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. Discussion 1: Big Urban Data
  2. Discussion 2: Smart Cities, Big Data and Urban policy
  3. Discussion 3: Spatialities of Gender and Energy Poverty
  4. Discussion 4: Assessing Mobility Models
  5. Discussion 5: Clustering the Urban Form
  6. Discussion 6: 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,

  • 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 - Big Urban Data

Big Data and Big Cities: The promises and limitations of improved measures of urban life


The following questions do not necessarily pursue the order of the paper

  1. What is big data? What are the sources of big data in the cities? What is big data analytics?
  2. How did the scholars use to address their research questions on urban science? What are the shortcomings of those approaches?
  3. What kinds of analyses are facilitated by big data analytics?
  4. How can crowdsourcing using digital apps and our digital traces (like mobile, OV-chipkaart, Google maps, Airbnb, etc.) help policymakers provide equitable distribution of resources (p135)?
  5. What are some of the differences between old and new data sources? Should we use a combination of big data and conventional data sources in urban data science (page 124 and p133)?
  6. How could we calculate the population of a city during the day and night? What kinds of data sources do you recommend? How should we use them?
  7. How could big data analytics help to manage racial conflicts in the cities?

Week 3 - 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 4 - No Discussion

Week 5 - 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 6 - Assessing Mobility Models

Diagnosing the performance of human mobility models at small spatial scales using volunteered geographical information


  1. What use does a lower scale spatial scale population movement model have compared to larger scale movement models?
  2. What are biases in the classical population movement models and why do these models perform badly on smaller scales?
  3. What is the main data used in population movement models techniques?
  4. How do the authors propose that simulation methods could be improved?
  5. The paper focusses a lot on the use of OpenStreetMap data: what are possible pitfalls related to this?

Week 7 - Clustering the Urban Form

The Spatially Varying Components of Vulnerability to Energy Poverty


  1. According to the authors, what is a common limitation of current vulnerability indexes?
  2. How is vulnerability defined in the context of energy poverty? What is the added value of sociospatial distribution?
  3. What are the structural causes of energy poverty? Why is an approach that relies on these drivers not spatial in nature?
  4. What are the main differences between a PCA and GWPCA from a methodological perspective? How was the quality of visualising the results of both approaches impacted?
  5. How did the local analysis alter the understanding of geographies of vulnerability to energy poverty? What are the advantages of conducting both global and local analyses?
  6. The authors mention an example of “Problem that policy forgot”. Building on the previous question, explain how certain challenges might fall through the cracks and remain unnoticed by policy makers.

Week 8 - Ethics of Smart Cities

The ethics of smart cities and urban science


  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?
  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?