Techniques & Politics of New Urban Analytics

Course Description & Objectives

This course is about different techniques used in assembling, managing, visualising, analysing and predicting using heterogeneous and messy data sets in urban environments. These include point, polygon, raster, vector, text, image and network data; data sets with high cadence and high spatial resolution; data sets that are inherently messy and incomplete. In addition to the mechanics of urban data analytics, we will also explore the issues of ethics and politics of data generation and analysis.


Much of the analytical techniques will be taught using R. A working knowledge of the R environment is useful, though the first couple of labs, we will go over the basics. However, the course moves quickly. You are advised to seek help to keep up


We will discuss the topics from the following two books in class. Students ae expected to read through the material before Day 1.

O’Neil, Cathy (2016). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. New York: Crown.

Townsend, Anthony M (2013). Smart cities: Big data, civic hackers, and the quest for a new utopia. WW Norton & Company.

The following books are recommended for reference.

Bivand, Roger S, Edzer Pebesma and Virgilio Gómez-Rubio (2013). Applied Spatial Data Analysis with R. 2nd ed. 2013 edition. New York Heidelberg Dordrecht London: Springer. ISBN: 978-1-4614-7617-7.

Brewer, Cynthia A. (2015). Designing Better Maps: A Guide for GIS Users. 2 edition. Redlands, California: Esri Press. ISBN: 978-1-58948-440-5.

Few, Stephen (2004). Show Me the Numbers: Designing Tables and Graphs to Enlighten. Oakland, Calif: Analytics Press. ISBN: 978-0-9706019-9-5.

Grolemund, Garrett and Hadley Wickham (2017). R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. Sebastopol, CA: O’ Reilly media,. URL: (visited on May. 25, 2018).

Tufte, E. R (2001). The Visual display of Quantitative Information. Cheshire, CT: Graphics Press.

Course Policies


Every student should have a working laptop that has R and Rstudio installed. The laptops should have sufficient memory and processing capacity to deal with large data sets.


  • 30% lab reports to be submitted at the end of the lab (Individual)

  • 30% Daily homework assignments due by 11:59 PM (Individual)

  • 20% Final project (Group)

  • 10% Class & lab participation

Grading of labs and homeworks will be through Canvas. Instructions will be provided on the first day of class.

Academic Conduct

I firmly believe in learning from your peers and from others. All homework and lab submissions could benefit from collaborations, however, the submissions are individual. This means that interpreting the data and the results, producing the visualisations, drawing appropriate conclusions from the data is necessarily individual even when the strategies can be discussed and developed with others in class or out of class. All help, however, should be explicitly acknowledged. Severe penalties are imposed for non-attribution.


Day 1

8:30 AM - 11:40 AM (Lec & Lab): Introduction

2:30 PM - 4:40 PM (Lab): Visualising urban data

  • Lab Session: Exploring large urban datasets. Vector data. Visualsing using small multiples, choropleth maps etc. Notes
  • Homework: Due Day 1 11:59 PM in Canvas

Day 2

8:30 AM - 11:40 AM (Lec & Lab): Raster Analysis

2:30 PM - 4:40 PM (Seminar) : The ethics of smart cities

Students are expected to read through the material and be prepared to discuss the topics in class. This is a student driven discussion. Instructor will only facilitate.

Assigned Readings

Goodspeed, Robert (2014). “Smart cities: moving beyond urban cybernetics to tackle wicked problems”. In: Cambridge Journal of Regions, Economy and Society 8.1, pp. 79-92.

Hill, Dan (2008). The street as platform. URL: (visited on Jun. 03, 2018).

Vanolo, Alberto (2014). “Smartmentality: The smart city as disciplinary strategy”. In: Urban Studies 51.5, pp. 883-898.

Wang, Tricia (2016). Why Big Data Needs Thick Data. URL: (visited on Jun. 03, 2018).

Day 3

8:30 AM - 11:40 AM (Lec & Lab): Classification & Machine Learning

  • Lecture. slides
  • Lab Session: Remote sensing classification, machine learning. Notes
  • Homework: Due Day 3 11:59 PM in Canvas

2:30 PM - 4:40 PM (Lec): Predictive Blackboxes & Algorithmic Biases

Assigned Readings

Rosenblat, Alex (2016). “The truth about how Uber’s app manages drivers”. In: Harvard Business Review.

Tufekci, Zeynep (2015). “Algorithmic Harms beyond Facebook and Google: Emergent Challenges of Computational Agency”. In: Colorado Technology Law Journal 13, p. 203. URL:

Ziewitz, Malte (2015). “Governing Algorithms”. In: Science, Technology, & Human Values 41.1, pp. 3-16. ISSN: 1552-8251. DOI: 10.1177/0162243915608948. URL:

Day 4

8:30 AM - 11:40 AM (Lab): Scraping the web for data

  • Lab Session: Points of Interest on Baidu. Notes
  • Homework: Due Day 4 11:59 PM in Canvas

2:30 PM - 4:40 PM (Research Talk): (Mis)adventures in urban analytics

Day 5

8:30 AM - 11:40 AM (Lec & Lab) : Visualising & Analysing Point Patterns

  • Lecture:
  • Lab Session: Analysing crime clusters in Manchester Notes

2:30 PM - 4:40 PM: Group Project Work

Day 6

8:30 AM - 11:40 AM: Short Project Presentations


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