Skip to main content

IM919 Urban data - theory and methodology

15/20/30 CATS - (7.5/10/15 ECTS)
30CAT - CORE FOR THE MSC IN URBAN INFORMATICS AND ANALYTICS

Cities have traditionally adapted to the raise of new technologies, like cars or telephones, for instance. Nowadays, digital technologies and data in particular are transforming the material, cultural, social and political spheres of the urban realm.

These transformations require new theories and research methods to understand the spaces, scales, and agents involved in the relationships between data and the urban. This module offers an insight into some of these current theories and methodologies, to question the notion of data itself, to challenge controversial notions like the smart city, and to expand the realms of inquiry of urban data. We will look at the modes and implications of producing, gathering, distributing and visualizing data in urban spaces, including sensors, datacenters, urban utopias or crowdsourcing maps. The module is open to students from all disciplines; no specific prior knowledge is required.

Module Convenor

Nerea Calvillo

Additional Teaching Staff: Greg McInerny

Indicative Syllabus

Week 1 (3hr lecture): Introduction to Urban Science-Cities and their relationship with technology and Smart cities?

How the three technological revolutions have restructured cities: industrial revolution, skyscrapers, proliferation of automobiles, and ubiquitous digital society.

Genealogy of smart cities. Top-down vs. Grass-Roots (Townsend 2013); Case studies from global cities: London, New York, Amsterdam, Rio De Janeiro, Barcelona; Experiments of Masdar City, UAE and Songdo, South Korea; Critical approaches to smart cities in geography, new media and art.

Week 2 (2hr lecture & 1hr seminar): Urban data ecosystems

Theory and Politics surrounding data; Policy-making urban data: land use and geographic data, Census & demographic data, traffic – mobility & flow data; Other types of urban data from social media and twitter to noise, urban lights and urban heat; Public and Open Data; Big Data of Cities.

Week 3 (2hr lecture & 1hr seminar): Against the ubiquitous city

The material infrastructures of urban data; From sensors to datacenters; Digital infrastructures and their transformation of territory; Impact of digital infrastructures in urban design and urban imaginaries.

Week 4 (2hr lecture & 1hr seminar): Data gathering, urban sensing

Theory, practices and examples of urban sensing; Why and how do we collect urban data?; From satelites to DIY devices; Institutional vs citizen science data.

Week 5 (2hr lecture & 1hr seminar): What is Data?

Using examples from the urban, examine the fundamental characteristics to build a critical and informed view of data; What are the hypotheses underlying and embedded within data?; Is there such a thing as “Raw Data”?; Observation errors – calibration, measurement, variation, sampling; Process errors – system effects, dynamic processes and dynamic states; Incomplete data and Metadata

Week 6 Reading week

Week 7 (2hr lecture & 1hr seminar): The Anatomy of Urban Visualisations

What is a visualisation composed of?; What is and isn’t a visualisation?; Form and function - What do visualisations do and what are they used for; The encoding and decoding model of communication; Visualisations as data… Maps!

Week 8-10 (3hr workshop): Ethnography of data. Group work

Analysis, critical and alternative visions of an existing urban cartography whose data is available. Search, analyse and propose changes in the type of data, how it has been gathered, which which other data it interacts, the material infrastructures that support the data, the agents involved, and the visualization.

Indicative Bibliography:

Batty, M. ,2013. The New Science of Cities. MIT Press.

Beecham, R. & Wood, J. ,2014. Exploring gendered cycling behaviours within a large-scale behavioural dataset. Transportation Planning and Technology, 37(1), pp. 83-97.

Bettencourt, L. & West, G. ,2010. A unified theory of urban living. Nature, 467, 912-913.

Bettencourt, L. et al. ,2007. Growth, innovation, scaling, and the pace of life in cities. PNAS. 104(17), 7301-7306.

Campbell, T. ,2012. Beyond Smart Cities: How Cities Network, Learn and Innovate. Routledge.

Crang, M. & Graham, S., 2007. SENTIENT CITIES Ambient intelligence and the politics of urban space. Information, Communication & Society, 10(6), pp.789–817

Farías, Ignacio; Bender, Thomas ed., 2010. Urban Assemblages, London, New York: Routledge.

Forman, R.T.T. ,2014. Urban Ecology: Science of Cities, Cambridge University Press.

Fujita, M., Krugman, P. and Venables, A.J. (1999) The Spatial Economy: Cities, Regions, and International Trade. London: MIT Press.

Gabrys, J., 2012. Sensing an Experimental Forest: Processing Environments and Distributing Relations. Computational Culture. Available at: http://computationalculture.net/article/sensing-an-experimental-forest-processing-environments-and-distributing-relations [Accessed May 31, 2013].

Gabrys, J., 2007. Automatic Sensation: Environmental Sensors in the Digital City. The senses and society, 2(2), pp.189–200.

Goldmith, S. and Crawford, S. ,2014. The Responsive City: Engaging Communities Through Data-Smart Governance, John Wiley.

Graham, Stephen & Marvin, Simon, 2009. Splintering Urbanism. Networked infrastructures, technological monilities and the urban condition, London, New York: Routledge.

Greene, R.P., and Pick, J.B. ,2006. Exploring the Urban Community: A GIS Approach. Pearson/Prentice Hall.

Greenfield, A., 2013. Against the Smart City. The City is Here for You to Use Do Projects.

Halpern, O. et al., 2013. Test-Bed Urbanism. Public Culture, 25(2 70), pp.272–306.

Harcourt, W, and Nelson, I. , 2015. Practising Feminists Political Ecologies, Zed Books.

Kuznetsov, S. & Paulos, E., 2010. Participatory sensing in public spaces: activating urban surfaces with sensor probes. In Proceedings of the 8th ACM Conference on Designing Interactive Systems. DIS ’10. New York, NY, USA: ACM, pp. 21–30.

Latour, B. & Hermant, E., 1998. Paris ville invisible, Paris: Institut Sythélabo pour le progrés de la connaissance.

Marres, N., 2011. The cost of public involvement Everyday devices of carbon accounting and the materialization of participation. Economy and Society, 40(4).

Mayer-Schonberger, V. and Cukier, K. ,2013. Big Data: A Revolution That Will Transform How We Live, Work and Think, John Murray.

Meirelles, I. ,2014. Design for Information - An Introduction to the Histories, Theories, and Best Practices Behind Effective Information Visualizations. Rockport.

Muller, N. Werner, P. & Kelcey, JG. ,2010. Urban Biodiversity and Design. Wiley Blackwell.

Sheppard, M. ed ,2011. Sentient City. Ubiquitous Computing, Architecture and the Future of Urban Space., The MIT Press.

Townsend, A.M. ,2013. Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia, W.W.Norton.

Spade, Dean, 2008. Documenting Gender. Hastings Law Journal, 59(731).

Weiser, Mark, 1991. The Computer for the 21st Century. Scientific American, 265(3).

Learning Outcomes

By the end of this module, students should be able to:

  • Demonstrate an understanding of how cities are shaped and transformed through technological developments;

  • Explain the basic propositions of smart cities, including their advantages, challenges and feasibility through examples;

  • Reflect on the implications of information and communication technologies and big data for contemporary cities and smart cities;

  • Develop an appreciation of the methodological and epistemological challenges involved in conducting inter-disciplinary research on cities using big and open data;

  • Demonstrate an understanding of the ways in which urban data is transforming traditional social research practices and processes;

  • Extend general and current knowledge in urban data to specific thematic context of urban challenges.

Important Information:

Please be advised that you may be expected to have access to a laptop for some of these courses due to software requirements; the Centre is unable to provide a laptop for external students.

Gaining the permission of a member of CIM teaching staff to take a module does not guarantee a place on that module. Nor does gaining the permission of a member of staff from your home department or filling in the eVision Module Registration (eMR) system with the desired module. You must contact the Centre Administrator (J.Sharp@warwick.ac.uk) to request a module place.

Please be advised that some modules may have restricted numbers. Places are not allocated on a first-come first-served basis, but instead all external students requesting a CIM module as optional, who submit their request by the relevant deadline are given equal consideration.

We are normally unable to allow students (registered or auditing) to join the module after the third week of it commencing. If you have any queries please contact the Centre Administrator.