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IM913 Spatial Methods and Practice in Urban Science

15/20/30 CATS - (7.5/10/15 ECTS)

30CAT - CORE FOR THE MSC IN URBAN INFORMATICS AND ANALYTICS

CIM, Computer Science and Engineering


Urban science is a rapidly growing field dedicated to use big data to better understand modern cities and the integration of emerging technologies in the urban space.

This module aims at improving the theoretical, methodological as well as the substantive understanding of urban science. This is achieved through the combination of three inter-related components: (1) theoretical foundations of urban science; (2) a methodological approach to the urban space with emphasis to theory and methods in spatial analysis; and (3) practice in urban science, carried out in the form of a student-led group project to solve an urban challenge using real-world scenarios and data. The module is open to students of all disciplines, no specific prior knowledge is required.

Module Convenor

Dr João Porto de Albuquerque

Indicative Syllabus

PART I: Introduction to Urban Science and Spatial Methods

Week 2 (2hr lecture and 1 hr seminar): Course overview, introduction to urban theory and science

Introduction to current trends in urban science methods, challenges posed by the evolution of modern cities, recent trends in urbanisation. Urban models and urban utopias: garden cities, modernist city design, new urbanism. Importance of spatial methods and geographic information in urban science.

Week 3 (2hr lecture and 1 hr seminar): Urban theory, the new urban science and smart cities

An overview on the development of urban theory and methods. Implications of recent technological developments (e.g. social web, big data analytics, internet of things) to urban planning and management. The emergence of new information sources and methods for understanding urban contexts. Vision and practice of smart cities.

Week 4 (2hr lecture and 1 hr seminar): Introduction to spatial methods and geographic information systems in urban science

Basic concepts on spatial thinking and geographic information systems. Main characteristics and features of geographic information, such as layers, maps, projection, scale, raster and vector data models. Types of geographic information systems and spatial data: vector and raster data.

PART II: Spatial Methods in Urban Science

Week 5 (3 hr practical workshop): Visualising the city - geovisualisation and thematic mapping

Principles of cartography, geovisualisation and digital mapping techniques. Methods for exploratory spatial analysis using geovisualisations of geographic information from different sources by means of a practical lab project.

Week 6 (3 hr practical workshop): Analysing the city - spatial data and pattern analysis

Methods for spatial analysis by location and distance, as well as identification of spatial patterns by means of a practical lab project.

Week 7 (3 hr practical workshop): Designing the city - spatial analysis and statistics

Methods for spatial analysis such as cluster detection, density estimation and spatial interpolation, also exploring the use of statistics for inferences and correlations by means of a practical lab project.

PART III: Urban Science in Practice

Weeks 8-9 (3hr project supervision each week): Student-led group projects in the context of urban science, to be supervised by member of CIM.

Week 10 (3 hr seminar): Final group presentations

Illustrative Bibliography

Arribas-Bel, D., 2014. Accidental, open and everywhere: Emerging data sources for the understanding of cities. Applied Geography, 49, 45–53.

Barnett, J., 2016. City design: Modernist, traditional, green and systems perspectives. Routledge.

Batty, M., 2013. The new science of cities. London, UK: The MIT Press.

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.

Burrough, P. A., McDonnell, R. A., & Lloyd, C. D., 2015. Principles of Geographical Information Systems, 3rd edition. Oxford, UK: Oxford University Press.

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.

Fischer, M. M., & Getis, A., 2010. Handbook of Applied Spatial Analysis. (M. M. Fischer & A. Getis, Eds.). Berlin, Heidelberg: Springer Berlin Heidelberg.

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

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.

Goodchild, M., 2007. Citizens as sensors: the world of volunteered geography. GeoJournal, 69(4), 211–221.

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.

Haklay, M., 2013. Citizen Science and Volunteered Geographic Information: Overview and Typology of Participation. In D. Sui, S. Elwood, & M. Goodchild (Eds.), Crowdsourcing Geographic Knowledge (pp. 105–122). Dordrecht: Springer Netherlands.

Kitchin, R., 2013. The real-time city? Big data and smart urbanism. GeoJournal, 79(1), 1–14.

Kitchin, R., 2014. The data revolution: Big data, open data, data infrastructures and their consequences. London, UK: Sage.

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.

LeGates, R.T. and Stout, F. eds., 2015. The city reader. Routledge.

Longley, P. A., Goodchild, M. F., Maguire, D. J., & Rhind, D. W. (2015). Geographic Information Science and Systems, 4th Edition. London, UK: Wiley.

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

Parker, S., 2015. Urban Theory and the Urban Experience: Encountering the City (2nd ed.). London, UK: Routledge.

Rogerson, P.A., 2010. Statistical Methods for Geography: A Student’s Guide, 3rd edition, Sage Publications.

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

Slocum, T.A., McMaster, R.B., Kessler, F.C., Howard, H.H. (2005) Thematic Cartography and Geographic Visualization, Second Edition, Prentice Hall.

Thrift, N., 2014. The “sentient” city and what it may portend. Big Data & Society, 1(1).

Townsend, A., 2015. Cities of Data: Examining the New Urban Science. Public Culture, 27(2 76), 201–212.

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

Tufte, E.R. , 2001. The Visual Display of Quantitative Information. Graphic Press.

Learning Outcomes

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

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

  • Explain the basic propositions of urban models, and what they tell us about the way cities take shape;

  • Reflect on the implications of ICTs and big data for contemporary cities and smart cities;

  • Compare spatial and non-spatial methods and understand the spatial aspect of data analytics;

  • Understand the current use of GIS and open source tools in the urban science domain and the use of such systems in applied situations;

  • Use different visual techniques to effectively present outcomes of data analytics;

  • Have acquired expertise in a range of spatial analysis skills including data handling, geo-processing and presentation.

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 external students (other than to linked departments) 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.