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IM913 Spatial Methods and Practice in Urban Science

Spatial Methods

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


CIM, Computer Science and Engineering

General info/abstract

Urban science is a rapidly growing field that investigates the way people interact within and are influenced by urban systems. It is dedicated to harnessing the wealth of social information available in our modern information society. In this way, urban science uses large amounts of heterogeneous data to better understand cities and other types of complex urban systems, as well as the integration of new technologies with them.

The aim of this module is to present the theoretical and practical methodological and substantive foundations of urban science. This is achieved by combining three interconnected components: (1) the substantive foundations of urban science, with an emphasis on urban geography; (2) a methodological approach to urban space, focusing on theory and methods of 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 scenarios and data. The module is open to students of all disciplines, no specific prior knowledge is required.

Module Convenor

Dr René Westerholt

Lab Leader

Vikki Houlden

Indicative Syllabus

PART I – Substantive: Introduction to urban science

Week 2 (2hr lecture and 1 hr seminar)

Besides introducing the students to basic terms and concepts, the first session covers different types of cities and urban systems. We will discuss the roles of different types of cities in urban networks, and why this is important for the way we live in these. Different scales are thereby considered, ranging from global to local perspectives.

Week 3 (2hr lecture, 1 hr seminar)

Week 3 is dedicated to a range of theories and models of urban structure to better understand urbanisation and the development of conurbations. We will further look at the internal structuring of cities. The topics thereby covered include morphogenetic, as well as functional and social structures.

Week 4 (2hr lecture, 1 hr seminar)

In this week we get to know culturally different types of cities. We will cover US, Latin-American, Islamic, Indian, Japanese, Chinese, South-East-Asian, and South-African cities. Further, it is discussed how globalisation impacts these kinds of urban areas, and how this leads to post-modern city developments.

PART II – Methods: GIS and spatial analysis in urban science

Week 5 (1 hr lecture, 2 hrs lab)

This session introduces to basic principles of spatial data and GIS. This is based on the topics data input, management, analysis, and presentation, and includes concepts like vector and raster data, layers, map projections, among others. The students will also work with GIS software in the accompanying practical lab session.

Week 6 (1 hr lecture, 2 hrs lab): Analysing the city - spatial data and pattern analysis

Having discussed basic foundations of GIS, week 6 will cover the manipulation of geographic data. This includes vector operations like buffering, clipping and intersection, as well as raster-based manipulations such as applying map algebra, or calculating slope and exposition from digital elevation models. The lab will reinforce the understanding of these topics by giving the students a chance to perform the introduced operations practically.

Week 7 (2 hrs lecture, 1 hr lab): Designing the city - spatial analysis and statistics

It is the intention of this session to introduce the students to a statistical account of spatial pattern disclosure. We will do an introduction to basic ideas of spatial analysis, and will cover some state of the art techniques to calculate spatial autocorrelation, for discovering geographic hot spots, and towards spatial regression and filtering. Again, the lab in the afternoon will provide the opportunity to apply these concepts to real-world scenarios. Note: There will be offered a "refresher in statistics", which will not be mandatory but optional for those who feel a need to refresh their basic understanding of stats. Further information on this will be announced soon in one of the first few sessions.

PART III: Urban Science in Practice

Weeks 8 (3hrs lab):

This week is reserved for practical work. You will do hands-on spatial statistical work by using GeoDa (a software for exploratory spatial analysis) and respective R packages. The topics covered this week include spatial autocorrelation and spatial regression.

Week 9 (3 hrs supervision) and week 10 (3 hrs seminar)

In weeks 9 and 10, we offer the students a supervision of their summative group works. The supervision in week 10 is thereby optional and will be in a time slot other than the usual one. Finally, in week 10, the students will present their works and we will discuss them in the plenary.


All students will take part in group work assessed by one final presentation (summative) and another joint as well as one individual report, documenting the group project and the own individual contribution (2,500 words combined; summative). These group projects are conceptualised alongside the course, and the success of the initial preparation is recorded in brief project proposals (500 words; formative). In addition to these group-based assessments, each student has to write an essay dealing with theoretical aspects taught in the course. These essays vary in length, depending on the number of CATS a student wishes to complete: 1,500 words (15 CATS), 3,000 words (20 CATS), or 4,000 words (30 CATS).

Illustrative Bibliography

Part I

Batty, M. (2013). The new Science of Cities. The MIT Press.

Dastbaz, M., Naudé, W., & Manoochehri, J. (Eds.). (2018). Smart Futures, Challenges of Urbanisation, and Social Sustainability. Springer.

Felgenhauer, T., & Gäbler, K. (Eds.). (2017). Geographies of Digital Culture. Routledge.

Hall, T., & Barrett, H. (2012). Urban Geography. Routledge.

Heineberg, H. (2017). Stadtgeographie (4th Ed.). UTB.

Jensen, R. R., Gatrell, J. D., & McLean, D. (Eds.). (2007). Geo-Spatial Technologies in Urban Environments: Policy, Practice, and Pixels. Springer.

Kitchin, R. (2014). The real-time city? Big data and smart urbanism. GeoJournal, 79 (1), 1-14.

Sarma, A. K., Singh, V. P., Bhattacharjya, R. K., & Kartha, S. A. (Eds.). (2018). Urban Ecology, Water Quality and Climate Change (Vol. 84). Springer.

Schintler, L. A., & Chen, Z. (Eds.). (2017). Big Data for Regional Science. Routledge.

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

Part II

Burrough, P. A., McDonnell, R. A., & Lloyd, C. D. (2015). Principles of Geographical Information Systems (3rd Ed.). Oxford University Press.

Chun, Y., & Griffith, D. A. (2013). Spatial statistics and geostatistics: theory and applications for geographic information science and technology. Sage.

Dovey, K., Pafka, E., & Ristic, M. (Eds.) (2018). Mapping Urbanities. Taylor & Francis.

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

Gaetan, C., & Guyon, X. (2010). Spatial statistics and modeling (Vol. 81). New York: Springer.

Gelfand, A. E., Diggle, P., Guttorp, P., & Fuentes, M. (Eds.). (2010). Handbook of spatial statistics. CRC press.

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

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

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

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 on Admission

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 (cim at warwick dot ac dot 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.