Urban Resilience, Disasters and Data
Interdisciplinary Postgraduate Modules
IM928 Urban Resilience, Disasters and Data
15/30 CATS - (7.5/15 ECTS)
This intensive module is aimed at introducing the topics of disaster risks and urban resilience with an emphasis on the use of innovative digital technologies to gather and analyse urban data for improving disaster resilience. It approaches, theoretically and practically, the main issues involved in disaster resilience and the way in which social media, mobile technologies and the web 2.0 are related to our collective experience of disasters and crisis events. By means of a practical project and field work conducted in the city of Coventry, students will learn how to collect urban data using open-source mobile data collection software (OpenDataKit), process and analyse this data with Geographic Information Systems (QGIS) and produce an interactive digital map to visualise urban aspects related to disaster resilience.
This is a five-day intensive module:
Introduction to the course and distribution of topics for the student-led seminar presentations (1 hour seminar).
1. Introduction to the week and lecture: "Disasters, Resilience and Urban Data" (2 hours): what is the connection between disasters, natural hazards, urban resilience and urban data? Difference disciplinary perspectives on the field of disaster management, urban resilience, risk reduction: social sciences, environmental sciences, computational sciences.
2. Seminars with student-led presentations on the following topics:
- Disaster Risk Reduction and Sustainable Development Goals
- Urban Resilience and the New Urban Agenda
- Disaster Risk Management and GIS
- Volunteered and Crowdsourced Geographic Information
- Social media and disasters
- Collaborative mapping, disasters and resilience
- Crowdsensing, citizen science and disasters
- Ethics and privacy of crowdsourced geographic information
1. Lecture on "Crowdsensing for disaster resilience in practice" (2 hours). Existing tools for mobile data collection and how they are used to support disaster resilience and humanitarian work.
2. Workshop: Mobile data collection and preparation for fieldwork (4 hours). Tutorial on the use of OpenDataKit and FieldPapers for data collection. Group work to design forms for mobile data collection and to print paper maps to be used in the field. Installation of the OpenDataClient in the smartphones and tablets.
Fieldwork. Mobile data collection in the field (6 hours)
1. Lecture: "Analysing Urban Data for Disaster Resilience" (2 hours). How to use GIS tools (QGIS, JOSM) to process and digitise data collected and produce interactive web maps.
2. Data analysis workshop (4 hours). Students digitise and analyse data with supervision of the teaching staff.
1. Data analysis workshop (2 hours). Students prepare interactive maps and presentations with supervision of the teaching staff.
2. Final group project presentations (4 hours). Student-led presentations with the results of the group projects.
Amin, S., & Goldstein, M. (Eds.). (2008). Data against Natural Disasters. The World Bank.
Coaffee, J., & Lee, P. (n.d.). Urban resilience : planning for risk, crisis and uncertainty. London: Palgrave.
Cova, T. J. (2005). GIS in emergency management. In P. A. Longley, M. F. Goodchild, D. J. Maguire, & D. W. Rhind (Eds.), Geographical Information Systems: Principles, Techniques, Management and Applications (2nd Editio., pp. 845–858). Wiley.
Crowley, J. (2014). Open Data for Resilience Initiative Field Guide (p. 134). Washington DC.
Hacklay, 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.
Kawasaki, A., Berman, M. L., & Guan, W. (2013). The growing role of web-based geospatial technology in disaster response and support. Disasters, 37(2), 201–21.
Konečný, M., & Reinhardt, W. (2010). Early warning and disaster management: the importance of geographic information (Part A). International Journal of Digital Earth, 3(3), 217–220.
Manfré, L. a., Hirata, E., Silva, J. B., Shinohara, E. J., Giannotti, M. a., Larocca, A. P. C., & Quintanilha, J. a. (2012). An Analysis of Geospatial Technologies for Risk and Natural Disaster Management. ISPRS International Journal of Geo-Information, 1(3), 166–185.
Roche, S., Propeck-Zimmermann, E., & Mericskay, B. (2013). GeoWeb and crisis management: issues and perspectives of volunteered geographic information. GeoJournal, 78(1), 21–40.
Vacano, M. von, & Zaumseil, M. (2014). Understanding Disasters: An Analysis and Overview of the Field of Disaster Research and Management. In M. Zaumseil, S. Schwarz, M. von Vacano, G. B. Sullivan, & J. E. Prawitasari-Hadiyono (Eds.), Cultural Psychology of Coping with Disasters (pp. 3–44). New York, NY: Springer New York.
Zook, M., Graham, M., Shelton, T., & Gorman, S. (2010). Volunteered Geographic Information and Crowdsourcing Disaster Relief: A Case Study of the Haitian Earthquake. World Medical & Health Policy, 2(2), 7.
The module aims to encourage students to be able to:
- Demonstrate an understanding of main concepts on the interdisciplinary research on disaster risks, natural hazards and urban resilience;
- understand the relationship between disaster management, disaster risk reduction, urban resilience and new urban agendas for sustainable development;
- explain the role of urban data in strategies for disaster risk reduction and urban resilience;
- understand how urban data for disaster resilience is traditionally collected and identify the emerging urban data sources based on crowdsourced geographic information;
- critically appreciate the potential of participatory digital technologies and crowdsourced geographic information to gather and process data;
- reflect on how social media, mobile technologies and the web 2.0 are related to changes in the collective experience of disasters and crisis events;
- reflect on the potentials and limitations of digital geospatial technologies to capture urban data related to urban resilience;
- use open-source geospatial tools (OpenDataKit, FieldPapers) to do mobile urban data collection in the field;
- use Geographic Information System software (QGIS, JOSM) to analyse the urban data collected and produce interactive digital maps that visualise urban resilience issues;
- work in interdisciplinary teams to analyse an urban challenge related to disaster resilience, design strategies for using open source geospatial tools to collect, process and analyse urban data.
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 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 Postgraduate Programmes Coordinator.
(1) one 500-word blog and presentation about one of the topics of the module (summative, 15%);
(2) a student-led oral presentation about the group project (summative, 25%);
(3) a 2,000-word individual project report (summative, 60%).
(1) one 500-word blog and presentation about one of the topics of the module (summative, 7.5%);
(2) a student-led oral presentation about the group project (summative, 12.5%)
(3) a 2,000-word individual project report (summative, 30%);
(4) a 3,500-word essay (summative, 50%).