Flexible resilience indicators for Australian cities

This was my lecture at AURIN's microsimulation symposium: http://aurin.org.au/blog/events/aurinnatsem-microsimulation-symposium/

Transcript

1. AURIN Microsimulation Symposium, Melbourne, 5th December 2014 Flexible resilience indicators for Australian cities Robin Lovelace, University of Leeds
2. Structure • Opening considerations – What is resilience? – Previous work – Ideal datasets • Data – Local -> Regional -> National -> Global • Methods • Discussion
3. Opening consider- ations http://etheses.whiterose.ac.uk/5027/ Pilbara, western Australia http://tinyurl.com/bde9y56
4. Oil prices - 92 to 2012
5. 2009 until present
6. The threat of LOW oil prices! http://www.theguardian.com/world/2014/dec/02/russia-warns-fall-into-recession-2015-sanctions-oil-price
7. Local natural disasters
10. Energy use for air con... http://www.pir.sa.gov.au/pirsa/ Southern Australia
11. Sea level rise http://www.ipcc.ch/report/ar5/wg1/
12. Human-caused shocks
13. What is resilience anyway? "the ability of a set of mutually reinforcing structures and processes to persist in the presence of disturbance and stresses (Holling, 1973; Gunderson, 2000)" Why has it replaced sustainability? Luers, A. et al. (2003). A method for quantifying vulnerability, applied to the agricultural system of the Yaqui Valley, Mexico. Global Environmental Change, 13(4), 255–267. doi:10.1016/S0959-3780(03)00054-2
14. The blunders of 'sustainability'
15. Some resilience metrics of interest: • Resilience to coastal flooding • Resilience to disease • Resilience to oil price shocks • Resilience to austerity
16. The ideal resilience indicator "Ultimately, we conclude that the social and spatial distribution of oil vulnerability depends on how an energy-constrained future is envisioned" (Lovelace and Philips, 2014). • Objective and quantifiable (like GDP) • Flexible enough for local contexts • Not so flexible as to become meaningless • Compatible with change
17. The ideal dataset
18. Data
19. Data considerations • Already hundreds of detailed datasets • With 'big data' revolution, ever increasing • 'Smart cities' projects • Variability • Order • Continuity
20. Example datasets • Individual • Local • Regional • National • Global
21. Individual level data Sleep deprivation: "percentage of those who report sleeping less than 7 hours on average on a typical weekday" (VicHealth Indicators Survey, 2011 Sedentary behaviour: "The proportion of people who sit for 7 hours or more per day." Method of Travel: http://www.abs.gov.au/censusContact Other variables (Travel surveys)
24. Data: Read the small print "Data was collected via telephone interviews. The survey was conducted in each of Victoria’s 79 Local Government Areas (LGAs), with a total sample of 25,075 participants aged 18 years and over." Population of Victoria: Almost 6 million Number of participants per zone: 300 Sample methodology and size issues with this one!
25. Local variables Proximity to... • Water source • Shops • Schools/hospitals Level of community cohesion
26. Regional variables • Renewable energy • Infrastructure • Urban morphology • Aggregate low- level data • Lack of compatibility • between states
27. National-level variables • Gross domestic product • Wellbeing indeces • Natural resources • Millitary defences • Conclusion: focus is more on 'energy security', over the head of most planners
28. Categories of metrics Need to impose order on the metrics • Social: how well people can 'bounce back' - linked to social capital, education • Economic: the dominant force in global system • Technological: e.g. number of electric cars • Infrastructure: e.g. renewable energy installations, bicycle paths • Environmental
29. Hard to separate out levels
30. Methods http://www.colorado.edu/hazards/gfw/
31. GIS • Identify appropriate geographic scales for study • Data merging and consolidation • Compute values, e.g. distances to services • Visualisation
33. The method: spatial microsimulation
34. Individual-level variability
35. Inequalities within areas
36. Links to agent-based models Spatial microsimulation applied to transport modelling problems. See Lovelace et al. (2014)
37. Method: online visualisation
38. Wider issues
39. Create flexible 'resilience' indicators "Ultimately, we conclude that the social and spatial distribution of oil vulnerability depends on how an energy-constrained future is envisioned" (Lovelace and Philips, 2014).
40. Datasets ready for deployment • Mode of travel to work • Australian travel dataset • Long-distance commute (Victoria) • Social variables (Victoria) • Likelihood of flooding (altitude) Variables to be calculated • Proximity to key services (OSM + GIS) • Distance of commute (ABS statistics)
41. Policy impact • Ultimately it's about 'evidence-based policy' • Because approach is not prescriptive, should be more attractive politically • But most use probably local urban planners and decision makers • Make discussion of future more realistic
42. Is resilience conservative?
43. The wider picture: reducing the need for resilience "We’re not going to be able to burn it all. Over the course of the next several decades, we’re going to have to build a ramp from how we currently use energy to where we need to use energy. And we’re not going to suddenly turn off a switch and suddenly we’re no longer using fossil fuels, but we have to use this time wisely, so that you have a tapering off of fossil fuels replaced by clean energy sources" (Obama, 2014)
44. Plug: R for big data workshop • To be held on 5th December • We'll be using the dplyr package • To analyse Tweet and OD data of Melbourne