Flexible resilience indicators for Australian cities

67b1027cca3877a76a9024425519ddde?s=47 Robin
December 05, 2014

Flexible resilience indicators for Australian cities

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

67b1027cca3877a76a9024425519ddde?s=128

Robin

December 05, 2014
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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

  8. None
  9. None
  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)
  22. None
  23. None
  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
  32. None
  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