Zones of Vulnerability II: The Sequel!Virtual Spatial Modelling for Ageing Policy Analysis

Authors

Hamish Robertson*

Nick Nicholas

Joanne Travaglia

Andrew Georgiou

Julie Johnson

Source: The New Yorker

Contents

• 2011 brief reprise – the prequel!

• Advancing the calculation of vulnerability

• Vulnerability in recent perspective

• Complexity and interconnections

• Problems of knowledge production

• Developing, building and refining spatial models

• Examples 1 and 2

• Discussion

• Conclusion

Brief Review of 2011 Presentation

• From patient safety to social vulnerability

• Hazards, events and the insurance Industry

• Population ageing and the policy future

• Sydney metro example – man-made hazards and ageing with EPA data

• Identifying and locating man-made hazards

• Quantifying social vulnerability

• Calculating spaces of vulnerability

Significant Trends and Vulnerability

• Environmental factors including climate, weather, water, sea levels, volcanic and seismic activity

• Human-environment interactions – climate, heat, wildfires, pollution, structural deformation (mining, run-off)

• Human systems – power/energy, transport, preparedness, planning, urbanisation, resource demands, politics and politicking

• Human behaviours – population growth, densities, population ageing

Complexity and Interconnections

• Growing recognition that complexity is central to the problems we face (but not fast enough and often continued resistance)

• Many issues are non-linear and highly dynamic with differential rates of change – varied sub-system interactions that we plan for poorly

• Human knowledge and understanding highly problematic – e.g. Victorian knowledge taxonomies

• Ideologies and expediency compromise our capacity to respond

• Vulnerabilities central to policy and planning processes including failures

• Our past experience is not a guarantee of our future experiences (and we should really know this by now)

Population – Growth and Ageing

• Current estimates suggest 7 billion people and growing• Compositional effects include significant ageing,

variable rates of ageing by country and diversification of the aged as a category construct (younger old, older old, oldest old, centenarians, super-centenarians)

• Ageing already effects the affluent countries but is growing rapidly in ‘developing’ countries with socio-economic differentiation (richer versus poorer)

• Population ageing and its effects will diversify and urban-rural effects increase

• Epidemiological and treatment impacts are significant –neurodegenerative diseases e.g. Alzheimer’s disease

• Important cognitive impacts including communication, resilience and response capacities – e.g. frail elderly

The Momentum of AgeingSource: UNFPA and HelpAge International 2012

Global Population and Health Trends

• Falling population growth rates (mostly)

• Rising older populations – totals and proportion

• Faster transitions to aged societies e.g. Iran

• More elderly people living alone in the community

• Rising numbers with chronic diseases

• Rising numbers with neuroepidemiological conditions (dementias, motor and gait)

• Rising numbers with disability (see above)

• Complexity of the epidemiology of ageing

Problems of Knowledge Production

• Knowledge production looking backwards e.g. high Victoriana of our education system, academic specialisations and policy domains

• Moral sciences masquerading as social science e.g. economics and economic discourse as dominant and detached from politics etc

• Misleading but prevalent heuristics undermine our capacity to deal with complexity – e.g. Malthus and Galton

• Rigid dichotomies are prevalent cultural tropes e.g. nature versus culture, rural versus urban, quantitative versus qualitative, objective versus subjective, micro versus macro, nature versus nurture etc

• Missing dimensions e.g. space and scale poorly configured and often absent entirely

• Utilisation of abstract (but powerful) points of reference as core e.g. Lanoix’s white educated middle-class male with full ‘capacity’ in health and disability praxis and Haraway’s ‘view from nowhere’

Drivers of VulnerabilitySource: Lewis and Kelman (2013) in Natural Hazards Obsever

• Endangerment

– environmental degradation

– discrimination (see also Briggs)

– displacement

• Impoverishment

– self-seeking public expenditure

– denial of access to resources

– corruption

– siphoning of public money

Spatial Vulnerability in Perspective

• Social vulnerability has always been spatial –Vaughan on Booth’s London maps

• Social relations are spatially patterned including urban segregation e.g. townships, barrios and many similar

• Environmental vulnerability is spatial, non-linear and highly dynamic

• Scale and interconnectedness are key to responding

Trend in Natural DisastersSource: Munich RE

Christchurch CBD

Hurricane SandySource: Baltimore Sun

Modelled Debris Spread from JapanSource: NOAA - National Oceanic and Atmospheric Administration

Spatial Visualisation of Risk at FukushimaSource: http://jciv.iidj.net/map/

Visualisation in Context

Modelling Vulnerability and Infrastructure

Spatial Vulnerability Model 2012

NSW Ambulance Service Locations

NSW Aged Care Assessment Teams (ACATS)

NSW Meals on Wheels Service Providers

Public Hospital Locations

Accessibility to Services

Extension and Visualisation

• National level model – draft

• Sub-indices and composite index (single measure)

• SA2 level analysis and visualisation

• Mapped in a GIS package and exported to Google Earth

• No environmental measures included as yet

• Aiming for a people-environment paradigm

Developing a Spatial Vulnerability IndexSA2 Relative SE Advantage Relative SE Disadvantage Economic Resources Education and Occupation

101011001 928.143411 934.870042 951.575402 921.01283

101011002 997.12448 1006.115735 1030.577313 987.741177

101011003 1001.418039 1012.276543 1029.160374 983.123817

101011004 1092.889708 1083.588709 1116.3517 1083.006258

101011005 929.596927 943.718316 956.612329 913.434263

101011006 943.435018 949.700272 976.603821 967.250272

101021007 997.887435 1012.752067 1011.785042 1018.549795

101021008 1001.309284 1003.94344 1014.996987 967.409828

101021009 989.797136 1000.57762 967.91556 998.776228

101021010 1032.476467 1035.647582 998.317759 1039.705345

101021011 1117.982613 1100.708465 1140.378306 1085.008914

101021012 1126.467077 1104.532241 1129.097261 1092.299664

101031013 936.13958 952.53529 969.547292 942.780502

101031014 950.80064 968.534123 953.263165 948.705914

101031015 1009.38213 1022.710674 1038.255488 1007.101874

101031016 1033.663569 1053.281085 1023.337245 1023.07632

101041017 923.747535 939.933602 950.013192 913.283582

101041018 948.077454 963.238323 965.27684 940.647397

Two Examples – Draft!1. Australian Model

2. Most Vulnerable 100 (SA2)

Brief Discussion

• The vulnerability concept can be deployed quantitatively, spatially and visually

• Increasingly, qualitative data can now be indexed and visualised spatially e.g. Atlas.ti

• Convergent and enabling technologies including visualisation mean policy options can now be ‘spatialised’

• Current evidence based policy still preferences certain kinds of information (usually not spatial)

• Spatial literacy for social science a significant and growing issue – geography fights back?

Conclusion

• Social policy is inherently spatial even when this is denied

• Spatial literacy will become more important as complexities grow and limitations of traditional knowledge paradigms are reached

• Vulnerability concept and characteristics can be quantified, qualified and visualised for exploration and development

• Increasingly pervasive spatial technologies including virtual earth environments (e.g. Google Earth)

• Expertise, utilisation and trust (ethical) issues

• Knowledge integration and evidence making – how to link and extend a complex system and break down knowledge silos