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“Predict a better future” COMMUNITY EMERGENCY RESPONSE MODEL (CERM) Victorian Fire Services Commissioner & ISD Analytics SimTecT 2013, Brisbane, Australia.

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Presentation on theme: "“Predict a better future” COMMUNITY EMERGENCY RESPONSE MODEL (CERM) Victorian Fire Services Commissioner & ISD Analytics SimTecT 2013, Brisbane, Australia."— Presentation transcript:

1 “Predict a better future” COMMUNITY EMERGENCY RESPONSE MODEL (CERM) Victorian Fire Services Commissioner & ISD Analytics SimTecT 2013, Brisbane, Australia

2 “Predict a better future” Simulait Online is like a real life “SimCity” application where businesses or Government can accurately predict and test strategies to influence the behavior of populations Simulait Online uses a broad range of data sources to explain:  What will consumers/communities do in the future?  How can I change or influence what consumers/communities will do?  What is the impact on my organisation or the community?  What is the impact of different or new scenarios, strategies, policies, trends, marketing campaigns, products, prices, competitive gaming, or future disruptive events? Simulation-based Big Data Predictive Analytics approach:  Applied to diverse consumer domains: water, energy, emergency response, retail, transport,...  Applied globally: Australia, Europe, USA  Cloud solution: on-demand access with a web browser Simulait Online

3 “Predict a better future” Following the 2009 bushfires that claimed 173 lives, the Victorian Royal Commission concluded that: "a more comprehensive policy is required-one that better accommodates the diversity of bushfires and human responses". 2009 Victorian Bushfires Royal Commission The Challenge

4 “Predict a better future” Need to predict community behaviour given:  The diverse and dynamic differences both within and between communities  E.g. demographic profile, level of preparedness & intentions  Degree of motivation to act and types of behaviours  E.g. response to warnings & communication, safety messages and visual cues  The uniqueness of each bushfire event  The implementation of new untested strategies or interventions  E.g. warnings communicated – when, what and how  Engagement with, and attitude towards, bushfire education and preparedness Sufficient data exists to simulate and predict community behaviour.... just need the right tools to manage the complexity and bring it all together! Complex Problem

5 “Predict a better future” Community Emergency Response Model (CERM) can accurately predict the behavioural responses of communities to bushfires  What people will do and when: Stay, Leave or “Wait and see” (undecided)  Where people will go: Neighbours, Designated shelter, Leave region or Open area  Community responses to communications and bushfire warnings  Warning type /content, mediums, schedule, intensity  Response to the arrival of the fire & its severity/size The Solution: CERM

6 “Predict a better future” CERM was developed in partnership with a team of emergency services professionals and researchers, using extensive emergence response and health research on community response and behaviour Can account for a broad range of factors influencing people’s response to emergencies  Census and socio-demographic data  Different levels of threat  Fire spread and severity  Household profile: e.g. preparedness, intentions, etc..  Warning schedules: mediums, timing, intensity  The presence of emergency services  Resource failures – e.g. water and power Comprehensive Evidence-Based Model

7 “Predict a better future” Benefits of CERM include accuracy, functionality and accessibility CERM was applied to two fires in Victoria (Churchill, 2009) and South Australia (Wangary, 2005) and demonstrated over 90% accuracy Accuracy is not the only important aspect of CERM... it is the scenarios you can test and the insights you can gain Simulait Online: on-demand access to CERM using a web- browser Accurate, Functional, Accessible

8 “Predict a better future” Better predictions of community behaviour, and testing of interventions that can influence their behaviour, can support community risk assessment, safety planning, and enable realistic and effective policies Application examples:  Shelter options  Warnings and community advice  Traffic management  Community risk assessment and protection  Strategies, policies and interventions to minimise community risk CERM is applicable to other emergencies/disasters, as well as health policy  Based on a human cognitive risk model when life is under threat Inform Decisions, Save Lives

9 “Predict a better future” CERM Insights

10 “Predict a better future” Warnings impacted on the community’s ability to respond appropriately Some people were caught unaware on fire impact, and thus were unprepared  Lack of warnings and communication regarding the fire progression and impact  High speed of the fire Some people that intended to stay changed their decision at the last moment  Warnings and communications underestimated the severity of the fire  Fire was more severe than people anticipated based on warnings  Resulted in people leaving at the worst/unsafe time - when the fire arrived Wangary Insights

11 “Predict a better future” Emergency Services Effect: identified unexpected factors that resulted in communities in different localities to respond differently  Presence of emergency services reduced the perceived threat by the community, resulting in most not making a decision to leave or stay  The wind then changed and the community were unprepared on fire impact  Late response limited the refuge options for those that decide to leave Churchill Insights

12 “Predict a better future” Applied the model to high risk communities to support safety planning Focus was on how many people would leave and when for a small and large fire, to assist with traffic modelling and risk assessment  Compared community response to a high severity (FFDI 50) and a catastrophic fire (FFDI 130)  Looked at the response of different communities that were impacted at different times by the fire and warnings Planning for High Risk Communities

13 “Predict a better future” Comparison: Community A High Severity Fire (FFDI 50) Catastrophic Fire (FFDI 130) Embers, fire impact Emergency Warnings Impact & Emergency Warnings Smoke visible, Watch & Act alerts

14 “Predict a better future” Impact A A D D E E Response timeline: Community A Scenario ‘Code red’ fire (FFDI 130) Up to 9 h warning prior to impact Predictions @ 30 min intervals Predicted response 60% of residents left in 2 ‘waves’ Observations 20% in 1 st wave ‘early responders’ (most vulnerable) 40% in 2 nd wave (less vulnerable) Events A1100Smoke visible B1130Watch & Act C1500Emergency Warnings D1800Embers E1830Fire Smoke, Watch & Act B B Emergency Warnings C C

15 “Predict a better future” Place of refuge: Community A Predicted place of refuge Early responders Outside the region Designated shelters 2 nd wave also went Neighbours Open areas (i.e. ‘last-minute’ refuges) Causal factors Why did 4% of residents seek refuge in open areas? 1.Too vulnerable to defend against a code red fire, 2.No vehicle, and 3.No neighbours that remained at home Predicted place of refuge 1 st wave 13%Outside region 7%Designated shelter 2 nd wave 34%Outside region 14%Designated shelter 9%Neighbour 4%Open area Watch & Act alerts Emergency Warnings

16 “Predict a better future” Comparison: Community B Embers & fire impact Smoke visible, Emergency Warnings Catastrophic Fire (FFDI 130) High Severity Fire (FFDI 50) Embers, fire impact

17 “Predict a better future” Response timeline: Community B Scenario ‘Code red’ fire (FFDI 130) Only up to 1 h warning prior to impact Predictions @ 30 min intervals Predicted response 53% of residents left Observations At impact, only 3% had left......and 31% were still preparing to leave Events A1100Smoke visible B1130Emergency Warnings C1200Embers & fire Smoke, Emergency Warnings Impact A A B B C C

18 “Predict a better future” Place of refuge: Community B Predicted place of refuge Ultimately: Outside the region (19%) Open areas (12%) Neighbours (11%) Designated shelters (8%) Observations A relatively high proportion went to open areas and neighbours (‘last-minute’ refuges) Consistent with having limited time to prepare Predicted place of refuge 19%Outside region 8%Designated shelter 11%Neighbour 12%Open area Emergency Warnings Impact

19 “Predict a better future” Web-Based Access

20 “Predict a better future” Copy, Edit, Configure & Share Scenarios

21 “Predict a better future” Run simulation Select simulation time period Select geographical regions to simulate Run Simulations

22 “Predict a better future” Download results Download Results

23 “Predict a better future” Results are available in different formats, and you can drill down by geographical region, time frame, response type, etc... Results

24 “Predict a better future” ISD Analytics 27 Chesser Street, Adelaide, South Australia, 5000 Phone: +61 8 7200 3589 www. Questions?

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