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Big Spatial Data for Disaster ManagementNovember 21st 2012
DIGITAL PRODUCTIVITY AND SERVICES FLAGSHIP
Agenda
1. Introduction
2. All Hazards/End-to-End Initiative
3. Case Studies
• Social Protection
• Enhanced Situational Awareness
• Dynamic Flood Modelling
4. Conclusion
Big Spatial Data for Disaster Management | November
2012
Digital Productivity & Services Flagship
Help business and government deliver new, faster, better services:
• Efficiently and effectively
• Doing old things in new ways
• Doing new things in completely new ways
What we doEvidence based policy and decision support
– Analytics
– Optimisation
Service delivery transformation
– Services innovation
– Business process and business rules
optimisation
Customer centric services
– Wrapping the service provider
around the customer
– Services personalisation
... Information is very directly about saving lives. If
we take the wrong decisions, make the wrong
choices about where we put our money and our
effort because our knowledge is poor, we are
condemning some of the most deserving to death
or destitution.
John Holmes UN Emergency Relief Coordinator and Under-
Secretary-General for Humanitarian Affairs (Amin and Goldstein
2008)
“
Impact Depends on Co-Location
Severity
Vulnerability
& protection
Probability
/ Frequency
People &
Property
Agenda
1. Introduction
2. All Hazards/End-to-End Initiative
3. Case Studies
• Social Protection
• Enhanced Situational Awareness
• Dynamic Flood Modelling
4. Conclusion
Big Spatial Data for Disaster Management | November
2012
Hazard Probability Impact Response
Flood
Storm Surge
/Tsunami
Bushfire
Storm
Cyclone
Earthquake
Research Activities Matrix in Flagship
Existing
None
Known gaps in all areas: opportunities for
collaboration
Partial
• Maximum foreseeable loss – ETSA
• Estimating Extreme Risk - Suncorp
• Social Media Monitoring – Queensland DCS and Victoria ESTA
• Social Protection in Indonesia – UN/AusAID
• Emergency Response Intelligence Capability – Dept Human Services
• Flood Modelling – BSMG
• Bushfire Modelling – Victorian Government
• Impacts Framework – Fire & Rescue NSW
• Urban Monitor - DSEWPAC
Current Activities
Need: Disaster
Management
Response
Emergency Response/Decision Support
Social Impact AnalysisBush fire AnalysisIntegrated Science
Domains/Systems
Landscape
ModellingFuel
Climate/We
ather
Modelling
Financial
Scenarios
Urban
Models
Applications: Specific
Science Domains
Landscape
Characteristics
Fuels Climate/
WeatherFinance Urban Data (pop.
Density etc)
Virtual
Libraries/Inputs:
Knowledge Bases
(Services)
Processing
Services
DataMiddleware
Processing
Services
DataMiddleware Processing
Services
DataMiddleware Processing
Services
DataMiddleware Processing
Services
DataMiddleware
Models & analytic
tools
Integrated Disaster Management Decision Support Platform
Prepared by Ryan Fraser, adapted from work by Lesley Wyborn (Geoscience Australia), 2012
S
Y
S
T
E
M
S
Applications
Business Need
Policy
Agenda
1. Introduction
2. All Hazards/End-to-End Initiative
3. Case Studies
• Social Protection
• Enhanced Situational Awareness
• Dynamic Flood Modelling
4. Conclusion
Big Spatial Data for Disaster Management | November
2012
AusAID Project
12 | CSIRO – Information Sharing for Emergency Management
•Gazetteer framework
• Infrastructure to bring data together
•Traditional Data Sources
• Income (240M people)
• Crops, etc
•Unconventional Data Sources
• Phone records (2Bn calls/day)
• Social media, etc
•Analytics
• Where are vulnerable populations?
• Getting worse or better?
• Exposure to natural hazards?
Social Protection
Discover Access Understand Extract, Transform, Load
Use
Time and effort
Goals
achieve fundamental, systemic improvement in information integration capability that enables more effective and cost-efficient sustained service delivery
Discover Access,
Understand
Extract Transform Load Use
Gazetteer framework - enable place names used
in different systems to be registered and used to
reference and integrate other information
14 | CSIRO – Information Sharing for Emergency Management
Multi-sectoral information for Social Protection
• Accurate
• Up to date
• Timely
• Integrated
• Presented in meaningful ways
Social Protection
“preventing, managing, and overcoming situations that adversely affect people’s well being.[1] “
- policies and programs to reduce poverty and vulnerability
-reducing exposure, enhancing capacity to manage risks
Project drivers
1United Nations Research Institute For Social Development
Example Use Case – Tsunami in Japan
30 km exclusion zone:
• How many people are inside?
• How many schools inside?
• Where are the exit roads; which ones are open?
• How many schools, hospitals, aged care homes inside?
• Which local governments areas?
• Which rivers flow through?
• Where are the nearest hospitals?
Presentation title | Presenter name | Page 16
.
Australian Application – Intensive Support
17 | CSIRO – Information Sharing for Emergency Management
Agenda
1. Introduction
2. All Hazards/End-to-End Initiative
3. Case Studies
• Social Protection
• Enhanced Situational Awareness
• Dynamic Flood Modelling
4. Conclusion
Big Spatial Data for Disaster Management | November
2012
.
19 | CSIRO – Australian Science, Australia's Future | Living in a Broadband World | Oppermann
The explosion of highly personalised social media is creating a wealth of rich data sources..
59
73
52
18
52
44
35
28
0 20 40 60 80
Send a text message to a response agency asking for
help
Ask people to help you reach a response agency
through a social network like facebook or Twitter to
get help
Post a request for help on a response agency's
Facebook page
Send a direct message via Twitter to a response
agency requesting help
% extremely/very likely
Current study American Red Cross
Social Media in Disasters and EmergenciesIf you needed help and 000 was busy...
Taylor M, Howell G, Raphael B. (2011). Use of social media. Presentation to the Joint Australia/US (DSTO/DHS) Technical Working Group. Melbourne, Victoria, 8th September 2011.
The US study was based on 1000 people – cross section of the general public (i.e. all ages over 18, and not necessarily social media users), Taylor et. a;l. data is based on 1170
people, with a more uncontrolled sample sent out via social media (so predominantly, if not entirely, based on people who use social media)
20 | CSIRO – Information Sharing for Emergency Management
CSIRO’s Enhanced Situation Awareness
21 | CSIRO – Information Sharing for Emergency Management
Capture
22 | CSIRO – Information Sharing for Emergency Management
Capture
23 | CSIRO – Information Sharing for Emergency Management
Detection and Alerting
Problem : Crisis coordinators need tools to manage issues arising from Tweet deluge
Solution : Alerts generated by our system are shown as a tag cloud. Alert colour and
size indicates deviation from expected.
Alerts are linked to clusters. Mouse click on alert tag to see cluster content.
Alert words are added to a tracking list. Tracking list can be clustered and displayed. Alerts can be tracked over time.
24 | CSIRO – Information Sharing for Emergency Management
Detecting and Alerting - Earthquakes
• When you don't know what to look for, such as with unexpected incidents, our Alert Monitor can provide some clues as to what is going on in Twitter Australia-wide within 3 minutes of the event.
• Moe earthquake as reported by our Melbourne Alert Monitor (last Friday 20th July 2012 19:13:08)
Christchurch 2011
26 | CSIRO – Information Sharing for Emergency Management
Condensing and Summarising
27 | CSIRO – Information Sharing for Emergency Management
Forensic AnalysisComparing alerts with other knowledge
28 | CSIRO – Information Sharing for Emergency Management
Agenda
1. Introduction
2. All Hazards/End-to-End Initiative
3. Case Studies
• Social Protection
• Enhanced Situational Awareness
• Dynamic Flood Modelling
4. Conclusion
Big Spatial Data for Disaster Management | November
2012
Mundaring Dam History
Concrete Gravity Dam
Built between 1898 and 1902
Height: 42 m (originally 32 m)
Extended by 10 m in 1940’s
Length: 308 m
Capacity: 64 GL
The weir leaks showing consistent
moisture stains where water moves
through the structure. This could be a
potential cause of dam failure.
Mundaring Dam Location
PERTH CBDDam Wall
39 km
Perth CBD Inundation
The water is coloured by velocity with blue 0 m/s and red 15 m/s
Much of the convention centre and the region around it are affected. Peak
flooding occurs at around 5.6 hours after the dam break event.
Perth CBD Inundation
The water is coloured by velocity with blue 0 m/s and red 15 m/s
Much of the convention centre and the region around it are affected. Peak
flooding occurs at around 5.6 hours after the dam break event.
tsunami approach to Fremantle harbour
The approach of the tsunami towards Fremantle harbour is simulated
using the SW solver. A wave train with a maximum amplitude of 3 m is used as
the tsunami source approximately 50 km offshore. The wave originates from
an earthquake source close to Sumatra.
tsunami approach to Fremantle harbour
The approach of the tsunami towards Fremantle harbour is simulated
using the SW solver. A wave train with a maximum amplitude of 3 m is used as
the tsunami source approximately 50 km offshore. The wave originates from
an earthquake source close to Sumatra.
tsunami run-up and inundation
The tsunami run-up and inundation into the Fremantle harbour is simulated
using SPH. The initial wave is setup 2 km off-shore by using the SW solution as
the basis.
Terrain resolution = 5 m
Fluid resolution = 2 m
Terrain particles = 800K
Fluid particles = 2 milion
Wave height = 7 m
Wave speed = 10 m/s
Run-up distance = 1.5 km
tsunami run-up and inundation
The tsunami run-up and inundation into the Fremantle harbour is simulated
using SPH. The initial wave is setup 2 km off-shore by using the SW solution as
the basis.
Terrain resolution = 5 m
Fluid resolution = 2 m
Terrain particles = 800K
Fluid particles = 2 milion
Wave height = 7 m
Wave speed = 10 m/s
Run-up distance = 1.5 km
Agenda
1. Introduction
2. All Hazards/End-to-End Initiative
3. Case Studies
• Social Protection
• Enhanced Situational Awareness
• Dynamic Flood Modelling
4. Conclusion
Big Spatial Data for Disaster Management | November
2012
Conclusions
Disaster management is a spatio-temporal, big data problem
CSIRO sees value in the all hazards, end-to-end approach
A robust spatial data infrastructure is essential
CSIRO is keen to collaborate with other organisations using common
data and decision support frameworks
New developments such as crowd sourcing add value and can be
incorporated
Today we have released a report available at:
http://www.csiro.au/disaster-management-report
Any Questions