Labs

Exposure modeling

Dr. Keiko Saito

GFDRRLabs, The World Bank

LabsRisk Modeling

Risk(e.g. probable loss)

Hazard(e.g. hurricane wind)

Exposure(e.g. houses)

Vulnerability(e.g. of house to wind)

Disaster Impact Analysis - Scenario or Stochastic -

From CAPRA definition of Risk

Labs

Risk Analysis

Vulnerability AnalysisHazard Analysis

Fragility/Susceptibility Analysis

Capacity/Resilience Analysis

SocialEconomicPhysicalEnvironmentalCultural

Analysis of weaknesses and gaps in existing protective and adaptive strategies.Legal/institutional frameworks and policiesSocial and economic development practices

GeologicalHydro-meteorologicalBiologicalTechnologicalEnvironmental

Analysis of fragilities in infrastructure and built environmentAnalysis of susceptibility in non-physical systems (e.g. populations, ecosystem, etc.)

By Dr. Bijan KarzaiCEDIM, Germany

Risk Analysis Components

Labs

• Estimating risk is fraught with lots of uncertainty.• Dealing with uncertainty is the essence of risk analysis..• Uncertainty is the state of having limited knowledge.• In probabilistic risk analysis, we can account for uncertainty.• Uncertainties are inherent (for example) in:

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Uncertainty

Hazard(spatial, temporal,

dimensional)

Susceptibility(physical, social,economic, etc.)

Exposure Database(acquisition, transformation

representation, change)

Benefits of risk reduction measures

LabsWhat elements are at risk?

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• All forms of man-made structures are at risk.

• Building Infrastructure - Any man-made construction, either urban or rural.

• Crops

• Population

Residential Commercial Industrial

Different forms of man-made structures

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What elements are at risk?

• Transportation

– Road, rail, air and other transport-related networks

• Large Loss Facilities

– Sports stadiums, marketplaces, churches/temples/mosques, schools and other high population density infrastructure

• Critical/High-Risk Loss Facilities

– Hospital and health care facilities, public buildings, telecommunications, airports, energy systems, bridges and other facilities critical to the recovery of a region post-earthquake

• Other Lifelines – Utilities, Pipelines

– Oil, gas and water supply pipelines/distribution systems, wastewater and electricity systems

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What type of data is needed for exposure

• Elements at risk characterizationNumber, type, location, size, height, age, construction cost, land value, irregularities, material and mechanical properties

• Government/Regional dataBuilding code knowledge, previous earthquake damage reports, social and economic datasets

• Population details

Day/night occupancy of people

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What do we collect for exposure data?

• Is the data there? It depends on scale and country type

• Local – Council data, local government agencies, aerial photos, individual architectural, structural drawings

• Provincial– State-based agencies, statistical offices, census data, investment and business listings, employment figures, existing GIS data.

• National – National statistical agencies, census data, global databases, remote sensing

Exposure scale, Local, Regional, National

Data resolution: the question of scale

Risk modeling by natural disaster type

• Earthquakes

• Tsunami

• Floods

• Hurricanes

• Volcanic

Level of analysis needs to be close to size of highest risk zone

1 km400 km

100 km Earthquake

Flood

Windstorm

Resolution of the hazard and exposure data

exposureWhen using

zones

When using250 m grid

cells

Number of collapsed buildings, zone 2

29 85

Number of deaths, zone 2

6 18

Number of serious injuries, zone 2

6 20

Eruption scenario (deterministic)

Out of 8206 buildings: 1%

Resolution of the input hazard model should also be taken into account

Exposure data guidelines (Buildings)

The data collection method depends on the purpose of the study, as well as data availability.

The building types defined should correspond to those for which vulnerability data already exists.

For exposure data, the necessary information is usually reduced to the following parameters :

Building type (location, material, regularity, and building shape in general, number of storeys/ building height, year of construction, use type, replacement cost, square footage, roof type, base elevation)

Context information e.g. the spatial position of a house in relation to other buildings

(Grunthal, 1998; HAZUS, 1999; Lang, 2002; Muller et at, 2006)

Unit of aggregation, geographical scale and data collection methodologies

A tiered concept depending on participants’ variable budgets, resources, and existing data

availability.

1. Spatial Tiers 2. Top Down - Bottom up

Integrating a range of data sources for inventory development, which might combine top down remote sensing and government records, with bottom-up expert opinion.

Tier 0

Tier 1

Tier 2

Tier 3 Building

Neighborhood

Country

Global

Region

Province

City

Copyright © ImageCat 2009

ImageCat Inc.

Exposure Assessment

Top Down

Bottom Up

Scale Per-structure Neighbourhood/City Region/country Global

Structures –possible attributes

•Building count•Occupancy (detailed e.g. sfd, mfd, factory, retail)•Height/stories•Sq ft•Structural type

•Building density•Occupancy (general e.g. res, com, ind, slum, service)•Probable height•Probable structural type

•Building density•Occupancy (broad e.g. res, com, ind)

•Urban/non-urban

Population –possible attributes

Population count per building

Population density per general occupancy class

Population density per broad occupancy class

Population density in populated areas

Datasets •Local expert knowledge used for calibration and validation or Tier 1 and Tier 2

•Moderate resolution satellite imagery•High resolution satellite and aerial imagery•Government statistics•Tier 3 expert knowledge

•Moderate resolution satellite •High-resolution satellite/aerial imagery•Census and other publicstatistics e.g.

•High-resolution satellite/aerial & in-field observations

Cost •Cost likely preclusive due to due to high-res imagery and time required

•Cost viable if access to Bing/Google imagery for calibration/validation

•Cost viable •Cost viable

Per-Building data

•For small areas, building by building surveys can be carried out on the ground. E.g. Pylos, Greece, approximately 1400 buildings surveyed in 2 weeks by 2 people.

• Countries like Japan have a long history of collecting per building data

•MasterMap in the UK now contains attributes and footprints at the per-building level. However, lacks information about the structure type.

•Padang data was collected with a view to use the data for DRM. Used combination of remotely sensed data (object oriented classification) and field survey.

1. Individual buildings manually digitized from high-resolution satellite imagery and field verified via in-situ inspections

• Coverage: PG, TO, VU, TV, SB, WS, CK, FJ, KI, PW, and FM.

2. Individual buildings manually digitized from high-resolution satellite imagery but not field verified

• Coverage: All 15 countries

3. Clusters of buildings delineated by polygons and manually enumerated, extracted from moderate to high-resolution imagery

• Coverage: PG, FJ, KI and, to a lesser extent, SB, CK, and MH.

4. Buildings in mostly rural areas, inferred using image processing techniques and/or census data, and aggregated to uniform gridded polygons (“cells”) with associated building counts

• Coverage: PG, TL, SB, VU, FJ, FM, MH, KI and, to a lesser extent, CK, TO, and TV.

Example 1: The Pacific Catastrophe Risk Assessment and Financing Initiative (PCRAFI) (combination of bottom up and

top down)

Building database with all categories

Advantage of having exhaustive footprints: Tohoku earthquake insurance claim handling using geospatial data

• Pre-event footprint and post-event aerial photographs used for claim handling by the Marine and Fire Insurance Association of Japan as well as local governments.

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Highlights the importance of data preparedness

Crowd sourced post-event building damage assessment

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Led by ImageCat, TomNod, EERI, EEFIT, CAR Ltd, Christchurch City Council, funded by Global Earthquake Model

• Another example of creating exposure model utilising satellite data.

• Assume homogeneous building type distribution for a particular land cover class. Building inventory Extrapolate based on land cover type across a wide region.

• Validation of the assumption is needed. Area of research.

Example 2: Indonesia exposure data

Pilot Exposure Development

Methodology

1. Delineating areas of urban development using remotely‐sensed data2. Categorizing land cover into homogenous areas of development3. Characterizing development within each use category using the results of

ground surveys and the best available information on Indonesian construction practices

4. Estimating number of buildings, square footage and distribution of building types for all delineated areas

Classification of uses

Typical land cover classes developed for study

• Residential

• Sparse residential (Residential located on Agricultural land)

• Moderate Density Residential

• High density residential (in dense urban settings)

• Commercial

• Industrial

• Port

• Resort

Sample use classes for Mataram, Lombok

Classification of structure types

Masonry

Rubble stone, field stone

Adobe (earth brick)

Simple stone

Massive stone

Unreinforced, with manufactured stone units

Unreinforced, with reinforced concrete floors

Reinforced masonry

Confined masonry (within a reinforced concrete frame)

Reinforced Concrete

Frame

Shear wall

Precast frames

Steel StructuresMoment frameBraced frameLight frame (transverse-frame; longitudinal-steel rod tension-only bracing)

Timber StructuresOpen frame at gradeShear wall at gradeDwelling anchored at gradeDwelling elevated on piers or stilts

Typical building classes developed for study

Field Surveys:

To characterize development in different use or occupancy classes

To validate classification

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6.012356 105.572678Mataram, Lombok

LOMBOK SURVEY EXAMPLE GROUND PHOTOGRAPHS VIEWED IN GOOGLE EARTH

Location Roof types Percentage

Lombok Clay tile 70%

Lombok Concrete slab 4%

Lombok Corrugated Metal 25%

Lombok Plywood <1%

Lombok Unknown <1%

Total 100%

Location Stories Percentage

Lombok 1 72%

Lombok 2 25%

Lombok 3 2%

Lombok 4 1%

Total 100%

Location Occupancy Percentage

Lombok Commercial 12%

Lombok Education 3%

Lombok Government 9%

Lombok Industrial <1%

Lombok Religious 2%

Lombok Residential 74%

100%

LOMBOK SURVEY BUILDING ATTRIBUTES SUMMARY AND SAMPLE PHOTOS

Critical infrastructure mapping

• It may be possible to re-use information on the location of critical infrastructure from previous mapping projects.

• If data does not exist, one avenue to investigate would be to use crowd sourcing. (e.g. Open Street Map or Google Map Maker etc).

Critical infrastructure mapping

Infrastructure Covered for the Pacific islands:

– Airports

– Bridges

– Road Networks

– Water Systems (pipe networks, pump stations, holding tanks, etc.)

– Power Systems (poles, substations, power boxes, etc.)

– Port Infrastructure

Collected Infrastructure Data –Papua New Guinea

Summary Statistics for All 15 Countries: InfrastructureAssets (Preliminary)

Labs

Hazards

Exposure

Vulnerability

Risk

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Applications

Visualization of hazard and risk

Territorial planning

Infrastructure design

Cost Benefit analysis for mitigation and

prevention investments

Scenario analysis for emergency

preparedness

Immediate damage assessment

Analysis of financial exposure

Climate Change

Thank you! Questions?