Labs. Exposure modeling. Dr. Keiko Saito GFDRRLabs, The World Bank

Transcription

1 Labs Exposure modeling Dr. Keiko Saito GFDRRLabs, The World Bank

2 Labs Risk Modeling Hazard (e.g. hurricane wind) Exposure (e.g. houses) Vulnerability (e.g. of house to wind) Risk (e.g. probable loss) From CAPRA definition of Risk Disaster Impact Analysis - Scenario or Stochastic -

3 Labs Risk Analysis Components By Dr. Bijan Karzai CEDIM, Germany Fragility/Susceptibility Analysis Analysis of fragilities in infrastructure and built environment Analysis of susceptibility in non-physical systems (e.g. populations, ecosystem, etc.) Hazard Analysis Geological Hydrometeorological Biological Technological Environmental Risk Analysis Vulnerability Analysis Social Economic Physical Environmental Cultural Capacity/Resilience Analysis Analysis of weaknesses and gaps in existing protective and adaptive strategies. Legal/institutional frameworks and policies Social and economic development practices

4 Labs Uncertainty 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: Hazard (spatial, temporal, dimensional) Susceptibility (physical, social, economic, etc.) Exposure Database (acquisition, transformation representation, change) Benefits of risk reduction measures 4

5 Labs What elements are at risk? 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 5

6 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 6

7 What type of data is needed for exposure Elements at risk characterization Number, type, location, size, height, age, construction cost, land value, irregularities, material and mechanical properties Government/Regional data Building code knowledge, previous earthquake damage reports, social and economic datasets Population details Day/night occupancy of people 7

8 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 8

9 Data resolution: the question of scale Risk modeling by natural disaster type Earthquakes Tsunami Floods Hurricanes Volcanic Windstorm 100 km Earthquake 400 km 1 km Flood Level of analysis needs to be close to size of highest risk zone

10 exposure Resolution of the hazard and exposure data Eruption scenario (deterministic) Number of collapsed buildings, zone 2 Number of deaths, zone 2 Number of serious injuries, zone 2 When using zones When using 250 m grid cells Resolution of the input hazard model should also be taken into account Out of 8206 buildings: 1%

11 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)

12 Copyright ImageCat 2009 Unit of aggregation, geographical scale and data collection methodologies 1. Spatial Tiers 2. Top Down - Bottom up Tier 3 Tier 2 Tier 1 Tier 0 Building Neighborhood City Province Country Region Global ImageCat Inc. A tiered concept depending on participants variable budgets, resources, and existing data availability. Integrating a range of data sources for inventory development, which might combine top down remote sensing and government records, with bottom-up expert opinion.

13 Exposure Assessment Top Down Bottom Up

14 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 public statistics 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

15 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.

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

17 Building database with all categories

18

19

20

21

22

23 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. Highlights the importance of data preparedness 23

24 Crowd sourced post-event building damage assessment Led by ImageCat, TomNod, EERI, EEFIT, CAR Ltd, Christchurch City Council, funded by Global Earthquake Model 24

25 Example 2: Indonesia exposure data 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.

26 Pilot Exposure Development

27 Methodology 1. Delineating areas of urban development using remotely sensed data 2. Categorizing land cover into homogenous areas of development 3. 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

28 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

29 Classification of structure types Typical building classes developed for study 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) Steel Structures Moment frame Braced frame Light frame (transverse-frame; longitudinal-steel rod tension-only bracing) Timber Structures Open frame at grade Shear wall at grade Dwelling anchored at grade Dwelling elevated on piers or stilts Reinforced Concrete Frame Shear wall Precast frames

30 Mataram, Lombok Field Surveys: To characterize development in different use or occupancy classes To validate classification 2

31 LOMBOK SURVEY EXAMPLE GROUND PHOTOGRAPHS VIEWED IN GOOGLE EARTH

32 LOMBOK SURVEY BUILDING ATTRIBUTES SUMMARY AND SAMPLE PHOTOS Location Occupancy Percentage Lombok Commercial 12% Lombok Education 3% Lombok Government 9% Lombok Industrial <1% Lombok Religious 2% Lombok Residential 74% 100% Location Stories Percentage Lombok 1 72% Lombok 2 25% Lombok 3 2% Lombok 4 1% Total 100% Location Roof types Percentage Lombok Clay tile 70% Lombok Concrete slab 4% Lombok Corrugated Metal 25% Lombok Plywood <1% Lombok Unknown <1% Total 100%

33 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).

34 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

35

36

37 Collected Infrastructure Data Papua New Guinea

38 Summary Statistics for All 15 Countries: Infrastructure Assets (Preliminary)

39 Labs Visualization of hazard and risk Hazards + Exposure + Vulnerability + Risk Territorial planning Infrastructure design Cost Benefit analysis for mitigation and prevention investments Scenario analysis for emergency preparedness Immediate damage assessment Applications Analysis of financial exposure Climate Change

40 Thank you! Questions?