co o) = <,, U' o..i (D E = o cf) O o c! O (5 qerar&6rx3r This IssL,e Defining area at risk and its effect on loss esiimation Sponsors ADE Benfield Greig Swiss Re Guy Carpenter IAG lnsurance (NRltll,A) A"on Re Suncr:rp-Metway Ernployers Re CGU lnsurance Gerling Global Group Roya! & Sun Alliance RACG MACQUARIE ll NIVERS TY-SYDNEY Defining are& at nlsk amd lts effect om loss estlmatior"l Catastrophe loss estimations combine both hazard and vulnerabilitydata. While hazard attributes such as intensity distributions are usually represented at a spatially-explicit pixel level, vulnerability information, such as dwellings, population and insurance portfolio data, is usually only available at coarse areal units, such as postcodes. This spatial mismatch can be a source of error since the elements at risk are implicitly assumed to be uniformly distributed across the areal unit and any spatial inhomogeneity within the unit ignored. As a consequence, the entire areal unit is improperly treated as being at risk. Here, in one of a suite of related studies on the impact of different averaging schemes on catastrophe modeling, we define occupied residential area as the area at risk and seek to assess its effect in a hailstorm loss estimation model. Figure 1(a) shows a symmetric pattern of hazard intensity about the epicenter of an event. lncreasing density of colour corresponds to increasing intensity - earth shaking intensity or peak ground acceleration in the case of an earthquake, or hailstone size in the case of a hailstorm. Traditional modeling practice attributes the intensity within the postcode to its centroid. (Postcode units were created for administrative purposes and their boundaries lack any physical meaning forcatastrophe loss estimation.)The centroid is determined by boundaries that can be arbitrary, and the centroid for a polygon of irregular shape may not even be located within the postcode! ln that case, assigning the hazard intensity to the centroid could be quite misleading. ln Figure 1(b), we see that the residential area is fudher away from the epicentre than the centroid of the postcode, and that a lower intensity should be assigned to the elements at risk or else losses will be overestimated. i M Study area Using Sydney as an example, residential areas at risk were identified through street buffers, and vul nerability data remodeled using an approach called dasymetric mapping that transforms data f rom arbitrary areal units to physical settlement areas. Occupied residential areas are assumed to be physically linked by street networks. A commercial street database StreetWorksrM, was used to derive the residential area with a buffer distance of 100 m either side of the street segment. Spatial (a) (b) Figure 1. A schematic representation of hazard intensity distribution, a postcode and identified residential areas
Transcript
coo)=<,,U'
o..i(D
E=o
cf)Ooc!
O(5
qerar&6rx3r
This IssL,e
Defining area at riskand its effect on lossesiimation
Sponsors
ADE
Benfield Greig
Swiss Re
Guy Carpenter
IAG lnsurance (NRltll,A)
A"on Re
Suncr:rp-Metway
Ernployers Re
CGU lnsurance
Gerling Global Group
Roya! & Sun Alliance
RACG
MACQUARIEll NIVERS TY-SYDNEY
Defining are& at nlsk amd lts effectom loss estlmatior"l
Catastrophe loss estimations combine both hazard and vulnerabilitydata. While hazard attributessuch as intensity distributions are usually represented at a spatially-explicit pixel level,vulnerability information, such as dwellings, population and insurance portfolio data, is usuallyonly available at coarse areal units, such as postcodes. This spatial mismatch can be asource of error since the elements at risk are implicitly assumed to be uniformly distributedacross the areal unit and any spatial inhomogeneity within the unit ignored. As a consequence,the entire areal unit is improperly treated as being at risk. Here, in one of a suite of relatedstudies on the impact of different averaging schemes on catastrophe modeling, we defineoccupied residential area as the area at risk and seek to assess its effect in a hailstorm lossestimation model.
Figure 1(a) shows a symmetric pattern of hazard intensity about the epicenter of an event.lncreasing density of colour corresponds to increasing intensity - earth shaking intensity orpeak ground acceleration in the case of an earthquake, or hailstone size in the case of ahailstorm. Traditional modeling practice attributes the intensity within the postcode to itscentroid. (Postcode units were created for administrative purposes and their boundaries lackany physical meaning forcatastrophe loss estimation.)The centroid is determined by boundariesthat can be arbitrary, and the centroid for a polygon of irregular shape may not even be locatedwithin the postcode! ln that case, assigning the hazard intensity to the centroid could be quitemisleading.
ln Figure 1(b), we see that the residential area is fudher away from the epicentre than thecentroid of the postcode, and that a lower intensity should be assigned to the elements at riskor else losses will be overestimated.
i
M
Study area
Using Sydney as anexample, residential areasat risk were identifiedthrough street buffers, andvul nerability data remodeledusing an approach calleddasymetric mapping thattransforms data f romarbitrary areal units tophysical settlement areas.Occupied residential areasare assumed to bephysically linked by streetnetworks. A commercialstreet databaseStreetWorksrM, was usedto derive the residentialarea with a buffer distanceof 100 m either side of thestreet segment. Spatial
(a) (b)
Figure 1. A schematic representation of hazard intensitydistribution, a postcode and identified residential areas
layers of local and national parks, lakes and rivers werealso used to refine boundaries. The final residential areawas then converted to a pixelformatwith a spatial resolutionof 100 m. The Sydney study area covers BB79 km'z,
whereas the derived residential area compris es only 22.0o/o
of this. Loss estimation should focus on this sub-set.
ln this example, separate houses were chosen as theinsured elements. Numbers of separate houses areavailable from CDATA 2001 for postcodes and censuscollection districts (CCD). There are a total of 255postcodes and 6674 CCDs contained within the studyarea. The numberof separate houses is 919,898, and anaverage sum insured of AUS $200,000 was assumed.Vulnerability data were represented at the two areal unitforms and their corresponding residential forms. Thepostcode represents the coarsest resolution whereas theCCD-based occupied residential areas form the finestscale.
Hailwas used as our hazard of choice to address the keytheme of this article. (The April 1999 Sydney hailstorm,which caused an estimated total insured loss of AUS $1.7billion, was the most costly natural disaster in Australia.)ln this example, identical scenario hailstorms with ahypothetical loss potentialgiven in Figure 2were imposedon the 140 km x 160 km grid - a total of 22 400 events.The distribution of loss was assumed to follow a lognormalfunction, with a SW-NE oriented elliptical footprint (semi-
major axis 25 km and semi-minor axis 10 km) andmaximum loss percentage for buildings (claims as apercentage of the total sum insured) of 3.5%. Thesecharacteristics are broadly consistentwith those of severeSydney hailstorms such as the March 1990 and April 1999events.
Results
Catastrophe losses were calculated for each hailstormover all postcode and CCD units and the values assignedto the pixelwhere each hailstorm was centred. We thencompared the spatial distribution of loss estimatescalculated using the vulnerability data in both postcodeand CCD-based residentialarea forms. Figure 3(a) showsthe spatial distribution of estimated losses usingvulnerability data at the CCD-based residential areas.Depending on the location of the hailstorm, losses rangefrom below $1 million to $600 million.
Figure 3(b) shows the spatial distribution of loss estimationdifferences on a pixel basis. These differences are relativeto the loss estimated forthe postcode form, and negativedifferences imply that the loss estimate for CCD-basedresidential areas was higher. Differences range betweenminus 161.3o/o and plus 99.5%. The areal proportions ofthe most extreme underestimation l-161.3%, -50.0%) andoverestimation [50.0%, 99.5%] groups occupy almost one
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Figure 2. Distribution of loss potential from a scenario hailstorm.
third of the study area! For inner city suburbs dominatedby continuous residential areas, loss estimationdifferences are modest because the chances of mis-allocating proportions of the portfolio to non-residentialareas are small. ln contrast, significant overestimationsoccurred for storms centred within non-residential areas(e.9., national parks, region of east Picton). Assuminghomogeneity at the coarse postcode level results inallocating portfolio data to these non-residentialareas andincorrectly computing larger losses. Such errors areunlikely when portfolio data at the CCD-based residentialarea, which more realistically reflects the true location ofdwellings, is used.
Loss estimation differences using the vulnerability data inother forms were also compared. While differences wereoften large, the main conclusion to emerge was that lossestimates using CCD and CCD-based residential areaforms were relatively close. This is good news forcatastrophe modelling and is attributed to the fact thatthe fine resolution of CCDs reflects the general distributtonof residential areas quite well.
Gonclusion
Current loss estimation practice largely uses portfolio dataat a postcode level; it is logical to anticipate that betterloss estimates could be achieved through a more accurate
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definition of the area at risk. Ourempiricalfindings suggestthat this is indeed the case, particularly for hazards thataffect only a small proportion of the area underconsideration, as is true for hailstorms.
lf the above analysis were repeated for some other hazardssuch as damaging earthquakes, which may have muchlarger areal footprints than do hailstorms, our results maywell be less sensitive to the way in which vulnerabilitydata is input. However in this case, it is the scale atwhich local soil conditions, which can amplify seismicground motion and increase damage to buildings, maychange which may dictate model accuracy. This, incombination with the non-linearity of relationships betweenbuilding damage and ground shaking intensity, means thatfine-grained averaging is again inescapable, if we are everto model Probable Maximum Losses with any confidence.
For more information please contact:
Keping Chen, John McAneney, Russell Blong or Roy LeighTelephone: +61-2-9850 9683Facsimile: +61-2-9850 9394email: [email protected]
Yigur* 3. (a) Calculated losses using vulnerability data at CCD-based residential area form for scenario hailstormsoccurring at difference locations. (b) Loss estimation differences using the vulnerability data at postcode form and CCD-based residential area form.
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