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Efficacy of Damage Data Integration: A Comparative Analysis of Four Major Earthquakes

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Abstract

Weeks after a disaster, crucial response and recovery decisions require information on the locations and scale of building damage. Geostatistical data integration methods estimate post-disaster damage by calibrating engineering forecasts or remote sensing-derived proxies with limited field measurements. These methods are meant to adapt to building damage and post-earthquake data sources that vary depending on location, but their performance across multiple locations has not yet been empirically evaluated. In this study, we evaluate the generalizability of data integration to various post-earthquake scenarios using damage data produced after four earthquakes: Haiti 2010, New Zealand February 2011, Nepal 2015, and Italy 2016. Exhaustive surveys of true damage data were eventually collected for these events, which allowed us to evaluate the performance of data integration estimates of damage through multiple simulations representing a range of conditions of data availability after each earthquake. In all case study locations, we find that integrating forecasts or proxies of damage with field measurements results in a more accurate damage estimate than the current best practice of evaluating these input data separately. In cases when multiple damage data are not available, a map of shaking intensity can serve as the only covariate, though the addition of remote sensing-derived data can improve performance. Even when field measurements are clustered in a small area-a more realistic scenario for reconnaissance teams-damage data integration outperforms alternative damage datasets. Overall, by evaluating damage data integration across contexts and under multiple conditions, we demonstrate how integration is a reliable approach that leverages all existing damage data sources to better reflect the damage observed on the ground. We close by recommending modeling and field surveying strategies to implement damage data integration in-real-time after future earthquakes.
Loos, S., Levitt, J., Tomozawa, K., Baker, J., Lallemant, D. (2022). “Efficacy of damage data integration: a
comparative analysis of four major earthquakes.” ASCE Natural Hazards Review, (in press).
This material may be downloaded for personal use only. Any other use requires prior permission of the American Society of Civil
Engineers.
Efficacy of damage data integration: a comparative analysis of four
major earthquakes
Sabine Loos, Jennifer Levitt, Kei Tomozawa, Jack Baker, David Lallemant
June 22, 2022
Abstract
Weeks after a disaster, crucial response and recovery decisions require information on the locations
and scale of building damage. Geostatistical data integration methods estimate post-disaster damage
by calibrating engineering forecasts or remote sensing-derived proxies with limited field measurements.
These methods are meant to adapt to building damage and post-earthquake data sources that vary
depending on location, but their performance across multiple locations has not yet been empirically
evaluated. In this study, we evaluate the generalizability of data integration to various post-earthquake
scenarios using damage data produced after four earthquakes: Haiti 2010, New Zealand February 2011,
Nepal 2015, and Italy 2016. Exhaustive surveys of true damage data were eventually collected for these
events, which allowed us to evaluate the performance of data integration estimates of damage through
multiple simulations representing a range of conditions of data availability after each earthquake. In all
case study locations, we find that integrating forecasts or proxies of damage with field measurements
results in a more accurate damage estimate than the current best practice of evaluating these input data
separately. In cases when multiple damage data are not available, a map of shaking intensity can serve
as the only covariate, though the addition of remote sensing-derived data can improve performance.
Even when field measurements are clustered in a small area–a more realistic scenario for reconnaissance
teams–damage data integration outperforms alternative damage datasets. Overall, by evaluating damage
data integration across contexts and under multiple conditions, we demonstrate how integration is a
reliable approach that leverages all existing damage data sources to better reflect the damage observed on
the ground. We close by recommending modeling and field surveying strategies to implement damage
data integration in-real-time after future earthquakes.
1 Introduction
From rapid forecasts to remote sensing-derived maps, novel sources of post-disaster building damage data
are needed to make crucial decisions for early recovery. For example, two to four weeks after a disaster,
the government of the affected region will often lead a Post-Disaster Needs Assessment (PDNA) to assess
metrics such as the number of damaged buildings and cost to reconstruct. The PDNA memorializes the losses
from an event and influences the aid a country receives for its recovery. Damage information also underlies
shorter-term response activities such as temporary shelter allocation and longer-term recovery policies such
as distribution of reconstruction aid (Bhattacharjee et al., 2021). In many cases, potentially useful data,
especially derived from satellites, is rapidly available. However, these data were often only used to guide the
collection of more precise damage data later on or to inform building safety, as they could not identify lower
damage grades necessary to support the PDNA which guides major reconstruction decisions (The European
Commission, 2017; Sextos et al., 2018; Eguchi et al., 2010; Government of the Republic of Haiti, 2010).
Post-earthquake damage maps come from a wide range of sources, including remote sensing-derived
or forecast-based estimates (Loos et al., 2020). We call these sources secondary datasets, which are
1
Loos, S., Levitt, J., Tomozawa, K., Baker, J., Lallemant, D. (2022). “Efficacy of damage data integration: a
comparative analysis of four major earthquakes.” ASCE Natural Hazards Review, (in press).
This material may be downloaded for personal use only. Any other use requires prior permission of the American Society of Civil
Engineers.
advantageous since they provide a rapid estimate of damage over a large region in less time than it would
take to collect primary field surveys of damage. They are highly uncertain, however, usually because they are
produced using methods developed for global use. Remote sensing-derived data is based on imagery from
any type of remote sensor, including satellites, planes, drones, among many others. Publicly available remote
sensing-derived data include NASA JPL-ARIA’s Damage Proxy Map (DPM) derived from Interferometric
Synthetic Aperture Radar (inSAR) data and the Department of Defense’s xView2 challenge, which called for
participants to use computer vision with high-resolution imagery to estimate multi-hazard building damage
(Yun et al., 2015; Gupta et al., 2019). Additionally, maps from manual interpretation of remote sensing
imagery exist, such as the crowdsourcing efforts carried out after the Haiti 2010 earthquake (Ghosh et al.,
2011) or damage grading maps from the Copernicus Emergency Management Service (Dorati et al., 2018).
Outside of remote sensing-derived maps, engineering forecasts are also produced as soon as a map of shaking
intensity becomes available (Erdik et al., 2014; Earle et al., 2009; Trendafiloski et al., 2009; Gunasekera
et al., 2018). Engineering forecasts are predictive models of damage that relate the estimated distribution
of shaking to consequence metrics, like building collapse, through models of exposure and vulnerability.
Alternative machine learning methods that similarly use hazard and building characteristic data to rapidly
forecast damage have also been developed (Mangalathu et al., 2020). An example of publicly available
engineering forecast is the United States Geological Survey’s PAGER system, which aggregates forecast
results to country-level estimates of economic loss or casualties (Jaiswal and Wald, 2011).
While abundant data might seem beneficial, three issues exist. First, rapid damage maps are produced
at varying resolutions with units that do not necessarily align with the needs of post-disaster planners. In
some cases, like with the DPM, the information provided is a proxy of damage, where each pixel contains
a unitless integer that indicates change between pre- and post-earthquake imagery, but has inconsistent
meanings between earthquakes. Second, many models are developed with data from prior events in other
places and therefore still need to be calibrated to the current disaster. Third, because of the fast-moving and
haphazard nature of post-disaster decision-making, many response workers or recovery planners use only
the data they trust, rather than considering all the available data at once (Liboiron, 2015; Bhattacharjee et al.,
2021; Hunt and Specht, 2019).
The Geospatial Data Integration Framework (G-DIF), based on the geostatistical method Regression
Kriging, addresses these issues (Loos et al., 2020). G-DIF is a general modeling framework that is agnostic to
different types of primary and secondary data, and therefore adapts to different places and new developments
in secondary data. The method decomposes the spatial distribution of damage into the trend, or the average
gradient in damage over the affected region, and spatially correlated and stochastic residuals around that
trend. The estimation of the trend depends on the secondary damage data, while the estimation of the
residuals depends on the expected spatial correlation in the residuals from the trend at the field survey
locations. Since our initial application of G-DIF to the Nepal 2015 earthquake, others have built upon this
idea with alternative models (Sheibani and Ou, 2021; Wilson, 2020).
Three main assumptions were made about the expected performance of G-DIF and alternative damage
data integration methods, which we evaluate and address in this paper. The first is that G-DIF will perform
better than any alternative secondary dataset alone. Without better performance, the effort of building a
G-DIF model would not be justified. The second is that the secondary data available in the earthquake-
affected country is of good enough quality to correlate with the damage seen on the ground. This assumption
might not be the case after earthquakes in regions with little remote sensing data and few seismic stations
to measure shaking intensity. The third assumption is that the field surveys used to calibrate the secondary
damage data to the local observations of building damage is collected from a spatially representative sample.
Field surveys may not be representative if engineering reconnaissance missions or local survey teams focus
on the communities that are easiest to reach immediately following a disaster or the areas where they expect
to find damage (resulting in a preferential sample).
In this study, we evaluate these assumptions by applying G-DIF to damage data that became available
2
Loos, S., Levitt, J., Tomozawa, K., Baker, J., Lallemant, D. (2022). “Efficacy of damage data integration: a
comparative analysis of four major earthquakes.” ASCE Natural Hazards Review, (in press).
This material may be downloaded for personal use only. Any other use requires prior permission of the American Society of Civil
Engineers.
after four major earthquakes: Haiti 2010, New Zealand February 2011, Nepal 2015, and Italy 2016. We
evaluate whether G-DIF’s damage estimate outperforms alternative secondary estimates of damage across
various contexts with different patterns of damage and quality of secondary data. Additionally, we examine
whether G-DIF is able to produce an accurate damage estimate with different sources of secondary data
available or more realistic field surveyed locations. Applying G-DIF to multiple real-world scenarios does
require assumptions to perform comparisons. To facilitate comparisons across events, we made several
simplifications to develop the inputs and models in G-DIF. While this might somewhat reduce predictive
performance, we still find clear and intuitive trends that allow us to understand the general performance of
the method.
We find that many of our assumptions hold, indicating that G-DIF generalizes well across contexts under
different scenarios of primary and secondary data availability. Overall, this study demonstrates how G-DIF
is a reliable approach that can leverage all available damage data after an earthquake to better reflect the
damage observed on the ground. Thus, G-DIF is an improvement over the current practice of qualitatively
evaluating each input damage data source, whether it be field surveys or remote sensing-derived, on their
own. We, therefore, close with both modeling and field surveying strategies to implement damage data
integration in-real-time after future earthquakes.
2 Case studies
We consider four major earthquakes from the past decade: 1) Haiti 2010, 2) New Zealand February 2011, 3)
Nepal 2015, and 4) Italy 2016. Table 1 summarizes key case study characteristics and Figure 1 shows maps
of the true damage obtained from field surveys. These case studies are vastly different in terms of the pattern
of damage, spatial scale, available data, and data quality, allowing us to evaluate performance of G-DIF in
varied circumstances.
The January 12, 2010 Haiti earthquake is our earliest case study. The Mw7.0 event occurred about
25 km southwest of Haiti’s capital of Port-au-Prince and was followed by three major aftershocks in the
week afterwards (DesRoches et al., 2011). Haiti had a weakly enforced building code in the dense city
of Port-au-Prince composed mostly of unreinforced concrete frame buildings (DesRoches et al., 2011),
resulting in an estimated 200,000-300,000 deaths (O’Connor, 2012). The Haiti earthquake was one of the
first earthquakes with a proliferation of damage data, pioneering many new techniques to evaluate damage
from remote sensing imagery (Corbane et al., 2011; Loos et al., 2020). However, because many of these
nontraditional damage datasets were originally tested after this event, Haiti’s datasets have relatively poorer
quality than the subsequent case studies. In addition, Haiti lacked a seismic network at the time of the
earthquake (DesRoches et al., 2011) and thus had a poorly constrained estimate of shaking.
Our next case study is the February 22, 2011 Christchurch, New Zealand Earthquake, the most damaging
of the Canterbury Earthquake Sequence (Potter et al., 2015; Comerio, 2014). The Mw6.3 earthquake was
an aftershock of the Mw7.1 Darfield earthquake of September 2010 and occurred only 10 km away from
downtown Christchurch. It caused damage throughout the Central Business District and residential areas of
Christchurch, ultimately leading to 185 deaths (Potter et al., 2015). Unlike the Haiti earthquake, the New
Zealand earthquake occurred in a country with relatively good quality secondary data and a strongly enforced
building code. The residential houses are predominantly engineered, light timber framed single story homes
(Buchanan et al., 2011). Much of the damage was liquefaction-induced, leading to high rates of foundation
damage (Van Ballegooy et al., 2014). Liquefaction damage traditionally may not be captured in engineering
forecasts but has the potential to be observed through remote sensing.
Our third case study is the April 25, 2015 Nepal earthquake. The Mw7.6 earthquake occurred in the
Gorkha district, about 80 km northwest of the capital of Kathmandu. This event and its aftershocks caused
nearly 9000 deaths and impacted both urban Kathmandu and the surrounding rural districts (Government
3
Loos, S., Levitt, J., Tomozawa, K., Baker, J., Lallemant, D. (2022). “Efficacy of damage data integration: a
comparative analysis of four major earthquakes.” ASCE Natural Hazards Review, (in press).
This material may be downloaded for personal use only. Any other use requires prior permission of the American Society of Civil
Engineers.
Fig. 1. Field surveys of damage for all four case studies. Row 1 shows the data locations for each case
study, plotted on the same scale to indicate their relative spatial extents. Row 2 shows the maps of damage
severity as obtained from field surveys. The units of measurement differ for each case study, but for all
locations, darker colors indicate higher damage.
of Nepal National Planning Commission, 2015). Like Haiti, this event resulted in a proliferation of non-
traditional data (Loos et al., 2020; Dennison and Rana, 2017). Nepal also has a long history of shifting
governmental institutions (Thapa, 2005; Sharma, 2006) and an inadequately enforced building code. Many
rural houses were low-strength stone masonry structures (Government of Nepal National Planning Commis-
sion, 2015). This led to the highest rates of collapse outside of Kathmandu in rural villages, especially near
the Himalayas. Because of the rural nature of this earthquake, the spatial extent of damage is much larger in
Nepal in other case studies (Figure 1).
Our final case study is the August 24, 2016 Central Italy earthquake. The Mw6.2 event occurred near the
village of Accumoli (Stewart et al., 2018; D’Ayala et al., 2019). It caused severe damage to nearby villages
including Amatrice and Arquata del Tronto, and ultimately nearly 300 deaths. While Italy is a higher-income
country with a steadily improving building code (Liel and Lynch, 2012), many of these towns had historic
unreinforced masonry structures that were prone to collapse (Sextos et al., 2018). Variations in building
stock led to differing rates of collapse among towns. Figure 1 shows that in Italy the damage is localized in
specific towns as opposed to the more continuous pattern of damage in Haiti, New Zealand, and Nepal.
2.1 Data description
Building damage data can generally be categorized into primary field measurements and secondary estimates
from remote sensing, engineering forecasts, or related geospatial covariates (Loos et al., 2020). Field
measurements of damage are usually obtained through field surveys, where surveyors assign a level of
damage to an entire building. Sources for field surveys include research-based reconnaissance teams,
government/stakeholder survey teams, and citizen science groups. Field measurements are the most accurate
measurement of damage, though have limited coverage in the few weeks that are required to make crucial
early recovery plans. Secondary damage data come in the form of inference from predictive forecasts and
4
Loos, S., Levitt, J., Tomozawa, K., Baker, J., Lallemant, D. (2022). “Efficacy of damage data integration: a
comparative analysis of four major earthquakes.” ASCE Natural Hazards Review, (in press).
This material may be downloaded for personal use only. Any other use requires prior permission of the American Society of Civil
Engineers.
observational estimates from remote sensing sources. Damage inference from engineering forecasts are often
based on an estimate of shaking intensity, exposure, and a function that translates shaking intensity to loss.
On the other hand, remote sensing-derived data provide observational estimates of damage based on sensors.
Another form of secondary data are geospatial covariates that are predictive of building damage, such as
shaking intensity itself. Secondary data sources are useful in that they become available in the first week
after an earthquake and have dense spatial coverage, though are highly uncertain and require calibration to
the locally observed building damage.
We chose these case studies largely because of their exhaustive field surveys that can be used for training
and validation, as well as their diversity of secondary data (Table 1). For Haiti, New Zealand, and Nepal,
the national governments each coordinated a large-scale field survey census of all buildings in the region to
inform recovery planning (MTPTC, 2010; Tonkin and Taylor, 2016; Government of Nepal Central Bureau
of Statistics, 2015; Lallemant et al., 2017). In Italy, Fiorentino2018DamageEarthquakes coordinated an
assessment of damage for 235 out of 300 buildings in the center of the town of Amatrice, which we
supplemented with 425 surveys from the European Commission’s Joint Research Centre (The European
Commission, 2017).
The field surveys in each country used different scales to represent damage. Nepal and Italy used the
EMS-98 damage grading system (Grünthal, 1998), Haiti used a modified ATC-13 grading system (Applied
Technology Council, 1985), and New Zealand used the building damage ratio, which is the ratio of repair
cost to the greater of the replacement cost or valuation of a building (Tonkin and Taylor, 2016).
We include at least one of each category of secondary data (remote sensing-derived, engineering forecast,
or geospatial covariate) for each case study, when available. The main geospatial covariate for each location
is the USGS Shakemap produced for each event (Worden et al., 2016). In addition, we include an estimate
of near-surface soil stiffness (Vs30) to represent site-conditions (Allen and Wald, 2009; Foster et al., 2019).
Damage Proxy Maps (DPM) are remote sensing-derived estimates of damage that are automatically
derived from inSAR data (Yun et al., 2015). The DPM provides a unitless measure of damage per pixel.
The Advanced Rapid Imaging and Analysis project started producing DPMs after the February 2011 New
Zealand earthquake, so we include them for the most recent three earthquakes (New Zealand, Nepal, and
Italy).
The Haiti and Italy earthquakes had remote sensing-derived datasets that were manually produced
through crowdsourced and expert interpretation, respectively. After the Haiti 2010 earthquake, the Joint
Research Centre of the European Commission (JRC), UNOSAT, and the World Bank coordinated a large
scale effort to assess point level damage from remote sensing imagery (Corbane et al., 2011). A team from
the JRC assigned building-level EMS-98 damage grades to all buildings in their study area. ImageCat and
the World Bank coordinated a crowdsourcing approach to damage assessment with the GEO-CAN (Global
Earth Observation–Catastrophe Assessment Network) community, a group of over 600 online engineering
and scientific experts (Corbane et al., 2011; Ghosh et al., 2011). The GEO-CAN effort identified heavily
damaged and destroyed buildings. We combine the two assessments, as they covered complementary areas.
Similarly, after the Italy earthquake, Copernicus assigned damage to individual buildings in satellite imagery
using the EMS-98 damage grading system (The European Commission, 2017).
We develop our own engineering forecasts, either predicting probability of collapse or average damage
grade using the ShakeMap and fragility curves available for each country’s housing types. More information
on the development of these forecasts is included in Appendix A.
5
Loos, S., Levitt, J., Tomozawa, K., Baker, J., Lallemant, D. (2022). “Efficacy of damage data integration: a
comparative analysis of four major earthquakes.” ASCE Natural Hazards Review, (in press).
This material may be downloaded for personal use only. Any other use requires prior permission of the American Society of Civil
Engineers.
Table 1. Case study context as well as the sources and characteristics of data used for analysis.
Haiti 2010 New Zealand
2011
Nepal 2015 Italy 2016
Context Density Urban Urban Rural Rural
Dominant
housing type
Concrete frame
single-story
Timber frame
single-story
Unreinforced
stone with mud
mortar
single-story
Unreinforced
masonry
multistory
Field survey
metric
(numerical scale)
ATC-13 damage
states (1-7)
Building damage
ratio (0-0.75)
EMS-98 damage
grades (1-5)
EMS-98 damage
grades (0-5)
Geospatial
covariates
Shaking intensity Shaking intensity Shaking intensity Shaking intensity
Vs30 Vs30 Vs30 Vs30
Original
Damage
Data
Remote
sensing-derived -
Automatic
N/A Damage Proxy
Map (DPM)
Damage Proxy
Map (DPM)
Damage Proxy
Map (DPM)
Remote
sensing-derived -
Manual
GEO-CAN / JRC
assessment
N/A N/A Copernicus
damage grading
Engineering
Forecast
Self-developed Self-developed Self-developed Self-developed
Granularity Gridded (100m x
100m)
Building-level Gridded (300m x
300m)
Building-level
Prepared
Dataset
Predicted value Collapse rate Building damage
ratio
Average damage
grade
Damage grade
Number of data
points
2353 58,426 28,190 660
Size of region
(𝑘𝑚2)
60 1060 45,000 80
We prepared the original damage data for modeling by extracting or transforming each secondary dataset to
the same level of granularity as the field surveys. In Haiti and Nepal, we translated the building level field
assessments to grid-level (100m and 300m, respectively), due to data availability and/or to match primary
and secondary data when coordinates did not align. New Zealand and Italy remained at the building-level.
The predicted value of G-DIF varied between case studies as well. In Haiti, we predicted collapse rate
per grid, since the secondary data from GEO-CAN and JRC focused on collapse. In Nepal, we predicted
average damage grade per grid. In New Zealand and Italy, we directly predicted the field surveyed value of
each building (i.e. building damage ratio or damage grade). Maps of the field data are included in Figure 2.
The size and scale of the final prepared dataset for each location also varies, as shown in Table 1 and
visualized in Figure 1. The New Zealand dataset is the largest with 58,426 buildings included. Italy, on
6
Loos, S., Levitt, J., Tomozawa, K., Baker, J., Lallemant, D. (2022). “Efficacy of damage data integration: a
comparative analysis of four major earthquakes.” ASCE Natural Hazards Review, (in press).
This material may be downloaded for personal use only. Any other use requires prior permission of the American Society of Civil
Engineers.
the other hand, only consists of 660 buildings. Because we converted the Haiti and Nepal data to gridded
datasets, Haiti and Nepal’s final datasets for modeling contain 2,353 and 28,190 grid points, respectively.
However, the size of the region that Nepal’s dataset covers is the largest, at approximately 45,000 𝑘 𝑚2. The
Haiti, New Zealand, and Italy data cover much smaller regions, with a maximum area of about 1060 𝑘𝑚2.
3 Methods
In this section, we first provide an overview of G-DIF, which is described in more detail in Loos2020G-
DIF:Damage. We then introduce the simulations used to evaluate G-DIF’s generalizability, the effect of the
secondary data, and the effect of the field sample.
3.1 G-DIF: Geospatial Data Integration Framework
The basis for G-DIF is regression kriging, a geostatistical method that uses a sparse sample of field surveys
with spatially exhaustive secondary data to predict building damage at all locations.
Consider a region affected by an earthquake that is composed of 𝑛grids or buildings, each with location
𝑠. The true damage in this region is a function of location, 𝑍(𝑠), and is expected to be spatially correlated
due to the factors that drive building damage (shaking intensity, building characteristics, etc.). Therefore,
we decompose the true damage into a spatial trend, or the average damage throughout space, with spatially
correlated errors:
𝑍(𝑠)=𝑚(𝑠) + 𝜀(𝑠),(1)
where 𝑚(𝑠)is the trend and 𝜀(𝑠)is the error.
After an earthquake, the data that is available is a set of 𝑝secondary datasets, X=𝑋1. . . 𝑋𝑝, at all 𝑛
locations and field surveys of damage Zat a subset of 𝑛𝑓 𝑠 locations. Our goal is to use these data to estimate
the damage at an unsurveyed location 𝑠0. Consider the simple example of one unsurveyed location, though
the method scales to multiple unsurveyed locations. To estimate the trend, 𝑚, we develop a regression
function 𝑓between field measured damage Zand the secondary datasets. We then predict the trend at 𝑠0:
ˆ𝑚(𝑠0)=𝑓(X(𝑠0)) .(2)
A residual will exist between the trend model and the true damage. The residuals at all locations have a
mean of zero, but are likely to be spatially correlated because the trend model will not capture all sources of
spatial correlation. The residual at 𝑠0can thus be estimated using the spatial covariance between residuals at
all 𝑛𝑓 𝑠 field surveyed locations. We derive the spatial covariance structure based on the semivariance, 𝛾, or
dissimilarity in the residual between two field surveyed locations as a function of their separation distance,
:
𝛾()=
1
2var𝜀(𝑠) 𝜀(𝑠+).(3)
Broadly, building damage varies in space, due to both the trend and spatial patterns in variability. Here,
we consider the trend through the function, 𝑓, which accounts for some of this variation in space. We also
assume second-order stationarity, or that the spatial patterns in variability have the same covariance structure
across the entire affected region. This allows us to develop a single covariance structure for the entire study
area through evaluating an empirical variogram.
An empirical variogram ( ˆ𝛾()) can be constructed using sample variances of observed residuals (at
field-surveyed locations) with separation distance . A theoretical variogram can then be fit through each
(ˆ𝛾, )pair. The theoretical variogram is used to solve for the kriging weights, 𝜆, which we implement in
7
Loos, S., Levitt, J., Tomozawa, K., Baker, J., Lallemant, D. (2022). “Efficacy of damage data integration: a
comparative analysis of four major earthquakes.” ASCE Natural Hazards Review, (in press).
This material may be downloaded for personal use only. Any other use requires prior permission of the American Society of Civil
Engineers.
Ordinary Kriging by weighing the known residuals at all field surveyed locations to estimate the unknown
residual at 𝑠0:
ˆ𝜀(𝑠0)=
𝑛𝑓 𝑠
𝛼=1
𝜆𝛼(𝑠) · 𝜀(𝑠𝛼).(4)
To obtain the final damage estimate, ˆ
𝑍(𝑠0), we substitute the results from Equation 2 and Equation 4 into
Equation 1.
3.1.1 Application to case studies
The above general integration framework is then applied to the case study data of Table 1. In New Zealand
and Italy, where building-level data is available, the direct survey of damage is used as the true damage,
𝑍(𝑠). In Haiti, where data is at grid-level, 𝑍(𝑠)is the collapse rate: the percentage of buildings in a grid
with a damage state of six or seven. In Nepal, where data is also at grid-level, 𝑍(𝑠)is the average damage
grade of buildings in a grid.
To model the trend, we mainly use a linear ordinary least squares (OLS) regression as our function
𝑓in this study. This is a common approach in Regression Kriging (Hengl et al., 2007), though assumes
independent residuals which is not entirely consistent with the spatial correlation structure of the residuals.
Other models, such as generalized least squares, general additive models, regression trees, and artificial
neural networks have also been used for Regression Kriging in order to allow for greater flexibility with
regard to these features (Chiles and Delfiner, 2012a; Hengl et al., 2003; Grujic, 2017; McBratney et al., 2000;
Motaghian and Mohammadi, 2011). Here, we apply OLS to be able to compare models across simulations
and across case study locations. All secondary data is standardized to have a mean of zero and standard
deviation of one. In cases where high collinearity exists between secondary data–enough so that it impedes
fitting of the coefficients of the trend model–we implement mixed stepwise selection (Hastie et al., 2009).
To model the spatially correlated residuals, we consider an exponential, spherical, or Matern theoretical
variogram and selected the model with the lowest sum of squared errors. For New Zealand and Nepal, with
large field sample sizes, we also apply local kriging to restrict the maximum number of points considered
for prediction at 𝑠0.
3.2 Simulation study to evaluate efficacy of G-DIF
We perform a simulation study to evaluate the efficacy of G-DIF in adapting to multiple contexts and damage
datasets. Each simulation uses the following procedure:
1. Sample one realization of field surveys.
2. Use the field survey sample to fit the models described above.
3. Use the fitted models to develop the final damage estimate.
4. Calculate performance metrics for the error between damage estimate and true damage at all unsurveyed
locations.
5. Repeat Steps 1 through 4 1000 times.
Rows one through three of Figure 2 demonstrate the model building process for one realization of field
surveys across all case study locations. By repeating this procedure with 1000 simulations, we estimate and
account for the uncertainty in G-DIF’s damage estimate due to the field survey sample.
Our goal is to compare G-DIF’s damage estimate to alternative damage estimates or alternative con-
figurations of G-DIF using this procedure. We evaluate each option using the error 𝑒between the damage
8
Loos, S., Levitt, J., Tomozawa, K., Baker, J., Lallemant, D. (2022). “Efficacy of damage data integration: a
comparative analysis of four major earthquakes.” ASCE Natural Hazards Review, (in press).
This material may be downloaded for personal use only. Any other use requires prior permission of the American Society of Civil
Engineers.
estimate and the true damage at each location 𝑖in all 𝑛𝑣𝑎𝑙 unsurveyed locations in the study area that were
not included in the field survey sample.
𝑒𝑖=ˆ
𝑍(𝑠𝑖) 𝑍(𝑠𝑖)(5)
The distribution of error for one realization of field surveys is shown in row four of Figure 2. The main
performance metric is mean squared error (MSE), which measures the overall bias and variance of the error
distribution. The MSE is
𝑀𝑆𝐸 =
1
𝑛𝑣𝑎𝑙
𝑛𝑣𝑎𝑙
𝑖=1
𝑒2
𝑖.(6)
We also calculate the bias (mean error, ME) and variance (variance of the error, VE) themselves.
𝑀𝐸 =
1
𝑛𝑣𝑎𝑙
𝑛𝑣𝑎𝑙
𝑖=1
𝑒𝑖(7)
𝑉 𝐸 =
1
𝑛𝑣𝑎𝑙
𝑛𝑣𝑎𝑙
𝑖=1
(𝑒𝑖𝑀𝐸 )2(8)
Values closer to zero are preferred for all three metrics. A lower ME means the average error is closer to
zero, a lower VE means the spread in error is closer to zero, and the MSE captures the combination of these
two. It is straight forward to calculate error for G-DIF, as the units are the same as the true damage from the
field surveys. However, for some engineering forecasts, the prediction varies from probability of collapse to
mean damage ratio. The calculation of error for these secondary datasets is included in Appendix B.
3.2.1 Baseline comparison
We first benchmark the accuracy of G-DIF’s damage estimate against that of secondary data alternatives,
focusing on sources that produce tangible damage estimates (i.e. manually derived estimates from remote
sensing and engineering forecasts). In this initial comparison, we use a set of field surveys that represents
the full distribution of damage and is spatially distributed throughout the entire region. We use two sample
sizes of field surveys: a consistent sample size of 100 points (i.e. buildings or grids) in all four cases and
a sample size that is likely to be collected within the first week after a disaster. By using two sample sizes,
we demonstrate how G-DIF’s damage estimate changes with different amounts of field surveys for all case
study locations, as we previously showed only in Nepal (Loos et al., 2020).
3.2.2 Utility of secondary data sources
The accuracy of G-DIF depends on the secondary data that is included in the integration. Certain secondary
data types are more informative than others. To evaluate the utility of each dataset we use only one secondary
dataset at a time in the trend model of G-DIF. We then evaluate the MSE of the damage estimate produced
by only the trend model (or row one in Figure 2), because the spatial correlation model tends to compensate
for secondary datasets that are poor predictors. We repeat this procedure 1000 times with different random
samples of field surveys. Within each case study, we use the same 1000 random samples with each secondary
dataset, to ensure a fair comparison.
3.2.3 Evaluating the effect of the field survey sample
Beyond the secondary data, the field survey size and sample configuration will also affect the G-DIF estimate.
For the previous comparisons, we used a random sample of field surveys used to calibrate the secondary
9
Loos, S., Levitt, J., Tomozawa, K., Baker, J., Lallemant, D. (2022). “Efficacy of damage data integration: a
comparative analysis of four major earthquakes.” ASCE Natural Hazards Review, (in press).
This material may be downloaded for personal use only. Any other use requires prior permission of the American Society of Civil
Engineers.
damage data in G-DIF. This is not realistic, as it is unlikely that surveyors will be able to reach a fully random
and spatially distributed set of locations in the aftermath of an earthquake.
We therefore compare G-DIF’s damage estimate from a random sample of field surveys to a more
realistic scenario where the surveys are spatially clustered in a small sub-region. The spatially clustered
sample emulates a situation where surveyors can only reach one neighborhood in the first week after a
disaster. We again compare the MSE, ME, and VE of G-DIF’s damage estimate using the above simulation
procedure, comparing random realizations of samples from the two field survey configurations.
For this comparison where the focus is on the field survey data, we also compare G-DIF to the alternative
where no secondary data is available and the field surveyed damage is interpolated directly. We do this by
using Ordinary Kriging to spatially interpolate the damage from the field survey sample. Ordinary kriging is
an univariate geostatistical prediction method as opposed to the multivariate regression kriging (Chiles and
Delfiner, 2012b). Ordinary kriging assumes the average damage throughout space is an unknown constant,
whereas in regression kriging the average is varying (and captured with the trend model). Here, we apply
Ordinary Kriging by developing a variogram directly with the field surveys of damage, using this to solve
for the kriging weights used for spatial interpolation.
4 Results and discussion
In this section, we first apply G-DIF to the four case studies to illustrate the components of the framework. We
then provide a benchmark comparison between damage estimates produced from G-DIF and each secondary
dataset. Analyzing this result further, we consider which secondary dataset leads to the most accurate
prediction of damage within G-DIF. Finally, we evaluate the effect of the configuration of the field surveys
on the prediction error of G-DIF. These analyses provide three main takeaways: 1) G-DIF is more accurate
than any forecast or remote sensing-derived dataset in each case study, 2) the most predictive secondary
source of data varies between case studies but generally a Shakemap can be sufficient as the only covariate,
and 3) G-DIF effectively predicts true damage even with spatially clustered field surveys.
4.1 Application of G-DIF to four case studies
We apply G-DIF to the four events’ data in Figure 2. This initial application uses the number of field surveys
we estimate to be possible to collect within a week. However, this number can vary between events and
is not well-documented, so, here, we assume that a field surveyor can carry out 20 damage surveys per
day and that there are more field surveyors available after more damaging earthquakes based upon personal
reconnaissance experience. In Haiti, we use a field sample of 50 grids (2.1% of all grids). With an average of
145 buildings per grid, this would result in about 7,250 buildings being surveyed, which could be completed
by 50 surveyors over 7 days. In New Zealand, we use a field sample of 3,000 buildings (5.1% of all buildings),
which could be completed by 30 surveyors over 5 days. In Nepal, we use 500 grids (1.8% of all grids). Nepal
has an average of 10 buildings per grid, leading to about 5,000 buildings being surveyed in total, which could
be completed by 50 surveyors over 5 days. Finally, in Italy, we use a sample of 60 buildings (9.1% of all
buildings), which could be completed by 3 surveyors in 1 day. As an alternative, we also consider a scenario
with a consistent field survey sample of 100 buildings or grids across all case study locations. Here, we
initially consider a random sample of field surveys.
Figure 2 demonstrates the step-by-step components of G-DIF and the resulting histogram of error when
comparing G-DIF’s damage estimate to the full set of field surveys. The first row of Figure 2 shows the
estimated trend (Equation 2) from a linear regression model after standardizing each secondary dataset
predictor to have a mean of zero and standard deviation of one. The second row then shows the residuals
at the field survey locations between the true damage and estimated trend, once they have been interpolated
using Ordinary Kriging (Equation 4). The third row is the final integrated damage estimate from G-DIF,
10
Loos, S., Levitt, J., Tomozawa, K., Baker, J., Lallemant, D. (2022). “Efficacy of damage data integration: a
comparative analysis of four major earthquakes.” ASCE Natural Hazards Review, (in press).
This material may be downloaded for personal use only. Any other use requires prior permission of the American Society of Civil
Engineers.
Fig. 2. Example application of G-DIF and associated error for one realization across four case studies.
The top three rows show the trend model, estimated residuals, and integrated damage estimate from G-DIF
using one realization of a random sample of field surveys that could be collected in one week. The fourth
row shows a histogram of errors between G-DIF’s damage estimate and the true damage in all case study
locations, for this one realization, with the mean error indicated as the vertical line.
which is the sum of the estimated trend in the first row and the estimated residuals in the second row
(Equation 1). The integrated damage estimate is then compared to the true damage from the full set of
field surveyed damage. Note that the prediction unit varies for each case study location depending on the
field data: Collapse Rate in Haiti (number of buildings in Damage States 6 or 7 over the total number of
buildings), Building Damage Ratio in New Zealand, Mean Damage Grade in Nepal, and Damage Grade in
Italy. Finally, the bottom row shows the distribution of error between the integrated damage estimate and the
true damage for this one realization. In this error distribution, we highlight the mean error (ME) with the
vertical line in the error distribution. In the following sections, we calculate the overall mean squared error
(MSE) of each realization’s error histogram to capture the change in G-DIF’s prediction error with different
11
Loos, S., Levitt, J., Tomozawa, K., Baker, J., Lallemant, D. (2022). “Efficacy of damage data integration: a
comparative analysis of four major earthquakes.” ASCE Natural Hazards Review, (in press).
This material may be downloaded for personal use only. Any other use requires prior permission of the American Society of Civil
Engineers.
field survey samples.
4.2 Baseline comparison of G-DIF to secondary data alternatives
Fig. 3. Baseline comparison of G-DIF to individual secondary damage data. Distribution of mean
squared error (MSE) of G-DIF’s damage prediction across all case studies is shown in orange and red. G-
DIF 1 week in orange uses the amount of field surveys that can be collected in one week and G-DIF 100 in red
uses 100 points in the field survey sample. Sample sizes are annotated next to each distribution. Each vertical
line is the MSE of G-DIF’s damage estimate using one field sample realization, the dark middle line is the
average MSE, the left line is the 25th percentile, and right line is 75th percentile. G-DIF’s MSE is compared
to predicting the average damage of the field surveys, remote sensing-derived estimates, and engineering
forecasts. The remote sensing-derived estimate is from crowdsourcing in Haiti and manually-interpreted in
Italy. The MSE from the remote sensing-derived estimates and engineering forecasts are single lines since
they do not depend on field surveys.
To evaluate the performance of G-DIF relative to any single secondary damage dataset, we compare
prediction errors from the two approaches. We compare the MSE’s of the G-DIF damage estimate and that
from single secondary data predictions. We consider 1000 realizations of random samples of surveys, and
compute MSE values for each, as shown in Figure 3. Again, a MSE closer to zero means that the predicted
damage is closer to the observed damage.
12
Loos, S., Levitt, J., Tomozawa, K., Baker, J., Lallemant, D. (2022). “Efficacy of damage data integration: a
comparative analysis of four major earthquakes.” ASCE Natural Hazards Review, (in press).
This material may be downloaded for personal use only. Any other use requires prior permission of the American Society of Civil
Engineers.
Figure 3 shows the distribution of the MSE from G-DIF predictions built using 100 buildings/grids in
the field survey sample (in red, second from the bottom) or with an amount of field surveys that could be
collected within a week (in orange, bottom). Figure 3 shows that G-DIF predictions, with both field survey
amounts, result in lower prediction errors than any alternative secondary damage dataset.
G-DIF’s lower MSE compared to the secondary data in the four case studies confirms that G-DIF is
indeed generalizable to multiple locations when using these secondary datasets and a random set of field
surveys. This better performance occurs because G-DIF includes all of the datasets available, but weighs
the more predictive datasets as more important to the final prediction. Even with a very predictive set of
secondary data, there will always be residuals between the trend and the field surveyed damage. The spatial
correlation model addresses this by spatially interpolating those residuals to all locations. Therefore, G-DIF
improves upon any secondary damage dataset by combining it with other data and also interpolating the
remaining residuals to more closely match the field surveyed damage.
This approach does require enough field surveys to build the trend model and the variogram. With fewer
field survey samples, the performance of G-DIF is worse—this can be seen in Figure 3, where the G-DIF
MSE’s are lower in all four case studies for the row with more field surveys. In addition, for a region with
few buildings that are far apart, like in Italy, it may be difficult to build an accurate variogram to capture
small-scale spatial correlations. This can affect the performance of G-DIF–a few of the Figure 3 realizations
in red and orange for Italy have an MSE similar to the manually-interpreted remote sensing-derived damage
estimate.
Notably, we also compare the MSE when predicting one value at all locations, the average damage of the
field survey sample, shown in gray in Figure 3. This is a naive comparison, although interestingly, the MSE
of the average of the field surveys falls between G-DIF and each secondary dataset. The better performance
of G-DIF than the average of the field surveys is to be expected, as G-DIF will be spatially heterogeneous as
compared to the single value of the average. G-DIF uses this same set of field surveys as calibration for the
secondary datasets in each location. The lower MSE of the average of the field surveys than each secondary
dataset indicates that the damage estimates in the secondary data may be over or underpredicting the overall
damage, leading to larger overall errors.
4.3 Predictive power of secondary data
Here, we examine the predictive power of each secondary dataset within G-DIF to evaluate which are most
useful to collect after an earthquake. For each case study location, we evaluate the error (MSE) of the
damage estimate from the trend model when using only one secondary dataset as the predictor, comparing
remote sensing-derived damage estimates, forecasts, distributions of shaking, and Vs30. Figure 4 shows the
distribution of error (MSE) of the trend estimate; we again vary the locations of the field surveys used to
train G-DIF.
The most predictive secondary dataset varies in each case study location. The secondary datasets that
show distributions closer to zero on the left in Figure 4 are more predictive of damage, and therefore
useful to collect post-earthquake and use within G-DIF. Generally, the shaking intensity provides a trend
estimate that is both consistent and has relatively low errors. Engineering forecasts are closely aligned
with shaking intensity and perform similarly—especially in Haiti and New Zealand where forecasts were
modeled assuming the same structure type across the entire affected region. On the other hand, remote
sensing-derived data can be more predictive of true damage than an engineering forecast, as seen in New
Zealand and Italy. In Haiti, the GEO-CAN crowdsourced data results in a lower MSE than the engineering
forecast in some cases, though it has larger variability.
The observational nature of remote sensing-derived damage data can lead to more accurate estimates
of damage compared to the predictive nature of forecasts. Observations from the event based on nadir
imagery (imagery seen from above) can capture small-scale variations in damage that cannot be captured in
13
Loos, S., Levitt, J., Tomozawa, K., Baker, J., Lallemant, D. (2022). “Efficacy of damage data integration: a
comparative analysis of four major earthquakes.” ASCE Natural Hazards Review, (in press).
This material may be downloaded for personal use only. Any other use requires prior permission of the American Society of Civil
Engineers.
Fig. 4. Comparison of secondary datasets’ predictive power in G-DIF. Each vertical line is the MSE of
G-DIF’s trend model when using each secondary dataset as the only covariate for one field sample realization.
Each realization uses a random sample of field surveys that could be collected within a week. The dark
middle line is the average MSE, the left line is the 25th percentile, and right line is the 75th percentile. The
manual remote sensing-derived damage estimate in Haiti is from crowdsourcing and in Italy is from expert
interpretation.
the predictive forecasts. The DPM data in New Zealand strongly correlates with the true damage compared
to the engineering forecast, as seen in Figure 5. This is because the DPM detected areas of liquefaction
that were not included in the forecast. On the other hand, in Nepal, where there was little liquefaction and
damaged rural houses had more potential to be shrouded by dense tree cover, the DPM performed similarly
to the engineering forecast. In Haiti, the GEO-CAN estimate shows small-scale patterns of collapse near the
center of Port-au-Prince that were not identified in the forecast. However, because the GEO-CAN estimate
is based on crowdsourcing, it overestimates the true collapse rates in the center of Port-au-Prince, leading to
the large variability in GEO-CAN’s resulting MSE for Haiti in Figure 4. Therefore, the method of deriving
damage from satellite imagery and the mechanisms of damage influence whether a remote sensing-derived
estimate is more predictive of the true damage than a forecast.
Figure 4 provides intuition behind which datasets are most useful to include in G-DIF after an earthquake
occurs. In all cases, the shaking intensity from the Shakemap is predictive of damage. This means that
after earthquakes where other types of secondary data are not available, this dataset is sufficient as the only
predictor in the trend. However, the addition of a remote sensing-derived dataset has the potential to improve
the accuracy of the trend estimate due to the increased spatial granularity of the remote sensing-derived
damage estimate. Using one field survey sample across all case study locations, we compared a trend model
with just shaking intensity to a model with both shaking intensity and a remote sensing-derived dataset as
predictors using the F test (Williams, 1959). We found that in each case study location, the model with a
remote sensing-derived dataset to the trend model was significantly different than a model without (𝑃0.05
for all five remote sensing datasets, and 𝑃0.001 for three). Thus, the addition of a remote sensing-derived
dataset will improve the accuracy of the trend model if it is available.
4.4 Effect of field survey configuration
In addition to the secondary datasets, the field surveyed damage data has a large influence on G-DIF’s
performance. Here we evaluate the effect of the configuration of the field survey sample on G-DIF’s damage
estimate, focusing on Haiti (the case study with relatively poor quality secondary data).
The prior simulations of G-DIF used a random sample of field surveys with locations scattered throughout
14
Loos, S., Levitt, J., Tomozawa, K., Baker, J., Lallemant, D. (2022). “Efficacy of damage data integration: a
comparative analysis of four major earthquakes.” ASCE Natural Hazards Review, (in press).
This material may be downloaded for personal use only. Any other use requires prior permission of the American Society of Civil
Engineers.
Fig. 5. Maps of remote sensing-derived damage estimates versus engineering forecasts. The remote
sensing-derived estimate in Haiti is manually interpreted using crowdsourcing (Ghosh et al., 2011) and
in New Zealand is automatically-interpreted (Yun et al., 2015). Haiti’s engineering forecast is in units of
probability of collapse.
15
Loos, S., Levitt, J., Tomozawa, K., Baker, J., Lallemant, D. (2022). “Efficacy of damage data integration: a
comparative analysis of four major earthquakes.” ASCE Natural Hazards Review, (in press).
This material may be downloaded for personal use only. Any other use requires prior permission of the American Society of Civil
Engineers.
the affected region. With a random sample of surveys, it is possible to directly interpolate the field surveys
using Ordinary Kriging without including any secondary datasets. Figure 6 compares the damage estimate
from G-DIF, which integrates every damage data available, to the damage estimate from Ordinary Kriging,
which only interpolates the field surveys. Figures 6a and b show the damage estimate in two dimensions,
whereas Figure 6c shows the damage estimate in one dimension. With a random sample, G-DIF and
interpolating the field surveys produce similar performance. This is because with a sufficient number of
field surveys at separation distances within the range of spatial correlation of the surveyed building damage,
Ordinary Kriging will provide a smooth interpolation.
As mentioned before, it is unlikely that survey teams will be able to reach a random sample of locations
within a week after an earthquake. With a random sample, directly interpolating the field surveys using
Ordinary Kriging can perform similarly to G-DIF, like in Figure 6. However, in most cases field surveys
will only be collected in certain localities. The trend model within G-DIF makes it preferable in these more
realistic scenarios, where field surveys are sampled in one area of the affected region. Figure 7 shows the
effect of a spatially clustered field sample G-DIF and direct interpolation of the field surveys using Ordinary
Kriging. G-DIF’s damage estimate is similar when using the clustered sample in Figure 7 and the random
sample in Figure 6. In Figure 7, G-DIF’s final damage estimate converges to the trend model’s damage
estimate in the area away from field surveyed data. Conversely, the damage estimate from Ordinary Kriging
converges to the average damage, a single value, from the field surveyed sample in this same area.
Ordinary kriging will predict the average field surveyed damage at all locations outside the range of
spatial correlation, as seen in Figure 7. Therefore, in a real scenario, Ordinary Kriging will predict the
average damage at locations that did not experience shaking, whereas G-DIF has the potential to predict zero
to low damage at those locations because of the trend model. Because we do not have data on buildings that
were outside of the affected areas in these four events, any calculated error of Ordinary Kriging presented
from hereon overestimates the performance of its damage estimate.
We repeat the comparison between G-DIF and Ordinary Kriging, simulating each method’s damage
estimate with 1000 different field survey samples from the random or clustered field survey configuration.
The clustered sample is constrained to the same small area of 54 grids, and we select a different sub-sample
of 40 field surveys within this area. The sample of random surveys can be at any location in the affected
region. The results of these simulations are shown in Figure 8.
The top row of Figure 8 shows the distributions of MSE, ME, and VE for the two methods’ damage
estimates when a random field survey sample is used to fit the models. With a random sample, G-DIF
and Ordinary Kriging damage estimates have similar average values of MSE, ME and VE, as shown by the
middle vertical lines in each distribution. Again, the actual performance for Ordinary Kriging is likely lower
than what we are able to calculate with our field survey data. The majority of field sample realizations for
G-DIF’s damage estimate have lower MSE values than Ordinary Kriging, as indicated by comparing the
25th and 75th percentile vertical lines for each. Though for a very small percentage (0.7%) of field sample
realizations, G-DIF does have higher MSE values than Ordinary Kriging. This is most likely due to a poor
trend model fit for G-DIF and Ordinary Kriging being a weighted average of nearby points. With the more
realistic, spatially clustered sample (bottom row of Figure 8), G-DIF’s performance is markedly better than
Ordinary Kriging, as seen by the lower interquartile range for G-DIF’s MSE distribution in the bottom row.
This is because interpolating the field surveys using Ordinary Kriging underestimates the true damage, as
shown in Figure 7, resulting in a biased estimate where the mean error is always negative in Figure 8.
These simulations comparing field survey configurations were applied to Haiti, the case study location
with poorer quality secondary data. We also used a small percentage (1.7%) of field survey samples to fit
the models in G-DIF and Ordinary Kriging. In a case study with more predictive secondary data and with
more field surveys, we expect the performance of G-DIF to further improve.
The effect of the field survey sample on G-DIF’s damage estimate indicates the need for thoughtful
planning of the field sampling strategy immediately after an earthquake. We discuss ways to improve rapid
16
Loos, S., Levitt, J., Tomozawa, K., Baker, J., Lallemant, D. (2022). “Efficacy of damage data integration: a
comparative analysis of four major earthquakes.” ASCE Natural Hazards Review, (in press).
This material may be downloaded for personal use only. Any other use requires prior permission of the American Society of Civil
Engineers.
Fig. 6. G-DIF versus Ordinary Kriging using a random sample of field surveys. The estimated
distribution of collapse in Haiti, shown in two dimensions, resulting from (a) G-DIF, which integrates field
surveyed damage with secondary data, (b) Ordinary Kriging, which only interpolates the field surveyed
damage, shown by the black points. Both methods use a randomly distributed set of field surveys. The
spatial variation in estimated collapse rate from G-DIF and Ordinary Kriging is also shown in one dimension
in (c), when plotting collapse along the teal line cutting horizontally across the map shown at the bottom.
17
Loos, S., Levitt, J., Tomozawa, K., Baker, J., Lallemant, D. (2022). “Efficacy of damage data integration: a
comparative analysis of four major earthquakes.” ASCE Natural Hazards Review, (in press).
This material may be downloaded for personal use only. Any other use requires prior permission of the American Society of Civil
Engineers.
Fig. 7. G-DIF versus Ordinary Kriging using a clustered sample of field surveys. The estimated
distribution of collapse in Haiti, shown in two dimensions, resulting from (a) G-DIF, which integrates field
surveyed damage with secondary data, (b) Ordinary Kriging, which only interpolates the field surveyed
damage. Both methods use a spatially clustered set of field surveys, shown by the black points. The spatial
variation in estimated collapse rate from G-DIF and Ordinary Kriging is also shown in one dimension in (c),
when plotting collapse along the teal line cutting horizontally across the map shown at the bottom.
18
Loos, S., Levitt, J., Tomozawa, K., Baker, J., Lallemant, D. (2022). “Efficacy of damage data integration: a
comparative analysis of four major earthquakes.” ASCE Natural Hazards Review, (in press).
This material may be downloaded for personal use only. Any other use requires prior permission of the American Society of Civil
Engineers.
Fig. 8. Effect of field survey configurations on G-DIF versus Ordinary Kriging. Each vertical line is
the error of G-DIF (red) or Ordinary Kriging’s (yellow) damage estimate for one field sample realization.
Error metrics shown include mean squared error (MSE), mean error (ME), or variance in the error (VE).
The dashed vertical line plotted at zero is the best possible value for each metric. Each realization uses a
sample of 40 field surveys in each field sample configurations, maps of which are shown on the left. The
dark middle line is the average MSE, the left line is the 25th percentile, and right line is the 75th percentile.
damage estimates through field samples in the next section.
5 Recommendations for developing G-DIF in real time
The above results point to several recommendations to implement G-DIF in real time. Here, we describe
considerations for modelers developing G-DIF damage predictions with the data from a future disaster. We
provide an interactive code to support this section as well, which is included in the Data Availability Statement
(Loos, 2022). We close this section with a summary of field survey sampling strategies to maximize G-DIF’s
damage prediction and examples of ways these ideas are being carried out in practice.
5.1 Developing G-DIF
Based on the testing and results summarized above, the following recommendations will enable a modeler
to maximize the efficacy of G-DIF in real time after a disaster.
1. Evaluate field survey sample size The size of the field survey sample will dictate how a modeler
trains and evaluates the accuracy of G-DIF’s damage estimate. Methods such as k-fold cross-validation,
leave-one-out cross-validation, and bootstrapping can be used for training and validating the models within
G-DIF (Hastie et al., 2009). In addition, with more field data, a modeler can create a separate test set to
evaluate G-DIF’s potential prediction performance on unsurveyed locations. Generally, more field data will
lead to a more accurate damage estimate with less variation as shown in Figure 3.
2. Establish boundary of prediction area In real time, the modeler would establish the spatial boundary
for the forward prediction of damage using G-DIF. An initial boundary could be the area of strong shaking
in the USGS ShakeMap, and users on the ground can help refine this by considering areas they will focus
on during response and recovery. To accommodate datasets that are not available throughout the entire
19
Loos, S., Levitt, J., Tomozawa, K., Baker, J., Lallemant, D. (2022). “Efficacy of damage data integration: a
comparative analysis of four major earthquakes.” ASCE Natural Hazards Review, (in press).
This material may be downloaded for personal use only. Any other use requires prior permission of the American Society of Civil
Engineers.
prediction area, separate trend models can be built for multiple subregions (Loos et al., 2020). Alternatively,
one can integrate these datasets using Bayesian methods (Booth et al., 2011; Foster et al., 2019; Lee and
Tien, 2018; Noh et al., 2020). However, developing explicit methods to do this in a spatial manner requires
future research.
3. Assess spatial distribution of field sample Combining the previous two steps, modelers should evaluate
whether the field sample is spatially distributed across the prediction area, like the evaluation completed in
Section 4.4. Modelers can also explore the separation distances of the field survey sample. Highly clustered
field samples with small separation distances, like in Figure 7, will converge to the trend model in regions
outside of the field surveyed area. Separated field survey samples at large distances greater than the expected
spatial correlation in trend residuals may result in a “nugget” variogram, or a variogram with constant
semivariance at all distances. In both cases, modelers should focus on building a predictive trend model that
accurately reflects the relationships between the field survey samples and secondary data (discussed in step
5), since the resulting variogram may not improve the overall performance of G-DIF. In addition, modelers
should suggest that additional field surveys be collected at those unobserved separation distances.
4. Compare secondary data at field surveyed sample to full study area The modeler should compare
values of secondary data at the field surveyed locations to the full distribution of secondary data at all
locations. As much as possible, the variance of the secondary data at the field surveyed locations should
reflect the variance over the entire affected region. Otherwise, the trend estimate may not extrapolate well
outside of sample distribution’s range. This might occur if field surveys are clustered so that there is little
variation in the secondary data, as evaluated in Section 4.4. If this occurs, additional field surveys should be
collected, if possible, for a wider range of secondary data values.
5. Explore relationships between primary and secondary data When building the trend model, the
modeler should incorporate the relationships that exist between each secondary dataset and the field survey
sample. In this study, many of the relationships between the secondary data and the field data are close to
linear. One can evaluate this by examining the moving average of the field damage at various bins of each
secondary data, or creating a “loess" curve. However, nonlinear trends can be considered by transforming
the secondary dataset using a nonlinear trend model.
6. Address redundant secondary data Some damage datasets may be collinear (e.g., an engineering
forecast may be closely aligned with a ShakeMap). Collinearity can lead to unreliable estimates of the
coefficients for each secondary dataset and to overfitting. Ways to evaluate collinearity of the secondary
datasets are to evaluate the variance inflation factor and correlation matrix of the secondary data at the
field survey locations. If collinearity is found, a modeler can address this by applying variable selection
techniques, like mixed stepwise selection which was applied in this study, or aggregating collinear variables
using principal component analysis.
7. Build and evaluate the trend model The modeler should choose an appropriate functional form
and regression algorithm to implement for the trend model, whether it be Ordinary Least Squares (OLS),
Generalized Least Squares, or a more complex regression function (Loos et al., 2020). In the case of OLS,
which we implement here, the modeler should check the coefficients for each secondary dataset. A coefficient
opposite from expectations (for example, predictions of decreasing damage with increasing ground shaking
intensity), may indicate that the secondary dataset is unreliable or that outliers exist, and should be addressed
by removing from the model. Otherwise the directions of the coefficients should reflect the relationships seen
in Step 5. To evaluate the relative utility of each secondary dataset, one should ensure that each variable is
20
Loos, S., Levitt, J., Tomozawa, K., Baker, J., Lallemant, D. (2022). “Efficacy of damage data integration: a
comparative analysis of four major earthquakes.” ASCE Natural Hazards Review, (in press).
This material may be downloaded for personal use only. Any other use requires prior permission of the American Society of Civil
Engineers.
standardized to ensure that coefficients are comparable. The coefficients and standard errors of each variable
in the trend model will provide intuition behind which secondary dataset has the most influence on the trend
estimate, similar to the results shown in Figure 4. Finally, the modeler should assess the distribution of the
residuals of the trend model to assess whether they meet the Gaussian assumptions for Kriging. If residuals
are non-Gaussian, the modeler can explore methods for transforming the residuals (Cecinati et al., 2017).
8. Build and refine the spatial correlation model The choice of variogram and kriging method affects
the final spatial pattern of damage, especially in situations where the secondary data are poor predictors of
the true damage. In addition to selecting the variogram based on best fit, modelers should also consider the
expected spatial pattern that result from the selected variogram. If the fitted variogram exhibits a trend (or the
semivariance increases with distance), the field surveyed damage may not have been successfully detrended
with the trend estimate or perhaps the selected variogram model is too flexible. In this case, it might
be preferable to consider local kriging, where only the closest surveyed points to the unsurveyed location
are considered when making the kriging prediction. If a nugget variogram is fit to the detrended survey
points, the trend model may have fully captured the spatial correlation in damage. However, the modeler
should compare the variogram fit to the detrended field surveys with the variogram fit to the original field
surveys, to ensure the nugget variogram is not arising from a lack of closely spaced field data (as discussed
in Step 3). Finally, we assume second-order stationarity in this formulation, meaning that semivariances
are constant across locations and in all directions, and therefore build a single variogram. The modeler can
explore developing several variograms depending on location in the study region, though this requires further
research with a full set of ground-truth damage data.
9. Calculate performance metrics Finally, the modeler should calculate the model performance on a
test set of field surveys held out from all model fitting. The modeler can use the fitted model to predict
damage at the test set locations and calculate the mean error, variance in the error, and mean squared error.
Prediction errors for newly acquired field data should be monitored—low errors would confirm the current
G-DIF damage estimate, whereas high errors would trigger a model update.
5.2 Strategizing field survey collection
Thoughtful on-the-ground collection of damage data after future disasters can have a meaningful impact
on resulting G-DIF estimates, since many of the above recommendations for maximizing G-DIF’s damage
prediction (including Steps 1, 3, 4, and 9) are influenced by the field survey sample. Decisions for field
survey collection can be strategized with respect to what measurements are collected, who is collecting the
measurements, and where those measurements are collected.
What: types of damage assessments G-DIF can adapt to different types of field survey assessments, as
decided upon by local stakeholders—the EMS-98 damage grading system was employed in Italy and Nepal;
a modified ATC-13 in Haiti; and the Building Damage Ratio, or Loss Ratio, in New Zealand. Therefore, it
is important that the field surveys that are used to calibrate secondary damage data are in the unit that will
ultimately be used for response and recovery planning purposes.
Who: sources of field surveys In this study, we demonstrate G-DIF using field surveyed damage data
collected mainly by the governments of each case study location. We use government-collected damage
data because the unit of measurement for the damage assessment was used later in the recovery to guide the
distribution reconstruction grants and insurance payouts.
However, field surveyed damage data can come from multiple sources outside of government or stake-
holder teams, including research-based reconnaissance teams or citizen science. Various groups organize
21
Loos, S., Levitt, J., Tomozawa, K., Baker, J., Lallemant, D. (2022). “Efficacy of damage data integration: a
comparative analysis of four major earthquakes.” ASCE Natural Hazards Review, (in press).
This material may be downloaded for personal use only. Any other use requires prior permission of the American Society of Civil
Engineers.
reconnaissance trips to locations recently affected by disaster including the Earthquake Engineering Research
Institute (EERI), Geotechnical Extreme Events Reconnaissance (GEER), the Earthquake Engineering Field
Investigation Team, and the Structural Extreme Events Reconnaissance (StEER) network. Reconnaissance
teams have the advantage of containing highly trained surveyors who may be able to reach the affected region
before a governmental survey is orchestrated. G-DIF makes it possible to integrate reconnaissance-collected
field surveys with secondary data to estimate the expected damage at places reconnaissance teams cannot
reach. Importantly, if reconnaissance teams conduct assessments in the same unit of measurement as that
employed by survey teams for the government—which often go beyond red-yellow-green safety tags—a
G-DIF damage estimate calibrated with reconnaissance assessments could be consequential for large-scale
planning.
G-DIF is appealing because it also allows for citizen science, or community-based data collection, to be
used as the field surveys to calibrate top-down assessments. This could be data collected from mobile phones,
as seen in disasters like Haiti (e.g. (Corbane et al., 2012)). Or, community-based disaster preparedness groups
like the Community Forest User Groups in Nepal (Gentle et al., 2020) can provide preparedness training
on how to collect field surveys of damage. In fact, after the 2021 Haiti earthquake, StEER organized teams
in Haiti to take multiple pictures of buildings throughout the affected area, which were then assessed for
damage by remote earthquake engineers. Community-based data would be ideal to use in the week after
an earthquake, when it is unlikely for government or reconnaissance survey teams to be in-country. In this
way, G-DIF is able to combine bottom-up data collection with top-down damage estimates, leading to more
participatory damage estimates.
Where: locations of surveys Finally, the locations of the field surveys from these sources have a direct
influence on the G-DIF results. The performance of G-DIF improves with field surveys that are distributed
throughout the prediction area (Step 3), have nearby separation distances within the range of the expected
spatial correlation of trend residuals (Step 3), and that have representative values of secondary data (Step
4). Many of the above groups have developed strategies for field survey collection, which can be used in
conjunction to gather a set of field surveys that are adequate for developing G-DIF.
The StEER network advocates for a “Hazard Gradient Survey,” a sampling strategy designed to collect
an unbiased estimate of damage across all hazard levels, the hazard being shaking intensity for earthquakes
(Kijewski-Correa et al., 2021). While not demonstrated in this study, we have found that a biased field survey
sample (e.g. when only collapsed buildings are assessed) leads to a biased G-DIF estimate. A “Hazard
Gradient” approach would lead to the unbiased sample necessary for developing G-DIF. However, additional
guidance could be provided on the sample design of the field data collection concerning other sources of
secondary data, such as forecasts or remote sensing-derived.
Practices for strategizing field survey samples in corroboration with alternative damage data have already
occurred after past disasters, usually by government or stakeholder survey teams. For example, after the
Haiti 2010 earthquake, the JRC, the World Bank, and UNOSAT collected data specifically to corroborate the
results from the crowdsourced damage collection (Corbane and Lemoine, 2010; Lemoine et al., 2013). After
Typhoon Haiyan in 2013, REACH, in conjunction with the Shelter Cluster and American Red Cross, orga-
nized their field sample to validate crowdsourced data from Humanitarian OpenStreetMap Team (Westrope
et al., 2014). Finally, after the Italy 2016 earthquake, the local government coordinated their field response
based on Copernicus’s damage grading map (The European Commission, 2017).
While it may not be possible to collect field surveys at an ideal set of locations in the days after an
earthquake, the prediction variance from an initial G-DIF model using early field surveys (for example, from
citizen science groups) can inform where to collect additional surveys.
22
Loos, S., Levitt, J., Tomozawa, K., Baker, J., Lallemant, D. (2022). “Efficacy of damage data integration: a
comparative analysis of four major earthquakes.” ASCE Natural Hazards Review, (in press).
This material may be downloaded for personal use only. Any other use requires prior permission of the American Society of Civil
Engineers.
6 Conclusion
In this study, we evaluate the ability of the Geospatial Data Integration Framework (G-DIF) to generalize,
or adapt to different contexts, new datasets, and realistic field survey scenarios. Specifically, we consider
real damage data from four earthquakes to understand the impacts of differing contexts and available data on
G-DIF’s damage estimate. We evaluate G-DIF in these four earthquakes and account for the uncertainty in
the G-DIF damage estimate by repeatedly building G-DIF with 1000 different field survey samples.
We find that G-DIF is a generalizable framework and predicts damage more accurately than alternative
damage datasets under various scenarios of data availability and with realistic field survey strategies. G-DIF’s
increased accuracy over alternative damage estimates results from the underlying regression kriging model
that calibrates secondary data to the true observations of damage from the ground.
Evaluating individual secondary datasets, we find that the shaking intensity from the Shakemap is a
reasonably effective initial predictor of the trend, even with poorly constrained Shakemaps. Adding a remote
sensing-derived dataset produces more detailed and granular estimates of damage. Though, the added utility
of remote sensing-derived data can vary between places and the source of the data. For example, the
manually-derived remote sensing estimate from Copernicus in Italy resulted in consistently lower errors than
other secondary datasets but the crowdsourced dataset in Haiti had less stable errors. These differences can be
evaluated during the model building process through looking at the coefficients in the trend model. Overall,
G-DIF’s damage estimate is only expected to improve with as remote sensing-derived and forecast-based
methods improve.
The accuracy of G-DIF also strongly depends on the field survey locations. G-DIF shines in comparison
to interpolating the field surveyed damage using Ordinary Kriging when a spatially clustered set of field
surveys has been collected. This means that G-DIF is able to predict damage in realistic post-earthquake
field collection scenarios, when it is difficult to reach multiple places due to building debris or damaged
infrastructure. In the unlikely case where a random, spatially distributed set of field surveys is collected,
G-DIF still performs better than Ordinary Kriging. This conclusion is especially salient given that Ordinary
Kriging will perform even worse at locations outside of the range of spatial correlation of damage, since it
will predict the average damage everywhere. The overall error of G-DIF’s damage estimate when using a
random sample is consistently lower than the damage estimate resulting from the clustered sample. These
differences illuminate the importance of the field surveys for estimating damage over an entire affected
region.
Based on these results, we provide recommendations for collecting field surveys and implementing G-
DIF in future disasters. An effective damage estimate benefits from the model forms selected by the analyst
and the locations of the field surveys. Fortunately, growing experience with this approach indicates how
these issues can be addressed systematically in order to develop confidence in resulting predictions.
By applying G-DIF to multiple case study datasets, we show how this framework can be used by
stakeholders to combine all the damage data that is available into a single, accurate estimate of damage.
Importantly, G-DIF calibrates secondary data from forecasts and remote sensing to the damage seen on the
ground. This means that secondary datasets, which in many cases are derived from global techniques or
models, are amended to more accurately reflect the patterns of damage that are specific to that location
and that earthquake. The necessity of field data for calibration poses opportunities for the engineering
community to strategize where to collect data, so field samples can be used to inform damage estimates
produced from G-DIF. More broadly, G-DIF provides a framework to connect top-down damage estimates,
like those produced with the PAGER system or NASA-JPL/ARIA, with bottom-up data collection, like
crowdsourced estimates of damage. This study shows that G-DIF is a flexible and reliable approach to
produce locally-specific damage estimates after future earthquakes.
23
Loos, S., Levitt, J., Tomozawa, K., Baker, J., Lallemant, D. (2022). “Efficacy of damage data integration: a
comparative analysis of four major earthquakes.” ASCE Natural Hazards Review, (in press).
This material may be downloaded for personal use only. Any other use requires prior permission of the American Society of Civil
Engineers.
7 Data Availability Statement
An interactive code to support the "Recommendations" section of this study are available at https://
sabineloos.github.io/GDIF-Gen/Diagnostics.html and the supporting code generated during this
study are available at the following repository: https://github.com/sabineloos/GDIF-Gen (Loos,
2022). Additional code and data that support the findings shared in this study are available from the
corresponding author upon reasonable request. Field data used during the study were provided by a third
party. Direct requests for these materials may be made to the provider as indicated in the Acknowledgments.
8 Acknowledgments
We thank the Ministry of Public works; the Earthquake Commission and Tonkin + Taylor; the Government
of Nepal and Kathmandu Living Labs; and the European Commission and the USGS for access to the field
data from Haiti, New Zealand, Nepal, and Italy, respectively. Specifically, thanks to Virginie Lacrosse,
Sjoerd van Ballegooy, Sang-Ho Yun, Keiko Saito, David Wald, Paolo Zimmaro in preparing and accessing
these datasets. Thank you to Kishor Jaiswal and Nicole Paul for providing input on the development of the
engineering forecasts used in this study. We also thank three anonymous reviewers who provided valuable
feedback that improved this manuscript and shared code. This work was funded by the Stanford Urban
Resilience Initiative; the John A. Blume Earthquake Engineering Center; the National Science Foundation
Graduate Research Fellowship; and the National Research Foundation, Prime Minister’s Office, Singapore
under the NRF-NRFF2018-06 award.
A Damage Data Sources
A.1 Development of engineering forecasts for each case study
The process of developing the engineering forecast for each case study is similar in that each combines the
spatial distribution of the estimated shaking intensity from the USGS ShakeMap (Worden et al., 2016) with
the building stock exposure and the vulnerability of that exposed building stock. Generally, we aimed to
replicate a model that could be rapidly produced with openly available datasets in each country. However,
due to data limitations, we do not expect these forecasts to be of the same accuracy as those produced by risk
modeling companies or agencies.
For all four case studies, we use the estimated distribution of shaking from the USGS ShakeMap, using
either the macroseismic intensity, peak ground acceleration, or peak spectral acceleration depending on the
intensity measure used in that case study’s vulnerability curve. Specific ShakeMaps used for each case study
are referenced in Table 2.
Exposure and vulnerability data varied by case study. In all cases, we prioritized using vulnerability
and fragility curves that were openly available. In Haiti, most buildings at the time of the earthquake were
unreinforced concrete frames with masonry infill and unreinforced masonry. Because of a lack of census
data, we assume all buildings correspond to the ‘C3’ structure type in PAGER’s collapse fragilities (Jaiswal
et al., 2011), recognizing that these fragilities underestimate the true collapse from the event. In New
Zealand, most residential buildings were light timber framed buildings. Again, we assume all buildings
correspond to the ‘W1’ structure type in PAGER’s collapse fragilities (Jaiswal et al., 2011). In Nepal, we
follow the same process employed in (Loos et al., 2020). Fragility curves come from Nepal’s National
Society of Earthquake Technology (Japan International Cooperation Agency and Ministry of Home Affairs,
His Majesty’s Government of Nepal, 2002). We define exposure from Nepal’s 2011 census and distribute
the exposure according the LandScan 2011 High Resolution Global Population Dataset (Bright et al., 2012).
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Loos, S., Levitt, J., Tomozawa, K., Baker, J., Lallemant, D. (2022). “Efficacy of damage data integration: a
comparative analysis of four major earthquakes.” ASCE Natural Hazards Review, (in press).
This material may be downloaded for personal use only. Any other use requires prior permission of the American Society of Civil
Engineers.
Finally, in Italy we estimate mean damage ratio using vulnerability curves from the Global Earthquake Model
(Martins and Silva, 2020). Here, we acknowledge the building material for each surveyed building and use
that as our exposure data, recognizing that this increases the precision of Italy’s forecast compared to other
locations.
Table 2. Damage data sources
Earthquake Variable description Damage data type Original units Source
Haiti 2010 Shaking Intensity Covariate MMI USGS (United
States Geological
Survey, 2010)
Haiti 2010 site characterization - Vs30
(time-averaged shear-wave
velocity to 30 m depth)
Covariate Vs30 per 30 arcsec
grid
USGS (Allen and
Wald, 2009)
Haiti 2010 % buildings tagged as grade
4 or 5 per grid, crowdsourced
Remote-sensing
based
% UNITAR,
UNOSAT, JRC,
GEO-CAN
(Corbane et al.,
2011)
Haiti 2010 Probability of collapse,
similar to PAGER
Engineering
Forecast
Probability of
collapse
Self developed,
using PAGER
collapse fragilities
(Jaiswal et al.,
2011)
Haiti 2010 Average central damage
factor of buildings per grid,
field assessed
Field survey CDF MTPTC Haiti
(MTPTC, 2010)
New Zealand 2011 Shaking Intensity Covariate MMI USGS (United
States Geological
Survey, 2011)
New Zealand 2011 Site characterization - Vs30
(time-averaged shear-wave
velocity to 30 m depth)
Covariate Vs30 per 100m
grid
Foster and Bradley
(Foster et al., 2019)
New Zealand 2011 Damage Proxy Map Remote-sensing
based
DPM value per
30m grid
NASA JPL-ARIA
(Yun et al., 2015)
New Zealand 2011 Probability of collapse,
similar to PAGER
Engineering
forecast
Probability of
collapse
Self-developed,
using PAGER
collapse fragilities
(Jaiswal et al.,
2011)
Continued on next page
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Loos, S., Levitt, J., Tomozawa, K., Baker, J., Lallemant, D. (2022). “Efficacy of damage data integration: a
comparative analysis of four major earthquakes.” ASCE Natural Hazards Review, (in press).
This material may be downloaded for personal use only. Any other use requires prior permission of the American Society of Civil
Engineers.
Earthquake Variable description Damage data type Original units Source
New Zealand 2011 Building damage ratio, field
assessed
Field survey Ratio Earthquake
Commission
(Tonkin and
Taylor, 2016)
Nepal 2015 Shaking Intensity Covariate MMI USGS (United
States Geological
Survey, 2015)
Nepal 2015 site characterization - Vs30
(time-averaged shear-wave
velocity to 30 m depth)
Covariate Vs30 per 30 arcsec
grid
USGS (Allen and
Wald, 2009)
Nepal 2015 Mean damage ratio per grid Engineering
forecast
Ratio Self developed,
using JICA
fragilities (Japan
International
Cooperation
Agency and
Ministry of Home
Affairs, His
Majesty’s
Government of
Nepal, 2002)
Nepal 2015 Damage Proxy Map Remote-sensing
based
DPM value per
30m grid
NASA JPL-ARIA
(Yun et al., 2015)
Nepal 2015 Average damage grade Field survey EMS Damage
Grade per building
Government of
Nepal
(Government of
Nepal Central
Bureau of
Statistics, 2015)
Italy 2016 Shaking Intensity Covariate MMI USGS (United
States Geological
Survey, 2016)
Italy 2016 Site characterization - Vs30
(time-averaged shear-wave
velocity to 30 m depth)
Covariate Vs30 per 30 arcsec
grid
USGS (Allen and
Wald, 2009)
Italy 2016 Probability of collapse,
similar to PAGER
Engineering
forecast
Mean loss ratio Self developed,
using GEM
vulnerability
curves (Martins
and Silva, 2020;
Martins, 2020)
Continued on next page
26
Loos, S., Levitt, J., Tomozawa, K., Baker, J., Lallemant, D. (2022). “Efficacy of damage data integration: a
comparative analysis of four major earthquakes.” ASCE Natural Hazards Review, (in press).
This material may be downloaded for personal use only. Any other use requires prior permission of the American Society of Civil
Engineers.
Earthquake Variable description Damage data type Original units Source
Italy 2016 Damage Proxy Map Remote-sensing
based
DPM value per
30m grid
NASA JPL-ARIA
(Yun et al., 2015)
Italy 2016 Damage Grade, assessed
from satellite imagery
Remote-sensing
based
EMS Damage
Grade per building
EC-JRC (The
European
Commission,
2017)
Italy 2016 Damage Grade, field
assessed
Field survey EMS Damage
Grade per building
Fiorentino et al.
(Fiorentino et al.,
2018), EC-JRC
(The European
Commission,
2017)
End
B Validation of secondary data
To compare the accuracies of each secondary damage dataset against the field survey data (as shown in
Figure 3), necessary transformations and assumptions were made. This is because the secondary data might
be in different units than the field survey data, as shown in Table 3. In addition, G-DIF’s damage prediction
results in real numbers, which requires binning when the field survey data is measured as a positive integer
(like a damage grade). We outline the procedures we took to compare each form of secondary data to the
full set field validation data in each case study location.
Table 3. Summary of damage estimate translation used for validation for all case study locations.
Datasets include damage information from field surveys, engineering forecasts, and remote sensing-derived
data. Final units are the units of the dataset after preparing for G-DIF. Possible values are the values of the
final units that dataset could take on. For New Zealand, the building damage ratio can take on values of
0-1, though the actual field data was truncated from 0-0.75. Translated? indicates whether the dataset was
translated to compare to the field surveyed data.
Case study Dataset Final units Possible values Translated?
Haiti 2010 Field surveys Collapse rate 0-1 /
GEO-CAN/JRC
assessment
Collapse rate 0-1 No
Engineering
forecast
Collapse probability 0-1 No
New
Zealand
2011
Field surveys Building damage ratio 0-1* /
27
Loos, S., Levitt, J., Tomozawa, K., Baker, J., Lallemant, D. (2022). “Efficacy of damage data integration: a
comparative analysis of four major earthquakes.” ASCE Natural Hazards Review, (in press).
This material may be downloaded for personal use only. Any other use requires prior permission of the American Society of Civil
Engineers.
Engineering
forecast
Collapse probability 0-1 No
Nepal 2015 Field surveys Mean damage grade 1-5 /
Self developed Mean damage ratio 0-1 Yes
Italy 2016 Field surveys Damage grade 0, 1, 2, 3, 4, 5 /
Copernicus as-
sessment
Damage grade 0, 1, 2, 3, 4, 5 No
Engineering
forecast
Loss ratio 0-1 Yes
Generally, there are limitations in comparing field surveys and forecasted damage estimates due to
differences in the damage measured and uncertainties in the exposure and vulnerability models (Silva and
Horspool, 2019). Understanding these limitations, we still aim to compare the forecast to the field surveys
damage to provide a relative benchmark of performance. Because different vulnerability curves were used for
each case study, the resulting engineering forecasts were not always in the same units as the field surveys. For
the Haiti earthquake, no translation was required, because our forecast predicts collapse probability which
is analogous to the field survey units of collapse rate per grid. In New Zealand, the units of the field surveys
and the forecast have the same range of possible values (0-1), so we therefore did not translate the forecast.
However, it is important to note that these are fundamentally different values, since the building damage
ratio is a measure of overall loss per building whereas the collapse probability is a measure of just collapse.
Therefore, the calculated mean squared error for the forecast in New Zealand is likely overestimated. In
Nepal, the forecast predicts the mean damage ratio per grid. To compare to the field surveys, we bin these
damage ratios according to EMS-98’s range of damage ratios per each damage grade (Grünthal, 1998).
Similarly, in Italy, we bin the forecast’s loss ratio per building to each damage grade using the same approach
as in Nepal.
The remote sensing derived proxies of damage, conversely, did not need to be translated to compare
with the field surveyed data. In Haiti, the GEO-CAN/JRC assessment derived from crowdsourcing assessed
collapse rate per grid, same as the gridded field surveys. In Italy, Copernicus’s damage assessment was also
in the EMS-98 damage scale and could be directly compared to the field surveys. NASAs damage proxy
map for New Zealand, Nepal, and Italy were omitted, as the assessments of damage are unitless and cannot
be translated to the field surveyed damage without a model (like G-DIF).
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