1 European Laboratory for Structural Assessment Unit, Joint Research CentreInstitute for the Protection and Security of the Citizen, Ispra, Italy
2 External Security Unit, Joint Research CentreInstitute for the Protection and Security of the Citizen, Ispra, Italy
3 Inland Marine Waters Unit, Joint Research CentreInstitute for Environment and Sustainability, Commission of the European Communities, Ispra, Italy
Reprint requests to Eugenio G. Gutiérrez, Joint Research CentreInstitute for the Protection and Security of the Citizen, European Laboratory for Structural Assessment, Via E. Fermi, 1, TP 480 Ispra 21020 (VA), Italy (e-mail: eugenio.gutierrez{at}jrc.it).
Received for publication July 8, 2004. Accepted for publication February 16, 2005.
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ABSTRACT |
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demography; mortality; multivariate analysis; natural disasters
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INTRODUCTION |
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New data retrieval and satellite remote sensing technologies are opening new avenues that may overcome the deficiencies mentioned above. Recently, with the development of geographic information systems, earthquake engineers, seismologists, and emergency relief agencies have collaborated to develop information technology systems to help decision makers rapidly assess potential damage and mortality in the immediate aftermath of an earthquake (17
). Such models have been calibrated by using detailed classification of buildings, soil types, and other key components from past, well-documented earthquakes. Although these models are continuously improving, the sophistication required, in terms of both the quality of input data and modeling resources, limits implementation of these techniques to developing countries. This limitation is primarily the result of the incompleteness of pertinent parametric data in some of the affected areas.
In this paper, we start by first identifying fatal earthquakes (those that resulted in deaths) and their location (events); we then report on other relevant physical and environmental information concerning each event, such as the magnitude of the earthquake according to the Richter scale and where on the earth's crust the earthquake took place. The second and more challenging phase is to compute the affected population and how it is distributed with respect to where the earthquake took place. Hence, from an epidemiologic point of view, earthquakes can be seen as a disease whose mortality can be linked to a number of contributing factors.
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MATERIALS AND METHODS |
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Each event is tagged to the relevant, quantifiable factors that may have a bearing on mortality. The worldwide earthquake casualty set was compiled from fatality and injury reports in the US Geological Survey (USGS) (8) database of significant earthquakes (those that either exceeded a magnitude of 6.5 on the Richter scale or caused fatalities, injuries, or substantial damage) dating back to 1980. The database also contains other attendant seismologic factors pertaining to the location and depth of the earthquake, discussed in more detail below.
We divided the data set into two classes. The first includes statistically derived demographic data. The second includes parameters that are physical or pertain to physically related environmental factors; here, we incorporate reported fatalities and injuries resulting from the earthquake.
Demarcation of the affected area
It is evident that the earthquake mortality analysis presented herein is inherently dependent on identification and demarcation of the affected area, for it establishes not only the boundaries of the affected population but also the values of the otherphysical and environmentalparameters to which the population was exposed during the earthquake. The toponomy types provided in the USGS data set are not homogeneous. Sometimes, the name of an affected town or city is given; in other instances, only the district or county is provided. To homogenize the definition of the affected area (and hence its population data sets), we demarcated the affected areas with circles, as described in the following three paragraphs.
Locate the affected area.
We used a geographic information system to locate a town or administrative unit provided by USGS. If the toponomy given by the USGS database could not be located, no circle was digitized and the event was discarded.
Demarcate the affected area with circles.
For a city, we ensured that most of the high-density areas were contained in the circle. We decided that suburban regions should be included unless they had a radial orientation, which would imply inclusion of a large portion of a rural area. The following information pertains to an administrative unit: 1) in a sparsely populated unit, we drew the circle in such a way that it covered most of the administrative unit and coincided as much as possible with its borders; 2) in a unit with population centers (towns or cities with higher population densities), we drew the circle so that it contained the major population centers.
Assign attributes.
For each circle, we assigned an identification tag along with specific demographic, physical, and environmental attributes. These attributes were compiled by mapping the circle to the other information layers contained in the geographic information system database (discussed in the sections below).
We provide a representative example from the El Quindío earthquake of January 25, 1999, in Colombia. For this earthquake, the USGS database reports 1,185 persons killed and more than 700 missing and presumed killed. In the city of Armenia, 907 fatalities are reported, with 60 percent of the buildings destroyed. However, whereas similar percentages of structural damage were reported in the neighboring towns of Calarcá and Pereira, no fatality figures are given for either. It is highly unlikely that such structural damage did not result in a high number of fatalities even though no fatalities are explicitly ascribed. In the case of the El Quindío earthquake, the affected area is taken as the large circle encompassing the urban centers mentioned in the USGS report, as shown in Web figure 1. (This figure is posted on the Journal's website (http://aje.oupjournals.org/).)
Reported casualties: fatalities and injuries
After examining the USGS database, we compiled a total of 366 earthquake events (those in which at least one fatality is reported and the geographic region is well defined) for our analysis. Although the sources from which the USGS draws its casualty reports are not provided, such figures are usually obtained from local governmental authorities as well as international nongovernmental aid agencies. Whereas casualty reporting in the developed world is means-supported by governmental and financial institutions for legal and financial claims reasons, it is expected that casualty reporting in the developing world is inaccurate. In these countries, the lack of a strong governmental social infrastructure where, quite often, population censuses are lacking and emergency relief mechanisms cannot always cope with large-scale emergencies, casualty reporting can at best be described as estimated.
Demographic data
Having established the locations where fatalities are reported for each significant earthquake, we computed the demographic data from a raster-based global population set (9) assembled on a geographic information system. The worldwide data set comprises geographically referenced digital elements, referred to as pixels; each spans an area of 1 square kilometer of the earth's surface. A synopsis of the demographic parameters is given in table 1. The population density (inhabitants per square kilometer) is automatically defined by the value of the population for each pixel. The ensembles of the pixels in the demarcated area constitute the affected population.
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Physical and environmental data types
Physical parameters (table 2) such as the earthquake's magnitude and the depth of the hypocenter from the epicenter (the point on the earth's surface vertically above the hypocenter) are intrinsic properties of the earthquake event that can be obtained from the USGS, which also provides their location with geographic coordinates. The position of the earthquake itself is therefore included as an information layer on the geographic information system. The type of fault that caused the earthquake was not considered in the analysis, nor was the distance of the population to major tectonic fault lines.
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There has been much speculation as to the time that an earthquake hits a population. There would seem to be compelling reasons to include this information in our analysis, so the local time of day when the event occurs is taken into account with a simple parameter, activity index, ranging from 0 (sleep) to 2 (active or working).
In the field of earthquake engineering (a branch of engineering that studies the behavior of structures and buildings subject to seismic action), certain parameters play a key role in the capacity of an earthquake to cause destruction. Of these parameters, the deep geology and the stratigraphic profiles that determine the local ground conditions influence the attenuation or amplification of seismic waves (11) and are considered of fundamental importance in structural seismic design (12
). We are not aware of a worldwide raster data set for the aforesaid parameters; instead, the topsoil texture (13
) is used as a surrogate, which may be indicative of the substrate parent rock that generated it.
Another key factor is the quality of construction, that is, the architectural, structural, and material qualities that enable a structure to withstand or dissipate the input energy from the earthquake without collapse. In developed countries, the quality of housing in earthquake-prone areas has improved considerably, and the use of structural steel and reinforced concrete has, in general, improved the capacity of most structures to withstand earthquakes. In the developing world, the quality of the materials and workmanship and the more predominant use of brick masonry and adobe as the main load-carrying elements make the housing stock more vulnerable to earthquakes. We are not aware of a worldwide database of housing stock that may be used to assess structural build quality; hence, we used the gross domestic product (GDP) per capita (14) of the affected country as an indicator of the overall quality of construction and as a surrogate for vulnerability (15
) of the affected population to natural disasters.
Mortality
Mortality is obtained by dividing the declared number of fatalities by the total population in the demarcated area. The error sources in estimating the total population inevitably result in over- and underestimation of mortality. Conversely, over- and underestimation of fatalities will have a similar effect. For example, the May 1995 Sakhalin Island 7.1-magnitude earthquake in Russia resulted in 1,998 reported fatalities, with a circumscribed population (according to our population data set and the demarcated area definition) of only 850. In spite of this incongruence, we have no systematic reason to exclude such cases because it is to be presumed that examples of overreporting of fatalities may statistically balance other examples in which the population is overestimated. Other errors concern the impact of the evolution of demographic trends over time that is not taken into account by the global population data set. How good can any mortality prediction be when confronted with such calibration data? We can claim to study worldwide earthquake mortality in statistical terms only.
Analysis methods
The analysis assumes that some of the parametric data (particularly those corresponding to demographic and casualty reporting) are contaminated by errors. From the unfiltered data, it is not possible to discern whether the correlations of the presumed casualty-inducing parameters with mortality are low because they have no influence or simply because they represent the complexity of some nonlinear dependence of mortality on the parameters selected. Moreover, a low correlation value is not a sign of noncausality if the distribution is not Gaussian. We conjecture that if a nonlinear correlation exists, filtering will reduce noise and highlight the relation between parameters. To do so, we screened the data with a moving window averaging filter. We examined both the value and the rate of convergence of the correlation factors as a function of the filter bandwidth. Having first performed a null-hypothesis test on randomly generated data, we consider that for the highest filter value (32-point average), only those correlations higher than 0.75 can be deemed significant. The aim of the analysis, then, is to monitor the parametric correlations of mortality as a function of the filter bandwidth. We claim that, by so doing, we can construct a ranking of the sensitivity of mortality to the parameters selected for this study.
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RESULTS |
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The absolute values of the correlations of magnitude and depth increase (respectively, positive and negative, i.e., high magnitude, shallow earthquakes increase mortality) with increasing filter size. Likewise, the trend in the correlation coefficient of the distance from the demarcated population to the epicenter is negative (short distances). Most of the other physical parameters seem to play a negligible role, the only exception being that flat terrain is moderately positively correlated with mortality. The correlations of soil texture with mortality are not particularly strong, indicating that such a parameter cannot be used as a surrogate of soil type as intended in earthquake engineering. Likewise, it may seem surprising that the correlation of seismic hazard with mortality is rather low, but, because most of these events take place in hazardous zones, the comparatively small variations in hazard may be hidden by the conditional dependence on other factors. The activity coefficient does not seem to play a significant role either, indicating perhaps that mortality can be equally high when people are congregated in buildings either when asleep or during working hours.
The correlation trends with the demographic data become more strongly negative (i.e., nonurban) with filter size, particularly regarding maximum population density; the mean, mean (standard deviation) density; and Xm. The increased negative coefficients with total population cannot be trivially explained by the fact that mortality is directly derived from the ratio of fatalities and total population: from the available demographic data at our disposal, there is a clear trend that indicates higher mortality in less populated zones. For example, the 2001 Bhuj, northwest India, earthquake was a relatively low mortality event even though it resulted in a high fatality count (above 20,000) where the circumscribed analysis area encompassed a high total population (more than 40 million).
Conversely, the December 2003 earthquake in Bam in southeast Iran (not included in this data set) is an example of a high-mortality event, for it is believed to have caused more than 30,000 fatalities in a population of less than 200,000 within a 50-km radius of the epicenter. The latter is representative of an earthquake occurring in sparsely populated semirural areas with no large, highly developed, urban centers. These findings are corroborated by the fact that Xm (which tends to highlight built-up urban areas surrounded by sparsely populated zones) is also highly negative.
Also of great relevance, from a humanitarian relief point of view, is the increasing negative correlation of GDP per capita with mortality. The initial correlation for the unfiltered set is 0.25, whereas, for the 32-point average, it is 0.83. However, even though GDP values vary considerably at both ends of the mortality spectrum for earthquakes of similar mortality rank, on average it was observed that the high end of the mortality range is populated with lower GDP values. Thus, conditional parametric sensitivity implies that, for all other parameters being equal, we expect a higher mortality if the GDP of the affected population is low, which is as expected in poor rural communities where the structural quality of buildings is lowest.
We now turn our attention to high-fatality events (i.e., those for which more than 500 fatalities are reported); of these, 33 events (as shown in figure 2) were compiled from the complete 366-event ensemble. It is important to note that the average magnitude for the high-fatality earthquake set was just higher than 7. For ease of identification, the sequence number now refers to mortality rank.
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The number of fatalities predicted for the 1988 Armenia earthquake (figure 2, #31) was 1,500, whereas 25,000 fatalities were reported. Although the prediction for the 1988 west Iran earthquake (#28) was of the order of 10,000 and the reported value was 45,000, we propose that, in terms of early warning, this would still be a valid reason to set up the appropriate emergency procedures. However, the 2001 Bhuj (#7) and 1999 Turkey (#29) fatality predictions were more accurate. The remaining fatality predictions were consistent with the reported values. Finally, the cumulative predicted fatality count for the 33-event ensemble was 130,000, whereas the reported one was
182,000. Considering that the cumulative fatality toll for the complete 366-event ensemble was
190,000 implies that the 33-event high-fatality ensemble accounted for nearly 95 percent of the reported death toll from 1980 until 2001.
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DISCUSSION |
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From our analysis, a pattern emerges of highly vulnerable rural and semirural areas with a poorly built environment. In fact, of the 50 highest mortality ratios, two thirds are for populations with fewer than 500,000 inhabitants. However, in terms of total fatalities, the worst earthquakes vary over a wide mortality range; the 2001 Bhuj and the 2003 Bam earthquakes are contrasting examples. Even though the worldwide postearthquake mortality prediction correlation for a selected set of high-fatality earthquakes is about 0.93, commission (false high fatality alerts) and omission (low numbers of fatalities predicted for high-fatality events) errors occur. We claim that the accuracy of the method improves when the causal correlations between parametric input data and mortality are sufficiently strong, and that, when used with more accurate input parameter sets (available for the developed world), the accuracy will improve further. However, as yet, this is not a truly blind prediction method because the demarcated area considered in the prediction was known beforehand.
The damaging effects of earthquakes are not limited to the immediate morbidity and mortality resulting from the structural collapse of buildings. The sequelae of earthquakes may result in other pathologies linked to the breakdown of essential social services, such as sanitation and the health care infrastructure, as well as concomitant factors from chronic diseases such as cardiovascular disorders (1921
). This study should complement other epidemiologic earthquake mortality studies by providing an approximate basis for assessing worldwide earthquake mortality and how regional differences may attenuate or aggravate the attendant mortality.
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ACKNOWLEDGMENTS |
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References |
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