2
Current Status of At-Risk Subnational Population Estimation

The principal goal of this chapter is to address the first task of the committee, which is to “assess the strengths and weaknesses of existing data, methods (e.g., gaps in spatial and thematic coverage, counting individuals, proxy measures such as those derivable from Earth observations), and tools for estimating population.” Because the committee was charged with the overall task of better identifying populations at risk—groups that are susceptible to the impact of natural or human-induced disasters—we recognize that there are three critical elements of the data, each of which is a scale issue: (1) spatial scale (how far below the national level can estimates be derived?); (2) temporal scale (for how recent a time period can estimates be made?); and (3) social “scale” (how detailed are the available population characteristics?).

A close corollary of the scale issues is the question of accessibility of the data. Data might be “available” in the sense that they have been collected, but access to them may be highly restricted and this will limit their usefulness to the humanitarian relief community. Each of these scale issues, along with the availability of existing data, is influenced by the trade-off between costs and “errors.” The finer the scale at which data are collected, the costlier the information will be, and making these more detailed data available to users will be proportionally more expensive.

As a preface to assessment of the current state of the art, the committee notes that input was sought from a range of individuals, familiar with planning and delivery of humanitarian relief, regarding the kinds of data that would be most useful to them. These individuals expressed complete agreement that very recent local data are extremely useful (without agreement,



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Tools and Methods for Estimating Populations at Risk from Natural Disasters and Complex Humanitarian Crises 2 Current Status of At-Risk Subnational Population Estimation The principal goal of this chapter is to address the first task of the committee, which is to “assess the strengths and weaknesses of existing data, methods (e.g., gaps in spatial and thematic coverage, counting individuals, proxy measures such as those derivable from Earth observations), and tools for estimating population.” Because the committee was charged with the overall task of better identifying populations at risk—groups that are susceptible to the impact of natural or human-induced disasters—we recognize that there are three critical elements of the data, each of which is a scale issue: (1) spatial scale (how far below the national level can estimates be derived?); (2) temporal scale (for how recent a time period can estimates be made?); and (3) social “scale” (how detailed are the available population characteristics?). A close corollary of the scale issues is the question of accessibility of the data. Data might be “available” in the sense that they have been collected, but access to them may be highly restricted and this will limit their usefulness to the humanitarian relief community. Each of these scale issues, along with the availability of existing data, is influenced by the trade-off between costs and “errors.” The finer the scale at which data are collected, the costlier the information will be, and making these more detailed data available to users will be proportionally more expensive. As a preface to assessment of the current state of the art, the committee notes that input was sought from a range of individuals, familiar with planning and delivery of humanitarian relief, regarding the kinds of data that would be most useful to them. These individuals expressed complete agreement that very recent local data are extremely useful (without agreement,

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Tools and Methods for Estimating Populations at Risk from Natural Disasters and Complex Humanitarian Crises however, on a strict definition of “recent” nor on the spatial scale at which data become “local”), whereas less agreement was evident about the kinds of population characteristics that data users routinely employ. Distributions of the population by age and gender were most often mentioned because women, children, and the elderly tend to have higher levels of vulnerability in almost any emergency situation. In some parts of the world, relief agencies are clearly aided by knowing the distribution of the population by characteristics such as income levels, housing, religion, race or ethnicity, and language. In culturally homogeneous areas, especially rural regions, some of these characteristics may be common knowledge and sophisticated data collection schemes are not necessary, but in urban areas and other places experiencing in-migration, it may be important to know the relative distribution of various cultural groups. In its discussion of the social scale of available data, the committee includes age, gender, and socioeconomic and cultural characteristics, with the caveat that not every emergency may demand these characteristics and not every relief agency may use them. This input to the committee’s report is consistent in all respects with the Hyogo Framework of Action for 2005-2015, which was adopted by the international community at the United Nations World Conference on Disaster Reduction held in Hyogo, Japan, in January 2005, shortly before this committee began its work. Particularly noteworthy are the following elements, which the Hyogo Framework (UNISDR, 2005) suggests should be incorporated into all disaster reduction planning: (1) A gender perspective should be integrated into all disaster risk management policies, plans, and decision-making processes, including those related to risk assessment, warning, information management, and education and training. (2) Cultural diversity, age, and vulnerable groups should be taken into account when planning for disaster risk reduction, as appropriate. (3) Systems of indicators of disaster risk and vulnerability at national and subnational scales should be developed that will enable decision makers to assess the impact of disasters on social, economic, and environmental conditions and disseminate the results to decision makers, the public, and populations at risk. Although tasked with evaluating both data and methods, the committee’s view is that methods themselves are likely less problematic than the data to which estimation techniques are applied, especially in those countries that are data-poor. The committee discusses the use of censuses, field and weighted population sample surveys, and remotely sensed imagery, as well as spatial modeling techniques designed to overcome deficiencies in the extant data sources. A key element in modeling and estimation techniques is the emphasis on spatially explicit demographic data—combining demographic characteristics of the population with the georeferenced location of people according to those characteristics. “Georeferenced” means a location in terms of an address or latitude-longitude, not just a place or

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Tools and Methods for Estimating Populations at Risk from Natural Disasters and Complex Humanitarian Crises regional name. The committee emphasizes that its focus is on pre-event population estimation—having knowledge about who is likely to be at risk when an emergency strikes, so that the size and scope of the response can be estimated properly and mobilized. However, the collection of data in the post-event environment is also discussed, especially in terms of its reliance on pre-event estimations of the population at risk. GAPS IN SPATIAL AND TEMPORAL COVERAGE The Ideal Census Database for Estimating Populations at Risk The ideal population database at the subnational level would probably be a population register, with data recorded for every person with respect to residence, place of employment, age, gender, and other relevant sociocultural characteristics, with the requirement that every person has to report each change in status and location. However, the cost and intrusiveness of such a scheme means that it is presently impracticable in all but a few countries. Data from the United Nations Statistics Division (UNSD) show that scarcely more than 70 million people worldwide (about 1 percent of the world’s population) live in a country with a population register, and all of them are in Europe (UNSD, 2005). The closest database that most countries come to this kind of register would be administrative sources for the purpose of voting, taxes, or driver’s licenses, which record age, gender, and residence and are updated routinely. However, such registers generally exclude children and may also exclude the most vulnerable individuals in a population because they do not drive, pay taxes, vote, or otherwise have need of a formal identification card. Population registers are maintained at the local level, with events being registered by the municipal authorities. From this point the data may be relayed up to regional and national levels, creating a central population register. Data could, of course, remain at the local level and be shared at higher administrative levels only as needed. This would be one of the “bottom-up” approaches that international organizations such as the Asian Disaster Preparedness Center (ADPC) suggest be combined with the more traditional “top-down” approaches to data collection and analysis (ADPC, 2006). The usefulness of local data depends, however, on the ability of relief organizations to access and integrate those data into standard methods of analysis. Thus, an integration of top-down standards and bottom-up data collection would probably yield the most reliable and useful type of population register. Until resources become available to generate ideal population databases in all countries, the best working set of data by which to estimate populations at risk almost certainly comes close to what is available for countries

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Tools and Methods for Estimating Populations at Risk from Natural Disasters and Complex Humanitarian Crises such as Mexico, the United Kingdom, Canada, South Africa, and the United States—census data for two or more time periods at the equivalent of the subcounty level (e.g., census tract, or preferably census block, in the United States and Canada; area geográfica estadística basica (AGEB) in Mexico; enumeration area in the United Kingdom), along with digital boundary files (e.g., Shapefiles) that cover that level of geography and to which the data can then be georeferenced. Preferable in almost every situation is to have data at the finest possible spatial resolution, especially when dealing with emergency situations. In general, it is easier to aggregate detailed data to coarser spatial resolution than it is to parse aggregated data into finer units. In an emergency situation, in particular, detailed data may help identify populations that are especially vulnerable and are thus likely to be in the greatest need of assistance. Censuses measure people at their place of residence (the “nighttime” population), not where they work or attend school. If emergencies occur during a time period when people are not at or near home (typically the daylight hours), then estimates of residential population are apt to overestimate the population at risk in some places (by inferring that people are at home when they are really at work) and underestimate it elsewhere (where people work, but do not live). Partial compensation for these daytime and nighttime differences is possible in countries such as the United States where an economic census (a census of businesses, rather than households) or the Place of Work data set produced by the Population Division of the U.S. Census Bureau is available. These data can be used to create at least rough estimates of daytime populations at the local level. In theory, every census has the potential to become this kind of versatile resource because each interviewed household should have an address associated with it, and thus the data can be aggregated (anonymously) to local administrative boundaries, although the committee notes that sample size issues can make this exercise challenging. The existence of paper maps for such boundaries means that digital maps can be produced, as long as the paper maps (or other legal description of boundaries that can be produced in a map) are made available to someone with the software and expertise to create digital maps. The committee notes that the spatial precision of maps rendered from imprecise paper maps will retain those aspects of impression in their digital forms. Producing digital maps is not an insurmountable issue for any place just as long as some type of hard-copy enumeration unit map exists. Once a digital map is made, it does not need to be remade, only updated as boundaries change. The utility of existing census data can be seen in Figure 2.1, which summarizes and updates UN data on the recency of national censuses throughout the world. Since 2000, 85 percent of the world’s population has been enumerated in a census or population register. To be sure, this

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Tools and Methods for Estimating Populations at Risk from Natural Disasters and Complex Humanitarian Crises FIGURE 2.1 Censuses by country as of the year 2005 based on data back to the year 2000. Most countries have taken a census in 2000 or more recently. Mozambique, shown in this figure as lacking a recent census, had conducted a full census in 1997 and has made these georeferenced data available digitally. Mozambique will conduct its decennial census in 2007 (see also Chapter 5). Thus, “recent” as a qualifier to the existence of reliable census data in a country must be evaluated on a country-by-country basis. SOURCE: Courtesy of and adapted and updated from United Nations Statistics Division (2005).

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Tools and Methods for Estimating Populations at Risk from Natural Disasters and Complex Humanitarian Crises still excludes nearly 1 billion people, about half of whom are in Asia and half in Africa. The latter region is particularly underrepresented in census counts, with scarcely half of the population having been enumerated in a recent census. A positive development in terms of maintaining current data is the increasing (albeit still relatively limited) use of continuous measurement programs, such as the American Community Survey in the United States, to supplement the regular (typically decennial) censuses. Peru is among the first developing countries to begin the implementation of such a program (Peru Instituto Nacional de Estadistica e Informatica, 2005). For the best-case scenario, the assumption is made that the information obtained in the census includes those attributes of people, households, and housing units that will be most useful for planning purposes. Such censuses would have followed the recommendations and guidelines on census content set forth by the United Nations and available at http://unstats.un.org/unsd/publication/SeriesM/SeriesM_67rev1E.pdf. At present, these guidelines are silent on overall principles of census taking, and the recommendations on subnational data collection are weak. Furthermore, there are no guidelines whatsoever for spatial data collection and dissemination. What would an ideal set of guidelines include? The population characteristics or attributes that may be especially useful in humanitarian relief situations include age and gender, household structure, religion and/or race or ethnicity (in diverse populations), levels of education, categories of economic activity, health status and skills, and characteristics of the housing unit (Box 2.1). However, in addition to the importance of the categories of each variable, the cross-tabulation of different variables is also significant. What is the age structure of the population by race or ethnicity? What is the household structure by religion or by educational attainment? These kinds of details help planners evaluate levels of vulnerability. Detailed tables could be assembled at a fine resolution in advance by statistical agencies, but this rarely occurs. Instead, such detailed tables are normally requested based on specific planning or research needs for particular projects or in response to some disaster situation. To develop such detailed cross-tabulations requires access to individual census records, or census microdata, as these resources are called. The principal drawback to micro-level data is the potential for violations of confidentiality. Privacy is a major concern with the use of any data. In the United States, aggregated census data are sometimes altered or even suppressed in order to maintain confidentiality (Abowd and Lane, 2004). One common way in which confidentiality is preserved when microdata are made available to the public is to strip each record of its specific address and assign it instead to an administrative unit that includes enough people so that no single individual can be identified on the basis of the

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Tools and Methods for Estimating Populations at Risk from Natural Disasters and Complex Humanitarian Crises BOX 2.1 The Gold Standard for Census Data That Permit an Assessment of Subnational Populations at Risk Regular censuses of population and housing with data aggregated at subnational geographic units at a geographic scale equivalent to the census tract/ or enumeration area or even block level Current and accurate digital boundary maps of the subnational units for which data are aggregated and georeferenced Detailed content of census questions, including age, gender, race or ethnicity, religion, education, economic attributes, household structure, health status, skills, and housing characteristics Availability of census microdata for special analyses at local geographies on an as-needed basis data included in the census. In the United States these are called Public Use Microdata Areas (PUMA), and depending on population size, they encompass several census tracts or several counties in less-populated areas of the country. Further complicating the situation is that PUMA boundaries do not coincide with the administrative units for which other information is collected, thus making it difficult to provide an integrated database of information that would be most useful in an emergency situation. Planning for emergencies, however, does not necessarily require that geographically precise microdata be publicly available, only that someone at the national statistical agency has the background, training, and authority to conduct analyses when requested. Of particular importance is that maps are available, and that population data are integrated into the maps. To date, most census bureaus or national statistical offices in which these confidential data reside are ill-equipped to produce on-demand estimates of census variables by hazard-specific geographic boundaries. Reality Compared with the Gold Standard Every country can be evaluated against the gold standard for census data (Box 2.1), and the results of that global comparison provide an estimate of the overall gap that needs to be bridged with regard to estimating subnational populations at risk. These gaps are roughly categorized into (1) lack of recent census or population data, (2) deficiencies in the existing census data, and (3) lack of or deficiencies in maps.

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Tools and Methods for Estimating Populations at Risk from Natural Disasters and Complex Humanitarian Crises Lack of Census or Population Data Conducting a census is, of course, an extremely expensive operation, and for this reason the availability of censuses at the appropriate level of detail is difficult in less-developed countries, especially Africa and Asia as noted above. However, an equally important issue is that in many less-rich countries, census data are collected but are not processed quickly or not made available for use. For the billion people not enumerated in a recent census, estimates of their number and location must be made by some modeling technique. These estimates require at least one reasonable source of information to fill in the gap left by the lack of a census. Two basic sources of such data exist—surveys and administrative data—as well as one important ancillary source, remotely sensed imagery. Survey Data. In addition to administrative registers and population censuses, surveys are an essential, but often underestimated, source of information for vulnerability assessment and disaster response. Given the absence of recent censuses and the rudimentary status of administrative data in many developing countries, survey data are generally the most important source of information. The wide range of local, national, and international actors involved in development and humanitarian activities carry out surveys on a regular basis to scope interventions for health, nutrition, access to water, housing, eradication of poverty, and so forth. These surveys all produce baseline population data and indicators relevant for the risk and vulnerability assessment. Such surveys are relevant even in countries where regular censuses are carried out, given the inherent aspects of census data, including their decennial frequency, potential difficulties of access, slowness in processing, and weighty administrative procedures. Publishing and centrally archiving population surveys and their indicators are essential steps in ensuring their quick availability in periods of looming or actual disaster. Sample survey data are important sources of information, especially with respect to detailed population characteristics, even in places where censuses are undertaken. The obvious advantage of the sample survey is that it is less costly to undertake a sample that includes only a small fraction of the total population. The disadvantage, of course, is the loss of geographic detail. However, it may be possible to use a variety of spatial modeling techniques to make estimates for places that were not surveyed based on the patterns found in those places that were surveyed. This estimation is made more or less difficult by the type of sampling strategy employed. The most common survey design is a multistage cluster probability sample, often stratified by urban and rural residence, which is based on sampling from defined administrative units using estimates of the enumerated population in each area. Typically, sample sizes within administrative

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Tools and Methods for Estimating Populations at Risk from Natural Disasters and Complex Humanitarian Crises units are based on probabilities proportional to the number of people (a self-weighting sample within each stratum), so the more populated areas are overrepresented. This approach is appropriate for obtaining national-level data but may lead to large geographic areas, especially sparsely populated rural areas, that are excluded from the sample. Another concern with sample surveys is the size of the sample itself. The sample size must be large enough within the subnational levels sampled for estimates of subnational populations to be made with a reasonably small margin of error (with the caveat that “small” has been left undefined). Furthermore, urban areas, often oversampled already (see above), may require even greater oversampling to obtain good estimates of at-risk populations living in marginal locations such as informal settlements and unofficial housing. The value of sample surveys is enhanced if the survey data can be combined with census data in those places where the survey was conducted. In developing nations where higher fractions of the population at risk of natural or human-induced disasters are especially vulnerable to the long-term disruption from such events, the scientific sample surveys most likely to be available are either the Demographic and Health Surveys (DHSs) conducted by Macro International Inc., an Opinion Research Corporation company (ORC Macro), with funding from the U.S. Agency for International Development (USAID), or the United Nations Children’s Fund (UNICEF) Multiple Indicator Cluster Surveys (MICS). These surveys almost always include at least one subnational administrative level and thus provide an important basis for modeling subnational populations. The list of currently available and planned surveys can be found at http://www.measuredhs.com and http://www.childinfo.org/MICS2/natlMICSrepz/MICSnatrep.htm, respectively. Household surveys have also been conducted over the past several years by the World Health Organization (WHO). These surveys are undertaken primarily in developing countries and may overlap coverage with the DHS. Information about the World Health Surveys can be found at http://www.who.int/healthinfo/survey/whsresults/en/index.html. The World Bank, in conjunction with the United Nations Development Programme, has sponsored a series of household surveys aimed at evaluating levels of poverty in developing countries. The Living Standards Measurement Surveys were conducted especially during the 1990s, but some others are more recent. A complete listing is available at http://www.worldbank.org/LSMS/guide/select.html. The World Bank has supported the creation of metadata for these and other household surveys (including the DHS, MICS, and WHO surveys), and this information has now been compiled by the International Household Survey Network and can be accessed at

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Tools and Methods for Estimating Populations at Risk from Natural Disasters and Complex Humanitarian Crises http://www.internationalsurveynetwork.org/home/?lvl1=activities&lvl2=catalog&lvl3=surveys/. The metadata include contact information for obtaining access to the micro-level data with identifiers removed. Rapid-assessment surveys conducted just after an emergency also fill in some of the data gaps by obtaining expert judgment estimates from well-informed local leaders—community leaders and planners or local nongovernmental organization (NGO) or national statistical office (NSO) heads. While the quality of these data is typically unknown Noji (2005) suggests that such data are preferable to no data at all. The committee would qualify this statement by indicating that consistent collection and updating of census and survey data would reduce reliance on data that may be of questionable or unknown reliability. Administrative Data. Administrative data refer to data collected by the government or other large entity for purposes other than demographic uses. These data might be land parcel data used for taxation and land tenure purposes or utility data collected for billing purposes. Committee members recognize, however, that areas lacking censuses are equally likely to lack these sources of information. When available, administrative records typically provide data on the number of households in a given region, and in combination with other estimates of household size, this information can be used to generate total population estimates for subnational areas. However, these sources of population estimates rarely provide data on the characteristics of the population or households. Quite often, administrative data in developing countries are not automated and thus not very practical for creating and updating statistical profiles. Remotely Sensed Data. Remotely sensed imagery includes data acquired from sensors positioned on satellites and other airborne vehicles and has generally been collected for the purpose of Earth science observation and monitoring. The total costs of undertaking a mission to acquire satellite data would far exceed the expenditures of undertaking frequent national censuses; in contrast, the marginal cost of acquiring already-collected imagery is fairly inexpensive at moderate resolution. The image itself is composed of a two-dimensional array of pixels from which radiant energy has been captured for an area on the ground that is equal to the spatial resolution of the image. The information recorded for each image depends on the particular sensor, but the brightness within a given band is assigned a digital number. The combination of digital numbers representing relative reflectance across the different bands of light yields the spectral signature of that pixel. Particular types of land cover (e.g., vegetation, soil, water, impervious surface) tend to have unique spectral signatures. The more bands that a sensor has, the more detailed is the land cover classification.

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Tools and Methods for Estimating Populations at Risk from Natural Disasters and Complex Humanitarian Crises Determining any personal information about individuals directly from remotely sensed imagery is nearly impossible, although we may indirectly infer the social status of residents in an area by interpreting characteristics such as building size and shape and amenities such as swimming pools and vegetation. These inferences build on the fact that humans generally transform their physical environment in ways that provide clues about their numbers, location, and overall well-being. In rural areas, people clear forests and plow fields to plant crops; they dam rivers to create reservoirs of water, build roads, and alter the environment in numerous other ways. These activities yield clear, aerially extensive signs of the human alteration of the physical environment. This transformation is even more intense in urban areas where people put infrastructure underground and then cover the surface with an almost endless variety of buildings and transportation networks, interspersed with bits of nature (parks) in the midst of vast areas of human-built impervious surfaces (de Sherbinin et al., 2002). In all cases, people build dwellings that can be counted and identified from satellite imagery pre- and post-disaster. Because of the direct and deliberate impact that humans have on the environment, the transformed environment may be used as an index to the population living there. Depending on the spatial, spectral, and temporal resolution of the data, it is possible to estimate the extent of human activity in an area. For example, nighttime light imagery has been used extensively to estimate the size and extent of urban populations (Balk et al., 2005a), and these images are probably among the more widely recognizable of all satellite images (Figure 2.2). These data are discussed later in the chapter, but it is useful at this point to note that in spite of the imagery’s popularity, a number of limitations to the use of these data exist (Elvidge, 2006). In highly developed areas, the lights tend to splash into unpopulated areas leading to an overestimation of population, whereas in less developed areas the lack of electricity leads to an underestimation of the population. Further, no intra-urban distinctions are possible, limiting the use of these data to the detection of entire settlements. Nonetheless, the usefulness of nighttime lights for social science purposes has led to proposals for launching satellites with more sophisticated light sensors that would at least diminish some of these problems and make the data even more useful for subnational population estimation purposes (Elvidge et al., 2007). Deficiencies in Existing Census Data Census information, such as tables on population and housing characteristics, is increasingly georeferenced using twenty-first century geospatial technologies, but it is still the case that census tables are often not released with census geographic boundaries. Even where a census has recently been

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Tools and Methods for Estimating Populations at Risk from Natural Disasters and Complex Humanitarian Crises FIGURE 2.5 Time-series composite of the Nile Delta nighttime lights for the years 1992 (blue), 1998 (green), and 2003 (red). Note the different types of information that can be extracted from the images of Cairo (Figure 2.4) compared to this image. Image courtesy of Chris Elvidge (National Oceanic and Atmospheric Administration). these data are not used in either GPW or LandScan products, but they are used in GRUMP. At present, funding is still uncertain for the launch of a new NPOESS VIIRS (National Polar-orbiting Operational Environment Satellite System Visible Infrared Imager Radiometer Suite) satellite. It was set to launch in 2009, but a review by the House Science Committee in June 2006 put the funding in doubt. If launched, this will be a medium-resolution sensor with more quantization levels than OLS and spatial resolution sufficient to observe primary features found in cities and towns. Once operational, Elvidge (2006) has indicated that this improved sensor would be able to provide the following: (1) products depicting the geographic footprints of human settlements of all sizes, including the outline of developed areas, specific estimates or measures of constructed area, and vertical structures in urban

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Tools and Methods for Estimating Populations at Risk from Natural Disasters and Complex Humanitarian Crises FIGURE 2.6 Simulation of 25-, 50-, 100-, 200-, and 742-meter (Visible Infrared Imager Radiometer Suite [VIIRS]) resolution nighttime lights imagery covering a portion of Las Vegas, Nevada, generated using a 1.5-meter resolution image. The images show the effect of spatial resolution on feature content in nighttime lights. Images courtesy of Chris Elvidge (National Oceanic and Atmospheric Administration).

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Tools and Methods for Estimating Populations at Risk from Natural Disasters and Complex Humanitarian Crises cores; (2) the location and extent of sparse development in rural areas; (3) objective identification of intra-urban classes, such as classes of residential areas, commercial and industrial areas, and the distribution of vegetation types and open lands within urban areas; (4) vectors for streets and roads; and (5) measures of economic activity such as the extent and stability of the electric power grids. Other Aerial Imagery During and after an emergency, remotely sensed imagery from satellites or lower-altitude aircraft may be useful in estimating the number of people potentially affected by a crisis or disaster (Jensen and Hodgson, 2005). The use of imagery to assess damage in places such as Kosovo (as a result of conflict), Indonesia, Sri Lanka (as a result of the tsunami), and the U.S. Gulf Coast (as a of Hurricane Katrina) has been instructive and offers a way of assessing the geographic extent (and the degree of physical change of the landscape) of the affected areas so that the size of the population affected by an event may be determined. Additionally, oblique-angle aerial photography and digital videography used in response to Hurricane Katrina were successful for assessing damage. Of course, repeated analyses of such places can offer estimates of the recovery of population in affected areas. Satellite imagery is constrained by its temporal resolution (how often it passes over a given place on Earth’s surface) and by the occurrence of cloud-free passes over that place. Virtually all of the satellite imagery used for population estimation purposes to date is optically sensed and thus requires cloud-free scenes to be used effectively. As a consequence, remote imagery in some emergency situations may have to be acquired by other fixed-wing aircraft that can fly under the cloud cover. With relatively modest training, these data can be analyzed by people on the ground to estimate damage to homes and infrastructure in the post-event period. Radar imagery can be used to “see through” the clouds, but the use of radar data in population estimation has been extremely limited. In one of the few published studies using radar imagery for these kinds of purposes, Tatem et al. (2004) utilized Japanese Earth Resources Satellite-1 (JERS-1) synthetic aperture radar (SAR) imagery in combination with Landsat TM imagery to map settlements in Kenya. To be useful for population estimation purposes however, the radar data must be combined with multispectral imagery such as Landsat TM, along with ancillary data such as road networks. Risk Indicators The world’s populations are not evenly distributed, nor are the risks and hazards to which they are exposed. The World Bank produced a set

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Tools and Methods for Estimating Populations at Risk from Natural Disasters and Complex Humanitarian Crises of maps (“Hotspots” maps) that spatially delimit the populations of the world (in total population counts) that are at risk from selected natural hazards (cyclones/or tornadoes, earthquakes, floods, drought, volcanoes, and landslides) (Dilley et al., 2005). These maps use GPW and the disaster impact data of CRED (both mentioned earlier in this chapter) to calculate disaster risk at the subnational level in order to contribute to development planning and disaster prevention. Some populations are at risk from more than one of these hazards, so their overall exposure to natural hazards is additive. While the spatial overlay of total population by multiple hazard sources helps to prioritize areas by exposure, a more important question is the extent to which these populations include vulnerable subpopulations that are more sensitive to a hazard and have less resilience to cope with an emergency, such as the young, the old, the sick, or the poor. This type of a vulnerability analysis requires the existence of subnational population attribute data and hazard data for each area of interest, whether at a county level (Cutter et al., 2006), city level (Pelling, 2003), or for a small island nation (Pelling and Uitto, 2002). SUMMARY We take for granted the assumption that better data will improve humanitarian relief efforts, although admittedly this is a difficult proposition to prove. All respondents to our study, including those involved directly with humanitarian and development aid projects, indicated that better georeferenced population census and survey data, and maps to which the data could be linked, were desired and preferred for planning and execution of these aid projects. Nonetheless, we do not have similarly situated emergencies, one with good data and one without, with all other conditions held constant, that would allow us to quantify the importance of good data. In particular, we are unable to specify how many lives or livelihoods would be saved if better data were available. Thus, we cannot readily weigh the costs and benefits of spending donor resources on better data, and better training of people to use and disseminate those data, compared to other uses of the same resources. The committee has thus chosen instead to consider the narrower question of how best to generate the subnational population estimates that relief agencies believe will be of assistance to them. Chapters 3 and 4 explore the question of coordination and training within and among various responders who might use these data, because good data alone will not guarantee an effective emergency response. The chapter began with an overview of the data required to prepare estimates of populations at risk of being involved in emergency situations. Since it is impossible to know in advance what events people might confront, the estimates for planning purposes need to be spatially explicit and

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Tools and Methods for Estimating Populations at Risk from Natural Disasters and Complex Humanitarian Crises sufficiently detailed in demographic terms so that they have broad applicability to a number of emergencies. Ideally, population registries would be universally accessible as sources of data at all geographic levels. In reality, censuses of population tend to be the best single source of data because they maximize the geographic coverage of populations and typically have enough detailed characteristics about each person in each household to permit the calculation of estimates of vulnerability. In theory, censuses are also ideal geographically because each household’s location had to be known to the statistical agency undertaking the census, so the characteristics of people should be capable of being spatially identified with considerable precision. In practice, the exact location may not be recorded and so is lost immediately after the data are collected; even if the location is known, it may not be converted to a digital format that can be mapped easily. The committee concludes, therefore, that in much of the world the real issue is not the collection of data per se, but rather what happens to the data after being collected. Census and other data about households and individuals need to be georeferenced (with proper privacy safeguards), linked to accurate maps, and then analyzed by individuals with the appropriate training to undertake tasks. A major shortcoming of census data (and most population registers as well) is that they are universally collected at places of residence, yet people are often at risk outside of their home. There is no simple answer to this dilemma of estimating the “daytime” populations (assuming that being away from homes is essentially a daytime activity), but modeling based on the results of survey data about out-of-home activities is the most common approach. Most other problems in creating estimates of the population at risk are related to the fact that censuses are not conducted everywhere on a regular basis, and even where they are conducted, the national statistical agency may not have the resources to provide data at a local level, to prepare local-level maps coinciding with the census geography. To work around some of these issues, global population databases such as LandScan and GPW were developed to create population “surfaces” for the globe, but at the moment they lack the breadth of demographic characteristics that would allow users to create estimates of vulnerability beyond population counts and density in a given area. Much of this chapter has examined what can be done when the ideal census data and corresponding maps are not available, recognizing that this is largely an issue for data-poor countries, which tend to have limited resources for training of personnel and collection of data. Proxy measures should not be preferably established over collection of census data and associated maps. The most important kinds of data that might be used in the absence of recent detailed census data include sample survey data, such as those collected in

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Tools and Methods for Estimating Populations at Risk from Natural Disasters and Complex Humanitarian Crises the DHS and MICS, as well as information derived from remotely sensed imagery. These kinds of proxy measures have tremendous potential to augment our knowledge of subnational populations, but they require the use of fairly sophisticated modeling techniques if they are to be employed in population estimation for at-risk groups, since they were not designed for that mission. Uncertainty is a component in any population estimate, but these proxy measures have less certainty associated with them than census-based estimates, and researchers must utilize the techniques that are emerging to evaluate error in these statistically unconventional models. While it is generally true that any data are better than no data in an emergency situation, the best decisions are those made with the best data or, at the very least, with the best understanding of the limitations of available data. Overall, it seems doubtful that one approach to estimating populations at risk will fit all situations. The reality is that countries have different kinds of data available, different populations in diverse geographic locations, and different probabilities of needing to have data available. The committee has tried to provide a road map for estimating populations at risk given the kinds of data that are currently used for that purpose. This is, however, an evolving area of policy research, and it is imperative that we learn from each new emergency or disaster situation what is needed and how it is used, so that meeting these data needs in future disasters becomes a high priority for governments and emergency responders. RECOMMENDATIONS The preceding discussion provides the basis for the following committee recommendations: Improve the capacity of census-poor countries, through training and technical assistance programs, to undertake censuses. Such improvement is critical for the long-term availability of subnational data that can assist in humanitarian emergency and development situations. Knowing the location, number, and critical characteristics of populations is pivotal to all planning, response, and long-term understanding of disasters. These data sets should have pre-existing protocols for data format, sharing, mapping, intercensal projections, and metadata that are consistent with international standards. [Report Recommendation 1] Support should be given to test the accuracy of estimates of size and distribution of populations based on remotely sensed imagery, particularly in rural and urban areas of countries with spatially, demographically, and temporally inadequate census data. Current

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Tools and Methods for Estimating Populations at Risk from Natural Disasters and Complex Humanitarian Crises efforts to render global spatial population estimation—LandScan and Gridded Population of the World—use different methodologies. An independent study of the state of the art in spatial population estimation would highlight the strengths and weaknesses of the existing methods and could serve as a guide for improvements in the methods and development of new ones for the purposes of understanding populations at risk. [Report Recommendation 8] REFERENCES Abowd, J.M., and J. Lane, 2004. New approaches to confidentiality protection: Synthetic data, remote access and research data centers. In J. Domingo-Ferrer and V. Torra (eds.), Lecture Notes in Computer Science. Berlin: Springer, pp. 282-289. ADPC (Asian Disaster Preparedness Center), 2006. Community Based Disaster Risk Management 2006. Available online at http://www.sheltercentre.org/shelterlibrary/items/pdf/ADPCCriticalGuidelines.pdf [accessed October 3, 2006]. Archer, E.R.M., 2004. Beyond the “climate versus grazing” impasse: Using remote sensing to investigate the effects of grazing system choice on vegetation cover in eastern Karoo. Journal of Arid Environments 57(3):381-408. Balk D., F. Pozzi, G. Yetman, U. Deichmann, and A. Nelson, 2005a. The distribution of people and the dimension of place: Methodologies to improve the global estimation of urban extents. The International Society for Photogrammetry and Remote Sensing Proceedings of the Urban Remote Sensing Conference, Tempe, Arizona, March. Balk, D., Y. Gorokhovich, and M. Levy, 2005b. Estimates of Coastal Population Exposed to the 26 December 2004 Tsunami, Note prepared for the Humanitarian Information Unit of the U.S. Department of State, January 7; available online at http://www.ciesin.columbia.edu/tsunami2004.html (and presented at the Columbia University School of International and Public Affairs, Tectonics, Politics and Ethics: The Tsunami and Its Aftermath, March 2005). Balk, D.L., U. Deichmann, G. Yetman, F. Pozzi, S.I. Hay, and A. Nelson, 2006. Determining global population distribution: Methods, applications and data. Advances in Parasitology 62(April):119-156. Bilsborrow, R., 2006. University of North Carolina; presentation to the committee on April 20 at the National Academies Keck Center, Washington, D.C. Presentation available through the National Academies Public Access Records Office. Brown, V., D. Coulombie, F. Belanger, G. Jacquier, S. Balandine, and D. Legros, 2001. Rapid assessment of population size by area sampling in disaster situations. Disasters 25:164-171. CIESIN (Center for International Earth Science Information Network), Columbia University; International Food Policy Research Institute (IFPRI), the World Bank; and Centro Internacional de Agricultura Tropical (CIAT), 2004. Global Rural-Urban Mapping Project (GRUMP). Available online at http://sedac.ciesin.columbia.edu/gpw/. Champion, T., and G. Hugo, 2004. New Forms of Urbanization: Beyond the Urban-Rural Dichotomy. London: Ashgate Publishing Limited, 444 pp. Christakos, G., R.A. Olea, M.L. Serre, H. Yu, and L. Wang, 2005. Interdisciplinary Public Health Reasoning and Epidemic Modeling: The Case of Black Death. New York: Springer, 319 pp. Coale, A., and P. Demeny, 1983. Regional Model Life Tables and Stable Populations. New York: Academic Press, 496 pp. Curran, L.M., S.N. Trigg, A.K. McDonald, D. Astiani, Y.M. Hardiono, P. Siregar, I. Caniago, and E. Kasischke, 2004. Lowland forest loss in protected areas on Indonesian Borneo. Science 303(5660):1000-1003.

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