Tools and Methods for Estimating Populations at Risk from Natural Disasters and Complex Humanitarian Crises (2007) / Chapter Skim
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2 Current Status of At-Risk Subnational Population Estimation
2 Current Status of At-Risk Subnational Population Estimation
Pages 28-71
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; and (3) social "scale" (how detailed are the available population characteristics?
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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.
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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.
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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.
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. Thus, "recent" as a qualifier to the existence of reliable census data in a country must be evaluated on a country-by-country basis.
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In the United States, aggregated census data are sometimes altered or even suppressed in order to maintain confidentiality (Abowd and Lane, 2004)
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lack of recent census or population data, (2) deficiencies in the existing census data, and (3)
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Two basic sources of such data exist -- surveys and administrative data -- as well as one important ancillary source, remotely sensed imagery. Survey Data.
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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)
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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.
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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)
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FIgURE 2.2 An example of global, stable nighttime lights (average visible band digital number) including city lights and fires.
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Another potential deficiency of existing census data is that important population characteristics may be missing. In particular, ethnic and religious differences have historically been triggers for internal violence, yet these characteristics are not always available in a census.
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In the United States, the Census Bureau has been at the forefront in the creation and distribution of digital boundary files that provide the spatial
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as the available census data. Researchers must anticipate that for most nations a great deal of time and effort will be required to obtain digital maps that correspond to the subnational boundaries referred to in census or survey data.
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Coming close to the gold standard for census data availability requires recognition on the part of people everywhere that georeferencing the data collected is the key to success in all subsequent efforts to create subnational population estimates. Once the link is lost between the data and their location, the task of creating subnational population estimates becomes tremendously more complicated.
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While baseline population counts are necessary, these counts should be sufficiently spatially disaggregated for event-specific denominators to be constructed. For example, the high-resolution census data for Indonesia (i.e., at the fourth administrative level, representing 60,000 units nationally and more than 2,000 within Aceh Province alone)
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. Although subnational data are, by definition, collected in the process of constructing national-level estimates, analysis of subnational population data currently is not seen as the responsibility of the United Nations or any other international organization.
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. As a result of this human resource deficit, these kinds of demographic estimates and projections are unlikely to be undertaken by national statistical agencies.
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. Spatial Demography As noted above, subnational population characteristics may be estimated by modeling data from georeferenced surveys, such as the DHS and the MICS, and similar household surveys.
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. global Population Data All of the data and methods discussed to this point are place specific, in the sense that we are asking what kinds of demographic data and digital geospatial data exist for a particular country for the most recent date possible.
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, at the University of California, Santa Barbara, with partial support from a CIESIN National Aeronautics and Space Administration (NASA) contract so that population data could be integrated with Earth science data.
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. LandScan starts with subnational population estimates provided by the Population Division of the U.S.
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To the extent that both GPW and the census inputs are spatially precise, of reasonably high spatial resolution, and recent, they are very likely to approximate the distribution of usual residence. LandScan, in contrast, represents an ambient population, and estimates of population distribution from this data set will tend to be better than GPW in places where the census data are spatially coarse and not recent (as long as the ancillary data are more current)
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for elevation data; Controlled Image Base (CIB) data for urban boundaries and settlement identification; MODIS for land cover data; Landsat Thematic Mapper Derived Land Cover Data; Digital urban boundaries updated and modified with IKONOS/Quickbrid high resolution imagery Population data source U.S.
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The LandScan model reallocates population in Saudi Arabia from large administra 2-3 tive regions to likely places of work or residence and produces a much more finely reduced for single page resolved population distribution map. Where underlying population inputs are high per unit area, as in the case of France, the output grids of population distribution appear quite similar.
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A reasonable conclusion is that the same problems that prevent existing census data from being directly usable by humanitarian relief agencies also prevent the data from being made available through GPW or LandScan. other Database Developments The GPW and LandScan concepts have encouraged construction and development of additional databases.
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PROXy MEASURES OF POPULATION SIZE AND DISTRIbUTION The increasing availability of remotely sensed imagery has led to investigations of the use of these data as proxy sources of population size and distribution, with the aim of improving population estimates and locations, especially for areas where little reliable information exists or at least is not available from other sources. Remotely sensed imagery has the advantage that it can be collected even for places where people otherwise cannot or will not venture on the ground.
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Accuracy increases with in situ data (typically census data) used to calibrate the average number of persons per dwelling and the number of homeless, seasonal, or migratory people, and with spatial resolution sufficient to identify individual structures and their uses (e.g., house, commercial building)
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BOX 2.3 Allometric Growth The "law of allometric growth" (Nordbeck, 1965) is an empirical relationship between simple urbanized built-up area classified from remotely sensed imagery and settlement population, posited as r = a × pb where r = radius of built-up area, a is constant of proportionality, p is population, and b is an empirically derived exponent.
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Census Bureau's TIGER system GIS road data proved better at estimating populations than land change data derived from Landsat imagery (Qui et al., 2003)
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Imagery and population Distribution Despite the limitations noted above, imagery both at fine- and coarsescale resolutions has been used successfully to identify and monitor the distribution of occupied landscapes. Major advances have been made in this domain, largely registered in land use and land cover change science as observed by Landsat and SPOT satellites and more recently by Moderate Resolution Imaging Spectroradiometer (MODIS)
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As mentioned earlier in the chapter, nighttime lights, detected by the DMSP-OLS (Defense Meteorological Satellite Program-Operational Linescan System) , represent the only system currently collecting low-light imaging data globally.
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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.
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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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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.
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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.
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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.
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[Re port 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.
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CIESIN (Center for International Earth Science Information Network) , Columbia University; International Food Policy Research Institute (IFPRI)
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Identify ways in which subnational demographic and geographic data and tools could be used to help decision makers provide useful information to populations at risk. In National Research Council, tools and Methods for estimating populations at Risk from Natural Disasters and Complex humanitarian Crises.
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Political geography and emergency Relief. In National Research Council, tools and Methods for estimating populations at Risk from Natural Disasters and Complex humanitarian Crises.
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Briggs, 2003. Modeling urban population growth from remotely sensed imagery and TIGER GIS road data.
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Hay, 2004. Defining approaches to settlement mapping for public health management in Kenya using medium spatial resolution satellite imagery.
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