EXISTING GEOREFERENCED POPULATION DATASETS

Transcription

1 CHAPTER 3 REVIEW OF EXISTING GEOREFERENCED POPULATION DATASETS The previous chapter reviewed definitions of urban and rural areas, and analysed what statistical data are available and what georeferenced datasets could be used as potential inputs into models of population distribution. In this chapter, the two most widely known and used georeferenced global population distribution databases that have been developed based on these sources are reviewed and several recent efforts to model population distribution, taking urban and rural areas explicitly into account are described. The Gridded Population of the World (GPW), originally developed at the National Center for Geographic Information Analysis (NCGIA) and subsequently updated by the Center for International Earth Science Network (CIESIN) at Columbia University, attributes population to the lowest subnational administrative units for which population counts are available. In GPW the population count for each administrative unit is distributed uniformly across all the gridcells of the unit, without considering whether the gridcell belongs to urban or rural area. The LandScan Global Population Database, produced by the Oak Ridge National Laboratories (ORNL), distributes national populations by land cover category, according to a model with assumed coefficients for population occurrence in each type of land cover. General information about how each database was produced is given below, along with the main advantages and disadvantages of each. In both cases, the primary sources of population are data from censuses and surveys compiled for political or administrative units. The term global is used to indicate that there is no explicit reference to urban or rural areas, and only overall total population counts and densities are given. As there is more than one global database available, each being produced by different methods, the most suitable database should be chosen largely on the basis of the type of application for which it is to be used. 3.1 GRIDDED POPULATION OF THE WORLD The GPW project was the first major attempt to generate a consistent global georeferenced population dataset. It was originally produced at the National Center for Geographic Information Analysis (NCGIA) in 1995 (Tobler et al., 1995), and subsequently updated by CIESIN in 2000 (Deichmann et al., 2001) and in 2004 (Balk and Yetman, 2004). 13

2 ] M A P P I N G G L O B A L U R B A N A N D R U R A L P O P U L A T I O N D I S T R I B U T I O N S [ GPW was the first global rasterized dataset of population totals based solely on administrative boundary data and population estimates associated with those administrative units. In the original version, two datasets at 2.5 arc-minutes were produced with the data for the year 1990: i) unsmoothed, where the gridding algorithm assigned population in grid cells with multiple input polygons by a straight majority rule, and ii) smoothed, where population was distributed based on a smoothing method called pycnophylactic interpolation (Tobler et al., 1995), which assumes that grid cells close to administrative units with higher population density tend to contain more people than those close to low density units. Since that first release, higher resolution population data sets have been compiled for various regions of the world. In 2000 CIESIN released an updated second version of GPW. GPWv2 is based on more detailed administrative units, resulting in an improved median resolution. The median resolution is defined as the ratio of total area of the country to number of administrative units; a lower number indicates a larger number of administrative units, and therefore a more spatially refined dataset. Nonetheless, no effort was made to model population distribution, and no ancillary data were used to predict population distribution or revise the population estimates. The only assumption made was that population is uniformly distributed within each administrative unit. The latest version, GPWv3 (Web site ref. 13), is based on the same assumptions as the previous version but relies on more recent data at higher resolutions (see Map 3.1). In particular, the number of administrative units has increased from approximately in GPWv2 to more than in GPWv3, and consequently the average median resolution has dropped from 33 in GPW2 to 18 in GPWv3. This new version contains unadjusted population data for the years 1990, 1995 and 2000, as well as data for those years adjusted to match United Nations population estimates. Data about land area and population density are also included. In order to avoid mismatches at the border between countries, most country boundaries have been matched to standard sources, namely Seamless Administrative Boundaries of Europe (SABE, Web site ref. 14) and DCW. The main advantages in using GPW are that it relies on a very simple area-weighting scheme for reallocation, and on the best possible census and administrative data available. GPW also provides updates every five years, allowing for a (short) time series analysis. Its main drawbacks are its coarse resolution of 2.5 arc-minutes, which corresponds to approximately 5 kilometres at the equator, and the lack of any modelling of population distribution within administrative units, causing population to be evenly distributed across any given administrative unit. This is unlikely to represent a realistic population distribution, especially within large units with significant variation in land cover characteristics. 14

3 MAP 3.1 Population density in 2000 from GPWv3 adjusted to UN totals 15 Source: Center for International Earth Science Information Network (CIESIN), Columbia University and Centro Internacional de Agricultura Tropical (CIAT) REVIEW OF EXISTING GEOREFERENCED POPULATION DATASETS

4 ] M A P P I N G G L O B A L U R B A N A N D R U R A L P O P U L A T I O N D I S T R I B U T I O N S [ 3.2 LANDSCAN GLOBAL POPULATION DATABASE The Oak Ridge National Laboratories developed LandScan (Web site ref. 15) in 1998 (Dobson et al., 2000) in order to overcome the limitations of GPW, and originally in response to a demand for distributed population data that would show emergency workers where populations were likely to be concentrated in the event of a disaster. It was subsequently updated in 2000, 2001 and LandScan was conceived as an effort to capture ambient population, more than decennial population counts. The difference between ambient and resident population is not significant as the results are quite coarse in all available population density maps. LandScan 2003 was released shortly before this report went to press. In this FAO study, a modified version of LandScan 2002 (LandScan a) was used, as explained in section (see Map 3.2). The sources used for the LandScan released in 1998, included DCW, Nighttime Lights, GLCC, high resolution aerial photography and satellite imagery. The methodology was subsequently updated and the input layers improved. In the 2000 version of LandScan, the major improvement was the use of VMap1 (see section 2.3.1) with its superior identification of the road networks, populated places and water bodies. In the 2001 version, the major improvement was better information about second order administrative boundaries for population distribution outside the United States; and, within the United States, newly-available high-resolution (30 metre) land cover data products. In 2002, refinements were made to the algorithm for its population models and MODIS land cover database was used as an input data sources. The LandScan methodology consists in an automated procedure to allocate population data to 30 arc-second cells, which correspond to approximately 1 square kilometre at the equator. The population estimates used as inputs are based primarily on aggregate data for second order administrative units compiled by the International Programs Center of the US Bureau of Census and represent the most recent census information for each country. These population counts are allocated to the individual 30 arc-second cells through a smart interpolation method that assesses the relative likelihood of population occurrence in cells on the basis of road proximity, slope, land cover, and Nighttime Lights. Probability coefficients are assigned to every value of each input variable, and a composite probability coefficient is calculated for each LandScan cell. The coefficients for all regions are based on the following factors: " Roads, weighted by distance from major roads. " Elevation, weighted by favourability of slope categories. " Land cover, weighted by type with exclusions for certain types. " Nighttime Lights of the World, weighted by frequency. The resulting coefficients are weighted values, independent of census data, which can then be used to apportion shares of actual population counts within any particular area of interest. Coefficients vary considerably from country to country even within different regions of the same country. 16

5 MAP 3.2 LandScan Global Population Database, adjusted to UN figure year Source: Oak Ridge National Laboratories (ORNL), Tennessee, USA REVIEW OF EXISTING GEOREFERENCED POPULATION DATASETS

6 ] Control totals can be based on any administrative unit (whether nation, province, district or minor civil division) or on any arbitrary polygon for which census data are available. The resulting population distribution is normalized and compared with appropriate control totals to ensure that aggregate distributions are consistent with census control totals. The advantages of LandScan, as compared with GPW, include its better output resolution of 30 arc-seconds, as opposed to 2.5 arc-minutes, and the use of an extensive model to predict population distribution within administrative units. Although LandScan takes urban areas into account, it does not distinguish urban and rural populations in the database. However, the input layers are such that urban areas can be inferred by analysing the population density. One problem with LandScan concerns the roads database. The model processes the input layers by country without taking into consideration the spatial continuity of the road networks between them, resulting in uneven changes of population density at country boundaries. Another problem is that, owing to the way in which the LandScan processing methods evolved, population comparisons between available revisions of the database are not possible. Although each revision date of LandScan represents the adjusted midyear July population estimates for that year, comparatively, the available 1998, 2000, 2001, 2002 and 2003 releases of these data do not represent a time series that can be used for pixel-by-pixel analyses or comparisons (see also Dooley, 2005). Also the underlying models have not been published, so the assumptions employed by LandScan to distribute population counts to pixels are not known. 3.3 GLOBAL RURAL URBAN MAPPING PROJECT In a recent project, CIESIN and partners such as the International Food Policy Research Institute (IPFRI), the World Bank and the Centro Internacional de Agricultura Tropical (CIAT), developed a model for redistributing population within administrative units by combining data from several sources. The description of the method and the datasets, in the box, draws on the working paper available at the GPW Web site (Balk et al., 2004a). [ M A P P I N G G L O B A L U R B A N A N D R U R A L P O P U L A T I O N D I S T R I B U T I O N S 18

7 REVIEW OF EXISTING GEOREFERENCED POPULATION DATASETS BOX 3.1 GLOBAL RURAL URBAN MAPPING PROJECT (GRUMP) DATASET What does the GRUMP dataset contain? Human settlements database of about settlements points that have a population of or more A global database of cities and towns (points). Each point, represented as a latitude/longitude pair, has associated tabular information on its population and data sources. Population data were gathered primarily from official statistical offices (census data) and secondarily from other sources, such as Gazetteer and City Population. Based on the data available and applying UN growth rates, population was estimated for the year 1990, 1995, and When the records for cities and town did not include latitude and longitude coordinates, those were taken from the NIMA database, based on a city name and administrative units match. As mentioned earlier, due to uncertainties in the positional accuracy of the NIMA coordinates, some of the cities and towns might not be accurately geolocated. Urban extent database of over areas The GRUMP urban mask represents an attempt to delineate extents associated with human settlements globally. The physical extents of settlements are derived from both raster and vector datasets. In particular, the team used the Nighttime Lights dataset for the period (Elvidge et al., 1997, 2001), DCW Populated Places, and cities from the Tactical Pilotage Charts (standard charts produced by the Australian Defense Imagery and Geospatial Organization, at a scale of 1: ) for selected countries in Africa. All the sources of urban extent (night-lights, DCW polygons and TPCs) were combined in order to obtain the maximum possible coverage for each country. The population values are assigned to the physical extents from points within a three kilometre buffer. For points that are not within the three kilometres buffer of an extent, circles were created based on the relationship between population size and areal extents for the points with known parameters. These newly created circles were added to the existing ones to create a complete coverage of urban extents with population information for each country. see next page 19

8 ] M A P P I N G G L O B A L U R B A N A N D R U R A L P O P U L A T I O N D I S T R I B U T I O N S [ ] Urban rural The urban-rural population grid was created by using a population grid, mass-conserving algorithm called GRUMPe (Global Rural with an output Urban Mapping Programme), developed by CIESIN, that resolution of 30 reallocates people into urban areas, within each arc-seconds administrative unit. In particular the data inputs are the administrative polygons, containing the total population for each admin unit, and the populated urban extents. The reallocation process works iteratively so that the output urban and rural proportions match, when possible, the UN ones. Although the UN totals are useful as a benchmark, in some cases the GRUMP output proportions have not been matched to the UN ones (when for example CIESIN s data includes many more small settlements than those corresponding to the urban threshold given by the country). What are GRUMP s main advantages? The main advantage of GRUMP is that it uses population data from the census, rather than predicting it based on probability coefficients or lighted areas. Also, it makes use of other GIS data to identify urban areas, compensating for the small settlements in poor countries that are not detected by the Nighttime Lights. The resulting grid is a dataset at moderate resolution that represents a more accurate distribution of human population than the existing datasets, and that makes explicit reference to urban and rural areas. What are GRUMP s main limitations? The lights are known to overestimate the actual extents of urban areas (Elvidge et al., 2004), but, as previously discussed, applying a threshold would reduce the number of small settlements that are not frequently lit, as in developing countries. Given the complexity of finding a single threshold that could work globally (Small et al., 2005), no light threshold was applied, resulting in an overestimation of the urban extents in some parts of the world. Although population is estimated for three time periods (1990, 1995, and 2000), users need to remember that the lights refer to one point in time only (the 1994/1995 time period), so it would not be advisable to use these extents for any analysis of change in urban areas. These data provide the first systematic assessment of the world s urban land area nearly three percent (Balk et al., 2004a), and how population distributions by ecosystems differ dramatically. Coastal zones are the most urban of all systems, and sustain the highest population densities, not only in the urban areas, but in the rural ones as well. The GRUMP grid is one of the key input datasets in the Millennium Ecosystem Assessment (McGranahan et al., 2005). 20

9 MAP 3.3 Population density in 2000 from GRUMP adjusted to UN totals 21 Source: Center for International Earth Science Information Network (CIESIN), Columbia University; International Food Policy Research Inst. (IPFRI), the World Bank and Centro Internacional de Agricultura Tropical (CIAT) REVIEW OF EXISTING GEOREFERENCED POPULATION DATASETS

10 ] M A P P I N G G L O B A L U R B A N A N D R U R A L P O P U L A T I O N D I S T R I B U T I O N S [ 3.4 POPULATION DATABASES FOR AFRICA, ASIA AND LATIN AMERICA Population databases for Africa, Asia and Latin America, compiled by the United Nation Environment Programme (UNEP) and partners (CIAT and CIESIN), build on the GPW tradition but take road networks and populated places into account in the redistribution of population (Web site ref. 16) As described in the documentation (Deichmann, 1996a; Hyman et al., 2000; Nelson, 2004), a model was created in the following stages. First, information about the transportation network and urban centres was collected. The transportation network included roads, railroads and navigable rivers using data from DCW, the World Boundary Databank II, and Michelin paper maps, while information about urban centres consists of location and size of towns and cities from the human settlements database of GRUMP. This information was then used to compute a simple measure of accessibility for each node in the network. This measure is the so-called population potential, which is the sum of the population of towns in the vicinity of a given node weighted by a function of distance, using network distances rather than straight-line distances. The computed accessibility estimates for each node were subsequently interpolated onto a regular raster surface. A simple inverse distance interpolation procedure was used, which resulted in a relatively smooth surface. Raster data for inland water bodies (lakes and glaciers), protected areas and altitude were then used to adjust the accessibility surface heuristically. Finally, the population totals estimated for each administrative unit were distributed in proportion to the accessibility index measures estimated for each grid cell. The input administrative units, with corresponding population numbers, are the same as those of GPW. The output resolution, as for GPW, is 2.5 arc-minutes. This model undoubtedly represents an improvement upon GPW, in that it takes into account road networks and populated places to achieve a better reallocation of population within administrative units. Unlike LandScan, only roads and populated places are used, and there is no explicit effort to capture the ambient quality of the LandScan approach. The resolution might still be too coarse for detailed studies at the local/national level, but it provides consistent population distributions across continents, allowing analysis at the regional scale. 3.5 OTHER RESEARCH EFFORTS TO MAP URBAN POPULATION In this section, other recent attempts to model population distribution are described. The first two use GPW as base population input and additional georeferenced datasets, while for the third the starting point is country-level demographic statistics. The first is work in progress, and is not available publicly. The first one was conducted by CIESIN, in a parallel effort to the GRUMP database. CIESIN pursued a method for improving on the GPW by using the Nighttime Lights dataset to identify urban areas (Pozzi et al., 2003). The project aimed to overcome some of the limitations of LandScan (extensive modelling), GPW (lack of modelling) and GRUMP (extensive data collection) by developing a simple model to redistribute population within administrative units according to human settlements. Human settlements are identified by the Nighttime Lights dataset produced for the year 1994/1995 (Elvidge et al., 1997, 2001). 22

11 REVIEW OF EXISTING GEOREFERENCED POPULATION DATASETS The reallocation of population within administrative units is based on a function derived from the relationship between the population density and Nighttime Light frequency for a sample of regions of the world with spatially detailed administrative areas. The result is spatial refinement in areas or countries with relatively large populations but poor spatial detail for administrative boundaries. As the identification of urban areas is based solely on Nighttime Lights, in countries with poor lights coverage (for instance in Africa) the accuracy of the reallocation may not be very precise. The second effort was conducted at the Department of Geography and Center of Remote Sensing at Boston University, as part of a larger project to map global land cover from MODIS data (Web site ref. 17). The authors present a method for mapping urban land cover at spatial resolution of one kilometre by fusing multiple sources of coarse resolution data (Schneider et al., 2003). The objective was to determine the boundaries and the extents of urban areas more accurately. Population density data were used as one of the sources for determining probable location of urban areas, but no effort was made to actually estimate urban population counts. Two major tasks were involved in this study. First, a supervised decision tree classification method was developed by fusing one kilometre MODIS data and two ancillary sources: the Nighttime Lights data (Elvidge et al, 1999) and population density data (GPW, see Tobler et al., 1995; Deichmann et al., 2001). The second task was to establish the best means for evaluating the accuracy of urban land cover maps produced over large regions, an issue that is especially problematic when the class of interest is a small fraction of the total area mapped. For most parts of the world, multiple data sources were fused to achieve the results. The fusion of these three data types improves urban classification results by resolving confusion between urban and other classes that occurs when any one of the data sets is used by itself. For Africa, the ancillary data were too problematic, and Africa was successfully mapped with MODIS data alone. Any city around the globe larger than a few square kilometres should be represented, barring those areas (such as the majority of the Congo basin) that have continuous cloud cover. In addition, the scale of cities in developing countries is quite different from the rest of the world, so that most small cities in Africa, India and China (which might only be one pixel) are not represented (Schneider, personal communications). The third project is part of the World Water Development Report II Indicators for World Water Assessment Programme (Web site ref. 18). The University of New Hampshire Water Systems Analysis Group has developed a compendium of Earth System and socio-economic databases describing the current state of global water resources, including associated human interactions and pressures. Global population fields were constructed for the year 2000 using country-level demographic statistics contained in the World Resources Institute (WRI) Earth Trends database. The urban and rural population data sets were developed by spatially distributing the WRI 2000 country-level urban population data among DMSP-OLS nighttime stable-lights imagery (Elvidge et al., 1997a) and ESRI Digital Chart of the World populated places points. Country-level urban population was evenly distributed among the DMSP-OLS city lights data set at one- 23

12 ] kilometre grid cell resolution with detectable lights in at least ten percent of the cloud free observations (Elvidge et al, 1997b). Where available, the spatial extents of major city locations with known demographic data (Tobler et al, 1995) were superimposed in the DMSP-OLS city lights data set to enhance the accuracy of the urban population distribution. Rural population was spatially distributed equally among the DCW populated places points falling outside of the DMSP-OLS city lights extent. Total population is simply the sum of urban and rural population data sets gridded to the 30 minute simulated topological river network (STN-30) (Fekete et al., 2001). [ M A P P I N G G L O B A L U R B A N A N D R U R A L P O P U L A T I O N D I S T R I B U T I O N S 24

13 CHAPTER 4 DETERMINING VARIATION IN THE DISTRIBUTION OF URBAN AND RURAL POPULATIONS BY PIXEL 4.1 OBJECTIVES This chapter describes the method used by FAO/SDRN to develop gridded urban and rural population databases for inclusion in the FIVIMS Global GIS Database (FGGD) (Huddleston et al., 2005). The main difference between this method and the ones reviewed in Chapter 3 is that it allows making of rural population maps in which pixel values reflect variations not only between subnational units, but also within the units. The method is based on detecting and masking out urban areas on the LandScan Global Population Database in order to make a global rural population grid at the same resolution as LandScan, that is at 30 arc-seconds. This task has been carried out as part of a larger effort within the context of a Poverty Mapping Project, implemented jointly by FAO, UNEP and CGIAR to promote the use of poverty maps in policy - making and in targeting assistance, particularly in the areas of food security and environmental management (Web site ref. 19). Poverty mapping, defined as the spatial representation and analysis of indicators of human well-being and poverty, provides a means for integrating biophysical and geophysical information with socio-economic indicators to provide a more systematic and analytic picture of human well-being and equity (Henninger and Snel, 2002). GIS-based analysis of links between environment and poverty would not be possible without gridded databases and maps showing the spatial distribution of the world s rural and urban populations at a very high resolution. The gridded rural population database developed by FAO/SDRN is particularly useful for comparing the distribution of rural populations with available natural resources and other environmental and geophysical indicators of the degree of vulnerability of rural livelihood systems in developing countries. In this context, the aim is to identify the spatial distribution of rural population globally, so that reasonable estimates of the numbers of people living in different rural environments around the world and within regions and countries can be generated, such as in different agro-ecological zones, farming systems or crop zones. Besides describing the method developed to detect the urban population grid cells in the LandScan global population database and create the urban area mask, this chapter also presents results in map and table formats and compares them with other similar databases. 25

14 ] M A P P I N G G L O B A L U R B A N A N D R U R A L P O P U L A T I O N D I S T R I B U T I O N S [ ] 4.2 METHODOLOGY The task of detecting the urban areas was not straightforward because, as discussed in section 2.1, there is no commonly accepted definition of what constitutes an urban area. Indeed, since most humans tend to congregate in settlements, by some definitions almost all people could be said to live in urban areas. But generally, human settlements occurring in areas that are largely agricultural are considered rural, even though the size of their population may sometimes be quite large. In this report these are referred to as rural settlements and the population living in these settlements is excluded from analyses reporting rural population. On the other hand, in some countries, particularly those where total population density is not very large, even some small settlements are considered urban. To create the gridded urban, rural and rural settlements population databases, four primary sources were used. LandScan 2002 was used as the reference database for population distribution. Nighttime Lights of the World 2000 was used to identify the extent of urban areas. UN population data for each country for the year 2000 were taken as the reference point for urban/rural population and for overall totals. Detailed information about these three sources was given in previous chapters. In addition, the UN (DPKO/UNCS) International Boundaries/Coastlines map for 2004 was used to delineate the country boundaries and coastlines (Web site ref. 20). The reasons behind the choice of LandScan as the reference database for global population distribution, and the technical steps for generating the urban mask, are given in sections and respectively Choice of population database Three global population datasets GPW, GRUMP and LandScan were evaluated in order to choose the most suitable one for this study. GPW, as described in section 3.1, has a fairly coarse resolution and the population is uniformly distributed within any given administrative unit. The GRUMP database has better spatial resolution (30 arc-seconds) and superior differentiation of urban and rural populations, but both are still uniformly distributed within any given administrative unit, and in any case the database was not available at the time of this study. Therefore, LandScan was chosen as the source database for global population distribution, because of its high resolution and its depiction of variation in population counts also within each administrative unit, rather than showing only their averages. An additional advantage of the LandScan database is that, although it does not provide direct information about urban and rural areas, its population model distinguishes urban and rural populations and their distribution. The ORNL has released five versions of LandScan (see section 3.2). Each version has included new refinements, reflecting improvements in the quality of the data sources as well as improved data manipulation. The 2002 version of LandScan has been used as the source for the spatial distribution of the world s population because the most recent version, for 2003, was not released until near the end of this study. Since the LandScan database does not contain administrative 26

15 DETERMINING VARIATION IN THE DISTRIBUTION OF URBAN AND RURAL POPULATIONS BY PIXEL boundaries, it was overlaid with the standard UN International Boundaries map in order to delineate national boundaries and populations. A comparison of UN population database figures for 2002 with LandScan 2002 showed that, at the global level and for most of the countries, the differences were insignificant. As the year 2000 was selected as the reference year for other time-sensitive variables analysed in this report series, the LandScan 2002 had to be adjusted to year 2000 population estimates. This was deemed to be a more accurate representation than using the LandScan 2000 database itself, which used a less refined population distribution model. To reconcile the two sets of data, FAO used the UN 2000 population data for the country totals and LandScan 2002 for the distribution of the population within each country. In other words, for every country included in the database, the total population numbers derived from LandScan 2002 were adjusted to the UN figure for The new totals were then distributed across the pixels in the same proportion as in the original LandScan 2002 database. The adjustment coefficient is calculated for each country and is the ratio between the total population from the UN database and the total population from the LandScan database using the UN International Boundaries. The result is a 30 arc-second grid of population distribution that is matched to the UN figures in terms of total population for each country. From here on, we will refer to this modified LandScan global population database for year 2000 as LandScan-a. The FAO/SDRN rural and urban population distribution grids have been generated at 30 arc-seconds on LandScan-a. Because these databases were developed to analyse the distribution of rural population in relation to environmental and geophysical factors which were available only at 5 arc-minute resolution, it was necessary to convert the rural population grid from 30 arc-seconds to 5 arc-minutes. However, an analysis of the country area calculations at 5 arcminute resolution indicated that at that resolution, GIS analysis in countries with areas less than square kilometres would not be sufficiently accurate. Therefore such countries were not included in the analysis, nor were the countries with a UN total population figure less than Table 4.1 lists the 154 countries included in the analysis Detection of urban areas Several methods were explored for determining urban area boundaries and extents that return urban population counts consistent with UN population data for each country. The simplest method is to classify all the pixels in LandScan-a with population density above a certain threshold as urban, for instance all pixels with greater than persons per square kilometre. A variant of this method is to establish a unique threshold for each region or country. However, this concept was found to be too simplistic for discriminating urban and rural populations as it produces a very fragmented urban mask. Another method that was considered was to use a threshold for the gradient of the population density, rather than the population density itself. This method seemed very promising as differences in population density between many urban and rural areas were quite easily detected. However this method and even its combination with the population density threshold method described above was also not sufficiently accurate in some countries, and was not pursued. 27

16 ] M A P P I N G G L O B A L U R B A N A N D R U R A L P O P U L A T I O N D I S T R I B U T I O N S [ ] TABLE 4.1 List of the 154 countries included in the urban and rural databases Afghanistan Costa Rica Indonesia Morocco Somalia Albania Côte d Ivoire Iran, Islamic Rep of Mozambique South Africa Algeria Croatia Iraq Myanmar Spain Angola Cuba Ireland Namibia Sri Lanka Argentina Cyprus Israel Nepal Sudan Armenia Czech Republic Italy Netherlands Swaziland Australia Denmark Jamaica New Zealand Sweden Austria Djibouti JapanNicaragua Switzerland Azerbaijan, Dominican Jordan Niger Syrian Arab Republic of Republic Republic Bangladesh Ecuador Kazakhstan Nigeria Tajikistan Belarus Egypt Kenya Norway Tanzania, United Rep of Belgium El Salvador Korea, Dem OmanThailand People s Rep Benin Eritrea Korea, Republic of Pakistan Timor-Leste Bhutan Estonia Kuwait Panama Togo Bolivia Ethiopia KyrgyzstanPapua New Guinea Trinidad and Tobago Bosnia and Finland Laos Paraguay Tunisia Herzegovina Botswana France Latvia Peru Turkey Brazil Gabon Lebanon Philippines Turkmenistan Bulgaria Gambia Lesotho Poland Uganda Burkina Faso Georgia Liberia Portugal Ukraine Burundi Germany Libyan Arab Puerto Rico United Arab Jamahiriya Emirates Cambodia Ghana Lithuania Qatar United Kingdom Cameroon Greece Macedonia, Romania United States of The Fmr Yug Rp America Canada Guatemala Madagascar Russian Federation Uruguay Central African Guinea Malawi Rwanda Uzbekistan Republic Chad Guinea-Bissau Malaysia Saudi Arabia Venezuela, Bolivar Rep of Chile Guyana Mali Senegal Viet Nam China Haiti Mauritania Serbia and Yemen Montenegro Colombia Honduras Mexico Sierra Leone Zambia Congo, Dem Hungary Moldova, Slovakia Zimbabwe Republic of Republic of Congo, Republic of India Mongolia Slovenia The third method and the one selected for producing the urban mask is based on delineating urban boundaries depending on the intensity of the lights from populated areas. In satellite images of the globe taken at night, urban areas appear highly lighted. The correlation between these lighted zones and urban areas had already been explored by other researchers (Imhoff et al., 1997; Sutton, 1997; Elvidge et al., 1997). More recently a research on the metrics for quantifying the relationships within geospatial datasets has been developed. A spatial cross correlation between population counts in the LandScan database and the Nighttime Lights was computed and the two were found to be highly correlated (Ganguly A., personal communication). The main idea is to determine light intensity threshold (LT) for delineating urban areas using the human settlements dataset of the Nighttime Lights (NTL) of the World for the year In this dataset the values indicate the Digital Number (DN) genereted by the 28

17 DETERMINING VARIATION IN THE DISTRIBUTION OF URBAN AND RURAL POPULATIONS BY PIXEL Operational Linescan System (OLS) satellite monitoring, where the value of DN correlates with the light intensity on the ground (see section 2.3.2). The range for the DN values is from These numbers are the average DN values for the year. The minimum value identifies a situation of no lights; the maximum of saturated lights.. Initially, it was found that the images generated from the NTL database did not have sufficiently high positional accuracy for a global analysis. There were considerable nonsystematic positional shifts, sometime as high as 15 Km when compared to accurate reference maps. Once the NTL images were geometrically corrected, there was sufficiently good registration between the NTL images and the coastlines map used. Figure 4.1 depicts the uncorrected and the corrected images of a very highly populated metropolitan area the city of Istanbul, Turkey. The waterway (Bosphorus) in the centre of the image has a width of approximately metres at the narrowest point and was covered with light in the uncorrected image but not in the corrected one. This is indicative of the positional accuracy achieved by the geometric correction applied. It should be noted that in order to use the NTL images for the delineation of the urban mask two problems needed to be resolved. First, since the lights are more linearly correlated with GDP and electrification than with population density (Doll et al., 2000), a given urban population density will produce lower light intensity where GDP and electrification are low than where they are high. Second, as mentioned in section 2.3.2, the lights tend to overestimate the actual extents of the urban areas because of the blooming effect (Elvidge et al., 2004). The solution to the first problem required the determination of a specific LT value for each country. In order to determine the LT value for a given country, first a histogram and FIGURE 4.1 Geometric correction of Nighttime Lights of the World 2000 to UN international coastline map: Istanbul area 29

18 ] M A P P I N G G L O B A L U R B A N A N D R U R A L P O P U L A T I O N D I S T R I B U T I O N S [ ] then a cumulative distribution of population in that country for each DN value were generated. This is done by locating rural population figure on the y-axis of the cumulative distribution (see Figure 4.2) and finding the DN (i.e. the x-axis) value corresponding most closely to it. This value of DN corresponds to the LT value which covers all the urban population given by the UN figures for each country. Figure 4.2 depicts the procedure above using the UN population figures for Italy as an example. In the UN figures the total population for Italy is and the total rural and urban populations are and respectively. The DN value of 44 is the value on the x-axis that comes closest to the UN figure for rural population on the y-axis. It identifies as rural population which is a difference of 1.3 percent compared to the UN figure. In the same way it is possible to talk in terms of urban population, that is with a difference of one percent. The above differences are due to rather coarse representation of the DN values that is by only 64 integer values. In most countries the difference was less than ten percent. As explained in section 2.1, the definition of urban and rural areas is controversial and therefore the UN urban and rural population figures could also be considered controversial. Nevertheless, it was considered essential to use some urban and rural population figures as a benchmark and the UN figures were chosen because of their international acceptance. There is a large variation in the LT values between countries. Figure 4.3 depicts the average LT for all the UN regions. All Africa, except the North, required the lowest value of DN to detect the urban population. Western Asia, Northern America and Japan required greater values of DN on average. FIGURE 4.2 Cumulative distribution of population versus DN value in Italy 30

19 DETERMINING VARIATION IN THE DISTRIBUTION OF URBAN AND RURAL POPULATIONS BY PIXEL FIGURE 4.3 Average light threshold (LT) value by UN region *See section 4.3 for the description of the UN regions. During the analysis of the NTL images, it was noted that in 50 countries there were not sufficient lights to account for UN urban population figures. Even taking the lowest DN value as the LT, in these 50 countries the estimates of urban population were far lower than the UN figures and had an error greater than ten percent. Not surprisingly 49 of them are developing countries and only one is developed, Australia (-11 percent). Of the 49 countries, 69 percent are in Africa, 18 percent in Latin America, ten percent in Asia and two percent in Oceania. Also, in these countries, there appears to be a number of high population pixels in LandScan-a, which could not be detected by the lights. The reason for this could not be explained as LandScan population distribution model is not available in the public domain. In most cases, they are isolated pixels with high population density outside the lights (see blue boxes in Figure 4.4), but in some other cases (see circles in Figure 4.4) they appear to be in the form of agglomerations. In these 50 countries those pixels were classified as urban if they had the same or greater population values than the urban population density of the country. Regarding the problem of the blooming effect of the lights for the actual extents of the urban areas, even if the urban population of a country were close to the UN figure, there could be some local overestimation of extents. That is, some scarcely populated pixels 31

20 ] M A P P I N G G L O B A L U R B A N A N D R U R A L P O P U L A T I O N D I S T R I B U T I O N S [ FIGURE 4.4 Cameroon: isolated pixels and very small agglomerations not detectable by LT within or near urban areas can have high nighttime lights. In order to eliminate these pixels, for each country where there was not underestimation, the urban population density was calculated and all the pixels in which the number of people was less than ten percent of this value were reclassified as rural. Finally, a 3x3 majority filter was applied to the urban mask to reduce the fragmentation. The procedure for generating the urban mask is illustrated in Figure 4.5 for the urban agglomeration of Johannesburg. The red pixels (a) indicate the areas detected by LT for South Africa. Pink areas (b) show the agglomeration after removing the pixels with population less than ten percent of the urban density of the country. The blooming effect is reduced considerably, but some areas were too fragmented. The blue pixels (c) indicate the final result after the application of the majority filter. Application of the procedures described above generated an urban mask, called Poverty Mapping Urban extents (PMUe), which was then used as a tool for deriving the rural and urban population distribution grids Derivation of population distribution grids All the pixels of LandScan-a corresponding to the urban extents grid generated the Poverty Mapping Urban population (PMUp) distribution grid at 30 arc-seconds. The Poverty Mapping Rural population (PMRp) distribution grid at 30 arc-seconds was defined by masking out the detected urban extents from LandScan-a. These are population distribution grids, where the value for each pixel represents the number of persons found on that pixel. It 32

21 DETERMINING VARIATION IN THE DISTRIBUTION OF URBAN AND RURAL POPULATIONS BY PIXEL FIGURE 4.5 Different stages in computing the urban mask for Johannesburg and vicinity (a) (b) (c) should be noted that this number does not represent persons per square kilometre, as pixel areas vary by latitude. Urban and rural population density grids, called PMUd and PMRd, were also computed by dividing each pixel value by the pixel area. Since almost all the maps of the Poverty Mapping project are at 5 arc-minute resolution, PMRe and PMRp were also converted to this lower resolution. These grids are denoted with the acronyms PMRe5 and PMRp5. PMRe5 values indicate the percent area occupied in each 5 arc-minute pixel by the rural pixels from the 30 arc-second grid. PMRp5 values are the sum of the rural population numbers on 30 arc-seconds pixels in each 5 arc-minute pixel. In some countries, rural pixels exhibit very high density. Such pixels were classified differently based on a study carried out by the International Institute for Applied Systems Analysis (IIASA) for the identification of cultivated areas. Based on data from China and Bangladesh, two countries with very high population density in certain areas, the relationship between population density and the land area required for buildings and infrastructure was estimated. It was determined that almost negligible land area would be left for agriculture at areas with population density greater than persons per square kilometre Therefore besides rural and urban classes, a third class, called rural settlement was created and rural pixels with population density values greater than were assigned to that class. A new mask called Poverty Mapping Rural Settlements extents (PMRSe) was generated for this class. The acronyms PMRSp and PMRSd denote the grids corresponding to population distribution and density grids respectively, corresponding to 33

22 ] M A P P I N G G L O B A L U R B A N A N D R U R A L P O P U L A T I O N D I S T R I B U T I O N S [ PMRSe. Subsequently a new grid PMURRS was generated in which each pixel belongs to one of the following three classes: Urban, Rural or Rural Settlement. All the grids described in this section are part of the Poverty Mapping Urban Rural (PMUR) database Crosschecking of the urban population results with the UN figures As noted above the UN urban population figures (UNup) were used as a benchmark for computing the PMUp grid. A comparison of the PMUp results with the UNup shows the PMUp results to be within -/+ ten percent of the UN urban population figures in 125 countries, which represent about 81 percent of the countries included in the analysis. Of these, 32 percent are developed countries, and their percent differences are less than +/- five, with the exception of Australia. The remaining 68 percent are developing countries and of these almost half of them are in Asia, and the other half is distributed equally between Africa and Central/South Americas. The 29 countries which are not within the -/+ ten percent above, are all developing countries. In 25 of these countries, the PMUp underestimates the UNup. Most of these countries are in Africa (76 percent), with a further 12 percent in Latin America, and the remaining are Mongolia and Timor-Leste in Asia and Papua New Guinea in Oceania (Table 4.2). TABLE 4.2 List of the countries with underestimated urban populations Country UNup PMUp Percent difference PMUp - UNup population in thousands percentage Benin 2,630 2, Argentina 32,700 28, Angola 4,236 3, Uruguay 3,071 2, Botswana Perú 18,885 16, Timor-Leste Cameroon7,395 6, Djibouti Madagascar 4,710 3, Papua New Guinea Tanzania, United Rep of 11,236 9, Namibia Mozambique 5,735 4, Burundi Mongolia 1,415 1, Congo, Republic of 2,254 1, Mali 3,594 2, Guinea-Bissau Gabon1, Burkina Faso 1,967 1, Central African Republic 1, Mauritania 1, Sierra Leone 1, Liberia 1,

23 DETERMINING VARIATION IN THE DISTRIBUTION OF URBAN AND RURAL POPULATIONS BY PIXEL The countries in which PMUp overestimates compared to the UNup are only four: Niger, Haiti, Kenya and Afghanistan (Table 4.3). TABLE 4.3 List of the countries with overestimated urban populations Country UNup PMUp Percent difference PMUp - UNup population in thousands percentage Kenya 10,194,000 11,577, Niger 2,209,000 2,447, Haiti 2,857,000 3,254, Afghanistan 4,680,000 5,891, Map 4.1 shows the geographic distribution of the countries for which the PMUp - UNup difference is more than ten percent. In general the countries where there is under and over estimation are more rural. In the countries where the PMUp estimates are within ten percent of the UNup, the average urban population share is about 75 percent for developed countries, and about 40 percent in the developing ones. In the countries, where the differences are greater than ten percent, the average urban population share is not quite 40 percent for the underestimated ones and 33 percent for the overestimated. 35

24 36 [ M A P P I N G G L O B A L U R B A N A N D R U R A L P O P U L A T I O N D I S T R I B U T I O N S ] MAP 4.1 Spatial distribution of the difference in urban population figure by country

25 DETERMINING VARIATION IN THE DISTRIBUTION OF URBAN AND RURAL POPULATIONS BY PIXEL 4.3 COMPARISON OF THE PMUE WITH OTHER SIMILAR DATABASES In this section, the PMUe results have been compared with similar data from other sources for the land area of urban extents in square kilometre and for the geographic coordinates of urban centers. This cannot be considered a measure of the accuracy of the PMUe results because of the conceptual problems for defining urban areas noted before. First there is not a clear and unique definition of an urban area that is applicable to all countries (see section 2.1). Furthermore there is a lack of non-controversial global statistical data aggregated at sufficiently high spatial resolution with accurate geographic coordinates and population figures (see section 2.2). However, the comparisons do generally confirm the validity of the PMUe. The reminder of this section describes the results of the comparisons, aggregated by UN region and/or continents. UN classification of regions (UN, 2002) is depicted in Map 4.2. MAP 4.2 UN classification of the world in regions 37

26 ] M A P P I N G G L O B A L U R B A N A N D R U R A L P O P U L A T I O N D I S T R I B U T I O N S Comparison of the urban land area results Comparison of the urban land area results of PMUe, GRUMP and Boston University Urban Area (BUUA) databases are listed in Table 4.4 by UN region. Generally the GRUMP data produced by CIESIN yielded the largest extents. However, in 49 of the countries analysed the PMUe results were greater than the GRUMP urban extents and in seven of twenty regions (three in Africa, one in America, one in Europe and two in Oceania) the PMUe identified a larger urban area than GRUMP. The land cover class called built-up area detected by BUUA was the smallest of the three databases, except in Japan. TABLE 4.4 Comparison of urban area by UN regions (km 2 ) Continent Region PMUe GRUMP BUUA Database with extents built-up area greater urban area Africa Eastern Africa 44,839 30,228 9,870 PMUe Middle Africa 17,059 16,402 3,486 PMUe Northern Africa 55,462 81,379 15,360 GRUMP Southern Africa 21,285 49,873 10,458 GRUMP Western Africa 60,416 39,482 13,566 PMUe Americas Caribbean20,327 26, GRUMP Central America 44, ,251 8,683 GRUMP Northern America 292, , ,510 GRUMP South America 425, ,434 42,221 PMUe Asia Eastern Asia 266, , ,554 GRUMP Japan26, ,210 52,067 GRUMP South-central Asia 153, ,989 85,313 GRUMP South-eastern Asia 83, ,044 17,603 GRUMP Western Asia 74, ,586 27,405 GRUMP Europe Eastern Europe 217, ,381 68,212 GRUMP Northern Europe 89, ,289 21,263 GRUMP Southern Europe 49, ,572 48,933 GRUMP Western Europe 225, ,379 52,797 PMUe Oceania Australia and New Zealand 42,123 44,601 8,990 GRUMP Melanesia 1,957 1, PMUe Developing countries 1,270,260 1,621, ,558 GRUMP Developed countries 943,236 1,863, ,772 GRUMP World Total 2,213,496 3,485, ,330 GRUMP [ 38

27 DETERMINING VARIATION IN THE DISTRIBUTION OF URBAN AND RURAL POPULATIONS BY PIXEL FIGURE 4.6 Histogram of the share of urban area in total area, by UN region 30% 25% % of urban area for PMUe % of urban area for GRUMP % of urban area for BUUA 20% 15% 10% 5% 0% Eastern Africa Middle Africa Northern Africa Southern Africa Western Africa Caribbean Central America Northern America South America Eastern Asia Japan South-central Asia South-eastern Asia Western Asia Eastern Europe Northern Europe Southern Europe Western Europe Australia and New Zealand Melanesia Africa America Asia Europe Oceania 39

28 ] M A P P I N G G L O B A L U R B A N A N D R U R A L P O P U L A T I O N D I S T R I B U T I O N S [ FIGURE 4.7 Comparison of the share of urban area in total area, by continent 4,0% 3,5% 3,0% 2,5% 2,0% 1,5% 1,0% 0,5% 0,0% % of urban area for PMUe % of urban area for GRUMP % of urban area for BUUA FIGURE 4.8 Comparison of the share of urban area in total area by developed/developing country 4,0% 3,5% 3,0% 2,5% 2,0% 1,5% 1,0% 0,5% % of urban area for PMUe % of urban area for GRUMP % of urban area for BUUA 0,0% Developed Countries Developing Countries Figures 4.6, 4.7 and 4.8 compare the share of the urban area in total area by UN region, by continent and by developed/developing country. As expected, developed countries are more urbanized than developing countries, with the largest difference in GRUMP. In conclusion, the urban land area of the world estimated by PMUe and GRUMP are 1.7 and 2.7 percent respectively. The BUUA figures are much smaller, only 0.5 percent. 40

29 DETERMINING VARIATION IN THE DISTRIBUTION OF URBAN AND RURAL POPULATIONS BY PIXEL Figure 4.9 compares boundaries of four urban extents defined by PMUe, GRUMP and BUUA. Three of these examples show the generally larger extents generated by GRUMP compared to PMUe and BUUA for most urban areas, both the agglomerations and the smaller settlements. One example from South America shows a larger extent estimated by PMUe, as was typical for that region. FIGURE 4.9 Visual comparison of urban extents (a) Atlanta, Georgia, USA (b) Mexico City, Mexico (c) Santiago, Chile (d) New Delhi, India Global Rural Urban Mapping Project (GRUMP) Poverty Mapping Urban extents (PMUe) Boston University Urban Area (BUUA) Rural Area Evaluation of the geographic coordinates of the human settlements in the PMUe database As noted in section 3.3, the GRUMP human settlements database contains the geographic coordinates and the estimated population for the years 1990, 1995 and 2000 for each human settlement. Therefore it could be used for comparing the geographic coordinates of the human settlements detected in PMUe. 41

30 ] M A P P I N G G L O B A L U R B A N A N D R U R A L P O P U L A T I O N D I S T R I B U T I O N S [ Using the original version of the PMUe (i.e. without a buffer around the human settlements), it was possible to detect about 72 percent of the GRUMP settlements globally; these held 88 percent of the population living in human settlements in The lowest detection percentage was again in Africa, where the results were about 60 percent, containing 86.5 percent of the population. In order to ascertain whether this relatively poor detection performance was merely the result of a slight difference in the positioning of the urban area and the points, or was caused by a more serious problem, a buffer of one kilometre around the PMUe human settlements was generated. With this buffer the detection results improved considerably. Globally, 92 percent of the GRUMP human settlements were captured, corresponding to 97 percent of the population (Table 4.5). With the buffer, the improvement in terms of the detected points was almost 30 percent, but in terms of population, the increment was only nine percent, as the buffer pixels were not highly populated. However the size of the global urban population not captured was very small around three percent. TABLE 4.5 The human settlements in GRUMP database detected by PMUe with one kilometre buffer Continent Region N. of human Estimated Percentage Percentage settlements population of human of population in grump for the human settlements detected with settlements detected PMUe in 2000 with PMUe Africa Eastern Africa ,455, Middle Africa ,202, Northern Africa ,405, Southern Africa ,502, Western Africa ,768, Americas Caribbean518 20,326, Central America ,845, Northern America 13, ,648, South America 6, ,398, Asia Eastern Asia 2, ,255, Japan ,854, South-central Asia 3, ,132, South-eastern Asia ,445, Western Asia ,620, Europe Eastern Europe 2, ,285, Northern Europe 2,057 59,720, Southern Europe 3,040 96,234, Western Europe 2, ,331, Oceania Australia and New Zealand ,387, Melanesia , Developing countries 17,764 1,506,993, Developed countries 24, ,461, World Total 42,576 2,317,455,

31 CHAPTER 5 UNRESOLVED ISSUES AND FUTURE CHALLENGES During the past decade a number of important advances have been made in GIS technologies that have allowed demographers and GIS experts, working together, to begin to map the spatial distribution of urban and rural populations globally. This report has reviewed the results of these efforts to date, and presented a new method for mapping variations in the distribution of rural populations by pixel. The unique contributions of this new method are: " creation of urban and rural population distribution grids (both pixel counts and densities), that assign values to each pixel approximating the actual number of people living in that location; " creation of a population grid for rural settlements, i.e., human settlements that are not classified as urban but where the population density is too high for them to be considered mostly agricultural. Both results are important and were required for achieving FAO s objectives for the Poverty Mapping project. Nevertheless, a number of issues and challenges remain, that, if resolved, would permit an even more refined analysis of the spatial distribution of the human population around the globe. The first issue, commented upon several times already, is the lack of standard definitions for what constitutes an urban area, and the criteria used for distinguishing urban from rural population. A second issue relates to the imprecision of the datasets currently available for determining the location of human settlements and their extents (DCW, NIMA points database, Nighttime Lights, GRUMP). Another relates to the lack of comparability of statistical data from different countries and sources, and the need to rely on statistical estimation procedures to create time series. These issues have limited the ability of researchers to validate their results, as no independent source exists that could serve this purpose. Until now, validation efforts for population distribution grids have been limited to crosschecking results with population totals reported by the UN (in the case of GPW, GRUMP, PMUR) or by official sources (in the case of LandScan). For urban extents, the Demographic Health Survey (DHS) points have been used to validate the location and extents of urban areas generated by the Nighttime Lights at country level for some countries, but the DHS coverage is not global. Some of the more promising approaches for resolving these issues include: " georeferencing of census and survey data at the time of collection and introducing data collection procedures that allow recording of results for lower level administrative units before aggregating to the national level; 43

32 ] " developing and publicly releasing models such as that used by LandScan to distribute population counts by pixel, and validating the results with a sampling frame and on-the-ground field surveys; " further improvement in the quality and accuracy of the Nighttime Lights databases and images; " reliance on medium and high resolution images now available from MODIS and other imaging satellites that can detect urban areas more reliably. One of the most pressing challenges of our time the reduction and eventual elimination of poverty and hunger from the globe cannot be effectively addressed without accurate knowledge about who the poor and hungry are, where they live, and what factors present in their immediate surroundings are contributing to their distress. Mapping the spatial distribution of the global population is an essential tool for generating this knowledge; continued effort to resolve remaining challenges will be required to obtain full benefit from this potentially powerful tool. [ M A P P I N G G L O B A L U R B A N A N D R U R A L P O P U L A T I O N D I S T R I B U T I O N S 44

33 REFERENCES Sources and notes Map 2.1 Source: Notes: The Nighttime Lights of the World superimposed on bathymetry (segment) National Oceanic and Atmospheric Administration (NOAA). Web site The database has a resolution of 30 arc-seconds. Map 2.2 The Global Land Cover, 2000 Source: Global Vegetation Monitoring Unit of the Joint Research Center (JRC), Global Land Cover of the Year 2000, Ispra (VA), Italy. Web site www-gvm.jrc.it/glc2000/ Notes: Copyright European Commission, Map 3.1 Source: Notes: Population density in 2000 from GPWv3 adjusted to UN totals Center for International Earth Science Information Network (CIESIN), Columbia University; and Centro Internacional de Agricultura Tropical (CIAT), Gridded Population of the World (GPW), Version 3, Palisades, NY, USA. Web site GPW grid is a population database at 2.5 arc-minutes resolution. Map 3.2 The LandScan Global Population Database, 2002 Source: Oak Ridge National Laboratories (ORNL), LandScan 2002 global population database, Oak Ridge, TN, USA. Web site Notes: LandScan 2002 dataset is a worldwide ambient population database compiled on a 30 arc-second grid. Map 3.3 Source: Notes: Map 4.1 Source: Notes: Map 4.2 Source: Notes: Population density in 2000 from GRUMP adjusted to UN totals Center for International Earth Science Information Network (CIESIN), Columbia University; International Food Policy Research Institute (IPFRI), the World Bank; and Centro Internacional de Agricultura Tropical (CIAT), Global Rural-Urban Mapping Project (GRUMP): Gridded Population of the World, version 3, with Urban Reallocation (GPW-UR), Palisades, NY, USA. Web site global.jsp GRUMP dataset is a population database at 30 arc-seconds resolution. Spatial distribution of the difference in urban population by country results of the analysis the map was made by FAO-SDRN GIS Unit following the results of the comparison the UN figure with the PMUp estimates. UN classification of the world in regions United Nations World Urbanization Prospects, the 2001 Revision. United Nations Publication sales No. E.02.XIII.16 The map was made by FAO-SDRN GIS Unit following the definitions of regions and major areas contained in the report. 45

34 ] M A P P I N G G L O B A L U R B A N A N D R U R A L P O P U L A T I O N D I S T R I B U T I O N S [ Document references Balk, D. & Yetman, G The Global Distribution of Population: Evaluating the Gains in Resolution Refinement. Documentation for GPW Version 3 available only at Balk, D., Pozzi F., Yetman, G., Deichmann, U. & Nelson, A. 2004a. The distribution of people and the dimension of place: methodologies to improve the global estimation of urban extents. Working Paper, CIESIN, Columbia University. Palisades, NY. Documentation for GRUMP also available at UR_paper_webdraft1.pdf. Clark, J.I. & Rhind, D.W Population Data and Global Environmental Change. Paris, IISC/UNESCO. Danko, D.M The Digital Chart of the World project. Photogrammetric Engineering and Remote Sensing, 58: Deichmann, U A review of spatial population database design and modeling. Technical Report TR-96-3, National Center for Geographic Information and Analysis, Santa Barbara. Deichmann, U. 1996a. Asia medium resolution population database documentation. Database documentation and digital database prepared in collaboration with UNEP/GRID Geneva for the UNEP/CGIAR Initiative on Use of GIS in Agricultural Research, National Center for Geographic Information and Analysis, University of California, Santa Barbara. (available also at Deichmann, U., Balk, D. & Yetman, G Transforming population data for interdisciplinary usages: from census to grid. Documentation for GPW Version 2 available only at Dobson, J.E., Bright, E.A., Coleman, P.R., Durfee, R.C. & Worley, B. A LandScan: a global population database for estimating populations at risk. Photogrammetric Engineering and Remote Sensing. 66(7): Doll, C.N.H., Muller, J.P. & Elvidge, C.D Nighttime imagery as a tool for global mapping of socio-economic parameters and greenhouse gas emissions. Ambio, 29(3): Dooley, J.F An inventory and comparison of globally consistent GIS databases and libraries. Environment and Natural Resources Series No 19, FAO - Rome. Elvidge, C.D., Baugh, K.E., Kihn, E.A., Kroehl, H.W. & Davis, E.R. 1997a. Mapping city lights with nighttime data from the DMSP Operational Linescan System. Photogrammetric Engineering and Remote Sensing, 63(6): Elvidge, C.D., Baugh, K.E., Kihn, E.A., Kroehl, H.W. & Davis, E.R. 1997b. Relation between satellite observed visible-near infrared emission, population, economic activity and electric power consumption. Int. Journal of Remote Sensing, 18(6): Elvidge, C.D., Baugh, K.E., Dietz, J.B., Sutton, P.C. & Kroehl, H.W Radiance calibration of DMSP-OLS low light imaging data of human settlements. Remote Sensing of Environment, 68: Elvidge, C.D., Imhoff, M.L., Baugh, K.E., Hobson, V.R., Nelson, I., Safran, J., Dietz, J.B. & Tuttle, B.T Nighttime Lights of the World: ISPRS Journal of Photogrammetry and Remote Sensing, 56:

35 Elvidge, C.D., Safran, J., Nelson, I.L., Tuttle, B.T., Hobson, V.R., Baugh, K.E., Dietz, J.B. & Erwin, E.H Area and position accuracy of DMSP night-time lights data. Chapter 20 in Remote Sensing and GIS Accuracy Assessment. Eds. R.S. Lunetta and J.G. Lyon, CRC Press, pp Fekete, B. M., C. J. Vorosmarty, and R. B. Lammers Scaling gridded river networks for macroscale hydrology: Development, analysis and control of error. Water Resources Research, 3(77): Fischer, G., van Velthuizen, H., Shah, M. & Nachtergaele, F.O Global agro-ecological assessment for agriculture in the 21st century: methodology and results. IIASA and FAO, Publication RR FAO Compendium of agricultural environmental indicators to Statistics Analysis Service, Statistics Division, Rome. Henninger, N. & Snel, M Where are the poor? Experiences with the development and use of poverty maps. WRI and UNEP/GRID-Arendal. Huddleston, B., Ataman, E., Salvatore, M. & Bloise, M A geospatial information framework for analysis of poverty and environment links. Environment and Natural Resources Series, FAO - Rome (forthcoming). Hyman, G., Nelson, A., Lema, G., Fosnight, G., Singh, A. & Deichmann, U Latin America and Caribbean Population Database Documentation (available only at globalpop/lac/intro.html). Imhoff, M.L., Lawrence, W.T., Stutzer, D.C. & Elvidge, C.D A technique for using composite DMSP/OLS city lights satellite data to map urban area. Remote Sensing of Environment, 61(3): Lee, T. F., Miller, S. D., Turk, F. J., Schueler, C., Julian, R., Elvidge, C., Deyo, S., Dills, P. & Wang, S The Day/Night Visible Sensor aboard NPOESS VIIRS, Proc. of the 13th Conference on Satellite Meteorology and Oceanography, 1.8, American Meteorological Society, Norfolk, VA. McGrahanan, G., Marcotullio, P., Bai, X., Balk, D., Braga, T., Douglas, I., Elmqvist, T., Rees, W., Satterthwaite, D., Songsore, J. & Zlotnik, H Urban Systems. Chapter 22 in Conditions and Trends Assessment of the Millennium Ecosystem Assessment. Forthcoming in October Nelson, A African population database documentation (available only at globalpop/africa/africa_index.html) Pozzi, F., Small, C. & Yetman, G Modeling the distribution of human population with nighttime satellite imagery and gridded population of the world. Earth Observation Magazine, 12 (4): Oak Ridge National Laboratory (ORNL) Documentation of LandScan Global Population 1998 Database and further releases 2000, 2001, 2002 and 2003 (available only at sci/gist/landscan/landscancommon/landscan_doc.html). Schneider, A., Friedl, M.A., McIver, D.K. & Woodcock, C.E Mapping urban areas by fusing multiple sources of coarse resolution remotely sensed data. Photogrammetric Engineering and Remote Sensing, 69 (12): Small, C., Pozzi, F. & Elvidge, C.D Spatial analysis of global urban extents from the DMSP- OLS Night Lights. Remote Sensing of the Environment, 96 (3-4): Sutton, P Modeling population density with night-time satellite imagery and GIS. Computers, Environment and Urban Systems, 21(3/4):

36 Tobler, W.R., Deichmann, U., Gottsegen, J. & Maloy, K The global demography project. National Center for Geographic Information and Analysis 95 6, University of California, Santa Barbara, California, 75 pp. United Nations Principles and Recommendations for Population and Housing Censuses. Revision 1. Series M, No. 67, Rev. 1 (United Nations publication, Sales No. E.98.XVII.8). United Nations World Urbanization Prospects, the 2001 Revision. United Nations Publication sales No. E.02.XIII.16 United Nations World Population Prospects, the 2002 Revision. United Nations Publication sales No. E.03.XIII.10 United Nations World Urbanization Prospects, the 2003 Revision. United Nations Publication sales No. E.04.XIII.6 [ ] M A P P I N G G L O B A L U R B A N A N D R U R A L P O P U L A T I O N D I S T R I B U T I O N S 48

37 Web site references 1. United Nations Population Division World Population Prospect: The 2004 Revision Population Database (available at 2. U.S. Census Bureau s International Program Center Statistical demographic and socio-economic data for 227 countries and areas of the world. (available at 3. World Gazetteer (available at 4. City Population (available at 5. Environmental Systems Research Institute, Inc. (ESRI) Digital Chart of the World (DCW) (available at 6. National Geospatial-Intelligence Agency Vector Smart Map level 0 (available at 7. National Geospatial-Intelligence Agency Vector Smart Map level 1 (available at 8. National Geospatial-Intelligence Agency GEONet Names Server (GNS) (formerly NIMA points database) (available at 9. National Oceanic and Atmospheric Administration (NOAA) Database of Nighttime Lights of the World (available at Global Land Cover Characteristics (GLCC) dataset (available at Global Vegetation Monitoring Unit (GVM) Global Land Cover 2000 database (GLC2000) (available at www-gvm.jrc.it/glc2000/) 12. National Aeronautics and Space Administration MOderate Resolution Imaging Spectroradiometer (MODIS) (available at Center for International Earth Science Information Network (CIESIN) Gridded Population of the World version 3 (GPW v3) dataset; Future Estimates 2015 dataset; Data products developed under the Global Urban-Rural Mapping Project (GRUMP). (Downloaded in April 2005) (available at 49

38 14. EuroGeographics Seamless Administrative Boundaries of Europe (available at Oak Ridge National Laboratory (ORNL) LandScan Global Population Database, 2002 (Downloaded in March 2003) (available at United Nations Environment Programme (UNEP) Global Resource Information Database (available at Boston University s Department of Geography Urbanization as a component of global change: a global map of urban areas (available at United Nations Educational, Social and Cultural Organization (UNESCO) World Water Development Report II Indicators for World Water Assessment Programme (available at Poverty Mapping Project (available at UN Geographic Information Working Group (DPKO/UNCS) International Boundaries dataset (Downloaded March 2004) (available at for members of the UN system) [ ] M A P P I N G G L O B A L U R B A N A N D R U R A L P O P U L A T I O N D I S T R I B U T I O N S 50

39 Annex Estimates of future global population distribution to 2015 Deborah Balk Melanie Brickman Bridget Anderson Francesca Pozzi Greg Yetman CIESIN Columbia University PO Box 1000 Palisades, NY Please correspond with: Acknowledgments for this annex We thank Lisa Lukang, Mirella Salvatore, Barbara Huddleston and Ergin Ataman, in particular, for their contributions. This database and map was prepared as part of the FAO Poverty Mapping Project (GCP/INT/761/NOR) funded by the Government of Norway and also with the support from National Aeronautics and Space Administration (Contract NAS ) to the Socioeconomic Data and Applications Center (SEDAC) at CIESIN. The data are freely available at the following sites:

40 DISCLAIMER FOR THIS ANNEX The designations employed and the presentation of material in this information product do not imply the expression of any opinion whatsoever on the part of CIESIN or the Food and Agriculture Organization of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. Ideas contained in this document are solely those of the author and do not necessarily represent the views of CIESIN and FAO. COPYRIGHT FOR THIS ANNEX All rights reserved. Reproduction and dissemination of material in this information product for educational or other non-commercial purposes are authorized without any prior written permission from the copyright holders provided the source is fully acknowledged. Reproduction of material in this information product for resale or other commercial purposes is prohibited without written permission of the copyright holders. Applications for such permission should be addressed to CIESIN or the Chief, Publishing and Multimedia Service, Information Division, FAO, Viale delle Terme di Caracalla, 00100, Rome, Italy or by to FAO and The Trustees of Columbia University in the City of New York 2005

41 ANNEX 1.INTRODUCTION There is considerable interest in the future distribution of human population. The United Nations Population Division produces biannual updates to its medium-term projections of population (UN, 2003) to insure that researchers and policy makers have the most recent information upon which to base their analysis and policies. The UN (and other projection making organizations, see review in O Neill et al., 2001) project population at the national level only, despite the fact that there is evidence to believe that future population growth, on average, is more likely to occur in urban areas than rural ones (e.g. UN, 2002). A recent National Research Council study has called for much greater attention to be paid toward understanding spatial issues in understanding future urbanization (NRC, 2003). In the near term, however, there are no formal demographic forecasts of population that are spatially explicit. This exercise is a stop gap measure to address a short-term scenario: If the current rates of population growth, as observed in the decade prior to 2000, continue for 15 years, what would the distribution of population look like in the year 2015? The Gridded Population of the World: Future Estimates, 2015 (GPW2015) provides estimates of the world s population, by country and continent, for the year 2015 and converts the distribution of human population from sub-national units to a series of 2.5 arc-minute quadrilateral grids. This 2015 data product is entirely derived from the spatial and population input data used to construct the Gridded Population of the World version 3 (GPWv3) (CIESIN and CIAT, 2005). This is comprised of administrative boundary and associated population data. The 2015 gridded population data was derived from almost 400,000 administrative units. For most countries of the world, roughly 75 percent of them, subnational estimates of population from the two most recent censuses (c and 2000) were used as the basis of the extrapolation. Sub-national rates of growth for the interval were then applied, in five year increments, as described in more detail below. Population estimates are projected to the year 2015 using the same simple extrapolation methods as the GPWv3 and prior GPW databases (Deichmann, Balk and Yetman, 2001; Tobler et al., 1997). The purpose of our 2015 projection is to show a scenario of future spatial distribution for the population at a subnational resolution. However, it assumes a continuation of recent demographic patterns and is not suitable for generating national population totals in and of itself. The UN method for projecting population (UN, 2001) follows a cohortcomponent methodology and incorporates more information about the baseline population (e.g. age structure) and future population trends (e.g. expected fertility and 55

42 ] M A P P I N G G L O B A L U R B A N A N D R U R A L P O P U L A T I O N D I S T R I B U T I O N S [ ] mortality). Therefore, our 2015 total population estimates are adjusted at the national level to the United Nations 2015 population projections. An adjustment factor (A) is applied to our administrative unit population totals (Pop NSO2015 ) via the following calculation, where Pop UN2015 represents the UN medium variant projected population for 2015: A=1+ (Pop UN 2015 Pop NSO 2015 ) Pop NSO 2015 The results of this method are shown in Map 1.1. As in the present, the most densely populated places are south and southeast Asia. Similarly, there are expected to be very densely populated regions of Africa (notably in Nigeria, and east Africa), in Brazil, parts of Central America (including an already dense Mexico City region) and North America (particularly the coastal portions of the urban north east and Los Angeles areas). Europe also continues to be densely populated. 56

43 MAP 1.1 Global population density in Source: Center for International Earth Science Information Network (CIESIN), Columbia University; Food and Agricultural Organization (FAO) and Centro Internacional de Agricultura Tropical (CIAT) ANNEX - 1. INTRODUCTION

44 ANNEX 2.INPUT DATA DESCRIPTION As previously stated, the input data for the 2015 database mirrors that in GPWv3. The specificity of these inputs varies greatly by country due to factors such as: date of most recent census, administrative level at which population and spatial data are released, degree to which the boundaries and population inputs match spatially, the relationship between the number of administrative units and the country land area, among other influences. All inputs are divided between two categories: boundary data and population data, as described in more detail below. 2.1 BOUNDARY INPUT SOURCES Geographic Information System (GIS) data sets of either administrative or statistical (census) reporting units are produced by national statistical and mapping agencies, research projects, and commercial data vendors. GPWv3 relied on a combination of publicly available boundary data sets and additional boundaries from commercial data vendors or statistical agencies that sell spatial data on license. The level of the spatial inputs utilized in GPWv3 was constrained to the level for which matching population data was available, which varies substantially by country. Levels are commonly ranked from low to high, where the lowest level (level one), refers to the first subnational administrative level below the national one, with higher levels representing subsequently finer administrative levels within each country. In general, while there is no consistent pattern between countries with regard to the number of administrative units, there tend to be higher levels available for more developed countries. Differences in administrative levels that can be used to generate our estimates are due in part, to data availability i.e., population and spatial inputs for the highest-level units are not always available or usable. In addition, the designation of administrative units is sometimes ambiguous. Often, administrative units are based on historic boundaries that are based on geographic and political features that were once historically important but which no longer translate to necessarily meaningful divisions. It also should be noted that for statistical data-reporting, some countries utilize geographic regions that serve no administrative purpose and therefore do not match the administrative boundaries. As demonstrated by Map 2.1, the number of administrative units included in GPWv3 varies greatly between countries and is not necessarily proportional to the land area of a nation. 59

45 ] M A P P I N G G L O B A L U R B A N A N D R U R A L P O P U L A T I O N D I S T R I B U T I O N S [ MAP 2.1 Number of administrative units included in GPWv3, by country The degree of resolution of administrative units provides a good representation of this variability in the number of administrative units. Resolution is calculated as: (country area) / (number of units) Resolution is to some extent determined by the geographic size and average population density of a country. Smaller countries have a relatively higher resolution even before adjusting for the number of administrative units. In other words, the national extent of a small country may already be smaller than an administrative unit of another country. Slovenia is an example of a small country with one of the highest resolutions both because of geographic size and number of units. Conversely, many countries with vast, mostly uninhabited areas tend to have large administrative units resulting in very low resolution (e.g. Mongolia, Libya). Additionally, the presence of relatively densely distributed populations generally necessitates a larger number of administrative units than a more sparsely populated country of equivalent size. This results in higher relative resolution. For example, India is much more densely populated and has higher resolution than similarly sized, but sparsely populated Algeria. Low resolution can be a result of inadequate data, in which higher resolution administrative units boundaries exist, but were simply not available for this project. It can 60

46 ANNEX - 2. INPUT DATA DESCRIPTION also stem from a combination of data quality, geographic and population density issues. As a comparison, Mongolia and Saudi Arabia have similarly low resolution, but for different reasons. These two countries are similar in geographic size, but Mongolia has approximately one tenth the population size of Saudi Arabia. The measure of average persons per administrative unit for Mongolia was 108 in 2000, but 672 in Saudi Arabia. Since we would expect higher resolution in more highly populated areas, the data quality for Saudi Arabia is considered to be inferior to that of Mongolia. For Saudi Arabia, more detailed administrative units would help considerably in the precise representation of population distribution. Table 2.1 demonstrates the countries with the highest and the lowest available resolution (excluding countries and areas smaller than 10,000 square kilometres in size, many of which consist of only one administrative unit). TABLE 2.1 Countries with the highest and lowest available resolution 10 lowest resolutions Km 10 highest resolutions Km Saudi Arabia 386 Slovenia 0.01 Chad 298 Malawi 1.84 Mongolia 265 Switzerland 3.21 Angola 264 South Africa 3.54 Libyan Arab Jamahiriya 254 France 3.66 Svalbard 246 Slovakia 3.87 Algeria 219 Ireland 4.09 Sudan171 Portugal 4.49 Yugoslavia 159 Indonesia 4.65 Botswana 156 Hungary 5.23 Within a given country, the mean resolution (across administrative units) depends considerably on a combination of geographic and demographic characteristics, some of which have been described above. Thus, mean resolutions are not always comparable between countries. For example, level-three administrative units in Canada can vary from a small, densely populated city-district to large tracts of uninhabited land whereas the same administrative level in the continental United States varies much less in area. By continent, the average level and total number of administrative units used are shown in Table 2.2. There are clear differences, with Europe, Oceania, and North America having higher average resolutions. All continents, however, have some countries with highresolution data, leading to a large number of units for each continent. As compared to the first version of GPW, undertaken a decade ago, there is nearly a 20 times improvement in GPWv3. 61

47 ] M A P P I N G G L O B A L U R B A N A N D R U R A L P O P U L A T I O N D I S T R I B U T I O N S TABLE 2.2 Average level and total number of administrative units, by continent 10 lowest resolutions Mode of administrative Sum of the Average Average Levels Number of units Resolution Persons per unit Africa 2 109, Asia 2 99, Europe 2 98, North america 2 74, Oceania 1 2, South america 2 15, World total 2 399, POPULATION INPUT SOURCES Population data were collected for each country via national statistical agencies and census bureaus. The most recent year and most detailed administrative level were acquired whenever possible. A large portion of the data was publicly available, however it was also necessary to purchase population information for many areas. Population data constraints such as censuses occurring in different years and inconsistent data availability result in disparities related to the most recent population data year employed for each country. This is illustrated by Map 2.2 below. MAP 2.2 Year of the most recent census data available, by country [ 62

48 ANNEX - 2. INPUT DATA DESCRIPTION Where possible, two data points were collected as close to the target years of 1990 and 2000 as possible. Obviously, the closer the data points were to 1990 and 2000, the less interpolation was required. The greatest source of uncertainty in the dataset occurred in cases where the available population data was far from the target years, and where only one population data year was available. Countries with only one data point occurred most often in areas where new data obtained for GPWv3 was at a higher spatial resolution than in past GPW iterations, thus affecting our extrapolation method (see section 3.2). Map 2.3 displays the number of population data years employed globally. MAP 2.3 Number of population data years employed, by country 63

49 ANNEX 3.METHODOLOGY In the following paragraphs we describe the methodology used to create the GPW2015, both in terms of the gridding approach used to produce the final raster grids, and in terms of the extrapolation methodology used to calculate the population distribution in THE GRIDDING APPROACH The GPWv3 administrative and population input data were used to produce raster grids demonstrating the estimated number of people residing in each grid cell. When the administrative units are converted to grids it is possible for more than one unit to fall into the same grid cell and for some units to be smaller than a single grid cell. To ensure that no administrative information is lost in the gridding process, we implemented a proportional allocation of population from administrative units to grid cells. Proportional allocation works on the assumption that the variable being modelled in this case population is distributed evenly over the administrative unit. Grid cells are assigned a portion of the total population for the administrative unit they fall within, dependent on the proportion of the area of administrative unit that the grid cell takes up. A simple example of proportional allocation (also known as areal weighting) would be an administrative unit with a population of that is filled exactly with 100 grid cells. For this case, each grid cell would be assigned a population of 50. In the creation of the population grids, the actual implementation of areal weighting uses the administrative unit s population density and the area of overlap between administrative unit and grid cell to calculate each unit s contribution to the cell population total (further description is given in Deichmann et al., 2001, and a comparison between this and other methods is given in Deichmann, 1996). 3.2 EXTRAPOLATION METHODOLOGY The methodology for the extrapolation of population data to 2015 is similar to that used for extrapolating population data in GPWv3 to 1990, 1995, and In both instances, the population inputs were collected for the most recent years and smallest sub-national units. The majority of these data were obtained via national censuses or official estimates. For the GPW2015, the official population estimates were then extrapolated forward by computing an average annual geometric growth rate that was then applied to the most recent population data. Because population numbers do not typically rise or fall in a linear fashion, a geometric growth rate was calculated for these estimates. 65

50 ] M A P P I N G G L O B A L U R B A N A N D R U R A L P O P U L A T I O N D I S T R I B U T I O N S The formula employed for calculating the growth rate is: LN [(P 2/P 1 )] r = (t 2-t 1 ) where, LN = the natural log, P1 and P2= population counts for the first and second reference years, t 1 and t 2 = time periods 1 and 2. The forward extrapolations are thereby computed with the following formula: e rt *P 1 where, r= the geometric growth rate (as defined above), t= the number of years the initial estimate will be projected forward/backward, P 1 = population counts for the first reference year. These extrapolations are not meant to be formal projections. As indicated initially, this is an extrapolation method that is commonly used for short-term projections and is not typically employed for longer-term projections because it lacks information useful for the longer-term adjustments to population composition and dynamics. The growth rates are held constant and the populations are accordingly estimated for 2015 without the aid of additional information. In the next section, we address when and under what circumstances adjustments beyond that of adjusting the national population totals to the UN medium-run project were made. [ 66

51 ANNEX 4.EXTRAPOLATION PROBLEMS AND SOLUTIONS In a number of instances, outstanding obstacles impeded our use of the above methodology for growth rate and projection calculations. Problems were dealt with on a case-by-case basis. Descriptions of the setbacks we encountered and explanations of our solution procedures are described below. 4.1 IRRECONCILABLE BOUNDARY DIFFERENCES In general, geographic boundaries are not static. Unfortunately, however, if an administrative unit changes size or shape between two data years it is impossible to use the above method to calculate a population growth rate for that particular unit. Thus, when faced with irreconcilable boundary differences between two data years, we implemented a three-tiered approach for determining a growth rate to be used in the population projections: (a) Whenever viable, we created hybrids of the administrative unit polygons (and their associated population figures) in order to form matching subnational datasets for two time periods. In instances where hybrids were created, our administrative units do not match those politically defined by the country of origin, but are still spatially and demographically accurate. (b) If a polygon-based hybrid was impractical, the next step was to consider using a coarser administrative level to calculate the growth rate. For example, if there were substantial boundary changes at the second administrative level, but the first administrative level remained unchanged, then a growth rate was computed at the first administrative level and applied to the higher resolution data. (c) When neither option (a) nor (b) were feasible, national level growth rates were calculated using United Nations population estimates and projections (UN, 2001). These rates were then uniformly applied to the most detailed and recent subnational data at our disposal. In cases were we suspected the data to be largely erroneous, United Nations derived growth rates were implemented as well. Map 4.1 illustrates countries for which we used subnational growth rates or, where necessary, national growth rates. 67

52 ] M A P P I N G G L O B A L U R B A N A N D R U R A L P O P U L A T I O N D I S T R I B U T I O N S [ MAP 4.1 Number of countries for which sub-national versus national growth rates were used 4.2 MIXED ADMINISTRATIVE LEVEL SPATIAL AND POPULATION DATA Administrative and population data are often collected and released by separate governmental offices as well as in unconnected years. Because the two types of data are not published together, a matched dataset at the lowest available administrative level may be unattainable. In this situation, two potential data scenarios occur: (a) The population data are at a smaller administrative level than the spatial data. When this situation transpired, it was necessary to aggregate the population data to the coarser level of the spatial data. As a result, we were unable to use the more detailed level of population data and will continue to be incapable of doing so until spatial data are made available at the same level. For example, if we had population estimates for the Delaware counties of Kent, New Castle, and Sussex but only had spatial boundaries for the state of Delaware, it would be necessary to combine the population figures up to the state level. or (b) The spatial data are at a smaller administrative level than the population data. Under these circumstances, the population growth rate for the larger unit could be applied to the smaller spatial units it encompassed. In this scenario, the more detailed geographic level was maintained. For example, if we only had population estimates for the state of Delaware but had spatial boundaries for counties of Kent, New Castle, and Sussex, we could calculate a Delaware growth rate and apply this same rate to each of the three counties. 68

53 ANNEX - 4. EXTRAPOLATION PROBLEMS AND SOLUTIONS 4.3 PARTICULARLY HIGH POPULATION GROWTH RATES Local area estimates of population are bound to have higher levels of error than larger units: rapid growth appearing in a small region would be absorbed by estimates of a larger region; and small area rates of growth may be unlikely to persist in the long-run. Rather, they may be localized in space and time. Even over a ten-year period highly localized growth may not be sustained. Thus, there is an optimal level of the administrative data at which to apply rates of growth, neither too coarse nor too fine. In general, if a country has very high-resolution data (such as level four or five), we do not use that information as the basis of the growth rate, rather we use a coarser unit (e.g., counties rather than tracts in the US) and apply those growth rates for the units that nest beneath it. We used a benchmark growth rate of five percent, because such a high level of growth is unusual for large administrative units (e.g. countries). Similar benchmarks have been implemented by the World Bank in a comparable exercise, in the World Development Report (WDR, 2002). (a) If population growth rates were higher than five percent for less than ten percent of all administrative units in a given country, growth rates were manually set to the five percent benchmark for the administrative units concerned. (b) If population growth rates were higher than five percent for more than ten percent of all administrative units in a given country, we suspected that the data were too flawed or unreliable to use; and United Nations derived growth rates were implemented as explained in section 4.1c of this annex. 69

54 ANNEX 5.CONCLUSION The Gridded Population of the World: Future Estimates, 2015 is a useful tool in conjunction with the UN 2015 projections as it shows a future scenario of the spatial distribution of populations. As already stated outright, this method has limitations for even short-run forecasting. Future investments should include further data development such that more rigorous estimates of future population, along with estimates of associated uncertainty, can be made at a subnational level. When shown with urban area extents for 2000, it is possible to see how the urban areas might grow over the next decade both in spatial extent and in population density compared to the year Map 5.1 shows scenarios for select urban areas from CIESIN s Global Rural-Urban Mapping Project database. They clearly emerge as much more densely populated than surrounding rural areas. Further improvements in resolution to the underlying population and boundary data will make it possible to gain greater insight in the expected future population of urban areas, and current and future peri-urban areas. Data constraints result in varying degrees of accuracy in the projected estimates between countries, making comparisons difficult in some circumstances, particularly for parts of Africa and Asia two regions of high concern for future urban and rural development. Recent investments in more timely, high-resolution, reliable population and boundary data have been made in many countries, such as Malawi, South Africa, Cambodia, Indonesia and Kenya. Using these countries as models for other nations in the same regions to follow, would go a long way to contributing to regional and global efforts to understand current and future population dynamics in urban and rural areas. 71

55 72 [ M A P P I N G G L O B A L U R B A N A N D R U R A L P O P U L A T I O N D I S T R I B U T I O N S ] MAP 5.1 Population density projections for the year 2015 with a focus on selected urban areas

56 REFERENCES Balk, D. & Yetman, G The global distribution of population: Evaluating the gains in resolution refinement. (available at documentation_final. pdf) Deichmann, U., Balk, D. & Yetman, G Transforming population data for interdisciplinary usages: from census to grid (available at: documentation.pdf) Deichmann, U A review of spatial population database design and modeling. Technical Report TR-96-3, National Center for Geographic Information and Analysis, Santa Barbara. National Research Council Cities transformed: demographic change and its implications in the developing world. Panel on Urban Population Dynamics, Eds. M.R. Montgomery, R. Stren, B. Cohen, & H.E. Reed, Committee on Population, Division of Behavioral and Social Sciences and Education. Washington, DC, The National Academies Press. O Neill, B., Balk, D., Brickman, M. & Ezra, M A guide to global population projections. Demographic Research 4(8): Tobler, W., Deichmann, U., Gottsegen, J. & Maloy, K World population in a grid of spherical quadrilaterals. International Journal of Population Geography, Volume 3, Issue 3, pp United Nations Department of International Economic and Social Affairs World population prospects: 2000 Revision. Volume 1 [Comprehensive Tables]. New York. United Nations World urbanization prospects, the 2001 revision. United Nations Publication sales No. E.02.XIII.16 United Nations World population prospects, the 2002 revision. United Nations Publication sales No. E.03.XIII.10 World Bank World development report: Sustainable development in a dynamic world. Washington DC. 73

57 - FAO ENVIRONMENT AND NATURAL RESOURCES SERIES 1. Africover: Specifications for geometry and cartography, 2000 (E) 2. Terrestrial Carbon Observation: The Ottawa assessment of requirements, status and next steps, 2002 (E) 3. Terrestrial Carbon Observation: The Rio de Janeiro recommendations for terrestrial and atmospheric measurements, 2002 (E) 4. Organic agriculture: Environment and food security, 2003 (E and S) 5. Terrestrial Carbon Observation: The Frascati report on in situ carbon data and information, 2002 (E) 6. The Clean Development Mechanism: Implications for energy and sustainable agriculture and rural development projects, 2003 (E)* 7. The application of a spatial regression model to the analysis and mapping of poverty, 2003 (E) 8. Land Cover Classification System (LCCS), version 2, 2005 (E) 9. Coastal GTOS. Strategic design and phase 1 implementation plan, 2005 (E) 10. Frost Protection: fundamentals, practice and economics- Volume I and II + CD, 2005 (E) Availability: March 2005 Ar Arabic F French Multil Multilingual C Chinese P Portuguese * Out of print E English S Spanish ** In preparation F A O E N A N D A S S E S S M E N T V I MO N I T O R I N G R O N M GEO-SPATIAL DATA E N T A N D AND INFORMATION ENVIRONMENTAL MANAGEMENT N A T U R A L R E S O C H A N G E C E U R E N V I R O N M E N TA L S G L O B A L S E E I C R V The FAO Technical Papers are available through the authorized FAO Sales Agents or directly from: Sales and Marketing Group - FAO Viale delle Terme di Caracalla Rome - Italy 75

58 M A P P I N G G L O B A L U R B A N A N D R U R A L P O P U L A T I O N D I S T R I B U T I O N S [ ] ENVIRONMENT AND NATURAL RESOURCES WORKING PAPERS 1. Inventory and monitoring of shrimp farms in Sri Lanka by ERS SAR data, 1999 (E) 2. Solar photovoltaics for sustainable agriculture and rural development, 2000 (E) 3. Energia solar fotovoltaica para la agricultura y el desarrollo rural sostenibles, 2000 (S) 4. The energy and agriculture nexus, 2000 (E) 5. World wide agroclimatic database, FAOCLIM CD-ROM v. 2.01, 2001 (E) 6. Preparation of a land cover database of Bulgaria through remote sensing and GIS, 2001 (E) 7. GIS and spatial analysis for poverty and food insecurity, 2002 (E) 8. Enviromental monitoring and natural resources management for food security and sustainable development, CD-ROM, 2002 (E) 9. Local climate estimator, LocClim 1.0 CD-ROM, 2002 (E) 10. Toward a GIS-based analysis of mountain environments and populations, 2003 (E) 11. TERRASTAT: Global land resources GIS models and databases for poverty and food insecurity mapping, CD-ROM, 2003 (E) 12. FAO & climate change, CD-ROM, 2003 (E) 13. Groundwater search by remote sensing, a methodological approach, 2003 (E) 14. Geo-information for agriculture development. A selection of applications. (E) ** 15. Guidelines for establishing audits of agricultural-environmental hotspots, 2003 (E) 16. Integrated natural resources management to enhance food security. The case for community-based approaches in Ethiopia, 2003 (E) 17. Towards sustainable agriculture and rural development in the Ethiopian highlands. Proceedings of the technical workshop on improving the natural resources base of rural well-being, 2004 (E) 18. The scope of organic agriculture, sustainable forest management and ecoforestry in protected area management, 2004 (E) 19. An inventory and comparison of globally consistent geospatial databases and libraries, 2005 (E) 20. New LocClim, Local Climate Estimator CD-ROM, 2005 (E) 21. AgroMet Shell: a toolbox for agrometeorological crop monitoring and forecasting CD-ROM, 2005 (E) ** 22. Agriculture atlas of the Union of Myanmar (agriculture year ), 2005 (E) 23. Better understanding livelihood strategies and poverty through the mapping of livelihood assets: a pilot study in Kenya, 2005 (E) Availability: October 2005 Ar C E Arabic Chinese English F P S French Portuguese Spanish Multil * ** Multilingual Out of print In preparation The FAO Technical Papers are available through the authorized FAO Sales Agents or directly from: Sales and Marketing Group - FAO Viale delle Terme di Caracalla Rome - Italy 76 Printed on ecological paper

59 - GEO-SPATIAL DATA AND INFORMATION This monograph is part of a series of reports that explain and illustrate methods for applying spatial analysis techniques to investigate poverty and F A O E N A N D V I MO N I T O R I N G A S S E S S M E N T R O N M E N ENVIRONMENTAL MANAGEMENT T A N D N A T U R A L R E S O C H A N G E C E U R E N V I R O N M E N TA L S G L O B A L S E E I C R V Nations and other sources, and various georeferenced sources are assessed for their usefulness to the geospatial analysis of population distribution. The report examines environment links worldwide. Analysing population distribution in relation to poverty and environmental factors is increasingly recognized as a valuable element in decision-making processes related to development issues. Accurately mapping and assessing vulnerable populations can provide a solid basis for recommendations on how best to reduce poverty and improve living conditions in developing countries. In this report, the various definitions of the terms urban and rural are reviewed, along with data from the United two widely used global georeferenced population datasets, reviews recent methodological developments for distinguishing urban and rural populations spatially and presents a method for creating an urban mask and determining variations in the distribution of urban and rural populations, by pixel. The report concludes with a brief discussion of unresolved issues and future challenges. Finally, the Annex details a method for estimating global population distribution to the year 2015 using data from over subnational units. Environment and Natural Resources Service (SDRN) publications SDRN contact: Environment@fao.org Food and Agriculture Organization of the United Nations (FAO)