Until recently, demographers and cartographers have had only two sources of information about population distribution:
paper maps and navigational guides showing the location of cities and towns and the administrative boundaries of countries and subnational administrative units within countries;
statistical data obtained from national censuses and other demographic surveys.
With this information, it has been possible for some time to present statistical information about national population size, and the location and population count of urban centres and other subnational administrative units, in map form. This combination of statistical data with information about administrative boundaries produces maps that give a geographic representation of the concerned parameters, but the usefulness of these maps is limited as they are not georeferenced and are not supported by databases that capture variations within the mapped units.
Researchers have been developing a number of techniques for mapping globally variations of parameters within countries. As these techniques have become more sophisticated, and the capacity of computers to handle very large datasets with great speed has increased, the interest in developing methods for distributing population data to the grid cells of GIS maps has also increased. Initially, GIS specialists tended to direct their efforts towards establishing the coordinates of coastlines and country boundaries, and generating georeferenced datasets for physical and environmental variables that could be derived from high-resolution aerial photography and satellite imagery. Less effort was directed towards the development of georeferenced socio-economic datasets, mainly because such data is collected by censuses and surveys and compiled for political or administrative units, and direct interpolation techniques to estimate the spatial distribution of socio-economic variables are still lacking (Clark and Rhind, 1992; Deichmann, 1996).
Despite these limitations, improvements in the quality and accessibility of georeferenced environmental data have generated growing demand for more accurate and up-to-date spatial information about the global distribution of population variables. This demand has been driven by two different concerns within the development community. One relates to the interest of demographers, sociologists and urban planners in mapping urbanization processes and defining the location and socio-economic characteristics of urban populations with more precision. The other relates to the interest of agricultural economists and rural planners in gaining access to current data showing the location and socio-economic characteristics of rural populations in relation to physical and environmental factors that affect their livelihood options and vulnerability to poverty and food insecurity.
The first issue to be addressed has been the problem of definition. Somewhat surprisingly, despite the importance of urban growth for development processes worldwide, there is no commonly accepted definition of what constitutes an urban area, and no commonly accepted spatial characterization of urban areas. The methods used to enumerate urban and rural populations differ from one country to another, and these national differences are reflected in the global population statistics maintained by the United Nations.
An exploration of definitions currently in use, along with an assessment of the quality of available statistical and geospatial data relevant for mapping population distribution, are the subject of chapter two of this report.
Chapter three describes the evolution of georeferenced population datasets and explains how information generated from paper maps, high-resolution aerial photography and satellite imagery has been combined with statistical data to develop GIS datasets of global population distribution. It presents a method developed by CIESIN, based on its Gridded Population of the World (GPW) dataset, which distinguishes urban from rural populations within subnational administrative units and maps the location and extents of urban areas. It also summarizes other work in progress to distinguish and map urban and rural population, in most cases using GPW as the reference GIS dataset for global population distribution.
Chapter four describes a method developed by FAO/SDRN to detect urban areas, using the LandScan Global Population Database, and to generate separate grids for urban and rural populations at 30 arc-seconds that facilitate spatial analyses of the environmental and socio-economic vulnerability of rural populations.
While both GPW and LandScan represent significant advances and already have considerable utility for the research community, the methods developed so far for mapping urban and rural populations with these two databases are not fully satisfactory. A brief summary of unresolved issues and future challenges is given in chapter five.
The Annex describes a method of estimating the distribution of the world's population to the year 2015, using GPW version 3 as the database; the particular relevance of 2015 is that it has been set as the target year for the World Food Summit and Millennium Development Goals (MDGs).