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;
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.