UNEP/GRID-Sioux Falls
uneplive atlas geas

Part I: Boundary and population data


Discussion of data sources

The Latin America and Caribbean administrative boundaries and population database was compiled from medium-scale maps at country and sub-national level, national population censuses and United Nations data. The United Nations data are for the smaller islands of the Caribbean. Population data for all of mainland Latin America, Cuba, Puerto Rico, Jamaica, Trinidad and Tobago, Haiti and the Dominican Republic are from population census data. The administrative boundary maps for mainland Latin America and the large Caribbean islands were digitized at the International Center for Tropical Agriculture (Jones and Bell 1997). The smaller island nations of the Caribbean do not have sub-national administrative units. The outlines of these countries are from the Digital Chart of the World. None of the input boundary or population data has been officially checked or endorsed by national statistical agencies or the United Nations.


The scale of the source boundary maps vary from 1:50,000 to 1:1,125,000.  More detailed information on the source maps is available in the appendix. In order to ensure a close match between different national coverages, and to obtain maximum compatibility with other standard medium resolution data sets, all national boundaries and coastlines were replaced with the political boundaries template (PONET) of the Digital Chart of the World (DCW). The DCW is a set of basic digital GIS data layers with a nominal scale of 1:1 million scale. The use of a very detailed international boundaries template for, in some cases, relatively coarse resolution data is quite misleading, but was required to ensure a close match between the national coverages. In any application the smaller cartographic scale (i.e., coarser resolution) of the administrative boundary data in comparison to the international and coastlines template should be kept in mind.

For a few countries very detailed boundary data were available for which the spatial referencing information was not known. In light of the objectives of this project, these were nevertheless incorporated in order to achieve maximum resolution. Yet, the ad hoc transformation, projection change and rubbersheeting required to make these data compatible with the DCW template have no doubt introduced positional error which may well reach a magnitude in the order of 1-2 km.

Population data

With few exceptions, we used official census figures or official estimates, which were taken from national publications (census reports or statistical yearbooks) or from secondary data sources (yearbooks and gazetteers). The specific sources are indicated for each country in the appendix. The accuracy of censuses obviously varies by country. It was beyond the scope of this project to evaluate the accuracy of every census used, or of any of the official estimates. This would be possible since most censuses are followed by a post-census enumeration that provides an accuracy estimate. We compared the country totals from this dataset with values from the Population Reference Bureau (PRB) and the Economic Commission for Latin America (ECLAC). ECLAC, PRB and other sources giving country totals are likely to have values that are closer to the true value for the nation. The country totals have been corrected to account for inaccuracy in the census. Our data is from the original disaggregated censuses and does not account for these corrections. In countries that differed by more than 10 percent from the PRB values, we made a uniform adjustment of the population data to bring it in line with the PRB country totals. In countries with functioning registration systems, population figures reach an accuracy within a fraction of a percent. In the US, census counts have been shown to have an accuracy of about 2 percent. With few exceptions, the accuracy of Latin American censuses is likely to be considerably lower.

The population data is generally from the early 1990's with an average census year of 1990. Costa Rica and Honduras carried out their last population censuses in 1984 and 1988 respectively. The sources for population data are listed in the appendix.


Population projections

In order to maximize comparability across national boundaries, all sub-national population figures from the 1990's census round were projected to 1995. The population for 1960, 1970, 1980, 1990 and 2000 was projected from the 1995 base year. These projections were based on historical population growth rates for departments in Latin America and the Caribbean. In some cases we used national level population growth rates for the projections. The volume of papers and monographs on population projection methods in the demographic literature is very large. It is matched, however, by the number of publications that emphasize the continuing inability of these methods to accurately forecast population figures over more than very short time periods (see the interesting discussion in Cohen, 1995).

For this project, we used ECLAC figures for population growth rates based on a mathematical trend forecast. In contrast to previous estimates for the global demography project, the current figures for each sub-national unit are based for most countries on a district-specific inter-censal growth rate between the last and the next to last enumeration. The inter-censal growth rate was calculated as


where r is the average rate of growth, P1 and P2 are the population totals, for example, in the first and second census, and t is the number of years between the two enumerations. The 1995 estimate was then derived using:


See, for example, Rogers (1985). For predictions over only a few years, mathematical trend projections are usually fairly accurate, and the specific type of function used has little influence on the results (Cohen 1995). A more elaborate estimation approach such as the cohort survival method would result in more reliable estimates, but the data requirements for this technique (age and sex distribution as well as age specific birth, death and migration rates) were far beyond what was possible in this project. Given the method used for the population forecasting, the characteristics of the available source data obviously have a significant impact.

The population estimates are at best a rough estimate which should be interpreted within wide confidence margins. In general we can expect the reliability of the estimates to be lower, the longer the census upon which they are based lies back - that means the confidence intervals around the point estimates become increasingly wider over time. The data for some countries for which data were available for the early eighties only, need to be regarded as a best-guess only.

The figures included in the database are directly taken from the estimation and thus show more significant digits than is justified by their accuracy. During data manipulation and processing one should preserve all significant digits, but for presentation purposes, the figures should be rounded to reflect the uncertainty of the data. Even the use of population numbers to the nearest thousand in the above table is clearly optimistic.

Given the limited amount and quality of the base population data, we checked the resulting total national population figures against standard benchmarks, the regularly published population estimates produced by ECLAC and data from PRB. In the summary table in the appendix, our total estimated population is compared with PRB and ECLAC figures for 1995. Obviously, the ECLAC and PRB data are by themselves associated with a considerable amount of uncertainty since the estimates are based on conditional forecasts that make a number of assumptions regarding the most recent and future fertility, mortality and migration rates. They are also based, for the most part, on official census figures which sometimes prove to be highly unreliable. In cases where our estimate was considerably different from the UN estimate, the intercensal growth rates were adjusted uniformly such that the resulting estimate was equal to or close to the UN estimate (United Nations 1998). Typically this is the case where the latest available population figures were very old, or where a country experienced significant reductions in fertility in recent years that are not sufficiently reflected in the population dynamics between the last two censuses. The adjustments are indicated in the specific country documentation below.

The United Nations population projections were used for all the Caribbean countries without sub-national boundaries, mainly the smaller islands. This group of Caribbean countries excludes Cuba, Jamaica, Puerto Rico, Trinidad and Tobago, Haiti and the Dominican Republic (United Nations 1998). United Nations population figures were used to calculate population growth rates in three cases: (1) for Puerto Rico where we had no information on population growth rates at sub-national levels; (2) for Surinam and Guyana where census figures were available for only one point in time or the next to last census was too long ago, we applied the average annual national growth rate for a ten-year period centered on the target date. This modification resulted in a uniform adjustment of population figures across these two countries; (3) for departments in Latin America with missing population growth rate data in the ECLAC database. This group included departments in Argentina (1), Brasil (2), Colombia (5), Cuba (1), Honduras (1), Mexico (1), Paraguay (4), and Venezuela (1).


Data Quality Estimates

Given our limited knowledge about the accuracy of the input data, it is impossible to make an objective assessment of data quality. The development of a qualitative index of boundary and population data quality was considered. However, such an index would be associated with considerable subjective judgment. Any question "how good are the data?" is incomplete since we also have to ask "for what purpose?" Data that are clearly inappropriate for high resolution applications at the province or sub-province level, are still sufficiently accurate to be used in regional or continental scale applications (the prime motivation for this project), or for the visualization of spatial patterns in a country. Thus, we only provide some informal summary measures in the table below, and refer to the individual country documentation that provides all known details about the lineage of the data (admittedly, this knowledge is too often very limited). The user can consider this information to make his or her own decision about whether the data are appropriate for the specific tasks.

As in previous databases of this nature, we included two useful summary measures of data resolution in the summary table in the appendix:

Mean resolution in km =

i.e., the length of a side of an administrative unit, if all the units were square.

Mean population per unit = total_national_population / number_of_units..

These two measures complement each other well. In countries where large areas are uninhabitable, the mean resolution in km gives a biased impression of available detail. In such cases, the number of people per unit is a more meaningful indicator. The following table shows how these measures of resolution compare for Africa, Asia and Latin America.



Mean resolution in km




Mean population per administrative unit ('000)









































There are 10,666 administrative units with population information in the data set. Much of the reduction of resolution in kilometers for Latin America and the Caribbean is due to the high level of detail for Brazil, with about one third of all the units for the data set. The population data for all the countries of Latin America was collected at a finer level of detail. The reduction in mean population per unit reflects the higher resolution in kilometers, and in comparison with Asia, lower population densities.


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