Techniques and Examples
By far the most widely used cartographic representation of census data is the choropleth, or shaded area, map. The example provided in Figure 1 shows the 2001 census percentage of white ethnic groups in the city of Southampton, England. Values in the map are based on the aggregation of all the individual census results within each output area, clearly revealing how the outer suburbs are almost entirely white while some central areas are dominated by nonwhite ethnic groups. It is generally inappropriate to map counts in this way, as the values will often be related to the size of the area rather than prevalence in the population. A disadvantage of all choropleth mapping is that the visual dominance of an area is related to its geographical area rather than its population size. The largest areas in Figure 1 comprise mostly docks and open water, while the most densely populated areas are so small as to be barely visible. This type of map is increasingly available online from the websites of national statistical agencies, such as that illustrated in Figure 2, from the Neighborhood Statistics service for England and Wales which again shows Southampton, this time the percentage of households without a car in 2001 for larger census areas called wards. In this case, additional interactivity is provided by dynamic linkage between the histogram and the map: pointing at an area on one will highlight its identity and position in the other. The figure also demonstrates the effects of generalization, compared to Figure 1, in which the map has been simplified to speed the web application, with the consequence that these area boundaries would be difficult to locate accurately on a street map of the city. The availability of some census data within online mapping and virtual globe software such as Google Earth has not to date produced new forms of mapping so much as increased options for access and interaction.
If area boundaries are not available, it is possible to produce simple maps by the generation of synthetic boundaries, known as Thiessen or Voroni polygons, or the placement of proportional symbols at centroid locations representing each area, although these types of representations tend to introduce additional interpretational difficulties, for example, due to altered area geometry or overlapping symbols in areas of high population density. All census mapping is affected by general cartographic design considerations. Choices of map scale, number and placement of class intervals, selection and order of colors, and other layout considerations will affect how the map reader interprets the data. A further challenge facing census users is that of comparing data from successive censuses when there have been changes in the area boundaries. True comparisons can only be made for areas which encompass equivalent population groups. Where boundaries have changed it is necessary either to aggregate to the smallest possible comparable areas or to interpolate values from one time period to another. This challenge makes long term comparison of local populations especially difficult where census outputs are tied to administrative boundaries which themselves experience high levels of intercensal change.
A variety of alternative techniques are available which address some of the deficiencies of choropleth mapping. In dasymetric mapping, additional information is used to restrict the shading of mapped values. For example, a map of inhabited areas might be superimposed on census boundaries and shading displayed only within the inhabited areas, thus providing a much more accurate impression of the population distribution. Another approach designed to address this type of difficulty is population surface modeling, whereby census variables are redistributed onto the cells of a regular grid, as illustrated in Figure 3. This figure again shows the Southampton region, here showing population density in 200200m grid cells from the 1991 census. In this case, many of the cells remain unpopulated and the resulting map more accurately reflects the true distribution of population. These regular grid cells can aid comparison over time or with the output of other spatial models but the unconventional appearance of the map can deter users familiar with choropleth representations.
An entirely different data visualization is produced by the use of a population cartogram, in which locational accuracy is relaxed in order that each area can be represented proportional to its population size. Using this approach, areas with larger populations will take up a greater proportion of the map, but with the consequence that distance and direction cannot be accurately interpreted. An example of this approach is illustrated in Figure 4. This map shows change in the percentage of households without a car between 1991 and 2001. The cartogram illustrates the enormous population dominance of the London conurbation, represented by the dark area to the lower right of the map, where growth in car ownership has been much slower than in more remote areas with smaller populations. The figure embodies two important innovations over a conventional choropleth map: first, the sizes of the areas (here, counties and unitary authorities) are proportional to their population sizes and second, the use of stable geographical areas has permitted the creation of a map showing change over time.
Censuses continue to provide an enormously rich resource for those concerned with social geography. The large statistical data volumes make mapping an essential key to understanding geographical trends and processes in these data, although much spatial analysis is performed without the production of intermediate maps. The choropleth method continues to dominate census mapping, both in the popular media and academic publications, although its representational weaknesses are well documented and a variety of alternative methods are available which allow the user to manipulate and interrogate census data with greater geographical and statistical rigor.