The Role of Census Mapping

Modern census organization involves the coordination of a very large workforce and geographical information systems (GISs) are routinely used to manage the logistics of enumeration. This organizational mapping does not generally form part of published outputs but nevertheless underpins the operational success of the census. Successful geocoding of individual census returns, linking them to the appropriate areas, is essential if counts are to be correctly aggregated. The management of address lists and geography lookup tables is a major element of census operations and often serves to underpin the geocoding of many noncensus data sources. Enumeration areas used are not necessarily the same as those used for publication of results, which usually need to be meaningful in terms of administrative and electoral geographies or are designed for general purpose statistical reporting.

Mapping makes a unique contribution to the interpretation of census results due to the ability of the human eye to identify pattern in graphical data. Thus, complex patterns of social geography may be readily conveyed by mapped census data which would be almost impossible to extract from tabular results. Ongoing fascination with these representations can be traced back to nineteenth century interests in the mapping of social conditions such as Charles Booth’s poverty maps of London, through to the development of social area analysis and geodemographic classification whereby areas are grouped according to their characteristics defined across a large pool of census variables. The production of a final map, for example, of an area classification scheme, may be the result of extensive unseen manipulation in GISs and statistical software. The dual independent map encoding (DIME) digital data structure developed to accompany the 1970 US census was influential in the emergence of GIS standards and one of the first examples of a statistical organization’s digital boundary data to accompany a census. Digital boundaries allow users to map and analyze census data using their own software and provide the population base layer for many GIS applications. While geodemographic classification tends to find commercial applications, such as direct marketing or site location for retail outlets, similar approaches also serve as the starting point for area based government policy, particularly the weighting of additional resources to neighborhoods with high levels of unemployment and multiple social deprivation.

All aggregated census data are susceptible to the modifiable areal unit problem (MAUP). This relates to the fact that observed patterns are a reflection of the number and placement of area boundaries as much as of the underlying population characteristics: by redesigning area boundaries it is frequently possible to change the apparent pattern in the map. Such aggregate representations are also subject to the ecological fallacy, whereby associations seen in the data at one level of aggregation will not necessarily be observed between individuals in the population, or at other levels of aggregation. These phenomena are particularly relevant to the use of census mapping in political districting. The general principle requires that small census areas be grouped into larger electoral districts of similar population size to achieve equality of representation, either by manual or automated means. The ability to concentrate supporters of particular parties by boundary redesign can also lead to the direct manipulation of boundaries for purposes of electoral gain, known as gerrymandering.