||A term applied to the analysis of social and economic data in a geographical context for commercial purposes related to marketing, site selection, advertising, and sales forecasting. Geodemographics has emerged as a significant area of application for geographic information systems (GIS; see also locational analysis; location-allocation models; market-area analysis; market potential model; retailing, geography of).
In most countries, the socio-economic data collected by the census every few years are an important source of information on detailed geographical distributions of population. In addition to simple counts, which are needed for purposes such as allocation of central funds and the administration of elections, censuses often include questions on economic status, age, housing quality, migration, and many other topics. geocoding of census returns in recent decades has allowed tabulations and cross-tabulations to be made for small areas, for an enormous array of socio-economic characteristics.
When these census tabulations are combined with digital information on the geometric outlines of census tracts and other reporting zones, it is possible for simple and widely available computer mapping packages to be used to create maps indicating areas of high market potential for particular products, suitable locations for businesses, etc. Other data sources can be used to plot the locations of competing businesses, or statistics on sales. But far more valuable insights are available when census data are manipulated within GIS. geocoding makes it possible to identify the residents living within a given census reporting zone; a company willing to make the assumption that all of these people have incomes similar to that reported by the census for the zone as a whole can now target mail specifically to those residents. Various techniques of \'clustering\' (Weiss, 1988) have been used to generalize the census-reported characteristics of area residents for such purposes. GIS makes it possible to reverse this process, by identifying the census zone containing each of a firm\'s customers, and thus to impute economic characteristics to customers.
Modern methods of geodemographic analysis go far beyond census-derived data. Retailers use many subtle and not-so-subtle methods to obtain street addresses from their customers, including \'loyalty\' cards, prizes, and credit and debit card records. When a customer uses a credit or debit card at a supermarket check-out, a record is created linking the customer, by name and home location, to every item purchased. Imaging systems can read vehicle licence numbers and link them to owners. Database and GIS technologies allow telephone numbers to be linked to street addresses and geocodes, and thus to census records. Geographical location of purchase is also a major factor in detecting likely misuse or theft of stolen credit cards.
Because of widespread commercial use, many governments have moved to recover part of the cost of collecting a census by selling its processed data, rather than distributing data at the cost of reproduction. The USA is now almost alone in maintaining a policy of cost-of-reproduction pricing on all data collected by the Federal government. Moreover the census is collected infrequently, often at ten-year intervals, opening a niche for commercial providers of more recent data, which can have great value to retailers. For all of these reasons commercial production of geodemographic data has grown rapidly in the past decade, and is likely to continue to grow in the future. Geodemographics is increasingly an international industry, gathering information for processing and resale wherever it is economically viable to do so.
Several geographers and others have commented on the privacy implications of geodemographics (Onsrud, 1994; Goss, 1995; Pickles, 1995; Curry, 1997). GIS allows otherwise unrelated data sets to be linked, by name, street address, or geographic location, and thus bypasses the legal constraints that have been built into the use of other unique personal keys, like social security numbers. It is technically straightforward to scan the contents of a telephone book, re-sort it by phone number, and geocode each individual address; the result is little different from painting the occupants\' names and telephone number on the outside of each house. Uncontrolled access to such information has been implicated in a small but growing number of cases of stalking, harassment, and other problems.Â (MG)
References Curry, M.R. 1997: The digital individual and the private realm. Annals of the Association of American Geographers 87 (4): 681-99.Â Goss, J.D. 1995: We know where you are and we know where you live. Economic Geography 71 (2): 171-98.Â Onsrud, H.J. 1994: Protecting privacy in using geographic information systems. Photogrammetric Engineering and Remote Sensing 60 (9): 1083-95.Â Pickles, J. 1991: Geography, GIS, and the surveillant society. Papers and Proceedings of Applied Geography Conferences 14: 80-91.Â Pickles, J., ed., 1995: Ground truth: the social implications of geographic information systems. New York: Guilford.Â Weiss, M.J. 1988: The clustering of America. New York: Harper and Row.
Suggested Reading Castle, G.H. III, ed., 1993: Profiting from a geographic information system. Fort Collins, CO.: GIS World Books.