Geodemographic generally refers to the analysis of people based on the statistical classification of the area they live in. According to Harris, Sleight & Weber (2005) the location where a person lives say something about the person, especially socioeconomic attributes. Geodemographic classifications organize areas into different categories based on the multiple socioeconomic attributes. For instance, wealthy neighborhoods normally comprise older individuals. Geodemographic classification is widely applied in various disciplines, particularly, urban planning, health planning and marketing.
Geodemographic classification has roots in the work of Charles Booth’s maps of poverty in London in 1880-1890. However, human ecologists in the 1920s and 1930s developed the concept further (Singleton & Spielman, 2014). With the rise of urbanization, human ecologists had to come up with new methods of classification to understand the complexities of human settlements. Over the years, geodemographic methods have improved. Harris, Sleight & Weber (2005) note that geodemographics assume positive spatial autocorrelation when residents of the same neighborhoods are taken into consideration. With the use of Zip codes/ postal codes to classify neighborhoods, the work of geodemographics researchers became simpler.
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Proponents of geodemographics believe that geographical patterns determine the form and functions of settlements (Dalton & Thatcher, 2015). The logic used to support geodemographic is that people tend to reside near similar people. Clichés such as “you are where you live” have proven to be true in most incidences, and they act as the motivation for the use of geodemographics. After all, it is economically impossible for a poor person to settle in an affluent neighborhood. In developing a geodemographic system, experts identify various socioeconomic clusters based on various indicators from the data sets. The data is divided into various classifications such as “Upper Crust,” “Affluent Achievers,” to “the Have-nots” (Dalton & Thatcher, 2015).
The geodemographic system will assign an algorithm to a given cluster/region, in most cases the algorithms are the postal codes for each region. The algorithms will be used to easily identify locations, and the characteristics of the people within those locations. Once the geodemographic classification is done, the government and private organizations use the information to address the specific needs of the consumers within different regions. This explains why marketers are more likely to advertise luxurious products postal codes identified as “upper crust” neighborhoods, while they advertise basic products in postal codes identified as “the Have-nots” (Dalton & Thatcher, 2015). Despite its flaws, geodemographics has been applied by marketers and public planners since the beginning of the 20th century. This shows that when it is done well, geodemographic classification is a representation of the population.
However, critics of geodemographics state that social identity cannot be reduced to measurable characteristics as seen in the algorithms used in geodemographics (Dalton & Thatcher, 2015). Social factors such as the level of education are hard to determine, and yet most geodemographic systems assume that people in poor neighborhoods have low levels of education based on the algorithms assigned to the neighborhood. Some qualitative aspects of social identity cannot be quantified. Geodemographics is faced with uncertainties that challenge its credibility, the uncertainties being: lack of adequate data to classify individuals and location well, and the negligence of the heterogenous nature of the society.
Harris, Sleight & Richard (2005) state that ecological fallacy is the biggest downfall of geodemographics. Dalton & Thatcher (2015 p. 16) define ecological fallacy as, “an error of deduction that involves deriving conclusions about individuals solely on the analysis of the group data.” Researchers often make assumptions about certain individuals based on the characteristics given to the entire neighborhood. For instance, assuming that all individuals in the “Have-nots” cluster are poor is wrong. However, ecological fallacy is hard to avoid given the extensive nature of geodemographic classification. It is easy to obtain aggregate data than individual data, and the aggregate date is often applied to all individuals within the cluster. Ecological inferences are made continuously in geodemographics, making ecological fallacy a perennial methodological issue that is unlikely to be solved in the near future.
Dalton & Thatcher (2015) also criticize the potential of the geodemographic system to limit consumer options. Once it is determined that a particular neighborhood is poor, the consumers in that neighborhoods are limited to the options that the marketers deem fit for them. Eventually, the geodemographics does not only produce insights on demographics, rather it produces social relations based on geographic spaces of consumption.
Regardless of its challenges, geodemographic classification is the only way a researcher can derive a clearer picture of the socioeconomic attributes of the entire population. With the invention of new technologies, geodemographic classification will be improved and some of its challenges will be addressed. Dalton & Thatcher (2015) note that the rise of big data from the use of technologies such as GIS, google maps, smartphones and social media make it easy to access more reliable data on individuals unlike in the past. In the past, geodemographic classification relied on public data from government census, which were quite inaccurate. Technology now enables researchers to come up with systems that meets the heterogenous nature of today’s society. It is now easy to know one’s socioeconomic patterns by analyzing their online presence. Additionally, most businesses now keep consumer data regardless of the location.
Researchers acknowledge that it is almost impossible to solve ecological fallacy in geodemographics, hence, they are adopting measures to improve the reliability of geodemographics by reducing the intensity of the ecological fallacy. Studying socioeconomic patterns on an individual level is almost impossible, though such studies would produce the most reliable results. To reduce the effects of ecological fallacy, researchers are now opting to use geodemographic classification to study smaller samples.By studying smaller geographic units, the researchers increase the accuracy of the results. Researchers can study smaller neighborhoods first in a certain locality to come up with more accurate patterns of the locality.
In conclusion, geodemographic classification enables researchers to understand the socioeconomic patterns that shape the society. It is impossible to conduct large scale research studies on the entire society, hence, geodemographic classification simplifies the process by attaching algorithms to various clusters of classification. Geodemographic classification is used by the government and businesses in planning. Marketers are the number one users of geodemographic classification, as it helps them to come up with the appropriate marketing mix for customers in different locations. Just like any research method, geodemographic classification has its problems. The biggest problem is ecological fallacy, whereby researchers make conclusions about individuals based on the aggregate data. The advent of advanced technologies and use of smaller geographic units has the potential of minimizing the effects of ecological fallacies. However, researchers have to come up with better ways of studying qualitative aspects of the population that are not often reflected in the geodemographic algorithms.
References
Dalton, C. M., & Thatcher, J. (2015). Inflated granularity: Spatial “Big Data” and geodemographics. Big Data & Society , 2 (2), 2053951715601144.
Harris, R., Sleight, P., & Webber, R. (2005). Geodemographics, GIS and neighbourhood targeting (Vol. 7). John Wiley and Sons.
Openshaw, S., & Blake, M. (1995). Geodemographic segmentation systems for screening health data. Journal of epidemiology and community health , 49 (Suppl 2), S34-S38.
Singleton, A. D., & Spielman, S. E. (2014). The past, present, and future of geodemographic research in the United States and United Kingdom. The Professional Geographer , 66 (4), 558-567.
