The Impact of Social Factors and Consumer Behavior on Carbon Dioxide Emissions (Baiocchi et al., 2010)

This paper applies geodemographic segmentation data to assess the direct and indirect carbon emissions associated with different lifestyles. As geodemographics are generally used to improve the targeting of advertising and marketing communications, I am curious about the use of geodemographics in GIScience.

In this paper, the authors argue that the top-down approach, which is conventionally used to classify lifestyle groups, fails to recognize spatial aspects associated with lifestyles. This is why they choose to use geodemographic lifestyle data. Because lifestyle data employs bottom-up techniques that draw spatial patterns out from the lifestyle data, as opposed to fitting it to some a priori classification of neighborhood types. However, it is important to note that the geodemographic classification systems are beset by Modifiable Areal Unit Problem and ecological fallacies in which the average characteristics of individuals within a neighborhood are assigned to specific individuals. For example, in ACORN groups that are labeled as “Prudent pensioners”, many people will be neither elderly single nor old. More importantly, many others who are both elderly single and old are located outside of “Prudent pensioners” groups. Also, as I know, the data used to build the classification systems mostly derive from the census, which becomes dated quickly and is not sufficient to capture the key dimensions that differentiate residential neighborhoods. Are there any alternative datasets for geodemographics?

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