Virtual Geodemographics to online resources which govern more informed consumer choices this will likely affect the aggre- gate retail behaviors of the "e-engaged"(Longley et al., 2008). This therefore has implications for those neighborhood level segmentations that do not account for such patterning of internet use. For example, a targeted mail shot advertising a new low price for a product may not be as effec- tive if the potential consumer has access to price comparison information indicating that the same product could be purchased elsewhere for the same or lower price. IMPLICATIONS AND CHALLENGES This brief review of those online technologies affecting the usefulness of geodemographics demonstrates a need to revisit the underpinning philosophy and methodology used to justify and construct these spatial representations. There are a number of implications which need to be investigated in this new research agenda. With an assertion that individuals subsume the role of neighborhood as the most appropriate scale of analysis; where transactional information creates a significant resource for targeting effective pro- motions linked through either a website logon, or virtual identity; this in turn requires new insight into issues of privacy and surveillance, particularly in the way in which information gathered about individuals online can be collected, collated and reused. Privacy concerns for geodemographic classification is not a new phenomenon (Goss, 1995), however, if these classification are to be extended to measure virtual as well as real geographies, further research is now necessary to address a growing body of concern about the way in which online information may impinge on privacy, security and civil liberties(see Alessandro & Ralph, 2006; Miyazaki, 2008; Whysall, 2000). Users of the internet are becoming increasingly aware of these risks (Madden, Fox, Smith, & Vitak, 2007), and indeed a number of companies now provide consumers with various ways of assessing their digital footprint, both in terms of data transferred2, and those occurrences of your details across various websites3. These issues are complex, and also have parallels with other real world methods of data collection, for example, in those activities of retailers operating store card schemes. When users collect points on their store card based on the value and items in their shop- ping basket, they also are providing retailers with a plethora of information about their shopping behaviour. This information is used by retailers to provide targeted promotions and inform store intelligence (Hunby, Hunt, & Philip, 2007), and in the case of some schemes, these information are available outside the borders of the stores in which the data was collected. A further implica- tion for geodemographic classification builders is a requirement for better understanding of how information gathered online relates to offline behaviors, and indeed analysis if these are either complementary and as such reinforcing, or; con- tradictory, thus providing new insights. Some research has been completed in terms of social capital accumulation (Wellman, Haase, Witte, & Hampton, 2001) and specifically how these con- structs may influence offline behaviors (Blanchard & Horan, 1998; Matei & Ball-Rokeach, 2001). Other researchers have looked at the relation- ship between engagement with new information communication technologies and the arrange- ment of these behaviors across real geographic space (Longley & Singleton, 2009a; Longley et al., 2008). The link between online behaviors for offline applications are beginning to be explored, for example Sulake, a Finnish provider of a virtual world have started utilizing the platform to produce market research data by surveying 42,000 consum- ers across 22 countries (Jana, 2007). Additionally, with the advent of geocoded online content, such 376