As with a wide variety of other research fields within the confines of GIScience, it will be interesting to see how geodemographics may change with technological advances in machine-learning. An example could be with the delineation of boundaries between clusters, which could be fractured or combined based on reasoning that could be quite difficult to understand for humans. These geodemographic generalizations of space could also be continuously computerized in a not so distant future, which could lead to an ever changing assessment of neighborhoods on a very short temporal scale. Micro-level analysis could also allow for a better representation of a neighborhood based on recent population inflow or outflow data, data that becomes increasingly accessible in the era of the Internet of Things (IoT).
The thresholds used to assess whether a neighborhood is more closely related to x rather than to y need to be defined quantitatively, which forces a certain cutoff and brings in a little subjectivity. An example could be demonstrated with the occurrence of a natural disaster in a hypothetical neighborhood, which could lead to a sufficient devaluation of houses to warrant changing how the neighborhood is characterized. In that case, a population possibly once seen as energetic and lively (or as defined by Parker et. al as a live/tame zone) could be completely changed to a dead/wild zone from one day to the next. Although these would be reassessed at some point in time by corporations or the government, technological advancements grant the ability to reassess neighborhoods much more rapidly.
As someone not well versed in the conceptualization of geodemographics, it becomes apparent that a balance needs to be made between the number of classes needed and the level of representativity desired; after all, every household could be considered unique enough to warrant its own neighborhood. Future advances in the field might incorporate a three-dimensional analysis of neighborhoods in densely populated urban centers, as residential skyscrapers present vertical spatial clustering.