When Psychology Trumps the Compulsion of Proximity (or why aren't my neighbours coming to my store ?)

Geo-demographers expend considerable energy studying the profile of customer populations based on proximity. This is all based on the principle that "birds of a feather flock together" (BOF). This psychological concept has been extended by geo-demographers using clever clustering technology to predict both regions where the population will behave in a similar fashion (e.g. will buy a similar car, have a similar income, have the same number of children) and to develop profiles of given regions (like postal code polygons).

During these studies there are occasionally awkward anomalies and typically the clever maths of the neural net or machine learning algorithms will gently slide those into the outlier bucket. What is being sought is a generalization that is statistically reliable.

However, in some cases these outliers are evidence of a behaviour pattern that should be included in the profile.

There is a very tight connection between psychological behaviour patterns and spatial proximity. As a simple example, consider the case of a person deciding which retail outlet to go to. Lets assume that all the facilities within reach have what I'm looking for. Between my location and closest facility is a railway but there is a bridge. The next closest facility is quite a bit further away but does not require me to cross the railway.

If the route to a facility was longer because of available crossings of the railway then this would be a physical impedance and the spatial model, clustering algorithms and predictions would incorporate this. However, in the case above, not only is the facility across the railway but there are several other bridges also potentially offering a shorter travel time. This is where the spatial model should be influenced by the psychology of the situation. There is a psychological impedance to crossing the railway tracks. There is probably some very good explanation of why this happens but it definitely does occur and our spatial models need to be aware of this. The evidence of course - if we are training a machine learning engine - is that a statistically significant number of real routes will go the longer route to stay on the same side of the railway line.

This is a simple example that skilled geo-demographers are aware of and take into consideration when the data supports it. However, there are greater depths of psychological factors that may need to be considered. Physically intimidating facilities for example can influence customers to go right past and travel a longer time to find a more "friendly" environment. Factors such as acres of pristine floor space make service desks "remote". Overwhelming lights and signage can deter visitors. Narrow or pokey entrances or entrances that go too close to a service desk can dissuade at least the casual visitor.

Ideally a clever spatial model, well trained would be able to surface anomalies that might be psychological but as spatial specialists, we're not really qualified to analyze these results. Perhaps it is time to sit down with the anthropologists and psychologists and make those outlier results as valuable as possible.