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.
Google has a patent that explains that autonomous vehicles should be able to decide on behaviour based on the proximity of other vehicles in traffic. For example, a vehicle on a highway with a large truck on one side and a car on the other may decide to move to one side of its lane for safety sake. Or it may decide to accelerate past the truck to a safer area ahead.
This seems like an inevitable evolution of the simulation of existing traffic behaviour.
Humans piloting vehicles understand proximity in a different way - even when they drive into each other or cut each other off in traffic. One might hypothesize that a contributing factor to road rage is that the humans driving are autonomous and there is no overseeing power that prevents them from developing conflicts.
Consider now an entire environment of autonomous vehicles in close proximity. In the above example, the vehicle in the middle lane moves over to allow a safer zone around the truck. But the car on the other side sees the middle vehicle move closer and compensates by also changing position in its lane. The middle car sees that and reacts. By the dynamic proximity variations, there is now an unstable situation that is exacerbated by the very fast reflexes of the controlling computers. If this were to happen with many autonomous vehicles near each other, there could be massive disruption of the flow with lane concertinas and swerving.
If we now add in some complications - such as an obstacle in the road, wet roadway, or a human driver in the midst of the traffic then the potential problem is even larger.
Perhaps the right way to think about this spatial problem is to rethink the autonomous vehicle approach. With the present designs we are perpetuating the idea that each vehicle is a unit. The V2V communications work is well developed but there doesn't seem to be much thought about the conflict/feedback situation mentioned above. The intense research is focussed on the actual vehicle being smart enough to figure out how to deal with the dynamics of a complex and unpredictable traffic situation.
Perhaps the real time oversight of many autonomous vehicles attempting to cooperate in a limited space is a spatial problem that deserves attention from the spatial professionals ?
