Is it possible that some of today's analytics results are fundamentally flawed ?

There are numerous authorities stridently claiming that analysts working today are missing a vital dimension in that they are ignoring the spatial aspect of the data. The neighbourhood relationship in other words. The claims indicate that spatial data included in the analysis will give better "insights" and add value to the results.

While this is provably true given numerous real and usable examples, the claims have to be coloured to some extent by the vested interests of the claimants. They are selling something.

However, what if the lack of use of neighbourhood in routine (and even extreme) analysis was giving rise to fundamentally flawed results but not because the spatial aspect was failing to contribute to the result but because its absence was actually invalidating the results ? What if the missing spatial variables were forcing a bias in the analytics simply because their presence is essential to a correct or at least predictably reliable result ?

There is a factor in statistical analysis called omitted-variable bias. Coarsely defined, it arises when the analytical model, missing a key variable set, compensates for that missing variable set by changing the influence of other variables. And thus the model is broken. Conventional literature suggests that if there is a risk of OVB then all the variables should be added as control variables. But that's scarcely a safe choice since now the model becomes so constrained that the results, while most likely trustworthy, reveal very little of compelling interest (meaning the return on investment is minimized). Kevin A. Clarke writes of the dangers of excessive inclusion of control variables (referring specifically to regression analysis).

There are extremely effective methods that permit an analyst to identify which variables are most strongly characteristic of a particular data set and in some previous work, I have seen practical and provably useful results from these methods. This is of course a different view of the analytical approach but the fact that neighbourhood was almost always a dominant variable in the various real life examples processed, suggests that the omission of neighbourhood from an analysis of these data sets could lead to results polluted by OVB.

In selecting the variables that will be used for analytics, proximity (neighbourhood) should be considered as at least as important to the model as any other (income, age gender etc). Without proximity, the results could be incomplete or inaccurate and if OVB has occurred due to the absence of proximity, the results could be quite seriously misleading.