Spatial Analytics is Simple - Really

Think of spatial information, location intelligence, GIS and the various other terms used with respect to the storage and use of spatial data and we think of the three basics :

1. spatial data type (for storage),
2. spatial indices and 
3. spatial operators.

These however are only the mechanics of the handling of the data and obscure the elegant simplicity of the application of the data. While these three elements are fundamental to and essential for the successful use of spatial data, once the data is used in an analytical sense, there is really only one asset that spatial data yields. Only one but it is a big one and at least as important as any other factor considered in data analytics.

Spatially enabled data stores permit analysis using neighbourhood.

Location intelligent engines have all kinds of fascinating and complex operators with very clever and refined algorithms but if we look at a few of them, its revealing just how they may be considered when analytics are being used.

• Buffer. Arguably the most used operator in the GIS world; the operator gives you a region around some feature you might be interested in. A truck carrying something toxic has driven down a certain road and has leaked the substance as it goes. A driver wants to know where there are gas stations near the road being driven. But here's the thing. The buffer is cool to look at on a map but it is never the result that a user wants. Its nothing more than a means to help a user understand what's near something interesting. Neighbourhood in other words.
• Topology management. The assembly of a topologically consistent fabric in a data store is a lovely and engaging problem for all spatial systems. There are the famous sliver polygons, the overshoots, the undershoots, the invalid connections, the missing connections and so forth. The purpose of all this fine and expensive processing is to prepare the data such that the maximum value can be extracted during analysis. Meaning that the neighbourhood factors derived have a reasonable chance of being accurate.
• Overlaps. Perhaps the second most used operator and fraught with all kinds of nuances for which spatial professionals have worked out all kinds of semantics. Why do I as an analyst care ? As an example, I may want to know which regions of my township are like to flood in the next 10 years. That's useful but its not the end result of the process. The analyst will want a result that can be acted on (such as the calculation of insurance risk) and now we are back to neighbourhoods again.

We could argue that GIS engines permit the measurement of distance (to see how far things are from other things), area (because the user wants to know whether he can practice the bagpipes in the house without being shot at), slope (for calculations to indicate what neighbourhoods will flood). But it seems to me that these are still the mechanics that lead to an analytics system being able to use neighbourhood effectively.

For at least 25 years, there has been a prevalent assertion that 80% of the world's data has a spatial component. There's a good discussion here that traces where this came from. While the number might be argued, it is very likely to be too low rather than ambitious given the sources of our data (phones, cars etc).

With that density of data concentrated around the single analytical factor of neighbourhood and the riveting actions that can be derived from combining that factor with the analytical factors already in play, it is fundamentally important that the spatial practitioners look after the plumbing such that analysts can add neighbourhood to their portfolio of power analytics.

Right now there is a big gap and the use of spatial data in analytics is being held up by concern and worry about the complexities of GIS, the difficulties of managing the data and consternation about things like "projection systems", "Hausdorff distances", "WFS filters" and so forth. We should be able to enable skilled analysts to make dramatic and lucrative progress with neighbourhood factors in traditional analytics without making their heads hurt.