While browsing The C Initiative earlier today, I happened upon a post by Milton, one of my MIS colleagues, titled Spatial Intelligence. The article distilled the core benefits of comtemporary GISs which currently give their audience visualised capabilities operating primarily over distance. As an enabler of decision-making I would even venture as far as to say that it is one of the most powerful technologies known to man since the advent of the conventional IS. Why do I say this? Because it takes something that is commonplace in much of our decisions (even implicitly) and sets it in a highly visible position. I'm speaking of course, about the variable of distance. Even more important is that fact that distance is often a parameter in viewing and/or organizing other pieces of data in which we might be interested, such as income, age, population spread and density, and even disease/infection patterns. Thus, as Milton indicates, a GIS is a rather apt bridge between spatial data and spatial intelligence.
All in all, said article was an interesting read. The GIS of today can be an extraordinary tool when put to good use. But it has a particularly huge limitation in that it's capabilities are largely concerned with spatial processing. Given thus, the representation of the data given will necessarily be largely static. What if we were to add a temporal element such that a GIS would not only be concerned with distance, but could perform temporal modeling? What would be the benefits which would accrue subsequent to such an implementation? Would spatial intelligence gain anything new?
The answer is...that depends. As Dr Duggan once said, you can't very well design a solution and go in search of a problem. There has to be a need for it. And I don't think that need has reached critical mass as of yet--but let's stretch our imaginations a bit. Where would be need time to be involved in a geographic representation? Truth be told, whenever we use a GIS we tend to focus primarily on only one variable changing while all others usually remain (fairly) constant. But the other variables may very well be changing as well. How then do we quantify and explore relationships between pieces of information that may very well vary with time? For example, what if we wanted to validate the hypothesis that people of lower incomes would opt to carpool (despite having public transport in the area) in districts where crime was prevalent and roads were bad? A single static representation of geographic data would assist perhaps in mapping out what routes were bad and how the transport system could best serve said districts by promoting the least expensive paths. It would also help the NWA in seeing where exactly the offending roads lay thus allowing them to be targeted before further deterioration. But it still doesn't help us to find out the answer to our question.
The fact of the matter is our answer would lie only in observations taken over a period of time. Also known as the 4th dimension, time is becoming an increasingly non-trivial variable in decision-making. Nothing speaks more powerfully than the power of historical data brought to bear in making predictions and forecasts. But to do so we must first determine correlations (and reliable ones at that) amongst all components that we seek to investigate. In the case of our example, we would be looking at income distribution, public transport availability, road wear, and crime. Some of these might be related but time gives us the ability to reason over which relationships are actually sensible. in so doing, the prime benefit is that of a robust data model that can be used to build interactive charting for shared use by more than one primary stakeholder (in this case the JUTC and NWA). This reinforces once of the key tenets of application design--that of reuse, which in turn makes for robust IS implementations and extensions. I daresay that spatiotemporal processing, if implemented successfully could be the next ICT wave to rock the world of computing for decades to come!
Your friend in Christ,
Josh.
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