Social networks have their foundations in the space-time continuum—you know, the funky coordinate system  that Einstein was so keen about.

Social network analysis is all about looking for patterns of “proximity” among people, considered in their cultural capacities as influencers and followers, innovators and imitators, first-movers and late adopters. Down deep, I consider social network analysis an important new branch of decision support systems as a discipline. The core question is: What unique situational chemistry causes various people, individually or collectively, to make various decisions at various places and times?

That’s where space and time enter the social network analysis equation. It’s not enough that I look up to your shining example and take my lead from what you say and do. It’s just as important that we be in the same city, neighborhood, or room. More than that, it’s important that you and I actually cross paths in order for you to actively influence me to buy that latte, or for you to calm me down and thereby stop me from storming out the door and severing my relationship with a retailer who has ignored my complaints one time too many.

People’s proximity—measured in social, spatial, and temporal coordinates—is the matrix within which we measure the situational complexity of real-world decisioning environments. That’s why I was impressed yesterday by my briefing and demo by Space-Time Insight, the vendor of a geospatial business intelligence (BI) platform. Most noteworthy about Space-Time Insight is their “space-time OLAP” engine, rules engine, and spatial processor, which are is optimized for in-memory, real-time correlation, calculation and visualization of location, time, and social proximity among individuals, as well as among any other “nodes” that can be located within these dimensions. By “nodes,” I’m also referring to the physical assets that come to play in transportation, logistics, supply chain, and other processes where human beings, with their shifting relationships, are just another moving part.

In addition to its cloud-oriented platform, Space-Time Insight also provides packaged geospatial (should I call it geo-temporal-social?) applications for energy & utilities, oil & gas, and other industries. Space-Time Insight’s solutions correlate real-time feed updates from social networks (Twitter, blogs, etc.), real-time sensors, satellite images, event feeds, GPS, GeoRSS providers, ERP data, and many other sources. Just as important, the vendor has embedded a workflow engine, mobile alerting, and portal into its geo-temporal-social environment to drive event-driven automated response and decision support.

But I’m not going to overstate the social network functionality that this vendor currently integrates into their platform and applications. It’s clearly a work in progress, and doesn’t yet have the sophistication found in social network analysis tools from SAS, KXEN, and some others covered in my recent Forrester Wave for Predictive Analytics and Data Mining Solutions. And we have yet to see social networks such as Twitter—which has recently added geo-locationing capabilities—add the proximity features necessary to optimize online communities for dynamic social-situational awareness.