Why we focus on spatial data science

The I in Information Technology is so broad – why is our first integrated data science problem focus on spatial data? It doesn’t fit when looking on face of our Services Catalog . We get asked this a lot and this is our reason, and like Geospatial, its multi-dimensional spanning different ways of thinking, audiences, maturity, progressions, science, modeling, and time:

whydata1

 

In green, x-axis, is the time progression of public web content. The summary point is data took the longest period – about 10-15 years. And data can only get better as it matures into being popular 25 years old on the web. We are in the information period now, but moving swiftly into the knowledge period. Just see how much more scientific data visualizations, and dependence we are on the internet. Just think how much you were on the web in 1998 compared to 15 years later – IT IS IN YOUR POCKET now.

whydata5

 

This isn’t just our theory.

RadarNetworks put together the visual of progressing through the web eras. Web 1.0 was websites or Content and early Commerce sites. Web 2.0 raised the web community with blogs and the web began to link collaboratively built information with wikis. Web 3.0 is ushering in the semantic direction and building integrated knowledge.

radarnetworkstowardsawebos

 

Even scarier, Public Web Content progression lags several business domains, but not necessarily in this leading order: Intelligence, Financial, Energy, Retail, and Large Corporate Analytics. Meaning, this curve reflects the Public maturity, and those other domains have different and faster curves.

The recent discussions on intelligence analysis linking social/internet data with profile, Facebook/Google Privacy and use for personalized advertising, level of detail SalesForce knows about you and why companies pay so much for a license/seat, how energy exploration is optimizing where to drill in some harder to find areas, or the absolute complexity and risk of the financial derivatives as the world market goes – these technologies usually lag in how we integrate public content for googling someone, or using the internet to learn more and faster. Reason: Those do not make money. Same reason why the DoD invented the internet – it was driven by security of the U.S. which makes money which makes power.

So, that digression aside (as we have been told “well, my industry is different”), the public progression does follow a parabolic curve that matches Moore’s Law driving factor in IT capability – every 2 years, computing power doubles in power, at same cost (paraphrasing). The fact that we can do more faster at quality levels means we can continue to increase our complexity of analysis in red. And there appears to be a stall not moving towards wisdom, but as we move toward knowledge. Its true our knowledge will continue to increase VERY fast, but what we do with that as a society is the “fear” as we move towards this singularity so fast.

Fast is an understatement, very fast even for logarithmic progression as its hard to emote and digest the magnitude of just how fast it is moving. We moved from

  • The early 90s simply placing history up there and experimentation and having general content with loose hyperlinking and web logs
  • to the late 90s conducting eCommerce and doing math/financial interaction modeling and simulations and building product catalogs with metadata that allowed us to relate and say if a user found that quality or metadata in something, it might like something else over here
  • to the early 2000s to engineering solutions including social and true community solutions that began to build on top of relational and the network effect and use semantics and continually share content on timelines and where a photo was taken as GPS devices began to appear in our pockets
  • To the 2010s or today where we are looking for new ways to collaborate, find new discoveries in cloud, and use the billions and billions on sensors and data streams to create more powerful more knowledgable applications

Another way to digest this progression is via the table below.

Web VersionTimeDIKWWeb MaturityKnowledge Domain Leading WebData Use Model on WebData Maturity on Web
.9early 90sDataContentHistoryExperimentalLogs
1.01995+InfoHistoryExperimentalContent
1.11997MathExperimentalRelational
1.21999+CommerceMathHypotheticalMetadata
1.32002EngineeringHypotheticalSpatial
2.02005+Knowledge+CommunityEngineeringComputationalTemporal
2.12010sEngineeringComputationalSemantic
3.02015 and predictable webKnowledge+CollaborationScienceData as 4th paradigm notTempoSpatial (goes public)
4.02020 -2030Wisdom in sectorsAdvancing Collaboration with 3rd world coreAdvancing Science into Shared Services – Philosophical is out yearRobot/Ant data qualitySentiment and Predictive (goes public/useful) – Sensitive is out year

Now, think of the last teenager that could maintain eye contact in a conversation with an adult while holding phone in their hand and not be distracted by the pavlovian response of a text, tweet, instagram, etc. Now imagine, ten years from now, when its not tidbits of data, but as a call comes up, auto-searching on terms they aren’t aware of come up in augmented reality. Advice on how to react on the sentiment they just received – not just the information. The emotional knowledge quotient will be google now – “What do I do when?” versus critical thinking and live and learn.

So, taking it back to the “now”, though this blog is lacking the specific citations (blogs do allow us to cheat, but our research sources will make sure to detail and source our analysis), if you agree that spatial mapping for professional occurred in early 2000s and agree now that it has hit the public and understand that spatially tagging data has pass the tipping points with advent of smartphones, map apps, local scouts, augmented reality directions, and multi-dimensional modeling integrating GIS and CAD with web, then you can see the data science maturity stage we are in that has the largest impact right now is – Geospatial.

Geospatial data is different. Prior to geospatial, data is non-dimension-based. It has many attributable and categorical facets, but prior to spatial data, that data does not have to be stored as a mathematical or picture form with specific relation to earth position. Spatial data – GIS, CAD, Lat/Longs, have to be stored in numerical fashion in order to calculate upon it. Further more, it hasn’t be be related to a grounding point. Essentially, geospatial is storing vector maps or pixel maps. When you begin to put that together for 10s of millions of streams, you get a a very large complicated spatially referenced hydrography dataset. It gets even more complicated when you overlay 15-minute time-based data such as water attributes (flow, height, temperature, quality, changes, etc.) with that. Even more complicated when you combine that data with other dimensions such as earth elevations and need to relate across domains of science, speaking different languages to be able to calculate how fast water may flow a certain containment down a slope after a river bank or levy collapses.

Before we can get to those more complex scenarios, geospatial data is the next progression in data complexity .

That said, definitely check out our Geospatial Integrated Services and Capabilities