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Déjà vu

by Andrew Clouston, Senior Consultant, on 22-Oct-2021 11:04:00

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I’ve recently had a sense of déjà vu. This, of course, is the feeling of having already experienced the present situation that, yet again, the spatial industry is trying to explain why it’s relevant, setting itself up as foundational digital infrastructure. What triggered this for me was a recently released whitepaper on the Geospatial Knowledge Infrastructure (GKI) published on GeoWorld.

So, is a GKI just a rebranded Spatial Data Infrastructure? A geospatial marketplace? Is SDI dead and long live GKI? Does this mean that the significant effort put in by various jurisdictions to build National Spatial Data infrastructures (SDI) to deliver data been wasted? I don’t think so. SDI’s (and geospatial data catalogues, etc.) were, and are, fundamental building blocks and core to a GKI. The primary issue with SDIs (and the marketing that goes with them), is that they are not dissimilar to the prophets of yesteryear, forever preaching how special spatial is and mainly delivering to the already converted (and those with the capability to use the data).

So the insight in this paper that “NSDIs are essentially a data infrastructure in a knowledge environment” is important. So too is the recognition that “data is not the endpoint; knowledge is”. The analogy used is if data is/was “the new oil”, then perhaps knowledge is “the new capital”.

A shift in focus from data to knowledge will be to many in the Geospatial domain a paradigm shift. I first experienced this shift when working in relation to the Canterbury Earthquake recovery (circa 2010). Data was being collected at a rapid rate, guided by experts in a variety of fields. However, to move forward, and to address both current and future social/administrative issues, executive-level decisions had to be made. It’s for this reason that I prefer a more linear flow diagram, rather than the traditional and widely known Data, Information, Knowledge, Wisdom (DIKW) model (as used in the DKI paper).

Wisdom (DIKW) modelOriginal Source unknown

Essentially, back in 2010, we were faced with two primary problems in moving from data to decisions. There was a technical problem where the data had to be transformed into information; and a people problem where the diversity in experience and responsibilities had to be overcome and knowledge created for the decision-makers. The technical problem was relatively easy. We had the capability, system, and expertise to collect relevant data and convert it into information, but this information was still highly specialised. The largest challenge was therefore to enabling the “computer-to-human” step which is where information is transformed into knowledge.

Decision-makers needed to be able to explore and understand the information for it to become knowledge. Bringing a geospatial visualisation tool with subject matter experts together with the decision-makers enables this knowledge transformation to occur more efficiently.

However, it is unrealistic for future Knowledge Systems to have experts “on tap” to assist, so geospatial visualisation tools need to evolve further to be more intuitive to guide users who have business acumen but not necessarily geospatial skills.

So what might a future geospatial knowledge system look like? It will have to be much more capable of automating the data to information step, as well as guiding users to understand the resultant information. The scope needs to go beyond data and current bespoke geospatial technologies to decisions, automation, and knowledge on-demand. This is nicely outlined as per the following diagram from the GKI paper.

Capability comparison between Spatial Data Infrastructure and Geospatial Knowledge Infrastructure
In addition, as the paper takes pains to point out, a GKI must be collaborative because industry is leading many aspects of knowledge creation. The paper further highlights that the industry should partner with governments to deliver GKI “for the benefit of all”, saying that partnerships are essential, and that knowledge is born through collaboration in the real world and so, likewise, in the digital world. From a knowledge perspective, geospatial expertise is not a prerequisite, as the needs of a much wider set of users must be accommodated.

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A GKI case study

If the vision as stated in the paper is to place “Geospatial knowledge at the heart of tomorrow’s sustainable digital society” then a great early example of how a GKI (aka a modern spatial decision support system) might operate, is our own recently released web-based decision support system, SwitchMyFleet, which was co-funded by New Zealand government through the Energy Efficiency and Conservation Authority to help support the government’s policy to increase the use of electric vehicles.

We specifically developed SwitchMyFleet to overcome the immense informational complexity challenges that commercial fleet operators face when planning the transition to electric. Combining the physics of moving vehicles, New Zealand geospatial data, and route optimisation algorithms, SwitchMyFleet is an easy-to-use tool that balances time, distance, and energy use to calculate the metrics that fleet operators and transport businesses need to build their business cases for transition.

SwitchMyFleet demonstrates how geospatial analysis can increase efficiencies in collections and deliveries. The knowledge that fleet managers and business owners develop enables them to make both short- and long-term decisions about how to manage daily operations and plan for the future. You can learn more about SwitchMyFleet here.

Summary

Therefore, seeing greater collaboration between the geospatial ecosystem, the wider digital ecosystem, and end-users and decision-makers will be crucial to delivering and combining advanced decision support systems, like SwitchMyFleet.

As more and more advanced geospatial decision support systems are built (often in collaboration which is a good thing), I believe that we will see the development of a GKI that benefits all of us - and not just those with access to (or knowledge of how to use) the data.

This is a significant step forward, as under historic SDI rollouts, the heavy lifting was largely done by local or central government.

The analytics, artificial intelligence (AI), modelling and gaming communities, who are vital contributors and users of data, will no longer be kept at arm’s length or treated as “customers”. They will be active participants and contributors to the GKI.

Fulfilling the GKI scope

GKI scopeIrrespective of what is in (or out) and what each of the elements of a GKI actually ends up as, it is clear that the geospatial ecosystem now needs to play into the wider digital, or knowledge ecosystem. 

Focusing on data and information alone will not achieve it. Therefore, maybe it’s not so much a feeling of déjà vu that I was experiencing, but rather early stages of the fulfillment of the visions of those early spatial pioneers, where those of us in the geospatial community have a means to contribute data, skills, and technology - and ways of approaching problems looking through a spatial lens - to support people, businesses, and the wider community to make meaningful decisions.

In other words, the geospatial ecosystem becomes a full and equal partner in the wider digital or knowledge ecosystem.

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About Critchlow Geospatial

Critchlow Geospatial are Location Intelligence Specialists.

Partnering with world-class solution providers, we enable organisations to see, share and understand information so they make the best business and operational decisions.

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