Why your geolocation skills can help in an artificial intelligence environment

earth-in-two-hemispheres

Geographic Information Systems experts have leveraged their existing knowledge of analysis, design, coding, cleansing, merging, management, and leadership — developed from their exposure to a divergent technology landscape over many years — to evolve and survive in a modern era beyond Google mapping.

But while these geo experts may have already leveraged their existing knowledge across various fields, they may not yet realize that there is another potential option available to them.  They possess the fundamental skills and capabilities necessary to understand data and data modelling, which in turn gives them the option to migrate to the world of AI.

Only by working in a world of AI have I realised that the terms between the two streams are so similar. Both derive heavily from the same or similar mathematical fundamentals, design principles and concepts. Statistical analysis, for example, is one of the fundamental elements of Geographic Information Systems and is leveraged heavily in AI as well. The principles behind GIS such as mapping of coordinates systems and datums, all contain math. Longitude, latitude, meridians, calculating scales and legends — it’s all rooted in math.

The interpolation methods often used in raster and vector analysis can also be used in spatial software including kriging, minimum curvature, polynomial regression, and nearest neighbour analysis. Then you have specific spatial statistics like K means, clustering and fuzzy C clustering, Delauney triangulation, minimum spanning tree, hot spot analysis and space time cluster analysis. All of these are methods which can be used to predict what is going to happen in certain environments. All of these solutions can then be used to put the data into a segmentation and classification toolset such as Decision Tree again using spatial software to analyse.

These worlds collide with the math, the environment and the predictive analysis. Here is where they seem to diverge: from a certain point of view, GIS is AI without an actuator. In other words, there is no direct action as a result of what we, as geolocation experts, find in the analysis in the spatial world.

But what if that’s an incomplete point of view? Maybe we just don’t necessarily see that direct action straight away. So often, our analysis is undertaken and we predict what may happen 5, 10 or even 15 years into the future – such as in the scenario of when transport patterns predict the formation of new towns or where mapping where floods might occur. Our results are used to make crucial decisions on whether a town should be built, or where transport should be delivered or where people need to be protected by flood walls. Maybe this is the reason we don’t call this Artificial Intelligence: we still make the decisions ourselves and our outputs are delivered to humans to make decisions.


Sarah James was ANZ lead for Authentic Leadership in DXC and an advocate for DXC’s Women in Leadership and STEM. Prior to leaving DXC in September 2017, Sarah founded the Empowering Future Leaders blog and was its primary author. With over 15 years of experience in the world of IT, Sarah’s specialty is spatial information and includes integration on projects as diverse as mapping volcanoes in Hawaii to delivering high-tech police vehicles.

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