The geospace race – Part 2: Geospatial in practice

How can geospatial data be used for the betterment of the built and natural environment? It’s the US$29tn question. Here are some use cases:

In 2017 fires raged through the Garden Route in South Africa, damaging beautiful holiday homes and ravaging businesses. Insurers who had insured risks in the affected region were overwhelmed by a flurry of fire claims. The resulting admin was crippling, and the risk administration process intimidating and expensive to resolve. Some firms had geocoded their properties (i.e. turned each of them into points). The fire range was known and was defined by a polygon. With these two knowns, the way forward was beautifully elegant. By throwing a polygon over their claims data, it was possible to very quickly, and without sending inspectors onsite, to identify spurious and opportunistic claims. And, unfortunately for the chancers, commence a separate process!

A major fuel retailer was investigating the profit potential of adding quick service restaurants (QSRs) to their sites. Could any of their real estate house QSRs? If so, could they list the applicable sites and quantify their under-sweated value? They started by geocoding all of their sites. Travel distance polygons were thrown around these points, defining a catchment area. This catchment area was cross referenced against census data to understand local demographics. By defining polygons describing onsite transport reticulation, existing structures and grades, retaining walls and servitudes, it was possible to identify strategic sites for standalone sites and redevelopment. In that way, a plan for QSR supply was agreed. The next priority was to identify all quick services restaurants in the country, and travel distances from each, in order to ascertain potential demand. The result: the retailer is looking for standalone sites where they do not have an existing QSR offering within a certain radius. Locations according to competitor activity, and where demographics satisfy customer criteria. At the conclusion of this exercise, these incredibly powerful answers could be delivered with the mere click of a button, to boardrooms of decision-makers. The outcome was the identification of significant retail space in locations where the quality and quantity of customer had been verified.

Enter a large office node. Vacancies are estimated between 15% to 30%, but nobody knows for sure. How do you answer the question conclusively? Well first you define the node using a polygon. A polygon can give you a very precise description of the node – in certain instances suburbs making up that node are bisected, in others they are enclosed. Certain roads are fully encapsulated by the polygon, while others are only partially enclosed in the polygon net. Next throw your geolocated vacancies data at the net and return results. To concerned property owners, eventually there is light, clarity and efficiency. Data is now firm, answers are auditable and eventually trustworthy, and better decisions are made – more efficiently.

Now for a fibre operator. Large sums of money are being spent on laying down high quality, cheap, lightning fast fibre. But how to efficiently introduce the superior product, at a lower price point for customers? How do these businesses, hungry for cost savings, efficiently get to know of the great opportunity available to them? Again, let’s look at what is known. The fibre routes are known. The buffer (area serviceable beyond a fibre route) is also known. So now you have a net of supply to throw at potential demand. But how to identify the customers who are needing this better priced, superior product? Well, if those customers are geospatially defined, you simply throw your net, and it will come back with gold.

For many people, the first experience of geospatial inspires a certain romance and feel of magic.

Following a deeper, practical understanding of geospatial’s levers and substance, empowers first time users with a twin sense of confidence and promise.

The real magic happens when you get to move from the theoretical, to the actual: joining the dots, identifying innovative and simple solutions to previously tricky problems. These solutions can be applied from data quality issues, through to revenue-enhancing insights.
 
And this is the beauty of this problem-solving paradigm. Geospatial can help drive business efficiencies, expose untapped markets, reduce risks, and provide data points to property decision makers starved of decision-relevant intelligence.