The geospace race – Part 1: The principles

Data is the oil on which property runs. Property players around the world are required to wrestle with the combination of maps, title deeds, land registries, registry of deeds, etc. Google Earth made a lot of previously complex data simple. It provided the missing golden thread that allowed property professionals to tie together numerous disparate data jewels. For many, it was a stepping-stone into the world of geospatial technology.

Further to this fresh, new perspective, it gave to decision-makers and analysts wondrous tools like measurement devices. Pins on maps could be customised with colours and shapes, even branded. Polygons could be made opaque and outlined. Files, when clicked, could navigate to a property on a map. This information could be stored neatly in folders. These collections of information could be emailed to colleagues, who could in turn view them on their own devices. It was powerful for presentations, and effective for communicating unique insights and perspectives.

Commercial property is all about location, and location is the heartbeat of geospatial data. By way of explanation, geospatial data can be categorised into the following three Ps.

• Points on a map
• Those points connected to become Paths
• And those paths that circle back to their beginnings to become Polygons.

Fusing commercial real estate with these three Ps allows you to ask different data questions and enables a whole new means of solving problems. Further, geospatial provides a common language for commercial real estate that transcends geographies and sidesteps subjectivity. What’s more, geospatial data can talk to other geospatial data. It is, in short, Esperanto for the world’s property professionals.

Let’s return to those three Ps, and unpack them further:

Points

A point is the same as a pin on a map. It is defined by a combination of a latitude and longitude, known as co-ordinates.

The beautiful thing about geospatial datapoints is that they talk to each other. So, as soon as your point is assigned to co-ordinates, it can talk to other geospatial data, and be enriched by that data.

For example, think about the point where you are reading this. This point probably lives inside of a polygon-shaped suburb. That suburb, along with others, is nested inside of a larger city or town area. Which, in turn, lives within either a county, province or state. This resides, again, in an even larger collection of counties, provinces or states, called a country.

So, from a single humble point (a latitude and longitude), it is possible, with razor sharp accuracy, in high speed, to enrich that point with a suburb, city, province, and country. In tech parlance – this point can inherit other attributes.
Thus, once you have a collection of these points, you can slice and dice your data by this, new, enriched information. Further, those points can be used in connection with the Ps that follow…

Paths

A collection of points – be they roads, public transport routes, or fibreoptic paths – are, in turn, interesting. Paths pass along or through points, and live inside of polygons. Paths can be assigned buffers on each side and these buffers can be turned into polygons. These buffers can communicate the catchment area of a bus route, or target customers for high speed fibre optics. Paths can be either Euclidian (straight line or as the crow flies), or they can conform to how people move through urban or natural geographies. Path distances can be measured by travel time or distance. The data harvesters for these travel times are humans, using navigation apps.

This data, anonymised and aggregated, empowers Uber drivers to predict time they at which will arrive to collect a passenger, and when that passenger will arrive at their destination. A path allows delivery vehicles to sequence delivery drop offs, ensuring prime cargo is delivered on time. Again, as with points, paths can inherit related point and polygon data.

Polygons

It’s perhaps easiest to think of a polygon as a net.

A simple suburb can be a polygon. So too, the floor plan of a property. So too, the land perimeter of said property. So too, all those locations that can be reached by car within 5 minutes of your house during rush hour. So too, collections of the closest possible points, relative to your competitors, that a consumer is likely to walk to, before that consumer walks to your competitor.

Why are polygons so useful in commercial real estate? They allow you to objectively define geographies for comparison purposes – for example, vacancy rates within two polygons of equal or comparable size. This allows analysts to eliminate subjectivity when comparing different data sets.

Polygons are also used in other ways. In dense cities, where the boundaries between one neighbourhood and another are often a matter of opinion, polygons alleviate the need for debate: simply draw a polygon, as if throwing a net, over selected geo-located data points.
 
When data is assigned geospatial attributes (co-ordinates), no matter what form the data takes, it is beautifully structured for analysis.

Geospatial may seem like a new age buzzword – either intimidating or irrelevant, and possibly both. However, in reading this, you may have realised that, as a topic, it is intuitive, simple. This article has laid out some core principles. Perhaps, while reading, you were applying these principles to your life – where you live, the routes you travel. Maybe, you started thinking about solving an existing real estate problem in a slightly different way? Or better yet, you experienced the green shoots of a business opportunity crystallising? This is the wonder of geospatial. It provides a simple but privileged perspective: a bird’s-eye view of the terrestrial realm.

In the second part of this piece, I will unpack practical examples of geospatial wins in our US$29tn industry. Until then, the good news is that these seeds, planted in the fertile ground of your brain, will keep on growing.