Geo-analytics provides an interesting domain due to its direct link to the physical world. From a predictive perspective, may real-world questions are linked to it. What is the impact of a new location or the temporary closure of a road. A new gas station might fail even when locations are only 500 meter apart. It can help branch managers of a bank to determine where best to compete and where risk from competition is highest.

A key aspect of geo-anaytics is the large set of constraints that must be imposed on the prediction models. One can not build a house everywhere, and you can not drive through a river. Changes over time might impose new constraints. A village might grow during the expansion of an airport, regulations might change causing delays in completion of a tunnel. That connection might have impact on the economic viability of a gas station.



For geo-analytics, visualisation is key. As complex socio-geographical data is used with a direct relation to the physicial world, it is essential to be represent this data in maps. Preferably this is done not only static but also dynamic as geographical information often has a time dimension as well.

Network Analyses

Whether it is finding new location for a gas station or identity geographic areas into which to expand business, the geographical data is key. Often, many parameters with their own geo-information need to be combined and incorporated in a predictive model. The relationships and locations require a solid network analyses to be able to predict the impact of adding a new retail outlet for example.

Heat Map Animation

Besides analyzing networks from a geo perspective, density distributions are also very important. Especially when these densities fluctuate over time. The simple example everybody knows is the flow of traffic. However, the same techniques can be used to simulate predict the impact of changes in large warehouses. Those predictions help avoid costly mistakes and capture potential efficiency gains over the life-cycle of a warehouse.

How Kentivo can help:

Underpinning geo-analytics combines many predictive and machine learning techniques in a socio-geographical context. With its expertise, Kentivo can help organisations do more with data in a geographical context:

  • Geo-analytical prediction models: Our consultants can create or improve prediction models for geo related questions.
  • Network Simulation Models: Kentivo van create network simulations to increase understanding and dynamics within a network. This can help avoid bottlenecks and provide insights to make optimal decisions.
  • Geo-information Animation: Plotting some data on a map is one thing, providing insights across multiple paramaters and over time is a more challenging step. For this animations of results can be of great help to understand the prediction models. Kentivo can create these animations/visualasations.