By Armando Alvarez
During the 2009 global alert, caused by the Influenza A (H1N1) virus, Google was able to identify the location of an outbreak way before than any health system. By starting to identify the exponential increase of searches of symptoms associated to this disease in a specific geographical area, with this information it was possible to identify if there was a presence of outbreak.
This process is an example of how a great scale of analysis of information can optimize a decision, in this case, a decision that can save many lives, from targeting prevention efforts to reduce the potentially devastating effects of an epidemic.
The essential component of analysis in this case, besides symptoms’ searching information, and the technical and technological tools that made this analysis possible, is the geographical component.
From the exponentially growing empowerment of geographical coordinates, not only of specific points on the map (branch offices’ addresses, offices, clients, etc.) but also of real time lines of movement (GPS in cellphones, computers, cars, etc.), it is now possible to begin to exploit the world of geographic correlations with a previously unimagined scope.
Nowadays it is possible to obtain information that gives us the location of clients, vehicle fleets, electronic devices, even endangered animals and “high risk” people. Besides that, it is also possible to identify their interactions and doing it in real time. This allows locating optimal trajectories, risky areas, potential and non-potential areas, geographical intersections.
It is possible to answer questions about complex events of great populations:
- Where there may be an epidemic?
- Where there may be an ecologic crisis?
- Where there may be economic or political problems?
Similarly there might be questions that allow optimizing specific business processes:
- Where do I find a new branch?
- Which routes should take my force of distribution to minimize delivery times?
- How do I target the territory in a geographically way so that division respond to the behavior of my market?
- How can I know my client from his geographical movements so I can offer him products and services that are related to that mobility?
And finally, proper questions from a common citizen:
- Which is the easiest route to my destination?
- Which is the best restaurant closer to me?
- Where’s a Hospital?
From solutions that integrate methodologies of advanced analytics, integration and storage of high volumes of information, geographical tools of digital maps and mobility components, and things from the internet, it is not only possible to find real time geographical phenomena but also to predict the evolution of those phenomenon, which can be helpful to optimize decisions based on those projections.
Definitively, today is possible to bring precise answers and in an agile way, many other questions that contain any geographical intrinsic component.