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As one of my Pitney Bowes colleagues professes, we GIS geeks are too "map-centric." We love maps; our map creations are a cross between art and science. Web maps, story maps and map manifestos are wonderful portrayals of the advantages of understanding the proximity of people, places and things. However, the current reality suggests that maps play only a small part in coping with the increasing volumes of location-based data. And sometimes, they are not needed at all.
Blasphemy! I know. But think.
With both Earth observation (EO) satellites and ground-based sensors capturing data at rates that will severely test the limits of geospatial professionals to consume and analyze relevant information, professionals need to question the practicality of always seeking better visualization capabilities. More and more, GIS professionals need to think of themselves as becoming the new "data geeks." Their professional future will depend on it.
Data geeks are in search of the "answers." These answers come from asking questions such as "how far away is my house to the 50-year flood zone?" GIS geeks always want a map; they want to "see" the answer. As a data geek you should determine "Do I need a map for this? Or is the "answer," simply "yes" or "no" … "in" or "out."
The users of business intelligence (BI) technology are now relying on maps as the "next big thing" in analytics. But they too are coming to the conclusion that while visualizations are useful, they are confronted with the reality that, for some analytics, data needs to be distilled more simply. They don't always default to a map as the visual manifestation of "the answer." GIS geeks need to question this as well.
This places the challenge of understanding the necessity and reliance on map visualization technology as the sole output of geospatial processing. In reality, map visualization is an optional step in the process of curating, managing, integrating, cleaning and analyzing location-based data. If there is a better understanding of the first several steps, the last few steps of "to map or not to map" become clearer in the mind of the analyst.
The flood zone example above is a simple geocoding exercise often used by insurance underwriters to determine risk. Insurance companies are processing millions of policy records, property attributes and distance functions to arrive at an answer. Maps are extraneous to the process. Context is not, however. The location, that is, the address of a home is the georeferenced starting point. The context is the flood zone and any additional attribute that contributes to peril. Without context, the answer is unknown. But you don't need a map to discern the answer.
Let's take another example using more dynamic data from traffic and video image analysis technology. Smart city technology promises to alert drivers of vacant parking spaces. Taking traffic congestion feeds, employing predictive analytics and image analysis of available parking spaces, a driver on the lookout for parking would be sent directions to their connected car of the nearest available spot. Does the driver need a map? Or do they simply need the verbal directions from the in-vehicle navigation system? Taking into account the geoprocessing requirements to analyze traffic sensor and image recognition technology, the location of open parking space and the communication thereof to the open spots, a map is truly just an option for the driver.
GIS is a great tool that enhances the profession of a geospatial analyst. But the impact of the volume of location-based data is changing the requirements on upward mobility. Location-based data is now a part of the broader IT spectrum. As such, the need for an individual to be narrowly focused on just learning a GIS software solution belies the need to be skilled in other IT specializations such as master data management. These added skills will help broaden the appeal of the geospatial professional as their domains become more integrated within organizations run by chief information and chief data officers.