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Content Management in Geospatial Applications

The greatness of geospatial information rests in its universal application to almost any subject. You can use geospatial information in population demographics, locating a new retail location, tabulating incidence of traffic accidents in different geographical areas, or for reviewing topo maps for a mining operation. Whatever the subject, there is a wealth of images, videos, text and other content that you can pin to any geo set of coordinates.

The flip side of this versatility is managing what can quickly become an overwhelming amount of content. For example, at what point do you become so buried in information that you can’t even get to the information that you wanted?

“There are many different types of geospatial information,” says Anthony Calamito, chief geospatial officer at Boundless, a provider of geospatial technology solutions. “But so many organizations still don’t understand geospatial information and how to optimize its use. Location is just one component. You also have rastervector, unstructured and tabular data that you can superimpose on location.”

Calamito references the use case of a large agribusiness company.

“The company wanted to take its geospatial data to a new level of usefulness,” he says. “They knew they were managing mass quantities of imagery, but they didn’t know how to optimize its use. They wanted better ways to index and reach this imagery. Out of this, they created a new way of indexing the data that was more effective for them.”

Calamito says that one of the hardest things for companies to do as they seek to better utilize their geospatial data is to understand the value that they have the potential of unlocking.

“The key is remembering that all things are related,” he says. “For example, if you want to understand the effect of gerrymandering on voting, you can use location to see how location affects different voter districts and what voting outcomes are likely to be.”

A second geospatial data construct organizations should consider is time.

“In some cases, your organization needs real-time or near real-time data in order to assess certain business situations,” Calamito says. “In other cases, it is okay to use data that’s only updated periodically.”

Understanding which data must be real-time or near real-time – and which can be of a more historical nature – is critical when it comes to how you store your data. For example, geospatial data that needs to be real-time will have to be stored in memory or on very rapid solid disk memory. Data that is seldom accessed can be archived away to cold storage, which only needs cheap hard disks. Between these two extremes is geospatial data that is regularly accessed, but not needed in real-time.

“How organizations make decisions on where geospatial data is stored matters,” Calamito says. “Do you need data that is instantaneous or at two-minute intervals or every month? Based on this, you can decide whether the data should be stored on a local server in a remote office, or uploaded to a cloud, or consolidated in your central data center.”

Finally, it’s important to think broadly about geospatial data because of its almost universal applicability.

“Many organizations tend to look at their data as unidirectional or applying to just a small set of business cases,” notes Calamito. “This type of approach doesn’t bode well for geospatial data, because of its breadth. The best way to get the most out of your geospatial data is to think broadly. In other words, what other rules, applications and different ways of processing this data can be exploited in the business? This is the way to get the most value out of your geospatial data.”


Reprint from: GeoDataPoint

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