Integrating Geospatial Data (Finally)

Making Sense of Big Data in the Real Business World

For many companies involved with the earth in some way, there are data people, and then there are GEO data people. One deals with the traditional IT kind of data. The other is devoted to GIS data, and they’re often a world into themselves.

It’s odd, really, because it all boils down to a data choice, probably made three to four decades ago, about imaging (raster) versus vector (GIS). Out of this arose specialized data skills, separate divisions and separate software for using one or the other.

That’s changing, according to a recent MapCite article, as more companies are integrating the two, using remote sensing and imaging to augment GIS data.

If you’ve ever seen Google Maps or Zillow, this is hardly shocking news. But cheaper LiDA sensors, new satellites and more access to sensed data means more organizations can afford to combine this mapping data, as well as overlay other datasets. Companies that once supported only GIS data are now adding integration capabilities, as well. GIS technologies are even offered as a cloud-based service, the article points out.

It’s led to wider use of what Mladen Stojic, VP of geospatial at Intergraph, calls “smart maps.”

That may be good news for government agencies, which often store large GIS data sets in their transportation or divisions focused on digging in the earth. Of course, some state and regional agencies have been working for years to solve the GIS integration problems, but for those who haven’t been, it may provide an easier, less expensive path to putting that data to use.

What will be really interesting, though, is using Big Data to quickly analyze GIS, sensing data and other types of data. The University of Michigan recently published a study showing how big datasets and tools can be used to help fight crime. GigaOm’s Derrick Harris wrote about the study, suggesting it might be used to prevent crime.

Prevent crime? Holy Hadoop, Batman! Now that’s a good reason to integrate geospatial data.