What is Geospatial Data? Geospatial Data Explained.
As the name suggests, geospatial data refers to data that has a geographic or locational component to it. This geospatial component could come in various forms, including but not limited to coordinates, longitude and latitude values, street addresses, ZIP codes, GPS data, or even GIS (geospatial information system) data. Basically, any information that can tie the data to its specific location on Earth.
On the flipside, non-spatial data is data that does not have a geographic element and cannot be tied to a specific location. However, it is possible to turn non-spatial data into geospatial data. For example, if you have data about an object, such as a car or a bike, geospatial information can be added to it.
As organisations continue to look for new ways to glean insights from their data, leveraging geospatial data provides an extra dimension to create value, address problems, and deliver more efficiently. Because of this, the popularity of geospatial data analysis is growing, as are the methods for generating and acquiring geospatial data.
With technological advancements, today we are creating more geospatial data than ever before through the expanding use of GPS-enabled computers, vehicles, mobile phones, and IoT devices.
The ways to use geospatial data are also diversifying rapidly, as people find new ways to leverage the power of location-based analytics.
To cite a few real-world examples, today geospatial data can be used to understand traffic patterns and predict crime in certain locations based on information gathered from the many endpoint devices available. Other uses include helping businesses derive information on the best retail location, or visualize the geographical distribution of their target customer segment.