In the previous chapter of this guide, we went over some uses for the different types of geospatial data out there, like polygons and points of interest. But geospatial data in and of itself isn’t that useful unless you know how to read it properly.
So what is geospatial data analysis, and why are many organizations incorporating it into their analytics and other operations? We’ll answer these questions and more as we look at the following:
Let’s start with the basics by explaining what geospatial data analysis is.
Geospatial data analysis involves collecting, combining, and visualizing various types of geospatial data. It is used to model and represent how people, objects, and phenomena interact within space, as well as to make predictions based on trends in the relationships between places.
Put another way, geospatial data analytics puts data in a more accessible format by introducing elements of space and time. Information that would be difficult to get out of reading line after line in a table or spreadsheet becomes much easier to understand in the context of a visual representation of what the world really looks like. This allows people to more easily pick up on patterns such as distance, proximity, density of a variable, changes over time, and other relationships.
In short, geospatial data analysis is about going beyond determining what happens to not only where and when it happens, but also why it happens at a specific place and/or time.
Geospatial big data analytics makes trends regarding space and time more visually obvious than they would be in a massive set of raw data. This, in turn, offers many advantages over analyzing datasets without this type of context. To illustrate, here are 4 benefits of using geospatial data in analytics:
It shouldn’t be a surprise that geospatial data is increasingly being integrated into several different industries and corporate functions. After all, it provides a lot of extra information and context that most other types of data don’t. Here are just a few business practices that are now leveraging geospatial data analysis methods.
You can learn more about these (and other) uses for geospatial data in this guide’s chapter on geospatial data use cases and examples.
The increasing number of use cases for geospatial data is steadily growing the geospatial data analytics market. Some market analysts estimate that the geospatial data industry will nearly double in size between 2021 and 2026.
The types of fields – both commercial and non-commercial – that geospatial data is being used in are diversifying as well. We already touched briefly on how the retail, private equity, and insurance industries are utilizing geospatial data. But utilities providers can also make use of it to predict where and when service disruptions may occur, and thus optimize when and where they should perform maintenance. And governments can use it to formulate better emergency response and public information protocols in the event of a natural disaster or other crisis.
All of this means that geospatial data analysis companies will be more in-demand than ever. Another prediction is that, as the fields of machine learning and geospatial data analysis intertwine, we will see the emergence of self-piloting vehicles and maybe even high-definition custom maps on demand.
Speaking of maps, they are the primary medium for visualizing geospatial data so it can be analyzed. But there are many different types of maps, and which type you use to display your data can sometimes have a big impact on what you get out of analyzing it. We’ll explain more in our next chapter on methods of visualizing geospatial data.
