GIS Analytics vs. Spatial Analytics
As businesses gather more and more location-based data, the need for tools to make sense of that data has grown dramatically. GIS analytics and spatial analytics are two tools for analyzing location-based data that come with their unique features and benefits. In this post, we'll break down both, compare them objectively and hopefully get a chuckle or two out of you.
What is GIS Analytics?
GIS (Geographic Information System) is a system of software, hardware, people, and data that allows us to visualize, manipulate, and analyze geographic data. GIS analytics focuses on spatial analysis and mapping, and it is useful for tasks such as visualizing demographic data or creating maps for disaster response planning.
GIS analytics uses data in a geographical context, such as data on population density, terrain, and natural resources. The data is often analyzed through maps, as these provide an intuitive way of visualizing and interpreting the information.
What is Spatial Analytics?
Spatial Analytics, on the other hand, is a broader term that encompasses any type of analytics that involves spatial data. It doesn't necessarily require geographic data like GIS, instead, it can use any data that has a location component. This approach enables spatial analytics to analyze data sets from various sources, such as social media or marketing trends.
Spatial Analytics tools can unearth hidden patterns and complex relationships connecting data points, detecting trends and predicting future trends, and detecting changing patterns in real-time.
How do GIS Analytics and Spatial Analytics compare?
GIS and Spatial Analytics are closely linked in their use of spatial data, albeit focusing on different components. In terms of strengths and weaknesses, GIS analytics is better suited for visual representation of geographical data such as maps, whilst Spatial analytics offers more sophisticated data analysis that can uncover hidden relationships within data and detect changing patterns in real-time.
GIS analytics provides a spatial query, data visualization, and data analysis support system. Often aimed at the physical world, GIS analytics deals with demographic, surveying, and urban planning data. Whereas, spatial analytics is helpful in detecting Geospatial relations when used with AI and ML applications.
Therefore, choosing which analytics tool to use largely depends on what kind of data the business is dealing with and the outcome it is seeks to obtain. It could be used to assess patterns that unfold over time or to locate the origin of an incident in the virtual world.
Conclusion
The comparisons between GIS analytics and spatial analytics are fewer than the similarities between them. They both make use of spatial data, and both are useful in their respective environments.
GIS analytics is more focused on spatial representation, while spatial analytics leverages more extensive data analytics capabilities. Both have specific use cases that businesses should consider when selecting them.
So, if you are into mapping, demographics, or urban planning, GIS analytics could be a great opportunity. But suppose you need to detect micro patterns or immediately identify parameter changes, spatial analytics provides a better solution.
References
- Nieves, Juan Pablo. (2021). GIS or Spatial Analytics? Choose The Right Tool For The Job
- Flare Compare. (2022). Spatial Analytics Vs. GIS Analytics: What's the Difference?