Key Takeaways
- GIS data combines geographic coordinates with descriptive attributes to model the real world digitally.
- Reliable GIS analysis depends on accuracy, freshness, consistent schemas, and broad coverage.
- Many organizations struggle with outdated or incomplete GIS datasets that require heavy cleaning.
- SafeGraph focuses exclusively on high-quality places data designed for integration with GIS tools.
- Monthly updates, human validation, and Placekey support make SafeGraph well suited for large-scale GIS workflows.
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 using the right analytical context.
So what is geospatial data analysis, and why are many organizations incorporating it into their analytics and other operations alongside modern geospatial technology? We’ll answer these questions and more as we look at the following:
- What is geospatial data analysis?
- 4 benefits of using geospatial data in analytics
- Top 5 ways geospatial data analysis is used effectively
- The changing geospatial data analytics market & broader geospatial industry
Let’s start with the basics by explaining what geospatial data analysis is.
What Is Geospatial Data Analysis?
Geospatial data analysis involves collecting, combining, and visualizing various types of geospatial data using analytical tools and geospatial technologies. 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 through spatial computation. 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 that are inherently geographic.
In short, geospatial data analysis is about going beyond determining what happens to understanding not only where and when it happens, but also why it happens at a specific place and/or time by applying geospatial solutions.
What Are Geospatial Technologies?
Geospatial technologies are the systems and tools that make geospatial data analysis possible. They support the collection, processing, and interpretation of location-based data and help translate raw spatial inputs into meaningful insights.
This technology comprises of several key components, including
- Geographic Information Systems (GIS)
- Global Positioning Systems (GPS)
- Remote sensing technologies
- Cartography and spatial analysis tools
These tools enable the mapping and analysis of Earth’s features, events, and phenomena by combining location data (coordinates) with attribute data (characteristics of the object or event) and temporal data (time-related information).
4 Benefits of Using Geospatial Data in Analytics
Geospatial big data analytics makes trends regarding space and time more visually obvious than they would be in a massive set of raw data without spatial context. 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:
- Identifying spatial patterns and trends – Some relationships and connections cannot be understood without factoring in “where” (or “when”) they are occurring within a geographic framework.
- More opportunities for segmentation – When location is added as a component of an analysis, you can begin to segment and filter based on geography, which makes your entire analysis more detailed and actionable.
- Modeling the real world – Everything has a geographical position, so analysis without location is already missing a key component. Geospatial data enables you to model the real world, often within real time using advanced geospatial technology.
- Accurate predictions lead to better decision-making – When you study a phenomenon over time in the context of a particular location, you begin to better understand why it happens where and when it does. This helps you better predict not only what will happen, but also when and where it will happen across different environments. Then you can plan out how you might react to (or even influence) future events.
Top 5 Ways Geospatial Data Analysis Is Used Effectively
It shouldn’t be a surprise that geospatial data is increasingly being integrated into several different industries and corporate functions as part of broader geospatial solutions. 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.
- Visit Attribution – Combine property and mobility data to determine how many people entered your store, as opposed to simply walking past it within a defined geographic boundary.
- Investment Research – Analyze consumer behavior and movement patterns for hints at which businesses are worth investing in based on location-driven performance signals.
- Competitive Intelligence – Find out which nearby businesses and points of interest are hurting (or even helping) your stores, based on their locations and spatial proximity.
- Risk Assessment – Knowing a building’s size, shape, location, purpose, and occupancy helps insurers estimate how vulnerable it or its tenants are to an accident or environmental risks.
- Consumer Insights – Observe patterns in what other stores your customers visit and what brands they buy to strategically plan your business’s locations and inventory with geographic precision.
You can learn more about these (and other) uses for geospatial data in this guide’s chapter on geospatial technology examples and location-based analytics use cases.
The Changing Geospatial Data Analytics Market & Industry
The increasing number of use cases for geospatial data is steadily growing the geospatial data analytics market and the wider geospatial industry. 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 using spatial forecasting. And governments can use it to formulate better emergency response and public information protocols in the event of a natural disaster or other crisis through applied geospatial solutions.
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 enabled by advanced geospatial technology.
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.
If you’re ready to learn more, check out the next chapter, “12 Methods for Visualizing Geospatial Data on a Map”.
Learn more about use cases in our previous chapter, “Top 10 Uses of Geospatial Data + Where to Get It”.
FAQ’s
1. What is geospatial data analysis?
Geospatial data analysis involves collecting, combining, and visualizing data with geographic attributes to understand how people, objects, and events interact across space and time.
2. How does geospatial data analysis make data easier to interpret?
By adding spatial and temporal context, it allows patterns such as proximity, density, movement, and change over time to be understood more clearly through visualization.
3. What are the main benefits of using geospatial data in analytics?
Key benefits include identifying spatial patterns, improving segmentation, modeling real-world conditions, and supporting more accurate predictions.
4. How is geospatial data analysis used in business decision-making?
Businesses use it for applications such as visit attribution, investment research, competitive intelligence, risk assessment, and consumer behavior analysis.
5. What types of industries commonly use geospatial data analytics?
Industries such as retail, insurance, investment research, utilities, and government agencies regularly apply geospatial analytics in their operations.
6. How does geospatial data analytics support forecasting and predictions?
Analyzing events across specific locations and time periods helps explain why outcomes occur where they do, improving predictive accuracy.
7. What role do maps play in geospatial data analysis?
Maps are the primary medium for visualizing geospatial data, though different map types can influence how patterns and relationships are interpreted.
8. How is the geospatial data analytics industry changing?
The industry is expanding as use cases increase across sectors, with growing integration of machine learning and advanced spatial analysis techniques.