SafeGraph is a company built around a belief in the importance of geospatial data. We’re built that way because we believe in knowing more than just what happens. We believe in knowing why it happens, and we also believe that you can’t know that without knowing where it happens.
Now, you may ask: what is geospatial data, exactly? And what makes it such a unique and valuable asset to so many organizations? If you’re curious about what geospatial data is or how it might be able to help your business, this guide is for you.
If you’re looking for the basics, jump down to these topics now:
If you need the full crash course on geospatial data use, from the types of data out there and where you can get them to what you can do with geospatial data and how you can work effectively with it, check out our detailed guides below.
Here’s what you can look forward to:
Everything has a geography, so almost any data can be made geospatial. In this guide, we’ll define geospatial data in terms of its most common categories and ways of representing places, people, and things. We break down everything from POIs to building footprints to mobility data and everything in between.
If you want to learn more about geospatial data types, check out “Geospatial Data Types and How You Can Use Them”.
Some geospatial data providers specialize in one type, while others produce a wide variety of datasets. Find where to get the data you need for a particular geospatial analysis. We list 9 types of sources for your geospatial data, and 20+ providers and vendors that specialize in each of those areas.
If your goal is to learn more about the sources and where you can actually get geospatial data, read through “Geospatial Data Sources — Where to Get the Data You Need”.
What is geospatial data used for? It’s obviously critical to mapping, but it’s also seeing increasing use in business analytics and strategy planning.
See the various ways geospatial data is being applied across industries and organizations, including in mapping, retail site selection, visit attribution, urban planning, network planning, investment research, and more.
Learn more about use cases in “Top 10 Uses of Geospatial Data + Where to Get It”.
The real value of geospatial data lies in the insights gained from analyzing it. In this guide, learn what geospatial data analysis is, the benefits of using it in analytics, the top ways it’s used most effectively, and about the changing geospatial data analytics market and industry.
“Geospatial Data Analytics — What It Is, Benefits, and Top Use Cases” will teach you everything you need to know about this topic.
Visualizations are critical for giving geospatial data meaning. We break down the top 12 methods used for visualizing geospatial data (with image examples), how to do these visualizations, and in what instances they are most useful.
If you need to learn more about the top “12 Methods for Visualizing Geospatial Data on a Map”, this is your step-by-step guide.
Using geospatial data is not without its complications, some of which you won’t find elsewhere in data science. This guide explains why data integration is necessary, and breaks down the top 5 challenges associated with geospatial data integration.
Ensure you have the right guidelines, know-how, and tools to utilize geospatial data effectively, and learn how to solve problems such as data standardization, address standardization, processing times, data quality, and more.
If you’re on the integration path and have questions about the process, make sure you check out “Geospatial Data Integration — Importance + Top 5 Challenges”.
Geospatial data has a few key differences from other types of data, and so requires a somewhat unique management style. If you are managing geospatial data or you need to soon, you need to identify your organization’s needs and optimize your strategy to accommodate those needs.
We break down these questions and concepts into 5 phases that will help you plan your strategy and implement it at your organization. And we include 15 best practices you can use as the base for your geospatial data management strategy.
If you’re already working with large-scale geospatial data management or you’re about to, make sure you check out “Geospatial Data Management Best Practices — 5 Steps to a Winning Strategy”.
Geospatial data is any information about an object, event, or phenomenon relative to its location on (or near) Earth’s surface. It can often also include more details than just the address or coordinates of where it is occuring, such as timestamps, categorization, and other attribution.
The location component is what’s critical to this geospatial data definition. It means that the data doesn’t just exist in a vacuum; it inherently points to a real place (or set of places) somewhere on Earth. This makes geospatial data behave a bit differently than other types of data, but it also makes it easier to visualize and conceptualize.
Adding other attributes to geospatial data provides even more context and opens up even more avenues for analysis. For example, adding a time component allows for monitoring dynamic objects and events, such as how close a delivery truck is to reaching its drop-off destination, or if/when a severe storm over the ocean is likely to make landfall. Possibilities like these are also part of what makes geospatial data unique.
It’s sometimes asked what the relationship between GIS and geospatial data is, since the two terms are often used together. Typically, GIS (i.e. geographic information systems) refers to a specialized system of computer software that collects, manages, analyzes, and maps geospatial data. In other words, it processes geospatial data into forms that are easier for humans to understand and use.
GIS platforms are more common than you might think, too. If you’ve ever used Google Maps to get driving directions or find the address of a local place to eat (both of which are types of geospatial data), you’ve used a GIS.
So why is geospatial data important? Well, as we mentioned, it adds spatial (and sometimes temporal) context to information. And being able to relate data to specific places and times in the physical world makes it easier to conceptualize. Patterns in things such as shopping habits, migrations, severe weather, and road traffic are much more apparent if they’re mapped to a representation of what the world actually looks like, as opposed to just being numbers in a table.
The ability to recognize more of these patterns, and faster, is what is giving organizations that use geospatial data a competitive advantage. Here are three reasons why:
This only scratches the surface of what geospatial data encompasses and what it’s capable of. Throughout this guide, we’ll discuss what types of geospatial data are out there, where to find them, what you can do with them, and how you can effectively put them to work towards building a better business strategy.
To lead off, we’ll cover some of the common forms that geospatial data comes in. This will help you recognize what they look like and, more importantly, understand what information they provide. You can use this to decide how they may fit into your organization’s operations.
If you're ready to learn more about geospatial data types, check out “Geospatial Data Types and How You Can Use Them”.