To many, the words 'spatial data' translates directly to 'map'. Maps are certainly a great way to display spatial data, but there is much more spatial data is good for. After all, everything we see and do has some kind of spatial component. Where we live, how we travel - the list doesn’t end.

By analyzing spatial data and how certain variables impacts our lives, we can learn more about why certain spatial relationships exist. Why are certain locations popular travel destinations? Why does a brand do successfully in one country and not another? It’s time to start adopting spatial and location data practices to better understand human behaviour and our influence on this planet.

Let's Start With an Example

Map of London in 1854

To explain how important and useful it can be to think about spatial data, let's look at the way John Snow analyzed a cholera outbreak in Soho, London in 1854. At this time in history, people believed that cholera was spread in the air. However, Dr. Snow was skeptical of this hypothesis.

To better understand this particular outbreak, he identified the homes of those who were impacted by the disease on a city map. By doing this, he realized there was a pattern and relationship between those infected and where they lived. From this simple type of spatial analysis, John Snow was able to identify the actual culprit of the cholera outbreak - a contaminated water well.

This analysis led to the shut down of this particular well, saving many lives. Not only did the mortality rate drop, but this provided new insight and evidence to the medical community who eventually identified that cholera was not in fact transmitted by air, but was a result of ingesting contaminated food and drink.

Discovering More With Spatial Data

The world is changing at an exponential rate and it can be challenging to keep up with all that is going on. One of the major changes that we see taking place before our eyes is the movement and growth of the human population. With this comes changes to businesses, communities, and the environment. To fully understand why and how changes are occurring and to help predict future trends while mitigating problems, spatial data needs to be a part of your analysis.


For your company or business to be successful, you must ask a variety of data-driven questions. Some of the most important questions are those that help you better understand your customer base. One of the first questions you may ask is “Where is my customer base?” Enter: spatial data.

Spatial data is a great way to understand trends that relate directly to your best and worst customer bases. There is plenty of free census and geographic data available from open data portals from government departments that you can integrate with your own customer data.

Problem: Business Site Selection
Let's say, for example, that you are a company with a product whose core customer base is college students. How are you going to ensure you choose the best location for a brand new store?

Solution: Spatial Data
You may begin your analysis by identifying the location of college campuses or use demographic data from open data portals to see where people aged 18-25 are concentrated. From here, you could incorporate transportation spatial data to see what locations are most accessible.

Let's say you find there is a very convenient transit service nearby. Where does this service take riders? After asking these kinds of questions and doing spatial analysis, you’ll be able to choose a location best suited for your store.

Spatial data in the business arena is not limited to retail. It’s also very important for real estate agencies, insurance companies, and any franchise to understand where they will and will not grow. With spatial data you can discover growth insights, manage facilities and networks, and provide location information to customer. Without considering spatial components and how they relate to your business, your risks and possibility of poor results will increase.


Human relationships with others and our surroundings create diverse communities throughout the world. Everything we do impacts our communities and the people within it. How can we continue to support each other in our environments? Spatial data can help us make better predictions about human behaviour and understand what variables may influence an individual's choices. By performing spatial analysis on our communities, we can ensure that neighbourhoods are accessible and usable by everyone.

Problem: Community Nuisances
Let's say you work in public service and have received complaints about graffiti or other types of public defacing. There are a few options you could take to address the issue. You could put in the resources to identify the suspects and find them, but that won’t necessarily stop other graffiti artists from drawing in the same area.

Solution: Spatial Data
Another approach might be to look at the spatial characteristics of that area and compare them to others. Perhaps you notice that well-lit areas in your community do not have problems with graffiti and a simple solution to solving your street-art issue in this location is to simply put up an additional lamp post.

Additional uses of spatial data in a societal context include informing citizens about their surroundings, keeping your community safe with timely information, and supporting your community in times of need. Short term solutions are useful for solving issues, but long term solutions can solve and prevent issues before they even occur. Using spatial data can help you understand these differences, weigh your options, and ultimately, save you time and money.


The environment is constantly changing due to human influence and other natural forces. To truly understand the environment and the science behind our ecosystems, we must understand how different elements interact with each other. Naturally, this is done using spatial data.

Problem: Environmental Relationships
Let's consider how the surrounding environment will be impacted by building a dam. There are many spatial components to consider here. How much will the reservoir expand behind the dam? How will the baseflow, infiltration, and drainage change as a result? There are potentially more spatial concerns than just these.

Solution: Spatial Data
Combining various sets of spatial data like soil type, river structure, elevation, and other variables can help you understand the relationships between each of the ecosystem’s variables. You can even take this a step farther and create simulations based on your understanding of the implementation of dams to show how changes will occur over time. Your final results will ultimately help you and others better understand the relationships between humans and our environment.

The beauty and complexity of nature is what makes it so fascinating. While various environments and habitats appear vastly different, they bear many similarities to each other. By analyzing the spatial relationships in a habitat we are familiar with, we can gain insights into other habitats that are more complex or have been studied less. Other environmental related uses for data include sharing information with volunteers of citizen science projects, providing location-specific weather alerts, and tracking plant and animal communities. There is no limit to how spatial data can be used in understanding natural environments.

Why Now?

You may be thinking to yourself “If spatial data has so much value, why am I only finding out about this now?” While spatial data has always been useful, it hasn’t always necessarily been usable, and we haven’t always had as much of it as we do today.

Typically, spatial data existed in very large file sizes and was difficult to use without advanced hardware. Now, with more high-tech computers and devices for processing and collecting data, more and more organizations are turning to spatial data to find answers to their problems. Smartphones, vehicle tracking, and satellites are helping to unlock the world of spatial and location data for organizations big and small.

What can still be difficult with spatial data (really, any data) is that it can be stuck in formats that cannot be easily used by all applications. While you may have the hardware to use it, you may not have the right software to understand it. This is why FME was created.

FME for Spatial Data

FME, which stands for feature manipulation engine, supports over 450 formats and applications to make it easier for you to work with the data you have in the software of your choice. There are a variety of spatial transformations tasks that can be easily implemented via drag-and-drop transformers and even more data integration tools to help you create the exact dataset you want. FME was built specifically for transforming spatial data and has always been the best integration tool for working with it. Spatial format structures and models are built directly into the software, so you don’t have to spend time on the mundane, tedious components of data integration.

Safe Software, the makers of FME, are leaders in the technology world that strive to stay one step ahead of the data integration trends. FME is continuously upgraded to ensure it is supporting the newest formats, updated versions of data formats, and large amounts of data. Gone is the idea that individual departments must work in their own data silos, with IT structures limiting the company’s potential to truly work as one. Data should be able to flow freely no matter where, when, or how it’s needed.

Related Resources

What is Spatial Data?


What is Spatial ETL?


How to Prep Mapping Data for Tableau


Geospatial Intelligence in the Cloud


38 Common GIS Tasks in FME


What is Application Integration?