A building footprint may seem like just a polygon (or set of polygons) on a map, but it’s so much more than that. It’s an important data point for geospatial analysis that can be used in fields like real estate, urban planning, and even insurance.
So exactly what is a building footprint? What can it be used for? And where can you get accurate data on building footprints? We’ll answer all of these questions in the following sections:
Let’s start with a building footprint definition to give you a better understanding of what a building footprint actually is.
The definition of a building footprint is a polygon, or set of polygons, representing a specific building in the physical world. It provides a ground-centered visual representation of a building’s location, shape, dimensions, and area. It may also include other geospatial information as well.
This information can include:
Building footprints are useful because they provide detailed delineations of structures or parts of properties. This offers more insight than simple point of interest (POI) data. For example, when comparing building footprint vs. building area, the former lets you visualize what a building is shaped like and how much space it takes up relative to its surroundings. With the right metadata, it can also tell you whether a building is its own entity or a unit inside another building (i.e. spatial hierarchy). The latter can tell you how large a building is, but not how that area is distributed.
Building footprint data is a great tool on its own, but can become even more useful when combined with other types of geospatial data. We’ll demonstrate how in the next section.
So what is a building footprint actually used for in a business context? Well, as we mentioned, it’s usually combined with other types of data for various applications. Here are 5 common ones.
Businesses with brick-and-mortar stores can combine polygon data with human mobility data to perform visit attribution. This allows them to see how customers interact with a specific store location. They can measure things like how close people get to the store, how many people actually enter the store, where people go in the store, and how long people stay in the store.
They can use this information in a number of ways. For example, they can plan out where they’re going to advertise in the surrounding area so they can direct more customers to the store. Also, based on where customers typically go in the store, they can rearrange the layout of the store (or future stores) to make the popular departments or products more accessible. And they can more accurately track how much foot traffic their store gets, which can be a factor in a decision to close down a store and move somewhere else.
When mapping a building, it’s important to use the footprint of the building to understand how it interacts with other nearby spaces. For example, some buildings may be irregularly-shaped to meet the constraints of the surrounding terrain, or because of other design choices. And if a building has multiple levels, it is possible that not all of them have the same profile as the main floor.
Another consideration is spatial hierarchy. Some basic address data, combined with basic POI or property data, can help trace a route to a particular building or point of interest. But it may not be much help when dealing with a building that has multiple smaller units inside it, such as a mall, apartment complex, or office tower. It can also not be precise enough information for a place such as a college or university campus, which is made up of multiple related buildings but can sometimes be classified as a singular point of interest.
In these cases, the building footprint area and shape must be calculated and drawn for each unit in the building and/or each building in the complex. Only then will these places have accurate mapping information – not only for navigation, but also for analyzing with other geospatial data (like specific unit or building addresses).
Telecommunications companies are expanding their services into more sparsely-populated areas in an effort to connect more people. So it’s useful for them to know how to calculate a building footprint in order to properly position cellular towers (and other infrastructure).
To illustrate, they need to compare the tower plans against the area of land available for development, as well as the range of a tower’s signal. They will also need to take into account the footprints of other buildings in the area, as their heights and/or locations could interfere with signal transmissions. This will help the company get the maximum amount of coverage with a minimum number of towers.
Insurance companies require accurate and precise polygon data to evaluate the risk factors of buildings and other properties. As a start, they need to be able to model a building or area to identify potential hazard spots where an accident is more likely to occur. But there is much more they can do with building footprints.
For one thing, by measuring the area of a building footprint on a plot of land, insurers can look at how vulnerable a building is to damage caused by the terrain or other nearby objects. For example, a building that’s in a low-lying area, and/or close to a river or other body of water, is at greater risk of flooding or other water-related damage. Or a building in a crowded urban area close to other buildings may be more vulnerable to damage from a vehicle accident or another building collapsing.
Insurers can also use spatial hierarchy metadata to assess risk. For instance, an apartment complex with a greater number of individual units can potentially house a greater number of people who could either cause or experience an accident. So that building’s risk profile goes up. Switching to a retail example, a store that sells clothing might have a relatively low base risk profile. However, that store may have an elevated risk profile anyway if it’s inside a mall and near a type of store with more hazards, such as a restaurant with stoves and grills.
Of course, understanding building footprint examples can also be helpful for government agencies trying to parcel out the use of city land. Knowing the dimensions and area a building takes up on a plot of land can help civil engineers plan infrastructure around it. That includes plumbing, electricity, sidewalks (and other transportation infrastructure), and constructs to aid with accessibility (like wheelchair ramps).
Another part of this is making sure that buildings and the infrastructure around them don’t take up too much space. Communities still need land for things like yards, public parks, and nature conservation areas. Analyzing the total area of building footprints relative to the area of available land helps city planners maintain this balance.
Studying building footprint GIS data also gives context to other geospatial data that urban planners might use. Chief among those is mobility data. Planners can look at the flow of population movements within a city on a daily or weekly basis, and assess how the layout of buildings may be affecting that. They can also identify which buildings people are congregating at, and where people are coming from to reach those buildings. All of this may suggest the need for improved transportation networks to make important or popular destinations more accessible. Or it may mean a city should build critical facilities closer to places where people are already active.
Building footprint calculation is tedious and time-consuming to do on your own. That’s why it’s much easier to purchase pre-processed data from a company like SafeGraph that specializes in collecting, cleaning, and organizing data. This allows you to start your analysis quicker without having to spend your own time and money on prep work.
SafeGraph’s Geometry dataset offers building polygon data with 15 different attributes. Our building footprint database includes spatial hierarchy metadata, as well as detailed information about each building as a point of interest.
Building footprints are versatile geospatial data that can be valuable in many different industries. But they’re only valuable insofar as they’re precise. That’s why we pride ourselves on the coverage, accuracy, and ability to compute spatial hierarchies of our polygon data.