Take a look around your city or local neighborhood. There’s a good chance you’ll see a handful of construction projections happening at this very moment. This is the direct result of successful urban planning and development in action.
However, going from breaking ground to opening a new building—whether it’s commercial real estate or a new apartment complex—requires a deep understanding of the environment surrounding that new development as well as the community it will primarily serve. Geospatial analysis plays a critical role in moving these types of projects and initiatives forward.
As a starting point, satellite imagery can help us visualize, at a quick glance, where buildings exist at any point in the world. But it, too, has its own limitations: satellite imagery is unable to capture buildings obstructed by landscapes or other overhangs just as it cannot reveal the presence of multiple sub-stores within a given building. It’s merely a starting point.
To go a level deeper, you need building footprints—also known as building polygons or building geofences—to fill in the blanks where satellite imagery and other geocoding solutions fall short. Here, we’ll review what building footprints are, where they can be put to use, and how SafeGraph helps businesses, urban planners, local governments, and other organizations perform accurate geospatial analysis that leads to more effective decision-making.
A building footprint is a form of geofencing capturing a building’s precise perimeters, excluding adjacent property features like parking lots and landscaping.
You can think of building footprints as the ground-level “outlines” of any given structure. These outlines can provide useful metadata and other spatial characteristics—such as location, shape, distribution, and relationship to surrounding structures—to aid in geospatial analysis.
Building footprints are composed of polygons that demarcate the boundaries of structures, from government buildings to offices to single-family homes to farms (and beyond).
Unfortunately, a lot of today’s geocoding solutions rely heavily on approximations. While this may be a decent starting point, this approach does not typically yield the most accurate data or results. This is just one of many reasons why building footprints have quickly become the de facto choice for geospatial analysis: they provide us with precise data that can enable a deeper and more accurate understanding of any given point-of-interest (POI).
It’s important to note, however, that for building footprints to provide meaningful analytical value, the data associated with them must come from accurate and up-to-date sources.
Articulating the actual dimensions that represent the true shape of a POI—in the form of a building polygon or a building geofence—provides us with useful data to better understand and visualize the true nature of POIs in ways that traditional geocoding data simply can’t.
For example, address-point and street-level geocodes, also known as centroids, only provide approximate measures of occupied space based on the center point of a building to its distance from either an adjoining parking lot or a nearby street curb. This creates a radius that is then used to explain, in broad strokes, a building’s general footprint.
Building footprint geocodes, on the other hand, can precisely identify the actual space a POI occupies by using real rooftop specifications (versus center points) to capture a building’s true footprint. This helps avoid overlaps and erroneous radiuses from being accounted for in the data—one of the biggest downsides of using centroid-based data—which can quickly undermine the overall accuracy and effectiveness of geospatial analysis.
A great example of how building footprint data is used regularly is store visit attribution. Simply put, store visit attribution is a method for predicting or measuring where foot traffic to brick-and-mortar POIs will (most likely) come from. This type of analysis typically relies on a combination of Census Block Group (CBG) data and other foot traffic data—like SafeGraph’s Neighborhood Patterns dataset—to paint a picture of how, when, and where consumers travel to and from throughout the day. Having specific data pertaining to the actual space a specific POI occupies is critical for accurate store visit attribution.
Using store location centroids—also known as the “radius approach”—tend to produce geocodes that are over-representative of sub-stores or under-representative of larger stores. Both errors can lead to an inaccurate understanding of actual foot traffic to those locations.
>>To learn more about why building polygon data provides a better and more accurate way for measuring foot traffic, be sure to download our Store Visit Attribution whitepaper today. <<
Building footprints, sometimes referred to as building geometry, can add value to every industry across a variety of different applications and use cases. Building footprint data can allow you to:
The information derived from building polygons can support:
These are only a handful of the ways that building footprint data can be put to work. Here are a few more examples of how building footprint and POI data can be used in the real estate, consulting, and financial services industries.
In all of these use cases above, one thing is clear: having access to precise data about a building’s actual footprint can lead to more accurate and effective geocoding. This drives better development and planning, more accurate data visualizations, stronger connections to other data sources, and, most importantly, more informed decision-making.
At SafeGraph, our mission is to be the source of truth about all physical places in the world. Highly accurate building polygon data is at the heart of our open-source data.
For example, SafeGraph’s Geometry dataset includes POI footprints with spatial hierarchy metadata that depicts when two tenants share the same polygon, available for ~6MM POIs in the U.S. and Canada.
To ensure our data is accurate and up-to-date at all times, we leverage thousands of sources —from satellite imagery to municipal records—to help generate the most accurate reference for a store’s or building’s actual footprint. Then, we supplement the building footprints derived from satellite imagery with hand-drawn polygons to ensure our dataset’s utmost precision. This helps eliminate the “noise” and inaccuracy caused by centroid approximations.
Additionally, we make it a priority to specify sub-stores within malls, stadiums, airports, and other similar building structures in relation to their parent store geometry. For this reason, SafeGraph’s store location geofences are the most precise in the market.
There’s one thing we know to be explicitly true: the physical world is constantly changing.
Businesses, non-profits, academic institutions, researchers, and local, state, and federal government organizations choose SafeGraph data for its industry-leading accuracy and precision. Our building footprint polygons are built using sophisticated machine learning, computer vision, and satellite imagery, allowing us to not only develop more detailed and accurate building geometry but also to provide the cleanest and most useful datasets.
Ready to begin using SafeGraph data? Our team is ready to help. Just schedule a call to get the ball rolling and, while you’re at it, feel free to download $100 worth of SafeGraph data for FREE when you use code “Footprints” at checkout at shop.safegraph.com.
That's it – that's all we do. We want to understand the physical world and power innovation through open access to geospatial data. We believe data should be an open platform, not a trade secret. Information should not be hoarded so that only a few can innovate.