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Building Footprints: Essential Data for Accurate Geospatial Analysis 

September 14, 2020
Bryan Bonack

A closer look at why building polygons and geofences are more effective than centroid-based geocodes for geospatial analysis.

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 polygons, geometry, or geofences—to fill in the blanks where satellite imagery and other geocoding solutions fall short. Here, we’ll review what they 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.

What is a building footprint? 

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 these polygons 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 geometries 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 polygons to provide meaningful analytical value, the data associated with them must come from accurate and up-to-date sources. 

Why use polygons? 

Articulating the actual dimensions that represent the true shape of a POI—in the form of a building polygon or 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 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 or rooftop 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. 

Building footprints for store visit attribution

A great example of how geometry 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 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 enable the most accurate store visit attribution.
Example of the potential for radius overlap “errors” when using centroids for building footprints.

Top use cases for building footprints

Geometry data can add value to every industry across a variety of different applications and use cases. Accurate polygons enable you to:

  • Identify the exact location of a building
  • Determine the number of buildings within a given area
  • Account for buildings hidden in aerial images by trees and other obstructions
  • Spot potential risks and hazards to a building 
  • Measure the square footage of a POI

The information derived from building polygons can support:

  • Mobile Marketing: Targeting, reaching, and engaging location-based audiences, based on their proximity to a property or structure. Want to see this in action? Learn how Media Storm used SafeGraph Places data to improve its mobile targeting.
  • City Planning: Identifying development constraints, informing landscape design, assessing property sale value, and estimating urban growth potential.
  • Risk Evaluation: Streamlining risk analysis by identifying surrounding elements that could potentially cause harm to a building. This information can also inform disaster planning and emergency response time (should a disaster ever occur).
  • Navigation: Creating highly accurate navigation maps with precise road geometry.
  • Insurance Risk Assessment: Leveraging risk analysis to estimate more accurate and reasonable insurance premiums and deductibles.
  • Retail Site Selection and Trade Area Analysis: Gaining a deeper understanding of a store’s surrounding to both manage and optimize potential and ongoing foot traffic. 

These are only a handful of the ways that polygon data can be put to work. Here are a few more examples of how geometry 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.

Spatial hierarchy is crucial for effectively analyzing risk and proximity.
SafeGraph data specifies sub-stores in relation to their parent store geometry. 

Why use SafeGraph building footprint data?

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 over 8 million POIs in the US, Canada, and UK.

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 geometry 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. 

Get started with SafeGraph today

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 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.

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