At a glance: A building footprint is a polygon, or set of polygons, used to represent the total area of coverage of a physical building. It's used for geospatial analysis and can be applied in real estate development and investment, city planning, insurance, and more.
The physical imprint of a given location is easy to measure using custom polygons. Interpreting a location’s human mobility imprint — how people interact with it — is a bit more complex. Here are a few tips to get started.
What is a building footprint, and how does it relate to footfall data?
A building footprint is a polygon or set of polygons that represents the total area a building covers to the outer edge of its foundation. To define that further, you can draw a polygon around any building and decide to include things like sidewalk space, parking areas, or walkways. It's can be far more detailed than just a building area. With the right data, a building footprint can also tell you whether a building is a standalone unit or a unit inside another building. Now you have a custom POI based on your building footprint.
Footfall data represents the foot traffic patterns of people who visit that building footprint. It includes when they come by and how often, but also more important stuff, such as where their visit originated from, the routes they travel to get there, and where else they go. That’s valuable intel for a range of use cases, which we’ll get into a little further below.
When you combine building footprint or POI data with footfall data, you start to see that elusive, fulsome picture of your building’s human mobility imprint. The next step is to better understand your visitors.
What can footfall data tell us about visitors to a given building footprint or POI?
Footfall data is highly valuable in measuring traffic flow around a given building footprint or POI. It’s also readily available and has been proven to be well-correlated to ground truth from in-store beacons, WiFi, automated counters, etc.
To begin crafting a 3-dimensional view of our visitors, we begin with their origin and destination -- where they are coming from and going to. A downtown office building with typical hours may draw most of its footfall from a commuting workforce Monday to Friday. A mall will probably see weekend spikes from the suburbs. A grocery store’s origin/destination footfall will greatly influence the visitor profiles and their cross-visitation to nearby stores and buildings.
Continuing to build our visitor view, by adding aggregate demographic data we can now understand our most typical visitor’s gender, ethnicity, age and even average income for a given home origin. Go a step further as a retailer, for example, and you can layer-in transactional data from both in-store and web and mobile commerce.
We now have a pretty fair, and privacy safe view of our building’s human mobility footprint. Great. Now how should you go about interpreting all this data to help inform your own business planning and management?
Using footfall data with custom POIs in different industries
When footfall data is complemented with different types of data, the result is powerful insights and improved decision-making for many use cases in real estate, insurance, finance, retail, telecommunications and others.
In many cases, the goal is to understand how to better execute investments, plans, and processes that are reliant on human mobility and/or the movement of people and goods through different locations and areas. Here's a little more about how different groups use building footprints / POIs and footfall data together.
Real Estate Investors - CRE investors can use POIs and footfall data to research areas with increasing foot traffic and accurately measure population and income flow. This helps to identify investment and development opportunities. CRE investors also use footfall in order to better understand current lease rates, or projected sales.
Retailers and Restaurateurs - Retailers and restaurateurs typically use footfall data to determine ideal sites for new locations, for geo-marketing, and to help determine staffing levels. Highly sensitive to changes in mobility, it was not a surprise to see both industries suffer through the Covid-19 pandemic.
Data Scientists - Data Scientists use footfall and POI data in a broad range of use cases based on industry and specific objectives in order to scale the breadth and depth of their vision into an industry, market, or problem -- it’s a powerful tool in the chest and a required one in the future of finance, insurance, retail and healthcare.
Public Sector - The public sector relies heavily on footfall and custom POI/footprint data in order to plan and execute projects that optimize infrastructure in order to meet evolving population density and footfall models.
- Planning and operations for public transit
- Real estate acquisition and development
- Maintenance and security
- Location planning for essential services
- Urban land use classification
