Spatial analytics combines public or private data sets with location data from geographic information systems.
You can make more informed business decisions by using spatial analytics to rank and score options based upon many potential factors, including proximity data.
It’s often used for site selection and can help companies optimize return-to-work strategies.
The COVID-19 pandemic accelerated our move to “virtual” while also emphasizing how much our physical place in the world matters. We’re creating oceans of data which, when viewed through geographic information systems technology, become a powerful new lens that enables us to see life on the ground. The result is an emerging field of business intelligence — spatial analytics — that is likely to become a defining capability.
Better, faster decisions come from knitting together location data with the universe of proprietary and public sources that track millions of phenomena and entities. This aggregation of information allows any imaginable data set — pandemic cases, household incomes, shipping routes and firm-specific data — to be harnessed in a way that reveals new insights. The spatial layer provides an organizing and clarifying lens that allows you to zoom in on specific data.
A parts distributor seeks to optimize its network of warehouses for faster distribution of products, taking into account existing customer locations, service centers, consumer patterns and national consumer data. Relocating its facilities in new locations could increase customer access, growing the top line, while decreasing operating costs and increasing profitability.
A private equity investor wants to maximize the value of its investments in restaurant chains and supermarkets and needs to analyze thousands of consumer markets, layering in household income, foot traffic, local tastes and seasonality. Adjustments to locations can allow the firm to improve return on investments across its portfolio companies.
An investor wants to manage risk exposure in a recently acquired chain of hospitality properties in the Pacific Northwest, and specifically to understand the potential exposure to earthquakes as well as extreme weather events. Moreover, the investor would like to understand how to better connect with future clientele. How do the new properties fit with local attractions and amenities? What do weekenders arriving from the major regional metro areas typically like to do? The answers are found by layering data to interrelate hazards, attractions and the investor’s portfolio.
Real estate investors applied spatial analytics to score and rank options for a single-family development. They weighed dozens of potential factors, including proximity to a high-end supermarket, distance from a railroad and variations in growth in household income over time.
Historically, such problems would be solved by connecting to intermediaries, such as real estate brokers or other local experts, or through imperfect proxies, such as the common practice of a new fast food chain locating its restaurants near rival chains in already thriving locations.
But by relating more data points, we move into the next wave of business intelligence. With spatial analytics, the number of data points being put to use toward determining — say the optimal location for a new restaurant — is potentially limitless, bound only by what makes sense rather than who’s available to add that layer of local knowledge or what’s been tried before. This emerging capability means creating more options — at least tenfold the number — for strategic decision-making in order to more fully understand investment risk and potential.
The most common application for spatial analytics is site selection. For instance, a food manufacturer sorts hundreds of potential brownfield sites in the southeast US against its core criteria: price, location, distance to residential areas and workforce availability, allowing it to focus its on-site diligence efforts on the handful of sites with the highest likelihood of success.
Embedding spatial analytics to perform this analysis saves time while potentially keeping the manufacturer from making a costly mistake. It used to take thousands of hours to evaluate several properties.
Using spatial analytics, it’s possible to evaluate thousands of properties in several hours — clearly an advantage as businesses prepare to grow in a post-pandemic economy.
Site selection isn’t the only use case. Spatial analytics extends to more esoteric applications, such as implementing artificial intelligence (AI) alongside location data to enable retail and consumer credit strategies, or scoring the societal impact of hundreds of large infrastructure projects.
Employers across the country are evaluating their return-to-work strategies. While many focus on the physical location of their office, the location of their employees is just as significant. Combining geospatial information with workforce data through spatial analytics can help employers optimize office locations and improve the employee experience. Some employers may save millions in commercial real estate costs and recruiting efforts if they do this correctly.
Spatial analytics can be used to measure a real estate portfolio’s ability to generate renewable energy, for instance, by layering things like sunlight intensity or wind speed, direction and consistency over top of a real estate footprint. That kind of capability is also being applied as a part of building information management (BIM) to optimize how office space is being used.
Healthcare policy, including access and affordability, are generation-defining challenges. We have rural areas where hospitals have closed, creating healthcare deserts. Emergencies can require an expensive airlift, and state insurers and regulators need to consider how to bridge the gap. Expressing the problem through mapping and data can reveal new solutions designed to develop a network to reach underserved communities.
Ultimately, location, easily represented by a map, becomes the organizing relational framework — the glue — that binds together and makes sense of all the data available to us today. Suddenly, economic data, workforce data and migration data all become more meaningful and interpretable, rather than overwhelming. Adding “where” will help you answer “why,” “how” and “when.”
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