Brad is a senior associate planner and co-director of Sasaki Strategies, an in-house team of designers, analysts, and software developers specializing in storytelling and problem-solving with data.
This blog originally appeared on Banker & Tradesman as an op-ed by Sasaki planner and senior associate Brad Barnett.
Like all urban development, successful retail is highly dependent on understanding a site’s context. But the nature of retail is changing.
Co-location, pop-ups and creative subleasing are trends challenging traditional notions of ground floor uses, making it especially important to understand how urban districts are actually being used. Social sites like Yelp, Instagram, and Foursquare give us unprecedented insights about how urban districts function and opportunities for investment if you know what to look for.
Traditional datasets about business locations typically capture data following a top-down approach, which poses two challenges. First, these data sources often struggle to keep up with fast-moving sectors like retail, quickly becoming out of date. Second, these datasets are often captured at larger geographic units like ZIP codes, which limits their use for understanding street level dynamics.
By contrast, emerging crowdsourced datasets are far more useful. Departing from top-down data collection, instead they are often crowdsourced by users, with larger companies providing oversight, data cleanup and monitoring. This hybrid approach creates datasets that are much more detailed and up-to-date.
One detail more readily available today via crowdsourced user data, is business hours–the hours when businesses open and close. This detail may not sound terribly interesting on its face, but much of the character of a district is driven by when businesses are open. Business hours give us a window into larger issues of activity—where can we expect to find people? What kind of traffic should we expect? As businesses open and close, other kinds of activity follow. This information can help developers, planners, designers and the cities they work with to create more vibrant places at any time of day or night.
Tracking Ebbs and Flows of Street Life
Looking at business hours for Boston, for example, we can examine the dynamics of a neighborhood like Fenway, which sees significantly more late-evening uses associated with ballpark-affiliated activities and nearby students than other parts of the city. By contrast, downtown—which often lacks open businesses after 6 p.m.—has far earlier business opening hours to cater to office workers commuting into work.
Taking it a step further, we can also look at these changes over the course of the week. Recent development in districts like Kendall Square and the Seaport has created a more balanced day/night mix of businesses during the week, but each district still shows a significant slump in activity on the weekends.
What does one do with this knowledge? One of the key drivers of a neighborhood’s overall business hours is the mix of businesses within that neighborhood. For instance, using these Boston datasets, we can see that retail shops all tend to open and close at similar times in the city (8-10 a.m. and 6-8 p.m.).
Armed with this knowledge, those of us who develop cities can become much more nuanced about how we mix and match ground floor uses to create the optimal balance throughout the day and across a week. Need more activity in the evenings? Grocery stores and restaurants in Boston both have average closing times of 10 p.m. or later. On the weekends, fitness and other active recreation tenants are significant activity-generators, often open for long stretches on Saturdays and Sundays.
As retail evolves and creating active ground floors and neighborhoods becomes more complex, developers and cities will need to become even more nuanced in their understanding of how cities work. Mined thoughtfully, emerging datasets will become an indispensable part of the toolkit necessary to build this understanding and design the next generation of great urban districts.