We used our pedestrian activity monitoring sensors to see how popular the Boston Marathon was. We learned at peak time it attracted more than 7X the number of people we would expect to see on average.

 
If we want to make agile cities, we need to know what works and what doesn’t. Data collection allows us to reflect in real-time on how spaces are used and make informed decisions about how to change these spaces to better serve the community. We studied the Boston Marathon to show the power of data collection from a highly recognizable event.
Katy Gero  |  Data Science Lead

Katy Gero  |  Data Science Lead

 

Sensor data revealed pedestrian activity peaked between 11am and 4pm in Boston along the race route on Marathon Monday. But that's only the beginning, see what else we learned. 

We studied the Audubon Circle intersection along the Boston Marathon race route, about one and a half miles from the finish line on Boylston Street. Using our Soofa Pro sensor we found that the peak time on Marathon Monday attracted more than 7X the number of people who normally pass through the intersection during the same time window on an average weekday. 

The graph below was created by analyzing three weeks of sensor data from a Soofa Pro Sensor on the corner of Beacon Street and Park Drive in Boston, also known as Audubon Circle.

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Through simple, intuitive data visualizations, we are able to see the impact the Boston Marathon had on pedestrian traffic at and near the Audubon Circle intersection by comparing baseline data with event specific data.

Here's how we did it: 

1. Set up a Soofa Pro Sensor on the south west corner of the Audubon Circle intersection.

 

2. Captured pedestrian traffic data on Marathon Monday, April 17, and for the three weeks following.

 

3. Took an average of pedestrian traffic every hour of every weekday for three weeks to build the blue line shown on the graph above.

 

4. Compared this average with the actual traffic on the event day, Marathon Monday, to analyze the impact the race had on pedestrian activity at and near the intersection.

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3.6 million rows of data tell us people congregated extremely quickly, stayed for a long time, but didn't stick around long after the main group of runners passed by.

As the photo above shows, the crowd size at and around Audubon Circle was many people deep on both sides of the race route. What we can't see from this photo is exactly when the crowd grew, how long most people tended to stay, and when the crowd dissipated. Over a three week period, our sensors gathered more than 3.6 million rows of data, letting us go beyond the basic understanding that the Boston Marathon attracts a lot of people to actually knowing how big the crowd size was at its peak (plus how this relates in relative terms to average traffic flow) and also how the public tends to value different points in the race throughout the day.

In this particular location, the bulk of the crowd was just about as equally excited to see the elite runners pass by as it was for the later waves of runners. This is illustrated by the peak traffic remaining generally consistent between 12pm and 4pm. 

The data visualization also shows that in the morning people came to watch in two waves, the first being about 9:30 - 10am and the second starting at about 11:30am. This is evidenced by the two different slopes on the graph in the morning hours. While we see the crowd build up in multiple phases, the crowd leaves nearly all at once shortly after 4pm when the average fourth wave runner was finishing the race.

 

At Soofa, our sensor data partners use this type of data and similar analytics to inform planning decisions, programming and event management, capital improvement budgeting, and more. Below are a few examples. 

The Boston Marathon is a great example to see the power of capturing pedestrian count numbers and quantifying baseline public space activity levels because it is such a recognizable event, but it's only the beginning. Soofa city partners like Las Cruces, NM and the Park District of Oak Park, IL use the same kind of sensor data to improve public spaces for the people in their communities. 

In Las Cruces, NM the town council and leaders from economic development and city planning are using sensor data to quantify pedestrian activity in the city's downtown core to evaluate the feasibility for a future public wifi network. Prior to working with Soofa, the city did not have a reliable nor accurate way to quantify the number of people visiting downtown on any given day and seasonally to justify an expense of over $400,000 on a public wifi deployment. 

Read the full Las Cruces, NM case study here>>

A Chicago suburb, home to just over 50,000 people, the Park District of Oak Park prides itself on serving its community with a network of numerous small parks and recreation facilities. Before launching a pilot project with Soofa to measure the use of four neighborhood parks, the Park District was left to do a lot of guessing as to which parks were the busiest, when they tended to get used, which events were the most popular, and how different marketing campaigns performed against others. Using Soofa Pro sensors the Park District has now gained insight into four parks like never before and, by integrating Soofa data into its district-wide dashboard, makes data-driven decisions. 

Read the full Park District of Oak Park, IL case study here>>

 

Do you want to analyze public space use in your community? 

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