One of the key ingredients to a successful real estate development project is foot traffic. This is particularly important in the retail sector. Physical stores need people to walk in and buy stuff (obviously), so the more people walking by, the better.
Data regarding pedestrian foot traffic can be extremely valuable in helping to predict which locations and neighborhoods provide the best future prospects based on past trends.
With that in mind, did you know that New York City performs bi-annual pedestrian traffic counts at 114 specific locations across the five boroughs?
Did you know that they have been doing this in a standardized fashion since at least 2007?
Did you know that they post their data online for anyone and everyone to see and analyze?
I didn’t this morning. I do now. Here’s a quick peak at what I built from this data:
As I’ve discussed before, New York City has done an amazing job aggregating and releasing their data. The more I search, the more I find. The NYC Department of Transit (DOT) is one of the many city entities that partakes in this open data program. They update and release at least 14 different sets of data each year.
One of those sets of data is bi-annual pedestrian counts for 114 different locations. Click here to view this data on the NYC DOT’s website.
About the data
These are the key characteristics about this dataset, according to the metadata:
114 locations, including 100 on-street locations (primarily retail corridors), 13 East River and Harlem River bridge locations, and the Hudson River Greenway
Counts taken twice a year, based on ITE recommended dates: May & September
Counts conducted on one midweek (Tue/Wed/Thu) day and an adjacent Saturday
Counts conducted from 7-9am, 4-7pm on weekday, 12-2pm on Saturday
Screenline counts conducted on sidewalk mid-block (or mid-bridge), on both sides of street where applicable
The dataset is available in Excel and as a Shapefile, so we can map whatever we find. NYC DOT also provides us with a map so we can see the different locations they track:NYC Pedestrian Traffic Count Locations
First of all, please be aware that I only analyzed the September morning data for the sake of expediency. I did not analyze evenings, May, or weekends, but the analytical process I used for September morning data can just as easily be applied to all the other segments. When I get more time I will post an update.
Now, after playing with the data and lining things up as necessary, I was able to build an intuitive and useful view:
This alone gives you a great overall picture and enables you to do some quick analyses.
I sorted the list by which locations in New York City have the most pedestrian traffic today. By that metric, West 34th Street, Seventh Avenue, East 42nd Street, Chambers Street, and Sixth Avenue are the top five most trafficked locations in the morning. That probably doesn’t surprise you. Midtown and Downtown are have very heavy foot traffic in the mornings.
I mapped that information across all of New York City. The red dots are the top five locations noted above. The green dots show everything outside of the top five. Darker green means heavier traffic.
Here it is for you to play with:
Back to the table above. On the right, I calculated compounded annual growth rates (CAGRs) from 2007 to 2015. With that metric you can see which locations have grown in traffic.
The teaser I posted at the top of this article shows the CAGRs. Here’s the full map:
Two things jump out to me:
- All of the East River Bridges have increases in foot traffic. The Brooklyn Bridge, the Manhattan Bridge, the Williamsburg Bridge, and the Queensboro Bridge all have dark green dots. You see the same pattern if you look at the crossings between The Bronx and Manhattan:
Since we’re looking at morning data, this likely shows that more people have been commuting on foot from the Bronx, Brooklyn, and Queens to Manhattan (or in the other direction). Either way, I think it supports the notion that the outer boroughs are growing and will likely continue to grow. That said, we shouldn’t overestimate based on these maps. The magnitude itself is not that much. Adding car and bike data would certainly be a valuable next step
- There are a number of decreases in Downtown and Midtown Manhattan. For example, Trinity Place in the Financial District has decreased at a CAGR of -69%. Wall Street and Broadway (FiDi) have decreased by 19% and 13%, respectively. There are a few decreases along 42nd Street and even West 34th Street (Penn Station) is showing a decrease of 1%.If you look at the detailed data above, I think there are a few different explanations. The West 34th Street data point appears to be an outlier. The trend across the years was steadily upwards, but it dropped significantly in 2015. If you look in the data, you’ll see that the 2015 point was measured on September 9th, but the earlier points in 2007 and 2008 were measured on September 25th. I wonder if the closeness of that measurement to September 11th had anything to do with the decrease.The city should try and review its processes to get more data samples and account for seasonal factors.Trinity Place, on the other hand, showed a pretty steady decrease across the years. There could be other factors at play such as construction and subway closures, but the trend looks clear.
I only analyzed a fraction of the data available and was able to build some interesting models about how people move throughout the city and how that movement has changed over the years.
With more time and thought, surely additional insights could be gleamed from this dataset alone. And if the city worked to expand its data collection practices, this source of information could become extremely valuable from real estate and urban development perspectives. Imagine if this information was collected daily and on a more micro level.
I wouldn’t want somebody counting people on every corner every single day and there would be privacy concerns, but technology should be able to do a lot of the legwork.
At the end of the day, this is a solid source of information that has the potential to get much better. I’m glad I found it.