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New program wants to improve cities with the power of tweets and Flickr uploads

It’s called Urban Pulse

Data about the comings and goings in hubs like New York City’s Union Square can help inform designers’ planning decisions.

Want to watch street life unfold outside of New York’s Metropolitan Museum of Art, long after it’s closed for the day? Or uncover the hidden ways both tourists and locals alike use Manhattan’s most famous landmarks? Now you can, all thanks to the power of data.

Urban Pulse, open-source software developed at NYU’s Tandon School of Engineering, uses data to create a map that visualizes how people move through cities. From Urban Pulse’s interface, you can observe, for example, how tourists navigate Central Park.

Sociologists Robert Park and Ernest Burgess, who worked in the early years of the 20th century, developed a theory of urban environments that used the human body as an organizing analogy: They likened the mundane processes of everyday urban life (things like phone calls and taxi rides) to the heartbeat. Urban Pulse brings that analogy into the 21st century, replacing statistics about phone calls with social media and other digital data.

Essentially, Urban Pulse is a dynamic, comparative heat map. And the hot spots on the map are made up of what the research team dubbed “pulses” and “beats,” terminology that was inspired by Park and Burgess’s original analogy.

But Urban Pulse’s findings don’t simply reinforce what we already know about cities. By pinpointing how, when, and by whom city spaces are most often used, the data has the power to upend our preconceptions about civic space. This has potentially far-reaching implications for urban planners, architects, and city planners.

But what is a “pulse?” And where is that social media data coming from? Simply put, a pulse is a graphic representation of the kinds of open source data that serve as proxies for human activity, like tweets and Flickr uploads. Though Urban Pulse currently only uses data from Flickr and Twitter, it is free to download on GitHub, and its creators are hoping to see a wider variety of data types input by the open source community.

The Flickr data (info about where and when people are uploading photos to the site) equals tourist activity and Twitter data (the where and when people tweet) equals human density. So, the more pictures uploaded to Flickr at the Empire State Building, for example, the busier it was with tourists that day, and the hotter it appears on the map. The more tweets are geo-tagged as coming from Union Square station, the more people were using public transit, and the hotter it appears on the map. The darkest circles on the map mark the areas with the most activity.

If they’re significant enough, those “pulses” are then broken up into three types of beats. For any given space, one beat indicates when the area becomes popular during the day, another indicates how popular it remains in the context of its surrounding area (as opposed to the whole city), and a third type indicates the change in popularity of that space over any period of time.

Manhattan’s Bryant Park.

This kind of information is useful for architects trying to guide their design decisions based on pedestrian traffic precedents. With Urban Pulse, they’re be able to search across a variety of cities and data types (from Twitter data to info about cab frequency) to find out how existing areas function in relation to others, instead of simply relying on popular opinion about those spaces. Turns out, a lot of areas that are expected to show the same use patterns—places like Bryant Park, Union Square, and Rockefeller Center in New York City—share fewer similarities in public use than we thought.

By looking at the difference in beats (i.e., frequency, time, and intensity of use), Bryant Park, Union Square, and Rockefeller Center (which are usually grouped together by urban planners and architects because of their similar land use, size, and success as popular landmarks) can clearly be seen playing different roles in the urban ecosystem: Rockefeller Center is consistently popular throughout the day, mostly with tourists, whereas Union Square is frequented primarily by locals towards the end of a working day.

And in the case of Bryant Park, locals and tourists alike flock most heavily to its grounds on weekends in July. Instead of functioning similarly across time, with relatively similar density and peaks and troughs of use—as was conventionally assumed—each New York City landmark is used quite differently.

With this kind of data, architects can also draw comparisons between spaces in different cities across the country. For example, Bryant Park’s use patterns are strikingly similar to those of San Francisco’s Mission Dolores Park because of its increase in popularity in the evenings and summer months. Information like this is helpful in the common design practice of researching existing spaces in order to build new and better ones.

Beyond comparing buildings or parks, the data in Urban Pulse can also be manipulated to contrast and compare patterns of behavior in various communities and enclaves.

In one example given by Fabio Miranda, the lead researcher and developer of Urban Pulse, you can see a clear divide between spots frequented by Spanish-speaking communities and English-speaking communities in Manhattan nightlife. You can identify which areas of the city get less foot traffic, or where people most often catch cabs.

Mission Dolores Park in San Francisco.

This means that Urban Pulse could be used to address challenges caused by rapid growth in cities: underutilization, gentrification, and poor transportation. Case studies for the project involved reconsidering how to improve outdated and outmoded landmarks based on their “twins” in other cities, identifying need for reprogramming of public spaces in San Francisco, and using tweets to identify cultural and linguistic divides in New York City.

While findings like these have the potential to improve the lives of city dwellers, and even help mitigate persistent problems, like rapid gentrification, Miranda made it clear to me that “this is purely based on the data.” For the project team, the project was less about the applications of the technology and more about using the data to challenge the way people understand the urban landscape.

This data “enables [the team] not only to help define new types of public spaces” by helping architects and urban planners more accurately understand contemporary use of urban space, but also leads to the better functioning of existing spaces.

Now that it’s been publicly released, opportunities for Urban Pulse’s application in city planning and architecture are virtually endless. If, as sociologists Park and Burgess wrote in 1925, the city “is a product of nature and particularly of human nature,” then we owe it to ourselves and the environment to continue to shape it and understand it in the digital age, using digital tools.