The Urban Attitudes Lab at Tufts University is leading the study of Big Data and the psychological dimensions of urban planning and public policy practice. Through the use of sophisticated data acquisition tools, sentiment analysis, eye-tracking technology, and conventional psychological tools, the UAL is advancing knowledge and developing new tools to improve planning and policy.  

The UAL is led by Justin Hollander Ph.D., AICP an Associate Professor of Urban and Environmental Policy and Planning at Tufts University. Justin has worked in land use and environmental planning at the local, regional, and federal levels, most recently for the Public Buildings Service of the U.S. General Services Administration as a Presidential Management Fellow. His research examines the role of planning and public policy in managing land use and environmental changes, with a focus on urban design, shrinking cities, and Big Data. Justin is an accomplished author and specializes in Big Data analytics. Dr. Hollander has written over 50 publications on these topics including seven books.   BLDUP spoke to Justin about how Big Data, technology, and social media are starting to change the field of planning.

BLDUP: Can you tell us how big data can be used in planning projects?

Justin Hollander: Social listening is an unobtrusive and inexpensive way for planning projects to capture big data and harness it. Social listening is listening to conversations, reading social media posts, following people geographically. Classic planning engagement tools have been mostly obtrusive: sending surveys, holding public meetings, doing interviews, these approaches have limited effectiveness in helping planners understand what matters to community members and they tend to not be very representative.

There is always going to be bias no matter the method. There are sock puppet accounts meaning fake accounts to retweet or repeatedly share a user or user groups postings. Big data, does have its challenges, but with millions of data points, you have the power of a large sample, so many observations.

BLDUP: Have you used any of these tools in your planning work? If so how?

Justin Hollander: I have used big data from Twitter, Flickr, Instagram, and google street view or google trends. I have done work with data analysis using IBM programs to do the analysis.

For example, I used data in my work in the city of Holyoke Massachusetts with a colleague from UMass Amherst, Max Page. Max Page is Professor of Architecture and Director of Historic Preservation Initiatives at the University of Massachusetts in Amherst. We received a grant from the 1772 Foundation to fund our research in Holyoke Massachusetts.

Holyoke is a historic city with difficulty identifying historic resources. Some properties are preserved at state and federal levels but there are several properties that have not been protected. We monitored social media data for months from Instagram and Twitter posts across the city to learn what areas were valued by residents in Holyoke Massachusetts. Many of the locations were not your traditional birthplace-of-a-famous-poet.  They were unofficial parks, funeral homes, and outdoor community gathering spots.

In this project, big data gave us a high-level sense of what was important to the residents of Holyoke. In talking to the residents, there was a lot of overlap between the images posted on social media, and what locations mattered to the community.

In a series of projects in Acton and Somerset (Massachusetts), we used an interactive 3D model of a real place. These kinds of virtual places can be used in planning to get the community excited and engaged with planning. The feedback helped us redesign the area by widening sidewalks, narrow streets, incorporating new stores, changing the landscape, and then asking the community for feedback.

BLDUP: Big data and social media are not words typically associated with planning. Can you elaborate on the connection between the two topics?

Justin Hollander: When we think about the planning process it should ideally begin with a conversation around what are some of the problems that a given place faces, and what are some solutions that make sense.  In order to do a good job at understanding the process and understanding what kinds of solutions are attractive to people, it is important to get better insight to what people are thinking about, and Big Data is the answer.

For example, we look at a company called Soofa; they build smart outdoor furniture such as street lamps and benches. The company puts sensors to constantly monitor who is sitting on the bench and for how long, bike traffic verse cars, and weather conditions. This stuff is all really powerful and gives so much more insight into what is going on in our communities.

BLDUP: What challenges did you face while working in such a new field, did you view this as an opportunity?

Justin Hollander: There are a lot of naysayers, but I don’t let it bother me. There is a willingness to do things the old way and the world is changing very rapidly.

This is a relatively new field. There has not been much work done in this space. In the planning industry technology has not been embraced or used enough. Social media will be used a lot more. There is so much content with social media. The location data is being made available through social media, one of my students is using social media patterns to understand traffic patterns in a community through posts on Twitter.

I have attended the American Planner Association Conference for the past 20 years and last year in New Orleans, I’ll bet you 90 percent of the exhibitors were tech firms. Each year there is the trend in planning to move toward technology,  If we are not careful tech experts will take over the industry of planners. Planners have to be nimble and adjust to change.

To learn more about this upcoming trend check out Justin’s book: Urban Social Listening: Potential and Pitfalls for Using Microblogging Data in Studying Cities. https://www.palgrave.com/us/book/9781137594907   or visit the Urban Attitudes Lab website at http://sites.tufts.edu/ualab/

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