City as a Service
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City as a Service

Gehl Institute explores how public-life data can be packaged into more compelling solutions

Team work at the We Count! Design Sprint, May 9–12. 2018 at Samsung Next in New York City.

Public spaces are an open living room, where a diverse spectrum of people and pursuits coexist and collide. Benches can serve as a spot to meet friends or take a nap; farmers markets organize official offerings while enabling informal economies; paved paths afford access to wheelchairs, strollers, and bikes. Good city design is open and inclusive, enabling multiple possibilities for experience and exchange in ways that reflect the diversity of the residents themselves.

But how do cities assess how well they accommodate the complexity of public life? How do they find out who and what are missing from the picture? In the previous article, we wrote about how the small data of everyday life is critical to the health and success of our cities — but the reality is that it can be quite elusive to quantify and apply.

Gehl Institute is focused on just this: giving cities tools to observe public space, gather insights, and apply them to design better places and policies.

View of public-life data collected in San Francisco’s downtown and visualized in Stae.

Last month, Stae teamed up with Gehl Institute to host a three-day design sprint to enhance the Public Life Data Protocol, a new open data standard for observing how people use public space. The Protocol includes more than 80 ways to note who and how space is occupied at any given moment — from talking on the phone to biking without a helmet. By including guidelines on how to observe the same places at different days, times, and weather conditions, Gehl Institute elevates people-watching into a rigorous research method, equipping cities with unbiased insights to inform how space should be designed and regulated.

To make it more accessible, Stae digitized the protocol alongside data from San Francisco, Seattle, and Vancouver that was previously in paper forms and disparate spreadsheets, impossible to view comparatively across cities. With the Protocol and data sets digitized, standardized, and consumable via API on Stae, we invited 115 designers, architects, urban planners, city officials, and technologists to co-create new tools that would make the Protocol easier to use. Below are snapshots of what some of the winning teams made and some big-picture learnings.

1. Collect what you need, not what you can.
Team: Data for Democracy (DFD)

Kushal Dagli, Stephen Larrick, Angela Khermouch, Caitlin Brady, Greg Jordan-Detamore, Katrina Johnston-Zimmerman, Keebaik Sim

Team DFD created the persona Eileen, an urban planner in Pawtucket, RI trying to assess the design needs of a new public park.

Design Challenge:
A misguided, yet common approach to urban data is to collect whatever you can and figure out what to do with it later. But this can waste limited city resources and leave teams with information overload rather than valuable insights. Team DFD tackled this problem by making the Public Life Data Protocol more of a self-service tool, so that city officials anywhere can easily filter through available resources and curate a targeted study.

Prototype:
The team built an improved intake tool, to make the Protocol more actionable for city officials. Before starting a study, a city official would use a simple Google form (pictured below) to enter relevant information about the scale of the project, the typology of the relevant public space, and research questions. After filling out the form, the city official receives a template survey with data fields catered to their specific city context. City officials will also be connected to other practitioners who have conducted similar public-life studies, along with relevant case studies.

Prototype survey to help focus the tool for city officials by team, Data for Democracy.

2. Mix the artisanal with the automated.
Team: HappenNow

J Russell Beaumont and Hannah House

Design Sprint participant gathering data in Madison Park using the existing paper forms.

Design Challenge:
While technology can help scale data capture, people are wary of things like surveillance cameras. How can cities gather the people data they need, without infringing on privacy rights? Team HappenNow explored ways to keep the Public Life Data Protocol as a people-to-people data collection method, while introducing digital tools to make fieldwork easier and more scalable.

Prototype:
Using the HappenNow app, researchers can snap a picture of their site and begin tagging the photo with data points such as perceived gender, age, and what activity they’re engaged in (E.G. sitting in a cafe, listening to music, sitting with a group). The tagged photo is scraped of Personally Identifiable Information and then sent to a server, where researchers can later continue tagging and analysis. This library of categorized photos can then be incorporated into a machine learning program so that photos can be automatically tagged and accuracy standards can be established.

Prototype survey tool enabling researchers to use smartphones or tablets to take pictures of an area and then quickly tag people by perceived demographic and activity — a method that could supplement or replace the current paper worksheets.

3. Add numbers to the stories
Team: Question Machine

Mariam Abdelazim, Mohamed Aly, Vivian Jara, Marwah Jarib, Esthi Zipori

Stae’s Community Engineer, Crystal (left), supports Team Question Machine map their user flow.

Design Challenge:
We have the data, now what do we do? This team wanted to make public-life data more immediate and compelling. They did a deep dive into the available data in San Francisco, Seattle, and Vancouver then extrapolated universal questions that researchers in any city — in this case Cairo — would want to know. Rather than make a database that is only tagged by data type or field, Team Question Machine brainstormed ways to make the data more human-centered and conducive to good storytelling.

Prototype:
The Question Machine is a friendly landing page with prepopulated questions (Think: magic eight ball for a complex data set). Because one of the benefits of adapting a shared protocol is being able to compare data across cities, the Question Machine aims to optimize for rapid-fire query across geographies by suggesting universal questions.These curated queries also spare researchers from duplicating existing studies, and in general make the data more comprehensible and compelling to the general public.

Prototype for making all public-life data queryable by curating compelling research questions.

Everyone Counts.
Beyond making easier onramps for cities to take on the task of measuring public life, we learned that designing inclusive systems indeed takes a village. The diverse skillsets and backgrounds of the participants themselves made the tools they built open, respectful and human-centered. We’re so grateful for everyone’s contributions and we hope to help bring these prototypes to life in the future.

Interested in learning more how Stae can help you manage and gain insight into your community’s civic data? Get in touch.

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