Public Life Diversity Toolkit 1.0

Public LifeDiversityToolkit: a prototype formeasuring social mixing and economic integration in public space

Credits

Gehl Studio SF - A Gehl Architects Company

Project TeamJeff Risom, Principle in ChargeAnna Muessig, Project ManagerEric Scharnhorst, Data ScientistTyler Jones, DesignerAlex DeCicco, DesignerCelsa Dockstader, Designer

www.gehlarchitects.com

Special thanks to:Benjamin de la Peña, The Knight FoundationDaniel Harris, The Knight Foundation Ariella Cohen, Next CityNoah Chrisman, SPURGreg Lindsay

April, 2015

Contents1 / Why Measure Diversity in Public Space?

2 / Prototyping New Measurement Tools

3 / Design Brief

Why measure diversity in public life?

6 Place Diversity Toolkit Why Measure Diversity in Public Space?

Background and motivation for a Public Life Diversity Toolkit

Economic integration between people of different socioeconomic strata matters. Spending time with people who come from different walks of life increases empathy across groups, builds tolerance as a society, may generate new ideas and economic opportunity, and represents our democratic society.

We care about diversity and integration in many civic systems in our cities. Public housing experts have grappled for decades with how to integrate people of different incomes in the same neighborhoods and buildings. Leaders in our public educational system have struggled with how to make childrens’ educational experience one where they are exposed to young people from different economic strata and backgrounds. In public space and the design of the built environment, we strive to design and program places that achieve the democratic promise of our public spaces by attracting a diversity of different people that represent our society.

Within each of these urban systems, at the core of these efforts to integrate those from different economic strata is a belief that a strong democratic society should foster civic spaces that are built on diversity, tolerance, and the democratic freedom of choice. By leading with these values we can create a connected society that allows individuals and cities to reach their full potential.

In public space design, diversity is often a benchmark of

Many of our cities are divided by economic means, and inequality is a rising topic on the national agenda. Yet, when we go into our squares, parks, and public places, we encounter people from different walks of life. This diversity is core to our cities’ economic competitiveness, civility, and democracy. So why don’t we have the tools to measure what we care about?

quality, from a diversity of types of activities taking place in the public realm, to a diversity of modes of transit, to a diversity of building types.

We care about the diversity of people spending time in public space because we want our public spaces to represent the communities they serve, and be able to function as a civic forum.

What do we mean when we say we care about social mixing in public space? Social mixing can be as passive as two people quietly sharing a bench at a park, or as active as two moms striking up a conversation at a playground and exchanging contact information about local daycare. There are many ways that people from different economic strata can mix with one another. It is the aim of this design brief to begin to understand how to identify where this mixing is happening, and the types of design and programming cues that can help invite for these interactions, in order to fulfil the important civic purpose of our cities’ public realms.

This research topic is timely. Although there is a growing interest in the relationship between the economic diversity of public life and the built environment, there exists no method to measure or identify it. Instead, urban planners and designers concerned with the question of spatial disbursement of economic inequality rely on data sets that measure where people live, like the Census and the American

ABOVE, Public life in Largo São Francisco, São Paulo - Image courtesy SP Urbanismo

Gehl Studio 7

Community Survey. However, these data sets don’t measure other essential qualities of our lived experience in cities like where we choose to spend our time, where we encounter people that are different from ourselves, or how we participate in civic life.

Opportunity

A method to measure the diversity of public life could help planners, designers, and urbanists understand the design and programming cues for social mixing between people in different economic strata, and use these cues to develop performance metrics, give us a new way to map the city that identified places that foster mixing, and recuperate places that don’t.

Background

My some measure, designing places that foster a diverse public life is fundamental to the very core of urban design and planning itself. Many contemporary urbanists have developed ways of talking about the

relationship between form and a diverse public life. Sociologist Elijah Anderson calls places that foster diversity the“cosmopolitan canopy.” William “Holly” White identified “triangulation” as a “third thing” that can create a bond between strangers. [More extensive ‘lit review.’]

Today, there are a host of projects that measure place preferences and behavior patterns using digital means. MIT’s Place Pulse translates human preferences for the built environment into machine-learning and an automated system for rating google street view public space data. Eric Fischer’s Locals and Tourist maps looks at the spatial divide of locals and tourists. [Enhance this section]

However, while many urbanists have developed their own typologies for encouraging social mixing, or probed one-off data sets for place-preferences, few have looked at the economic dimension of social mixing in public space, and few have developed methods for understanding what fosters a diverse public life.


Why Gehl

At Gehl, we have been measuring the way the people interact with space for 40 years. We measure pedestrian flows, stationary activities, and the mix of age and genders in space, along with physical qualities of space.

However, beyond these categories, Gehl has not focused on the differences between people in space. Adding new layers to our understanding of how people use space is a natural extension from our work in human-scaled design. To tackle these questions, we bring our mixed-methods practice that blends primary observational and interview data with new digital tools.

Key Challenges

There is a reason the relationship between public life diversity and the built environment is still a mystery. It is not because it is difficult or the data unavailable. It is primarily because calling out inequality and difference among people can be an uncomfortable

and controversial exercise. To measure social mixing among people from different economic strata do you ask people how much money they make? Do you judge people from afar based on how they look? Can you use “big data”? It makes sense that many have come to this research question and turned away, it confronts both some timeless taboos and some next-generation data frontiers. In addition to these challenges, social mixing among people from different economic strata is quite difficult to measure: accurate data sets are a challenge, there are limitations on primary data collection, and even if one finds the data, it is tough to pinpoint impact given all the intervening variables. Even if we measure this topic well, the data is hard to make sense of.

Despite these challenges, the questions remain vitally important. Developing these tools will give us a new layer of badly-needed information about the health of public life and public spaces in our cities.

ABOVE, Minneapolis’ Northern Spark. Photo by Patrick Kelley

Gehl Studio 9

Project Approach

We took this challenge to develop better tools to understand the relationship between diversity of public life and the built environment. Our approach unfolds in three steps. This document is evidence of the first step.

1. Develop tools to identify places where there is social mixing among economic groups. Before understanding design cues that invite for social mixing, one must first define a way to identify locations of this social mixing. Our efforts documented here describe the results of a working prototyped toolkit that begins to answer this question.

2. Design research in these places, specifically looking at role of public space in social mixing Once an effective research tool uncovers locations of social mixing, we will perform design research in these places to learn about the design and programming cues for social mixing.

3. Process and Design Inspiration for economic mixing. Finally, this design research will be translated into a guide for process and design inspiration for civic leaders to measure what they care about and create a feedback loop between people first metrics and design and investment decisions.

Guiding research questions to apply to test sites, Phase 1

We framed our objectives with three questions to apply to test sites:

1. Do people from different economic groups spend time in this place?

2. Are people from different economic groups socializing with each other? A clarification: we are assuming people from different economic groups can be either friends or strangers. Some of our tools ask questions about interactions with strangers based on this assumption.

3. If so, what prompted this social interaction?

ABOVE, Chess players in New York’s Washington Square Park

Call to Action: We Needa Census for City Streets

10 Place Diversity Toolkit

Today the average American makes more data before breakfast than George Washington did over the course of his entire life. Worldwide, we’re generating over 2.5 quintillion bytes of data every day, most of it from interacting with digital content like email and Facebook. But cities and the people using them are physical.

Our understanding of how people interact with each other offline, in public spaces, and how these public spaces then impact communities is often a mystery.

The classic approach to collecting physical data about people in cities is to record how people use the public realm. Jan Gehl began collecting and using

data to unravel this mystery in Copenhagen 45 years ago. Since then, city by city, street by street, the Gehl database has grown to capture the life of thousands of streets and public places. Now we can compare the life of a street in Sydney, Australia to one in Xalapa, Mexico. The approach has been fine-tuned over the years, and the scope has

Density, count of estimated home locations of 10,000 Instagram users who took a photo in the plaza

5 Miles5 Miles Patricia’s Green Union Square

< 5

5 - 9

10 - 19

20 - 49

50+



< 5

5 - 9

10 - 19

20 - 49

50+


< 5

5 - 9

10 - 19

20 - 49

50+

LOCAL REACHPATRICIA’S GREEN

LOCAL REACHUNION SQUARE

To frame our research questions, we wrote this article, which was published in Next City as part of In Public, an article series co-curated by Gehl. By Eric Scharnhorstwww.nextcity.org/column/in-public

RIGHT, Powell St, San Francisco. Photo by Justin Ennis via Flickr

11 RIGHT, Powell St, San Francisco. Photo by Justin Ennis via Flickr


expanded incrementally every time we work in a new city.Our data at Gehl Architects is unique because of its human focus. In addition to hourly pedestrian counts, we record the number and diversity of stationary activities in the public realm. Stationary activity data is a good proxy for the public life of a place. A space dominated by people waiting for the bus has a different life than one with a balanced mixture of children playing, people standing, and people sitting and talking. We incorporate age and gender information to the stationary and pedestrian data to get a better sense of who is using a space.

This kind of data gives urbanists a platform to advocate for cities for people. We used some of this empirical data to champion

the pedestrian and public space improvements along Broadway and throughout New York, throughout Melbourne’s network of alley ways and civic destinations, and other public realm improvements all over the world. The methodology also presents some opportunities and challenges to use empirical observations relevant to addressing a variety of urban problems, from inclusion and equity to sustainability and resilience.

One such challenge is to understand the character of urban diversity in public space. Cities are celebrated for their heterogeneity, and piles of studies are written on the economic and social benefits of interacting with people different from you. Yet, our tools for

understanding this heterogeneity are crude and often based on information separate from the people and their daily routines, which truly define a city’s character.For example, take a look at a map of census data showing household incomes in New York. There is a concentration of wealth in the center, and a clear contrast north of Central Park. But is this representative of the social life of New York City? Of course not. Despite the clearly delineated income inequality on the map, New York’s public spaces are melting pots for the economic and social diversity of the entire city, and they are far more diverse than the blocks that surround them. You would never know that from looking at the map. How can we learn more about socioeconomic mixing at

13

the human scale?

There are a number of new tools and datasets from governments, academia and industry that address socioeconomic mixing. In government, the US Census and the American Community Survey (ACS) are still the go-to sources. The ACS is now updated on a rolling basis, and the US Census is still on a 10-year refresh cycle. And although the resolution of Census data has improved incrementally every 10 years, the highest level of detail is the city block, and at that resolution only a limited set of attributes are available.

The ACS and the Census are both used by city planners to understand social and demographic trends. But the large grain size of the data means it can’t be used to zoom into a plaza to find out how people are spending time, or the quality of the experience on the ground. The Census will return values from a few years ago that characterized the socioeconomic characteristics of people living nearby. But asking the Census or the ACS what is happening at the human scale is like trying to find your favorite raindrop in a frozen pond.

Cities have been innovating and augmenting the Census and ACS data by opening up their own datasets. Open city data covers things like building and demolition permits, parking, food sources, car traffic and even the locations of rat sightings. This information can be used to map place and building characteristics — critical information for city planners. But everyday city life is rarely the focus. In these datasets,

“people data” is usually limited to pedestrian injuries or worse.

Some new tools developed by hobbyists, industrialists and academics are leading the way in automating people data measurements. Different types of “smart” sensors now read license plates and count traffic, estimate pedestrian volumes by sensing the number of nearby WiFi devices and estimate stadium crowds by scanning for phones that have their Bluetooth sensors turned on. Tweets, photo locations, cellphone data and even taxicab data are mapped to learn more about what’s happening on the ground in cities. Video feeds of thermal imagery are used to count people walking and bicycling. Even arial drones outfitted with cameras and scanners feed their footage into software that generates detailed 3D models of city spaces.

But new types of traffic counters and more detailed 3D models are not providing new categories of information. These innovations are only doing old work more efficiently. And although they can make it easier collect more data, in doing so, they sometimes remove the stakeholder and volunteer data collectors who are most excited to contribute to the public discourse that follows the data collection and analysis phase. The innovations are not helping us understand how people interact with one another in the public realm. The reason cities don’t have this information is not because of technological limitations. It’s because of a shared resistance to doing the work in the first place. Although city planners map citywide Census-level socioeconomic

categories like income and family size, they are hesitant to measure how people of different “categories” mix in the public realm.

The hesitation is justified. It comes from real concerns about potential political backlash. It is one thing to compare the median income of different neighborhoods. It’s quite another to compare the incomes of people sitting next to each other on a bench, and to try and understand how that affects their urban experience.

If this job isn’t getting done because it’s uncomfortable work, then waiting for a technology-based solution may take forever. The first step is to address the question of how the Gehl methodology can be adapted to collect socioeconomic data at the human scale. We will explore this question over the course of the next few months and will report back in an “In Public” post soon.

PrototypingNew Tools

2

3

2

1

3

intercept survey

observational analysis

census for city streets


A toolkit not a tool: fitting the right tool to the question

There is no one-tool-fits-all for measuring public life diversity. We prototyped three different tools, which work together to give us a full picture of social mixing between people in different economic groups.

Public Life Diversity Toolkit: Sample quality and research question fit

Census for City Streets: Potential data sets

High usership*Spatial and public focus

Smartphoneonly

User data is mostly public

Instagram Yes (300 million) Yes Yes YesFlickr No (92 million) Yes No Yes

Twitter Yes (288 million) No No YesFoursquare No (55 million) No (merchant-focus) Yes No

Facebook Yes (1393 million) No No No* Global active user numbers from Digital Marketing Ramblings, http://expandedramblings.com


No No Yes

Do people from different economic groups spend time in this place? Yes No Yes

Are people socializing? Yes Yes No

Are people from different economic groups socializing with each other? Yes No No

What prompts social interactions between people from different economic groups? Yes No No

Intercept survey Observational Analysis Census for city streets

No Yes No

Does the data cover a long period of time?

Does the data represent everyone in a public space during the analysis?

Research question fit

Sample quality

Gehl Studio 17

Testing different tools

Understanding the complexity of public life diversity, who is mixing in a place, how they are interacting, and how their relationships change over time, cannot be summarized in a single tool. Some information can only be gleaned from one-on-one dialogue with a person, while other information is best captured passively through the digital realm.

We prototyped three tools at three scales that address three facets of our research questions. They are best used together because each collects complementary information that fills in holes left by other tools. The table below describes these complementary tools.

Gehl always begins with people and how they use space. Therefore, our first tool is an intercept survey which captures nuanced information about the triggers for connections between strangers and helps us validate our Instagram data. This tool gives very complete data, but is limited by small sample size.

Our second tool relies on empirical observational analysis in-place, and uses Gehl’s Public Space/Public Life methodology to count social grouping. It captures more people than the intercept survey tool, but doesn’t give information about social cues for mixing or economic strata.

Our final tool is powered by social media data and allows us to look at areas as big as an entire city at flexible points in time. This tool is excellent because it can be deployed at any time, over any geographic area, and it gives us an estimation of the economic qualities of the neighborhoods that people are coming from. But, because social media is biased towards young people (among other things), its sample is biased. It is also proxy data for who might be in a space at any given time, and so cannot tell us about what is actually taking place on the ground.

Using these tools together allow us to put these various findings together into a well-rounded understanding of how the diversity of public life plays out in space.

The following sections describe in more detail our the methods, findings, and an evaluation of the performance of each tool.


1 Patricia’s Green is a central plaza in downtown surrounded by shopping and hotels

2 Union Square is a central plaza in downtown surrounded by shopping and hotels

1

2

Test Sites

We wanted to test our prototypes on public spaces that were close to home so they would be easy to come back and tweak our tools, places that we already had hunches about that we could test, and that had different qualities of public life.

Patricia’s Green is a small park adjacent to a temporary pop-up public space called PROXY. It is a neighborhood-serving park that is a common landmark in the mixed neighborhood of Hayes Valley. It features a play structure for children, a grassy lawn, and shaded fixed benches. PROXY is a popular parking lot-turned pop-up retail destination that offers coffee, juice, retail, ice-cream, cultural pop-up programming, and a beer garden (our test only captured the public space, not the beer garden).

Union Square is the central public space of San Francisco’s downtown shopping district. It is host to many seasonal iconic events including a large Christmas tree, ice skating, and other attractions that draw tourists from around the world.

We had a hunch that Patricia’s Green was more popular with locals and those from the immediate neighborhood and were able to test our hunches with our tools.

Gehl Studio 19

ABOVE: People interacting at the Living Innovation Zone on Market Street, San Francisco

Intercept Survey

There are some things you can only learn about a space from asking someone directly, like why they feel comfortable in a space, and when they feel comfortable meeting a new person. We developed an intercept survey to ask these questions.

Method

Gehl staff approached visitors at random in a public space and asked them to take a short, paper survey. Survey questions asked about demographics, how visitors used the public space, and if they used Instagram.

As we tweaked our survey instrument in the prototyping process, we asked different questions, from “did you talk to people” to “do you recognize people.” We found that

some people felt uncomfortable talking to strangers in general, but enjoyed recognizing people from the neigborhood.

The nuances between “talking’ and “seeing” different people sparked much debate around the value of recognition versus connection, and what we are looking for in our analysis. This discussion of the spectrum of connection is presented in our article for Next City on page 32.

20 Place Diversity Toolkit Prototyping New Tools

Gehl Studio 21

Results + Analysis

In Union Square, 60% of surveyed people talked to new people in the square. The most common trigger for this interaction, other than “other”, was programming (an event, concert, or class) at 28% of respondents. In Hayes Valley 60% of respondents said they did recognize or know someone, with 15% reporting they had made new acquaintances because of interactions they had in the plaza.

Have you talked to people in this plaza?

(Union Square Survey Responses)

Do you recognize neighbors or friends in this

plaza? (Patricia’s Green Survey Responses)

If yes, what brought about your interaction with

them?

8

11

5

Yes, friends of friends

Yes, stranger I struck up conversation with

Yes, both friends of friends and a stranger (A and B)

No Yes0

5

10

15

20

25

16

24

12

20

6

8

5

0

5

10

15

20

25

No

Yes I recognize or know someone in the plaza (59%)

Yes, I recognize a few faces, but have not talked to anyone (11%)

Yes, I have made a few acquintances (15%)

Yes, I have made new friends that I met because of the plaza (9%)

30

35

40

Yes

39

PetsChildrenEvent / concert / classVolunteering / Religious event OtherSports / physical exercise

3%

10%

14%

28%7%

38%

ABOVE, A Gehl surveyor in Queens, New York.


Evaluating the tool

Our first tool gives us a very complete picture of how people experience a place with respect to connections with different people.

We were able to gather valuable information about how often people recognized others from the public space, when they had interactions with people different from them, what triggered this interaction.

We also were able to identify when we gave the survey to groups of people, and could conceivable compare the economic qualities of groups to answers in the survey. Unfortunately our sample size for this data was so small (only a handful of groups were surveyed), so we cannot share the findings here.

Because we also asked about social media usage, this tool also allows us to verify the reliability of our third “census for city streets” tool by asking the same questions to our interviewees and seeing if their responses match up.

However, there are some sampling problems with this tool. Because this method must be administered in person, limitations of staffing and budget mean sample sizes will be small. It also may be skewed to those who say “yes.” With this tool, there is lots of room for misinterpretation of questions, and people can lie about potentially uncomfortable answers like income.

ABOVE: Market Street Prototyping Festival

Gehl Studio 23

Observational Analysis

The Public Space / Public Life surveys capture pedestrian, age, gender, and stationary activities for a place. We added social groups to our survey to learn more about the dynamics of social activity in place.

0238

Method

We used Gehl’s ethnographic and observational analysis methods to observe behavior of people spending time in our test sites. We looked at age/gender breakdowns, stationary activities, and groups of people engaging in social activity.

We quickly learned that measuring groups of people can be a very subjective exercise. How can you tell if two people are engaging with one another? What if there

is a group of four people but at any given moment only two people are talking to one another? If two people are sitting on a bench together are they a group?

We decided to delineate between active and passive social activities in public space. We recognize that while both are valuable, other tools in our toolbox measure the economic levels of who is in a space at a given time much better than observational analysis.

This tool looks specifically at the social-ness of a space.


On the spectrum of social activities, we measured only active social activities, categorized by an active engagement like talking, playing, an exchange, or a purposeful social activity.

Passive social activities such as sitting on a bench together, sharing in a public performance, or even waiting in line for a cashier are also important on the spectrum of social mixing, but are captured through other tools.

Gehl Studio 25

Active Social Activity

Passive Social Activity

dogwalkers taking photos for strangers

children playing active recreation

eating together strolling with acquaintances talking with strangers

watching cultural activity

parallel activity co-working commercial activity

8

11

5

Patricia’s Green Union Square

Summary statistics of estimated home census tracts, 10,000 people each

< $10,000

$10,000-14,999

$15,000-24,999

$25,000-34,999

$35,000-49,999

$50,000-74,999

$75,000-99,999

$100,000 +

MEDIAN INCOME

< $50,000

$50,000-99,999

$100,000 +

MEDIAN INCOME

< 3%

3 - 5.9%

6 - 8.9%

9 - 11.9%

12 - 15%

> 15%

UNEMPLOYMENT

< 6%

6% +

UNEMPLOYMENT




Union SquarePatricia’s Green


Summary statistics of reportedincome, 40 people each


< $10,000

$10,000-14,999

$15,000-24,999

$25,000-34,999

$35,000-49,999

$50,000-74,999

$75,000-99,999

$100,000 +

ACTUAL INCOME

< $50,000

$50,000-99,999

$100,000 +

ACTUAL INCOME

0%

100%

0%

100%

Men

Women

Union SquarePatricia’s GreenGender

50%50%

11 12

65+

31-64

15-30

7-14

< 7

Union SquarePatricia’s GreenAge

Union Square

10+987654321

Patricia’s Green

10 AM 11 12

Group size

10 AM 11 120

50

100

150

200

250

300

0

50

10 AM 11 12

StandingWaiting for TransportBench SeatingCafe SeatingSecondary SeatingFolding/Moveable ChairsLying DownChildren PlayingCommercial ActivitySocial ActivityCultural ActivityPhysical Activity

Standing

Sitting

Active

Union SquareSTATIONARY ACTIVITY

Patricia’s GreenSTATIONARY ACTIVITY

10 AM 11 12

10 AM 11 12 10 AM 11 12

Romare Bearden Park

Summary statistics of estimated home census tracts, all 2600 instagram users in 2014

Union Square

Romare Bearden Park

10 AM 11 12 10 AM 11 12

Romare Bearden Park

Romare Bearden Park

Patricia’s Green:

19% (of 40 people) posted instagram from the plaza

Union Square:

15% (of 36 people) posted to instagram from the plaza

White Black or African American American Indian or Alaska Native Asian Native Hawaiian or Other Pacific Islander Some other race Two or more races Hispanic or Latino

Flatiron CensusRACE

Flatiron InterceptRACE

Corona CensusRACE

Corona InterceptRACE



Yes, both friends of friends and a stranger

Have you talked to new peoplein this plaza?

No Yes0

5

10

15

20

25

Union SquareSURVEY RESPONSES

16

24

12

20

6

1

8

5

0

5

10

15

20

25

No




30

35

40

Yes

Patricia’s GreenSURVEY RESPONSES

Do you recognize neighbors or friendsin this plaza?


3%

10%

14%

28% 7%

38% If Yes, what brought about your interaction with them?


no response (1%)

0%

100%

0%

100%

50%50%

0%

100%

0%

100%

50%50%

8

11

5



< $10,000

$10,000-14,999

$15,000-24,999

$25,000-34,999

$35,000-49,999

$50,000-74,999

$75,000-99,999

$100,000 +

MEDIAN INCOME

< $50,000

$50,000-99,999

$100,000 +

MEDIAN INCOME

< 3%

3 - 5.9%

6 - 8.9%

9 - 11.9%

12 - 15%

> 15%

UNEMPLOYMENT

< 6%

6% +

UNEMPLOYMENT








< $10,000

$10,000-14,999

$15,000-24,999

$25,000-34,999

$35,000-49,999

$50,000-74,999

$75,000-99,999

$100,000 +

ACTUAL INCOME

< $50,000

$50,000-99,999

$100,000 +

ACTUAL INCOME

0%

100%

0%

100%

Men

Women


50%50%

11 12

65+

31-64

15-30

7-14

< 7


Union Square

10+987654321

Patricia’s Green

10 AM 11 12

Group size

10 AM 11 120

50

100

150

200

250

300

0

50

10 AM 11 12


Standing

Sitting

Active



10 AM 11 12

10 AM 11 12 10 AM 11 12

Romare Bearden Park


Union Square

Romare Bearden Park

10 AM 11 12 10 AM 11 12

Romare Bearden Park

Romare Bearden Park

Patricia’s Green:


Union Square:



Flatiron CensusRACE


Corona CensusRACE






No Yes0

5

10

15

20

25


16

24

12

20

6

1

8

5

0

5

10

15

20

25

No




30

35

40

Yes




3%

10%

14%

28% 7%



no response (1%)

0%

100%

0%

100%

50%50%

0%

100%

0%

100%

50%50%

8

11

5



< $10,000

$10,000-14,999

$15,000-24,999

$25,000-34,999

$35,000-49,999

$50,000-74,999

$75,000-99,999

$100,000 +

MEDIAN INCOME

< $50,000

$50,000-99,999

$100,000 +

MEDIAN INCOME

< 3%

3 - 5.9%

6 - 8.9%

9 - 11.9%

12 - 15%

> 15%

UNEMPLOYMENT

< 6%

6% +

UNEMPLOYMENT








< $10,000

$10,000-14,999

$15,000-24,999

$25,000-34,999

$35,000-49,999

$50,000-74,999

$75,000-99,999

$100,000 +

ACTUAL INCOME

< $50,000

$50,000-99,999

$100,000 +

ACTUAL INCOME

0%

100%

0%

100%

Men

Women


50%50%

11 12

65+

31-64

15-30

7-14

< 7


Union Square

10+987654321

Patricia’s Green

10 AM 11 12

Group size

10 AM 11 120

50

100

150

200

250

300

0

50

10 AM 11 12


Standing

Sitting

Active



10 AM 11 12

10 AM 11 12 10 AM 11 12

Romare Bearden Park


Union Square

Romare Bearden Park

10 AM 11 12 10 AM 11 12

Romare Bearden Park

Romare Bearden Park

Patricia’s Green:


Union Square:



Flatiron CensusRACE


Corona CensusRACE






No Yes0

5

10

15

20

25


16

24

12

20

6

1

8

5

0

5

10

15

20

25

No




30

35

40

Yes




3%

10%

14%

28% 7%



no response (1%)

0%

100%

0%

100%

50%50%

0%

100%

0%

100%

50%50%

Results + Analysis

We observed more social group activity in Union Square, and a broader array of group sizes. This makes sense as it is a tourist destination and is popular with families.

Union Square also demonstrated a more even split between men and women, as well as age breakdown perhaps also because it is more of a family destination.

It is interesting that more social activity at Union Square also correlates to gender parity.


Age

Group Size

Gender

8

11

5



< $10,000

$10,000-14,999

$15,000-24,999

$25,000-34,999

$35,000-49,999

$50,000-74,999

$75,000-99,999

$100,000 +

MEDIAN INCOME

< $50,000

$50,000-99,999

$100,000 +

MEDIAN INCOME

< 3%

3 - 5.9%

6 - 8.9%

9 - 11.9%

12 - 15%

> 15%

UNEMPLOYMENT

< 6%

6% +

UNEMPLOYMENT








< $10,000

$10,000-14,999

$15,000-24,999

$25,000-34,999

$35,000-49,999

$50,000-74,999

$75,000-99,999

$100,000 +

ACTUAL INCOME

< $50,000

$50,000-99,999

$100,000 +

ACTUAL INCOME

0%

100%

0%

100%

Men

Women


50%50%

11 12

65+

31-64

15-30

7-14

< 7


Union Square

10+987654321

Patricia’s Green

10 AM 11 12

Group size

10 AM 11 120

50

100

150

200

250

300

0

50

10 AM 11 12


Standing

Sitting

Active



10 AM 11 12

10 AM 11 12 10 AM 11 12

Romare Bearden Park


Union Square

Romare Bearden Park

10 AM 11 12 10 AM 11 12

Romare Bearden Park

Romare Bearden Park

Patricia’s Green:


Union Square:



Flatiron CensusRACE


Corona CensusRACE






No Yes0

5

10

15

20

25


16

24

12

20

6

1

8

5

0

5

10

15

20

25

No




30

35

40

Yes




3%

10%

14%

28% 7%



no response (1%)

0%

100%

0%

100%

50%50%

0%

100%

0%

100%

50%50%

Stationary Activity

8

11

5



< $10,000

$10,000-14,999

$15,000-24,999

$25,000-34,999

$35,000-49,999

$50,000-74,999

$75,000-99,999

$100,000 +

MEDIAN INCOME

< $50,000

$50,000-99,999

$100,000 +

MEDIAN INCOME

< 3%

3 - 5.9%

6 - 8.9%

9 - 11.9%

12 - 15%

> 15%

UNEMPLOYMENT

< 6%

6% +

UNEMPLOYMENT








< $10,000

$10,000-14,999

$15,000-24,999

$25,000-34,999

$35,000-49,999

$50,000-74,999

$75,000-99,999

$100,000 +

ACTUAL INCOME

< $50,000

$50,000-99,999

$100,000 +

ACTUAL INCOME

0%

100%

0%

100%

Men

Women


50%50%

11 12

65+

31-64

15-30

7-14

< 7


Union Square

10+987654321

Patricia’s Green

10 AM 11 12

Group size

10 AM 11 120

50

100

150

200

250

300

0

50

10 AM 11 12


Standing

Sitting

Active



10 AM 11 12

10 AM 11 12 10 AM 11 12

Romare Bearden Park


Union Square

Romare Bearden Park

10 AM 11 12 10 AM 11 12

Romare Bearden Park

Romare Bearden Park

Patricia’s Green:


Union Square:



Flatiron CensusRACE


Corona CensusRACE






No Yes0

5

10

15

20

25


16

24

12

20

6

1

8

5

0

5

10

15

20

25

No




30

35

40

Yes




3%

10%

14%

28% 7%



no response (1%)

0%

100%

0%

100%

50%50%

0%

100%

0%

100%

50%50%

Patricia’sGreen

StationaryActivity

UnionSquare

StationaryActivity

8

11

5



< $10,000

$10,000-14,999

$15,000-24,999

$25,000-34,999

$35,000-49,999

$50,000-74,999

$75,000-99,999

$100,000 +

MEDIAN INCOME

< $50,000

$50,000-99,999

$100,000 +

MEDIAN INCOME

< 3%

3 - 5.9%

6 - 8.9%

9 - 11.9%

12 - 15%

> 15%

UNEMPLOYMENT

< 6%

6% +

UNEMPLOYMENT








< $10,000

$10,000-14,999

$15,000-24,999

$25,000-34,999

$35,000-49,999

$50,000-74,999

$75,000-99,999

$100,000 +

ACTUAL INCOME

< $50,000

$50,000-99,999

$100,000 +

ACTUAL INCOME

0%

100%

0%

100%

Men

Women


50%50%

11 12

65+

31-64

15-30

7-14

< 7


Union Square

10+987654321

Patricia’s Green

10 AM 11 12

Group size

10 AM 11 120

50

100

150

200

250

300

0

50

10 AM 11 12


Standing

Sitting

Active



10 AM 11 12

10 AM 11 12 10 AM 11 12

Romare Bearden Park


Union Square

Romare Bearden Park

10 AM 11 12 10 AM 11 12

Romare Bearden Park

Romare Bearden Park

Patricia’s Green:


Union Square:



Flatiron CensusRACE


Corona CensusRACE






No Yes0

5

10

15

20

25


16

24

12

20

6

1

8

5

0

5

10

15

20

25

No




30

35

40

Yes




3%

10%

14%

28% 7%



no response (1%)

0%

100%

0%

100%

50%50%

0%

100%

0%

100%

50%50%

Evaluating the tool

This tool gives us an exciting way to generate proxy data for the “socialness” of a space. Although we routinely measure other elements of public life, we have never routinely added social grouping to understand this in a quantitative way, which opens up new types of questions we can ask and correlations we can draw.

Through prototyping this tool, we found that while the nuance of group size was interesting, what was most valuable to us was the binary of whether or not someone was in a group. In future iterations of this tool, we will develop a binary way to measure whether someone is in a group or not, and refine what “counts” as social activity for our purposes.

Another reason why this tool is valuable is that observational analysis generates an extremely accurate portrait of the dynamics of a place in time. This technique relies on the nuances of empirical analysis by a trained volunteer, who also builds a tacit knowledge of a place and can add additional insights to a public space evaluation

However, although we can capture who is socializing, we cannot capture economic data of those people

socializing, whether or not their interaction was planned or unplanned, or any longitudinal data about how social interactions change or impact people over time. And, although the data collected is extremely accurate, it only represents one snapshot in time that does not correct for changes over time or any outliers.

Finally, like our other tools, this tool has some sampling weakness. The data is from one or two days and only a few sites because it is expensive and time consuming to collect by putting a person in public space all day.

ABOVE, Patricia’s Green Gehl Studio 27

In search of typologies of social mixing and social spacesWe begin to describe what a gradient of closeness in social mixing, and the design cues that invite for that mixing. By Blaine Merkerwww.nextcity.org/column/in-public


Can the design a public place help people from different walks of life to connect? Over the last two months, my colleagues at Gehl Studio and I have asked hundreds of people in San Jose to describe their favorite places. Not just strictly public places, but malls and churches, dive-bars and street cafes, corner stores and markets. Most of them are not designed specifically to encourage different people to mix, though most of them do.

It turns out that the places people love are usually the places where they also feel comfortable meeting new people. More than three-quarters of those we heard from not only socialized in their favorite place, but also met new people there. And more than half of them continued to see those new acquaintances in other places. What we’re observing may not just be social mixing—it might just be mixing that “sticks.”

Could a designer reverse-engineer a public space to support social mixing by cracking the code of places that already mix people well? We know the opposite can be true: plenty of urban spaces suppress interaction and empathy between people by seeming unsafe, uncomfortable, just plain too crowded…or not crowded enough. But if we really understood the mechanics of mixing, could we design for it?

Since Frederick Law Olmsted declared in 1870 that his newly-opened Central Park “exercises a distinctly harmonizing and refining … influence favorable to courtesy, self-control, and temperance,” the design of civic space has been intertwined with progressivism that can, at times, border on social engineering. Designers ever since have wrestled with crafting public spaces that invite diverse groups to mingle while communicating codes of behavior that aim to

minimize social friction. It’s those codes that are the tricky part.

Olmsted observed that the visitors to his new park were “school-girl daughters”, “country people”, “gentleman”, “visitors” from out of town, even “ruffians”—in short, almost sort of person in New York City. My colleagues Eric Scharnhorst and Anna Muessig have recently prototyped intriguing new tools that decode today’s digital signatures into profiles of diversity in public space. But even this data-rich portrait of the diversity in public spaces doesn’t tell us whether the gentleman and the ruffian trade pleasantries and if they do, whether it knits them more closely or reinforces existing boundaries.

I am a designer, and for most of my career I’ve been fascinated by what physical environments do to set up human interaction. Design is

RIGHT, Member of the Turf Feinz Eric “eNinga” Davis, Photo: Mike Kepka, The Chronicle

29 RIGHT, Member of the Turf Feinz Eric “eNinga” Davis, Photo: Mike Kepka, The Chronicle


often over-burdened with social and political agendas—and usually disappoints as a way of achieving them. Still, I believe that urbanists can do better to understand not what design makes happen but what design makes possible. A city where people of any background can co-occupy places and affect each other’s experience is, to me, at the heart of civic life. Do strangers need to talk? Not necessarily. But a range of interactions should be comfortable and not uncommon among people from different walks of life for the city to earn its status as humankind’s prevailing habitat.

My own theory of mixing is, so far, based on stories from people I know who tend to meet people in public. It’s mostly hunches, but ones that could be tested with data. This “mechanics of mixing” breaks down into two distinct but related phenomena. First is the type of connection that can develop between people in public. The second is the quality of the space where that interaction takes place.

What happens when strangers meet: Towards a spectrum of mixing

Interactions between people who don’t know each other span a spectrum of increasing reciprocity, and the level of connection has something to do with the spatial qualities of the place that set up the interaction in the first place.

At the low end of the spectrum, imagine a New York City subway car like the one photographed here by a young Stanley Kubrick. Patrons eye each other, exchange a few words, but generally exhibit what Erving Goffman called “civil inattention”,

a low level of social connection that could also just be called tolerance. However there is evidence to support the idea that simply encountering difference leads people to become more tolerant of it (known as the “contact hypothesis”).For a slightly more engaged connection ride the BART from San Francisco to Oakland today. Turfing (“taking up room on the floor”) is an Oakland-based hip hop dance where performers use the train car’s handholds to flip and spin, inviting commuters to break away from their cellphones (or at least to start recording on them). This direct engagement, often across class and culture lines, is noted for creating appreciation of the performance that temporarily binds strangers together, lifting the mood and creating conversation in otherwise silent cars.

A catalyst like commerce can spark it but a true exchange may need several of the spatial qualities in the next section to get started. It is also possible to build up to a moment of exchange through repeated habits of tolerance and appreciation (imagine waving to a neighbor daily until one of you stops and says hello). In San Jose, many residents we surveyed reported this taking place spontaneously in social places like the San Pedro Square Market and Jackson Street in Japantown where the pace is slow and the spaces full of people sitting or strolling. Flea Markets are also a prototypical site for social exchange between buyers and sellers.

A watershed is crossed when people agree to reconnect on their own terms, outside of the happenstance of a mixing space. It often takes place at physical thresholds between public and private spaces: lobbies and entrances, beginnings and ends of events. Even though the act may be as low risk as exchanging emails,

it extends the connection, however tentatively, into the future.

But the success of real social bonds finally depends on many actions outside the initial meeting, moving towards greater reciprocity between people. Jane Jacobs described neighbors communally looking out for each other’s children. Pickup at the local basketball court builds bonds between the players that outlast the game. It’s likely that most connections that reach this level are between neighbors or people who have routines or interests in common. Whatever form it takes, these habits of meeting build trust over time.

These are some of the ways people mix in space. But in what type of space would you talk to a stranger?

The DNA of a mixing space

Assuming that the demographics and social context provide diverse groups access to a space (we have some new tools to assess where this is already happening), what qualities of a space make higher levels of mixing possible?

Cues that are explicit (regulations, security personnel and the messages in signs) and implicit (invitations for various uses or users like benches for seniors or play equipment for kids) give permission for a diversity of users to stay. Similarly, a clear sense of territory creates a safe space for observing others and interacting more comfortably. Copenhagen’s recently renovated Israels Plads has well defined skateboard terrain, basketball courts, play areas, seating and cafes all within range for mutual observation.

An artificial closeness or “compression” of personal space helps overcome the bubble of

31

privacy. While the right amount of compression to kindle an interaction between strangers differs between places and cultures, a little of it seems to help create opportunities to interact without the need to make an uncomfortable overture.

Respondents to our surveys in San Jose ranked feelings of relaxation and safety most highly in places they also feel most social. Besides basic requirements like good lighting and “eyes on the street”, comfortable spaces seem also have an important feature: an exit strategy. The exit strategy can be a physical exit, or graceful social one. The Alameda Flea Market provides an ideal amount of compression and exit strategy, since moving between stalls is an acceptable way to leave an interaction.

The ability to catch one another’s

attention is also critical to even in the most casual engagement. Advertising and technology increasingly compete with the people directly around us for this scarce commodity.

If all these previous conditions set up the potential for a social interaction, there still must be an excuse for it to take place. Triangulation provides the momentary connection between two new people, the “glue” that causes the interaction to stick long enough to possibly set. In our surveys, people gave myriad reasons for meeting new people, from musical events to children playing and pets running, and—frequently—friendly baristas. Triangulation can even be sent in the form of a postcard, as illustrated in the work of artist Hunter Franks.

We should be interested in how strangers meet in public because we aren’t connecting across class and culture in very many other places. Peer-to-peer and e-commerce has made it much easier to connect with culturally similar, albeit unfamiliar, people to meet basic needs without connecting with people who are physically closer though further away economically or culturally. In our increasingly digital, culturally sorted, spatially segregated and economically unequal society, public spaces still perform that basic function of collapsing difference through proximity—whether at the civic or neighborhood scale—better than anything else humans have come up with.

This month, the Market Street Prototyping Festival tests fifty placemaking projects along two miles of San Francisco’s most

RIGHT, Alameda Flea Market. Photo: Ganesha_Isis via Flickr


prominent thoroughfare. Market Street unites seven of the city’s diverse neighborhoods, from the Tenderloin to the Financial District. The majority of these crowd-sourced designs aim to encourage social interaction. While these projects can’t on their own overcome the economic and political forces that sort urban populations, they create a temporary site for countervailing experience. And with a better understanding of the mechanics of mixing, cities can focus investment in places like Market Street that are already primed to engage diverse groups. Will it make society more integrated? Probably not: but better mixing spaces can provide an opportunity that is increasingly hard

to come by—people from different walks of life, meeting comfortably, in public.

Blaine Merker is the Head of Team at Gehl Studio’s San Francisco office, co-founder of the art and design studio Rebar, and one of the creators of Park(ing) Day. Gehl Studio is evaluating the impact of the Market Street Prototyping Festival during the month of April.

ABOVE Stanley Kubrick. Life and Love on the New York City Subway. Man carrying flowers on a crowded subway. 1946. Museum of the City of New York. X2011.4.10292.37C

33

ABOVE Copenhagen’s Isreals Plads. Photo: Blaine Merker

Census for City Streets

This tool brings census data down to the street level by connecting data about people in place to the neighborhoods they come from. It opens up a new method for mapping places we care about, and engages a large number of people passively, without asking them to do anything extra.


Collect the 10,000 most recent photos posted in a space

Map each photo-taker’s photos, estimate their home neighborhood

Using census data, find median income and % unemployed data for each user’s neighborhood

Aggregate data for all photo-takers

ABOVE, sample instagram photos from our test sites

Union Square

Hayes Valley

Gehl Studio 35

Method

We draw a bounding box around a place of interest. This could be a square, a park, or a large boulevard. Using this bounding box, we went to our social media API of choice, which for this prototype is Instagram. We chose instagram because it satisfied our selection criteria (see table at right). It is designed for people to post about places they love, while they are there. It has high usership, is smart-phone only so it captures people on-the-go, and was easy to capture data for this test. Using the Instagram API, we collected the most recent 10,000 photos posted by Instagram users within our bounding box who have their locations services turned on. We cannot capture those who do not have their location services turned on. The location data we are looking for is embedded in the EXIF data (short for Exchangeable Image File, a format that is a standard for storing interchange information in digital photography image file).

Once we have identified who has been posting to Instagram from a given public place, we then collect up to 1,000 of the most recent photos taken by each of those people. We can estimate their home

neighborhood based on the geolocated data in each of their other photos. Then, we use the Census API to gather economic information about each of our users’ neighborhoods. Finally, we put all the data from each of those 10,000 users together to paint a portrait of potential economic mix of this public space.

It is important to note that this method doesn’t capture information about each of the photo-takers, but rather about the neighborhood where they live, or who they are likely to be. This is one way to bring census data down to the street level.

< $10,000

$10,000-14,999

$15,000-24,999

$25,000-34,999

$35,000-49,999

$50,000-74,999

$75,000-99,999

$100,000 +

Median Income, ACS 2013

1 mile

Granularity of Census Tracts

< $10,000

$10,000-14,999

$15,000-24,999

$25,000-34,999

$35,000-49,999

$50,000-74,999

$75,000-99,999

$100,000 +

Median Income, ACS 2013

Connecting Census data... ...to people-data

Time Photo Was Posted Midnight

11pm

Noon

6 AM 6 PM

Instagram Data (1,000,000 most recent photos)



Who are we counting?

Many people take and post photographs of places and people they love, and post these photos on Instagram every day. 26% of all internet users in the U.S. use Instagram. 50% of these users use instagram more than once per day. (source: Pew Reserch Center)


High usership*Spatial and public focus

Smartphoneonly

User data is mostly public

Instagram Yes (300 million) Yes Yes YesFlickr No (92 million) Yes No Yes

Twitter Yes (288 million) No No YesFoursquare No (55 million) No (merchant-focus) Yes No

Facebook Yes (1393 million) No No No* Global active user numbers from Digital Marketing Ramblings, http://expandedramblings.com


No No Yes






No Yes No




Sample quality

ABOVE, Ice skating in Union Square

Patricia’s Green 61% use Instagram and 19% posted in the plaza (out of 49 respondents)

At Union Square 51% use Instagram and 15% posted in the plaza (out of 76 respondents) (Source: Toolkit intercept surveys)

Gehl Studio 37

Results and Analysis

We were happy that this tool validated some of our hunches about who was spending time in our test sites.

Our hunch was that Hayes Valley was more of a neighborhood place, and Union Square more of a tourist place. This hypothesis was validated in our data set. In fact, the “magnetism” of the different public space virtually mirrored one another. 59% of visitors to Patricia’s Green are from the bay area, while 62% of visitors to Union Square are not from the Bay Area.



< 5

5 - 9

10 - 19

20 - 49

50+



< 5

5 - 9

10 - 19

20 - 49

50+


Patricia’s Greenattracts 59% of its visitors

from the Bay AreaUnion Squareattracts 62% of its visitors

from outside the Bay Area


< 5

5 - 9

10 - 19

20 - 49

50+

LOCAL REACHPATRICIA’S GREEN

LOCAL REACHUNION SQUARE

Gehl Studio 39

Global visitors to Patricia’s Green and Union Square

GLOBAL REACH - PATRICIA’S GREEN

GLOBAL REACH - UNION SQUARE

Median Income (Census for City Streets)

8

11

5



< $10,000

$10,000-14,999

$15,000-24,999

$25,000-34,999

$35,000-49,999

$50,000-74,999

$75,000-99,999

$100,000 +

MEDIAN INCOME

< $50,000

$50,000-99,999

$100,000 +

MEDIAN INCOME

< 3%

3 - 5.9%

6 - 8.9%

9 - 11.9%

12 - 15%

> 15%

UNEMPLOYMENT

< 6%

6% +

UNEMPLOYMENT








< $10,000

$10,000-14,999

$15,000-24,999

$25,000-34,999

$35,000-49,999

$50,000-74,999

$75,000-99,999

$100,000 +

ACTUAL INCOME

< $50,000

$50,000-99,999

$100,000 +

ACTUAL INCOME

0%

100%

0%

100%

Men

Women


50%50%

11 12

65+

31-64

15-30

7-14

< 7


Union Square

10+987654321

Patricia’s Green

10 AM 11 12

Group size

10 AM 11 120

50

100

150

200

250

300

0

50

10 AM 11 12


Standing

Sitting

Active



10 AM 11 12

10 AM 11 12 10 AM 11 12

Romare Bearden Park


Union Square

Romare Bearden Park

10 AM 11 12 10 AM 11 12

Romare Bearden Park

Romare Bearden Park

Patricia’s Green:


Union Square:



Flatiron CensusRACE


Corona CensusRACE






No Yes0

5

10

15

20

25


16

24

12

20

6

1

8

5

0

5

10

15

20

25

No




30

35

40

Yes




3%

10%

14%

28% 7%



no response (1%)

0%

100%

0%

100%

50%50%

0%

100%

0%

100%

50%50%

8

11

5



< $10,000

$10,000-14,999

$15,000-24,999

$25,000-34,999

$35,000-49,999

$50,000-74,999

$75,000-99,999

$100,000 +

MEDIAN INCOME

< $50,000

$50,000-99,999

$100,000 +

MEDIAN INCOME

< 3%

3 - 5.9%

6 - 8.9%

9 - 11.9%

12 - 15%

> 15%

UNEMPLOYMENT

< 6%

6% +

UNEMPLOYMENT








< $10,000

$10,000-14,999

$15,000-24,999

$25,000-34,999

$35,000-49,999

$50,000-74,999

$75,000-99,999

$100,000 +

ACTUAL INCOME

< $50,000

$50,000-99,999

$100,000 +

ACTUAL INCOME

0%

100%

0%

100%

Men

Women


50%50%

11 12

65+

31-64

15-30

7-14

< 7


Union Square

10+987654321

Patricia’s Green

10 AM 11 12

Group size

10 AM 11 120

50

100

150

200

250

300

0

50

10 AM 11 12


Standing

Sitting

Active



10 AM 11 12

10 AM 11 12 10 AM 11 12

Romare Bearden Park


Union Square

Romare Bearden Park

10 AM 11 12 10 AM 11 12

Romare Bearden Park

Romare Bearden Park

Patricia’s Green:


Union Square:



Flatiron CensusRACE


Corona CensusRACE






No Yes0

5

10

15

20

25


16

24

12

20

6

1

8

5

0

5

10

15

20

25

No




30

35

40

Yes




3%

10%

14%

28% 7%



no response (1%)

0%

100%

0%

100%

50%50%

0%

100%

0%

100%

50%50%

8

11

5



< $10,000

$10,000-14,999

$15,000-24,999

$25,000-34,999

$35,000-49,999

$50,000-74,999

$75,000-99,999

$100,000 +

MEDIAN INCOME

< $50,000

$50,000-99,999

$100,000 +

MEDIAN INCOME

< 3%

3 - 5.9%

6 - 8.9%

9 - 11.9%

12 - 15%

> 15%

UNEMPLOYMENT

< 6%

6% +

UNEMPLOYMENT








< $10,000

$10,000-14,999

$15,000-24,999

$25,000-34,999

$35,000-49,999

$50,000-74,999

$75,000-99,999

$100,000 +

ACTUAL INCOME

< $50,000

$50,000-99,999

$100,000 +

ACTUAL INCOME

0%

100%

0%

100%

Men

Women


50%50%

11 12

65+

31-64

15-30

7-14

< 7


Union Square

10+987654321

Patricia’s Green

10 AM 11 12

Group size

10 AM 11 120

50

100

150

200

250

300

0

50

10 AM 11 12


Standing

Sitting

Active



10 AM 11 12

10 AM 11 12 10 AM 11 12

Romare Bearden Park


Union Square

Romare Bearden Park

10 AM 11 12 10 AM 11 12

Romare Bearden Park

Romare Bearden Park

Patricia’s Green:


Union Square:



Flatiron CensusRACE


Corona CensusRACE






No Yes0

5

10

15

20

25


16

24

12

20

6

1

8

5

0

5

10

15

20

25

No




30

35

40

Yes




3%

10%

14%

28% 7%



no response (1%)

0%

100%

0%

100%

50%50%

0%

100%

0%

100%

50%50%

8

11

5



< $10,000

$10,000-14,999

$15,000-24,999

$25,000-34,999

$35,000-49,999

$50,000-74,999

$75,000-99,999

$100,000 +

MEDIAN INCOME

< $50,000

$50,000-99,999

$100,000 +

MEDIAN INCOME

< 3%

3 - 5.9%

6 - 8.9%

9 - 11.9%

12 - 15%

> 15%

UNEMPLOYMENT

< 6%

6% +

UNEMPLOYMENT








< $10,000

$10,000-14,999

$15,000-24,999

$25,000-34,999

$35,000-49,999

$50,000-74,999

$75,000-99,999

$100,000 +

ACTUAL INCOME

< $50,000

$50,000-99,999

$100,000 +

ACTUAL INCOME

0%

100%

0%

100%

Men

Women


50%50%

11 12

65+

31-64

15-30

7-14

< 7


Union Square

10+987654321

Patricia’s Green

10 AM 11 12

Group size

10 AM 11 120

50

100

150

200

250

300

0

50

10 AM 11 12


Standing

Sitting

Active



10 AM 11 12

10 AM 11 12 10 AM 11 12

Romare Bearden Park


Union Square

Romare Bearden Park

10 AM 11 12 10 AM 11 12

Romare Bearden Park

Romare Bearden Park

Patricia’s Green:


Union Square:



Flatiron CensusRACE


Corona CensusRACE






No Yes0

5

10

15

20

25


16

24

12

20

6

1

8

5

0

5

10

15

20

25

No




30

35

40

Yes




3%

10%

14%

28% 7%



no response (1%)

0%

100%

0%

100%

50%50%

0%

100%

0%

100%

50%50%

8

11

5



< $10,000

$10,000-14,999

$15,000-24,999

$25,000-34,999

$35,000-49,999

$50,000-74,999

$75,000-99,999

$100,000 +

MEDIAN INCOME

< $50,000

$50,000-99,999

$100,000 +

MEDIAN INCOME

< 3%

3 - 5.9%

6 - 8.9%

9 - 11.9%

12 - 15%

> 15%

UNEMPLOYMENT

< 6%

6% +

UNEMPLOYMENT








< $10,000

$10,000-14,999

$15,000-24,999

$25,000-34,999

$35,000-49,999

$50,000-74,999

$75,000-99,999

$100,000 +

ACTUAL INCOME

< $50,000

$50,000-99,999

$100,000 +

ACTUAL INCOME

0%

100%

0%

100%

Men

Women


50%50%

11 12

65+

31-64

15-30

7-14

< 7


Union Square

10+987654321

Patricia’s Green

10 AM 11 12

Group size

10 AM 11 120

50

100

150

200

250

300

0

50

10 AM 11 12


Standing

Sitting

Active



10 AM 11 12

10 AM 11 12 10 AM 11 12

Romare Bearden Park


Union Square

Romare Bearden Park

10 AM 11 12 10 AM 11 12

Romare Bearden Park

Romare Bearden Park

Patricia’s Green:


Union Square:



Flatiron CensusRACE


Corona CensusRACE






No Yes0

5

10

15

20

25


16

24

12

20

6

1

8

5

0

5

10

15

20

25

No




30

35

40

Yes




3%

10%

14%

28% 7%



no response (1%)

0%

100%

0%

100%

50%50%

0%

100%

0%

100%

50%50%

8

11

5



< $10,000

$10,000-14,999

$15,000-24,999

$25,000-34,999

$35,000-49,999

$50,000-74,999

$75,000-99,999

$100,000 +

MEDIAN INCOME

< $50,000

$50,000-99,999

$100,000 +

MEDIAN INCOME

< 3%

3 - 5.9%

6 - 8.9%

9 - 11.9%

12 - 15%

> 15%

UNEMPLOYMENT

< 6%

6% +

UNEMPLOYMENT








< $10,000

$10,000-14,999

$15,000-24,999

$25,000-34,999

$35,000-49,999

$50,000-74,999

$75,000-99,999

$100,000 +

ACTUAL INCOME

< $50,000

$50,000-99,999

$100,000 +

ACTUAL INCOME

0%

100%

0%

100%

Men

Women


50%50%

11 12

65+

31-64

15-30

7-14

< 7


Union Square

10+987654321

Patricia’s Green

10 AM 11 12

Group size

10 AM 11 120

50

100

150

200

250

300

0

50

10 AM 11 12


Standing

Sitting

Active



10 AM 11 12

10 AM 11 12 10 AM 11 12

Romare Bearden Park


Union Square

Romare Bearden Park

10 AM 11 12 10 AM 11 12

Romare Bearden Park

Romare Bearden Park

Patricia’s Green:


Union Square:



Flatiron CensusRACE


Corona CensusRACE






No Yes0

5

10

15

20

25


16

24

12

20

6

1

8

5

0

5

10

15

20

25

No




30

35

40

Yes




3%

10%

14%

28% 7%



no response (1%)

0%

100%

0%

100%

50%50%

0%

100%

0%

100%

50%50%

Unemployment

Actual Income (intercept survey)

Using the Census for City Streets, both Patricia’s Green and Union Square represented people from a diverse mixture of neighborhood median incomes.

Patricia’s Green attracts people from neighborhoods that are a little wealthier and a little more employed.

In evaluating our tool, we compared our intercept findings with our Census for City Streets findings. We found that our intercept interviewees were more likely to be in a higher income bracket than our other tool. This may indicate that our sampling methods for intercept surveys misses those that make less money. It could also mean that our Census for City Streets tool is skewed. Or both.

For a first test, we are pleased to be able to look at real figures and continue to improve our methods.

ABOVE, PROXY in Hayes Valley


Evaluating the Tool

We were very sceptical of this tool. We knew that users of Instagram are skewed towards young people who can afford a smart phone. We predicted that few people interviewed would even use Instagram, and if they did that even fewer people would use it in the plaza. We were pleasantly surprised by higher-than-expected Instagram user rates in both places. In our sample, we found that 18% of people interviewed in our two test plazas had posted a photo to Instagram from the plaza, meaning that we were capturing about 18% of people present in these plazas.

Looking at research on smart phone and Instagram usage, we feel confident that this is a good proxy data set. Although not everyone uses Instagram, those who do are frequent users, with half using the site daily, more than any other major social media network except Facebook (compared to 70% on Facebook and 13-36% in other social media. Source: Pew, 2014). In San Francisco, 74% of all residents have a smart phone or tablet, including nearly 50% of people who make less than $30,000 per year (source: Pew Research Center 2014, Nielson 2013).

In addition, we are happy to have found a people-data proxy that has a place-in-time focus like Instagram. It’s a great data set. Of all the popular social media tools for capturing how people move around space, Instagram is the only one that is place-based and mobile-only by design. It is built around taking and posting photos in the moment, meaning that people are more likely to use it in the place they are spending time, not waiting to read an article, or pin a photo when they get back home or to the office. This data set is cheaper then observational analysis because it takes less time, and it allows us to toggle back and forth in time. We can identify sites remotely and do the analysis from anywhere, and on any site.

This tool allows us to ask new questions and gain new knowledge about how people use the city. We were able to show the characteristics of the “neighborhoods” that were represented at a place, and local and global social reach of public places - this has huge implications for bottom-up planning we will address in our design brief.

However, our tool has limitations. Not everyone uses Instagram (21% of all U.S. adults), meaning our tool only captures a fraction of people spending time in public places. In the US, Instagram’s user base is skewed to be younger - 53% of users are between the ages of 18-

29. Besides young adults, women are particularly likely to be on Instagram, along with Hispanics and African Americans. Additionally, the groups of people who are less likely to be on the internet on their smartphone, such as very low income people, very young people, or the elderly, are precisely the people we want to capture. We have yet to correct for age, income, and racial skews in Instagram users in the data. In places with censored internet access like China, this tool has major limitations. There are tools that exist that guess gender and race from profile information that we could explore to try to correct this, as well as other corrections to the data based on known biases.

Although some of our home location tests were extremely accurate (it guessed the exact building of one of our colleagues based on her Instagram account), guessing home location based on photo locations was not always accurate. Popular tourist destinations like Las Vegas seemed to be over-represented in the data. This is because people may take more photos in Las Vegas then they do in their own neighborhood.

Another issue with geolocation data is its accuracy - down to the foot. Does it know if you are inside a private space or in the adjacent plaza? In our next iteration, we will define a “minimum viable square footage” of our open space tests in order to take more precautions that our tool measures only public life.

This tool runs on proxy data, and one of the limitations is that we are describing the economic qualities not of people but of the census tracts that these people likely come from. This means that the method describes characteristics of the “neighborhoods” represented in a plaza, but not characteristics of the individuals. Nor does it tell us who is interacting with one another, only who is in the same place at the same time.

And finally, the elephant in the room: is this type of analysis as sneaky as the research the NSA, Google, Facebook, and other opaque enterprises are doing? And if so, is taking the “socioeconomic temperature” of a place a good enough reason to do the work? We think that transparently using user-generated big data in the service of making cities more just and diverse might be an excellent use of this information. But we, along with everyone making and thinking about big civic data, want to respect this digital ethical frontier, and engage stakeholders and generators of this data, if they agree.

Gehl Studio 41


Reporting Back: What your Instagram Feed has in Common with a Ballot Box

This article was published in Next City as part of the In Public article series. by Eric Scharnhorst www.nextcity.org

At first, it seems like voting and taking pictures have little in common.

But begin with the idea that a photo helps one memory stand taller than the others in the same way a vote bumps a candidate ahead in the race. And remember that a ballot box is a collection of people’s votes, sort of like Instagram is a collection of people’s photos.

What if we could tally up these public photos like they were votes in a ballot box?

This idea is worth exploring because there’s something about photos. I don’t take a lot of them. But when I do, I take pictures of the people I love, special events, and scenic places. My favorites combine all three.

And when I take a photo, my phone invisibly stamps it with mechanical notes, sort of like the clues written by hand on the back of an old Polaroid. Sometimes this metadata includes the location of where

the photo was taken. This means that some photos can be mapped.

When an Instagrammer’s photos of a city are mapped together, an interesting thing happens: Parts of the map are empty, and others are crowded with photos. The best example I’ve found of this is a series of maps by data artist Eric Fischer. Fischer mapped photos he found on Flickr of San Francisco according to whether the photo was taken by a local or a tourist, based on the the invisible digital stamps embedded in the photos. The two sets of maps were completely different; tourists traveled one San Francisco, while locals saw another.

These maps hint at a new form of public engagement that can include not only the 100 people who show up at a community meeting, but also the 10,000 people who skip the meeting to spend time at nearby public spaces.

Building upon Eric’s work, my colleagues at Gehl Studio and I recently experimented with using photos to learn about the mixture of people at different places in San Francisco. We looked at Union Square, a central plaza in downtown surrounded by shopping and hotels; and Patricia’s Green, a grassy neighborhood park with benches and a playground. We were curious about the social life and demographic mixing at each of these public spaces.

Our curiosity was piqued when we went looking for a census for city streets. Unlike the census that captures where people live, there is no regularly collected dataset for where people spend time in public spaces. Places that welcome a good mixture of different people can help foster connections between cultures and across social strata. But if diversity in public space is so important, why don’t we have the tools to find it? We asked ourselves, how could people help us build this dataset automatically?


At first, it seems like voting and taking pictures have little in common.

But begin with the idea that a photo helps one memory stand taller than the others in the same way a vote bumps a candidate ahead in the race. And remember that a ballot box is a collection of people’s votes, sort of like Instagram is a collection of people’s photos.

What if we could tally up these public photos like they were votes in a ballot box?

This idea is worth exploring because there’s something about photos. I don’t take a lot of them. But when I do, I take pictures of the people I love, special events, and scenic places. My favorites combine all three.

And when I take a photo, my phone invisibly stamps it with mechanical notes, sort of like the clues written by hand on the back of an old Polaroid. Sometimes this metadata includes the location of where the photo was taken. This means that some photos can be mapped.

When an Instagrammer’s photos of a city are mapped together, an interesting thing happens: Parts of the map are empty, and others are crowded with photos. The best example I’ve found of this is a series of maps by data artist Eric Fischer. Fischer mapped photos he found on Flickr of San Francisco according to whether the photo was taken by a local or a tourist, based on the the invisible digital stamps embedded in the photos. The two sets of maps were completely different; tourists traveled one San Francisco, while locals saw

another.

These maps hint at a new form of public engagement that can include not only the 100 people who show up at a community meeting, but also the 10,000 people who skip the meeting to spend time at nearby public spaces.

Building upon Eric’s work, my colleagues at Gehl Studio and I recently experimented with using photos to learn about the mixture of people at different places in San Francisco. We looked at Union Square, a central plaza in downtown surrounded by shopping and hotels; and Patricia’s Green, a grassy neighborhood park with benches and a playground. We were curious about the social life and demographic mixing at each of these public spaces.

Our curiosity was piqued when we went looking for a census for city streets. Unlike the census that captures where people live, there is no regularly collected dataset for where people spend time in public spaces. Places that welcome a good mixture of different people can help foster connections between cultures and across social strata. But if diversity in public space is so important, why don’t we have the tools to find it? We asked ourselves, how could people help us build this dataset automatically?

To do this, we decided to use Instagram because it is simple and popular, and there’s something special about its photos. Instagram provides a reliable, fine-grained, frequently updated dataset without any forms or complicated opt-

in mechanisms. Many of us participate in this tool not as a cumbersome task, but because we want to. The low barrier to engagement of social media-related data is what we call “passive engagement” because we are able to include people passively, by carefully sifting through the compost pile of public data.

Our first test looked at the geographic reach of Union Square and Patricia’s Green. We cycled through the Instagram photos posted from each place. Then we roughly estimated where each Instagrammer came from based on other Instagram photos posted on the account.

Mapping the data globally reminded us of what we intuitively knew. Union Square is popular with tourists while Patricia’s Green had a stronger local reach, with 59 percent of its Instagramming visitors from the Bay Area.

Patricia’s Green global reach. (Gehl Studio)By contrast, only 38 percent of Instagramming visitors were local at Union Square.

After mapping the social reach, we predicted the demographic mix of each space. We did this by seeing each Instagrammer as a representative of his or her home neighborhood. Then we drilled into the census data to get a demographic picture of the entire set of home neighborhoods represented by each place’s Instagrammers. Together, the neighborhoods of 10,000 of the most recent Instagrammers in each site helped paint a portrait of the economic diversity of these

45

public spaces. We found that Instagrammers in both places represented a diverse mixture of incomes, and Patricia’s Green was a little wealthier and a little more employed.

Bringing census data down to the street level can help us see how economic diversity plays out in public space. And for a first test, we’re happy that 20,000 people were represented. Imagine handing out 20,000 questionnaires, or hosting 20,000 people at a community meeting.

But this tool doesn’t do it all. It gives us an overview of people in a space, and how economically different they might be. But group size, the mixture of stationary activities, pedestrian flows, and how and why different people engage with one another are all unknown.

Plus, not everyone posts photos to Instagram. 15 percent of people we interviewed at Union Square and 19 percent at Patricia’s Green used Instagram

and posted in the plaza. This was a pleasant surprise because we expected it to be lower. But it’s nowhere close to including everyone. And it doesn’t tell us if people are talking to each other, or if they form lasting social bonds. And predicting a photographer’s neighborhood doesn’t tell us anything about the photographer. This is great news for privacy. But it means we are registering the places represented by people, not the people themselves.

This is why we see passive public engagement as just one tool in our toolbox. To finish the job, we enhanced our traditional analysis of pedestrians and stationary activities to include social grouping. And we used intercept surveys to investigate the triggers for unplanned social encounters. This combination of tools works together to build a better understanding of the social life and economic diversity of public places.

Even with its limitations, we

think the potential is huge. This approach can help us see how people respond differently to a place over time, and how, or if, its mixture of people changes as the space changes. We can look at the details, like how a sunny day compares to a rainy day. And we can look at the bigger picture, like how providing more room for sitting and walking affects the social and economic composition of the people using a space.

If we scale this tool up, we can imagine a whole new map of the city – a place diversity map. A better understanding of how different places invite different mixtures of people can help us make places that are inviting for everyone. Instead of experts picking sites from the top-down, the photo-posting-public can collectively build their own grassroots map. And their photos can be cast like votes that represent people in the places that bubble up to the map’s surface.

Design Brieffor a Public Life Diversity Toolkit

No No






No Yes No




Sample quality

Yes

48 Place Diversity Toolkit Design Brief

Evaluating our prototype Toolkit

All in all, this was a successful first test. The three tools work in tandem to solve for sampling issues in each. But, in our prototypes we found new holes, and thought of new tools to answer our research questions. We want to do more tests to Measure/Test/Refine this toolkit. Fortunately, we have opportunities to do this with current Gehl Studio projects and through Gehl Institute.

Specific suggestions for how to improve our tools are described below. However, in general the most important next step to improve the toolkit will be to improve the rigor of our methods. We need to correct for known biases and discover new biases and then correct for those. Normalizing our data sets based on biases in our data set and comparing our findings to local census data are important next steps.

We can also complement our vision and value set by formalizing relationships with skilled researchers and data providers in the social sciences, sensor technology, and urban data community who will improve the effectiveness of our toolbox.

Once we improve the methods of our toolkit, we can move to phases two and three of this project: perform design research in places with high levels of public life diversity, and from this research generate a guide that shares process and design inspiration to inviting for public life diversity.

We took our prototypes and gave them a hard look: what worked and what didn’t? How did our new tools achieve our goals and answer our research questions? What new ideas did our prototypes generate? We present an analysis of our prototyping process here and present a design brief for ways to continue to hone the Public Life Diversity Toolkit.


Gehl Studio 49

1

2 3

InterceptSurvey

What the tool adds to the Toolkit

• Accurate data about who is in a space at a given time, not a proxy• Captures data about social mixing: who is in space, whether or not they mix with strangers• Captures some data about economic integration by asking groups about their income levels.• Captures qualitative data about place: what triggers interactions with others• Helps correct skew of Census for City Streets data by validating social media participation estimates• People collecting data immerse themselves in the place, and come away with a deep understanding of how the place works

Core Challenge

• Sample sizes are small, and not representative of all users• Sample is skewed towards who says “yes,” and who surveyor feels comfortable approaching • Samples are limited by time and space because they require a person to collect data

Next Steps for Iteration

• More rigorous methodology for sampling, and other best-practices for correcting sampling biases in social science research• Improve survey questions about income to capture income groups socializing


ObservationalAnalysis


• Accurate data about who is socializing in space at a given time (not a proxy)• People collecting data immerse themselves in the place, and come away with a deep understanding of how it works• Ability to capture public space quality data (next iteration of toolkit)

Core Challenge

• No ability to capture economic data about people in place• Samples are limited by time and space because they require a person to collect data


• Enhance tool with qualitative place-data: Correlate public life diversity data with other qualitative place-data such as public space quality criteria and favorite places. What are design signals that tell diverse groups of people they are welcome?• More rigorous methodology for sampling, and other best-practices in social science research

1

2 3

1

2 3

Gehl Studio 51

Census forCity Streets


• Ability to estimate economic mix of people in a public space (proxy data, based on home neighborhoods)• Ability to estimate this economic mix at any point in time, over large area• Can perform these tasks quickly, not as limited by space or time as other tools

Core Challenge

• Economic data is proxy for estimated home location of user, not user herself• Sample is limited to Instagram users who have their location services turned on, and biased to the users of this app• Does not collect data on social interactions, only who was in the same place at the same time


• Normalize data based on known bias of smartphone users in the Bay Area and intercept survey data • Test tool on other social media platforms. Accessing better data with more coverage will lessen biased sample of instagram methods.

• One option to do this is through environmental sensors. Sensors can be cheap and capture various data about how people move through place: pedestrians and bicycle counts, path-tracing, velocity, grouping, duration of staying activities, and other qualities. Working in strategic partnerships with technology developers will enhance this tool in a mutually-beneficial way by giving developers to what they need: test cases and research questions to investigate.

• Form strategic partnerships with data providers such as google, cell phone carriers, facebook, certain online dating sites, and others.

• Improve method for only capturing people in public space (not buildings, e.g. thresholds for “minimum viable public space” for this tool]• Scale up: generate a citywide map that can score public spaces according to economic diversity and local/global reach and generate city-specific, city-wide baselines


Adding new toolsto the Toolkit

We are thrilled that our prototype works and that it’s helping us answer new questions about social mixing - and helping us ask better questions about the relationship between public space and public life. As we develop our questions and our methods, we see the potential for new data and new methods to help us answer these questions in better ways.

The following tools represent ideas, hunches, bursts of inspiration, and nagging questions that came up during the prototyping process.

They are questions and ideas that we were only able to have after understanding the limitations of the tools we were prototyping, conversations with colleagues and peer researchers, and trying to visualize or explain our methods.

Gehl Studio 53

Public LifeDiversity Index

Most urban research occurs because a top-down directive to uncover information about how a certain place of interest works. Often in urban design and development, this is informed by politics, infrastructure investments, or, sometimes, lack of creativity.

Once a site is selected, research is performed and presented. But, this research because of limitations of data collection, often leaves out a city-wide context. Maybe a different location in the city was more appropriate to look at?

The tools prototyped in the Census for City Streets open up a new way to see the city and to identify sites for further research or investment.

A Public Life Diversity Index could generate a city-specific, city-wide baseline for Public Life diversity using the Toolkit. It could then use this baseline to generate a public life economic diversity score to compare different public places.

A tool like this could change the way we select sites for research, investment, or other things, by allowing sites with high public life diversity to rise to the top, from the bottom up.

1


Social / SpatialNetwork Mapping

Social network mapping is often used to identify “connector” people who serve a connecting function between two or more social or professional networks. What if public spaces acted the same way?

We could explore the social dimension of data by analyzing social networks through “likes”, “retweets” etc, and layering this over where people are spending time.

This analysis could reveal overlapping connections between online social networks and real-time spatial social networks.

Neighborhood PricePoints

In our existing tools, we measured economic diversity by looking at people in space. Another approach to understanding the economic diversity of a place would be to measure the economic diversity of surrounding retail offerings. Certain amenity-mapping services such as Yelp track the price-point of different amenities using a standard $$ rating system. Capturing the diversity of amenity price-points around a given public place could act as a proxy for the diversity of economic buying power of people who visit those places.

We could also collect related data about spending patterns such as point-of-sale and credit card transaction data to understand price point of sales, and price ratios compared to area median income.

3

2

Gehl Studio 55

Platforms for Sharing

We can think about ways to crowd-source our data collection for the Toolkit, and/or make data visualization more open and compelling for the public

This might involve building out our current Public Space Public Life global database to include a platform for sharing and comparing Place Diversity Index data across places and other variables.

This tool could build local capacity, and create a set of tools for communities to share data.

Process and Design Inspiration for economic mixing

Once we deploy the Toolkit on a city-scale, we can see places with high rates of different economic groups in the same place, and perform design research on these places. What do they have in common? Are there design cues that correlate to high levels of mixing? We can use findings from this analysis to generate design inspiration for designing processes for cities and advocates to measure what they care about and create a feedback loop between people first metrics and design and investment decisions.

0238

0214

5

4


Appendix

Gehl Studio 57

Intercept Survey

1. Do you use Twitter? Y / N

2. Have you ever posted while in this plaza? Y / N

3. Do you use Instagram? Y / N

4. Have you ever posted while in this plaza? Y / N

5. My income is within this range:

$0 - 10K

$10 - 15K

$15 - 25K

$25 - 35K

$35 - 50K

6. What is the street intersection closest to your home?_______________ and _______________

7. What is your home zip code? ___________________

8. Have you talked to new people in this plaza?

Yes - friends of friends

Yes - stranger I struck up conversation with

No

No - but sometimes I recognize people I don’t know

9. If Yes - What brought about your interaction with them?

Pets

Sports / physical exercise

Children

Event / concert / class

Volunteering / religious event

Other ______________

1

Thank You!@citiesforpeoplegehlarchitects

www. gehlarchitects.com

Thank you for participating in this survey about social mixing and public life. Your responses will be kept strictly confidential.

$50 - 75K

$75 - 100K

$100 - 150K

$150 - 200K

$200K or more

10. If Yes - have you seen them outside of this plaza?

Yes

No

Sometimes

Other ______________

Sharing knowledge and inviting dialogue in the prototyping process


ABOVE, Jeff Risom gave a webinar on the topic Pitting Public Life back into Public Space at SPUR

To broaden our impact and ask our questions to peers in the field, we used Next City as a platform for dialogue, and organized a Webinar as a means of sharing our findings and deepening dialogue among an informed and engaged audience.

Gehl Studio 59

Putting Public Life Back into Public Space Webinar

Registered: 775Attended: 440Asked at least one question: 27In person attendees at SPUR: 45

Webinar questions, arranged by topic:

Institutional Process + Collaborations• Can you share some strategies for working with transit and transportation agencies for reclaiming public spaces?• How do you work in relation to city staff when pulling permits for the pilot studies? Without naming the city, we are finding that the Risk Management department and the Engineers are the hardest members of the groups to work with in terms of allowing some leniency in pulling Right-of-Way permits when testing things like parklets. We tend to have a great reaction from the citizens, but many times hit a wall when working with city staff that have traditionally made decisions based on urban municipal code that has not been updated in years. Do you have any suggestions, as we are trying to make policy change...• Is there a strategy your company has used to suggest certain programs or plans to the city that are not an RFP?• Can he talk to the role of BIDs, which manage most new ped plaza spaces in NYC - what they are doing right and how can they improve?• How can bike infrastructure be better integrated to enhance tactical open/public spaces?• Do all these projects and proposals usually come from governments willing to change the city already, or do you sometimes have to try to “convince” city representatives that these changes were positive -and necessary- for the community?• Prior to the testing phase, what are the best ways to convince the business community that a more people focused place is good for business?• What is the role of the library in public placemaking?• What roles do public and/or urban universities have in coordinating, funding or programming initiatives to activate public spaces? Are there examples of universities that have done this (successfully or not)?Urban Design • How can we design public spaces in a larger scale as a network? And not only as separate dynamics in the city? What does connectivity of the public realm require?

• How do you see your methodologies changing buildings - can they facilitate an improved exchanged between the exterior public and interior private space?• Especially since Gehl studio has european beginnings, this question is relevant: How do you get people to imagine public space alternatives that they are not familiar with.• Are there any examples of people designing cities to reduce sexual (street) harassment or other forms of violence?• Many of the examples cited are geared toward engaging adults in public spaces. Do you have any examples that would appeal more to children and families?• Much of the work is heavily driven by data and assumes familiarity with precedent. How do you interact with users that don’t generate data (or won’t) or have completely different social relationships with the precedents?• Following up on your comments regarding public space in NY, Harlem was a place where streets were public- until all activity on the street was criminalized. How do you reconcile innovation social


policy and stereotype? How would this work in a place like the grosse point/Detroit border or Trenton?• This all sounds great and I hate to be “debbie downer” but do you have any data or experience with “downsides” - like more people leading to more crime (pickpockets, homeless sleeping...)? and if so has that let to further compensatory iterations that helped?• This feels a lot like PPS’s “Lighter, Cheaper, Quicker”. Are there major differences that you can discuss?• How can these methods be utilized in areas that do not have an existing culture of public space?• Your projects seem to have a definitive aesthetic that you didn’t focus much on, can you talk a little bit about that?

Finance• Can you give us 3 or 4 very different types of public space finance?• What do you think will be the affects of the trend toward public private partnerships on the public realm?• What successful sustainable funding models have you seen? What is the role of corporate sponsorship?

Census for City Streets Instagram Method / Data• Given the selection biases of Instagram and the inaccuracy of Census data, what other types of data sets would be ideal for baselining life in public space?• Not all studios and firms are able to undertake the intensive process of meeting people where they are. Is there a database of these urban metrics which designers can utilize to strengthen their projects, and can we build on information that is collected?• Are you making any of your research materials (specifically the social media algorithm and GIS maps) available? Could this be a developer’s kit or package for other places?• Is the instagram algorithm proprietary or is it something others could use?

Grab Bag• How does technology / media detract / encourage social interaction in public space?

Professions Listed when Jeff asked who was in the audience• Librarians (x 3)• Developer (x 3)• City Planner (x 3)• Community foundation leader (x 2)• Coordinator of a child and youth friendly city

initiative• Nonprofit that works with people of low income to find jobs and start businesses ... not a planner!• Environmental Urban Manager• Economic Development• Public Art Curator• Communication• Citizens

Gehl Studio 61

In Public, a Next City article series co-curated by Gehl

Our goal for the series was to better understand how public realm can bring people together, inspire new directions for the public realm, and engage people. We looked into whether a vibrant public realm connects underrepresented citizens to opportunity, fosters innovation and the spread of ideas, and builds strong, diverse communities. This 6-month article series touched on each of these goals, with a special focus on how public spaces expand opportunity.

The column has about 79,300 pageviews in total (as of4/8/15).

Gehl participated in two “tweet-chat” on popular articles, and two of the articles we authored for the series are included here.

The top articles are:What Long-Distance Trains Teach Us About Public Space in America - 9,031Hacking Public Space With the Designers Who Invented Park(ing) Day - 7,373How Much Public Space Does a City Need? - 5,220How Dating Apps Are Changing the Way We Behave in Public - 4,547We Found Jane Jacobs and Robert Moses’ Love Child - 3,779

Top links:LongReads.complanetizen.comusa.streetsblog.orgla.curbed.comarchitectmagazine.comtheguardian.compolitico.comgoverning.com

Public Life Diversity Toolkit 1.0

Documents

Transcript of Public Life Diversity Toolkit 1.0