StreetGo!

project overview

Now more and more people choose to walk to get around. But following the navigation from digital maps, such as Google Maps, Apple Maps, can be struggled, noisy, stressful, and full of traffic, unpleasant elements accompanied with all the route, even though it's time-saving - that is a problem. We developed StreetGo that uses data to empower walkers to know the walkability environment around them and get the most suitable route.

PARTNER

Xinyue Zhang

TIMELINE

Feb. - Apr. 2020
6 weeks

TOOLS

Figma, Principle
After Effects, Premiere

my CONTRIBUTIONS

User Research

Held interviews with usrs, finding their pain points and analyzing design opportunities.

Technical Implementation

Assisted in discovering potential technical opportunities and analyzing algorithms.

Co-design Session

Held interviews and voting sessions with users, dividing their walking preferences into 4 categories.

Iteration

Assisted in testing prototypes with users, iterating the information hierarchy and UI of the Website.

Futher Steps

Created the multi-device end concept which included smart bus stop boards and phones.

efficiency-related routes
and walkers' diverse needs

From interviewing multiple pedestrians, we learned that their walking preferences are multiple - each pedestrian may have unique walking preferences, and scenarios can change their walking preferences. However, navigation maps only utilized efficiency-related algorithms to select routes for walkers. Even though those routes are time-saving, they could be struggling, noisy, stressful, or full of traffic, not aligning with users' needs.
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Meanwhile, data on walkability exists only for city planners, and was not available in a simplified, accessible way to pedestrians. We hope to use the data to offer walkers customized walking experiences.

THE FULL EXPERIENCE

Design concept video featuring the story of Luna and Jayce, who set Memo together and company each other.

Main product features

Set a walking character

Users set a character based on their needs and get personalized walking information around them.

Check the walkability environment

StreetGo gives users all-rounded walking information and points of interest according to their characters.

Dive into each subelement

Users can dive into details they want to explore, including street rankings, street views, and rating factors of each subelement.

Receive recommended routes

According to users' characters, StreetGo can recommend the most suitable routes to users.

Customize route

Users can also select their preferred parts with detailed walking information and combine them to form their routes.

01 Exploratory research —

Navigation applications don't always give walkers the most suitable routes.

Walking is great because it's a great way to get exercise, it's good for the planet, and it helps to save money. According to WALKABILITY OF CHINESE CITIES, 20% of Chinese people who live in big cities choose to walk to get around. So, If walkers have good walk experiences or not? This question pushes us to move forward.

To figure out whether walkers have good walking experiences and why we developed a survey through questionnaires among 70 participants. From effective responses, we found that three reasons why walkers don't have good walking experiences:

Undesirable Navigation Apps

52% of walkers said the navigation apps give them undesirable routes.

Unfamiliar Environment Nearby

28% of walkers said that they weren't familiar with the environment nearby.

Undesirable Routes

20% of walkers said that some routes give them negative impressions.

01 Exploratory research —

Walkers have multiple preferences and aims, and these can vary by time.

In this research phase, our activities focused on the exploration of the problem space, including why navigation applications can't cater to walkers and where are the opportunities that we can improve people's walking experience. With these two questions in mind, we interviewed seven walkers to find their pain points and look for opportunities.

We discovered that walkers had different considerations to choose routes because they had different preferences such as trees, parks, and even routes. But the navigation applications can only give walker recommended routes according to walking time or distance.

To dive deeper, we developed a user journey map to articulate our findings and users' pain points, as well the opportunities we could target as intervention points to offer a better walking experience:

Key takeaways:

01

02

Walkers have multiple walking preferences and aims.
Walker's preferences can vary by time, but navigation maps only offer a single walker route every time.
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Walkers don't have enough information about the walking environment.
Most walkers don't know the walking environment around them.

02 Generative Research —

How might we use walkability data to facilitate walkers' walking experiences?

We used a combination of primary and secondary research methods during the exploratory research phase. For primary research, we expected to identify walkers' pain points and challenges. For secondary research, we reviewed the literature on pedestrians and walkability, conducted voting sessions with walkers, and held competitive analysis to discover opportunities from data.

How might we divide pedestrians' preferences?

Work with pedestrians to classify their walking bias and analyze the weight each walking preference.

How might we process the walkability data?

Analyze existing walking navigation and data maps to find the data's opportunities.

02 Generative Research —

What walkability data can we use to provide customized walking experiences?

Each walker has specific needs. According to our literature research, we found that 87% of all the walkers can be divided into seven parts: commuters, shoppers, students, leisure walkers, food walkers, tourists, and runners. We finally got 32 needs and divided these needs into six categories: comfortable, convenient, enjoyable, efficient, safe, and relaxing.

According to these six categories, we researched the indicator specifics and calculation formula to figure out the relationship between walkability data and real-world context.

Walkability data and indicators can be used▼

02 Generative Research —

What elements are walkers always concerned about?

To validate the rationality of the categories, we invited ten interviewees to participate in a selecting activity. In this activity, we asked volunteers to play a character and selecting the most relevant two elements according to their character. So, we could select appropriate preferences and the weights of each preference on each character.

Votting session▼

Analyze pedestrians' walking preference▼

Weight each walkability element▼

Key takeaways:

01

02

‍Most Walkers care about 4 kinds of walkability elements
Comfortable, convenient, enjoyable, and efficient are the four elements that all kinds of walkers most care about.

Walkers' walking preferences are different
Different types of walkers have different weights for each element.

02 Generative Research —

How might we collect and apply data to facilitate walkers' walking experience?

In this process, we looked through the data processing procedures of walkability map to ensure we can get and process street data in the real world.

Opportunities from walkability maps and navigation maps▼

In general, the procedures followed five fixed processes:

#1.Obtain and filter road network data and build street models; #2.Take a feature point every 50m on the street and calculate the latitude and longitude coordinates of each feature point on GIS; #3.By writing a Python program to call the Baidu Map panoramic static image service interface to obtain street view images, Baidu Map POI and other data; #4.Give weight to get total score; #5.Data visualization.
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We found three important problems in the third, fourth, and fifth steps by analyzing these processes. The three problems caused that they couldn't give needed information to every walker, and walkers couldn't fully understand the information in maps.

Data processing and visualizing procedures▼

Key takeaways:

01

02

03

We need to simplify the walkability data
Considering too much data in one map, but not all the data is necessary for every walker.
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We need to weigh walkability data based on each walkers' preference
Using a single group of weights to calculate the total score, but different walkers have different preferences.

We need to combine walkability data and navigation maps
Compared to navigation maps, data visualization maps can't be used in walkers' daily life.

03 Ideate —

Streetgo: Walkers can select a character to get personalized walkability information and a suitable navigation route.

Functionality 01

Functionality 02

Functionality 03

Setting a character

Walking information

Customized walking routes

Setting a character, getting default elements' weights, and users can modify the character and weights later.

Help users to know walking information and interesting points according to their characters.

Give users customized routes based on their characters, and users can change or modify their routes.

Setting a character

Setting a character, getting default elements' weights, and users can modify the character and weights on their needs.

Customized walking information

Help users to know walking information and interesting points according to their characters.

Customized walking routes

Give users customized routes based on their characters, and users can change or modify their routes.

03 Ideate —

Concept Ideation

Guided by our three main features, we generated 30 ideas in the ideation stage. :Setting a character, Customized walking information and Customized walking routes.
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03 Ideate —

How to visualize data for walkers?

In designing an experience to view this data, we developed two narrations and tested them on ten walkers. Our purposes were to respond to the walkers' confusing data visualization form that we found before. But the two narrations had some differences. We asked for feedback on readability and clarity – short or long? metaphor or realistic? 3D or 2D?
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Key takeaways:

01

02

03

Reduce the introduction part
Although the relationships between the characters and the elements are important, the introduction part can't be too long.

Detail the walker character
Walkers aren't always classified into characters preciously; they need to modify their preferences.
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Explain how does each element influence the overall walkability
The interpretation of the relationship between each element and the walkability score is necessary.

04 Prototying and testing —

Low-Fi Prototypes

One of the biggest challenges of this project is to induct walkers to choose their characters and visualize the walkability information and the navigation routes based on their characters to them. To overcome this barrier, we try to conduct as much user testing as possible with multiple walkers and iterate based on their feedback.

Character and walkability environment▼

Character's preference and personalized navigation▼

04 Prototying and testing —

Help walkers get the walkability information they need

Streetgo established walking roles for users to choose from. Users can set their characters first, and the application can understand user preferences and provide customized walking experience maps and customized routes. In addition, users can adjust their preferences according to their needs at any time, and the map information will be adjusted accordingly. In order to enable users to understand the information they need, we developed a series of testings:

Adjust character's preferences▼

The 3D street model helps walkers quickly obtain the meaning of the ralated walkability information.

Show points of interest according to walk characters.

Information hierarchy: the structure of the overall score and subscores▼

Customize the route according to the role▼

Color matching and removal of redundant information▼

05 Next steps —

Combine StreetGo with city's transport system

To find more possibilities to solve the walking problem, we tried to explore more after StreetGo. As a product designed to enhance the walking experience, StreetGo can be combined with the city's transport system, such as bus stops, bike stop sites, and sidewalks. No matter the purpose and preference of walkers, they can know the walkability information around them and find a suitable route between their position and destination.

07 Reflection —

Project Takeaways

The consideration of the algorithms can influence the design directions.

The "walkability map" algorithm allowed me to understand how to collect and process walkability data. Learning algorithms allowed me to optimize it from a designer's perspective to suit the user's needs.

Bringing users the products they need in the right place.

For pedestrians, what they need most is a route that suits their preferences, but not everyone plans routes in advance. Considering more ways to present the walkability map, such as cell phones and bus stop boards, can bring the design products to more users.