MapinHood

Harnessing the power of AI to optimize pedestrian-oriented travel. MapinHood is a Navigation app that prioritizes and personalizes routing for people.

NAVIGATION FOR PEOPLE, NOT CARS

A data gap exists for street-level features on most navigation systems. With the help of machine learning, crowdsourced information, and local open-source data, MapinHood is bringing pedestrian mobility and accessibility down to the human scale.

Most existing navigation apps are designed to serve vehicular transportation, which often overlooks street level features such as pathways, street evenness, obstacles, or amenities that could be useful for pedestrian navigation. MapinHood gives users full control of their routing options, allowing them to interact with their environment and move around on their own terms.

ROLE

UI & UX Design

Interaction Design

TIMELINE

Jun 2018 – Dec 2018

TOOLS

Sketch

Figma

Illustrator

The tech company piloting Mapinhood using Toronto as a testing ground for big cities

LEVERAGING LOCALS TO CREATE NETWORK EFFECTS

Who knows more about their community than locals?

Users play a key role in identifying and geotagging street-level features essential to mapping out the pedestrian landscape. One of the most important goals of this project was to incentivize users to share useful, accurate, and high quality contributions to fill in local data gaps. In order to do so, we had to make the process as fun and smooth as possible to keep users engaged and motivated.

PROJECT SCOPE

I had 8 months to design the mobile app interface and user interactions before the official launch. During this time, my team and I worked closely with locals and visually impaired communities to pilot test the app.

ROLE & TEAM

I was involved in the early design and development stages as a UI/UX Designer, where I worked closely with the Executives, and Research & Development teams.

USERS & AUDIENCE

Our target users included pedestrians from all walks of life: locals, tourists, commuters, hikers, runners, dog owners, walkers, moms with strollers, explorers, blind or sight impaired individuals, individuals in wheelchairs and more.

PROJECT GOALS

To build the navigation system’s routing engine, we had to think about the role of users in filling local data gaps and generating a positive network effect. 

These considerations prompted us to think of ways to obtain and promote user-generated content. The following points are key areas on which we chose to focus our design and development efforts:

  1. Simplify creation process: design smooth and intuitive interactions with buttons and geotagging functions (i.e. tagging and uploading data) 
     

  2. Grow creator base: increase the percentage of users who create content and encourage users to build out connections through self-expression, self-promotion, referrals, in-app engagement, and incentives
     

  3. Maintain data quality: obtain accurate, reliable, and relevant data from users through community review processes, user scores, and other metrics

“When a product’s value increases with more data, and when additional usage of that product yields data, then you have a Data Network Effect.”
– James Currier, The Network Effects Manual

FINDING MY BEARINGS

Once we identified our main challenges and goals, I started by sorting key features into high-level categories to break down into smaller processes. With findings from the research team about how people in our target audience use navigation apps, I created task flows for each of the core uses to determine key screens and points of interaction that needed to be designed prior to wireframing.

Trip Routing:

User-generated geotags:

Crowd-data validation:

DESIGN ITERATIONS

After building conceptual models for the main focus areas, I began designing wireframes for key pages with various alternatives.

Trip Routing

With the app’s unique trip customization feature, I had to think about how users prefer to personalize their routing settings. We wanted to enable users to have control over which features to approach and which to avoid while taking into consideration the sequence users customize their trip and how this influences their travel preferences.

Geotagging

My team and I went through several iterations of the geotagging function as we discovered several usability challenges regarding gestures and system feedback. Throughout this process, I worked with developers to improve tagging interactions. I measured its effectiveness by a) the speed and ease of tagging, b) allowance for error correction/ prevention, and c) adequacy of system responses to user actions.

Crowd-data validation

Designing a gamified experience that was not to be mistaken for a game was an important consideration in maintaining data quality. By introducing a relatively competitive landscape, we wanted to ensure users were not generating low-quality data for the sake of scoring more user points. To that effect, we established a rating system to assess the quality, accuracy, and reliability of contributions. 

DECISIONS ABOUT DATA

Once we had built the data collection systems, we had to determine what features were useful for geotagging

While some features immediately came to mind, I attempted to fill in the gaps by taking the role of a pedestrian. During my walk through the city, I noted the street-level features I encountered, ranging from interesting to unpleasant. After coming up with my list, I regrouped with my team to sort through the data. From there, I sorted these features into broader categories.

PROTOTYPES

After five design iterations of the app’s user interface, we were ready to start fully implementing the changes before launch. While some elements were refined, changed, and adapted throughout the development process, we landed on the following screens:

 

FINAL OUTCOMES

Following my involvement in the project, there was still plenty of work needed to build the routing engine and ensure its smooth performance. I stayed on the project as a Geographic Information System Specialist for a couple of months, working on other facets of the app, including collecting and encoding local geographic data. 

The launch was met with a lot of enthusiasm and positive feedback from users. The app was successful in gaining enough traction and became the first Canadian recipient of Microsoft’s AI for Accessibility grant program, which strives to amplify human capacity through AI-powered technology. 

While I was only part of the early development stages of the project, it was an exciting opportunity to learn about the different ways people travel and get to their destination, as well as how technology and design can blend the lines between the digital and physical world.

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