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RS21: UX DESIGN CHALLENGE

 

Spring 2018
UX Design
5 Days
Affinity Diagramming, Stakeholder Maps, Personas, User Journey Maps, Competitive Analysis, Prototyping, User Flows

 
 

 
 

Prompt

The city of Albuquerque, New Mexico has a significant air quality problem. Recognizing this, RS21 helped to deploy hundreds of sensors across the city to monitor air quality. The city is also working with RS21 to develop an app that helps people to select the best walking route to various locations around town in order to avoid bad air.

 
 

Hi-Fidelity Screens

 
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Outline

To tackle this design challenge, I divided it into several parts to make it easier to digest. First, I developed key insights through secondary research and smart assumptions. With those insights, I defined the audience and understood the user through personas and user journey maps. After looking at similarities between pain points, I came up with key features the app will have to accomplish the goal of helping people select the best walking route around town to avoid bad air. The app will be explained through low fidelity user flows. Then I dove deeper into individual screens with high fidelity mock ups that include explanations for key interactions on the screen and the reason it accomplishes the main goal.

 
 

Secondary Research & Key Insights

 
STEEP Forces (Social, Technological, Environmental, Economical, Political)

STEEP Forces (Social, Technological, Environmental, Economical, Political)

 
 
A lot of it comes from burning dirty fuels, like coal, oil and gas,” Perkins said. “We have a lot of oil and gas production here, and that’s definitely a cause of this air pollution.
— http://www.publicnewsservice.org/2017-04-13/environment/report-no-easy-breathing-in-new-mexico/a57243-1
 
 
 

After secondary research, I’ve made a couple of assumptions to understand the stakeholders more. Albuquerque is a pretty dangerous place, especially when it comes to vehicle safety. Therefore, low-income households cannot afford to lose a car, hindering them to transportation by walking, metro, and biking. This causes these households to be exposed to more bad air on a daily basis. And since industrial installations are closer to the poorer areas, these people are affected the most to the pollution. Those further away from these mills still experience bad air quality but not as severe as the poor.

 
 
 

Stakeholders

The stakeholder map lists several audiences that could possibly be affected by bad air quality in Albuquerque. It ranges from the low-income households to working middle class and politicians. Based on the secondary research, the policies for air quality control have been sub-par which leads to a lot of unrest within the city. Those who can afford to live far away from pollution heavy areas still suffer the effects of malpractice.

I’ve decided to focus on two very different audiences for personas and user journey maps to understand the different needs and pain points for these people to better design the app.

 
 
 
 

Those in the circle of the stakeholder map have the highest degree of influence in terms of interaction with poor air quality. Low income residents compose of that audience because they live near areas with high concentration of air pollutants and poor living conditions. Those near the outer areas of the circle such as the working middle class experience less exposure to pollutants and have better living conditions.

 
 
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Some takeaways I got from doing the personas and user journey maps was that both stakeholders spend a lot of time exposed to bad air while walking or doing other activities regardless of income or location. Although Maria might have it much worse due to her financial situation and locale, Mark can experience the same amount of stress to breathing throughout the day. The app design needs to tackle better routes for walking to avoid bad air and strategies to minimize the exposure of bad air while walking.

 
 

Competitive Analysis

Google Maps

Waze

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Komoot

inRoute Route Planner

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CleanSpace

Inrix

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After looking at several apps with a map and statistic feature, I've organized several features that I wanted to include in the final application: Crowdsourced info, live camera feed, danger notifications, and a pollution level meter. Utilizing these features, the final app will be both user friendly and provide information that is backed up by the opinions of the majority.

 
 

Initial Sketches

 

These were some initial concepts I had for the final app design. I incorporated a main user system centered around the navigation and the ability to choose between several good/ bad routes according to the Air Quality Index. I also had a secondary system which was the user’s menu bar. This included other components such as leaving feedback, live video, and danger points.

 
 

UX Flow

 

The main screens in the UX Flow include the home screen, rating a specific route, and the user's menu. The home screen is where all the navigation happens. User's can select a starting location and an end destination to see if the path avoids pollutants on a scale of 1-5. User's can rate these paths and watch a live feed of the location in the Path Details. This allows the user to gauge whether a path is safe or not and to contribute to the crowd sourced information about the path's air quality.

The main secondary screen is the user's menu which consists of four main tabs: Route history, Live feed, Warnings, and AQI. Both route history and warnings give the user information about each route taken and shows the potential danger to air quality in live time. The AQI section gives specific details on the quality of air that is experienced through different routes and shows data of previous and future predictions of the AQI in that area. This allows the user to fully understand the location they are walking in and gives people tools to plan their walking routes.

 
 

High Fidelity Screens

 

The higher fidelity screens bring more visual hierarchy to the mobile application, making it easier for the user to navigate through the app. With the consistent blue theme throughout the app, it gives off a "clean" tone which reinforces the idea that this is an app for air quality. The main navigational page is simple, with colors that only highlight certain functions within the app such as choosing a location and destination. The profile page is changed so that the space is fully utilized to show the user's average ratings on their paths and the number of paths they have traveled, giving the user a sense of time and history within their app. For the AQI screen, I've added more detail within that section that is accessed through horizontal swipes to see the weekly average AQI and the hot spots within the city that focus on higher and lower concentrations of poor air quality.

 
 
 

Conclusion

The final mobile app conquers the main goal: helping users select the best walking route to avoid bad air. I’ve included the crowd sourcing aspect to create a community that shares and assists each other to help everyone achieve the same task. The different features such as live feed and analytics help users understand why a route is preferred over others and gives them an opportunity to see the surrounding areas. This utilizes the placements of live cameras around the city of Albuquerque and gives more insight to users of the app and creators of the app. Over time, this app can help people navigate through the neighborhood, decreasing the chance of asthma and other air pollutant conditions. Also, this could further push this by starting conversations about certain areas and investigating in the true cause of pollution in the city of Albuquerque.

 
 
 

Moving Forward

If given more time and a larger scope for this design challenge, I would do user interviews in the city of Albuquerque to better understand the people that live there and the population that walks the most. This would allow me to create a more accurate app that will tackle the pain points these people face. Also, I would like to spend more time learning about the capabilities of incorporating live video feed, analytics, and mobile data to create a system that can utilize machine learning and help detect future areas of “bad air” so that preventative measures can be taken. Overall, I think this design challenge is a good start in solving a very complex problem.