User Research - Safety on University Avenue

University Avenue (the Ave) in University District is one of most traveled areas by students near the University of Washington, yet traveling the Ave is often regarded as dangerous by most pedestrians. With reports backing these claims from the FBI crime report that had the overall district at a crime rate of 151% higher than that of the national average. However it’s not just crime that creates this sense of uneasiness amongst pedestrians.

With a team we had to find these reasons for uneasiness and create potential solutions from the research we would complete. The research completed would be not just to address students, but all pedestrians who used University Avenue.

My Roles

  • UX Researcher

  • Interviewer

  • Surveyor

  • Data Analyst/Visualizer

  • Course Project

Research Process

Tools

  • Otter AI

  • Figma

Initial Plan

The research done was split up into three separate forms. The first being deep-hanging out a form of observations. This would be followed by 1-on-1 interviews and surveys to complete the research. Being in a team of three I was tasked with completing my portion of the research individually and meeting up with the other members several times a week to combine and analyze our data.

Observations

Deep-hanging out was the form of observation I applied for the first portion of research. I did three separate thirty-minute observations with each instance being farther north, beginning at 42nd St followed by 45th St and 50th St. Each was chosen because they were major intersections along University Avenue. Time of day was also a factor considered so each member was given a time of either 2:30 PM, 7:00 PM, and 12:00 AM. I volunteered to take the 12:00 AM slot as most data would be collected during that time and personal preference to help my teammates out from being out late on the Ave. The data collected from the observations were a starting point for the next steps of research.

Interviews

There was three 1-on-1 interviews to be done with an aim for them to be thirty-minutes each. Searching for a range of participants who used the Ave regularly and recently was the goal. Once I had found my participants I set up a time and location for our interview where I would then ask for their permission to record. I took notes during the interviews but also was able to use Otter AI to transcribe. However I had to revise some parts of the transcription as they were either incomplete or incoherent. When all three were complete I coded the transcriptions and then met with my team to also code theirs as well. We worked as a team to read our coded transcripts and put them together in a Figma diagram, where we then performed thematic analysis.

Surveys

From the information learned through the interviews and observations we began brainstorming questions for a survey. We wanted to learn more about people’s perceptions of the Ave, areas they avoid, and what constitutes the most risk to them. I was tasked with surveying fifteen people with as little bias as possible, the only condition was they traveled on the Ave prior to the survey. Once each member had their surveys complete we compiled the data into Excel to analyze.

Duration

March 2023 - June 2023

 

Conclusion

At each stage of our research we were tasked with coming up with potential solutions from the information we gained. We used the information we gained into the next steps of our research as starting points that we would build upon. From the initial observations we understood that people when traveling are often distracted, the lighting caused different interactions, and conditions were dependent on location. With these results we tried to focus our interviews more on the participants experiences on the Ave due to distractions, lighting, and location.

From our interview we gained new insights on what we had learned prior from the observations. From observations we initially believed time of day was a major part of lighting, but from our participants it was soon revealed they experienced problems throughout the day. Our participants also admitted to being distracted on the Ave but their attention to the unhoused population was often high as most of their negative experiences occurred from interactions with them. Also brought up often was congestion on the Ave, something we had not thought about as much. This was a new insight from our participants that we set to explore greater in our surveys.

Surveys was our final form of research as we were now tasked with gathering qualitative data to confirm our solutions and present them. With our survey we wanted to dig deeper to understand what people thought of the lighting on the Ave, the current unhoused situation, areas on the Ave, and space usage. From the data we concluded with our final three solutions. The first was to address the distressed unhoused population by creating more affordable housing, invest more in well-run shelters, and targeting structural issues and not policies. The second was to address people’s fear of traveling at night. To solve this we recommended more lighting on darker streets specifically farther north on the Ave and more incentives for people to be out later. Our third and final recommendation was to make the Ave more pedestrian friendly by limiting cars on the Ave. This could be done with better parking lots and systems as well as making sections of the Ave pedestrian only.