My team was given an ambiguous problem space: we were asked to explore the role of data in public safety. We chose to tackle community-police relations, motivated by the passion we felt to understand and, if possible, make meaningful change to policing in America.
We designed a web app that builds empathy and recognition between citizens and officers by connecting citizens with officers on their street and connecting officers with the citizens in their beat.
I interviewed more than 10 people, from former police detectives to police reform activists to parents involved in community organizing. We also conducted a heuristic evaluation of current solutions in this space. Our goal was to understand the barriers and challenges for communication between police and communities today.
Heuristic evaluation showed that current community-facing public safety solutions did not empower users to take action or empathize with each other.
We synthesized our findings into two key insights which would inform the design of a tool that builds empathy and promotes communication between police agencies and civilians.
Our project was divided into a trio of three-week sprints, each building upon the learnings of the previous. We moved from low-fidelity prototypes of our solution to a coded proof-of-concept, iterating with each sprint.
We brainstormed low-fidelity prototypes to understand the types of interactions and impact users cared about. We prioritized three key features to develop for our minimum viable prototype.
Our user research led to the three key features of our final solution, shown below.
We learned that users want data about public safety to feel relevant and actionable. Using My Beat, My Street, citizens can identify officers who are relevant to them and their area, building recognition and empathy.
We designed the officer profile and community reviews to build transparency and trust. Citizens can understand an officer’s qualifications and see their performance ranked by other community members. Officers can provide information about their human side and skills.
We designed an intuitive oboarding process that allows citizens to help police serve them by providing information about their specific needs. Officers can use this data to learn about the citizens in the community they serve, recognize them as humans, and better meet their needs.
Evaluate concepts with citizens & public safety stakeholders. Test the usability of features and information architecture with public safety and community users.
Return to features left on the cutting room floor, such as messaging with officers and a community concerns board.
Our work and research findings will contribute to Motorola Solutions' ongoing expansion of citizen-facing features and platforms, such as CommandCentral Community.
Moving from an open-ended problem statement about data and public safety to a coded proof-of-concept at times felt like an insurmountable task. Breaking the job into sprints kept us on track and allowed us to adapt to new findings and constraints.
As the research lead, I recruited people with diverse backgrounds and opinions on public safety in communities in order to understand the barriers our solution needed to address. When designing, the team built ethics checkpoints and workshops into our process to be sure we held true to our users' needs and understood the risks.
The intern team was a group of three with diverse backgrounds and no prior expertise in the world of public safety - and to complicate matters, our internship was fully remote due to COVID-19. We made sure to include each other in each step of the process, from research to development, by communicating frequently and supportively.
