Different communities across Toronto have various needs that need to be addressed in emergency and non-emergency situations. Though Toronto Police Service (TPS) can offer immediate assistance in emergency situations, they are ill equipped to provide resources and guidance that can help citizens receive ongoing support, which could help reduce the strain on TPS resources. Currently officers do not have a consistent format to deliver community referrals to citizens which impedes TPS from understanding the success of these referrals, understanding the best places to refer people to depending on their needs, and knowing what services are available to citizens in each neighbourhood.
The idea for a system to track, disseminate, and rate referrals was considered and brought to my team to research to understand the validity and possibilities of such a system.
I co-led the UX team for all user research and documentation. We spoke to community agencies (settlement agencies, city encampment teams, shelters, 311, Victim Services, Medic Alert, and more), officers (primary response officers, neighbourhood community officers, commanding officers, etc.), and various TPS units (Guns and Gangs, Youth Diversions, Equity, Inclusion & Human Rights,, Communications Services, etc.).
For this project I reported directly to the Chief Information Officer and worked in tandem with him to ensure that whatever results the research uncovered could be adhered to by the Toronto Police Service and would be a benefit to officers and citizens alike.
- Azure Dev Ops
User Research Approaches
User research and design steps used for this project:
- Detailed user research plan
- Competitive scans of referral type systems
- Deep research into community agencies and supports for citizens in the Greater Toronto Area
- Multiple rounds of user research sessions spanning community agencies, support services, officers of all ranks, TPS units and specialty services, and City of Toronto services
- Affinity Mapping with user research results
- Features documentation and discussion with developers
- Journey mapping
- Possible system flows
- Understand how officers currently find and distribute community referrals and how that process could be improved
- Understand community needs and privacy issues
- Understand agency needs and how they currently work with officers and citizens and how that process could be improved
- Understand citizen needs, confidentiality concerns, their view of officers and how that could influence or impact a referral
- Understand accessibility issues and how to meet those needs through tech
- Find opportunities to help officers find and distribute referrals in an easy and intuitive way
Scanned existing systems that offer referrals to understand:
- Layouts and features
- Filters and search
- Accessibility concerns
- Necessary user information
- User flow
- Use of stylistic elements (colour, text, buttons, etc.)
- Navigation and layout
- Permission sets and statuses
- Mapping functionality
Officers, citizens, and community agencies were interviewed to understand how they felt about a possible referral system, what features it would need to make it useful, privacy concerns, and how it might impact their day-to-day life/work.
Discovery Artifacts and Findings
After rounds of user research and discovery we found that a referrals application is needed and wanted by all parties but that citizens were wary of a police agency owning referral data. Because of this, multiple outside agencies have decided to take on the building of this application to relieve pain points around who owned the data and what it would be used for. We are currently working with the agencies to pass on all research and documentation and to consult with them on the design and build of the application.
Because of the vast amount of research and data synthesis that went into this project, we were able to build trust within the community by listening to them and passing the building and ownership to a trusted medical firm for data ownership.