Police Force case studies using SenseMaker®

Documented / Reported Uses

• Northern Ireland Policing & Community Relations

After the Good Friday Agreement, SenseMaker® was used in community–police engagement projects to capture citizens’ micro-narratives about safety, trust, and legitimacy. The goal was to help the Police Service of Northern Ireland (PSNI) understand shifting perceptions in a post-conflict environment and adapt policing strategies accordingly.

→ This is one of the most cited law enforcement–related SenseMaker® applications.

• Counterterrorism & Radicalization Monitoring (UK & Europe)

Police and security services have used SenseMaker® to gather community stories around extremism, radicalization, and social cohesion. Instead of relying solely on surveys, they collected short narratives from citizens and had respondents self-signify their meaning. This allowed police to detect weak signals of community tension before they escalated.

• Domestic Violence & Vulnerable Populations (Canada & Australia)

While not always branded as “police projects,” SenseMaker® has been deployed in collaborations between police, social services, and NGOs to capture frontline stories of domestic abuse and vulnerable populations. Police forces involved used the insights to adjust victim support protocols and inter-agency coordination.

Adjacent Public Safety / Security Uses

• Military & Peacekeeping (Afghanistan, Africa, Balkans)

NATO and allied forces used SenseMaker® to understand local population sentiment, which often overlapped with policing functions in stabilization missions. These insights informed both military and police-style community engagement.

• Urban Safety & Policing Partnerships

Municipal governments in Europe have piloted SenseMaker® with local police to understand citizen experiences of crime, disorder, and trust in authorities. The emphasis was on patterns of perception rather than incident counts.

Why Police Forces Use It

• Traditional surveys give averages; SenseMaker® reveals patterns and outliers.
• It helps detect weak signals (e.g., early signs of gang recruitment, community mistrust).
• It supports safe-to-fail experiments in policing strategy, rather than one-size-fits-all reforms.
• It bridges community narratives with operational decision-making.