Belleville Police prioritizes algorithmic patrols over mental health programs despite active State of Emergency

Belleville declared a State of Emergency in February 2024 for the growing addiction, mental health and homelessness crisis.

On October 17, 2025, the Ford government announced that they had approved Belleville Police Services Board’s grant application to the Community Safety and Policing Grant Program‘s Local Priorities Funding Stream in the amount of $235,475 for the following:

Belleville Police Service will launch a new data-informed team policing patrol strategy to improve how officers respond to community needs, in alignment with the local community safety and well-being plan and the service’s strategic plan. The project includes officer training, upgraded mapping technology and data tools to guide patrols based on real-time information. This approach will help officers respond more effectively, build stronger community connections and support long-term safety planning.

The grant program gives Police Service Boards flexibility, allowing them to them decide which risks are most pressing (eg. youth engagement, crime prevention, or school safety programs) when applying for funding for local initiatives.

Police leadership in Belleville supported predictive policing

Strong communities are built on strong partnerships. Effective policing depends on collaboration to ensure our officers have the tools, resources, and support they need to serve our residents. This funding reflects a share commitment to innovation, community safety, and the well-being of everyone in Belleville. By investing in both technology and people, we’re helping to build a policing model that’s proactive and prepared for the future.

Belleville Police Services Board Chair Heather Smith

Our community is growing, and with that growth comes new challenges. Rising demands related to property crime and social disorder require a modern approach that combines supportive, data-driven technology with community policing principles. This funding from the Ontario government allows us to enhance front-line support through a data-informed Team Policing Patrol Strategy, implement predictive analytics and advanced mapping tools, anticipate emerging challenges, and allocate resources where they are needed most to keep Belleville safe.

Deputy Chief Sheri Meeks

Note: Police Service Boards in Ontario are responsible for providing “adequate and effective” policing.

Other municipal police services applied and received funding for mental health and crisis response and crime prevention outreach initiatives

Many police service boards applied for and received funding to expand and/or enhance their mental health and addictions programs:

• Mobile Crisis Response Team (MCRT)
• Crisis Outreach and Support Team (COAST)
• Community Response Unit (CRU)
• Integrated Mobile Police and Crisis Team (IMPACT)

• Guelph received $861,728 to enhance their Integrated Mobile Police And Crisis Team (IMPACT), which pairs officers with mental health personnel to provide a more effective response to mental health calls for service.
• Brantford received $529,701 to enhance its Mobile Crisis Response Team (MCRT) partnerships with mental health professionals and integrating registered practical nurses and $191,568 for its Crisis Outreach and Support Team (COAST) program, a proactive mental health and addictions outreach initiative.
• South Simcoe received $299,781 to strengthen its mental health response through Project COMPASS (Community-Oriented Mental Health Partnership and Support Strategy), supporting co-response teams and community outreach. The initiative promotes trauma-informed care, reduces repeat crises and enhances data-driven service delivery.
• Brockville received $56,209 to enhance its community outreach program and youth-focused initiatives to address rising mental health and addiction challenges. The project supports collaborative crisis response, diversion programming and proactive engagement with at-risk youth.
• Cobourg was granted $149,000 to establish a community safety hub to coordinate multi-agency responses to complex safety issues like mental health crisis, substance use and unsafe housing. The initiative promotes prevention, early intervention and data-driven collaboration to reduce risk and improve outcomes.
• Kingston received $890,520 in partnership with Addictions and Mental Health Services Kingston, Frontenac, Lennox and Addington to maintain and build on the positive outcomes of the Mobile Crisis Rapid Response Team and the COAST.
• Hamilton was granted $2.26M to support the Crisis Response Branch, a collaborative model for crisis response for vulnerable populations. It will support more efficient hospital transfers to increase crisis response availability and officer mental health education and training. It will also support patrol on complex issues through training and on-site collaboration, divert non-urgent calls to reduce uniform patrol burden and utilize data for proactive intervention.

Belleville enlists Norigen Consulting to support data-driven decision-making

Toronto-based Norigen Consulting, who “specialize in integrating analytics with operations” and whose work involves “agreements on the sharing of information and providing data sharing models” and “played a pivotal role in modernizing the issuance, management and delivery of provincial offence tickets”.

Onsite Niche Data Integration:
Belleville has led an OPTIC-wide initiative to bring near real-time Niche data on-premises to power the Norigen Business Analytics tools.

2024 Annual Report – IT Unit

Business Analytics (Norigen Analytics)

Ongoing enhancements to our Business Analytics platform included the integration of vehicle tracking and key sign-out data, along with
expanded reporting and analytical capabilities. These improvements support data-driven decision-making across the Service.

2024 Annual Report

What is predictive policing?

Predictive analytics refers to algorithmic systems that analyze large datasets, including historical crime data, to try to predict or ‘forecast’ future crime.

In their 2020 report To Surveil and Protect, The Citizen Lab at the University of Toronto clarifies that there are two main categories of algorithmic predictive policing:

• Location-based algorithmic policing systems “purport to identify where and when potential criminal activity might occur [by using] algorithms that drive these systems [to] examine patterns and correlations in historical police data to attempt to make predictions about the future.”
• Person-based algorithmic policing systems are “designed to identify individuals who are likely to be involved in future criminal activity [… or]
  to assess what level of risk a particular individual has for either engaging in or being the victim of future criminal activity [… by processing] personal details, such as information about family, friends, or associates; their social media activity; criminal records; or appearance in other police databases…”

Law Commission of Ontario

Major US cities have redesigned their police patrols based on predictive policing models through which “officers are provided daily computer-generated maps of areas to patrol, and… patrol car mobile devices provide almost real-time updates of crime patterns as they patrol.”

Law Commission of Ontario

The use of predictive policing technology is typically characterized as a “technological promise” aimed at “revolutionizing law enforcement.” Professor Andrew Ferguson, a leading US scholar on predictive policing, writes of the promise where “data-driven insights [are] operationalized into concrete decisions about police priorities and resource allocation… offering police administrators the ability to identify higher crime locations, to restructure patrol routes, and to develop crime suppression strategies based on the new data.

Law Commission of Ontario

Predictive policing risks intensifying existing over-policing in minority/low-income areas by amplifying historical biases in data

Predictions about people and places based on historical data and patterns of policing raise several significant concerns and question the efficacy and reliability of the technology as an investigative tool.

Reallocating police to forecasted crime “hotspots” risks creating a feedback loop (self-fulfilling prophecy) of surveillance and arrests, thereby undermining fairness and civil liberties under the guise of “unbiased” tech. While proponents tout efficiency, critics argue it perpetuates old biases, masks systemic issues, and demands community engagement for responsible use.

At its worst, [predictive policing] can create a proxy for racially biased police presence in already over-policed neighborhoods and generate increased police-citizen tension.

Professor Andrew Ferguson, a leading US scholar on predictive policing

A 2020 report by the Citizen Lab at the Munk School of Global Affairs & Public Policy found that:

The Canadian legal system currently lacks sufficiently clear and robust safeguards to ensure that use of algorithmic surveillance methods—if any—occurs within constitutional boundaries and is subject to necessary regulatory, judicial, and legislative oversight mechanisms. Given the potential damage that the unrestricted use of algorithmic surveillance by law enforcement may cause to fundamental freedoms and a free society, the use of such technology in the absence of oversight and compliance with limits defined by necessity and proportionality is unjustified.

To Surveil and Predict (2020) – Citizen Lab at the Munk School of Global Affairs & Public Policy

Systemic racism in the Canadian criminal justice system must inform any analysis of algorithmic policing, particularly its impacts on marginalized communities. The seemingly ‘neutral’ application of algorithmic policing tools masks the reality that they can disproportionately impact marginalized communities in a protected category under equality law (i.e., communities based on characteristics such as race, ethnicity, sexual orientation, or disability). The social and historical context of systemic discrimination influences the reliability of data sets that are already held by law enforcement authorities (such as data about arrests and criminal records). Numerous inaccuracies, biases, and other sources of unreliability are present in most of the common sources of police data in Canada. As a result, drawing unbiased and reliable inferences based on historic police data is, in all likelihood, impossible.

To Surveil and Predict (2020) – Citizen Lab at the Munk School of Global Affairs & Public Policy

And included the following recommendations:

Governments must place moratoriums on law enforcement agencies’ use of technology that relies on algorithmic processing of historic mass police data sets, pending completion of a comprehensive review through a judicial inquiry, and on use of algorithmic policing technology that does not meet prerequisite conditions of reliability, necessity, and proportionality.

Law enforcement authorities must not have unchecked use of algorithmic policing technologies in public spaces: police services should prohibit reliance on algorithmic predictions to justify interference with individual liberty, and must obtain prior judicial authorization before deploying algorithmic surveillance tools at public gatherings and in online environments.

To Surveil and Predict (2020) – Citizen Lab at the Munk School of Global Affairs & Public Policy

Several independent investigations in the press have found that:

popular “predictive” policing tools trained on historical crime data often replicate long-held biases, offering law enforcement, at best, a veneer of scientific legitimacy while perpetuating the over-policing of predominantly Black and Latino neighborhoods. An October headline from
The Markup states bluntly: “Predictive Policing Software Terrible At Predicting Crimes.” The story recounts how researchers at the publication recently examined 23,631 police crime predictions—and found them accurate roughly 1 percent of the time.

US Lawmakers Tell DOJ to Quite Blindly Funding ‘Predictive’ Police Tools – Wired

Additionally, Ferguson outlines several other concerns that predictive policing systems:

• Are all relatively new with most continuing to evolve and change
• Are all relatively untested with only a handful of studies or empirical validation studies
• May be based on proprietary technology owned by private entities
• May be procured by law enforcement with little public oversight or input, and based on local rules.