From Data to Decisions: How Crime Analytics Informs Community Wellbeing

The work of community safety doesn’t always begin with a policy memo or a meeting – for Patrick Roncal, it often begins with a map.

“In community safety work, seeing where something happens can matter just as much as knowing how often,” says Patrick, who is a Policy and Data Lead with the City of Toronto, working on the SafeTO initiative within a social data team focused on community safety, self-safety, and wellbeing.

His role bridges the gap between technical data analysis and real-world decision-making, helping City partners understand where to focus their efforts. Patrick, who is an alumnus of the Certificate in Crime Analytics at The Chang School, describes the certificate as “incredibly practical”, closely mirroring the work he now does at SafeTO.

Using spatial analysis and data visualization to inform real-world decisions is one of the main skillsets alumni like Patrick have taken away from the certificate program. It’s designed to help analysts move beyond raw data and into policy- and community-focused decision making.

“The certificate didn’t introduce abstract theory,” he says. “It mirrored the realities of my work, where data is imperfect, context matters, and insights must be communicated clearly to people responsible for acting on them.”

Mapping is one of the key ways data supports community-based decision making in Patrick’s role.

Mapping as a Starting Point for Understanding Risk

One of the most common requests Patrick’s team receives is to map and visualize data. Plotting incidents across Toronto’s 158 neighbourhoods helps surface recurring geographic patterns and emerging hotspots that aren’t immediately visible in raw tables or reports.

These visualizations become a starting point for discussion with program teams and decision-makers – not conclusions in themselves. Drawing on the training he gained through the Crime Analytics certificate, Patrick uses maps as decision-support tools, guiding where attention and deeper analysis should be focused.

This way of thinking is built into the Crime Analytics certificate, says Ian Williams, Academic Coordinator for the certificate.

“We spend a lot of time with spatial analysis tools like GIS, dashboards, and infographics,” he says. “The emphasis is always on translating that analysis into something usable, like maps, visuals, and written interpretations, that help decision-makers understand what they’re seeing.”

Once it’s determined where deeper analysis is needed, Patrick and his team can combine this data with socioeconomic and other data sets.

Layering Datasets to Understand Community Context

Patrick routinely works with multiple data sources at once, including Toronto Police Service open data, as well as census data and employment statistics from Statistics Canada. By layering these datasets spatially, SafeTO teams can explore how safety trends intersect with broader social and economic conditions. This approach supports a more holistic understanding of community wellbeing.

Applying a multidisciplinary approach, integrating criminology, public policy, and quantitative analysis, is something Patrick credits the certificate with helping him hone in on.

“The program helped bring together concepts I had encountered in previous roles, giving me a cohesive framework for interpreting complex community data,” says Patrick.

Ian says the certificate not only teaches analytical skills, as mentioned above, but also how to turn that analysis into meaningful decision support.

“Learners understand how to bring together data from different sources – crime data alongside broader community and social data – and use spatial analysis and visualization to understand what’s happening in context, not in isolation,” he says.

However, because of the sensitive nature of socioeconomic data, it’s important to make sure that it’s reported ethically and free from bias.

Grounding Data in Context and Ethics

Patrick emphasizes that data must always be interpreted alongside frontline and community context. Issues such as underreporting, bias, or targeted campaigns can distort how patterns appear on a map if they’re not properly understood.

“It’s easy to make a map and jump to conclusions,” says Patrick. “The real work is understanding the context – underreporting, bias, what’s happening on the ground – and that only comes from talking to people.”

From an academic perspective, Ian says this critical lens is built into the program from the outset.

This emphasis on ethics and critical judgment prepares graduates like Patrick to use data responsibly – especially in community safety contexts where decisions have real human consequences.

A concrete example of community-based decision-making is Patrick and his team’s work on the Toronto Community Crisis Service (TCCS) pilot. His team analyzes incident data (or more technically-speaking, calls for service data) by subway station using frequency counts and time-based analysis to understand where and when crisis responses are occurring. These insights help City planners adjust coverage, timing, and resource planning as the pilot evolves.

Once data is grounded in context and community insight, the next step is understanding how patterns shift over time and what those changes mean for action.

Pattern Detection, Time-Based Analysis, and Adaptive Planning

Patrick regularly monitors month-over-month and year-over-year changes in safety-related data to understand how conditions evolve over time. This longitudinal view helps City partners assess whether interventions are working, where risks may be increasing, and when plans need to be adjusted.

Ian says that this kind of analytical thinking is central to the certificate.

“Once students build a repeatable analytical product – like a map or dashboard – they spend more of their time asking: Why is this pattern happening? What’s the context? What do we do about it? That’s where the real learning happens,” he explains.

This mirrors Patrick’s experience at SafeTO, where data is continuously reviewed and refined to support adaptive, responsive decision-making. Data is used to inform resource planning and program adjustments, with it feeding directly into discussions about resource deployment. By highlighting where patterns are changing, Patrick and his team’s analysis helps City partners decide where to focus their attention next and how to refine programming.

That ability to move from identifying patterns to making informed adjustments is exactly what Patrick honed through the Crime Analytics certificate – and what he now applies daily in his work at SafeTO.

“The certificate felt less like an academic exercise and more like real life – working with imperfect data, pivoting when things don’t work, and presenting analysis to decision-makers.”

Explore how the Crime Analytics certificate can help you build the analytical and spatial skills to turn data into informed, community-centred decisions.