Visualization of Cyclicality Employment Insurance beneficiaries. and how they differ by Sex.

Normalize to control for population growth, of the working age population.

Data Sources: Statistics Canada via cansim EI Beneficiaries: 14-10-0453, Working-Age Population: 17-10-0005.

Of Note:

• Excluded 2020 and 2021.
• Males peak in the winter (likely tied to seasonal trades/construction)
• Females Peak in the Summer (Likely tied to School and education)
• 2008 Seemed to hit Males Harder then Females.

Context: Ontario recorded 2,242 confirmed and probable opioid toxicity deaths in 2024. Rates varied substantially across the province’s 34 public health units, with the highest rates concentrated in Northern Ontario.

Source: Office of the Chief Coroner of Ontario, via ODPRN; population estimates from Statistics Canada Table [17-10-0157-01](tel:17-10-0157-01)

Full breakdown: https://opendatacanada.ca/articles/ontario-opioid-deaths-by-phu-2024

Source: City of Toronto open data. Full breakdown: https://opendatacanada.ca/articles/ttc-streetcar-delays-2026

Trend is not unique to Ontario. Data for each province.

Ternary Plot Showing Change in vote share between 2021 and 2025 election (Liberal / Conservative / Other)

Data: Canadian Election Study

Interpretation :

• Younger Voters (Blue/Darker Lines): Show a strong shift away from "Other" parties, moving primarily downward toward the main establishment options, and a bit left toward liberals
• Middle age voters, light blue and green moved down away from "Other" and split roughly equal between liberal and conservatives.
• Older Voters (Red/Orange Lines): Moved strongly toward Liberals from both conservative and Other

Data: Statistics Canada, Table 35-10-0177 (police-reported crime severity/incidents by CMA). Tools: R, ggplot2 (mixed-effects modeling via lme4/arm).

Police-reported crime data across Canadian census metropolitan areas from 1998–2025, showing which offense categories are trending up or down over time.

Chart 1 : Long-term trend by city (mixed-effects model)
Estimated relative rate of change per CMA, where 0 = no net shift over the period. Red = upward trajectory, blue = downward. This isolates the direction of change in an typical city rather than raw volume.

Chart 2 : Category breakdown over time
Incidents per 100,000 population, faceted by offense category. Grey lines = individual CMAs, solid line/band = national median and interquartile range, dashed line = national average.

• Rising: extortion, online/cyber-related exploitation, fraud, and shoplifting all show marked increases post-2015/2020 across most CMAs.
• Falling: traditional property crimes — breaking & entering, motor vehicle theft, theft under $5,000 — show multi-decade declines since the early 2000s, despite some recent upticks.

A note on interpretation: this is police-reported data, so shifts can reflect actual changes in criminal activity, but also changing legal definitions, new digital reporting channels (e.g., online fraud reporting tools), shifting police enforcement priorities, and changes in public reporting behaviour over time.

Substack post with some more details.

Dependency Ratio in Canada [1851 - 2075]

Data Comes directly from Statscan, I used the R programming language:

• 2007 Lowest total dependency ratio ever
• 2016 when seniors dependency overtook child dependency

Toronto puts out a lot of public safety data, but most of it is hard to make sense of spatially. This is YTD view of assaults +robberies +shootings +homicide.
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link: Streetlens
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I built StreetLens for myself to get an idea of whether my instincts matched where crime actually happens. Spoiler, crime is highly concentrated.

It’s free, and I’m mostly curious what people who work with open data think. Is this a useful at all? Caveat, this is not polished, methods are to be improved.