Overview
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Headline indicators
32,000 546 11,421Row
Trend of crashes by severity
This month-by-month view shows how crash counts ebb and flow across the year and how the mix of severity changes with them. Stacked areas make it easy to see whether serious and fatal incidents rise and fall with the total, or move differently. Keep in mind: the most recent months can read low because of reporting delays, so short-term dips should be treated cautiously. Use this trend to anchor the story: when incidents are most active and whether the severity mix is stable over time.
Weekday vs weekend severity share
This 100% column chart compares the severity mix on weekdays versus weekends. A larger share of severe outcomes on weekends often reflects who is driving and when — night-time leisure and regional trips — rather than a simple weekday/weekend risk switch. Read this as context for targeted messaging and enforcement windows, not as a standalone causal claim.
Where
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Heatmap of crash concentration
The heatmap spotlights where crash reports cluster across Victoria. Darker tiles point to higher concentration, typically following major corridors and busy intersections. This is a picture of where activity happens, not a rate of risk — places with more traffic will naturally show more incidents. Use it to flag hotspots worth a closer engineering or enforcement look.
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Top LGAs by reported crashes
This ranking lists Local Government Areas with the most recorded crashes. It is useful for prioritising effort, resourcing and engagement. Remember that raw counts reflect workload more than risk: population, traffic volume and network complexity vary by LGA, so totals are not directly comparable without exposure data.
Why/Context
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Light conditions and severity
Daylight records the most crashes simply because that is when most trips occur. The colour bands reveal how outcomes differ across lighting conditions — daylight, dusk and night. Treat these as descriptive patterns rather than proof that light alone drives severity. The takeaway for policy is to pair lighting with context (traffic volume, speed environment, roadside hazards) when judging risk.
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Severity share across speed zones
Within each posted speed band, the dots mark the proportion of outcomes by severity. A higher severe share in a band does not automatically mean that the speed limit itself is the driver — road type, intersection density and traffic mix change across bands. Use this chart to identify candidates for deeper rate-based analysis (e.g., per vehicle‑kilometre), provided exposure data can be sourced.
References (no divider)
Sources & notes (plain language).
We use the
Victorian Government’s open crash dataset from the Department of
Transport & Planning (public CKAN API). It covers police‑reported
road crashes with injury and fatal outcomes. Counts are descriptive, not
exposure‑adjusted — for true risk you would divide by traffic volume or
kilometres travelled, which aren’t included in this release. R and the
tidyverse were used for analysis and plotting. If you reuse this work,
please cite the data portal and acknowledge the open licence.
• Victorian Government — Department of Transport & Planning (2025),
Road crash statistics (open data portal).
• R Core Team
(2025), R: A Language and Environment for Statistical
Computing.
• Wickham et al. (2019), The Tidyverse, JOSS
4(43), 1686.