u/Fun-Investigator7818

I am Showing the full dashboard this time after feedback on my previous post! Reposting with the actual dashboard pages after feedback from the community — thanks for pushing me to show the real data!

Reposting with the actual dashboard pages after community feedback on my first post!

Key findings from 1,000,243 real crashes (2017-2022) from NYC Open Data: Staten Island has the highest fatality rate at 0.16% despite fewest total crashes — likely due to higher highway speeds.

Belt Parkway is the most dangerous street as far as 14,649 collisions are concerned. But Fatality rate varies as some streets have low collsions but deaths are high comparatively. 60,000 pedestrians killed across all 5 boroughs over 6 years.

Midday 10am-3pm has more crashes than morning rush hour — gridlock actually protects people at low speeds. Driver inattention is #1 cause across every single borough without exception. Tools: Power BI Desktop, Power Query, DAX Data: NYC Open Data / Dataotgov (free) Happy to answer any questions about the methodology or DAX measures!

I built a 4-page NYC Collision Overview Dashboard using 1,000,243 real crash records from NYC Open Data (2017-2022).

Dashboard revealed important insights

1. Staten Island has the highest fatality rate — nearly double Manhattan's despite having the fewest crashes. Likely due to higher highway speeds.

Data further drilled down to on Street level. All this data was sourced from Datadotgov.

OC: Built this in Power BI using 1 million NYC

collision records from Datdotgov (2017–2022)

Key findings that surprised me:

- Staten Island has the HIGHEST fatality rate

at 0.49% — nearly double Manhattan's 0.33%

despite having the fewest total crashes

- Belt Parkway is the most dangerous street —

14,649 collisions and 36 deaths

- Midday 10am–3pm has MORE crashes than rush

hour — completely counterintuitive

- Backhoe loaders and tow trucks: 100% fatality

rate on every collision

- Driver inattention is #1 cause across all

5 boroughs without exception

Tools used: Power BI Desktop, Power Query, DAX

Data source: DataDOTgov (publicly available)

Happy to answer any questions about the

methodology or how the measures were built.