We've been building a routing engine for NYC that treats "Safety" as a cost function similar to "Traffic" or "Distance."
To do this, we had to ingest and normalize about 2M+ crime data points and correlate them with the NYC LION street grid.
One of the most surprising findings from the data was this "Last Mile" risks for logistics: 33% of auto thefts happen when the car is running. It turns out that standard optimization algorithms often inadvertently route expensive assets through high-risk corridors that local drivers would instinctively avoid.
The post details our data sources (NYPD, OpenData) and the visualization. Happy to answer any questions on the data pipeline!
mednosis•1h ago
We've been building a routing engine for NYC that treats "Safety" as a cost function similar to "Traffic" or "Distance."
To do this, we had to ingest and normalize about 2M+ crime data points and correlate them with the NYC LION street grid.
One of the most surprising findings from the data was this "Last Mile" risks for logistics: 33% of auto thefts happen when the car is running. It turns out that standard optimization algorithms often inadvertently route expensive assets through high-risk corridors that local drivers would instinctively avoid.
The post details our data sources (NYPD, OpenData) and the visualization. Happy to answer any questions on the data pipeline!