How might we alert sanitation workers to the safety profile of a particular district?
UX design, web development
Being a sanitation worker is one of the most dangerous jobs in America. From research and conversations with sanitation workers, we found that a key reason for the high fatality rate was that the workers had become numb to the traffic hazards on the job. To raise worker alertness on the job, I built a quick map of recent motor collisions across NYC as a way to indicate how dangerous particular streets or intersections in the city might be. I drew upon data provided by the NYPD to create an interactive map, full of information.
Designed, coded and deployed the site.
Being a sanitation worker is one of the most dangerous jobs in America. I elaborate further on this problem and its root causes in the description for WristGuard.
Sanitation workers are well aware of the hazards on their job:
"In Manhattan, you got [sic] more traffic so you've gotta be careful when you come out of the truck so you don't get hit by a cab or anything."— Sanitation worker Edwin Nieves
The most important reason behind the high injury and fatality rate is not because these workers aren't aware of the dangers, or that they are flouting safety practices: it is because they are immersed in the danger so frequently (daily) that they become desensitized to the hazards.
Would it be represented by rate of traffic accidents? Or some rating system for road safety? Thanks to the NYC Open Data initiative, I was able to find a regularly-updated database on Motor Collisions released by the NYPD, complete with longitude-latitude location information.
These collisions are sortable by borough: by clicking on the borough names in the left column, you can filter the data points for specific regions.
The markers are color-coded to demonstrate the severity of the accident. Yellow markers indicate no injuries, orange indicates 1 person injured, and red indicates more than 1 person injured. Each marker can also be clicked on for further information about the collisions.
On its own, the map might not have a significant impact on reducing sanitation worker injuries or fatalities. It would be difficult to convince workers to constantly refer to the map as they go about their daily routine. However, the map remains valuable as it translates individual collision data into a broader picture of district-level safety profile.
I plan on evaluating how this map information can be incorporated into the WristGuard smart glove project to enhance the accuracy of alerts to sanitation workers. Imagine if alerts about oncoming vehicles could be more urgent in districts with a poorer safety profile: sanitation workers would be able stay even more alert in districts where they are in more danger.