This map is an entry for the Street Safety Challenge. Each colored circle is located at an intersection. The color of the circle is a shade between red (bad) and green (good), based on a metric of bike-friendliness within a 0.25 mile radius of that intersection.
Where did the metric come from? Open data on Cambridge's traffic accidents, pothole-repair requests, bike theft, and existing bicycle infrastructure. I weighted these factors in a way that made sense to me, but you can weight them differently (see the "options" section below). For example, a confident cyclist who owns an expensive bike might be especially concerned with bike theft, while a new cyclist might want to know which streets have good bicycle infrastructure and few traffic accidents.
This page was created by Jennifer Melot. For a more detailed description of the process, data, and resources I used, see the "details" section below.
ACCIDENTS: I used absolute numbers of all types of accidents instead of scaling by traffic volume, partially because a quick search did not yield traffic volume data I could easily incorporate. I did not include just bike-related accidents because I wanted to capture the unfriendliness of a street where vehicles frequently have accidents and where few cyclists will ride (thus resulting in few bike-related accidents).
This was the only dataset that I was able to divide up by time of day in what I felt was a meaningful way.
CRIME REPORTS: This dataset contained the bicycle theft data. Again, lacking a way to normalize for number of bicycles parked at a certain location, I used absolute numbers of reports. I had initially planned to divide this dataset by time of day, but unfortunately a large portion of the reports were not very specific about when the bicycle was stolen.
POTHOLES: Each pothole location was counted once, to avoid bias toward potholes in high-traffic locations. What I was trying to get at with this dataset was the unfriendliness of a road with lots of rough pothole patches -- excessively bumpy roads aren't comfortable on a bike! I think that this is the most marginally relevant or informative of the datasets I included.
BIKE INFRASTRUCTURE: This was by far the most difficult dataset to incorporate. I put the work in because it is arguably the most important. I used the KML file from the Bike Facilities layer at the link above, and extracted the line segments corresponding to existing infrastructure. I treated all kinds of infrastructure (bike lanes, multi-use paths, etc.) the same, because it was not obvious to me how the different kinds of infrastructure were indicated in the KML file.
Once I had the line segments, for each intersection i, I calculated the total length of all the line segments within a 0.25 mi radius of i. The assumption here was that a location with a lot of bike infrastructure nearby is relatively bicycle friendly.
INTERSECTIONS: I chose intersections as locations to display bike friendliness because they are (more or less) evenly dispersed, follow roadways, and were available as an open dataset.
Note that intersections very close to the Cambridge line may have scores of lower accuracy if the town line passes through a 0.25 mi radius circle around the intersection, due to lack of data outside the Cambridge city limits. Given more time to develop this map (or data from surrounding towns), I could have more carefully scored these intersections. Still, it isn't apparent to me that this has had a large effect on the accuracy of the current map.