Reflection

I. Lessons Learned

Regarding using SQL to help filtering data, I think coding is definitely a lot of fun, however, it took some time to make things/codes right. I’m more comfortable of using ArcGIS, so the majority of heavy load was done in ArcGIS. However, there are not too many challenges encountered, most of the data processing is very straight forward, just a little time consuming.

Selecting the datasets to reflect correlation of urban environment and other urban data was a little difficult, and how to forming a story line to tell, because cyclists were moving on their routes, it was difficult to interpret their demographic and relationship to cycling route settings. I selected income was to show the possible neighborhood maintenance, such as street pave, signage, landscape visibility (vision triangle of intersections), status, as in the perception, higher income neighborhood would have better public maintenance.

Also, after comparing street with crash locations, it seems better landscape could improve the cycling safety as lower crashes appear on the these better landscaped routes. It might because these trees could lower the traffic speed in the neighborhood compares to Baum and Center.

The majority of crashes happened in cautious routes which was highlighted in black, so the perception could also reflects the route safety.

II. Speculations

These comparison could help create a sense of different factor could contribute to bicycle crashes, and how could be improve, such as follows:

1. A large number of intersection crashes in the neighborhood, including total 125 crashes in and around Shadyside and 101 happened at intersections, indicates there should be certain improvement at these intersections, such as bicycle safety boxes or prioritized intersection signalization to help cyclists crossing.

2. Adding bike sharrows or bike lanes to certain streets like Baum Boulevard, Negley Ave, which could encourage sharing traffic and improve the cycling safety.