Outcome

Story

City of Pittsburgh is named one of the most livable cities by Economist. The city has great infrastructure and amenities to support varies of public life. Cycling is one of trending life style for citizens, and city is taking effort to improve and provide more cycling infrastructures to create a sustainable living environment and better transportation options. In order to make bicycle infrastructure planning process more efficient, and put limited budge to make sufficient impact, planners need a comprehensive understanding of historical bicycle crash data to better make design decisions. 

Collection

In order to have a better understanding what factors contribute to the bicycle crashes at different locations, I used Allegheny County Crash Data to start with the data analysis. I combined all crash data from 2004 to 2015 in Allegheny County, then filtered out all bicycle related crashes in GIS and exported as a new shape file. (Bicycle Indicator as 1)

With all bicycle crash data, I imported the shape file into Carto to visualize the location of bicycle crashes and started looking into different factors that were related to the bicycle crashes.

Analysis

I differentiated Weekdays and Weekend crash data and see if there is different location pattern between commuting and leisure cycling. 

Report

From the Crash Dataset, it shows 1096

1. From Map 2, the location distribution of crashes for weekdays had certain concentration around major employment centers, such as Oakland, Northside, and also concentrated along some major commuting routes, such as Penn Ave from Lawrenceville to Downtown, Fifth and Forbes Ave in Shadyside and Oakland. It is more obvious from heat map below.

2. From two environment indicators of crashes, illumination and intersection, we can tell there are many area in the county need improvement to improve the visibility for drivers to see the cyclists. Or enforce cyclists to wear visual stimulating equipment, such as bike light or outfit. From map 4, we saw there are many concentrations in highly used commuter corridors. It indicates a urgent improvement for these areas.

3. Regarding the topography influence on crash distribution, from Map 8, the bicycle crashes mainly happened around flat areas in neighborhoods.

4. For total drivers' personal behavior related bicycle crash (327 crash in total) reflects a potential for law enforcement or education program to help change drivers behaviors on driving or sharing roads with cyclists. For the location distribution, alcohol related and speeding related were evenly distributed, however, aggressive driving has little concentration in Shadyside, Squirrel Hill and Northside. I think it could use some enforcement in these areas to protect cyclists and pedestrians, as the area has high concentration of student population.

Reflection

Through the data visualization process, it was difficult to process/filter the data to eliminate the biases of single factors. The dataset always has certain factors influences the accuracy of visualization. For example, I was trying to visualize the aggressive driving behavior related bicycle crashes, and cleaned out all driver behavior related factors, however, there were still many environment related factors would contribute to the bicycle crashes. Also, the heavy dataset requires higher performance computers to process the data, especially overlay different layers of data, and adding contours data made the visualization extremely difficult. Nevertheless, the visualized data could still reflect a concentration of crash area where planners or city officials could pay more attention to and make more impactful improvement around the area.