Module 2 - Data Windows

CRASH DATA OF 3 NEIGHBORHOODS

Process

The dataset I used for this exercise is the Allegheny County Crash Data (2004-2016) from WPRDC. The dataset has over 157,800 records. The challenge is to filter the data and get the useful information for the representation.

The question I proposed is comparing the neighborhood of Squirrel Hill, Shadyside and Oakland to see what's the factors that related to the crash accidents.

The first assumption is the human factor which I choose the Alcohol column because alcohol-related crash accidents are among highest crash records all over the states. The second assumption is the illumination condition. I filtered the data into the night-time and dark illumination-related to see how different degree of street lighting will affect the crashes. This is an external and environmental factor. The last assumption I made is the age of the drivers' involved in the crashes which is an uncontrollable factor.

The ways I choose to represent these 3 factors are different. The alcohol-related data is purely represented by the location dots. The illumination-related data is represented by simulating the lighting condition. The age distribution map was colored like a heat map which shows the variations and concentrations.

The Matrix

The findings

The crash dataset contains locations and information about every crash incident reported to the police in Allegheny County from 2004 to 2016. By comparing the 3 neighborhood, I found that the crashes caused by human factors like alcohol-related accidents are more concentrated on main roads. If related to the drivers' age map, there are several teenagers drinking-related accidents which can definitely be controlled by regulation and education. Through improvement of street lighting can reduce some crashes especially on the secondary roads in the neighborhoods. The analysis of drivers age shows a surprising result that large percentage of the crash-related drivers are over 65 years old. This may suggest that the city should pay attention to restrict the qualification for the senior citizens to drive.