Visualization Deliverable

User persona:

Mike is a junior at University of Washington. He commutes to school every day by bike. His apartment is near U Village and he always feels unsafe riding bikes during rush hours. When he gets to school, he’s worried that his bike will get stolen. It also frustrates him when the bike gets a flat tire or breaks down. During the weekends, Mike enjoys riding bikes on the Burke-Gilman Trail with friends, but he’s afraid of running into joggers. Mike wants to know more about bike incidents in U district so that he can feel prepared when it actually happens to him. Other possible users are bikers who passes U district in a daily basis .

Research questions:

Mike wants to know how the amount of the bike incidents changed over the past two year. Mike is also interested in what month of the year has the most the amount of the bike incidents. Knowing what kind of incidents occurred the most can help Mike get prepared and prevent future incidents. He needs to find the correct data he wants first. But the data Mike finds online contains a lot of extra information that he doesn’t need. Mike always get confused when he sees a huge list of incidents. He wonders if there is a better way to visualize the data.

Visualization:

2014–2015 bike incidents in U district

The map shows the location of each bike incidents in 2014 and 2015. The number of the circles show the number of incidents in each area and the years are differed by the size of the circles. Each month is color coded, so users can identify which month has more incidents. The label shows the area of the U district.

2014–2015 bike incidents comparison in each month

The line graph clearly shows the incidents increased in 2015. Thickness of the line compares the total amount of incidents happened in each year and the lines are color coded by years. Users can easily tell July 2015 has the most incidents by finding the highest peak.

2015 weekly bike incidents types in July

The bubble graph shows the types of incidents that happened in July 2015. The incidents with bigger circles have higher occurance. The color of the circles represent different weekdays. Users can find what kind of incidents happened most often by looking at the labels of the biggest circles.

an example of the visualization