For our team project, we decided to focus on manipulating a set of data that was both relevant and interesting to us: local crime. After a brief search of The Western Pennsylvania Regional Data Center (WPRDC)’s repositories, we were able to find a CSV file containing information about a multitude of crimes committed by people in Allegheny county. The dataset itself included race, gender, location, crime description, and neighborhood information among a few other police-specific codes. When choosing which variables to include in our visualization, we decided to use those that would be most relatable to the general public—knowing the code numbers of the crimes, for example, would not help most people understand this topic more greatly.
Our first visualization shows a distribution of crimes by neighborhood and age, with each dot color representing one crime and the color corresponding to the race and gender of the person who committed it.
Our second visualization is a bar graph that plots the neighborhood where a crime was committed on the x-axis with the number of crimes on the y-axis. Using the drop-down menu, viewers can toggle between crimes committed by men or women who are white or black and see the bars respond to those changes. Rolling over a bar gives the user more insight into the crimes of each category of person by listing the crime code and description of crime committed as well as that person’s age.
The last visualization presents a map of the same data, this time giving the user the exact latitudinal and longitudinal coordinates of the crime as well as a description of the crime committed.
I believe that our group had a good work balance, and we each had an important role to play Since I have a limited background in coding, my contribution to the group’s work was through providing an HTML and CSS skeleton for the final presentation of the work. To make the website look like it was Pittsburgh-focused, I used a black and gold color palette when designing the site. I also learned how to ‘stick’ a navigation bar to the bottom of a webpage and included that in my work as well! Though the design may be simple, I think the minimalist approach looks clean and professional, and that is why I stuck to it for this project. Lastly, I made sure to turn on GitHub Pages so our website would be visible!
As a resident of Pittsburgh, I believe that our visualizations serve a dual purpose: One, they can help people track the frequency and types of crimes happening in their own neighborhoods, and two, they break the data down into categories to help viewers comprehend it more easily. This data also could be useful for a multitude of different social groups. Students such as myself may look at it in order to pick out a place to live, but others—those with small children, for example—could use this map and chart to caution their children about unsafe neighborhoods. The police could also use this visualization to try and concentrate their efforts in those areas that have higher rates of crime, making the force strongest where it is needed the most. In the future, perhaps these visualizations could be expanded to include up-to-date neighborhood crime data.