Introduction
As a resident who’s relatively new to New York, I have recently had gained significantly awareness into public safety issue. In this case, the shooting incident. Because additional information related to shooting incident can be included as suspect and victim. By dive further into the available data to understand the scale of the issue, therefore, to bring awareness to the public to explore the nature of shooting/criminal activity in NYC. I will be looking for dataset from recommended websites and utilizing Tableau Public to visualize and analyze the data related to the topic. To see the final visualizations, check out my visualization dashboard on Tableau Public.
Inspiration
Before creating the graphs, I first went to explore existing visualizations about related topics. I wanted to find out what other people expanded on this issues and what possibilities there are. I also wanted to find out what types of presentation could be useful to my study. I came across two cases that were appealing to me.
The New York Times published an article in 2015 on using line chart to claim that the gun death could be extremely dangerous to people (Figure 1). This definitely inspired me on gathering related data to analysis each incident in a yearly basis.
After deciding on the year to be one of the important dimension, I then look for other potential encoding methods to match in this scenario. I decided to go with a stacked chart (Figure 2) to show percentage of incident occurred in five boroughs of NYC by year.
Materials
Tools –
Microsoft Excel – A spreadsheet software by Microsoft, part of the MS Office suite.
OpenRefine – A tool for working with messy data, which helps in cleaning it up quickly and effectively.
Tableau Public – A data visualization software, which has many visualization techniques built into it.
Dataset –
The dataset I used for my lab is ‘NYPD Shooting Incident Data (Historic)’, which I found while browsing through datasets on NYC OpenData.
Process
Cleaning up the data
After I had selected the dataset, I started off with cleaning up the data to make it ready for analysis. The data set I have been chosen was in a good shape. So instead of using the OpenRefine to make alterations. I directly unify the data in the Excel sheet. I deleted the columns that are unwanted and only remain data such as ‘Occur Year’, ‘Location’, ‘Sex’, and ‘Age’. I also transformed many of the number cells’ datatype from ‘text’ to ‘number’.
Transferring the data into Tableau
After the data was clean and ready for analysis, I exported the data from OpenRefine as an Excel and import into Tableau.
Creating Visualizations
After loading the data into Tableau, I first started off by trying to create a visualization for the number of reported shooting incident happing per year (2006-2018). I chose the line chart to visualize since it is great for showcasing time series data and immediately, the trend in the number of reported incident in time was revealed. I then use the first chart as a basis for my following creation. Therefore, I added age and race as my second and third dimension combined with year. I limited the year from 2014 to 2018 because people are more relative to the most recent results. For the age chart, I was curious by how different age groups might effect the result. After consulting with Pro. Sula, I decided to keep the original grouping due to the unchangeability of the original data set. For the last visualization, I wanted to show percentage of incident occurred over time by borough, so I decided to use a stacked chart filtered by each of the five boroughs in New York City with the 68 null values excluded out.
Screen layouts in Tableau
After I was happy with how each of my graphs looked, I added them to a dashboard in Tableau and then added a screen layout for mobile, tablet, and desktop views. By adjusting the screen size the layouts automatically response to how the visualizations look on a dashboard depending on the viewer’s screen size.
Results
Please access tableau project – Shooting incident that occurred in NYC (Year 2006 to 2018)
In this project (Figure 3), I hope to bring viewers attention on how shooting incident has changed over the years, and how efficiently the problem has been taking care of. Because from the line charts, audience can see an overall downward trend over time. I also hope to make users understand the difference of incident occurred in the changing from borough to borough.
Visualization 1: Number of Shooting Incident Reported in NYC
In figure 4, there was a downward trend in shooting incident from 2006 until 2018. Presumably the police have been making a solid effort in lowering the incident in the city, the record was witnessed a steady dropped down to the lowest point of 951 in 2018.
Visualization 2: The Record of Shooting Incident Grouped by Age
In figure 5, the highest age range is observed in 18-24 and 25-44, which is significantly higher than all other groups, but it steeply drops below in 2015.
Except the group ranged from 45 to 64 only witnessed a upward, the rest of the groups are steadily going downward.
Visualizations 3: Shooting Incident Map of 4 Boroughs
In figure 6, it is very interesting to see how the incident are spread across the boroughs. Certain neighborhoods that are worst affected over the years can easily be identified by looking at the largest size of the chart. For instance, Brooklyn is the worst affected overall with 33.45% of incident been reported over the years. Bronx is next in the worst affected neighborhoods with 27.66% of incident reports, which is relatively similar than Brooklyn.
The Queens and Manhattan are overall quite evenly affected throughout. Staten Island emerges as the least affected borough with scarcely smallest space.
Reflection
I enjoyed working with Tableau and found it easy to manipulate data with. When I finally gathered all the charts together to see the overall effect. I also learned that in order to create a consistent visual language, I have to go back to the individual chart and play with its color and transitions several times. So that each of the chats has its own story but can also combined together to indicate the trend as a whole. In that case, I use color red to represent the downward trend.
In terms of future directions, I would like to continue exploring significant parts of the data I did not have time to go into. This includes the victim of shooting, sex, and victim location. I would also like to try using spots on maps to indicate the incident into a geographical presentation. There is a lot of manual work required during the process, but I think they have the potential to produce very interesting charts.