Visualizations Process Blog
This week’s sprint was focused on creating visualizations with the 911 incident reports data given. I thought the police would be interested in seeing the user group because they are the ones who respond to and record these events, and they better analyze their data with my visualizations. The people who assign policemen to precincts should do so depending on the demand. Some areas are more dangerous than others, and need more policemen nearby.
Even though we did not have the chance to do user research, I chose the user group of policemen and those who work at the police department. I had to make the assumption that 911 phone operator respondents work at the police department, and the police were the ones addressing all reported incidents. I also had to assume that the extent of my knowledge on this topic is accurate.
My research question is that the police department wants to know if Seattle is becoming a dangerous place to live in over time, and what areas are the most dangerous. They may want to know which stations should have the most policemen by looking at 911 incident reports and also want to see the types of incident reports to see if there are a lot of urgent matters such as criminal activity, home invasions, or if a large percentage is just made by public disturbances. With this information, they can better make adjustments to where more police are stationed, and add the new recorded data to see if their adjustments are helpful or not. The police need information on types of reports, location of reports, and the trend of reports over time.
My first visualization, a segmented bar graph, shows that areas such as Districts D, E, K, and M receive the most 911 incident reports, whereas Districts F, G, and O receive the least. Places like Downtown, Central District, SoDo, and Pioneer Square may get the most reports because of certain reasons while places like Delridge, Beacon Hill, and Industrial District may have less incident reports because of other reasons. The key shows what types of incidents are making up the highest percentages of reports in each district. Various police stations can use this information to better prepare for specific types of illegal activities that are popular among their district.
The line graph visualization can help the police department distinguish which times of the year they get the most 911 calls in. For example, there is generally a peak of incident reports from about every May to September, so the stations should prepare by having more police on duty. In addition, they can use this data for other uses, such as determining that December through February would be the best time to train cadets since there are less reports. Overall, there is no significant decrease or increase in reported incidents.
The police department can use the pie chart visualization to determine how safe Seattle currently is for the average citizen, and which threats to public safety they should prioritize to reduce. About half of the incident descriptions are activities that do not threaten citizens’ well-beings, such as parking violations, false alarms, and disturbances. However, the police should aim to reduce the amount of reports such as theft, burglary, and assaults, which make up a fairly large percentage when combined.
From Studio and this sprint, I learned a lot about types of visualizations to display data, such as symbol maps, treemaps, and highlight tables, that I had never heard of or thought of using before. I really enjoyed using Tableau because it is such a great tool to efficiently and effectively organize data into visualizations. The recommended visualization types under “Show Me” helped me better understand how to analyze the data and evaluate how the data can be applied to the research question. For example, in Viz 1, the segmented bar chart allows the user group to quickly and easily see which districts have the most incidents and and which are the most common types of incidents.
It is important to have successful visualizations that are clear and organized in this way, not only for user research and design challenges but also for other purposes such as statistical sciences and marketing meeting. Good visualizations help with communicating ideas between the people analyzing the data and those they are presenting the data to. It is also important to choose the most appropriate form of visualization to best address the questions that users and viewers have.
In the future, I want to practice creating visualizations that can clearly display multiple dimensions and measures of the data. Although I tried to integrate other dimensions and measures into “Marks” such as size and color, some of my visualizations got confusing or cluttered. I think getting more familiar with Tableau’s features and how data is organized would help me improve on making visualizations.
Data analyzing skills are important to have, no matter what job you have or what your major is. In future business-related applications and presentations, I want to use these skills and Tableau to make better infographics and visualizations. Tableau is very user-friendly, and now that I know the process of how to import, organize, and create a visualization with data, I will be using it for my future classes and presentations.