UCD Charrette Process Blog — Entry 8 Visualization Memo
November 20, 2016
- The User (persona)
The user for my visualization of Seattle’s noise pollution levels are people who are planning to move into Seattle, or real estate agents who are researching the area’s noise levels. The house buyers are a couple looking for a fairly quiet place to move into but the city must be in Seattle. Therefore, their decisions are affected by the noise pollution in their potential housing area. On the other hand, the real estate agent can look for areas that the couple may find favorable, and raise or lower the price according to how noisy the area can get. Knowing the noisiness (frequency of disturbances) of the location, time, and day is important for both customers and the agent.
2. Research Question
The research question the user is trying to answer is: “Where can I find a place to live in a fairly quiet area in Seattle, according to the noise disturbance data from the Seattle police department?” The user needs the data on noise disturbances from Seattle’s police department in order to address the question. The question makes it a little complicated because “fairly quiet” is subjective. The disturbances may or may not be coherent to the noise level the user is thinking about. In addition, there may be situations where one district has fewer disturbances but each disturbance may be louder, and another district may have more disturbances but each may be quieter.
3. Descriptions for Visualizations
The top visualization titled, “Noise Disturbances of each District in Relation to the Time of Day,” shows data for the number of disturbances for each district is shown in the form of a continuous linear graph. The areas of each section represent the frequency of 911 calls that are related to disturbances. This addresses the research question by showing which district has the most recorded disturbances. I believe this is effective the different colors separate each district so it is easier to see, which encourages the eye to compare different pieces of data. This visual is also closely integrated with the statistical descriptions of the data because this graph simply resizes the data into proportional areas.
Next, the graph on the bottom left corner titled, “Noise Disturbances of the Days of the Week and District,” shows data for the number of disturbances for a particular day of the week, which are individually color coded so the viewer has an easier job navigating through the data. The size of each section for the day of the week is larger or smaller according to the number of disturbances; the more disturbances, the larger the size. In addition, the sections for each day of the week are cut up into the districts, so the user can also see which district is the most noisiest on a certain day of the week because the noisiest districts make up larger portions of each day of the week. This addresses the research question because it allows the user to see which day and district have the most calls for disturbances. This visualization is effective because it makes large data more coherent, and it induces the user to think about the data.
Finally, the visualization in the bottom right corner titled, “Noise Pollution Map,” shows the most disturbed areas by the size and colors of the points on the map of Seattle. The points indicate where the disturbance had occurred, the size of the points represents the number of disturbances that had occurred in the same place, and the color separates the districts. This addresses the research question by showing specific areas to avoid or to look for in housing depending on the number of disturbances. This is an effective visualization because this map reveals the data at several levels of detail, depending on how much the user zooms into the map. The zooming in makes the data more specific, and zooming out makes the data more of an overview.
Link to my visualizations: