Creating information visualizations to present Seattle’s 911 Incident Response data on noise pollution
This past week, I practiced utilizing information visualization to make an extensive database on Seattle’s 911 Incident Response more comprehensible. My mission was to create coherent and beautiful visualizations to allow a specified user group (citizen scientists) to easily address a research question (How can the city of Seattle begin to combat noise pollution?). The citizen scientists of this study encompass people of Seattle who voluntarily contribute to scientific research, even without a licensed background in a scientific field. The question these users were investigating involve understanding the frequency and distribution of noise disturbance incidents in Seattle. Because noise pollution directly affects the city and its citizens, I figured this user group would be motivated to engage in this study.
To physically create these illustrations, I tinkered with Tableau, a software that allows designers (like me) to help others see and understand large data. Considering the information citizen scientists would need to tackle the research question, I formulated three visuals: a map of Noise Disturbances Per District/Sector, a graph of Noise Disturbances Per Month, and a treemap of Noise Disturbances Per Zone/Beat. By utilizing an array of pretty colors and large text, I felt the users would be able to better understand patterns and trends of noise pollution. And with this information, citizen scientists would know where to concentrate their human and fiscal resources, and ultimately, could reduce levels of noise pollutants around Seattle.
Learning Tableau — what I enjoyed about this project
As an overwhelmingly visual learner, I often have difficulty comprehending data that is strictly textual—and I imagine I am far from the only one. For this reason, it was extremely rewarding to learn the ropes of Tableau, a software I had no previous experience with. Though mastering the navigation of Tableau wasn’t without its hindrances, being able to create an illustration that once only existed in my imagination was highly satisfying. I commend Tableau for its ability to facilitate the production of aesthetically pleasing visuals via expansive and flexible features.
The applications and limitations of Tableau visualizations going forward
Most of the world’s population isn’t able to effectively interpret and analyze extensive databases. By introducing decent visualizations into the mix however, that number is presumably cut in half. With this in mind, I expect to be creating an endless number of such visuals within my future career in human centered design and engineering. Being able to expertly create and navigate informative illustrations could provide useful in team meetings, design proposals, and user research (just to name a few).
The projects for which information visualizations are applicable are extensive, but perhaps more limited than those for ideation or user research. A fitness app, for example, would surely profit by incorporating aesthetic and revealing visuals that present a user’s progress. And when fashioning physical products, such as a dining chair or garden shovel even, visualizations could contribute by unveiling sale trends. For projects that hardly benefit from thorough data, however, visualizations are less relevant. This limitation might include research that is exclusively qualitative, or perhaps personalized products that never reach a public market — such as a line of grandma’s best sweaters.