I loved your decision to utilize a traditional scatterplot as a means of providing the viewer with a feel for the enormity of the problem while attempting to preserve the humanity and identity of the individuals represented by each datum. As we’ve extensively discussed over the course of this semester, negotiating this tension between abstraction/ aggregation and personalization is often the most challenging and difficult component of data visualization, so I found the scatterplot to be an especially impressive and impactful contribution to discourse surrounding police brutality and racial injustice. As I mentioned when you presented last week in class, what I find truly compelling about your graph, aside from the sheer size and scale of the victim pool, are the outliers, the people that don’t fall into the typical age range. The data points for Jeremy Mardis and Kameron Prescott particularly struck me, as they were both only six years old when they were murdered by the police. This, I suppose, is one of your visualization’s greatest attributes- it evokes larger questions, it encourages the audience to learn more about the people behind the data points. Through its sort of humanized abstraction, your graph tells a crucial story of an entrenched system of violence perpetrated against the very people police officers are sworn to protect, which successfully challenges traditional characterizations of these shootings as isolated incidents committed by a “few bad apples”.
For future analysis, it might be valuable to further contextualize this data with more information about the victims beyond simply their government name, such as their date of death, race, state of legal residence, or some determined metric for socio-economic status (occupation, etc). While I am a firm proponent of the power of names, it is ultimately limited in terms of capturing a person’s identity. For example, after conducting some cursory research, I discovered that Jeremy Mardis, the six-year-old victim I previously mentioned, was actually white, which indicates an important nuance that should probably be accounted for in the future. I also think it would be extremely interesting to examine the perpetrating police officers to obtain further insight into the context enabling and facilitating these shootings. What was the racial composition of the officers that pulled the trigger? How many of them actually faced repercussions, and how were these punishments distributed? The answers to these questions may help us further understand why interactions between the police and certain demographics are often unsuccessful. I truly loved your visualization, thank you for tackling such an important issue. Have a great Thanksgiving break!
Hi Jerome!
I loved your decision to utilize a traditional scatterplot as a means of providing the viewer with a feel for the enormity of the problem while attempting to preserve the humanity and identity of the individuals represented by each datum. As we’ve extensively discussed over the course of this semester, negotiating this tension between abstraction/ aggregation and personalization is often the most challenging and difficult component of data visualization, so I found the scatterplot to be an especially impressive and impactful contribution to discourse surrounding police brutality and racial injustice. As I mentioned when you presented last week in class, what I find truly compelling about your graph, aside from the sheer size and scale of the victim pool, are the outliers, the people that don’t fall into the typical age range. The data points for Jeremy Mardis and Kameron Prescott particularly struck me, as they were both only six years old when they were murdered by the police. This, I suppose, is one of your visualization’s greatest attributes- it evokes larger questions, it encourages the audience to learn more about the people behind the data points. Through its sort of humanized abstraction, your graph tells a crucial story of an entrenched system of violence perpetrated against the very people police officers are sworn to protect, which successfully challenges traditional characterizations of these shootings as isolated incidents committed by a “few bad apples”.
For future analysis, it might be valuable to further contextualize this data with more information about the victims beyond simply their government name, such as their date of death, race, state of legal residence, or some determined metric for socio-economic status (occupation, etc). While I am a firm proponent of the power of names, it is ultimately limited in terms of capturing a person’s identity. For example, after conducting some cursory research, I discovered that Jeremy Mardis, the six-year-old victim I previously mentioned, was actually white, which indicates an important nuance that should probably be accounted for in the future. I also think it would be extremely interesting to examine the perpetrating police officers to obtain further insight into the context enabling and facilitating these shootings. What was the racial composition of the officers that pulled the trigger? How many of them actually faced repercussions, and how were these punishments distributed? The answers to these questions may help us further understand why interactions between the police and certain demographics are often unsuccessful. I truly loved your visualization, thank you for tackling such an important issue. Have a great Thanksgiving break!