The ‘Death Dataset’ compares various causes of death, relative to state. All fifty states are featured, and categories of death include the total number of deaths, death by heart failure, cancer, respiratory failure, stroke, accidental death, vehicle death, Diabetes, Alzheimer’s, the flu, nephritis, suicide, homicide, and AIDS.
For this data visualization, I’ve chosen to compare rates of suicide vs rates of homicide, varying from state to state. I began by uploading the raw data onto a Google Fusion table, managing to create a very basic column chart. I found this format to be the most feasible for comprehending the data — suicide rates are represented with blue, while rates of homicide are depicted in red. The x and y axis allows for an easy comparison of death tolls relative to state.
While the data visualization allows the viewer to access trends easily — i.e., one may quickly notice that the District of Columbia and Maryland both have greater rates of homicide, vs. greater rates of suicide. I may be lead to make some sort of inference as to why this is — close proximity, similar social circumstances, or etc. With other states visible on the chart, the rate of suicide typically surpass the rates of homicide. My only hesitation with this data vis is related to the raw data itself. There is no annotation for year, and there is no clue offered as to what year (or years) this information was extracted from. Alongside this, the dataset makes it unclear if the information is presented as deaths per state, or death per capita of each state (I highly doubt that total rate of homicide in the state of California is 7 per year?!).
Good job on this post! I also used Google Fusion Table because of its simplicity and easy-to-use data visualization tools. It’s good that you are able to point out the flaws in this dataset and the lack of information/context it provides. This is definitely as important as identifying the most visible points.