Pittsburgh Current
Towards Data Science
Popular Mechanics
CONTEXT
In the Spring of 2020, many tools emerged to track new confirmed cases of the coronavirus from online digital maps to detailed dashboards. These tools help public health officials coordinate response efforts and allow citizens to better understand the reach of the virus so that they can prepare accordingly.
However, many of these tools lack the granular scale necessary to contextualize new confirmed cases of the virus’ across large swaths of area. In large states such as California, seeing cases on a state-wide scale may be valuable to a national health official, but is not as helpful for state officials, local officials, or regular citizens. In addition, cases often skew towards larger urban areas due to their high population density at the expense of rural and suburban areas.
However, many of these tools lack the granular scale necessary to contextualize new confirmed cases of the virus’ across large swaths of area. In large states such as California, seeing cases on a state-wide scale may be valuable to a national health official, but is not as helpful for state officials, local officials, or regular citizens. In addition, cases often skew towards larger urban areas due to their high population density at the expense of rural and suburban areas.
Visualization from Johns Hopkins University is overly specific and makes the data hard to grasp
Visualization from HealthMap is too broad and makes the data indecipherable
