Creating Heat Maps to Visualize Epidemiological Data
Heat maps (or heatmaps) are wonderful visual representations of how points cluster on a map. This is ideal for plotting epidemiological data based on the longitude and latitude of events.
Epidemiologist can quickly identify where more cases exist and where more resources may be required.
I have created a short video tutorial about how to create a heat map using the folium package in Python. Folium generates interactive plots that can be exported for use in other media.
In the tutorial I generate some synthetics data for cases in Brooklyn, New York. I show how to change the parameters of the plot to change the plot for better visualization of the data.
I also create a time-series plot. These can be used to visualize how the data changes over time.
The link below shows the video tutorial. The Jupyter notebook (that contains the code) can be found HERE.