This blog post is aimed at creating meaningful visualizations that may or may not be available elsewhere, while instructing users on how to source, analyze, and visualize COVID-19 infection case and rate data using Python. All of the data used herein is publicly available for anyone interested in replicating the figures, with code and links where necessary. The methods used here have been uniquely conceived and developed by Maker Portal, and in no way reflect preferred methods of either the government or any other private entities. Several Python toolboxes will be implemented below, and it is recommended that users install and verify their functionality before attempting to replicate the forthcoming figures.

Python FTP for Data Mining and Analysis

Python’s file transfer protocol (FTP) library is used to parse weather station data from the publicly available automated surface observing system (ASOS) from the U.S.A.’s National Climatic Data Center (NCDC). Several programmatic tools available in Python are used to automate the parsing of weather data, as well as visualizing the resulting data.

Arduino GPS Tracker

The NEO-6 is a miniature GPS module designed by u-blox to receive updates from up to 22 satellite on 50 different channels that use trilateration to approximate fixed position of a receiver device every second (or less, for some modules). The particular module used in this tutorial, the NEO-6M, is capable of updating its position every second and communicates with an Arduino board using UART serial communication. The NEO-6M uses the National Marine Electronics Association (NMEA) protocol which provides temporal and geolocation information such as Greenwich Mean Time (GMT), latitude, longitude, altitude, and approximate course speed. The NEO-6M and Arduino board will also be paired with an SD module to create a portable logger that acts as a retrievable GPS tracker.

GOES-R Satellite Latitude and Longitude Grid Projection Algorithm

Calculating latitude and longitude from a GOES-R L1b data file. The GOES-R L1b radiance files contain radiance data and geometry scan information in radians. This information is not enough to plot geographic radiance data right from the file, however, after some geometric manipulation harnessing satellite position and ellipsoid parameters, we can derive latitude and longitude values from the one-dimensional scan angles and plot our data in projected formats familiar to many geographic information tools.

Geographic Mapping from a CSV File Using Python and Basemap