Merge pull request #44 from databridgevt/mitch_documentation

Mitch: Updated the README.md, added notebook and visualizations

analysis/db/mitch/README.md

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+-What does this figure show?
+
+This figure shows the changes in NO2 concentrations for Milan, Italy and Wuhan, China.
+
+-Why did you choose to look at the values shown in the figure?
+
+Largely because NO2 is a commonly used substance to measure air quality and it was
+the most available data I had out of my dataset.
+
+-Why should someone care about these results?
+
+Air pollution, especially from gases like nitrogen dioxide, are major sources of respiratory illnesses
+and deaths in numerous cities. Although rooted in unfortunate circumstances, having cleaner air in cities will 
+help save lives. Levels of air quality can also be an indicator of economic activity and how different governmets
+reacted to the pandemic.
+
+-What are the limitations/caveats to your data analysis?
+
+	-What decisions did you make in the data processing that could under/overestimate your results
+
+	I did not go back very far in my start date for my visualizations. In order to get a fuller view of 
+	air quality changes from COVID-19, I should have at least started a full year back to accomodate other 
+	potential factors such as seasonal changes.
+
+	-What external factors could affect your results?
+
+	Recent NO2 changes may not have been solely affected by COVID-19 quarantines. There could be countless of other variables
+	that play a role in air quality. For example, there may be areas that have a high concentration of essential businesses 
+	that require fossil fuel consumption and emission.
+
+	-Another way to think about this is this: if you were to present your findings to the public and people are going to take your
+	word to make some actionable decision, what would you tell them could skew the results and affect the decision making?
+
+	I only measured the concentrations of one substance. There are many other pollutants to measure such as SO2, CO, CO2, and particulate matter.
+