Using this normalized data, we can really get to the core of our question about neighborhood change (or, 311 noise complaint behavior). I chose to use an AD Fuller test to assess stationarity. The AD Fuller test's null hypothesis is that there's a unit root, meaning there is not stationarity. If we can reject the null, it generally means the data is stationary\cite{documentation}.
sm.tsa.adfuller(station_gw['ratio'])[1])
The one-line test resulted in a p-value of 0.00132. We can reject the null. Although there are visual changes, this data is stationary. When looking at just the past few years, the p-value remained close to zero, also denoting stationarity.
Commercial Noise Complaints
Seeing the results of the stationarity test was a bit surprising, but it may not tell the full story. One larger deviation from our exploratory analysis showed that there were large deviations in noise complaint subdivisions. To look at these in particular, I duplicated the analysis, looking at only Commercial noise complaints.