To get a different visual perspective, we can plot the percentage increase in crime and traffic, instead of the raw count. Let's divide all the data by the first row and plot again:

>>> crime_begin = crime_quarterly.iloc[0]
>>> crime_begin
IS_CRIME      7882
IS_TRAFFIC    4726
Name: 2012-03-31 00:00:00, dtype: int64

>>> crime_quarterly.div(crime_begin) 
                   .sub(1) 
                   .round(2) 
                   .plot(**plot_kwargs)