

In [29]:

    
from __future__ import division, print_function, unicode_literals

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline

Flooding Calls Over Time

The city of Chicago Disaster Recovery Plan was made largely in response to severe floods in April of 2013. The city also did most of its analysis by zip code, and while Austin generally had the most calls per event, interestingly for this specific event which prompted the funding request, it had fewer 311 calls than Chatham, but still the second highest out of the other top 6 community areas for flooding.



In [14]:

    
comm_df = pd.read_csv('311_data/flood_calls_311_comm.csv')
# Taking top 6 community areas for flooding 
comm_df = comm_df[['Created Date','AUSTIN','ROSELAND','CHICAGO LAWN','AUBURN GRESHAM','WASHINGTON HEIGHTS','CHATHAM']].copy()
comm_df['Created Date'] = pd.to_datetime(comm_df['Created Date'])
comm_df = comm_df.set_index(pd.DatetimeIndex(comm_df['Created Date']))
# Just get calls since the beginning of 2010
comm_df = comm_df['2010-01-01':]
comm_df.head()









    Out[14]:






  
    
      
      Created Date
      AUSTIN
      ROSELAND
      CHICAGO LAWN
      AUBURN GRESHAM
      WASHINGTON HEIGHTS
      CHATHAM
    
  
  
    
      2010-01-01
      2010-01-01
      0
      1
      0
      0
      0
      0
    
    
      2010-01-02
      2010-01-02
      0
      0
      0
      0
      0
      0
    
    
      2010-01-03
      2010-01-03
      0
      0
      0
      1
      0
      0
    
    
      2010-01-04
      2010-01-04
      1
      2
      2
      0
      0
      1
    
    
      2010-01-05
      2010-01-05
      0
      0
      0
      0
      0
      0



In [15]:

    
comm_month_grp = comm_df.groupby([comm_df.index.year,comm_df.index.month]).sum()
comm_month_grp.head()









    Out[15]:






  
    
      
      
      AUSTIN
      ROSELAND
      CHICAGO LAWN
      AUBURN GRESHAM
      WASHINGTON HEIGHTS
      CHATHAM
    
  
  
    
      2010
      1
      17
      20
      9
      9
      10
      17
    
    
      2
      17
      17
      11
      9
      4
      6
    
    
      3
      26
      39
      15
      24
      20
      11
    
    
      4
      51
      48
      31
      32
      27
      11
    
    
      5
      134
      73
      205
      76
      49
      39

Results Over Time

While Austin still has nearly double the next highest amount of 311 calls for flooding since 2010, it didn't happen to have quite as many as Chatham in April of 2013. Still, it had the second highest, so it's worth looking at why it didn't have as much focus in the city's plan.



In [25]:

    
comm_month_grp.sum().plot(title='Top 6 Community Areas by Flooding 311 Calls - 2010-Present', kind='bar')









    Out[25]:





<matplotlib.axes._subplots.AxesSubplot at 0x103185e48>



In [24]:

    
plt.rcParams["figure.figsize"] = [15, 5]
comm_month_grp.plot(title='Flooding 311 Calls by Top 6 Community Areas - 2010-Present')









    Out[24]:





<matplotlib.axes._subplots.AxesSubplot at 0x109c8b5f8>



In [23]:

    
april_df = comm_df['2013-04-01':'2013-05-01']
april_df.plot(title='Flooding 311 Calls by Top 6 Community Areas - April 2013')









    Out[23]:





<matplotlib.axes._subplots.AxesSubplot at 0x10bc41208>



In [22]:

    
april_df.sum().plot(title='Flooding 311 Calls - April 2013', kind='bar')









    Out[22]:





<matplotlib.axes._subplots.AxesSubplot at 0x10bbbbb70>



In [28]:

    
mid_april_df = comm_df['2013-04-15':'2013-04-25']
mid_april_df.plot(title='Flooding 311 Calls by Top 6 Community Areas - Mid-April 2013')









    Out[28]:





<matplotlib.axes._subplots.AxesSubplot at 0x10d8826d8>



In [ ]:

	Created Date	AUSTIN	ROSELAND	CHICAGO LAWN	AUBURN GRESHAM	CHATHAM
2010-01-01	2010-01-01	0	1	0	0	0
2010-01-02	2010-01-02	0	0	0	0	0
2010-01-03	2010-01-03	0	0	0	1	0
2010-01-04	2010-01-04	1	2	2	0	1
2010-01-05	2010-01-05	0	0	0	0	0

		AUSTIN	ROSELAND	CHICAGO LAWN	AUBURN GRESHAM	WASHINGTON HEIGHTS	CHATHAM
2010	1	17	20	9	9	10	17
	2	17	17	11	9	4	6
	3	26	39	15	24	20	11
	4	51	48	31	32	27	11
	5	134	73	205	76	49	39