In [1]:

    

%matplotlib inline
import pandas as pd
from pandas import DataFrame
import pickle
import folium
from IPython import display


    

In [2]:

    

df = pd.read_pickle('.././data/pickle/pypf_prep.pkl')
regionlookup = pd.read_csv('.././data/maps/oldnewlookupregions.csv', names=['GOR10CD', 'Area_code', 'Region'])
regionlookup['Region'] = regionlookup['Region'].str.upper()
regionlookup['Region'] = regionlookup['Region'].str.strip()
regionlookup['Region'] = regionlookup['Region'].str.replace('EAST OF ENGLAND', 'EAST')
#or_geo = '.././data/maps/gor.json'


    

In [3]:

    

maps = {}

for cause in df.Cause.unique():
    grp = df[(df['Cause'] == cause) & (df['Sex'] == 'Male')].groupby('Region')['Rate per 100,000 (standardised)'].mean()
    grp = DataFrame(grp).reset_index()
    maps[cause] = pd.merge(grp, regionlookup, on='Region')


    

In [4]:

    

maps['All Mesothelioma']


    

    
Out[4]:






  

    

      

      
Region
      
Rate per 100,000 (standardised)
      
GOR10CD
      
Area_code
    
  
  

    

      
0
      
EAST
      
7.861819
      
E12000006
      
C000G
    
    

      
1
      
EAST MIDLANDS
      
6.482373
      
E12000004
      
C000E
    
    

      
2
      
LONDON
      
8.220906
      
E12000007
      
C000H
    
    

      
3
      
NORTH EAST
      
17.543943
      
E12000001
      
C000A
    
    

      
4
      
NORTH WEST
      
8.965068
      
E12000002
      
C000B
    
    

      
5
      
SOUTH EAST
      
9.205414
      
E12000008
      
C000J
    
    

      
6
      
SOUTH WEST
      
7.618154
      
E12000009
      
C000K
    
    

      
7
      
WALES
      
6.184247
      
W92000004
      
A0004
    
    

      
8
      
WEST MIDLANDS
      
6.525374
      
E12000005
      
C000F
    
    

      
9
      
YORKSHIRE AND THE HUMBER
      
8.646171
      
E12000003
      
C000D
    
  

In [5]:

    

maps['IPF'].describe()


    

    
Out[5]:






  

    

      

      
Rate per 100,000 (standardised)
    
  
  

    

      
count
      
10.000000
    
    

      
mean
      
13.155275
    
    

      
std
      
1.394424
    
    

      
min
      
11.214911
    
    

      
25%
      
12.187088
    
    

      
50%
      
12.841522
    
    

      
75%
      
14.435973
    
    

      
max
      
15.200417
    
  

In [6]:

    

map = folium.Map(location=[54.2, -2.45], zoom_start=5)
map.geo_json(geo_path='gor.json', data_out='GorIPF.json', data=maps['IPF'],
      columns=['GOR10CD', 'Rate per 100,000 (standardised)'],
      key_on='feature.properties.GOR10CD',
      threshold_scale=[11, 12, 13, 14, 15],
      fill_color='PuBu', fill_opacity=0.7, line_opacity=0.3,
      legend_name='Male IPF deaths by region per 100,000 population (1975-2012)')
map.create_map(path='IPF_death_per100000.html')

map = folium.Map(location=[54.2, -2.45], zoom_start=5)
map.geo_json(geo_path='gor.json', data_out='GorMES.json', data=maps['All Mesothelioma'],
      columns=['GOR10CD', 'Rate per 100,000 (standardised)'],
      key_on='feature.properties.GOR10CD',
 #%%!     threshold_scale=[6, 7, 8, 9, 10, 16],
      fill_color='PuBu', fill_opacity=0.7, line_opacity=0.3,
      legend_name='Male Mesothelioma deaths by region per 100,000 population (1975-2012)')
map.create_map(path='Mesothelioma_death_per100000.html')

map = folium.Map(location=[54.2, -2.45], zoom_start=5)
map.geo_json(geo_path='gor.json', data_out='GorASB.json', data=maps['Asbestosis'],
      columns=['GOR10CD', 'Rate per 100,000 (standardised)'],
      key_on='feature.properties.GOR10CD',
     # threshold_scale=[11, 12, 13, 14, 15],
      fill_color='PuBu', fill_opacity=0.7, line_opacity=0.3,
      legend_name='Male Asbestosis deaths by region per 100,000 population (1975-2012)')
map.create_map(path='Asbestosis_death_per100000.html')


    

In [7]:

    

display.IFrame('IPF_death_per100000.html', '100%', 500)


    

    
Out[7]:





        
        

In [8]:

    

display.IFrame('Mesothelioma_death_per100000.html', '100%', 500)


    

    
Out[8]:





        
        

In [9]:

    

display.IFrame('Asbestosis_death_per100000.html', '100%', 500)


    

    
Out[9]:





        
        

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