Analysis of LSOA results: mRS mean shift#

file_name = 'lsoa_base'
limit_to_england = False

data_field = 'mean_shift'
data_name = 'mRS mean shift'

Proportions of stroke types: Analysis of SAMueL data#

Data from SAMueL using NIHSS 11+ as a surrogate for LVO:

Admission type

All arrivals

Arrival within 6 hrs known onset

Arrival within 4 hrs known onset

Proportion all admissions

100

42.9

37.1

Proportion haemorrhagic

11.5

13.6

14.1

Proportion ischaemic

88.5

86.4

85.9

Proportion ischaemic with NIHSS 0-10

74.9

67.4

65.7

Proportion ischaemic with NIHSS 11+

25.1

32.6

34.3

For original analysis see: https://samuel-book.github.io/samuel-1/descriptive_stats/10_using_nihss_10_for_lvo.html

Import packages#

import contextily as ctx
import geopandas
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import os

pd.set_option('display.max_rows', 150)

Load data#

Load shape file#

filename = "zip://./data/Lower_layer_super_output_areas_(E+W)_2011_Boundaries_(Generalised_Clipped)_V2.zip"
lsoa_gdf = geopandas.read_file(filename, crs='EPSG:27700')
lsoa_gdf.head()
LSOA11CD LSOA11NM LSOA11NMW geometry
0 E01000001 City of London 001A City of London 001A POLYGON ((532105.092 182011.230, 532162.491 18...
1 E01000002 City of London 001B City of London 001B POLYGON ((532746.813 181786.891, 532671.688 18...
2 E01000003 City of London 001C City of London 001C POLYGON ((532135.145 182198.119, 532158.250 18...
3 E01000005 City of London 001E City of London 001E POLYGON ((533807.946 180767.770, 533649.063 18...
4 E01000006 Barking and Dagenham 016A Barking and Dagenham 016A POLYGON ((545122.049 184314.931, 545271.917 18...
# Load country outline
outline = geopandas.read_file('./data/Countries_(December_2022)_GB_BFC.zip')
if limit_to_england:
    mask = outline['CTRY22NM'] == 'England'
    outline = outline[mask]
else:
    mask = (outline['CTRY22NM'] == 'England') | (outline['CTRY22NM'] == 'Wales')
    outline = outline[mask]
outline
FID CTRY22CD CTRY22NM CTRY22NMW BNG_E BNG_N LONG LAT GlobalID SHAPE_Leng SHAPE_Area geometry
0 1 E92000001 England Lloegr 394883 370883 -2.07811 53.23497 {096C3540-B006-4D2D-BCDF-B3E945B80D51} 1.358939e+07 1.304623e+11 MULTIPOLYGON (((83962.840 5401.150, 83970.680 ...
2 3 W92000004 Wales Cymru 263405 242881 -3.99417 52.06741 {1DFA050A-C94B-4E58-8210-DE8274C7C33E} 3.273688e+06 2.078260e+10 MULTIPOLYGON (((322081.699 165165.901, 322082....

Load hospital info#

hospitals_gdf = geopandas.read_file('./data/stroke_hospitals_2022.csv')
hospitals_gdf.geometry = geopandas.points_from_xy(
        hospitals_gdf.Easting, hospitals_gdf.Northing)
# Set crs for geometry, using epsg4326 for lat/long
hospitals_gdf = hospitals_gdf.set_crs(epsg=27700)

if limit_to_england:
    mask = hospitals_gdf['Country'] ==  'England'
    hospitals_gdf = hospitals_gdf[mask]
hospitals_gdf.head()
Postcode Hospital_name Use_IVT Use_MT Use_MSU Country Strategic Clinical Network Health Board / Trust Stroke Team SSNAP name ... ivt_rate Easting Northing long lat Neuroscience 30 England Thrombectomy Example hospital_city Notes geometry
0 RM70AG RM70AG 1 1 1 England London SCN Barking Havering and Redbridge University Hospitals N... Queens Hospital Romford HASU ... 11.9 551118 187780 0.179030640661934 51.5686465521504 1 0 Romford POINT (551118.000 187780.000)
1 E11BB E11BB 1 1 1 England London SCN Barts Health NHS Trust The Royal London Hospital Royal London Hospital HASU ... 13.4 534829 181798 -0.0581329916047372 51.5190178361295 1 1 Royal London POINT (534829.000 181798.000)
2 SW66SX SW66SX 1 1 1 England London SCN Imperial College Healthcare NHS Trust Charing Cross Hospital, London Charing Cross Hospital HASU ... 9.9 524226 176487 -0.212736111308184 51.4737165811988 1 1 Charing Cross POINT (524226.000 176487.000)
3 SE59RW SE59RW 1 1 1 England London SCN King's College Hospital NHS Foundation Trust King's College Hospital, London King's College Hospital HASU ... 15 532536 176228 -0.0932514945350423 51.4695052907757 1 0 Kings College POINT (532536.000 176228.000)
4 BR68ND BR68ND 1 0 0 England London SCN King's College Hospital NHS Foundation Trust Princess Royal University Hospital Princess Royal University Hospital HASU ... 13.3 543443 165032 0.0591464390000555 51.3662434074157 0 0 Princess Royal POINT (543443.000 165032.000)

5 rows × 22 columns

Load LSOA model output data#

lsoa_data = pd.read_csv(f'./output/{file_name}.csv')
lsoa_data.head(3).T
0 1 2
lsoa Adur 001A Adur 001B Adur 001C
closest_ivt_unit BN25BE BN25BE BN112DH
closest_ivt_time 17.6 18.7 17.6
closest_mt_unit BN25BE BN25BE BN25BE
closest_mt_time 17.6 18.7 19.8
transfer_mt_unit BN25BE BN25BE BN25BE
transfer_mt_time 0.0 0.0 31.6
mt_transfer_required False False True
msu_unit BN25BE BN25BE BN25BE
msu_time 17.6 18.7 19.8
ivt_drip_ship 107.6 108.7 107.6
ivt_mothership 107.6 108.7 109.8
mt_drip_ship 167.6 168.7 259.2
mt_mothership 167.6 168.7 169.8
drip_ship_lvo_untreated_probs [0.05 0.08 0.14 0.16 0.24 0.14 0.19] [0.05 0.08 0.14 0.16 0.24 0.14 0.19] [0.05 0.08 0.14 0.16 0.24 0.14 0.19]
drip_ship_nlvo_untreated_probs [0.2 0.26 0.12 0.13 0.14 0.06 0.09] [0.2 0.26 0.12 0.13 0.14 0.06 0.09] [0.2 0.26 0.12 0.13 0.14 0.06 0.09]
drip_ship_lvo_ivt_probs [0.09 0.09 0.13 0.15 0.22 0.12 0.2 ] [0.09 0.09 0.13 0.15 0.22 0.12 0.2 ] [0.09 0.09 0.13 0.15 0.22 0.12 0.2 ]
drip_ship_lvo_mt_probs [0.18 0.12 0.14 0.17 0.18 0.09 0.12] [0.18 0.12 0.14 0.17 0.18 0.09 0.12] [0.13 0.11 0.14 0.17 0.2 0.1 0.15]
drip_ship_nlvo_ivt_probs [0.36 0.22 0.11 0.12 0.1 0.03 0.06] [0.35 0.23 0.11 0.12 0.1 0.03 0.06] [0.36 0.22 0.11 0.12 0.1 0.03 0.06]
drip_ship_lvo_untreated_mean_utility 0.3319 0.3319 0.3319
drip_ship_nlvo_untreated_mean_utility 0.5997 0.5997 0.5997
drip_ship_lvo_ivt_mean_utility 0.3664 0.3664 0.3664
drip_ship_lvo_mt_mean_utility 0.4962 0.4962 0.441
drip_ship_nlvo_ivt_mean_utility 0.7045 0.7036 0.7045
drip_ship_lvo_ivt_added_utility 0.0345 0.0345 0.0345
drip_ship_lvo_mt_added_utility 0.1643 0.1643 0.1091
drip_ship_nlvo_ivt_added_utility 0.1048 0.1039 0.1048
drip_ship_lvo_untreated_cum_probs [0.05 0.13 0.27 0.43 0.67 0.81 1. ] [0.05 0.13 0.27 0.43 0.67 0.81 1. ] [0.05 0.13 0.27 0.43 0.67 0.81 1. ]
drip_ship_nlvo_untreated_cum_probs [0.2 0.46 0.58 0.71 0.85 0.91 1. ] [0.2 0.46 0.58 0.71 0.85 0.91 1. ] [0.2 0.46 0.58 0.71 0.85 0.91 1. ]
drip_ship_lvo_ivt_cum_probs [0.09 0.18 0.31 0.46 0.68 0.8 1. ] [0.09 0.18 0.31 0.46 0.68 0.8 1. ] [0.09 0.18 0.31 0.46 0.68 0.8 1. ]
drip_ship_lvo_mt_cum_probs [0.18 0.3 0.44 0.61 0.79 0.88 1. ] [0.18 0.3 0.44 0.61 0.79 0.88 1. ] [0.13 0.24 0.38 0.55 0.75 0.85 1. ]
drip_ship_nlvo_ivt_cum_probs [0.36 0.58 0.69 0.81 0.91 0.94 1. ] [0.35 0.58 0.69 0.81 0.91 0.94 1. ] [0.36 0.58 0.69 0.81 0.91 0.94 1. ]
drip_ship_lvo_untreated_mrs<=2 0.27 0.27 0.27
drip_ship_nlvo_untreated_mrs<=2 0.58 0.58 0.58
drip_ship_lvo_ivt_mrs<=2 0.31 0.31 0.31
drip_ship_lvo_mt_mrs<=2 0.44 0.44 0.38
drip_ship_nlvo_ivt_mrs<=2 0.69 0.69 0.69
drip_ship_lvo_ivt_shift [ 0.04 0.01 -0.01 -0.01 -0.02 -0.02 0.01] [ 0.04 0.01 -0.01 -0.01 -0.02 -0.02 0.01] [ 0.04 0.01 -0.01 -0.01 -0.02 -0.02 0.01]
drip_ship_lvo_mt_shift [ 0.13 0.04 0. 0.01 -0.06 -0.05 -0.07] [ 0.13 0.04 0. 0.01 -0.06 -0.05 -0.07] [ 0.08 0.03 0. 0.01 -0.04 -0.04 -0.04]
drip_ship_nlvo_ivt_shift [ 0.16 -0.04 -0.01 -0.01 -0.04 -0.03 -0.03] [ 0.15 -0.03 -0.01 -0.01 -0.04 -0.03 -0.03] [ 0.16 -0.04 -0.01 -0.01 -0.04 -0.03 -0.03]
drip_ship_lvo_untreated_mean_mRS 3.64 3.64 3.64
drip_ship_nlvo_untreated_mean_mRS 2.29 2.29 2.29
drip_ship_lvo_ivt_mean_mRS 3.48 3.48 3.48
drip_ship_lvo_mt_mean_mRS 2.8 2.8 3.1
drip_ship_nlvo_ivt_mean_mRS 1.71 1.72 1.71
drip_ship_lvo_ivt_mean_shift -0.16 -0.16 -0.16
drip_ship_lvo_mt_mean_shift -0.84 -0.84 -0.54
drip_ship_nlvo_ivt_mean_shift -0.58 -0.57 -0.58
drip_ship_lvo_ivt_improved 0.17 0.17 0.17
drip_ship_lvo_mt_improved 0.75 0.75 0.54
drip_ship_nlvo_ivt_improved 0.58 0.57 0.58
mothership_lvo_untreated_probs [0.05 0.08 0.14 0.16 0.24 0.14 0.19] [0.05 0.08 0.14 0.16 0.24 0.14 0.19] [0.05 0.08 0.14 0.16 0.24 0.14 0.19]
mothership_nlvo_untreated_probs [0.2 0.26 0.12 0.13 0.14 0.06 0.09] [0.2 0.26 0.12 0.13 0.14 0.06 0.09] [0.2 0.26 0.12 0.13 0.14 0.06 0.09]
mothership_lvo_ivt_probs [0.09 0.09 0.13 0.15 0.22 0.12 0.2 ] [0.09 0.09 0.13 0.15 0.22 0.12 0.2 ] [0.09 0.09 0.13 0.15 0.22 0.12 0.2 ]
mothership_lvo_mt_probs [0.18 0.12 0.14 0.17 0.18 0.09 0.12] [0.18 0.12 0.14 0.17 0.18 0.09 0.12] [0.18 0.12 0.14 0.17 0.18 0.09 0.12]
mothership_nlvo_ivt_probs [0.36 0.22 0.11 0.12 0.1 0.03 0.06] [0.35 0.23 0.11 0.12 0.1 0.03 0.06] [0.35 0.23 0.11 0.12 0.1 0.03 0.06]
mothership_lvo_untreated_mean_utility 0.3319 0.3319 0.3319
mothership_nlvo_untreated_mean_utility 0.5997 0.5997 0.5997
mothership_lvo_ivt_mean_utility 0.3664 0.3664 0.3664
mothership_lvo_mt_mean_utility 0.4962 0.4962 0.4962
mothership_nlvo_ivt_mean_utility 0.7045 0.7036 0.7036
mothership_lvo_ivt_added_utility 0.0345 0.0345 0.0345
mothership_lvo_mt_added_utility 0.1643 0.1643 0.1643
mothership_nlvo_ivt_added_utility 0.1048 0.1039 0.1039
mothership_lvo_untreated_cum_probs [0.05 0.13 0.27 0.43 0.67 0.81 1. ] [0.05 0.13 0.27 0.43 0.67 0.81 1. ] [0.05 0.13 0.27 0.43 0.67 0.81 1. ]
mothership_nlvo_untreated_cum_probs [0.2 0.46 0.58 0.71 0.85 0.91 1. ] [0.2 0.46 0.58 0.71 0.85 0.91 1. ] [0.2 0.46 0.58 0.71 0.85 0.91 1. ]
mothership_lvo_ivt_cum_probs [0.09 0.18 0.31 0.46 0.68 0.8 1. ] [0.09 0.18 0.31 0.46 0.68 0.8 1. ] [0.09 0.18 0.31 0.46 0.68 0.8 1. ]
mothership_lvo_mt_cum_probs [0.18 0.3 0.44 0.61 0.79 0.88 1. ] [0.18 0.3 0.44 0.61 0.79 0.88 1. ] [0.18 0.3 0.44 0.61 0.79 0.88 1. ]
mothership_nlvo_ivt_cum_probs [0.36 0.58 0.69 0.81 0.91 0.94 1. ] [0.35 0.58 0.69 0.81 0.91 0.94 1. ] [0.35 0.58 0.69 0.81 0.91 0.94 1. ]
mothership_lvo_untreated_mrs<=2 0.27 0.27 0.27
mothership_nlvo_untreated_mrs<=2 0.58 0.58 0.58
mothership_lvo_ivt_mrs<=2 0.31 0.31 0.31
mothership_lvo_mt_mrs<=2 0.44 0.44 0.44
mothership_nlvo_ivt_mrs<=2 0.69 0.69 0.69
mothership_lvo_ivt_shift [ 0.04 0.01 -0.01 -0.01 -0.02 -0.02 0.01] [ 0.04 0.01 -0.01 -0.01 -0.02 -0.02 0.01] [ 0.04 0.01 -0.01 -0.01 -0.02 -0.02 0.01]
mothership_lvo_mt_shift [ 0.13 0.04 0. 0.01 -0.06 -0.05 -0.07] [ 0.13 0.04 0. 0.01 -0.06 -0.05 -0.07] [ 0.13 0.04 0. 0.01 -0.06 -0.05 -0.07]
mothership_nlvo_ivt_shift [ 0.16 -0.04 -0.01 -0.01 -0.04 -0.03 -0.03] [ 0.15 -0.03 -0.01 -0.01 -0.04 -0.03 -0.03] [ 0.15 -0.03 -0.01 -0.01 -0.04 -0.03 -0.03]
mothership_lvo_untreated_mean_mRS 3.64 3.64 3.64
mothership_nlvo_untreated_mean_mRS 2.29 2.29 2.29
mothership_lvo_ivt_mean_mRS 3.48 3.48 3.48
mothership_lvo_mt_mean_mRS 2.8 2.8 2.8
mothership_nlvo_ivt_mean_mRS 1.71 1.72 1.72
mothership_lvo_ivt_mean_shift -0.16 -0.16 -0.16
mothership_lvo_mt_mean_shift -0.84 -0.84 -0.84
mothership_nlvo_ivt_mean_shift -0.58 -0.57 -0.57
mothership_lvo_ivt_improved 0.17 0.17 0.17
mothership_lvo_mt_improved 0.75 0.75 0.75
mothership_nlvo_ivt_improved 0.58 0.57 0.57
drip_ship_nlvo_ivt_added_mrs<=2 0.11 0.11 0.11
drip_ship_lvo_ivt_added_mrs<=2 0.04 0.04 0.04
drip_ship_lvo_mt_added_mrs<=2 0.17 0.17 0.11
mothership_nlvo_ivt_added_mrs<=2 0.11 0.11 0.11
mothership_lvo_ivt_added_mrs<=2 0.04 0.04 0.04
mothership_lvo_mt_added_mrs<=2 0.17 0.17 0.17
weighted_drip_ship_added_utility 0.031414 0.031311 0.025895
weighted_mothership_added_utility 0.031414 0.031311 0.031311
weighted_drip_ship_added_mrs<=2 0.03305 0.03305 0.02705
weighted_mothership_added_mrs<=2 0.03305 0.03305 0.03305
weighted_drip_ship_mean_shift -0.1643 -0.16315 -0.1343
weighted_mothership_mean_shift -0.1643 -0.16315 -0.16315
LSOA_predicted_admissions 2.550427 2.550427 2.550427
# Merge with shape file

lsoa_data_gdf = lsoa_gdf.merge(lsoa_data, left_on='LSOA11NM', right_on='lsoa', how='right')
lsoa_data_gdf.head()
LSOA11CD LSOA11NM LSOA11NMW geometry lsoa closest_ivt_unit closest_ivt_time closest_mt_unit closest_mt_time transfer_mt_unit ... mothership_nlvo_ivt_added_mrs<=2 mothership_lvo_ivt_added_mrs<=2 mothership_lvo_mt_added_mrs<=2 weighted_drip_ship_added_utility weighted_mothership_added_utility weighted_drip_ship_added_mrs<=2 weighted_mothership_added_mrs<=2 weighted_drip_ship_mean_shift weighted_mothership_mean_shift LSOA_predicted_admissions
0 E01031349 Adur 001A Adur 001A POLYGON ((525328.061 105725.349, 525436.843 10... Adur 001A BN25BE 17.6 BN25BE 17.6 BN25BE ... 0.11 0.04 0.17 0.031414 0.031414 0.03305 0.03305 -0.16430 -0.16430 2.550427
1 E01031350 Adur 001B Adur 001B POLYGON ((524713.079 106781.551, 524753.189 10... Adur 001B BN25BE 18.7 BN25BE 18.7 BN25BE ... 0.11 0.04 0.17 0.031311 0.031311 0.03305 0.03305 -0.16315 -0.16315 2.550427
2 E01031351 Adur 001C Adur 001C POLYGON ((523976.094 108414.594, 524038.094 10... Adur 001C BN112DH 17.6 BN25BE 19.8 BN25BE ... 0.11 0.04 0.17 0.025895 0.031311 0.02705 0.03305 -0.13430 -0.16315 2.550427
3 E01031352 Adur 001D Adur 001D POLYGON ((524263.733 106628.407, 524317.000 10... Adur 001D BN112DH 17.6 BN25BE 19.8 BN25BE ... 0.11 0.04 0.17 0.025895 0.031311 0.02705 0.03305 -0.13430 -0.16315 2.550427
4 E01031370 Adur 001E Adur 001E POLYGON ((522978.319 106148.604, 523260.859 10... Adur 001E BN112DH 16.5 BN25BE 19.8 BN25BE ... 0.11 0.04 0.17 0.026245 0.031311 0.02705 0.03305 -0.13530 -0.16315 2.550427

5 rows × 105 columns

Calculate difference between Mothership and Drip and Ship#

lsoa_data_gdf[f'nlvo_ivt_{data_field}_mothership_minus_dripship'] = \
    lsoa_data_gdf[f'mothership_nlvo_ivt_{data_field}'] - lsoa_data_gdf[f'drip_ship_nlvo_ivt_{data_field}']

lsoa_data_gdf[f'lvo_mt_{data_field}_mothership_minus_dripship'] = \
    lsoa_data_gdf[f'mothership_lvo_mt_{data_field}'] - lsoa_data_gdf[f'drip_ship_lvo_mt_{data_field}']

Plots#

fig = plt.figure(figsize=(18,12))

################### Plot data values ###################

cols = [f'drip_ship_nlvo_ivt_{data_field}',
    f'mothership_nlvo_ivt_{data_field}',
    f'drip_ship_lvo_mt_{data_field}',
    f'mothership_lvo_mt_{data_field}']

titles = [f'Drip and ship nLVO {data_name}',
    f'Mothership nLVO {data_name}',
    f'Drip and ship LVO {data_name}',    
    f'Mothership LVO {data_name}']

# Indexes for subplots (to leave gaps on right for diff between Mothership and Drip and Ship)
indexes = [1, 2, 4, 5]

# Find maximum of data
vmin = np.min([lsoa_data_gdf[col].min() for col in cols])
vmax = np.max([lsoa_data_gdf[col].max() for col in cols])

for i in range(4):
    ax = fig.add_subplot(2, 3, indexes[i])

    # Plot data
    lsoa_data_gdf.plot(ax=ax, # Set which axes to use for plot (only one here)
                 column=cols[i], # Column to apply colour
                 antialiased=False, # Avoids artifact boundry lines
                 edgecolor='face', # Make LSOA boundry same colour as area
                 vmin=vmin, # Manual scale min (remove to make automatic)
                 vmax=vmax, # Manual scale max (remove to make automatic)
                 cmap='inferno_r', # Colour map to use
                 # Adjust size of colourmap key, and add label
                 legend_kwds={'shrink':0.5, 'label':f'{data_name.capitalize()}'},
                 # Set to display legend
                 legend=True)
    
    # Add country boarder
    outline.plot(ax=ax, edgecolor='k', facecolor='None', linewidth=1.0)
    
    # Add hospitals
    mask = hospitals_gdf['Use_IVT'] == '1'
    hospitals_gdf[mask].plot(ax=ax, edgecolor='k', facecolor='w', markersize=40, marker='o')
    mask = hospitals_gdf['Use_MT'] == '1'
    hospitals_gdf[mask].plot(ax=ax, edgecolor='k', facecolor='r', markersize=40, marker='o')

    # Tighten map around mainland England and Wales
    ax.set_xlim(120000)

    ax.set_title(titles[i])
    ax.set_axis_off() # Turn of axis line and numbers

################### Plot differences between mothership and drip and ship ###################

cols = [f'nlvo_ivt_{data_field}_mothership_minus_dripship',
    f'lvo_mt_{data_field}_mothership_minus_dripship']

# Find absolute maximum of data extent
vmax = np.max((np.abs([lsoa_data_gdf[col].min() for col in cols]), 
               np.abs([lsoa_data_gdf[col].max() for col in cols])))
vmin = -vmax

titles = [f'nLVO advantage of Mothership ({data_name})',
          f'LVO advantage of Mothership ({data_name})']

# Indexes for subplots
indexes = [3, 6]

for i in range(2):
    ax = fig.add_subplot(2, 3, indexes[i])

    lsoa_data_gdf.plot(ax=ax, # Set which axes to use for plot (only one here)
                 column=cols[i], # Column to apply colour
                 antialiased=False, # Avoids artifact boundry lines
                 edgecolor='face', # Make LSOA boundry same colour as area
                 vmin=vmin, # Manual scale min (remove to make automatic)
                 vmax=vmax, # Manual scale max (remove to make automatic)
                 cmap='bwr', # Colour map to use
                 # Adjust size of colourmap key, and add label
                 legend_kwds={'shrink':0.5, 'label':f'Advantage of Mothership ({data_name.capitalize()})'},
                 # Set to display legend
                 legend=True)

    # Add country boarder
    outline.plot(ax=ax, edgecolor='k', facecolor='None', linewidth=1.0)
    
    # Add hospitals
    mask = hospitals_gdf['Use_IVT'] == '1'
    hospitals_gdf[mask].plot(ax=ax, edgecolor='k', facecolor='w', markersize=40, marker='o')
    mask = hospitals_gdf['Use_MT'] == '1'
    hospitals_gdf[mask].plot(ax=ax, edgecolor='k', facecolor='r', markersize=40, marker='o')

    ax.set_title(titles[i])
    ax.set_axis_off() # Turn of axis line and numbers    

    # Tighten map around mainland England and Wales
    ax.set_xlim(120000)
    
plt.tight_layout(pad=1)
plt.savefig(f'./output/{data_field}_six_in_one.jpg', dpi=300, bbox_inches='tight')
plt.show()
../_images/ca41669bf745c720eebdc408f1a1fd75ce9fb03186dcc68d134b3b982974ad84.png

Plot weighted benefit

# Check weighted data exists
col = f'weighted_drip_ship_{data_field}'

if col not in lsoa_data_gdf.columns:
    print ('Required data does not exist')
else:
    lsoa_data_gdf[f'weighted_mothership_advantage_{data_field}'] = (
        lsoa_data_gdf[f'weighted_mothership_{data_field}'] - 
        lsoa_data_gdf[f'weighted_drip_ship_{data_field}'])

    fig = plt.figure(figsize=(18,6))

    # Set up columns to map
    cols = [
        f'weighted_drip_ship_{data_field}', 
        f'weighted_mothership_{data_field}',
        f'weighted_mothership_advantage_{data_field}']

    titles = [
        f'Weighted drip and ship for {data_name}',
        f'Weighted mothership for {data_name}',
        f'Weighted advantage of mothership for {data_name}']

    cmaps = ['inferno_r', 'inferno_r', 'bwr']

    # Find absolute maximum of data extent
    vmax = np.max([lsoa_data_gdf[col].max() for col in cols[0:2]])
    vmin = np.min([lsoa_data_gdf[col].min() for col in cols[0:2]])

    for i, col in enumerate(cols):
        ax = fig.add_subplot(1, 3, i+1)

        # Radjust vmin and max for advantage plot
        if i == 2:
            vmax = lsoa_data_gdf[col].max()
            vmin = lsoa_data_gdf[col].min()
            vmax = max(abs(vmax), abs(vmin))
            vmin = -vmax

        # Plot data
        lsoa_data_gdf.plot(ax=ax, # Set which axes to use for plot (only one here)
                    column=col, # Column to apply colour
                    antialiased=False, # Avoids artifact boundry lines
                    edgecolor='face', # Make LSOA boundry same colour as area
                    vmin=vmin, # Manual scale min (remove to make automatic)
                    vmax=vmax, # Manual scale max (remove to make automatic)
                    cmap=cmaps[i], # Colour map to use
                    # Adjust size of colourmap key, and add label
                    legend_kwds={'shrink':0.5, 'label':f'{data_name.capitalize()}'},
                    # Set to display legend
                    legend=True)
        
        # Add country boarder
        outline.plot(ax=ax, edgecolor='k', facecolor='None', linewidth=1.0)
        
        # Add hospitals
        mask = hospitals_gdf['Use_IVT'] == '1'
        hospitals_gdf[mask].plot(ax=ax, edgecolor='k', facecolor='w', markersize=40, marker='o')
        mask = hospitals_gdf['Use_MT'] == '1'
        hospitals_gdf[mask].plot(ax=ax, edgecolor='k', facecolor='r', markersize=40, marker='o')

        # Tighten map around mainland England and Wales
        ax.set_xlim(120000)

        # Add title
        ax.set_title(titles[i])
        ax.set_axis_off() # Turn of axis line and numbers
    
    plt.tight_layout(pad=1)
    plt.savefig(f'./output/{data_field}_weighted_results.jpg', dpi=300, bbox_inches='tight')
    plt.show()
../_images/a32c350a6e01fd1989baebe4a6a37e10049296a1e99b9aca695863eaf6b8ef67.png