In [ ]:

#https://www.gov.uk/government/statistics/sub-national-electricity-and-gas-consumption-at-lsoa-msoa-and-igz-level-in-2015

In [1]:

import pandas as pd

In [ ]:

gasurl='https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/585723/LSOA_domestic_gas_2015.xlsx'
elecurl='https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/585872/LSOA_domestic_electricity_2015.xlsx'

In [2]:

gas=pd.read_excel('data/LSOA_domestic_gas_2015.xlsx',sheetname=1,skiprows=1)
gas.head()

Out[2]:

	Local Authority Name	Local Authority Code	MSOA Name	Middle Layer Super Output Area (MSOA) Code	LSOA Name	Lower Layer Super Output Area (LSOA) Code	Consumption (kWh)	Number of meters	Mean consumption (kWh per meter)	Median consumption (kWh per meter)
0	City of London	E09000001	City of London 001	E02000001	City of London 001A	E01000001	647907.34	58	11171	4046
1	City of London	E09000001	City of London 001	E02000001	City of London 001B	E01000002	292191.84	15	19479	7669
2	City of London	E09000001	City of London 001	E02000001	City of London 001C	E01000003	4483592.81	567	7908	6804
3	City of London	E09000001	City of London 001	E02000001	City of London 001E	E01000005	2867670.67	459	6248	3697
4	Barking and Dagenham	E09000002	Barking and Dagenham 016	E02000017	Barking and Dagenham 016A	E01000006	6003821.96	406	14788	14053

In [3]:

elec=pd.read_excel('data/LSOA_domestic_electricity_2015.xlsx',sheetname=1,skiprows=1)
elec.head()

Out[3]:

	Local Authority Name	Local Authority Code	Middle Layer Super Output Area (MSOA) Name	Middle Layer Super Output Area (MSOA) Code	Lower Layer Super Output Area (LSOA) Name	Lower Layer Super Output Area (LSOA) Code	Total domestic electricity consumption (kWh)	Total number of domestic electricity meters	Mean domestic electricity consumption (kWh per meter)	Median domestic electricity consumption (kWh per meter)
0	Hartlepool	E06000001	Hartlepool 001	E02002483	Hartlepool 001A	E01011954	2968986.80	966	3073	2611
1	Hartlepool	E06000001	Hartlepool 001	E02002483	Hartlepool 001B	E01011969	2036143.40	639	3186	2856
2	Hartlepool	E06000001	Hartlepool 001	E02002483	Hartlepool 001C	E01011970	1621503.90	453	3579	3231
3	Hartlepool	E06000001	Hartlepool 001	E02002483	Hartlepool 001D	E01011971	2111305.50	530	3984	3460
4	Hartlepool	E06000001	Hartlepool 001	E02002483	Hartlepool 001F	E01033465	2906157.53	827	3514	3134

In [4]:

iwLA='E06000046'
def geoJlocal(code):
    return '{}.json'.format(code)
geoJlocal(iwLA)

Out[4]:

'E06000046.json'

In [13]:

import folium
#color brewer palettes: ‘BuGn’, ‘BuPu’, ‘GnBu’, ‘OrRd’, ‘PuBu’, ‘PuBuGn’, ‘PuRd’, ‘RdPu’, ‘YlGn’, ‘YlGnBu’, ‘YlOrBr’, and ‘YlOrRd’.

import fiona

geojson_local='travel-times/E06000046.json'
fi=fiona.open(geojson_local)
centre_lat,centre_lon=((fi.bounds[0]+fi.bounds[2])/2,(fi.bounds[1]+fi.bounds[3])/2)

In [14]:

elecmap = folium.Map([centre_lon,centre_lat], zoom_start=11)
elecmap.choropleth(
    geo_path=geojson_local,
    data=elec[elec['Local Authority Code']=='E06000046'],
    columns=['Lower Layer Super Output Area (LSOA) Code', 'Total domestic electricity consumption (kWh)'],
    key_on='feature.properties.LSOA11CD',
    fill_color='PuBuGn', fill_opacity=1.0
    )

In [20]:

gasmap = folium.Map([centre_lon,centre_lat], zoom_start=11)
gasmap.choropleth(
    geo_path=geojson_local,
    data=gas[gas['Local Authority Code']=='E06000046'],
    columns=['Lower Layer Super Output Area (LSOA) Code', 'Consumption (kWh)'],
    key_on='feature.properties.LSOA11CD',
    fill_color='RdPu', fill_opacity=1.0
    )

In [16]:

elecmap

Out[16]:

In [21]:

gasmap

Out[21]: