Journey time Statistics¶

https://www.gov.uk/government/collections/journey-time-statistics

https://www.gov.uk/government/statistical-data-sets/journey-times-to-key-services-by-lower-super-output-area-jts05 https://www.gov.uk/government/statistical-data-sets/journey-times-to-key-services-by-local-authority-jts04

In [1]:

from __future__ import division

%matplotlib inline
import pandas as pd

In [2]:

gp='https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/485260/jts0505.xls'
xl=pd.ExcelFile(gp)
xl.sheet_names

Out[2]:

[u'Metadata', u'JTS0505']

In [3]:

tname='JTS0505'
dbd=xl.parse(tname,skiprows=6)
dbd[:3]

Out[3]:

	LSOA_code	Region	LA_Code	LA_Name	GP_pop	GPPTt	GPPT15n	GPPT30n	GPPT45n	GPPT60n	...	GPCyc60pct	GPCart	GPCar15n	GPCar30n	GPCar45n	GPCar60n	GPCar15pct	GPCar30pct	GPCar45pct	GPCar60pct
0	E01000001	London	E09000001	City of London	809	3.348711	2.117429	10	10	10	...	100	7.768724	10	10	10	10	100	100	100	100
1	E01000002	London	E09000001	City of London	947	8.154639	1.645195	10	10	10	...	100	8.527993	10	10	10	10	100	100	100	100
2	E01000003	London	E09000001	City of London	846	6.424466	2.365248	10	10	10	...	100	8.935269	10	10	10	10	100	100	100	100

3 rows × 32 columns

In [4]:

foodstore='https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/485262/jts0507.xls'
xl=pd.ExcelFile(foodstore)
xl.sheet_names

Out[4]:

[u'Metadata', u'JTS0507']

In [5]:

tname='JTS0507'
dbd2=xl.parse(tname,skiprows=6)
iwfood=dbd2[dbd2['LA_Code']=='E06000046']

In [6]:

iwfood[:3]

Out[6]:

	LSOA_code	Region	LA_Code	LA_Name	Town_pop	TownPTt	TownPT15n	TownPT30n	TownPT45n	TownPT60n	...	TownCyc60pct	TownCart	TownCar15n	TownCar30n	TownCar45n	TownCar60n	TownCar15pct	TownCar30pct	TownCar45pct	TownCar60pct
16777	E01017282	South East	E06000046	Isle of Wight	600	7.259422	2.000000	10.000000	10	10	...	100	6.593640	10.000000	10.000000	10.000000	10.000000	100	100	100	100
16778	E01017283	South East	E06000046	Isle of Wight	677	11.211987	2.035451	10.000000	10	10	...	100	7.705013	9.805022	9.805022	9.805022	9.805022	100	100	100	100
16779	E01017284	South East	E06000046	Isle of Wight	797	6.786982	1.146801	4.833124	10	10	...	100	6.948125	5.594730	10.000000	10.000000	10.000000	100	100	100	100

3 rows × 32 columns

In [7]:

iw=dbd[dbd['LA_Code']=='E06000046']

In [17]:

import fiona
code='E06000046'
geojson='https://github.com/martinjc/UK-GeoJSON/raw/master/json/statistical/eng/lsoa_by_lad/{}.json'.format(code)
!wget {geojson}
#fi=fiona.open('../IWgeodata/wards_by_lad/E06000046.json')
geojson_local='{}.json'.format(code)
fi=fiona.open(geojson_local)
centre_lat,centre_lon=((fi.bounds[0]+fi.bounds[2])/2,(fi.bounds[1]+fi.bounds[3])/2)

--2015-12-18 16:56:01--  https://github.com/martinjc/UK-GeoJSON/raw/master/json/statistical/eng/lsoa_by_lad/E06000046.json
Resolving github.com... 192.30.252.130
Connecting to github.com|192.30.252.130|:443... connected.
HTTP request sent, awaiting response... 302 Found
Location: https://raw.githubusercontent.com/martinjc/UK-GeoJSON/master/json/statistical/eng/lsoa_by_lad/E06000046.json [following]
--2015-12-18 16:56:01--  https://raw.githubusercontent.com/martinjc/UK-GeoJSON/master/json/statistical/eng/lsoa_by_lad/E06000046.json
Resolving raw.githubusercontent.com... 23.235.43.133
Connecting to raw.githubusercontent.com|23.235.43.133|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 288412 (282K) [text/plain]
Saving to: 'E06000046.json.1'

E06000046.json.1    100%[=====================>] 281.65K  --.-KB/s   in 0.09s  

2015-12-18 16:56:02 (3.22 MB/s) - 'E06000046.json.1' saved [288412/288412]

In [12]:

import folium

In [24]:

import folium
m = folium.Map([centre_lon,centre_lat], zoom_start=11)

m.choropleth(
    geo_path=geojson_local,
    data=iw,
    columns=['LSOA_code', 'GPPTt'],
    key_on='feature.properties.LSOA11CD',
    fill_color='PuBuGn', fill_opacity=1.0
    )

'''
m.choropleth(
    geo_path=geojson_local,
    data=iwfood,
    columns=['LSOA_code', 'FoodPTt'],
    key_on='feature.properties.LSOA11CD',
    fill_color='PuBuGn', fill_opacity=1.0
    )
'''
m

/Users/ajh59/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:9: FutureWarning: 'threshold_scale' default behavior has changed. Now you get a linear scale between the 'min' and the 'max' of your data. To get former behavior, use folium.utilities.split_six.

Out[24]:

In [113]:

import os
import time
from selenium import webdriver
from selenium.webdriver.support.ui import WebDriverWait

delay=5
fn='testmap.html'
tmpurl='file://{path}/{mapfile}'.format(path=os.getcwd(),mapfile=fn)
m.save(fn)


browser = webdriver.Firefox()
browser.get(tmpurl)
#Give the map tiles some time to load
time.sleep(delay)
browser.save_screenshot('map.png')
browser.quit()

In [ ]: