anisotropi4/.blocks

## .blocks
Released license:Apache2
height:780
border:no

Population Density and Distance from a Railway Station in Great Britain

Source: Office for National Statistics and Library of Scotland Output Area datasets licensed under the Open Government Licence v.3.0.

Source: Library of UK Parliament Output Area classifications licensed under the Open Government License v.3.0

## README.md

      
    Raw
  

              README.md
            
          
    Railway Station Distance in Great Britain

The Office for National Statistics and the Nation Record of Scotland provide population and shapefile data for the 2011 census for Great Britain. This calculates and shows the distance from railway stations to the central point of the 2011 Census population Output Area (OA11) in a number of formats including an interactive web visualisation.
Creating the datafiles and associated GeoJSON and vector tiles

Once the tippecanoe build and python are met run the script to create the population density output for England, Scotland and Wales:
$ ./prepublish

This downloads Census Output Area population ESRI Shape files and creates the shp, GeoJSON in the base directory, and the vector-tiles in the tiles directory. The vector-tiles layer created using a local build of the Mapbox tippecanoe toolset uses the Leaflet JavaScript library to create an interactive web visualisation.
Dependencies

tippecanoe dependencies

To download and install the Mapbox tippecanoe tool run the script:
$ ./build.sh

If tippecanoe is missing compile dependencies

Install the build-essential, libsqlite3 and zlib1g-dev libraries.
On an Debian based Linux system:
$ sudo apt install build-essential libsqlite3-dev zlib1g-dev

python dependencies

For ease of use manage package python packages dependencies with a local virtual environment venv:
$ virtualenv venv
$ source venv/bin/activate
$ pip install -r requirements.txt

If virtualenv is missing install the python virtualenv package

$ sudo apt install virtualenv

Data License

Licenses for the digital boundaries and reference map data:

Source: Office for National Statistics licensed under the Open Government Licence v.3.0.
Source: National Records Scotland data (c) Crown copyright and database right 2021.
Contains OS data (c) Crown copyright and database right 2021.


## density.py
#!/usr/bin/env python3

import os
from functools import partial
import numpy as np
from fiona.transform import transform_geom

import pandas as pd
import geopandas as gp
from shapely.ops import transform
from shapely.geometry import Point

from scipy.spatial import cKDTree

pd.set_option('display.max_columns', None)

# EPSG:4326 WG 84
# EPSG:32630
# EPSG:27700 OS GB36

URBANTYPES = {'Large Town',
         'Large Town in Conurbation',
         'Core City (outside London)',
         'Village or small community in Conurbation',
         'Other City',
         'Small Town in Conurbation',
         'Small Town',
         'Medium Town',
         'Medium Town in Conurbation',
         'Core City (London)',
         'Village or Small Community in Conurbation'}

def _set_precision(precision=6):
    def _precision(x, y, z=None):
        return tuple([round(i, precision) for i in [x, y, z] if i])
    return partial(transform, _precision)

def nearest_stations(c, stations):
    stree = cKDTree(stations['geometry'].apply(lambda v: (v.x, v.y)).to_list())
    return stree.query(c.centroid.apply(lambda v: (v.x, v.y)).to_list(), k=1)

print('Load Output Area Data')
TOWNDATA = pd.read_csv('oa-classification-csv.csv')

TOWNDATA['name'] = TOWNDATA['bua_name']
IDX1 = TOWNDATA['bua_name'] == 'None'
TOWNDATA.loc[IDX1, 'name'] = TOWNDATA.loc[IDX1, 'la_name']
TOWNDATA['Town'] = TOWNDATA['name']
for k in [' BUA in Conurbation', ' BUASD', ' BUA']:
    TOWNDATA['Town'] = TOWNDATA['Town'].str.replace(k, '')

TOWNS = TOWNDATA.groupby(['bua_code', 'name', 'region_name']).filter(lambda v: v['population'].sum() > 1)
del TOWNDATA

IDX2 = TOWNS['citytownclassification'].isin(URBANTYPES)
TOWNS['urban'] = 'N'
TOWNS.loc[IDX2, 'urban'] = 'Y'

print('Loaded Output Area Data')
print('Load Scotland')
CRS = 'EPSG:32630'

SC = gp.read_file('work/OutputArea2011_MHW.shp')
SC = SC.to_crs(CRS)
KEYS = ['code', 'Popcount', 'SHAPE_1_Ar', 'DataZone', 'geometry']
G1 = SC[KEYS].set_index('DataZone').join(TOWNS.set_index('lsoa_code', drop=False))
G1 = G1.rename(columns={'population': 'lsoa_population'})
G1 = G1.rename(columns={'code': 'OA11CD', 'SHAPE_1_Ar': 'Area', 'Popcount': 'population'})
G1 = G1.drop(columns='outputarea_code')
print('Loaded Scotland')

print('Load England and Wales')
EW = gp.read_file('work/Output_Areas__December_2011__Boundaries_EW_BGC.shp')
EW = EW.to_crs(CRS)
KEYS = ['OA11CD', 'Shape__Are', 'geometry']
G2 = EW[KEYS].set_index('OA11CD', drop=False).join(TOWNS.set_index('outputarea_code'))
G2 = G2.rename(columns={'code': 'OA11CD', 'Shape__Are': 'Area'})
G2['lsoa_population'] = G2['population']
print('Loaded England and Wales')

DENSITY = G1.append(G2).reset_index(drop=True)
DENSITY['density'] = DENSITY['population'] / DENSITY['Area']

print('Nearest station')
def nearest_stations(c, stations):
    stree = cKDTree(stations['geometry'].apply(lambda v: (v.x, v.y)).to_list())
    return stree.query(c.centroid.apply(lambda v: (v.x, v.y)).to_list(), k=1)

_precision = _set_precision(5)
STATIONS = gp.read_file('download/output-stations.json')
for k in [' Rail Station', ' Railway', ' Station']:
    STATIONS['Station_Name'] = STATIONS['Station_Name'].str.replace(k, '')

STATIONS = STATIONS.to_crs(CRS)
IDXS = STATIONS['Status'] == 'active'
KEYS = ['Type', 'TIPLOC', 'CRS', 'Station_Name', 'geometry']

ACTIVE = STATIONS.loc[IDXS, KEYS].reset_index(drop=True).rename(columns={'Station_Name': 'Station', 'Type': 'type'})

POINTS = gp.GeoDataFrame(geometry=DENSITY.centroid, crs=CRS)

D, IDXD = nearest_stations(POINTS, ACTIVE)

DF1 = ACTIVE.drop(columns='geometry').loc[IDXD].reset_index(drop=True)
DF1['distance'] = D.round(0)
DENSITY = DENSITY.join(DF1)

POINTS['geometry'] = POINTS['geometry'].to_crs('EPSG:4326').apply(_precision)
POINTS['data'] = POINTS['geometry'].apply(lambda v: [v.x, v.y])
DENSITY['longitude'], DENSITY['latitude'] = zip(*POINTS.pop('data'))

DENSITY = DENSITY.to_crs('EPSG:4326')
DENSITY['geometry'] = DENSITY['geometry'].apply(_precision)

print("Write all")
IDX3 = DENSITY['urban'] == 'Y'
IDX4 = (DENSITY['density'] > 0.0015) & (DENSITY['bua_code'] != 'None') & ~IDX3
DENSITY.loc[IDX4, 'urban'] = 'S'

KEYS = ['OA11CD', 'region_name', 'Town', 'bua_code', 'lsoa_code', 'msoa_code', 'population', 'Area', 'density', 'constituency_name', 'urban', 'type', 'TIPLOC', 'CRS', 'Station', 'distance', 'geometry']

DENSITY[KEYS].to_file('shp/all_density.shp', crs='EPSG:4326')
DENSITY[KEYS].to_file('all_density.geojson', crs='EPSG:4326', driver='GeoJSON')

print("Write urban")
DENSITY.loc[IDX3, KEYS].to_file('shp/urban_density.shp', crs='EPSG:4326')
DENSITY.loc[IDX3, KEYS].to_file('urban_density.geojson', crs='EPSG:4326', driver='GeoJSON')

print("Write semiurban")
DENSITY.loc[IDX4, KEYS].to_file('shp/semiurban_density.shp', crs='EPSG:4326')
DENSITY.loc[IDX4, KEYS].to_file('semiurban_density.geojson', crs='EPSG:4326', driver='GeoJSON')

## index.html
<!DOCTYPE html>
<html>
    <head>
	      <title>Population Density and Distance from a Railway Station in Great Britain</title>
	      <meta charset="utf-8" />
	      <meta name="viewport" content="width=device-width, initial-scale=1.0">
	      <link rel="stylesheet" href="https://unpkg.com/leaflet@1.7.1/dist/leaflet.css"/>
        <link rel="stylesheet" href="//azavea.github.io/Leaflet.zoomdisplay/css/leaflet.zoomdisplay.css"/>
    </head>
    <body style='margin:0'>
	      <div id="map" style="width: 100vw; height: 100vh; background: PowderBlue"></div>

	      <script type="text/javascript"  src="https://unpkg.com/leaflet@1.7.1/dist/leaflet.js"></script>
	      <script type="text/javascript"  src="https://unpkg.com/leaflet.vectorgrid@1.3.0"></script>
        <script src="//azavea.github.io/Leaflet.zoomdisplay/js/leaflet.zoomdisplay.js"></script>
	      <script>
         const minZoom = 5;
         const maxZoom = 14;
         var map = L.map('map', {
             minZoom: minZoom,
             maxZoom: maxZoom,
             zoomControl: false,
         }).setView([55.53, -4.53], 6);

         function get_color(density, distance) {
             const colours = {
                 u0: '#ffffff',
                 u1: '#ffac8f',
                 u2: '#ff585f',
                 u3: '#ff642f',
                 u4: '#ff4000',
                 v0: '#ffffff',
                 v1: '#ffe0d5',
                 v2: '#ffc0ab',
                 v3: '#ffa080',
             };
             if (distance < 5000.0) return colours.v0;
             if (distance < 10000.0) return colours.v1;
             if (distance < 32000.0) return colours.v2;
             return colours.v3;
         };

         var vectorTileStyling = {
             density: function (properties, zoom) {
                 var density = properties.density;
                 var distance = properties.distance;
                 var color = get_color(density, distance);
                 return ({fill: true,
                          weight: 1.0,
                          fillColor: color,
                          color: color,
                          fillOpacity: 1.0,
                          opacity: 1.0,
                          zIndex: 1,
                 });
             },
             station: {
                 radius: 1.0,
                 color: "blue",
                 zIndex: 3,
             },
         };
         var mapUrl = "https://anisotropi4.github.io/distance/tiles/{z}/{x}/{y}.pbf";
         var mapVectorTileOptions = {
			       rendererFactory: L.canvas.tile,
             interactive: true,
			       attribution: '&copy; <a href="http://www.openstreetmap.org/copyright">OpenStreetMap</a> contributors, <a href="https://creativecommons.org/licenses/by-sa/2.0/">CC-BY-SA</a>, ONS and National Records Scotland<a href="http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/"> Open Government License</a>',
             maxNativeZoom: maxZoom,
             minZoom: minZoom,
			       vectorTileLayerStyles: vectorTileStyling,
         }
         var mapPbfLayer = L.vectorGrid.protobuf(mapUrl, mapVectorTileOptions).addTo(map);
         var mapVectorTileOptions = {
			       rendererFactory: L.canvas.tile,
             interactive: true,
			       attribution: '&copy; <a href="http://www.openstreetmap.org/copyright">OpenStreetMap</a> contributors, &copy;',
             maxNativeZoom: maxZoom, // minZoom must be higher than minNativeZoom
             minZoom: minZoom,
			       vectorTileLayerStyles: vectorTileStyling,
         }
         mapPbfLayer.bindPopup(function (layer) {
             const lookup = {
                 "OA11CD": "OA11 code",
                 "region_name": "Region",
                 "lsoa_code": "LSOA",
                 "msoa_code": "MSOA",
                 "population": "population",
                 "Area": "area",
                 "distance": "station distance",
                 "Station": "station name",
             };
             var p = layer.properties;
             var popupContent = "<b>" + p.Town + "</b>";
             p.Area = Math.round(p.Area);
             for (var [k, v] of Object.entries(p)) {
                 if (k == 'Area') v = Math.round(v / 100.0) / 100.0 + ' Ha';
                 if (k == 'distance') v = Math.round(v / 100.0) / 10.0 + ' km';
                 if (k in lookup) popupContent += "<br>" + lookup[k] + ": " + v;
             }
             return popupContent;
         });
         var zoomcontrol = new L.Control.Zoom({ position: 'bottomright' }).addTo(map)
	      </script>
    </body>
</html>

## LICENSE
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## prepublish
#!/bin/sh

export PYTHONUNBUFFERED=1

for i in download work shp output
do
    if [ ! -d ${i} ]; then
        mkdir -p ${i}
    fi
done

URL=https://www.nrscotland.gov.uk/files/geography
FILENAME=output-area-2011-mhw.zip
if [ ! -s download/${FILENAME} ]; then
    curl -L -o download/${FILENAME} ${URL}/${FILENAME}
fi

if [ ! -s work/OutputArea2011_MHW.shp ]; then
    (cd work; unzip ../download/${FILENAME})
fi

URL=https://opendata.arcgis.com/datasets
#FILENAME=d74074ae6dec4de59fdcd2744fefc1f9_0.zip
#FILENAME=f79fc19485704ce68523d8d70d84a913_0.zip
FILENAME=a76b2f87057b43d989d8f01733104d62_0.zip
if [ ! -s download/${FILENAME} ]; then
    curl -L -o download/${FILENAME} ${URL}/${FILENAME}
fi

if [ ! -s work/Output_Areas__December_2011__Boundaries_EW_BGC.shp ]; then
    (cd work; unzip ../download/${FILENAME})
fi

URL=https://researchbriefings.files.parliament.uk/documents/CBP-8322
FILENAME=oa-classification-csv.csv
if [ ! -s ${FILENAME} ]; then
    curl -L -o ${FILENAME} ${URL}/${FILENAME}
fi

URL=https://gist.githubusercontent.com/anisotropi4/54c29e6e6192cf758e12279e1981e889/raw/9639ccfdc24f6e7a585dc3340d7d9da4e828af5d/
FILENAME=output-stations.json
if [ ! -s download/${FILENAME} ]; then
    curl -L -o download/${FILENAME} ${URL}/${FILENAME}
fi

if [ ! -s all_density.geojson ]; then
    pip install -r requirements.txt
    ./density.py
fi

if [ ! -d tiles ]; then
    bin/tippecanoe --no-tile-compression --force -l density -Z5 -z14 --coalesce-smallest-as-needed --extend-zooms-if-still-dropping --detect-shared-borders --coalesce --reorder --hilbert -e tiles all_density.geojson
fi

## requirements.txt
geopandas >= 0.9.0
scipy >= 1.6.2
	Released license:Apache2
	height:780
	border:no

	Population Density and Distance from a Railway Station in Great Britain

	Source: Office for National Statistics and Library of Scotland Output Area datasets licensed under the Open Government Licence v.3.0.

	Source: Library of UK Parliament Output Area classifications licensed under the Open Government License v.3.0
	#!/usr/bin/env python3

	import os
	from functools import partial
	import numpy as np
	from fiona.transform import transform_geom

	import pandas as pd
	import geopandas as gp
	from shapely.ops import transform
	from shapely.geometry import Point

	from scipy.spatial import cKDTree

	pd.set_option('display.max_columns', None)

	# EPSG:4326 WG 84
	# EPSG:32630
	# EPSG:27700 OS GB36

	URBANTYPES = {'Large Town',
	'Large Town in Conurbation',
	'Core City (outside London)',
	'Village or small community in Conurbation',
	'Other City',
	'Small Town in Conurbation',
	'Small Town',
	'Medium Town',
	'Medium Town in Conurbation',
	'Core City (London)',
	'Village or Small Community in Conurbation'}

	def _set_precision(precision=6):
	def _precision(x, y, z=None):
	return tuple([round(i, precision) for i in [x, y, z] if i])
	return partial(transform, _precision)

	def nearest_stations(c, stations):
	stree = cKDTree(stations['geometry'].apply(lambda v: (v.x, v.y)).to_list())
	return stree.query(c.centroid.apply(lambda v: (v.x, v.y)).to_list(), k=1)

	print('Load Output Area Data')
	TOWNDATA = pd.read_csv('oa-classification-csv.csv')

	TOWNDATA['name'] = TOWNDATA['bua_name']
	IDX1 = TOWNDATA['bua_name'] == 'None'
	TOWNDATA.loc[IDX1, 'name'] = TOWNDATA.loc[IDX1, 'la_name']
	TOWNDATA['Town'] = TOWNDATA['name']
	for k in [' BUA in Conurbation', ' BUASD', ' BUA']:
	TOWNDATA['Town'] = TOWNDATA['Town'].str.replace(k, '')

	TOWNS = TOWNDATA.groupby(['bua_code', 'name', 'region_name']).filter(lambda v: v['population'].sum() > 1)
	del TOWNDATA

	IDX2 = TOWNS['citytownclassification'].isin(URBANTYPES)
	TOWNS['urban'] = 'N'
	TOWNS.loc[IDX2, 'urban'] = 'Y'

	print('Loaded Output Area Data')
	print('Load Scotland')
	CRS = 'EPSG:32630'

	SC = gp.read_file('work/OutputArea2011_MHW.shp')
	SC = SC.to_crs(CRS)
	KEYS = ['code', 'Popcount', 'SHAPE_1_Ar', 'DataZone', 'geometry']
	G1 = SC[KEYS].set_index('DataZone').join(TOWNS.set_index('lsoa_code', drop=False))
	G1 = G1.rename(columns={'population': 'lsoa_population'})
	G1 = G1.rename(columns={'code': 'OA11CD', 'SHAPE_1_Ar': 'Area', 'Popcount': 'population'})
	G1 = G1.drop(columns='outputarea_code')
	print('Loaded Scotland')

	print('Load England and Wales')
	EW = gp.read_file('work/Output_Areas__December_2011__Boundaries_EW_BGC.shp')
	EW = EW.to_crs(CRS)
	KEYS = ['OA11CD', 'Shape__Are', 'geometry']
	G2 = EW[KEYS].set_index('OA11CD', drop=False).join(TOWNS.set_index('outputarea_code'))
	G2 = G2.rename(columns={'code': 'OA11CD', 'Shape__Are': 'Area'})
	G2['lsoa_population'] = G2['population']
	print('Loaded England and Wales')

	DENSITY = G1.append(G2).reset_index(drop=True)
	DENSITY['density'] = DENSITY['population'] / DENSITY['Area']

	print('Nearest station')
	def nearest_stations(c, stations):
	stree = cKDTree(stations['geometry'].apply(lambda v: (v.x, v.y)).to_list())
	return stree.query(c.centroid.apply(lambda v: (v.x, v.y)).to_list(), k=1)

	_precision = _set_precision(5)
	STATIONS = gp.read_file('download/output-stations.json')
	for k in [' Rail Station', ' Railway', ' Station']:
	STATIONS['Station_Name'] = STATIONS['Station_Name'].str.replace(k, '')

	STATIONS = STATIONS.to_crs(CRS)
	IDXS = STATIONS['Status'] == 'active'
	KEYS = ['Type', 'TIPLOC', 'CRS', 'Station_Name', 'geometry']

	ACTIVE = STATIONS.loc[IDXS, KEYS].reset_index(drop=True).rename(columns={'Station_Name': 'Station', 'Type': 'type'})

	POINTS = gp.GeoDataFrame(geometry=DENSITY.centroid, crs=CRS)

	D, IDXD = nearest_stations(POINTS, ACTIVE)

	DF1 = ACTIVE.drop(columns='geometry').loc[IDXD].reset_index(drop=True)
	DF1['distance'] = D.round(0)
	DENSITY = DENSITY.join(DF1)

	POINTS['geometry'] = POINTS['geometry'].to_crs('EPSG:4326').apply(_precision)
	POINTS['data'] = POINTS['geometry'].apply(lambda v: [v.x, v.y])
	DENSITY['longitude'], DENSITY['latitude'] = zip(*POINTS.pop('data'))

	DENSITY = DENSITY.to_crs('EPSG:4326')
	DENSITY['geometry'] = DENSITY['geometry'].apply(_precision)

	print("Write all")
	IDX3 = DENSITY['urban'] == 'Y'
	IDX4 = (DENSITY['density'] > 0.0015) & (DENSITY['bua_code'] != 'None') & ~IDX3
	DENSITY.loc[IDX4, 'urban'] = 'S'

	KEYS = ['OA11CD', 'region_name', 'Town', 'bua_code', 'lsoa_code', 'msoa_code', 'population', 'Area', 'density', 'constituency_name', 'urban', 'type', 'TIPLOC', 'CRS', 'Station', 'distance', 'geometry']

	DENSITY[KEYS].to_file('shp/all_density.shp', crs='EPSG:4326')
	DENSITY[KEYS].to_file('all_density.geojson', crs='EPSG:4326', driver='GeoJSON')

	print("Write urban")
	DENSITY.loc[IDX3, KEYS].to_file('shp/urban_density.shp', crs='EPSG:4326')
	DENSITY.loc[IDX3, KEYS].to_file('urban_density.geojson', crs='EPSG:4326', driver='GeoJSON')

	print("Write semiurban")
	DENSITY.loc[IDX4, KEYS].to_file('shp/semiurban_density.shp', crs='EPSG:4326')
	DENSITY.loc[IDX4, KEYS].to_file('semiurban_density.geojson', crs='EPSG:4326', driver='GeoJSON')
	<!DOCTYPE html>
	<html>
	<head>
	<title>Population Density and Distance from a Railway Station in Great Britain</title>
	<meta charset="utf-8" />
	<meta name="viewport" content="width=device-width, initial-scale=1.0">
	<link rel="stylesheet" href="https://unpkg.com/leaflet@1.7.1/dist/leaflet.css"/>
	<link rel="stylesheet" href="//azavea.github.io/Leaflet.zoomdisplay/css/leaflet.zoomdisplay.css"/>
	</head>
	<body style='margin:0'>
	<div id="map" style="width: 100vw; height: 100vh; background: PowderBlue"></div>

	<script type="text/javascript" src="https://unpkg.com/leaflet@1.7.1/dist/leaflet.js"></script>
	<script type="text/javascript" src="https://unpkg.com/leaflet.vectorgrid@1.3.0"></script>
	<script src="//azavea.github.io/Leaflet.zoomdisplay/js/leaflet.zoomdisplay.js"></script>
	<script>
	const minZoom = 5;
	const maxZoom = 14;
	var map = L.map('map', {
	minZoom: minZoom,
	maxZoom: maxZoom,
	zoomControl: false,
	}).setView([55.53, -4.53], 6);

	function get_color(density, distance) {
	const colours = {
	u0: '#ffffff',
	u1: '#ffac8f',
	u2: '#ff585f',
	u3: '#ff642f',
	u4: '#ff4000',
	v0: '#ffffff',
	v1: '#ffe0d5',
	v2: '#ffc0ab',
	v3: '#ffa080',
	};
	if (distance < 5000.0) return colours.v0;
	if (distance < 10000.0) return colours.v1;
	if (distance < 32000.0) return colours.v2;
	return colours.v3;
	};

	var vectorTileStyling = {
	density: function (properties, zoom) {
	var density = properties.density;
	var distance = properties.distance;
	var color = get_color(density, distance);
	return ({fill: true,
	weight: 1.0,
	fillColor: color,
	color: color,
	fillOpacity: 1.0,
	opacity: 1.0,
	zIndex: 1,
	});
	},
	station: {
	radius: 1.0,
	color: "blue",
	zIndex: 3,
	},
	};
	var mapUrl = "https://anisotropi4.github.io/distance/tiles/{z}/{x}/{y}.pbf";
	var mapVectorTileOptions = {
	rendererFactory: L.canvas.tile,
	interactive: true,
	attribution: '© <a href="http://www.openstreetmap.org/copyright">OpenStreetMap</a> contributors, <a href="https://creativecommons.org/licenses/by-sa/2.0/">CC-BY-SA</a>, ONS and National Records Scotland<a href="http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/"> Open Government License</a>',
	maxNativeZoom: maxZoom,
	minZoom: minZoom,
	vectorTileLayerStyles: vectorTileStyling,
	}
	var mapPbfLayer = L.vectorGrid.protobuf(mapUrl, mapVectorTileOptions).addTo(map);
	var mapVectorTileOptions = {
	rendererFactory: L.canvas.tile,
	interactive: true,
	attribution: '© <a href="http://www.openstreetmap.org/copyright">OpenStreetMap</a> contributors, ©',
	maxNativeZoom: maxZoom, // minZoom must be higher than minNativeZoom
	minZoom: minZoom,
	vectorTileLayerStyles: vectorTileStyling,
	}
	mapPbfLayer.bindPopup(function (layer) {
	const lookup = {
	"OA11CD": "OA11 code",
	"region_name": "Region",
	"lsoa_code": "LSOA",
	"msoa_code": "MSOA",
	"population": "population",
	"Area": "area",
	"distance": "station distance",
	"Station": "station name",
	};
	var p = layer.properties;
	var popupContent = "<b>" + p.Town + "</b>";
	p.Area = Math.round(p.Area);
	for (var [k, v] of Object.entries(p)) {
	if (k == 'Area') v = Math.round(v / 100.0) / 100.0 + ' Ha';
	if (k == 'distance') v = Math.round(v / 100.0) / 10.0 + ' km';
	if (k in lookup) popupContent += "<br>" + lookup[k] + ": " + v;
	}
	return popupContent;
	});
	var zoomcontrol = new L.Control.Zoom({ position: 'bottomright' }).addTo(map)
	</script>
	</body>
	</html>
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	limitations under the License.
	#!/bin/sh

	export PYTHONUNBUFFERED=1

	for i in download work shp output
	do
	if [ ! -d ${i} ]; then
	mkdir -p ${i}
	fi
	done

	URL=https://www.nrscotland.gov.uk/files/geography
	FILENAME=output-area-2011-mhw.zip
	if [ ! -s download/${FILENAME} ]; then
	curl -L -o download/${FILENAME} ${URL}/${FILENAME}
	fi

	if [ ! -s work/OutputArea2011_MHW.shp ]; then
	(cd work; unzip ../download/${FILENAME})
	fi

	URL=https://opendata.arcgis.com/datasets
	#FILENAME=d74074ae6dec4de59fdcd2744fefc1f9_0.zip
	#FILENAME=f79fc19485704ce68523d8d70d84a913_0.zip
	FILENAME=a76b2f87057b43d989d8f01733104d62_0.zip
	if [ ! -s download/${FILENAME} ]; then
	curl -L -o download/${FILENAME} ${URL}/${FILENAME}
	fi

	if [ ! -s work/Output_Areas__December_2011__Boundaries_EW_BGC.shp ]; then
	(cd work; unzip ../download/${FILENAME})
	fi

	URL=https://researchbriefings.files.parliament.uk/documents/CBP-8322
	FILENAME=oa-classification-csv.csv
	if [ ! -s ${FILENAME} ]; then
	curl -L -o ${FILENAME} ${URL}/${FILENAME}
	fi

	URL=https://gist.githubusercontent.com/anisotropi4/54c29e6e6192cf758e12279e1981e889/raw/9639ccfdc24f6e7a585dc3340d7d9da4e828af5d/
	FILENAME=output-stations.json
	if [ ! -s download/${FILENAME} ]; then
	curl -L -o download/${FILENAME} ${URL}/${FILENAME}
	fi

	if [ ! -s all_density.geojson ]; then
	pip install -r requirements.txt
	./density.py
	fi

	if [ ! -d tiles ]; then
	bin/tippecanoe --no-tile-compression --force -l density -Z5 -z14 --coalesce-smallest-as-needed --extend-zooms-if-still-dropping --detect-shared-borders --coalesce --reorder --hilbert -e tiles all_density.geojson
	fi