Interactive plot of simple vector features (polygons) with Bokeh and GeoPandas¶

Data used: Boundary-Line™ (https://osdatahub.os.uk/downloads/open/BoundaryLine)

In [2]:

# import libraries
import geopandas as gpd
from bokeh.io import output_notebook
from bokeh.models import (
    CategoricalColorMapper, LogColorMapper, GeoJSONDataSource
)
from bokeh.palettes import viridis
from bokeh.plotting import figure, show
from bokeh.tile_providers import CARTODBPOSITRON_RETINA, get_provider

In [3]:

# set inline plots
output_notebook()

Loading BokehJS ...

In [4]:

# specify map tile provider
tile_provider = get_provider(CARTODBPOSITRON_RETINA)

In [2]:

# import data
data = gpd.read_file(
    "data/os_bdline/data/bdline_gb.gpkg", layer="greater_london_const"
)
data["Name"] = data["Name"].str.slice(stop=-18)

In [3]:

# view data
data.head(5)

Out[3]:

	Name	Area_Code	Area_Description	File_Name	Feature_Serial_Number	Collection_Serial_Number	Global_Polygon_ID	Admin_Unit_ID	Census_Code	Hectares	Non_Inland_Area	Area_Type_Code	Area_Type_Description	Non_Area_Type_Code	Non_Area_Type_Description	geometry
0	Havering and Redbridge	LAC	Greater London Authority Assembly Constituency	GREATER_LONDON_AUTHORITY	1	1	112074	41452	E32000009	17089.120	215.025	VA	CIVIL VOTING AREA	None	None	MULTIPOLYGON (((552057.328 178434.366, 552047....
1	Croydon and Sutton	LAC	Greater London Authority Assembly Constituency	GREATER_LONDON_AUTHORITY	2	2	112056	41443	E32000005	13033.641	0.000	VA	CIVIL VOTING AREA	None	None	MULTIPOLYGON (((531409.308 171042.621, 531439....
2	Bexley and Bromley	LAC	Greater London Authority Assembly Constituency	GREATER_LONDON_AUTHORITY	3	3	112066	41448	E32000002	21444.820	373.731	VA	CIVIL VOTING AREA	None	None	MULTIPOLYGON (((549944.025 181074.392, 549992....
3	South West	LAC	Greater London Authority Assembly Constituency	GREATER_LONDON_AUTHORITY	4	4	112076	41453	E32000013	15259.573	198.064	VA	CIVIL VOTING AREA	None	None	MULTIPOLYGON (((519458.306 179569.530, 519465....
4	Ealing and Hillingdon	LAC	Greater London Authority Assembly Constituency	GREATER_LONDON_AUTHORITY	5	5	112054	41442	E32000006	17124.643	0.000	VA	CIVIL VOTING AREA	None	None	MULTIPOLYGON (((504288.900 193543.098, 504292....

In [6]:

# reproject to web mercator
data = data.to_crs(3857)

In [7]:

# convert data source to GeoJSON
geo_source = GeoJSONDataSource(geojson=data.to_json())

Categorical¶

In [8]:

# generate unique colours for each constituency
const = list(set(data["Name"]))
palette = viridis(len(const))
color_map = CategoricalColorMapper(factors=const, palette=palette)

In [9]:

# define plot title
TITLE = (
    "Greater London Constituencies. Contains OS Data" +
    " © Crown copyright and database right 2021."
)

In [10]:

# configure plot
p = figure(
    title=TITLE,
    tools="wheel_zoom, pan, reset, hover, save",
    x_axis_location=None,
    y_axis_location=None,
    tooltips=[("Name", "@Name"), ("Hectares", "@Hectares")],
    x_axis_type="mercator",
    y_axis_type="mercator"
)

p.grid.grid_line_color = None

p.hover.point_policy = "follow_mouse"

p.patches(
    "xs",
    "ys",
    source=geo_source,
    fill_color={"field": "Name", "transform": color_map},
    line_color="white",
    line_width=.5,
    fill_alpha=.7
)

p.add_tile(tile_provider)

# display plot
show(p)