Get Started with GeoViews¶
Table of Contents
Introduction¶
GeoViews is a Python library that makes it easy to explore and visualize geographical
GeoViews is built on the HoloViews library for building flexible visualizations of multidimensional data
You can install GeoViews and its dependencies using conda:
In [1]:
import geoviews as gv
import geoviews.feature as gf
import xarray as xr
from cartopy import crs
gv.extension('bokeh', 'matplotlib')
We can visualize the global as following
In [2]:
gf.ocean
Out[2]:
and we can improve it as follows
In [3]:
gf.ocean.options(
'Feature', projection=crs.Geostationary(), global_extent=True, height=525,width=525,
)
Out[3]:
We can show the lands only too
In [4]:
gf.land.options(
'Feature', projection=crs.Geostationary(), global_extent=True, height=525,width=525,
)
Out[4]:
with the borders
In [5]:
(gf.ocean * gf.land * gf.coastline * gf.borders).options(
'Feature', projection=crs.Geostationary(), global_extent=True, height=525,width=525,
)
Out[5]:
World Population Map¶
Map 1¶
We will consider first some datasets already uploaded in geoviews Library
In [6]:
import geopandas as gpd
In [7]:
d1=gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
In [8]:
d1.head(n=10)
Out[8]:
World population Map
In [9]:
gv.Polygons(d1, vdims=[('pop_est','Population'), ('name', 'Country')]).options(
tools=['hover'], width=800, height=500, projection=crs.Robinson()
)
Out[9]:
Saving the map into an html file
In [10]:
g1=gv.Polygons(d1, vdims=[('pop_est','Population'), ('name', 'Country')]).options(
tools=['hover'], width=800, height=500, projection=crs.Robinson()
)
In [11]:
renderer = gv.renderer('bokeh')
In [12]:
renderer.save(g1, 'population')
Map 2¶
In [13]:
import pandas as pd
import geoviews as gv
gv.extension('bokeh')
In [14]:
cities = pd.read_csv('cities.csv', encoding="ISO-8859-1")
In [15]:
population = gv.Dataset(cities, kdims=['City', 'Country', 'Year'])
In [16]:
population.columns
Out[16]:
In [17]:
points = population.to(gv.Points, ['Longitude', 'Latitude'], ['Population', 'City', 'Country'])
tiles = gv.tile_sources.StamenWatercolor
In [18]:
%%opts Points (size=0.003 cmap='viridis') [tools=['hover'] size_index=2 color_index=2 width=600]
tiles * points
Out[18]:
In [19]:
%%opts Points (size=0.003 cmap='viridis') [tools=['hover'] size_index=2 color_index=2 width=800 height=600 xaxis=None yaxis=None toolbar=None]
g2=tiles * points
In [20]:
renderer = gv.renderer('bokeh')
In [21]:
renderer.save(g2, 'population2')
In [22]:
g2
Out[22]:
World GDP Map¶
Loading Libraries
In [23]:
import pandas as pd
import geoviews as gv
gv.extension('bokeh')
Importing the data: The data that I will be using here is available here
In [24]:
df = pd.read_csv('gdp_gps_years.csv', encoding="ISO-8859-1")
In [25]:
df.columns
Out[25]:
In [26]:
gdp = gv.Dataset(df, kdims=['Country', 'Year'])
In [27]:
gdp.columns
Out[27]:
In [28]:
points = gdp.to(gv.Points, ['Longitude', 'Latitude'], ['GDP(current$)', 'Country'])
tiles = gv.tile_sources.StamenWatercolor
In [29]:
%%opts Points (size=0.000015 cmap='viridis') [tools=['hover'] size_index=2 color_index=2 width=800 height=600 xaxis=None yaxis=None toolbar=None]
tiles * points
Out[29]:
In [30]:
renderer = gv.renderer('bokeh')
In [31]:
gdp1=tiles * points
In [32]:
renderer.save(gdp1, 'gdp1')