Polyplot and Pointplot Using Geoplot Library

First, we will import Geoplot library. Next, we will load one of the sample datasets(geojson file) present in geoplot. In the below example, we are going to use “world” ,”contiguous_usa”,”usa_cities”,”melbourne” and “melbourne_schools” datasets. List of datasets present in geoplot are mentioned below:

• usa_cities
• contiguous_usa
• nyc_collision_factors
• nyc_boroughs
• ny_census
• obesity_by_state
• la_flights
• dc_roads
• nyc_map_pluto_sample
• nyc_collisions_sample
• boston_zip_codes
• boston_airbnb_listings
• napoleon_troop_movements
• nyc_fatal_collisions
• nyc_injurious_collisions
• nyc_police_precincts
• nyc_parking_tickets
• world
• melbourne
• melbourne_schools
• san_francisco
• san_francisco_street_trees_sample
• california_congressional_districts

We can add our own datasets by editing the datasets.py file. Click here for some free sample datasets. 

• If you have polygonal data, you can plot that using a geoplot polyplot.
• If your data consists of a bunch of points instead, you can display those points using pointplot.

Syntax :

geoplot.datasets.get_path(str)

Syntax for plotting:

geoplot.polyplot(var)
geoplot.pointplot(var)

Example:

World Dataset:

USA Dataset:

USA Cities Dataset:

Melbourne Dataset:

Melbourne Schools Dataset:

We can combine these two plots using overplotting. Overplotting is the act of stacking several different plots on top of one another, useful for providing additional context for our plots:

Example:

Output:

You may have noticed that this map of the United States appears to be odd. Because the Earth is a sphere, it is difficult to depict it in two dimensions. As a result, we use some type of projection, or means of flattening the sphere, whenever we take data off the sphere and place it on a map. When you plot data without a projection, or “carte blanche,” your map will be distorted. We can “correct” the distortions by picking up a projection method. Here we are going to use Albers equal-area and WebMercator projection.

Along with this, we are also going to add some other parameters such as hue, legend, cmap, and scheme.

• The hue parameter applies a colormap to a data column.
• The legend parameter toggles a legend.
• Change the colormap using matplotlib’s cmap.
• For a categorical colormap, use a scheme.

Example:

Output:

Spatial data, also known as geospatial data, GIS data, or geodata, is a type of numeric data that defines the geographic location of a physical object, such as a building, a street, a town, a city, a country, or other physical objects, using a geographic coordinate system. You may determine not just the position of an object, but also its length, size, area, and shape using spatial data.

To work with geospatial data in python we need the GeoPandas & GeoPlot library

GeoPandas is an open-source project to make working with geospatial data in python easier. GeoPandas extends the data types used by pandas to allow spatial operations on geometric types. Geometric operations are performed shapely. Geopandas further depends on fiona for file access and matplotlib for plotting. GeoPandas depends on its spatial functionality on a large geospatial, open-source stack of libraries (GEOS, GDAL, and PROJ). See the Dependencies section below for more details. 

Required dependencies:

• numpy
• pandas (version 0.24 or later)
• shapely (interface to GEOS)
• fiona (interface to GDAL)
• pyproj (interface to PROJ; version 2.2.0 or later)

Further, optional dependencies are:

• rtree (optional; spatial index to improve performance and required for overlay operations; interface to libspatialindex)
• psycopg2 (optional; for PostGIS connection)
• GeoAlchemy2 (optional; for writing to PostGIS)
• geopy (optional; For plotting, these additional for geocoding)

packages may be used:

• matplotlib (>= 2.2.0)
• mapclassify (>= 2.2.0)

Geoplot is a geospatial data visualization library for data scientists and geospatial analysts that want to get things done quickly. Below we’ll cover the basics of Geoplot and explore how it’s applied. Geoplot is for Python 3.6+ versions only.

Note: Please install all the dependencies and modules for the proper functioning of the given codes.