Introduction to geospatial vector data in Python
DS Python for GIS and Geoscience
October, 2020© 2020, Joris Van den Bossche and Stijn Van Hoey (mailto:jorisvandenbossche@gmail.com, mailto:stijnvanhoey@gmail.com). Licensed under CC BY 4.0 Creative Commons
%matplotlib inline
import pandas as pd
import geopandas
Geospatial data is often available from specific GIS file formats or data stores, like ESRI shapefiles, GeoJSON files, geopackage files, PostGIS (PostgreSQL) database, ...
We can use the GeoPandas library to read many of those GIS file formats (relying on the fiona
library under the hood, which is an interface to GDAL/OGR), using the geopandas.read_file
function.
For example, let's start by reading a shapefile with all the countries of the world (adapted from http://www.naturalearthdata.com/downloads/110m-cultural-vectors/110m-admin-0-countries/, zip file is available in the /data
directory), and inspect the data:
countries = geopandas.read_file("zip://./data/ne_110m_admin_0_countries.zip")
# or if the archive is unpacked:
# countries = geopandas.read_file("data/ne_110m_admin_0_countries/ne_110m_admin_0_countries.shp")
countries.head()
countries.plot()
What do we observe:
.head()
we can see the first rows of the dataset, just like we can do with Pandas.geometry
column and the different countries are represented as polygons.plot()
method to quickly get a basic visualization of the dataWe used the GeoPandas library to read in the geospatial data, and this returned us a GeoDataFrame
:
type(countries)
A GeoDataFrame contains a tabular, geospatial dataset:
Such a GeoDataFrame
is just like a pandas DataFrame
, but with some additional functionality for working with geospatial data:
.geometry
attribute that always returns the column with the geometry information (returning a GeoSeries). The column name itself does not necessarily need to be 'geometry', but it will always be accessible as the .geometry
attribute.countries.geometry
type(countries.geometry)
countries.geometry.area
It's still a DataFrame, so we have all the Pandas functionality available to use on the geospatial dataset, and to do data manipulations with the attributes and geometry information together.
For example, we can calculate average population number over all countries (by accessing the 'pop_est' column, and calling the mean
method on it):
countries['pop_est'].mean()
Or, we can use boolean filtering to select a subset of the dataframe based on a condition:
africa = countries[countries['continent'] == 'Africa']
africa.plot();
REMEMBER:
GeoDataFrame
allows to perform typical tabular data analysis together with spatial operationsGeoDataFrame
(or Feature Collection) consists of:Spatial vector data can consist of different types, and the 3 fundamental types are:
And each of them can also be combined in multi-part geometries (See https://shapely.readthedocs.io/en/stable/manual.html#geometric-objects for extensive overview).
For the example we have seen up to now, the individual geometry objects are Polygons:
print(countries.geometry[2])
Let's import some other datasets with different types of geometry objects.
A dateset about cities in the world (adapted from http://www.naturalearthdata.com/downloads/110m-cultural-vectors/110m-populated-places/, zip file is available in the /data
directory), consisting of Point data:
cities = geopandas.read_file("zip://./data/ne_110m_populated_places.zip")
print(cities.geometry[0])
And a dataset of rivers in the world (from http://www.naturalearthdata.com/downloads/50m-physical-vectors/50m-rivers-lake-centerlines/, zip file is available in the /data
directory) where each river is a (multi-)line:
rivers = geopandas.read_file("zip://./data/ne_50m_rivers_lake_centerlines.zip")
print(rivers.geometry[0])
type(countries.geometry[0])
To construct one ourselves:
from shapely.geometry import Point, Polygon, LineString
p = Point(0, 0)
print(p)
polygon = Polygon([(1, 1), (2,2), (2, 1)])
polygon.area
polygon.distance(p)
REMEMBER:
Single geometries are represented by shapely
objects:
single_shapely_object.distance(other_point)
-> distance between two pointsgeodataframe.distance(other_point)
-> distance for each point in the geodataframe to the other pointax = countries.plot(edgecolor='k', facecolor='none', figsize=(15, 10))
rivers.plot(ax=ax)
cities.plot(ax=ax, color='red')
ax.set(xlim=(-20, 60), ylim=(-40, 40))
See the visualization-02-geopandas.ipynb notebook for more details on visualizing geospatial datasets.
Throughout the exercises in this course, we will work with several datasets about the city of Paris.
Here, we start with the following datasets:
paris_districts_utm.geojson
data/paris_bike_stations_mercator.gpkg
Both datasets are provided as spatial datasets using a GIS file format.
Let's explore further those datasets, now using the spatial aspect as well.
EXERCISE:
We will start with exploring the bicycle station dataset (available as a GeoPackage file: data/paris_bike_stations_mercator.gpkg
)
stations
.type(..)
to check any Python object type.shape
attribute to get the number of features# %load _solutions/02-introduction-geospatial-data1.py
# %load _solutions/02-introduction-geospatial-data2.py
# %load _solutions/02-introduction-geospatial-data3.py
# %load _solutions/02-introduction-geospatial-data4.py
EXERCISE:
stations
dataset.plot
method accepts a figsize
keyword).# %load _solutions/02-introduction-geospatial-data5.py
A plot with just some points can be hard to interpret without any spatial context. Therefore, in the next exercise we will learn how to add a background map.
We are going to make use of the contextily package. The add_basemap()
function of this package makes it easy to add a background web map to our plot. We begin by plotting our data first, and then pass the matplotlib axes object (returned by dataframe's plot()
method) to the add_basemap()
function. contextily
will then download the web tiles needed for the geographical extent of your plot.
EXERCISE:
contextily
.stations
, but assign the result to an ax
variable.markersize
keyword of the plot()
method for this).add_basemap()
function of contextily
to add a background map: the first argument is the matplotlib axes object ax
.# %load _solutions/02-introduction-geospatial-data6.py
# %load _solutions/02-introduction-geospatial-data7.py
EXERCISE:
df['col_name']
hist()
method to plot a histogram of its values.# %load _solutions/02-introduction-geospatial-data8.py
EXERCISE:
Let's now visualize where the available bikes are actually stationed:
stations
dataset (also with a (12, 6) figsize).'available_bikes'
columns to determine the color of the points. For this, use the column=
keyword.legend=True
keyword to show a color bar.# %load _solutions/02-introduction-geospatial-data9.py
EXERCISE:
Next, we will explore the dataset on the administrative districts of Paris (available as a GeoJSON file: "data/paris_districts_utm.geojson")
districts
..shape
attribute)districts
dataset (set the figure size to (12, 6)).# %load _solutions/02-introduction-geospatial-data10.py
# %load _solutions/02-introduction-geospatial-data11.py
# %load _solutions/02-introduction-geospatial-data12.py
# %load _solutions/02-introduction-geospatial-data13.py
EXERCISE:
What are the largest districts (biggest area)?
districts
dataframe.df['new_col'] = values
sort_values()
method, specifying the colum to sort on with the by='col_name'
keyword. Check the help of this method to see how to sort ascending or descending.# %load _solutions/02-introduction-geospatial-data14.py
# %load _solutions/02-introduction-geospatial-data15.py
# %load _solutions/02-introduction-geospatial-data16.py
EXERCISE:
'population_density'
representing the number of inhabitants per squared kilometer (Note: The area is given in squared meter, so you will need to multiply the result with 10**6
).'population_density'
to color the polygons. For this, use the column=
keyword.legend=True
keyword to show a color bar.# %load _solutions/02-introduction-geospatial-data17.py
# %load _solutions/02-introduction-geospatial-data18.py
# %load _solutions/02-introduction-geospatial-data19.py
fiona
¶Under the hood, GeoPandas uses the Fiona library (pythonic interface to GDAL/OGR) to read and write data. GeoPandas provides a more user-friendly wrapper, which is sufficient for most use cases. But sometimes you want more control, and in that case, to read a file with fiona you can do the following:
import fiona
from shapely.geometry import shape
with fiona.Env():
with fiona.open("zip://./data/ne_110m_admin_0_countries.zip") as collection:
for feature in collection:
# ... do something with geometry
geom = shape(feature['geometry'])
# ... do something with properties
print(feature['properties']['name'])
geopandas.GeoDataFrame({
'geometry': [Point(1, 1), Point(2, 2)],
'attribute1': [1, 2],
'attribute2': [0.1, 0.2]})
For example, if you have lat/lon coordinates in two columns:
df = pd.DataFrame(
{'City': ['Buenos Aires', 'Brasilia', 'Santiago', 'Bogota', 'Caracas'],
'Country': ['Argentina', 'Brazil', 'Chile', 'Colombia', 'Venezuela'],
'Latitude': [-34.58, -15.78, -33.45, 4.60, 10.48],
'Longitude': [-58.66, -47.91, -70.66, -74.08, -66.86]})
gdf = geopandas.GeoDataFrame(
df, geometry=geopandas.points_from_xy(df.Longitude, df.Latitude))
gdf
See http://geopandas.readthedocs.io/en/latest/gallery/create_geopandas_from_pandas.html for full example