%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 can we observe:
.head()
we can see the first rows of the dataset, just like we can do with Pandas..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()
The rest of the tutorial is going to assume you already know some pandas basics, but we will try to give hints for that part for those that are not familiar.
A few resources in case you want to learn more about pandas:
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 04-more-on-visualization.ipynb notebook for more details on visualizing geospatial datasets.
For the exercises, we are going to use some data of the city of Paris:
paris_districts_utm.geojson
paris_sharing_bike_stations_utm.geojson
Both datasets are provided as GeoJSON files.
Let's explore those datasets:
EXERCISE:
districts
and stations
.type(..)
)# %load _solved/solutions/01-introduction-geospatial-data1.py
# %load _solved/solutions/01-introduction-geospatial-data2.py
# %load _solved/solutions/01-introduction-geospatial-data3.py
# %load _solved/solutions/01-introduction-geospatial-data4.py
EXERCISE:
districts
dataset.plot
method accepts a figsize keyword).# %load _solved/solutions/01-introduction-geospatial-data5.py
EXERCISE:
stations
dataset (also with a (12, 6) figsize).'available_bikes'
colums to determine the color of the points. For this, use the column=
keyword.legend=True
keyword to show a color bar.# %load _solved/solutions/01-introduction-geospatial-data6.py
EXERCISE:
stations
and districts
datasets together on a single plot (of 20, 10)).districts
dataset with an alpha of 0.5, but use black lines (tip: edgecolor
).ax.set_axis_off()
to remove the axis (tick)labels.# %load _solved/solutions/01-introduction-geospatial-data7.py
# %load _solved/solutions/01-introduction-geospatial-data8.py
# %load _solved/solutions/01-introduction-geospatial-data9.py
EXERCISE:
# %load _solved/solutions/01-introduction-geospatial-data10.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.# %load _solved/solutions/01-introduction-geospatial-data11.py
# %load _solved/solutions/01-introduction-geospatial-data12.py
A coordinate reference system (CRS) determines how the two-dimensional (planar) coordinates of the geometry objects should be related to actual places on the (non-planar) earth.
For a nice in-depth explanation, see https://docs.qgis.org/2.8/en/docs/gentle_gis_introduction/coordinate_reference_systems.html
A GeoDataFrame or GeoSeries has a .crs
attribute which holds (optionally) a description of the coordinate reference system of the geometries:
countries.crs
For the countries
dataframe, it indicates that it used the EPSG 4326 / WGS84 lon/lat reference system, which is one of the most used.
It uses coordinates as latitude and longitude in degrees, as can you be seen from the x/y labels on the plot:
countries.plot()
The .crs
attribute is given as a dictionary. In this case, it only indicates the EPSG code, but it can also contain the full "proj4" string (in dictionary form).
Under the hood, GeoPandas uses the pyproj
/ proj4
libraries to deal with the re-projections.
For more information, see also http://geopandas.readthedocs.io/en/latest/projections.html.
There are sometimes good reasons you want to change the coordinate references system of your dataset, for example:
We can convert a GeoDataFrame to another reference system using the to_crs
function.
For example, let's convert the countries to the World Mercator projection (http://epsg.io/3395):
# remove Antartica, as the Mercator projection cannot deal with the poles
countries = countries[(countries['name'] != "Antarctica")]
countries_mercator = countries.to_crs(epsg=3395) # or .to_crs({'init': 'epsg:3395'})
countries_mercator.plot()
Note the different scale of x and y.
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]})
df['Coordinates'] = list(zip(df.Longitude, df.Latitude))
df['Coordinates'] = df['Coordinates'].apply(Point)
gdf = geopandas.GeoDataFrame(df, geometry='Coordinates')
gdf