Spatial Vector Data
Contents
Learning Objectives
Create a Geopandas GeoSeries and Dataframe
Plot a basic map
Review
Spatial Vector Data#
Intro to GeoPandas#
The goal of GeoPandas is to make working with spatial data in python easier. It combines the capabilities of pandas and shapely, providing spatial operations in pandas and a high-level interface to multiple geometries to shapely. GeoPandas enables you to easily do operations in python that would otherwise require a spatial database such as PostGIS.
Data Structures#
GeoPandas implements two main data structures, a GeoSeries and a GeoDataFrame. These are subclasses of pandas Series and DataFrame, respectively.
GeoSeries#
A GeoSeries is essentially a vector where each entry in the vector is a set of shapes corresponding to one observation. An entry may consist of only one shape (like a single polygon) or multiple shapes that are meant to be thought of as one observation (like the many polygons that make up the State of Hawaii or a country like Indonesia).
geopandas has three basic classes of geometric objects (which are actually shapely objects):
Points / Multi-Points
Lines / Multi-Lines
Polygons / Multi-Polygons
import geopandas
from shapely.geometry import Point
s = geopandas.GeoSeries([Point(1, 1), Point(2, 2), Point(3, 3)])
s
0 POINT (1.00000 1.00000)
1 POINT (2.00000 2.00000)
2 POINT (3.00000 3.00000)
dtype: geometry
from shapely.geometry import LineString
l= geopandas.GeoSeries([LineString([Point(-77.036873,38.907192), Point(-76.612190,39.290386,), Point(-77.408456,39.412006)])])
l
0 LINESTRING (-77.03687 38.90719, -76.61219 39.2...
dtype: geometry
from shapely.geometry import Polygon
p= geopandas.GeoSeries([Polygon([Point(-77.036873,38.907192), Point(-76.612190,39.290386,), Point(-77.408456,39.412006)])])
p
0 POLYGON ((-77.03687 38.90719, -76.61219 39.290...
dtype: geometry
Note that all entries in a GeoSeries need not be of the same geometric type, although certain export operations will fail if this is not the case.
GeoDataFrame#
A GeoDataFrame is a tabular data structure that contains a GeoSeries.
The most important property of a GeoDataFrame is that it always has one GeoSeries column that holds a special status. This GeoSeries is referred to as the GeoDataFrame’s “geometry”. When a spatial method is applied to a GeoDataFrame (or a spatial attribute like area is called), this commands will always act on the “geometry” column.
The “geometry” column – no matter its name – can be accessed through the geometry attribute (gdf.geometry), and the name of the geometry column can be found by typing gdf.geometry.name.
Note
A GeoDataFrame may also contain other columns with geometrical (shapely) objects, but only one column can be the active geometry at a time. To change which column is the active geometry column, use the GeoDataFrame.set_geometry() method.
An example using the worlds GeoDataFrame:
world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))
world.head()
| pop_est | continent | name | iso_a3 | gdp_md_est | geometry | |
|---|---|---|---|---|---|---|
| 0 | 920938 | Oceania | Fiji | FJI | 8374.0 | MULTIPOLYGON (((180.00000 -16.06713, 180.00000... |
| 1 | 53950935 | Africa | Tanzania | TZA | 150600.0 | POLYGON ((33.90371 -0.95000, 34.07262 -1.05982... |
| 2 | 603253 | Africa | W. Sahara | ESH | 906.5 | POLYGON ((-8.66559 27.65643, -8.66512 27.58948... |
| 3 | 35623680 | North America | Canada | CAN | 1674000.0 | MULTIPOLYGON (((-122.84000 49.00000, -122.9742... |
| 4 | 326625791 | North America | United States of America | USA | 18560000.0 | MULTIPOLYGON (((-122.84000 49.00000, -120.0000... |
world.plot()
<AxesSubplot:>
