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234 lines
7.6 KiB
ReStructuredText
234 lines
7.6 KiB
ReStructuredText
.. _geometric_manipulations:
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Geometric Manipulations
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========================
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*geopandas* makes available all the tools for geometric manipulations in the `*shapely* library <http://shapely.readthedocs.io/en/latest/manual.html>`_.
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Note that documentation for all set-theoretic tools for creating new shapes using the relationship between two different spatial datasets -- like creating intersections, or differences -- can be found on the :doc:`set operations <set_operations>` page.
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Constructive Methods
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~~~~~~~~~~~~~~~~~~~~
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.. method:: GeoSeries.buffer(distance, resolution=16)
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Returns a ``GeoSeries`` of geometries representing all points within a given `distance`
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of each geometric object.
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.. attribute:: GeoSeries.boundary
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Returns a ``GeoSeries`` of lower dimensional objects representing
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each geometries's set-theoretic `boundary`.
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.. attribute:: GeoSeries.centroid
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Returns a ``GeoSeries`` of points for each geometric centroid.
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.. attribute:: GeoSeries.convex_hull
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Returns a ``GeoSeries`` of geometries representing the smallest
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convex `Polygon` containing all the points in each object unless the
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number of points in the object is less than three. For two points,
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the convex hull collapses to a `LineString`; for 1, a `Point`.
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.. attribute:: GeoSeries.envelope
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Returns a ``GeoSeries`` of geometries representing the point or
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smallest rectangular polygon (with sides parallel to the coordinate
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axes) that contains each object.
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.. method:: GeoSeries.simplify(tolerance, preserve_topology=True)
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Returns a ``GeoSeries`` containing a simplified representation of
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each object.
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.. attribute:: GeoSeries.unary_union
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Return a geometry containing the union of all geometries in the ``GeoSeries``.
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Affine transformations
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~~~~~~~~~~~~~~~~~~~~~~~~
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.. method:: GeoSeries.affine_transform(self, matrix)
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Transform the geometries of the GeoSeries using an affine transformation matrix
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.. method:: GeoSeries.rotate(self, angle, origin='center', use_radians=False)
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Rotate the coordinates of the GeoSeries.
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.. method:: GeoSeries.scale(self, xfact=1.0, yfact=1.0, zfact=1.0, origin='center')
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Scale the geometries of the GeoSeries along each (x, y, z) dimensio.
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.. method:: GeoSeries.skew(self, angle, origin='center', use_radians=False)
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Shear/Skew the geometries of the GeoSeries by angles along x and y dimensions.
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.. method:: GeoSeries.translate(self, xoff=0.0, yoff=0.0, zoff=0.0)
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Shift the coordinates of the GeoSeries.
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Examples of Geometric Manipulations
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------------------------------------
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.. sourcecode:: python
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>>> import geopandas
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>>> from geopandas import GeoSeries
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>>> from shapely.geometry import Polygon
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>>> p1 = Polygon([(0, 0), (1, 0), (1, 1)])
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>>> p2 = Polygon([(0, 0), (1, 0), (1, 1), (0, 1)])
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>>> p3 = Polygon([(2, 0), (3, 0), (3, 1), (2, 1)])
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>>> g = GeoSeries([p1, p2, p3])
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>>> g
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0 POLYGON ((0 0, 1 0, 1 1, 0 0))
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1 POLYGON ((0 0, 1 0, 1 1, 0 1, 0 0))
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2 POLYGON ((2 0, 3 0, 3 1, 2 1, 2 0))
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dtype: geometry
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.. image:: ../../_static/test.png
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Some geographic operations return normal pandas object. The ``area`` property of a ``GeoSeries`` will return a ``pandas.Series`` containing the area of each item in the ``GeoSeries``:
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.. sourcecode:: python
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>>> print(g.area)
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0 0.5
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1 1.0
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2 1.0
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dtype: float64
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Other operations return GeoPandas objects:
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.. sourcecode:: python
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>>> g.buffer(0.5)
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0 POLYGON ((-0.3535533905932737 0.35355339059327...
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1 POLYGON ((-0.5 0, -0.5 1, -0.4975923633360985 ...
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2 POLYGON ((1.5 0, 1.5 1, 1.502407636663901 1.04...
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dtype: geometry
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.. image:: ../../_static/test_buffer.png
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GeoPandas objects also know how to plot themselves. GeoPandas uses `descartes`_ to generate a `matplotlib`_ plot. To generate a plot of our GeoSeries, use:
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.. sourcecode:: python
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>>> g.plot()
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GeoPandas also implements alternate constructors that can read any data format recognized by `fiona`_. To read a zip file containing an ESRI shapefile with the `borough boundaries of New York City`_ (GeoPandas includes this as an example dataset):
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.. sourcecode:: python
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>>> nybb_path = geopandas.datasets.get_path('nybb')
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>>> boros = geopandas.read_file(nybb_path)
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>>> boros.set_index('BoroCode', inplace=True)
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>>> boros.sort_index(inplace=True)
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>>> boros
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BoroName Shape_Leng Shape_Area \
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BoroCode
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1 Manhattan 359299.096471 6.364715e+08
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2 Bronx 464392.991824 1.186925e+09
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3 Brooklyn 741080.523166 1.937479e+09
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4 Queens 896344.047763 3.045213e+09
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5 Staten Island 330470.010332 1.623820e+09
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geometry
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BoroCode
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1 MULTIPOLYGON (((981219.0557861328 188655.31579...
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2 MULTIPOLYGON (((1012821.805786133 229228.26458...
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3 MULTIPOLYGON (((1021176.479003906 151374.79699...
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4 MULTIPOLYGON (((1029606.076599121 156073.81420...
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5 MULTIPOLYGON (((970217.0223999023 145643.33221...
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.. image:: ../../_static/nyc.png
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.. sourcecode:: python
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>>> boros['geometry'].convex_hull
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BoroCode
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1 POLYGON ((977855.4451904297 188082.3223876953,...
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2 POLYGON ((1017949.977600098 225426.8845825195,...
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3 POLYGON ((988872.8212280273 146772.0317993164,...
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4 POLYGON ((1000721.531799316 136681.776184082, ...
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5 POLYGON ((915517.6877458114 120121.8812543372,...
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dtype: geometry
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.. image:: ../../_static/nyc_hull.png
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To demonstrate a more complex operation, we'll generate a
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``GeoSeries`` containing 2000 random points:
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.. sourcecode:: python
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>>> import numpy as np
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>>> from shapely.geometry import Point
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>>> xmin, xmax, ymin, ymax = 900000, 1080000, 120000, 280000
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>>> xc = (xmax - xmin) * np.random.random(2000) + xmin
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>>> yc = (ymax - ymin) * np.random.random(2000) + ymin
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>>> pts = GeoSeries([Point(x, y) for x, y in zip(xc, yc)])
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Now draw a circle with fixed radius around each point:
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.. sourcecode:: python
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>>> circles = pts.buffer(2000)
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We can collapse these circles into a single shapely MultiPolygon
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geometry with
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.. sourcecode:: python
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>>> mp = circles.unary_union
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To extract the part of this geometry contained in each borough, we can
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just use:
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.. sourcecode:: python
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>>> holes = boros['geometry'].intersection(mp)
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.. image:: ../../_static/holes.png
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and to get the area outside of the holes:
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.. sourcecode:: python
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>>> boros_with_holes = boros['geometry'].difference(mp)
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.. image:: ../../_static/boros_with_holes.png
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Note that this can be simplified a bit, since ``geometry`` is
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available as an attribute on a ``GeoDataFrame``, and the
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``intersection`` and ``difference`` methods are implemented with the
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"&" and "-" operators, respectively. For example, the latter could
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have been expressed simply as ``boros.geometry - mp``.
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It's easy to do things like calculate the fractional area in each
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borough that are in the holes:
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.. sourcecode:: python
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>>> holes.area / boros.geometry.area
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BoroCode
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1 0.579939
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2 0.586833
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3 0.608174
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4 0.582172
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5 0.558075
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dtype: float64
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.. _Descartes: https://pypi.python.org/pypi/descartes
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.. _matplotlib: http://matplotlib.org
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.. _fiona: http://fiona.readthedocs.io/en/latest/
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.. _geopy: https://github.com/geopy/geopy
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.. _geo_interface: https://gist.github.com/sgillies/2217756
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.. _borough boundaries of New York City: https://data.cityofnewyork.us/City-Government/Borough-Boundaries/tqmj-j8zm
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.. toctree::
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:maxdepth: 2
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