Geometric manipulations

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Geometric manipulations

2024-01-26 05:48| 来源: 网络整理| 查看: 265

Examples of geometric manipulations# >>> import geopandas >>> from geopandas import GeoSeries >>> from shapely.geometry import Polygon >>> p1 = Polygon([(0, 0), (1, 0), (1, 1)]) >>> p2 = Polygon([(0, 0), (1, 0), (1, 1), (0, 1)]) >>> p3 = Polygon([(2, 0), (3, 0), (3, 1), (2, 1)]) >>> g = GeoSeries([p1, p2, p3]) >>> g 0 POLYGON ((0 0, 1 0, 1 1, 0 0)) 1 POLYGON ((0 0, 1 0, 1 1, 0 1, 0 0)) 2 POLYGON ((2 0, 3 0, 3 1, 2 1, 2 0)) dtype: geometry

Some geographic operations return normal pandas objects. The area property of a GeoSeries will return a pandas.Series containing the area of each item in the GeoSeries:

>>> print(g.area) 0 0.5 1 1.0 2 1.0 dtype: float64

Other operations return GeoPandas objects:

>>> g.buffer(0.5) 0 POLYGON ((-0.3535533905932737 0.35355339059327... 1 POLYGON ((-0.5 0, -0.5 1, -0.4975923633360985 ... 2 POLYGON ((1.5 0, 1.5 1, 1.502407636663901 1.04... dtype: geometry

GeoPandas objects also know how to plot themselves. GeoPandas uses matplotlib for plotting. To generate a plot of a GeoSeries, use:

>>> g.plot()

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):

>>> import geodatasets >>> nybb_path = geodatasets.get_path('nybb') >>> boros = geopandas.read_file(nybb_path) >>> boros.set_index('BoroCode', inplace=True) >>> boros.sort_index(inplace=True) >>> boros BoroName Shape_Leng Shape_Area \ BoroCode 1 Manhattan 359299.096471 6.364715e+08 2 Bronx 464392.991824 1.186925e+09 3 Brooklyn 741080.523166 1.937479e+09 4 Queens 896344.047763 3.045213e+09 5 Staten Island 330470.010332 1.623820e+09 geometry BoroCode 1 MULTIPOLYGON (((981219.0557861328 188655.31579... 2 MULTIPOLYGON (((1012821.805786133 229228.26458... 3 MULTIPOLYGON (((1021176.479003906 151374.79699... 4 MULTIPOLYGON (((1029606.076599121 156073.81420... 5 MULTIPOLYGON (((970217.0223999023 145643.33221... >>> boros['geometry'].convex_hull BoroCode 1 POLYGON ((977855.4451904297 188082.3223876953,... 2 POLYGON ((1017949.977600098 225426.8845825195,... 3 POLYGON ((988872.8212280273 146772.0317993164,... 4 POLYGON ((1000721.531799316 136681.776184082, ... 5 POLYGON ((915517.6877458114 120121.8812543372,... dtype: geometry

To demonstrate a more complex operation, generate a GeoSeries containing 2000 random points:

>>> import numpy as np >>> from shapely.geometry import Point >>> xmin, xmax, ymin, ymax = 900000, 1080000, 120000, 280000 >>> xc = (xmax - xmin) * np.random.random(2000) + xmin >>> yc = (ymax - ymin) * np.random.random(2000) + ymin >>> pts = GeoSeries([Point(x, y) for x, y in zip(xc, yc)])

Now draw a circle with fixed radius around each point:

>>> circles = pts.buffer(2000)

You can collapse these circles into a single MultiPolygon geometry with

>>> mp = circles.unary_union

To extract the part of this geometry contained in each borough, you can just use:

>>> holes = boros['geometry'].intersection(mp)

and to get the area outside of the holes:

>>> boros_with_holes = boros['geometry'].difference(mp)

Note that this can be simplified a bit, since geometry is available as an attribute on a GeoDataFrame, and the intersection() and difference() methods are implemented with the “&” and “-” operators, respectively. For example, the latter could have been expressed simply as boros.geometry - mp.

It’s easy to do things like calculate the fractional area in each borough that are in the holes:

>>> holes.area / boros.geometry.area BoroCode 1 0.579939 2 0.586833 3 0.608174 4 0.582172 5 0.558075 dtype: float64

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Geometric manipulations

Geometric manipulations

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