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Joris Van den Bossche
82dd425e17
* API: provide datasets submodule in top-level namespace * DOC: loading example datasets with new method
121 lines
3.8 KiB
ReStructuredText
121 lines
3.8 KiB
ReStructuredText
.. currentmodule:: geopandas
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.. ipython:: python
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:suppress:
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import geopandas as gpd
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Mapping Tools
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=========================================
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*geopandas* provides a high-level interface to the ``matplotlib`` library for making maps. Mapping shapes is as easy as using the ``plot()`` method on a ``GeoSeries`` or ``GeoDataFrame``.
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Loading some example data:
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.. ipython:: python
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world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
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cities = gpd.read_file(gpd.datasets.get_path('naturalearth_cities'))
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We can now plot those GeoDataFrames:
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.. ipython:: python
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# Examine country GeoDataFrame
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world.head()
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# Basic plot, random colors
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@savefig world_randomcolors.png width=5in
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world.plot();
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Note that in general, any options one can pass to `pyplot <http://matplotlib.org/api/pyplot_api.html>`_ in ``matplotlib`` (or `style options that work for lines <http://matplotlib.org/api/lines_api.html>`_) can be passed to the ``plot()`` method.
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Chloropleth Maps
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-----------------
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*geopandas* makes it easy to create Chloropleth maps (maps where the color of each shape is based on the value of an associated variable). Simply use the plot command with the ``column`` argument set to the column whose values you want used to assign colors.
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.. ipython:: python
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# Plot by GDP per capta
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world = world[(world.pop_est>0) & (world.name!="Antarctica")]
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world['gdp_per_cap'] = world.gdp_md_est / world.pop_est
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@savefig world_gdp_per_cap.png width=5in
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world.plot(column='gdp_per_cap');
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Choosing colors
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~~~~~~~~~~~~~~~~
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One can also modify the colors used by ``plot`` with the ``cmap`` option (for a full list of colormaps, see the `matplotlib website <http://matplotlib.org/users/colormaps.html>`_):
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.. ipython:: python
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@savefig world_gdp_per_cap_red.png width=5in
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world.plot(column='gdp_per_cap', cmap='OrRd');
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The way color maps are scaled can also be manipulated with the ``scheme`` option (if you have ``pysal`` installed, which can be accomplished via ``conda install pysal``). By default, ``scheme`` is set to 'equal_intervals', but it can also be adjusted to any other `pysal option <http://pysal.org/1.2/library/esda/mapclassify.html>`_, like 'quantiles', 'percentiles', etc.
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.. ipython:: python
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@savefig world_gdp_per_cap_quantiles.png width=5in
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world.plot(column='gdp_per_cap', cmap='OrRd', scheme='quantiles');
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Maps with Layers
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-----------------
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There are two strategies for making a map with multiple layers -- one more succinct, and one that is a littel more flexible.
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Before combining maps, however, remember to always ensure they share a common CRS (so they will align).
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.. ipython:: python
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# Look at capitals
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# Note use of standard `pyplot` line style options
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@savefig capitals.png width=5in
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cities.plot(marker='*', color='green', markersize=5);
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# Check crs
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cities = cities.to_crs(world.crs)
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# Now we can overlay over country outlines
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# And yes, there are lots of island capitals
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# apparently in the middle of the ocean!
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**Method 1**
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.. ipython:: python
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base = world.plot(color='white')
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@savefig capitals_over_countries_1.png width=5in
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cities.plot(ax=base, marker='o', color='red', markersize=5);
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**Method 2: Using matplotlib objects**
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.. ipython:: python
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import matplotlib.pyplot as plt
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fig, ax = plt.subplots()
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# set aspect to equal. This is done automatically
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# when using *geopandas* plot on it's own, but not when
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# working with pyplot directly.
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ax.set_aspect('equal')
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world.plot(ax=ax, color='white')
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cities.plot(ax=ax, marker='o', color='red', markersize=5)
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@savefig capitals_over_countries_2.png width=5in
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plt.show();
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Other Resources
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-----------------
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Links to jupyter Notebooks for different mapping tasks:
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`Making Heat Maps <http://nbviewer.jupyter.org/gist/perrygeo/c426355e40037c452434>`_
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