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final doc updates (#328)
This commit is contained in:
Nick Eubank
committed by
Joris Van den Bossche
Joris Van den Bossche
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9344ddb7ac
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dc4b548696
@@ -0,0 +1,51 @@
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.. ipython:: python
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:suppress:
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import geopandas as gpd
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Aggregation with dissolve
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=============================
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It is often the case that we find ourselves working with spatial data that is more granular than we need. For example, we might have data on sub-national units, but we're actually interested in studying patterns at the level of countries.
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In a non-spatial setting, we aggregate our data using the ``groupby`` function. But when working with spatial data, we need a special tool that can also aggregate geometric features. In the *geopandas* library, that functionality is provided by the ``dissolve`` function.
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``dissolve`` can be thought of as doing three things: (a) it dissolves all the geometries within a given group together into a single geometric feature (using the ``unary_union`` method), and (b) it aggregates all the rows of data in a group using ``groupby.aggregate()``, and (c) it combines those two results.
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``dissolve`` Example
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~~~~~~~~~~~~~~~~~~~~~
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Suppose we are interested in studying continents, but we only have country-level data like the country dataset included in *geopandas*. We can easily convert this to a continent-level dataset.
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First, let's look at the most simple case where we just want continent shapes and names. By default, ``dissolve`` will pass ``'first'`` to ``groupby.aggregate``.
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.. ipython:: python
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world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
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world = world[['continent', 'geometry']]
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continents = world.dissolve(by='continent')
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@savefig continents.png width=5in
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continents.plot();
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continents.head()
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If we are interested in aggregate populations, however, we can pass different functions to the ``dissolve`` method to aggregate populations:
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.. ipython:: python
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world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
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world = world[['continent', 'geometry', 'pop_est']]
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continents = world.dissolve(by='continent', aggfunc='sum')
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@savefig continents.png width=5in
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continents.plot(column = 'pop_est', scheme='quantiles', cmap='YlOrRd');
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continents.head()
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.. toctree::
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:maxdepth: 2
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@@ -1,7 +1,7 @@
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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://toblerity.org/shapely/manual.html>`_.
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*geopandas* makes available all the tools for geometric manipulations in the `*shapely* library <http://toblerity.org/shapely/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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@@ -40,6 +40,11 @@ Constructive Methods
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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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@@ -59,16 +64,10 @@ Affine transformations
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Shift the coordinates of the GeoSeries.
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Aggregation Methods
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~~~~~~~~~~~~~~~~~~~~
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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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Examples of Geometric Manipulations
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------------------------------------
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------------------------------------
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.. sourcecode:: python
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@@ -128,7 +127,7 @@ GeoPandas also implements alternate constructors that can read any data format r
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3 Brooklyn 1.959432e+09 726568.946340
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4 Queens 3.049947e+09 861038.479299
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5 Staten Island 1.623853e+09 330385.036974
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geometry
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BoroCode
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1 (POLYGON ((981219.0557861328125000 188655.3157...
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@@ -138,7 +137,7 @@ GeoPandas also implements alternate constructors that can read any data format r
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5 (POLYGON ((970217.0223999023437500 145643.3322...
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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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@@ -183,7 +182,7 @@ just use:
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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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@@ -191,7 +190,7 @@ and to get the area outside of the holes:
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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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@@ -221,5 +220,3 @@ borough that are in the holes:
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.. toctree::
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:maxdepth: 2
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@@ -33,6 +33,7 @@ such as PostGIS.
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Managing Projections <projections>
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Geometric Manipulations <geometric_manipulations>
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Set Operations with overlay <set_operations>
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Aggregation with dissolve <aggregation_with_dissolve>
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Merging Data <mergingdata>
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Geocoding <geocoding>
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Reference to All Attributes and Methods <reference>
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@@ -67,8 +67,8 @@ In a Spatial Join, two geometry objects are merged based on their spatial relati
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cities.head()
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# Execute spatial join
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from geopandas.tools import sjoin
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cities_with_country = sjoin(cities, countries, how="inner", op='intersects')
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cities_with_country = gpd.sjoin(cities, countries, how="inner", op='intersects')
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cities_with_country.head()
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@@ -57,8 +57,7 @@ To select only the portion of countries within 500km of a capital, we specify th
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.. ipython:: python
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from geopandas.tools import overlay
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country_cores = overlay(countries, capitals, how='intersection')
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country_cores = gpd.overlay(countries, capitals, how='intersection')
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@savefig country_cores.png width=5in
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country_cores.plot();
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@@ -66,7 +65,7 @@ Changing the "how" option allows for different types of overlay operations. For
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.. ipython:: python
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country_peripheries = overlay(countries, capitals, how='difference')
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country_peripheries = gpd.overlay(countries, capitals, how='difference')
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@savefig country_peripheries.png width=5in
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country_peripheries.plot();
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