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<div class="section" id="set-operations-with-overlay">
<h1>Set-Operations with Overlay<a class="headerlink" href="#set-operations-with-overlay" title="Permalink to this headline"></a></h1>
<p>When working with multiple spatial datasets &#8211; especially multiple <em>polygon</em> or
<em>line</em> datasets &#8211; users often wish to create new shapes based on places where
those datasets overlap (or don&#8217;t overlap). These manipulations are often
referred using the language of sets &#8211; intersections, unions, and differences.
These types of operations are made available in the <em>geopandas</em> library through
the <code class="docutils literal"><span class="pre">overlay</span></code> function.</p>
<p>The basic idea is demonstrated by the graphic below but keep in mind that
overlays operate at the DataFrame level, not on individual geometries, and the
properties from both are retained. In effect, for every shape in the first
GeoDataFrame, this operation is executed against every other shape in the other
GeoDataFrame:</p>
<img alt="_images/overlay_operations.png" src="_images/overlay_operations.png" />
<p><strong>Source: QGIS Documentation</strong></p>
<p>(Note to users familiar with the <em>shapely</em> library: <code class="docutils literal"><span class="pre">overlay</span></code> can be thought
of as offering versions of the standard <em>shapely</em> set-operations that deal with
the complexities of applying set operations to two <em>GeoSeries</em>. The standard
<em>shapely</em> set-operations are also available as <code class="docutils literal"><span class="pre">GeoSeries</span></code> methods.)</p>
<div class="section" id="the-different-overlay-operations">
<h2>The different Overlay operations<a class="headerlink" href="#the-different-overlay-operations" title="Permalink to this headline"></a></h2>
<p>First, we create some example data:</p>
<div class="highlight-ipython"><div class="highlight"><pre><span class="gp">In [1]: </span><span class="kn">from</span> <span class="nn">shapely.geometry</span> <span class="kn">import</span> <span class="n">Polygon</span>
<span class="gp">In [2]: </span><span class="n">polys1</span> <span class="o">=</span> <span class="n">gpd</span><span class="o">.</span><span class="n">GeoSeries</span><span class="p">([</span><span class="n">Polygon</span><span class="p">([(</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">),</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">0</span><span class="p">),</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">),</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">2</span><span class="p">)]),</span>
<span class="gp"> ...: </span> <span class="n">Polygon</span><span class="p">([(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">),</span> <span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">2</span><span class="p">),</span> <span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">4</span><span class="p">)])])</span>
<span class="gp"> ...: </span>
<span class="gp">In [3]: </span><span class="n">polys2</span> <span class="o">=</span> <span class="n">gpd</span><span class="o">.</span><span class="n">GeoSeries</span><span class="p">([</span><span class="n">Polygon</span><span class="p">([(</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">),</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">1</span><span class="p">),</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">),</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">)]),</span>
<span class="gp"> ...: </span> <span class="n">Polygon</span><span class="p">([(</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">),</span> <span class="p">(</span><span class="mi">5</span><span class="p">,</span><span class="mi">3</span><span class="p">),</span> <span class="p">(</span><span class="mi">5</span><span class="p">,</span><span class="mi">5</span><span class="p">),</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">5</span><span class="p">)])])</span>
<span class="gp"> ...: </span>
<span class="gp">In [4]: </span><span class="n">df1</span> <span class="o">=</span> <span class="n">gpd</span><span class="o">.</span><span class="n">GeoDataFrame</span><span class="p">({</span><span class="s">&#39;geometry&#39;</span><span class="p">:</span> <span class="n">polys1</span><span class="p">,</span> <span class="s">&#39;df1&#39;</span><span class="p">:[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">]})</span>
<span class="gp">In [5]: </span><span class="n">df2</span> <span class="o">=</span> <span class="n">gpd</span><span class="o">.</span><span class="n">GeoDataFrame</span><span class="p">({</span><span class="s">&#39;geometry&#39;</span><span class="p">:</span> <span class="n">polys2</span><span class="p">,</span> <span class="s">&#39;df2&#39;</span><span class="p">:[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">]})</span>
</pre></div>
</div>
<p>These two GeoDataFrames have some overlapping areas:</p>
<div class="highlight-ipython"><div class="highlight"><pre><span class="gp">In [6]: </span><span class="n">ax</span> <span class="o">=</span> <span class="n">df1</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="s">&#39;red&#39;</span><span class="p">);</span>
<span class="gp">In [7]: </span><span class="n">df2</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s">&#39;green&#39;</span><span class="p">);</span>
</pre></div>
</div>
<a class="reference internal image-reference" href="_images/overlay_example.png"><img alt="_images/overlay_example.png" src="_images/overlay_example.png" style="width: 5in;" /></a>
<p>We illustrate the different overlay modes with the above example.
The <code class="docutils literal"><span class="pre">overlay</span></code> function will determine the set of all individual geometries
from overlaying the two input GeoDataFrames. This result covers the area covered
by the two input GeoDataFrames, and also preserves all unique regions defined by
the combined boundaries of the two GeoDataFrames.</p>
<p>When using <code class="docutils literal"><span class="pre">how='union'</span></code>, all those possible geometries are returned:</p>
<div class="highlight-ipython"><div class="highlight"><pre><span class="gp">In [8]: </span><span class="n">res_union</span> <span class="o">=</span> <span class="n">gpd</span><span class="o">.</span><span class="n">overlay</span><span class="p">(</span><span class="n">df1</span><span class="p">,</span> <span class="n">df2</span><span class="p">,</span> <span class="n">how</span><span class="o">=</span><span class="s">&#39;union&#39;</span><span class="p">)</span>
<span class="gp">In [9]: </span><span class="n">res_union</span>
<span class="gh">Out[9]: </span><span class="go"></span>
<span class="go"> df1 df2 geometry</span>
<span class="go">0 1 NaN POLYGON ((2 1, 2 0, 0 0, 0 2, 1 2, 1 1, 2 1))</span>
<span class="go">1 1 1 POLYGON ((2 1, 1 1, 1 2, 2 2, 2 1))</span>
<span class="go">2 NaN 1 POLYGON ((2 1, 2 2, 3 2, 3 1, 2 1))</span>
<span class="go">3 NaN 1 POLYGON ((2 2, 1 2, 1 3, 2 3, 2 2))</span>
<span class="go">4 2 NaN POLYGON ((3 2, 3 3, 4 3, 4 2, 3 2))</span>
<span class="go">5 2 1 POLYGON ((3 3, 3 2, 2 2, 2 3, 3 3))</span>
<span class="go">6 2 NaN POLYGON ((3 3, 2 3, 2 4, 3 4, 3 3))</span>
<span class="go">7 NaN 2 POLYGON ((4 3, 4 4, 3 4, 3 5, 5 5, 5 3, 4 3))</span>
<span class="go">8 2 2 POLYGON ((3 4, 4 4, 4 3, 3 3, 3 4))</span>
<span class="gp">In [10]: </span><span class="n">ax</span> <span class="o">=</span> <span class="n">res_union</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
<span class="gp">In [11]: </span><span class="n">df1</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">facecolor</span><span class="o">=</span><span class="s">&#39;none&#39;</span><span class="p">);</span>
<span class="gp">In [12]: </span><span class="n">df2</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">facecolor</span><span class="o">=</span><span class="s">&#39;none&#39;</span><span class="p">);</span>
</pre></div>
</div>
<a class="reference internal image-reference" href="_images/overlay_example_union.png"><img alt="_images/overlay_example_union.png" src="_images/overlay_example_union.png" style="width: 5in;" /></a>
<p>The other <code class="docutils literal"><span class="pre">how</span></code> operations will return different subsets of those geometries.
With <code class="docutils literal"><span class="pre">how='intersection'</span></code>, it returns only those geometries that are contained
by both GeoDataFrames:</p>
<div class="highlight-ipython"><div class="highlight"><pre><span class="gp">In [13]: </span><span class="n">res_intersection</span> <span class="o">=</span> <span class="n">gpd</span><span class="o">.</span><span class="n">overlay</span><span class="p">(</span><span class="n">df1</span><span class="p">,</span> <span class="n">df2</span><span class="p">,</span> <span class="n">how</span><span class="o">=</span><span class="s">&#39;intersection&#39;</span><span class="p">)</span>
<span class="gp">In [14]: </span><span class="n">res_intersection</span>
<span class="gh">Out[14]: </span><span class="go"></span>
<span class="go"> df1 df2 geometry</span>
<span class="go">0 1 1 POLYGON ((2 1, 1 1, 1 2, 2 2, 2 1))</span>
<span class="go">1 2 1 POLYGON ((3 3, 3 2, 2 2, 2 3, 3 3))</span>
<span class="go">2 2 2 POLYGON ((3 4, 4 4, 4 3, 3 3, 3 4))</span>
<span class="gp">In [15]: </span><span class="n">ax</span> <span class="o">=</span> <span class="n">res_intersection</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
<span class="gp">In [16]: </span><span class="n">df1</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">facecolor</span><span class="o">=</span><span class="s">&#39;none&#39;</span><span class="p">);</span>
<span class="gp">In [17]: </span><span class="n">df2</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">facecolor</span><span class="o">=</span><span class="s">&#39;none&#39;</span><span class="p">);</span>
</pre></div>
</div>
<a class="reference internal image-reference" href="_images/overlay_example_intersection.png"><img alt="_images/overlay_example_intersection.png" src="_images/overlay_example_intersection.png" style="width: 5in;" /></a>
<p><code class="docutils literal"><span class="pre">how='symmetric_difference'</span></code> is the opposite of <code class="docutils literal"><span class="pre">'intersection'</span></code> and returns
the geometries that are only part of one of the GeoDataFrames but not of both:</p>
<div class="highlight-ipython"><div class="highlight"><pre><span class="gp">In [18]: </span><span class="n">res_symdiff</span> <span class="o">=</span> <span class="n">gpd</span><span class="o">.</span><span class="n">overlay</span><span class="p">(</span><span class="n">df1</span><span class="p">,</span> <span class="n">df2</span><span class="p">,</span> <span class="n">how</span><span class="o">=</span><span class="s">&#39;symmetric_difference&#39;</span><span class="p">)</span>
<span class="gp">In [19]: </span><span class="n">res_symdiff</span>
<span class="gh">Out[19]: </span><span class="go"></span>
<span class="go"> df1 df2 geometry</span>
<span class="go">0 1 NaN POLYGON ((2 1, 2 0, 0 0, 0 2, 1 2, 1 1, 2 1))</span>
<span class="go">1 NaN 1 POLYGON ((2 1, 2 2, 3 2, 3 1, 2 1))</span>
<span class="go">2 NaN 1 POLYGON ((2 2, 1 2, 1 3, 2 3, 2 2))</span>
<span class="go">3 2 NaN POLYGON ((3 2, 3 3, 4 3, 4 2, 3 2))</span>
<span class="go">4 2 NaN POLYGON ((3 3, 2 3, 2 4, 3 4, 3 3))</span>
<span class="go">5 NaN 2 POLYGON ((4 3, 4 4, 3 4, 3 5, 5 5, 5 3, 4 3))</span>
<span class="gp">In [20]: </span><span class="n">ax</span> <span class="o">=</span> <span class="n">res_symdiff</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
<span class="gp">In [21]: </span><span class="n">df1</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">facecolor</span><span class="o">=</span><span class="s">&#39;none&#39;</span><span class="p">);</span>
<span class="gp">In [22]: </span><span class="n">df2</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">facecolor</span><span class="o">=</span><span class="s">&#39;none&#39;</span><span class="p">);</span>
</pre></div>
</div>
<a class="reference internal image-reference" href="_images/overlay_example_symdiff.png"><img alt="_images/overlay_example_symdiff.png" src="_images/overlay_example_symdiff.png" style="width: 5in;" /></a>
<p>To obtain the geometries that are part of <code class="docutils literal"><span class="pre">df1</span></code> but are not contained in
<code class="docutils literal"><span class="pre">df2</span></code>, you can use <code class="docutils literal"><span class="pre">how='difference'</span></code>:</p>
<div class="highlight-ipython"><div class="highlight"><pre><span class="gp">In [23]: </span><span class="n">res_difference</span> <span class="o">=</span> <span class="n">gpd</span><span class="o">.</span><span class="n">overlay</span><span class="p">(</span><span class="n">df1</span><span class="p">,</span> <span class="n">df2</span><span class="p">,</span> <span class="n">how</span><span class="o">=</span><span class="s">&#39;difference&#39;</span><span class="p">)</span>
<span class="gp">In [24]: </span><span class="n">res_difference</span>
<span class="gh">Out[24]: </span><span class="go"></span>
<span class="go"> df1 df2 geometry</span>
<span class="go">0 1 None POLYGON ((2 1, 2 0, 0 0, 0 2, 1 2, 1 1, 2 1))</span>
<span class="go">1 2 None POLYGON ((3 2, 3 3, 4 3, 4 2, 3 2))</span>
<span class="go">2 2 None POLYGON ((3 3, 2 3, 2 4, 3 4, 3 3))</span>
<span class="gp">In [25]: </span><span class="n">ax</span> <span class="o">=</span> <span class="n">res_difference</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
<span class="gp">In [26]: </span><span class="n">df1</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">facecolor</span><span class="o">=</span><span class="s">&#39;none&#39;</span><span class="p">);</span>
<span class="gp">In [27]: </span><span class="n">df2</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">facecolor</span><span class="o">=</span><span class="s">&#39;none&#39;</span><span class="p">);</span>
</pre></div>
</div>
<a class="reference internal image-reference" href="_images/overlay_example_difference.png"><img alt="_images/overlay_example_difference.png" src="_images/overlay_example_difference.png" style="width: 5in;" /></a>
<p>Finally, with <code class="docutils literal"><span class="pre">how='identity'</span></code>, the result consists of the surface of <code class="docutils literal"><span class="pre">df1</span></code>,
but with the geometries obtained from overlaying <code class="docutils literal"><span class="pre">df1</span></code> with <code class="docutils literal"><span class="pre">df2</span></code>:</p>
<div class="highlight-ipython"><div class="highlight"><pre><span class="gp">In [28]: </span><span class="n">res_identity</span> <span class="o">=</span> <span class="n">gpd</span><span class="o">.</span><span class="n">overlay</span><span class="p">(</span><span class="n">df1</span><span class="p">,</span> <span class="n">df2</span><span class="p">,</span> <span class="n">how</span><span class="o">=</span><span class="s">&#39;identity&#39;</span><span class="p">)</span>
<span class="gp">In [29]: </span><span class="n">res_identity</span>
<span class="gh">Out[29]: </span><span class="go"></span>
<span class="go"> df1 df2 geometry</span>
<span class="go">0 1 NaN POLYGON ((2 1, 2 0, 0 0, 0 2, 1 2, 1 1, 2 1))</span>
<span class="go">1 1 1 POLYGON ((2 1, 1 1, 1 2, 2 2, 2 1))</span>
<span class="go">2 2 NaN POLYGON ((3 2, 3 3, 4 3, 4 2, 3 2))</span>
<span class="go">3 2 1 POLYGON ((3 3, 3 2, 2 2, 2 3, 3 3))</span>
<span class="go">4 2 NaN POLYGON ((3 3, 2 3, 2 4, 3 4, 3 3))</span>
<span class="go">5 2 2 POLYGON ((3 4, 4 4, 4 3, 3 3, 3 4))</span>
<span class="gp">In [30]: </span><span class="n">ax</span> <span class="o">=</span> <span class="n">res_identity</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
<span class="gp">In [31]: </span><span class="n">df1</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">facecolor</span><span class="o">=</span><span class="s">&#39;none&#39;</span><span class="p">);</span>
<span class="gp">In [32]: </span><span class="n">df2</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">facecolor</span><span class="o">=</span><span class="s">&#39;none&#39;</span><span class="p">);</span>
</pre></div>
</div>
<a class="reference internal image-reference" href="_images/overlay_example_identity.png"><img alt="_images/overlay_example_identity.png" src="_images/overlay_example_identity.png" style="width: 5in;" /></a>
</div>
<div class="section" id="overlay-countries-example">
<h2>Overlay Countries Example<a class="headerlink" href="#overlay-countries-example" title="Permalink to this headline"></a></h2>
<p>First, we load the countries and cities example datasets and select :</p>
<div class="highlight-ipython"><div class="highlight"><pre><span class="gp">In [33]: </span><span class="n">world</span> <span class="o">=</span> <span class="n">gpd</span><span class="o">.</span><span class="n">read_file</span><span class="p">(</span><span class="n">gpd</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">get_path</span><span class="p">(</span><span class="s">&#39;naturalearth_lowres&#39;</span><span class="p">))</span>
<span class="gp">In [34]: </span><span class="n">capitals</span> <span class="o">=</span> <span class="n">gpd</span><span class="o">.</span><span class="n">read_file</span><span class="p">(</span><span class="n">gpd</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">get_path</span><span class="p">(</span><span class="s">&#39;naturalearth_cities&#39;</span><span class="p">))</span>
<span class="go"># Select South Amarica and some columns</span>
<span class="gp">In [35]: </span><span class="n">countries</span> <span class="o">=</span> <span class="n">world</span><span class="p">[</span><span class="n">world</span><span class="p">[</span><span class="s">&#39;continent&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s">&quot;South America&quot;</span><span class="p">]</span>
<span class="gp">In [36]: </span><span class="n">countries</span> <span class="o">=</span> <span class="n">countries</span><span class="p">[[</span><span class="s">&#39;geometry&#39;</span><span class="p">,</span> <span class="s">&#39;name&#39;</span><span class="p">]]</span>
<span class="go"># Project to crs that uses meters as distance measure</span>
<span class="gp">In [37]: </span><span class="n">countries</span> <span class="o">=</span> <span class="n">countries</span><span class="o">.</span><span class="n">to_crs</span><span class="p">(</span><span class="s">&#39;+init=epsg:3395&#39;</span><span class="p">)</span>
<span class="gp">In [38]: </span><span class="n">capitals</span> <span class="o">=</span> <span class="n">capitals</span><span class="o">.</span><span class="n">to_crs</span><span class="p">(</span><span class="s">&#39;+init=epsg:3395&#39;</span><span class="p">)</span>
</pre></div>
</div>
<p>To illustrate the <code class="docutils literal"><span class="pre">overlay</span></code> function, consider the following case in which one
wishes to identify the &#8220;core&#8221; portion of each country &#8211; defined as areas within
500km of a capital &#8211; using a <code class="docutils literal"><span class="pre">GeoDataFrame</span></code> of countries and a
<code class="docutils literal"><span class="pre">GeoDataFrame</span></code> of capitals.</p>
<div class="highlight-ipython"><div class="highlight"><pre><span class="go"># Look at countries:</span>
<span class="gp">In [39]: </span><span class="n">countries</span><span class="o">.</span><span class="n">plot</span><span class="p">();</span>
<span class="go"># Now buffer cities to find area within 500km.</span>
<span class="go"># Check CRS -- World Mercator, units of meters.</span>
<span class="gp">In [40]: </span><span class="n">capitals</span><span class="o">.</span><span class="n">crs</span>
<span class="gh">Out[40]: </span><span class="go">&#39;+init=epsg:3395&#39;</span>
<span class="go"># make 500km buffer</span>
<span class="gp">In [41]: </span><span class="n">capitals</span><span class="p">[</span><span class="s">&#39;geometry&#39;</span><span class="p">]</span><span class="o">=</span> <span class="n">capitals</span><span class="o">.</span><span class="n">buffer</span><span class="p">(</span><span class="mi">500000</span><span class="p">)</span>
<span class="gp">In [42]: </span><span class="n">capitals</span><span class="o">.</span><span class="n">plot</span><span class="p">();</span>
</pre></div>
</div>
<a class="reference internal image-reference" href="_images/world_basic.png"><img alt="_images/world_basic.png" src="_images/world_basic.png" style="width: 5in;" /></a>
<a class="reference internal image-reference" href="_images/capital_buffers.png"><img alt="_images/capital_buffers.png" src="_images/capital_buffers.png" style="width: 5in;" /></a>
<p>To select only the portion of countries within 500km of a capital, we specify the <code class="docutils literal"><span class="pre">how</span></code> option to be &#8220;intersect&#8221;, which creates a new set of polygons where these two layers overlap:</p>
<div class="highlight-ipython"><div class="highlight"><pre><span class="gp">In [43]: </span><span class="n">country_cores</span> <span class="o">=</span> <span class="n">gpd</span><span class="o">.</span><span class="n">overlay</span><span class="p">(</span><span class="n">countries</span><span class="p">,</span> <span class="n">capitals</span><span class="p">,</span> <span class="n">how</span><span class="o">=</span><span class="s">&#39;intersection&#39;</span><span class="p">)</span>
<span class="gp">In [44]: </span><span class="n">country_cores</span><span class="o">.</span><span class="n">plot</span><span class="p">();</span>
</pre></div>
</div>
<a class="reference internal image-reference" href="_images/country_cores.png"><img alt="_images/country_cores.png" src="_images/country_cores.png" style="width: 5in;" /></a>
<p>Changing the &#8220;how&#8221; option allows for different types of overlay operations. For example, if we were interested in the portions of countries <em>far</em> from capitals (the peripheries), we would compute the difference of the two.</p>
<div class="highlight-ipython"><div class="highlight"><pre><span class="gp">In [45]: </span><span class="n">country_peripheries</span> <span class="o">=</span> <span class="n">gpd</span><span class="o">.</span><span class="n">overlay</span><span class="p">(</span><span class="n">countries</span><span class="p">,</span> <span class="n">capitals</span><span class="p">,</span> <span class="n">how</span><span class="o">=</span><span class="s">&#39;difference&#39;</span><span class="p">)</span>
<span class="gp">In [46]: </span><span class="n">country_peripheries</span><span class="o">.</span><span class="n">plot</span><span class="p">();</span>
</pre></div>
</div>
<a class="reference internal image-reference" href="_images/country_peripheries.png"><img alt="_images/country_peripheries.png" src="_images/country_peripheries.png" style="width: 5in;" /></a>
</div>
<div class="section" id="more-examples">
<h2>More Examples<a class="headerlink" href="#more-examples" title="Permalink to this headline"></a></h2>
<p>A larger set of examples of the use of <code class="docutils literal"><span class="pre">overlay</span></code> can be found <a class="reference external" href="http://nbviewer.jupyter.org/github/geopandas/geopandas/blob/master/examples/overlays.ipynb">here</a></p>
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