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<li class="toctree-l2"><a class="reference internal" href="#dissolve-example"><code class="docutils literal"><span class="pre">dissolve</span></code> Example</a></li>
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<div class="section" id="aggregation-with-dissolve">
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<h1>Aggregation with dissolve<a class="headerlink" href="#aggregation-with-dissolve" title="Permalink to this headline">¶</a></h1>
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<p>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.</p>
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<p>In a non-spatial setting, we aggregate our data using the <code class="docutils literal"><span class="pre">groupby</span></code> function. But when working with spatial data, we need a special tool that can also aggregate geometric features. In the <em>geopandas</em> library, that functionality is provided by the <code class="docutils literal"><span class="pre">dissolve</span></code> function.</p>
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<p><code class="docutils literal"><span class="pre">dissolve</span></code> 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 <code class="docutils literal"><span class="pre">unary_union</span></code> method), and (b) it aggregates all the rows of data in a group using <code class="docutils literal"><span class="pre">groupby.aggregate()</span></code>, and (c) it combines those two results.</p>
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<div class="section" id="dissolve-example">
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<h2><code class="docutils literal"><span class="pre">dissolve</span></code> Example<a class="headerlink" href="#dissolve-example" title="Permalink to this headline">¶</a></h2>
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<p>Suppose we are interested in studying continents, but we only have country-level data like the country dataset included in <em>geopandas</em>. We can easily convert this to a continent-level dataset.</p>
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<p>First, let’s look at the most simple case where we just want continent shapes and names. By default, <code class="docutils literal"><span class="pre">dissolve</span></code> will pass <code class="docutils literal"><span class="pre">'first'</span></code> to <code class="docutils literal"><span class="pre">groupby.aggregate</span></code>.</p>
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<div class="highlight-ipython"><div class="highlight"><pre><span class="gp">In [1]: </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">'naturalearth_lowres'</span><span class="p">))</span>
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<span class="gp">In [2]: </span><span class="n">world</span> <span class="o">=</span> <span class="n">world</span><span class="p">[[</span><span class="s">'continent'</span><span class="p">,</span> <span class="s">'geometry'</span><span class="p">]]</span>
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<span class="gp">In [3]: </span><span class="n">continents</span> <span class="o">=</span> <span class="n">world</span><span class="o">.</span><span class="n">dissolve</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="s">'continent'</span><span class="p">)</span>
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<span class="gp">In [4]: </span><span class="n">continents</span><span class="o">.</span><span class="n">plot</span><span class="p">();</span>
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<span class="gp">In [5]: </span><span class="n">continents</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<span class="gh">Out[5]: </span><span class="go"></span>
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<span class="go"> geometry</span>
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<span class="go">continent </span>
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<span class="go">Africa (POLYGON ((49.54351891459575 -12.4698328589405...</span>
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<span class="go">Antarctica (POLYGON ((-159.2081835601977 -79.497059421708...</span>
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<span class="go">Asia (POLYGON ((120.7156087586305 -10.2395813940878...</span>
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<span class="go">Europe (POLYGON ((-52.55642473001839 2.50470530843705...</span>
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<span class="go">North America (POLYGON ((-61.68000000000001 10.76, -61.105 1...</span>
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</pre></div>
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</div>
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<a class="reference internal image-reference" href="_images/continents.png"><img alt="_images/continents.png" src="_images/continents.png" style="width: 5in;" /></a>
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<p>If we are interested in aggregate populations, however, we can pass different functions to the <code class="docutils literal"><span class="pre">dissolve</span></code> method to aggregate populations:</p>
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<div class="highlight-ipython"><div class="highlight"><pre><span class="gp">In [6]: </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">'naturalearth_lowres'</span><span class="p">))</span>
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<span class="gp">In [7]: </span><span class="n">world</span> <span class="o">=</span> <span class="n">world</span><span class="p">[[</span><span class="s">'continent'</span><span class="p">,</span> <span class="s">'geometry'</span><span class="p">,</span> <span class="s">'pop_est'</span><span class="p">]]</span>
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<span class="gp">In [8]: </span><span class="n">continents</span> <span class="o">=</span> <span class="n">world</span><span class="o">.</span><span class="n">dissolve</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="s">'continent'</span><span class="p">,</span> <span class="n">aggfunc</span><span class="o">=</span><span class="s">'sum'</span><span class="p">)</span>
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<span class="gp">In [9]: </span><span class="n">continents</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">column</span> <span class="o">=</span> <span class="s">'pop_est'</span><span class="p">,</span> <span class="n">scheme</span><span class="o">=</span><span class="s">'quantiles'</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s">'YlOrRd'</span><span class="p">);</span>
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<span class="gp">In [10]: </span><span class="n">continents</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<span class="gh">Out[10]: </span><span class="go"></span>
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<span class="go"> geometry pop_est</span>
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<span class="go">continent </span>
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<span class="go">Africa (POLYGON ((49.54351891459575 -12.4698328589405... 993281878</span>
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<span class="go">Antarctica (POLYGON ((-159.2081835601977 -79.497059421708... 3802</span>
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<span class="go">Asia (POLYGON ((120.7156087586305 -10.2395813940878... 4085852698</span>
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<span class="go">Europe (POLYGON ((-52.55642473001839 2.50470530843705... 728131201</span>
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<span class="go">North America (POLYGON ((-61.68000000000001 10.76, -61.105 1... 539350981</span>
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</pre></div>
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