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<div class="section" id="geometric-operations">
<h1>Geometric operations<a class="headerlink" href="#geometric-operations" title="Permalink to this headline">¶</a></h1>
<div class="section" id="overlay-analysis">
<h2>Overlay analysis<a class="headerlink" href="#overlay-analysis" title="Permalink to this headline">¶</a></h2>
<p>The aim here is to make an overlay analysis where we select only specific polygon cells from the data
based on the borders of municipality of Helsinki.</p>
<p>Let’s first read the data.</p>
<div class="code python highlight-default"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">geopandas</span> <span class="k">as</span> <span class="nn">gpd</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="c1"># File paths</span>
<span class="n">border_fp</span> <span class="o">=</span> <span class="s2">"/home/geo/data/Helsinki_borders.shp"</span>
<span class="n">grid_fp</span> <span class="o">=</span> <span class="s2">"/home/geo/data/TravelTimes_to_5975375_RailwayStation.shp"</span>
<span class="c1"># Read files</span>
<span class="n">grid</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">grid_fp</span><span class="p">)</span>
<span class="n">hel</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">border_fp</span><span class="p">)</span>
</pre></div>
</div>
<p>Let’s check that the coordinate systems match.</p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [1]: </span><span class="n">hel</span><span class="o">.</span><span class="n">crs</span>
<span class="gh">Out[1]: </span><span class="go">{'init': 'epsg:3067'}</span>
<span class="gp">In [2]: </span><span class="n">grid</span><span class="o">.</span><span class="n">crs</span>
<span class="go"> Out[2]: {'init': 'epsg:3067'}</span>
</pre></div>
</div>
<p>They do.</p>
<p>Let’s see how our datasets look like, let’s use the Helsinki municipality layer as our basemap and
plot the other layer on top of that.</p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [3]: </span><span class="n">basemap</span> <span class="o">=</span> <span class="n">hel</span><span class="o">.</span><span class="n">plot</span><span class="p">()</span>
<span class="gp">In [4]: </span><span class="n">grid</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">basemap</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.02</span><span class="p">);</span>
<span class="go"># Use tight layout</span>
<span class="gp">In [5]: </span><span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
</pre></div>
</div>
<a class="reference internal image-reference" href="_images/helsinki_grid_borders.png"><img alt="_images/helsinki_grid_borders.png" src="_images/helsinki_grid_borders.png" style="width: 7in;" /></a>
<p>Let’s do an overlay analysis and select polygons from grid that intersect with our Helsinki layer.</p>
<p><strong>Notice!</strong> This can be a slow procedure with less powerful computer. There is a way to
overcome this issue by doing the analysis <strong>in batches</strong> which is explained below, see the <a class="reference internal" href="#performance-tip">performance-tip</a>.</p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [6]: </span><span class="n">result</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">grid</span><span class="p">,</span> <span class="n">hel</span><span class="p">,</span> <span class="n">how</span><span class="o">=</span><span class="s1">'intersection'</span><span class="p">)</span>
</pre></div>
</div>
<p>Let’s plot our data and see what we have.</p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [7]: </span><span class="n">result</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="s2">"b"</span><span class="p">)</span>
<span class="gh">Out[7]: </span><span class="go"><matplotlib.axes._subplots.AxesSubplot at 0x12466e10></span>
<span class="go"># Use tight layout</span>
<span class="gp">In [8]: </span><span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
</pre></div>
</div>
<a class="reference internal image-reference" href="_images/helsinki_grid_borders_intersect.png"><img alt="_images/helsinki_grid_borders_intersect.png" src="_images/helsinki_grid_borders_intersect.png" style="width: 7in;" /></a>
<p>Cool! Now as a result we have only those grid cells included that intersect with the Helsinki borders
and the grid cells are clipped based on the boundary.</p>
<p>Whatabout the data attributes? Let’s see what we have.</p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [9]: </span><span class="n">result</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
<span class="gh">Out[9]: </span><span class="go"></span>
<span class="go"> car_m_d car_m_t car_r_d car_r_t from_id pt_m_d pt_m_t pt_m_tt \</span>
<span class="go">0 15981 36 15988 41 6002702 14698 65 73 </span>
<span class="go">1 16190 34 16197 39 6002701 14661 64 73 </span>
<span class="go">2 15727 33 15733 37 6001132 14256 59 69 </span>
<span class="go">3 15975 33 15982 37 6001131 14512 62 73 </span>
<span class="go">4 16136 35 16143 40 6001138 14730 65 73 </span>
<span class="go"> pt_r_d pt_r_t pt_r_tt to_id walk_d walk_t GML_ID NAMEFIN \</span>
<span class="go">0 14698 61 72 5975375 14456 207 27517366 Helsinki </span>
<span class="go">1 14661 60 72 5975375 14419 206 27517366 Helsinki </span>
<span class="go">2 14256 55 62 5975375 14014 200 27517366 Helsinki </span>
<span class="go">3 14512 58 70 5975375 14270 204 27517366 Helsinki </span>
<span class="go">4 14730 61 72 5975375 14212 203 27517366 Helsinki </span>
<span class="go"> NAMESWE NATCODE geometry </span>
<span class="go">0 Helsingfors 091 POLYGON ((391000.0001349226 6667750.00004299, ... </span>
<span class="go">1 Helsingfors 091 POLYGON ((390750.0001349644 6668000.000042951,... </span>
<span class="go">2 Helsingfors 091 POLYGON ((391000.0001349143 6668000.000042943,... </span>
<span class="go">3 Helsingfors 091 POLYGON ((390750.0001349644 6668000.000042951,... </span>
<span class="go">4 Helsingfors 091 POLYGON ((392500.0001346234 6668000.000042901,... </span>
</pre></div>
</div>
<p>Nice! Now we have attributes from both layers included.</p>
<p>Let’s see the length of the GeoDataFrame.</p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [10]: </span><span class="nb">len</span><span class="p">(</span><span class="n">result</span><span class="p">)</span>
<span class="gh">Out[10]: </span><span class="go">3836</span>
</pre></div>
</div>
<p>And the original data.</p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [11]: </span><span class="nb">len</span><span class="p">(</span><span class="n">grid</span><span class="p">)</span>
<span class="gh">Out[11]: </span><span class="go">13231</span>
</pre></div>
</div>
<p>Let’s save our result grid as a GeoJSON file that is another commonly used file
format nowadays for storing spatial data.</p>
<div class="code python highlight-default"><div class="highlight"><pre><span></span><span class="n">resultfp</span> <span class="o">=</span> <span class="s2">"/home/geo/data/TravelTimes_to_5975375_RailwayStation_Helsinki.geojson"</span>
<span class="c1"># Use GeoJSON driver</span>
<span class="n">result</span><span class="o">.</span><span class="n">to_file</span><span class="p">(</span><span class="n">resultfp</span><span class="p">,</span> <span class="n">driver</span><span class="o">=</span><span class="s2">"GeoJSON"</span><span class="p">)</span>
</pre></div>
</div>
<p>There are many more examples for different types of overlay analysis in <a class="reference external" href="http://geopandas.org/set_operations.html">Geopandas documentation</a> where you can go and learn more.</p>
<div class="admonition hint" id="performance-tip">
<p class="first admonition-title">Hint</p>
<p>Overlay analysis such as ours with around 13 000 polygons is quite CPU intensive task which can be quite slow to execute. Luckily, doing such analysis in <em>batches</em> improves the
performance dramatically (spatial lookups are much quicker that way). The code snippet below shows how to do it with batch size of 10 rows which takes only around 1.5 minutes to run the analysis in our computer instance.</p>
<div class="code python last highlight-default"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">geopandas</span> <span class="k">as</span> <span class="nn">gpd</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="c1"># File paths</span>
<span class="n">border_fp</span> <span class="o">=</span> <span class="s2">"/home/geo/data/Helsinki_borders.shp"</span>
<span class="n">grid_fp</span> <span class="o">=</span> <span class="s2">"/home/geo/data/TravelTimes_to_5975375_RailwayStation.shp"</span>
<span class="c1"># Read files</span>
<span class="n">grid</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">grid_fp</span><span class="p">)</span>
<span class="n">hel</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">border_fp</span><span class="p">)</span>
<span class="c1"># Batch size</span>
<span class="n">b</span> <span class="o">=</span> <span class="mi">10</span>
<span class="c1"># Number of iterations (round up with np.ceil) and convert to integer</span>
<span class="n">row_cnt</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">grid</span><span class="p">)</span>
<span class="n">iterations</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">row_cnt</span> <span class="o">/</span> <span class="n">b</span><span class="p">))</span>
<span class="c1"># Final result</span>
<span class="n">final</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="c1"># Set the start and end index according the batch size</span>
<span class="n">start_idx</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">end_idx</span> <span class="o">=</span> <span class="n">start_idx</span> <span class="o">+</span> <span class="n">b</span>
<span class="k">for</span> <span class="n">iteration</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">iterations</span><span class="p">):</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Iteration: </span><span class="si">%s</span><span class="s2">/</span><span class="si">%s</span><span class="s2">"</span> <span class="o">%</span> <span class="p">(</span><span class="n">iteration</span><span class="p">,</span> <span class="n">iterations</span><span class="p">))</span>
<span class="c1"># Make an overlay analysis using a subset of the rows</span>
<span class="n">result</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">grid</span><span class="p">[</span><span class="n">start_idx</span><span class="p">:</span><span class="n">end_idx</span><span class="p">],</span> <span class="n">hel</span><span class="p">,</span> <span class="n">how</span><span class="o">=</span><span class="s1">'intersection'</span><span class="p">)</span>
<span class="c1"># Append the overlay result to final GeoDataFrame</span>
<span class="n">final</span> <span class="o">=</span> <span class="n">final</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">result</span><span class="p">)</span>
<span class="c1"># Update indices</span>
<span class="n">start_idx</span> <span class="o">+=</span> <span class="n">b</span>
<span class="n">end_idx</span> <span class="o">=</span> <span class="n">start_idx</span> <span class="o">+</span> <span class="n">b</span>
<span class="c1"># Save the output as GeoJSON</span>
<span class="n">outfp</span> <span class="o">=</span> <span class="s2">"/home/geo/data/overlay_analysis_speedtest.geojson"</span>
<span class="n">final</span><span class="o">.</span><span class="n">to_file</span><span class="p">(</span><span class="n">outfp</span><span class="p">,</span> <span class="n">driver</span><span class="o">=</span><span class="s2">"GeoJSON"</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="section" id="aggregating-data">
<h2>Aggregating data<a class="headerlink" href="#aggregating-data" title="Permalink to this headline">¶</a></h2>
<p>Aggregating data can also be useful sometimes. What we mean by aggregation is that we basically merge Geometries into together by some common identifier.
Suppose we are interested in studying continents, but we only have country-level data like the country dataset.
By aggregation we would convert this into a continent-level dataset.</p>
<p>Let’s aggregate our travel time data by car travel times, i.e. the grid cells that have the same travel time to Railway Station will be merged together.</p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [12]: </span><span class="n">result_aggregated</span> <span class="o">=</span> <span class="n">result</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="s2">"car_r_t"</span><span class="p">)</span>
<span class="go"># What do we have now</span>
<span class="gp">In [13]: </span><span class="n">result_aggregated</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
<span class="gh">Out[13]: </span><span class="go"></span>
<span class="go"> geometry car_m_d car_m_t \</span>
<span class="go">car_r_t </span>
<span class="go">-1 (POLYGON ((388000.0001354737 6669000.000042855... -1 -1 </span>
<span class="go"> 0 POLYGON ((386000.0001357812 6672000.000042388,... 0 0 </span>
<span class="go"> 7 POLYGON ((386250.0001357396 6671750.000042424,... 1059 7 </span>
<span class="go"> 8 (POLYGON ((386250.0001357467 6671500.000042468... 1207 8 </span>
<span class="go"> 9 (POLYGON ((387000.0001355996 6671500.000042449... 1768 8 </span>
<span class="go"> car_r_d from_id pt_m_d pt_m_t pt_m_tt pt_r_d pt_r_t pt_r_tt \</span>
<span class="go">car_r_t </span>
<span class="go">-1 -1 5996387 -1 -1 -1 -1 -1 -1 </span>
<span class="go"> 0 0 5975375 0 0 0 0 0 0 </span>
<span class="go"> 7 1059 5977007 447 6 6 447 6 6 </span>
<span class="go"> 8 1207 5978638 1026 9 11 1026 9 11 </span>
<span class="go"> 9 1768 5980269 1274 11 15 1274 11 15 </span>
<span class="go"> to_id walk_d walk_t GML_ID NAMEFIN NAMESWE NATCODE </span>
<span class="go">car_r_t </span>
<span class="go">-1 -1 -1 -1 27517366 Helsinki Helsingfors 091 </span>
<span class="go"> 0 5975375 0 0 27517366 Helsinki Helsingfors 091 </span>
<span class="go"> 7 5975375 447 6 27517366 Helsinki Helsingfors 091 </span>
<span class="go"> 8 5975375 774 11 27517366 Helsinki Helsingfors 091 </span>
<span class="go"> 9 5975375 1210 17 27517366 Helsinki Helsingfors 091 </span>
</pre></div>
</div>
<p>Let’s compare the number of cells in the layers before and after the aggregation.</p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="go"># Number of rows before the aggregation</span>
<span class="gp">In [14]: </span><span class="nb">len</span><span class="p">(</span><span class="n">result</span><span class="p">)</span>
<span class="gh">Out[14]: </span><span class="go">3836</span>
<span class="go"># Number of rows after the aggregation</span>
<span class="gp">In [15]: </span><span class="nb">len</span><span class="p">(</span><span class="n">result_aggregated</span><span class="p">)</span>
<span class="go"> Out[15]: 51</span>
</pre></div>
</div>
<p>Indeed the number of rows in our data has decreased and the Polygons were merged together.</p>
</div>
<div class="section" id="simplifying-geometries">
<h2>Simplifying geometries<a class="headerlink" href="#simplifying-geometries" title="Permalink to this headline">¶</a></h2>
<p>Docs will be added here soon.</p>
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