Update documentation

Author: spatialthoughts

_sources/notebooks/geopandas_flood_frequency.ipynb

@@ -915,7 +915,7 @@
       "source": [
         "## Duplicate Flood Event Detection\n",
         "\n",
-        "One of the problems with the dataset is the presence of many duplicate records for the same flood event. While Google did perform some spatio-temporal aggregation, the dataset still has overlapping records from thesame flood event that have different geographic extent (captured from different articles) and/or slightly varying dates. Our goal is to count the total unique flood events aggregated for each grid. Such duplicates would show up as spatially intersecting polygons with `start_date` values within a few days of each other. We use a vectorized `STRtree` bulk query to find all such candidate pairs efficiently."
+        "One of the problems with the dataset is the presence of many duplicate records for the same flood event. While Google did perform some spatio-temporal aggregation, the dataset still has overlapping records from the same flood event that have different geographic extent (captured from different articles) and/or slightly varying dates. Our goal is to count the total unique flood events aggregated for each grid. Such duplicates would show up as spatially intersecting polygons with `start_date` values within a few days of each other. We use a vectorized `STRtree` bulk query to find all such candidate pairs efficiently."
       ]
     },
     {

notebooks/geopandas_flood_frequency.html

@@ -1115,7 +1115,7 @@ <h2>Pre-Processing Flood Data<a class="headerlink" href="#pre-processing-flood-d
 </section>
 <section id="duplicate-flood-event-detection">
 <h2>Duplicate Flood Event Detection<a class="headerlink" href="#duplicate-flood-event-detection" title="Link to this heading">#</a></h2>
-<p>One of the problems with the dataset is the presence of many duplicate records for the same flood event. While Google did perform some spatio-temporal aggregation, the dataset still has overlapping records from thesame flood event that have different geographic extent (captured from different articles) and/or slightly varying dates. Our goal is to count the total unique flood events aggregated for each grid. Such duplicates would show up as spatially intersecting polygons with <code class="docutils literal notranslate"><span class="pre">start_date</span></code> values within a few days of each other. We use a vectorized <code class="docutils literal notranslate"><span class="pre">STRtree</span></code> bulk query to find all such candidate pairs efficiently.</p>
+<p>One of the problems with the dataset is the presence of many duplicate records for the same flood event. While Google did perform some spatio-temporal aggregation, the dataset still has overlapping records from the same flood event that have different geographic extent (captured from different articles) and/or slightly varying dates. Our goal is to count the total unique flood events aggregated for each grid. Such duplicates would show up as spatially intersecting polygons with <code class="docutils literal notranslate"><span class="pre">start_date</span></code> values within a few days of each other. We use a vectorized <code class="docutils literal notranslate"><span class="pre">STRtree</span></code> bulk query to find all such candidate pairs efficiently.</p>
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># This step is computationally expensive and may take a few minutes</span>