Update documentation

Author: spatialthoughts

_sources/notebooks/geopandas_flood_frequency.ipynb

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@@ -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 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."
+        "One of the problems with aggregating this dataset over a grid is the presense of overlapping polygons 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 and slightly varying dates (an article may talk about flooding in the city while another will talk about the same flood in a neighborhood). Our goal is to count the total unique flood events aggregated for each grid and we want to only count unique flood events. We apply a pre-processing step to find all pairs of spatially intersecting polygons with `start_date` values within a few days of each other and assign the the same `flood_event` id."
       ]
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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 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>
+<p>One of the problems with aggregating this dataset over a grid is the presense of overlapping polygons 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 and slightly varying dates (an article may talk about flooding in the city while another will talk about the same flood in a neighborhood). Our goal is to count the total unique flood events aggregated for each grid and we want to only count unique flood events. We apply a pre-processing step to find all pairs of spatially intersecting polygons with <code class="docutils literal notranslate"><span class="pre">start_date</span></code> values within a few days of each other and assign the the same <code class="docutils literal notranslate"><span class="pre">flood_event</span></code> id.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <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>