From 301a181465ac1c451f74ff96edeb0bd9ee347676 Mon Sep 17 00:00:00 2001
From: Gabriele Filomena <gfilo@liverpool.ac.uk>
Date: Tue, 16 Apr 2024 13:58:38 +0100
Subject: [PATCH] docs

---
 docs/Web-Mapping-and-Geovisualisation.pdf     | Bin 6923076 -> 6924222 bytes
 docs/labs/w09_advanced.html                   | 686 +++++++++---------
 ...ll-8-output-1.png => cell-10-output-1.png} | Bin
 .../figure-html/cell-14-output-1.png          | Bin 40028 -> 0 bytes
 .../figure-html/cell-16-output-1.png          | Bin 16858 -> 40028 bytes
 .../figure-html/cell-18-output-1.png          | Bin 0 -> 16858 bytes
 docs/search.json                              |   6 +-
 docs/sitemap.xml                              |   4 +-
 8 files changed, 357 insertions(+), 339 deletions(-)
 rename docs/labs/w09_advanced_files/figure-html/{cell-8-output-1.png => cell-10-output-1.png} (100%)
 delete mode 100644 docs/labs/w09_advanced_files/figure-html/cell-14-output-1.png
 create mode 100644 docs/labs/w09_advanced_files/figure-html/cell-18-output-1.png

diff --git a/docs/Web-Mapping-and-Geovisualisation.pdf b/docs/Web-Mapping-and-Geovisualisation.pdf
index 96f77ad305723d0b30fcb0139326159b84691010..e6498ea429828e3a989cc7895deb84dfc93a039e 100644
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diff --git a/docs/labs/w09_advanced.html b/docs/labs/w09_advanced.html
index 688738b..e08a729 100644
--- a/docs/labs/w09_advanced.html
+++ b/docs/labs/w09_advanced.html
@@ -244,7 +244,7 @@ <h2 id="toc-title">Table of contents</h2>
   </ul></li>
   <li><a href="#animations-in-folium" id="toc-animations-in-folium" class="nav-link" data-scroll-target="#animations-in-folium"><span class="header-section-number">8.2</span> Animations in Folium</a>
   <ul class="collapse">
-  <li><a href="#animating-a-gps-track" id="toc-animating-a-gps-track" class="nav-link" data-scroll-target="#animating-a-gps-track"><span class="header-section-number">8.2.1</span> Animating a GPS track</a></li>
+  <li><a href="#animating-gps-tracks" id="toc-animating-gps-tracks" class="nav-link" data-scroll-target="#animating-gps-tracks"><span class="header-section-number">8.2.1</span> Animating GPS tracks</a></li>
   <li><a href="#other-animations-with-folium" id="toc-other-animations-with-folium" class="nav-link" data-scroll-target="#other-animations-with-folium"><span class="header-section-number">8.2.2</span> Other Animations with Folium</a></li>
   </ul></li>
   </ul>
@@ -291,7 +291,7 @@ <h1 class="title"><span class="chapter-number">8</span>&nbsp; <span class="chapt
 <span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="co"># pip install geoviews</span></span>
 <span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="co"># pip install movingpandas</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
-<div id="cell-3" class="cell" data-execution_count="2">
+<div id="cell-3" class="cell" data-execution_count="1">
 <div class="sourceCode cell-code" id="cb2"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> pandas <span class="im">as</span> pd, geopandas <span class="im">as</span> gpd</span>
 <span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> movingpandas <span class="im">as</span> mpd</span>
 <span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a></span>
@@ -318,99 +318,118 @@ <h2 data-number="8.1" class="anchored" data-anchor-id="getting-gps-trajectories-
 <li>Trace Summary (e.g., distance, duration)</li>
 </ul>
 <p>It’s not possible to download traces from OSM using <code>OSMnx</code>. One has to script their own code</p>
-<p>This first part of the notebook has been readapted from this <a href="https://github.com/movingpandas/movingpandas-examples/blob/main/2-analysis-examples/osm-traces.ipynb">notebook</a> created by Anita Graser.</p>
+<p>Parts of this section of the notebook have been readapted from this <a href="https://github.com/movingpandas/movingpandas-examples/blob/main/2-analysis-examples/osm-traces.ipynb">notebook</a> created by Anita Graser.</p>
 <section id="downaloding-gps-traces-from-the-openstreetmap" class="level3" data-number="8.1.1">
 <h3 data-number="8.1.1" class="anchored" data-anchor-id="downaloding-gps-traces-from-the-openstreetmap"><span class="header-section-number">8.1.1</span> Downaloding GPS traces from the OpenStreetMap</h3>
-<p>def bbox_to_osm_format(bbox): ““” Convert bounding box coordinates to OSM API format.</p>
-<pre><code>Parameters
-----------
-bbox: tuple
-    A tuple containing the bounding box coordinates in the order (north, south, east, west).
-
-Returns
--------
-bbox_str: str
-    A string representing the bounding box in the format "west,south,east,north".
-"""
-north, south, east, west = bbox
-bbox = f"{west},{south},{east},{north}"
-return bbox</code></pre>
 <p>We can try to get OSM traces around the Uni of Liverpool Campus. Feel free to change the area.</p>
-<div id="cell-7" class="cell" data-execution_count="4">
-<div class="sourceCode cell-code" id="cb4"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> osmnx <span class="im">as</span> ox</span>
-<span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a><span class="co"># Define the place name</span></span>
-<span id="cb4-3"><a href="#cb4-3" aria-hidden="true" tabindex="-1"></a>place_name <span class="op">=</span> <span class="st">"University of Liverpool, UK"</span></span>
-<span id="cb4-4"><a href="#cb4-4" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb4-5"><a href="#cb4-5" aria-hidden="true" tabindex="-1"></a><span class="co"># Get the latitude and longitude</span></span>
-<span id="cb4-6"><a href="#cb4-6" aria-hidden="true" tabindex="-1"></a>latitude, longitude <span class="op">=</span> ox.geocoder.geocode(place_name)</span>
-<span id="cb4-7"><a href="#cb4-7" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb4-8"><a href="#cb4-8" aria-hidden="true" tabindex="-1"></a><span class="co"># Create the bbox</span></span>
-<span id="cb4-9"><a href="#cb4-9" aria-hidden="true" tabindex="-1"></a>bbox <span class="op">=</span> ox.utils_geo.bbox_from_point((latitude, longitude), dist <span class="op">=</span> <span class="dv">1500</span>)</span>
-<span id="cb4-10"><a href="#cb4-10" aria-hidden="true" tabindex="-1"></a>bbox <span class="op">=</span> bbox_to_osm_format(bbox) <span class="co"># needs to be {west},{south},{east},{north} for OSM Api</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</div>
-<p>The <code>get_osm_traces</code> function below retrieves GPS traces from OpenStreetMap within a specified bounding box, processing up to a user-defined maximum number of pages. It uses a while loop to fetch and parse GPS data into GeoDataFrames, querying the OSM API by incrementally updating the page number until no more data is available or the maximum page limit is reached.</p>
-<p>Upon fetching the data, the function concatenates these individual GeoDataFrames into a single comprehensive GeoDataFrame. Before returning the final GeoDataFrame, it cleans the dataset by dropping a predefined list of potentially irrelevant or empty columns.</p>
-<div id="cell-9" class="cell" data-execution_count="5">
-<div class="sourceCode cell-code" id="cb5"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> get_osm_traces(max_pages <span class="op">=</span> <span class="dv">2</span>,  bbox<span class="op">=</span><span class="st">'16.18, 48.09, 16.61, 48.32'</span>):</span>
+<div id="cell-5" class="cell" data-execution_count="2">
+<div class="sourceCode cell-code" id="cb3"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> osmnx <span class="im">as</span> ox</span>
+<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a><span class="co"># Define the place name</span></span>
+<span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a>place_name <span class="op">=</span> <span class="st">"University of Liverpool, UK"</span></span>
+<span id="cb3-4"><a href="#cb3-4" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb3-5"><a href="#cb3-5" aria-hidden="true" tabindex="-1"></a><span class="co"># Get the latitude and longitude</span></span>
+<span id="cb3-6"><a href="#cb3-6" aria-hidden="true" tabindex="-1"></a>latitude, longitude <span class="op">=</span> ox.geocoder.geocode(place_name)</span>
+<span id="cb3-7"><a href="#cb3-7" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb3-8"><a href="#cb3-8" aria-hidden="true" tabindex="-1"></a><span class="co"># Create the bbox</span></span>
+<span id="cb3-9"><a href="#cb3-9" aria-hidden="true" tabindex="-1"></a>bbox <span class="op">=</span> ox.utils_geo.bbox_from_point((latitude, longitude), dist <span class="op">=</span> <span class="dv">1500</span>)</span>
+<span id="cb3-10"><a href="#cb3-10" aria-hidden="true" tabindex="-1"></a>bbox</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-display" data-execution_count="2">
+<pre><code>(53.420732655032396,
+ 53.39375304496761,
+ -2.9432044446907044,
+ -2.988462877347038)</code></pre>
+</div>
+</div>
+<p>We need the <code>bbox_to_osm_format</code> function below to convert our bounding box coordinates into the format required by the OpenStreetMap (OSM) API. The input tuple contains coordinates in the order (north, south, east, west). The function rearranges these values into the string “west,south,east,north”, which is the format expected by OSM for API queries.</p>
+<div id="cell-7" class="cell" data-execution_count="3">
+<div class="sourceCode cell-code" id="cb5"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> bbox_to_osm_format(bbox):</span>
 <span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a>    <span class="co">"""</span></span>
-<span id="cb5-3"><a href="#cb5-3" aria-hidden="true" tabindex="-1"></a><span class="co">    Retrieve OpenStreetMap GPS traces within a specified bounding box.</span></span>
+<span id="cb5-3"><a href="#cb5-3" aria-hidden="true" tabindex="-1"></a><span class="co">    Convert bounding box coordinates to OSM API format.</span></span>
 <span id="cb5-4"><a href="#cb5-4" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb5-5"><a href="#cb5-5" aria-hidden="true" tabindex="-1"></a><span class="co">    Parameters</span></span>
 <span id="cb5-6"><a href="#cb5-6" aria-hidden="true" tabindex="-1"></a><span class="co">    ----------</span></span>
-<span id="cb5-7"><a href="#cb5-7" aria-hidden="true" tabindex="-1"></a><span class="co">    max_pages: int, optional</span></span>
-<span id="cb5-8"><a href="#cb5-8" aria-hidden="true" tabindex="-1"></a><span class="co">        The maximum number of pages to retrieve. Defaults to 2.</span></span>
-<span id="cb5-9"><a href="#cb5-9" aria-hidden="true" tabindex="-1"></a><span class="co">    bbox: str, optional</span></span>
-<span id="cb5-10"><a href="#cb5-10" aria-hidden="true" tabindex="-1"></a><span class="co">        The bounding box coordinates in the format 'west, south, east, north'. Defaults to '16.18, 48.09, 16.61, 48.32'.</span></span>
-<span id="cb5-11"><a href="#cb5-11" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb5-12"><a href="#cb5-12" aria-hidden="true" tabindex="-1"></a><span class="co">    Returns</span></span>
-<span id="cb5-13"><a href="#cb5-13" aria-hidden="true" tabindex="-1"></a><span class="co">    -------</span></span>
-<span id="cb5-14"><a href="#cb5-14" aria-hidden="true" tabindex="-1"></a><span class="co">    final_gdf: GeoDataFrame</span></span>
-<span id="cb5-15"><a href="#cb5-15" aria-hidden="true" tabindex="-1"></a><span class="co">        A GeoDataFrame containing the retrieved GPS traces.</span></span>
-<span id="cb5-16"><a href="#cb5-16" aria-hidden="true" tabindex="-1"></a><span class="co">    """</span></span>
-<span id="cb5-17"><a href="#cb5-17" aria-hidden="true" tabindex="-1"></a>    </span>
-<span id="cb5-18"><a href="#cb5-18" aria-hidden="true" tabindex="-1"></a>    all_data <span class="op">=</span> []</span>
-<span id="cb5-19"><a href="#cb5-19" aria-hidden="true" tabindex="-1"></a>    page <span class="op">=</span> <span class="dv">0</span></span>
-<span id="cb5-20"><a href="#cb5-20" aria-hidden="true" tabindex="-1"></a>    </span>
-<span id="cb5-21"><a href="#cb5-21" aria-hidden="true" tabindex="-1"></a>    <span class="cf">while</span> (<span class="va">True</span>) <span class="kw">and</span> (page <span class="op">&lt;=</span> max_pages):</span>
-<span id="cb5-22"><a href="#cb5-22" aria-hidden="true" tabindex="-1"></a>        <span class="co"># Constructing the URL to query OpenStreetMap API for GPS trackpoints within a specified bounding box and page number</span></span>
-<span id="cb5-23"><a href="#cb5-23" aria-hidden="true" tabindex="-1"></a>        url <span class="op">=</span> <span class="ss">f'https://api.openstreetmap.org/api/0.6/trackpoints?bbox=</span><span class="sc">{</span>bbox<span class="sc">}</span><span class="ss">&amp;page=</span><span class="sc">{</span>page<span class="sc">}</span><span class="ss">'</span></span>
-<span id="cb5-24"><a href="#cb5-24" aria-hidden="true" tabindex="-1"></a>        </span>
-<span id="cb5-25"><a href="#cb5-25" aria-hidden="true" tabindex="-1"></a>        <span class="co"># Sending a GET request to the constructed URL</span></span>
-<span id="cb5-26"><a href="#cb5-26" aria-hidden="true" tabindex="-1"></a>        response <span class="op">=</span> requests.get(url)</span>
-<span id="cb5-27"><a href="#cb5-27" aria-hidden="true" tabindex="-1"></a>        </span>
-<span id="cb5-28"><a href="#cb5-28" aria-hidden="true" tabindex="-1"></a>        <span class="co"># Checking if the response status code is not 200 (indicating success) or if the response content is empty</span></span>
-<span id="cb5-29"><a href="#cb5-29" aria-hidden="true" tabindex="-1"></a>        <span class="co"># If either condition is met, the loop breaks, indicating no more data to retrieve</span></span>
-<span id="cb5-30"><a href="#cb5-30" aria-hidden="true" tabindex="-1"></a>        <span class="cf">if</span> response.status_code <span class="op">!=</span> <span class="dv">200</span> <span class="kw">or</span> <span class="kw">not</span> response.content:</span>
-<span id="cb5-31"><a href="#cb5-31" aria-hidden="true" tabindex="-1"></a>            <span class="cf">break</span></span>
-<span id="cb5-32"><a href="#cb5-32" aria-hidden="true" tabindex="-1"></a>        </span>
-<span id="cb5-33"><a href="#cb5-33" aria-hidden="true" tabindex="-1"></a>        <span class="co"># Reading the content of the response, which contains GPS trackpoints data, into a GeoDataFrame</span></span>
-<span id="cb5-34"><a href="#cb5-34" aria-hidden="true" tabindex="-1"></a>        <span class="co"># The 'layer' parameter specifies the layer within the GeoDataFrame where trackpoints will be stored</span></span>
-<span id="cb5-35"><a href="#cb5-35" aria-hidden="true" tabindex="-1"></a>        gdf <span class="op">=</span> gpd.read_file(BytesIO(response.content), layer<span class="op">=</span><span class="st">'track_points'</span>)</span>
-<span id="cb5-36"><a href="#cb5-36" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb5-37"><a href="#cb5-37" aria-hidden="true" tabindex="-1"></a>        <span class="cf">if</span> gdf.empty:</span>
-<span id="cb5-38"><a href="#cb5-38" aria-hidden="true" tabindex="-1"></a>            <span class="cf">break</span></span>
-<span id="cb5-39"><a href="#cb5-39" aria-hidden="true" tabindex="-1"></a>            </span>
-<span id="cb5-40"><a href="#cb5-40" aria-hidden="true" tabindex="-1"></a>        all_data.append(gdf)</span>
-<span id="cb5-41"><a href="#cb5-41" aria-hidden="true" tabindex="-1"></a>        page <span class="op">+=</span> <span class="dv">1</span></span>
-<span id="cb5-42"><a href="#cb5-42" aria-hidden="true" tabindex="-1"></a>        </span>
-<span id="cb5-43"><a href="#cb5-43" aria-hidden="true" tabindex="-1"></a>    <span class="co"># Concatenate all GeoDataFrames into one</span></span>
-<span id="cb5-44"><a href="#cb5-44" aria-hidden="true" tabindex="-1"></a>    final_gdf <span class="op">=</span> gpd.GeoDataFrame(pd.concat(all_data, ignore_index<span class="op">=</span><span class="va">True</span>))</span>
-<span id="cb5-45"><a href="#cb5-45" aria-hidden="true" tabindex="-1"></a>    </span>
-<span id="cb5-46"><a href="#cb5-46" aria-hidden="true" tabindex="-1"></a>    <span class="co"># dropping empty columns</span></span>
-<span id="cb5-47"><a href="#cb5-47" aria-hidden="true" tabindex="-1"></a>    columns_to_drop <span class="op">=</span> [<span class="st">'ele'</span>, <span class="st">'course'</span>, <span class="st">'speed'</span>, <span class="st">'magvar'</span>, <span class="st">'geoidheight'</span>, <span class="st">'name'</span>, <span class="st">'cmt'</span>, <span class="st">'desc'</span>,</span>
-<span id="cb5-48"><a href="#cb5-48" aria-hidden="true" tabindex="-1"></a>                       <span class="st">'src'</span>, <span class="st">'url'</span>, <span class="st">'urlname'</span>, <span class="st">'sym'</span>, <span class="st">'type'</span>, <span class="st">'fix'</span>, <span class="st">'sat'</span>, <span class="st">'hdop'</span>, <span class="st">'vdop'</span>,</span>
-<span id="cb5-49"><a href="#cb5-49" aria-hidden="true" tabindex="-1"></a>                       <span class="st">'pdop'</span>, <span class="st">'ageofdgpsdata'</span>, <span class="st">'dgpsid'</span>]</span>
-<span id="cb5-50"><a href="#cb5-50" aria-hidden="true" tabindex="-1"></a>    </span>
-<span id="cb5-51"><a href="#cb5-51" aria-hidden="true" tabindex="-1"></a>    final_gdf <span class="op">=</span> final_gdf.drop(columns<span class="op">=</span>[col <span class="cf">for</span> col <span class="kw">in</span> columns_to_drop <span class="cf">if</span> col <span class="kw">in</span> final_gdf.columns])</span>
-<span id="cb5-52"><a href="#cb5-52" aria-hidden="true" tabindex="-1"></a>    <span class="cf">return</span> final_gdf</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<span id="cb5-7"><a href="#cb5-7" aria-hidden="true" tabindex="-1"></a><span class="co">    bbox: tuple</span></span>
+<span id="cb5-8"><a href="#cb5-8" aria-hidden="true" tabindex="-1"></a><span class="co">        A tuple containing the bounding box coordinates in the order (north, south, east, west).</span></span>
+<span id="cb5-9"><a href="#cb5-9" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb5-10"><a href="#cb5-10" aria-hidden="true" tabindex="-1"></a><span class="co">    Returns</span></span>
+<span id="cb5-11"><a href="#cb5-11" aria-hidden="true" tabindex="-1"></a><span class="co">    -------</span></span>
+<span id="cb5-12"><a href="#cb5-12" aria-hidden="true" tabindex="-1"></a><span class="co">    bbox_str: str</span></span>
+<span id="cb5-13"><a href="#cb5-13" aria-hidden="true" tabindex="-1"></a><span class="co">        A string representing the bounding box in the format "west,south,east,north".</span></span>
+<span id="cb5-14"><a href="#cb5-14" aria-hidden="true" tabindex="-1"></a><span class="co">    """</span></span>
+<span id="cb5-15"><a href="#cb5-15" aria-hidden="true" tabindex="-1"></a>    north, south, east, west <span class="op">=</span> bbox</span>
+<span id="cb5-16"><a href="#cb5-16" aria-hidden="true" tabindex="-1"></a>    bbox <span class="op">=</span> <span class="ss">f"</span><span class="sc">{</span>west<span class="sc">}</span><span class="ss">,</span><span class="sc">{</span>south<span class="sc">}</span><span class="ss">,</span><span class="sc">{</span>east<span class="sc">}</span><span class="ss">,</span><span class="sc">{</span>north<span class="sc">}</span><span class="ss">"</span></span>
+<span id="cb5-17"><a href="#cb5-17" aria-hidden="true" tabindex="-1"></a>    <span class="cf">return</span> bbox</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</div>
+<div id="cell-8" class="cell" data-execution_count="4">
+<div class="sourceCode cell-code" id="cb6"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a>bbox <span class="op">=</span> bbox_to_osm_format(bbox) <span class="co"># needs to be {west},{south},{east},{north} for OSM Api</span></span>
+<span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a>bbox</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-display" data-execution_count="4">
+<pre><code>'-2.988462877347038,53.39375304496761,-2.9432044446907044,53.420732655032396'</code></pre>
+</div>
+</div>
+<p>The <code>get_osm_traces</code> function below retrieves GPS traces from OpenStreetMap within a specified bounding box, processing up to a user-defined maximum number of pages. It uses a while loop to fetch and parse GPS data into GeoDataFrames, querying the OSM API by incrementally updating the page number until no more data is available or the maximum page limit is reached.</p>
+<p>Upon fetching the data, the function concatenates these individual GeoDataFrames into a single comprehensive GeoDataFrame. Before returning the final GeoDataFrame, it cleans the dataset by dropping a predefined list of potentially irrelevant or empty columns.</p>
+<div id="cell-10" class="cell" data-execution_count="5">
+<div class="sourceCode cell-code" id="cb8"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> get_osm_traces(max_pages <span class="op">=</span> <span class="dv">2</span>,  bbox<span class="op">=</span><span class="st">'16.18, 48.09, 16.61, 48.32'</span>):</span>
+<span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a>    <span class="co">"""</span></span>
+<span id="cb8-3"><a href="#cb8-3" aria-hidden="true" tabindex="-1"></a><span class="co">    Retrieve OpenStreetMap GPS traces within a specified bounding box.</span></span>
+<span id="cb8-4"><a href="#cb8-4" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb8-5"><a href="#cb8-5" aria-hidden="true" tabindex="-1"></a><span class="co">    Parameters</span></span>
+<span id="cb8-6"><a href="#cb8-6" aria-hidden="true" tabindex="-1"></a><span class="co">    ----------</span></span>
+<span id="cb8-7"><a href="#cb8-7" aria-hidden="true" tabindex="-1"></a><span class="co">    max_pages: int, optional</span></span>
+<span id="cb8-8"><a href="#cb8-8" aria-hidden="true" tabindex="-1"></a><span class="co">        The maximum number of pages to retrieve. Defaults to 2.</span></span>
+<span id="cb8-9"><a href="#cb8-9" aria-hidden="true" tabindex="-1"></a><span class="co">    bbox: str, optional</span></span>
+<span id="cb8-10"><a href="#cb8-10" aria-hidden="true" tabindex="-1"></a><span class="co">        The bounding box coordinates in the format 'west, south, east, north'. Defaults to '16.18, 48.09, 16.61, 48.32'.</span></span>
+<span id="cb8-11"><a href="#cb8-11" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb8-12"><a href="#cb8-12" aria-hidden="true" tabindex="-1"></a><span class="co">    Returns</span></span>
+<span id="cb8-13"><a href="#cb8-13" aria-hidden="true" tabindex="-1"></a><span class="co">    -------</span></span>
+<span id="cb8-14"><a href="#cb8-14" aria-hidden="true" tabindex="-1"></a><span class="co">    final_gdf: GeoDataFrame</span></span>
+<span id="cb8-15"><a href="#cb8-15" aria-hidden="true" tabindex="-1"></a><span class="co">        A GeoDataFrame containing the retrieved GPS traces.</span></span>
+<span id="cb8-16"><a href="#cb8-16" aria-hidden="true" tabindex="-1"></a><span class="co">    """</span></span>
+<span id="cb8-17"><a href="#cb8-17" aria-hidden="true" tabindex="-1"></a>    </span>
+<span id="cb8-18"><a href="#cb8-18" aria-hidden="true" tabindex="-1"></a>    all_data <span class="op">=</span> []</span>
+<span id="cb8-19"><a href="#cb8-19" aria-hidden="true" tabindex="-1"></a>    page <span class="op">=</span> <span class="dv">0</span></span>
+<span id="cb8-20"><a href="#cb8-20" aria-hidden="true" tabindex="-1"></a>    </span>
+<span id="cb8-21"><a href="#cb8-21" aria-hidden="true" tabindex="-1"></a>    <span class="cf">while</span> (<span class="va">True</span>) <span class="kw">and</span> (page <span class="op">&lt;=</span> max_pages):</span>
+<span id="cb8-22"><a href="#cb8-22" aria-hidden="true" tabindex="-1"></a>        <span class="co"># Constructing the URL to query OpenStreetMap API for GPS trackpoints within a specified bounding box and page number</span></span>
+<span id="cb8-23"><a href="#cb8-23" aria-hidden="true" tabindex="-1"></a>        url <span class="op">=</span> <span class="ss">f'https://api.openstreetmap.org/api/0.6/trackpoints?bbox=</span><span class="sc">{</span>bbox<span class="sc">}</span><span class="ss">&amp;page=</span><span class="sc">{</span>page<span class="sc">}</span><span class="ss">'</span></span>
+<span id="cb8-24"><a href="#cb8-24" aria-hidden="true" tabindex="-1"></a>        </span>
+<span id="cb8-25"><a href="#cb8-25" aria-hidden="true" tabindex="-1"></a>        <span class="co"># Sending a GET request to the constructed URL</span></span>
+<span id="cb8-26"><a href="#cb8-26" aria-hidden="true" tabindex="-1"></a>        response <span class="op">=</span> requests.get(url)</span>
+<span id="cb8-27"><a href="#cb8-27" aria-hidden="true" tabindex="-1"></a>        </span>
+<span id="cb8-28"><a href="#cb8-28" aria-hidden="true" tabindex="-1"></a>        <span class="co"># Checking if the response status code is not 200 (indicating success) or if the response content is empty</span></span>
+<span id="cb8-29"><a href="#cb8-29" aria-hidden="true" tabindex="-1"></a>        <span class="co"># If either condition is met, the loop breaks, indicating no more data to retrieve</span></span>
+<span id="cb8-30"><a href="#cb8-30" aria-hidden="true" tabindex="-1"></a>        <span class="cf">if</span> response.status_code <span class="op">!=</span> <span class="dv">200</span> <span class="kw">or</span> <span class="kw">not</span> response.content:</span>
+<span id="cb8-31"><a href="#cb8-31" aria-hidden="true" tabindex="-1"></a>            <span class="cf">break</span></span>
+<span id="cb8-32"><a href="#cb8-32" aria-hidden="true" tabindex="-1"></a>        </span>
+<span id="cb8-33"><a href="#cb8-33" aria-hidden="true" tabindex="-1"></a>        <span class="co"># Reading the content of the response, which contains GPS trackpoints data, into a GeoDataFrame</span></span>
+<span id="cb8-34"><a href="#cb8-34" aria-hidden="true" tabindex="-1"></a>        <span class="co"># The 'layer' parameter specifies the layer within the GeoDataFrame where trackpoints will be stored</span></span>
+<span id="cb8-35"><a href="#cb8-35" aria-hidden="true" tabindex="-1"></a>        gdf <span class="op">=</span> gpd.read_file(BytesIO(response.content), layer<span class="op">=</span><span class="st">'track_points'</span>)</span>
+<span id="cb8-36"><a href="#cb8-36" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb8-37"><a href="#cb8-37" aria-hidden="true" tabindex="-1"></a>        <span class="cf">if</span> gdf.empty:</span>
+<span id="cb8-38"><a href="#cb8-38" aria-hidden="true" tabindex="-1"></a>            <span class="cf">break</span></span>
+<span id="cb8-39"><a href="#cb8-39" aria-hidden="true" tabindex="-1"></a>            </span>
+<span id="cb8-40"><a href="#cb8-40" aria-hidden="true" tabindex="-1"></a>        all_data.append(gdf)</span>
+<span id="cb8-41"><a href="#cb8-41" aria-hidden="true" tabindex="-1"></a>        page <span class="op">+=</span> <span class="dv">1</span></span>
+<span id="cb8-42"><a href="#cb8-42" aria-hidden="true" tabindex="-1"></a>        </span>
+<span id="cb8-43"><a href="#cb8-43" aria-hidden="true" tabindex="-1"></a>    <span class="co"># Concatenate all GeoDataFrames into one</span></span>
+<span id="cb8-44"><a href="#cb8-44" aria-hidden="true" tabindex="-1"></a>    final_gdf <span class="op">=</span> gpd.GeoDataFrame(pd.concat(all_data, ignore_index<span class="op">=</span><span class="va">True</span>))</span>
+<span id="cb8-45"><a href="#cb8-45" aria-hidden="true" tabindex="-1"></a>    </span>
+<span id="cb8-46"><a href="#cb8-46" aria-hidden="true" tabindex="-1"></a>    <span class="co"># dropping empty columns</span></span>
+<span id="cb8-47"><a href="#cb8-47" aria-hidden="true" tabindex="-1"></a>    columns_to_drop <span class="op">=</span> [<span class="st">'ele'</span>, <span class="st">'course'</span>, <span class="st">'speed'</span>, <span class="st">'magvar'</span>, <span class="st">'geoidheight'</span>, <span class="st">'name'</span>, <span class="st">'cmt'</span>, <span class="st">'desc'</span>,</span>
+<span id="cb8-48"><a href="#cb8-48" aria-hidden="true" tabindex="-1"></a>                       <span class="st">'src'</span>, <span class="st">'url'</span>, <span class="st">'urlname'</span>, <span class="st">'sym'</span>, <span class="st">'type'</span>, <span class="st">'fix'</span>, <span class="st">'sat'</span>, <span class="st">'hdop'</span>, <span class="st">'vdop'</span>,</span>
+<span id="cb8-49"><a href="#cb8-49" aria-hidden="true" tabindex="-1"></a>                       <span class="st">'pdop'</span>, <span class="st">'ageofdgpsdata'</span>, <span class="st">'dgpsid'</span>]</span>
+<span id="cb8-50"><a href="#cb8-50" aria-hidden="true" tabindex="-1"></a>    </span>
+<span id="cb8-51"><a href="#cb8-51" aria-hidden="true" tabindex="-1"></a>    final_gdf <span class="op">=</span> final_gdf.drop(columns<span class="op">=</span>[col <span class="cf">for</span> col <span class="kw">in</span> columns_to_drop <span class="cf">if</span> col <span class="kw">in</span> final_gdf.columns])</span>
+<span id="cb8-52"><a href="#cb8-52" aria-hidden="true" tabindex="-1"></a>    <span class="cf">return</span> final_gdf</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>We initially download 2 pages of GSP traces. We can try with higher numbers to get more data. More recent traces are downloaded first.</p>
-<div id="cell-11" class="cell" data-execution_count="6">
-<div class="sourceCode cell-code" id="cb6"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a>max_pages <span class="op">=</span> <span class="dv">2</span> <span class="co"># pages of data, </span></span>
-<span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a>gps_track_points <span class="op">=</span> get_osm_traces(max_pages, bbox)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div id="cell-12" class="cell" data-execution_count="6">
+<div class="sourceCode cell-code" id="cb9"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a>max_pages <span class="op">=</span> <span class="dv">2</span> <span class="co"># pages of data, </span></span>
+<span id="cb9-2"><a href="#cb9-2" aria-hidden="true" tabindex="-1"></a>gps_track_points <span class="op">=</span> get_osm_traces(max_pages, bbox)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
-<div id="cell-12" class="cell" data-execution_count="7">
-<div class="sourceCode cell-code" id="cb7"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a>gps_track_points.head()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div id="cell-13" class="cell" data-execution_count="7">
+<div class="sourceCode cell-code" id="cb10"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a>gps_track_points.head()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="7">
 <div>
 
@@ -473,12 +492,12 @@ <h3 data-number="8.1.1" class="anchored" data-anchor-id="downaloding-gps-traces-
 </div>
 </div>
 </div>
-<div id="cell-13" class="cell" data-execution_count="8">
-<div class="sourceCode cell-code" id="cb8"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a>gps_track_points.plot(markersize <span class="op">=</span> <span class="fl">0.5</span>) <span class="co"># still are trackpoints still</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div id="cell-14" class="cell" data-execution_count="8">
+<div class="sourceCode cell-code" id="cb11"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a>gps_track_points.plot(markersize <span class="op">=</span> <span class="fl">0.5</span>) <span class="co"># still are trackpoints still</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display">
 <div>
 <figure class="figure">
-<p><img src="w09_advanced_files/figure-html/cell-8-output-1.png" class="img-fluid figure-img"></p>
+<p><img src="w09_advanced_files/figure-html/cell-10-output-1.png" class="img-fluid figure-img"></p>
 </figure>
 </div>
 </div>
@@ -489,16 +508,16 @@ <h3 data-number="8.1.2" class="anchored" data-anchor-id="transform-the-point-geo
 <p><code>MovingPandas</code> is a Python library designed for analyzing movement data using <code>Pandas</code>. It extends <code>Pandas</code>’ capabilities to handle temporal and spatial data jointly. It is the ideal library for trajectory data analysis, visualization, and exploration. MovingPandas provides functionalities for processing and analyzing movement data, including trajectory segmentation, trajectory aggregation, and trajectory generalization.</p>
 <p><strong>See examples and documentation at https://movingpandas.org</strong></p>
 <p>The following code creates a <code>TrajectoryCollection</code> object by providing the GPS track points data, specifying the column that identifies trajectories, and indicating the column containing timestamps.</p>
-<div id="cell-16" class="cell" data-execution_count="9">
-<div class="sourceCode cell-code" id="cb9"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a>osm_traces <span class="op">=</span> mpd.TrajectoryCollection(gps_track_points, <span class="st">'track_fid'</span>, t<span class="op">=</span><span class="st">'time'</span>)</span>
-<span id="cb9-2"><a href="#cb9-2" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(<span class="ss">f'The OSM traces download contains </span><span class="sc">{</span><span class="bu">len</span>(osm_traces)<span class="sc">}</span><span class="ss"> tracks'</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div id="cell-17" class="cell" data-execution_count="9">
+<div class="sourceCode cell-code" id="cb12"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a>osm_traces <span class="op">=</span> mpd.TrajectoryCollection(gps_track_points, <span class="st">'track_fid'</span>, t<span class="op">=</span><span class="st">'time'</span>)</span>
+<span id="cb12-2"><a href="#cb12-2" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(<span class="ss">f'The OSM traces download contains </span><span class="sc">{</span><span class="bu">len</span>(osm_traces)<span class="sc">}</span><span class="ss"> tracks'</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code>The OSM traces download contains 14 tracks</code></pre>
 </div>
 </div>
-<div id="cell-17" class="cell" data-execution_count="10">
-<div class="sourceCode cell-code" id="cb11"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a><span class="cf">for</span> track <span class="kw">in</span> osm_traces: </span>
-<span id="cb11-2"><a href="#cb11-2" aria-hidden="true" tabindex="-1"></a>    <span class="bu">print</span>(<span class="ss">f'Track </span><span class="sc">{</span>track<span class="sc">.</span><span class="bu">id</span><span class="sc">}</span><span class="ss">: length=</span><span class="sc">{</span>track<span class="sc">.</span>get_length(units<span class="op">=</span><span class="st">"km"</span>)<span class="sc">:.2f}</span><span class="ss"> km'</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div id="cell-18" class="cell" data-execution_count="10">
+<div class="sourceCode cell-code" id="cb14"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb14-1"><a href="#cb14-1" aria-hidden="true" tabindex="-1"></a><span class="cf">for</span> track <span class="kw">in</span> osm_traces: </span>
+<span id="cb14-2"><a href="#cb14-2" aria-hidden="true" tabindex="-1"></a>    <span class="bu">print</span>(<span class="ss">f'Track </span><span class="sc">{</span>track<span class="sc">.</span><span class="bu">id</span><span class="sc">}</span><span class="ss">: length=</span><span class="sc">{</span>track<span class="sc">.</span>get_length(units<span class="op">=</span><span class="st">"km"</span>)<span class="sc">:.2f}</span><span class="ss"> km'</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code>Track 0: length=9.96 km
 Track 1: length=3.02 km
@@ -518,13 +537,13 @@ <h3 data-number="8.1.2" class="anchored" data-anchor-id="transform-the-point-geo
 </div>
 <p>Below we set up some visualization options for <code>holoviews</code>. Holoviews is a library for creating interactive visualizations of complex datasets with ease. Holoviews provides a declarative approach to building visualizations, allowing you to express your visualization intent concisely. Holoviews combines the usage of Matplotlib, Bokeh, and Plotly and supports creating interactive visualizations. You can easily add interactive elements like hover tooltips, zooming, panning, and selection widgets to your plots. Compared to <code>folium</code>, however, it does not allow for as much freedom and development outside the box. Folium is primarily designed for creating web-based maps with features like tile layers and GeoJSON overlays, offering a high degree of customization for map-based visualizations. Holoviews, on the other hand, focuses more on general-purpose interactive visualizations beyond just maps, offering a broader range of visualization options.</p>
 <p>See examples and documentation at https://holoviews.org.</p>
-<div id="cell-19" class="cell" data-execution_count="11">
-<div class="sourceCode cell-code" id="cb13"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> holoviews <span class="im">import</span> opts, dim</span>
-<span id="cb13-2"><a href="#cb13-2" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> hvplot.pandas </span>
-<span id="cb13-3"><a href="#cb13-3" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb13-4"><a href="#cb13-4" aria-hidden="true" tabindex="-1"></a>plot_defaults <span class="op">=</span> {<span class="st">'linewidth'</span>:<span class="dv">5</span>, <span class="st">'capstyle'</span>:<span class="st">'round'</span>, <span class="st">'figsize'</span>:(<span class="dv">9</span>,<span class="dv">3</span>), <span class="st">'legend'</span>:<span class="va">True</span>}</span>
-<span id="cb13-5"><a href="#cb13-5" aria-hidden="true" tabindex="-1"></a>opts.defaults(opts.Overlay(active_tools<span class="op">=</span>[<span class="st">'wheel_zoom'</span>], frame_width<span class="op">=</span><span class="dv">500</span>, frame_height<span class="op">=</span><span class="dv">400</span>))</span>
-<span id="cb13-6"><a href="#cb13-6" aria-hidden="true" tabindex="-1"></a>hvplot_defaults <span class="op">=</span> {<span class="st">'tiles'</span>:<span class="va">None</span>, <span class="st">'cmap'</span>:<span class="st">'Viridis'</span>, <span class="st">'colorbar'</span>:<span class="va">True</span>}</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div id="cell-20" class="cell" data-execution_count="11">
+<div class="sourceCode cell-code" id="cb16"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb16-1"><a href="#cb16-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> holoviews <span class="im">import</span> opts, dim</span>
+<span id="cb16-2"><a href="#cb16-2" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> hvplot.pandas </span>
+<span id="cb16-3"><a href="#cb16-3" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb16-4"><a href="#cb16-4" aria-hidden="true" tabindex="-1"></a>plot_defaults <span class="op">=</span> {<span class="st">'linewidth'</span>:<span class="dv">5</span>, <span class="st">'capstyle'</span>:<span class="st">'round'</span>, <span class="st">'figsize'</span>:(<span class="dv">9</span>,<span class="dv">3</span>), <span class="st">'legend'</span>:<span class="va">True</span>}</span>
+<span id="cb16-5"><a href="#cb16-5" aria-hidden="true" tabindex="-1"></a>opts.defaults(opts.Overlay(active_tools<span class="op">=</span>[<span class="st">'wheel_zoom'</span>], frame_width<span class="op">=</span><span class="dv">500</span>, frame_height<span class="op">=</span><span class="dv">400</span>))</span>
+<span id="cb16-6"><a href="#cb16-6" aria-hidden="true" tabindex="-1"></a>hvplot_defaults <span class="op">=</span> {<span class="st">'tiles'</span>:<span class="va">None</span>, <span class="st">'cmap'</span>:<span class="st">'Viridis'</span>, <span class="st">'colorbar'</span>:<span class="va">True</span>}</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display">
 <script type="application/javascript">
 (function(root) {
@@ -1095,10 +1114,10 @@ <h3 data-number="8.1.2" class="anchored" data-anchor-id="transform-the-point-geo
 </div>
 <p><strong>Traces generalisation</strong></p>
 <p>The <code>MinTimeDeltaGeneralizer</code> class from MovingPandas is applied to the <code>osm_traces</code> object. This class is responsible for simplifying or generalizing the GPS traces based on a minimum time delta. In this case, traces are being generalized to a tolerance of one minute.</p>
-<div id="cell-21" class="cell" data-execution_count="12">
-<div class="sourceCode cell-code" id="cb14"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb14-1"><a href="#cb14-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> datetime <span class="im">import</span> datetime, timedelta</span>
-<span id="cb14-2"><a href="#cb14-2" aria-hidden="true" tabindex="-1"></a>osm_traces <span class="op">=</span> mpd.MinTimeDeltaGeneralizer(osm_traces).generalize(tolerance<span class="op">=</span>timedelta(minutes<span class="op">=</span><span class="dv">1</span>))</span>
-<span id="cb14-3"><a href="#cb14-3" aria-hidden="true" tabindex="-1"></a>osm_traces.hvplot(title<span class="op">=</span><span class="st">'OSM Traces'</span>, line_width<span class="op">=</span><span class="dv">3</span>, width<span class="op">=</span><span class="dv">700</span>, height<span class="op">=</span><span class="dv">400</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div id="cell-22" class="cell" data-execution_count="12">
+<div class="sourceCode cell-code" id="cb17"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb17-1"><a href="#cb17-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> datetime <span class="im">import</span> datetime, timedelta</span>
+<span id="cb17-2"><a href="#cb17-2" aria-hidden="true" tabindex="-1"></a>osm_traces <span class="op">=</span> mpd.MinTimeDeltaGeneralizer(osm_traces).generalize(tolerance<span class="op">=</span>timedelta(minutes<span class="op">=</span><span class="dv">1</span>))</span>
+<span id="cb17-3"><a href="#cb17-3" aria-hidden="true" tabindex="-1"></a>osm_traces.hvplot(title<span class="op">=</span><span class="st">'OSM Traces'</span>, line_width<span class="op">=</span><span class="dv">3</span>, width<span class="op">=</span><span class="dv">700</span>, height<span class="op">=</span><span class="dv">400</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display">
 
 </div>
@@ -1171,11 +1190,11 @@ <h3 data-number="8.1.2" class="anchored" data-anchor-id="transform-the-point-geo
 </div>
 </div>
 <p>Visualising by speed</p>
-<div id="cell-23" class="cell" data-execution_count="13">
-<div class="sourceCode cell-code" id="cb15"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a>osm_traces.get_trajectory(<span class="dv">0</span>).add_speed(overwrite<span class="op">=</span><span class="va">True</span>, units<span class="op">=</span>(<span class="st">"km"</span>,<span class="st">"h"</span>))</span>
-<span id="cb15-2"><a href="#cb15-2" aria-hidden="true" tabindex="-1"></a>osm_traces.get_trajectory(<span class="dv">0</span>).hvplot(</span>
-<span id="cb15-3"><a href="#cb15-3" aria-hidden="true" tabindex="-1"></a>    title<span class="op">=</span><span class="st">'Speed (km/h) along track'</span>, c<span class="op">=</span><span class="st">'speed'</span>, cmap<span class="op">=</span><span class="st">'RdYlBu'</span>,</span>
-<span id="cb15-4"><a href="#cb15-4" aria-hidden="true" tabindex="-1"></a>    line_width<span class="op">=</span><span class="dv">3</span>, width<span class="op">=</span><span class="dv">700</span>, height<span class="op">=</span><span class="dv">400</span>, tiles<span class="op">=</span><span class="st">'CartoLight'</span>, colorbar<span class="op">=</span><span class="va">True</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div id="cell-24" class="cell" data-execution_count="13">
+<div class="sourceCode cell-code" id="cb18"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb18-1"><a href="#cb18-1" aria-hidden="true" tabindex="-1"></a>osm_traces.get_trajectory(<span class="dv">0</span>).add_speed(overwrite<span class="op">=</span><span class="va">True</span>, units<span class="op">=</span>(<span class="st">"km"</span>,<span class="st">"h"</span>))</span>
+<span id="cb18-2"><a href="#cb18-2" aria-hidden="true" tabindex="-1"></a>osm_traces.get_trajectory(<span class="dv">0</span>).hvplot(</span>
+<span id="cb18-3"><a href="#cb18-3" aria-hidden="true" tabindex="-1"></a>    title<span class="op">=</span><span class="st">'Speed (km/h) along track'</span>, c<span class="op">=</span><span class="st">'speed'</span>, cmap<span class="op">=</span><span class="st">'RdYlBu'</span>,</span>
+<span id="cb18-4"><a href="#cb18-4" aria-hidden="true" tabindex="-1"></a>    line_width<span class="op">=</span><span class="dv">3</span>, width<span class="op">=</span><span class="dv">700</span>, height<span class="op">=</span><span class="dv">400</span>, tiles<span class="op">=</span><span class="st">'CartoLight'</span>, colorbar<span class="op">=</span><span class="va">True</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display">
 
 </div>
@@ -1248,20 +1267,19 @@ <h3 data-number="8.1.2" class="anchored" data-anchor-id="transform-the-point-geo
 </div>
 </div>
 <p>One can also convert the <code>TrajectoryCollection</code> (MovingPandas’ object) to a LineString <code>GeoDataFrame</code>. This would allow us to take advantage of what we learnt across the previous sessions. For example, we can plot through <code>GeoPandas</code> and <code>matplotlib</code> after having projected the layer.</p>
-<div id="cell-25" class="cell" data-execution_count="14">
-<div class="sourceCode cell-code" id="cb16"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb16-1"><a href="#cb16-1" aria-hidden="true" tabindex="-1"></a>crs <span class="op">=</span> <span class="st">'EPSG:27700'</span></span>
-<span id="cb16-2"><a href="#cb16-2" aria-hidden="true" tabindex="-1"></a>gps_tracks <span class="op">=</span> osm_traces.to_traj_gdf()</span>
-<span id="cb16-3"><a href="#cb16-3" aria-hidden="true" tabindex="-1"></a>gps_tracks <span class="op">=</span> gps_tracks.set_crs(<span class="st">"EPSG:4326"</span>).to_crs(crs)</span>
-<span id="cb16-4"><a href="#cb16-4" aria-hidden="true" tabindex="-1"></a>gps_tracks.plot()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div id="cell-26" class="cell" data-execution_count="14">
+<div class="sourceCode cell-code" id="cb19"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb19-1"><a href="#cb19-1" aria-hidden="true" tabindex="-1"></a>crs <span class="op">=</span> <span class="st">'EPSG:27700'</span></span>
+<span id="cb19-2"><a href="#cb19-2" aria-hidden="true" tabindex="-1"></a>gps_tracks <span class="op">=</span> osm_traces.to_traj_gdf()</span>
+<span id="cb19-3"><a href="#cb19-3" aria-hidden="true" tabindex="-1"></a>gps_tracks <span class="op">=</span> gps_tracks.set_crs(<span class="st">"EPSG:4326"</span>).to_crs(crs)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display">
 <div>
 <figure class="figure">
-<p><img src="w09_advanced_files/figure-html/cell-14-output-1.png" class="img-fluid figure-img"></p>
+<p><img src="w09_advanced_files/figure-html/cell-16-output-1.png" class="img-fluid figure-img"></p>
 </figure>
 </div>
 </div>
 </div>
-<p>As you can see, from both visualisations, OSM traces may include also odd traces that jump from different locations of the city.</p>
+<p>As you can see, from the map above, OSM traces may include also odd traces that jump from different locations of the city.</p>
 <div class="alert alert-success">
 <p><strong>Exercise</strong>: Explore the <code>gps_tracks</code> dataset and try to compute the average speed of each of the tracks. Try to understand, also using the <code>gps_track_points</code> (do not forget to project it, in case), if you can find a way to filter out traces that follow very odd routes.</p>
 </div>
@@ -1271,20 +1289,20 @@ <h3 data-number="8.1.2" class="anchored" data-anchor-id="transform-the-point-geo
 <h2 data-number="8.2" class="anchored" data-anchor-id="animations-in-folium"><span class="header-section-number">8.2</span> Animations in Folium</h2>
 <section id="animating-gps-tracks" class="level3" data-number="8.2.1">
 <h3 data-number="8.2.1" class="anchored" data-anchor-id="animating-gps-tracks"><span class="header-section-number">8.2.1</span> Animating GPS tracks</h3>
-<div id="cell-28" class="cell" data-execution_count="15">
-<div class="sourceCode cell-code" id="cb17"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb17-1"><a href="#cb17-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> folium.plugins <span class="im">import</span> TimestampedGeoJson</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div id="cell-29" class="cell" data-execution_count="15">
+<div class="sourceCode cell-code" id="cb20"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb20-1"><a href="#cb20-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> folium.plugins <span class="im">import</span> TimestampedGeoJson</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Download this <a href="&quot;https://github.com/GDSL-UL/wma/blob/main/data/geolife_small.gpkg&quot;">file</a> in your data folder.</p>
-<div id="cell-30" class="cell" data-execution_count="16">
-<div class="sourceCode cell-code" id="cb18"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb18-1"><a href="#cb18-1" aria-hidden="true" tabindex="-1"></a>gdf <span class="op">=</span> gpd.read_file(<span class="st">'../data/geolife_small.gpkg'</span>)</span>
-<span id="cb18-2"><a href="#cb18-2" aria-hidden="true" tabindex="-1"></a>gdf[<span class="st">'t'</span>] <span class="op">=</span> pd.to_datetime(gdf[<span class="st">'t'</span>])</span>
-<span id="cb18-3"><a href="#cb18-3" aria-hidden="true" tabindex="-1"></a>gdf <span class="op">=</span> gdf.set_index(<span class="st">'t'</span>).tz_localize(<span class="va">None</span>)</span>
-<span id="cb18-4"><a href="#cb18-4" aria-hidden="true" tabindex="-1"></a>gdf.index.name <span class="op">=</span> <span class="va">None</span></span>
-<span id="cb18-5"><a href="#cb18-5" aria-hidden="true" tabindex="-1"></a>gdf.plot(column <span class="op">=</span> <span class="st">'trajectory_id'</span>, markersize <span class="op">=</span> <span class="dv">1</span>, cmap <span class="op">=</span> <span class="st">'Set1'</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div id="cell-31" class="cell" data-execution_count="16">
+<div class="sourceCode cell-code" id="cb21"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb21-1"><a href="#cb21-1" aria-hidden="true" tabindex="-1"></a>gdf <span class="op">=</span> gpd.read_file(<span class="st">'../data/geolife_small.gpkg'</span>)</span>
+<span id="cb21-2"><a href="#cb21-2" aria-hidden="true" tabindex="-1"></a>gdf[<span class="st">'t'</span>] <span class="op">=</span> pd.to_datetime(gdf[<span class="st">'t'</span>])</span>
+<span id="cb21-3"><a href="#cb21-3" aria-hidden="true" tabindex="-1"></a>gdf <span class="op">=</span> gdf.set_index(<span class="st">'t'</span>).tz_localize(<span class="va">None</span>)</span>
+<span id="cb21-4"><a href="#cb21-4" aria-hidden="true" tabindex="-1"></a>gdf.index.name <span class="op">=</span> <span class="va">None</span></span>
+<span id="cb21-5"><a href="#cb21-5" aria-hidden="true" tabindex="-1"></a>gdf.plot(column <span class="op">=</span> <span class="st">'trajectory_id'</span>, markersize <span class="op">=</span> <span class="dv">1</span>, cmap <span class="op">=</span> <span class="st">'Set1'</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display">
 <div>
 <figure class="figure">
-<p><img src="w09_advanced_files/figure-html/cell-16-output-1.png" class="img-fluid figure-img"></p>
+<p><img src="w09_advanced_files/figure-html/cell-18-output-1.png" class="img-fluid figure-img"></p>
 </figure>
 </div>
 </div>
@@ -1293,67 +1311,67 @@ <h3 data-number="8.2.1" class="anchored" data-anchor-id="animating-gps-tracks"><
 <li><code>gdf['t'] = pd.to_datetime(gdf['t'])</code>: This converts the values in the ‘t’ column of <code>gdf</code> into datetime objects using <code>pd.to_datetime()</code>. This is useful for time-series data where ‘t’ likely represents time information.</li>
 <li><code>gdf = df.set_index('t').tz_localize(None)</code>: This sets the index to the column <code>t</code> and removes the timezone information using <code>tz_localize(None)</code></li>
 </ul>
-<div id="cell-32" class="cell" data-execution_count="19">
-<div class="sourceCode cell-code" id="cb19"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb19-1"><a href="#cb19-1" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> geo_df_to_timestamped_geojson(geo_df, colour<span class="op">=</span><span class="st">"blue"</span>, marker_type<span class="op">=</span><span class="st">"LineString"</span>):</span>
-<span id="cb19-2"><a href="#cb19-2" aria-hidden="true" tabindex="-1"></a>    <span class="co">"""</span></span>
-<span id="cb19-3"><a href="#cb19-3" aria-hidden="true" tabindex="-1"></a><span class="co">    Convert a GeoDataFrame to a list of timestamped GeoJSON features.</span></span>
-<span id="cb19-4"><a href="#cb19-4" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb19-5"><a href="#cb19-5" aria-hidden="true" tabindex="-1"></a><span class="co">    Parameters</span></span>
-<span id="cb19-6"><a href="#cb19-6" aria-hidden="true" tabindex="-1"></a><span class="co">    ----------</span></span>
-<span id="cb19-7"><a href="#cb19-7" aria-hidden="true" tabindex="-1"></a><span class="co">        geo_df: GeoDataFrame</span></span>
-<span id="cb19-8"><a href="#cb19-8" aria-hidden="true" tabindex="-1"></a><span class="co">            The GeoDataFrame containing the geographic data.</span></span>
-<span id="cb19-9"><a href="#cb19-9" aria-hidden="true" tabindex="-1"></a><span class="co">        colour:str</span></span>
-<span id="cb19-10"><a href="#cb19-10" aria-hidden="true" tabindex="-1"></a><span class="co">            The color of the line or marker. Defaults to "blue".</span></span>
-<span id="cb19-11"><a href="#cb19-11" aria-hidden="true" tabindex="-1"></a><span class="co">        marker_type: str</span></span>
-<span id="cb19-12"><a href="#cb19-12" aria-hidden="true" tabindex="-1"></a><span class="co">            The type of marker to use, e.g., "LineString" or "Point". Defaults to "LineString".</span></span>
-<span id="cb19-13"><a href="#cb19-13" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb19-14"><a href="#cb19-14" aria-hidden="true" tabindex="-1"></a><span class="co">    Returns</span></span>
-<span id="cb19-15"><a href="#cb19-15" aria-hidden="true" tabindex="-1"></a><span class="co">    -------</span></span>
-<span id="cb19-16"><a href="#cb19-16" aria-hidden="true" tabindex="-1"></a><span class="co">        feature_list: list</span></span>
-<span id="cb19-17"><a href="#cb19-17" aria-hidden="true" tabindex="-1"></a><span class="co">            A list of GeoJSON features, each representing a timestamped marker or line.</span></span>
-<span id="cb19-18"><a href="#cb19-18" aria-hidden="true" tabindex="-1"></a><span class="co">    """</span></span>
-<span id="cb19-19"><a href="#cb19-19" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb19-20"><a href="#cb19-20" aria-hidden="true" tabindex="-1"></a>    df <span class="op">=</span> geo_df.copy()</span>
-<span id="cb19-21"><a href="#cb19-21" aria-hidden="true" tabindex="-1"></a>    x_coords <span class="op">=</span> df[<span class="st">"geometry"</span>].x.to_list()</span>
-<span id="cb19-22"><a href="#cb19-22" aria-hidden="true" tabindex="-1"></a>    y_coords <span class="op">=</span> df[<span class="st">"geometry"</span>].y.to_list()</span>
-<span id="cb19-23"><a href="#cb19-23" aria-hidden="true" tabindex="-1"></a>    coordinate_list <span class="op">=</span> [[x, y] <span class="cf">for</span> x, y <span class="kw">in</span> <span class="bu">zip</span>(x_coords, y_coords)]</span>
-<span id="cb19-24"><a href="#cb19-24" aria-hidden="true" tabindex="-1"></a>    times_list <span class="op">=</span> [i.isoformat() <span class="cf">for</span> i <span class="kw">in</span> df.index.to_list()]</span>
-<span id="cb19-25"><a href="#cb19-25" aria-hidden="true" tabindex="-1"></a>    </span>
-<span id="cb19-26"><a href="#cb19-26" aria-hidden="true" tabindex="-1"></a>    feature_list <span class="op">=</span> [</span>
-<span id="cb19-27"><a href="#cb19-27" aria-hidden="true" tabindex="-1"></a>       {</span>
-<span id="cb19-28"><a href="#cb19-28" aria-hidden="true" tabindex="-1"></a>           <span class="st">"type"</span>: <span class="st">"Feature"</span>,</span>
-<span id="cb19-29"><a href="#cb19-29" aria-hidden="true" tabindex="-1"></a>           <span class="st">"geometry"</span>: {</span>
-<span id="cb19-30"><a href="#cb19-30" aria-hidden="true" tabindex="-1"></a>               <span class="st">"type"</span>: marker_type,</span>
-<span id="cb19-31"><a href="#cb19-31" aria-hidden="true" tabindex="-1"></a>               <span class="st">"coordinates"</span>: coordinate_list,</span>
-<span id="cb19-32"><a href="#cb19-32" aria-hidden="true" tabindex="-1"></a>           },</span>
-<span id="cb19-33"><a href="#cb19-33" aria-hidden="true" tabindex="-1"></a>           <span class="st">"properties"</span>: {</span>
-<span id="cb19-34"><a href="#cb19-34" aria-hidden="true" tabindex="-1"></a>               <span class="st">"times"</span>: times_list,</span>
-<span id="cb19-35"><a href="#cb19-35" aria-hidden="true" tabindex="-1"></a>               <span class="st">"style"</span>: {</span>
-<span id="cb19-36"><a href="#cb19-36" aria-hidden="true" tabindex="-1"></a>                   <span class="st">"color"</span>: colour,</span>
-<span id="cb19-37"><a href="#cb19-37" aria-hidden="true" tabindex="-1"></a>                   <span class="st">"weight"</span>: <span class="dv">3</span>,</span>
-<span id="cb19-38"><a href="#cb19-38" aria-hidden="true" tabindex="-1"></a>               },</span>
-<span id="cb19-39"><a href="#cb19-39" aria-hidden="true" tabindex="-1"></a>           },</span>
-<span id="cb19-40"><a href="#cb19-40" aria-hidden="true" tabindex="-1"></a>       }</span>
-<span id="cb19-41"><a href="#cb19-41" aria-hidden="true" tabindex="-1"></a>    ]</span>
-<span id="cb19-42"><a href="#cb19-42" aria-hidden="true" tabindex="-1"></a>    </span>
-<span id="cb19-43"><a href="#cb19-43" aria-hidden="true" tabindex="-1"></a>    <span class="cf">return</span> feature_list</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div id="cell-33" class="cell" data-execution_count="19">
+<div class="sourceCode cell-code" id="cb22"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb22-1"><a href="#cb22-1" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> geo_df_to_timestamped_geojson(geo_df, colour<span class="op">=</span><span class="st">"blue"</span>, marker_type<span class="op">=</span><span class="st">"LineString"</span>):</span>
+<span id="cb22-2"><a href="#cb22-2" aria-hidden="true" tabindex="-1"></a>    <span class="co">"""</span></span>
+<span id="cb22-3"><a href="#cb22-3" aria-hidden="true" tabindex="-1"></a><span class="co">    Convert a GeoDataFrame to a list of timestamped GeoJSON features.</span></span>
+<span id="cb22-4"><a href="#cb22-4" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb22-5"><a href="#cb22-5" aria-hidden="true" tabindex="-1"></a><span class="co">    Parameters</span></span>
+<span id="cb22-6"><a href="#cb22-6" aria-hidden="true" tabindex="-1"></a><span class="co">    ----------</span></span>
+<span id="cb22-7"><a href="#cb22-7" aria-hidden="true" tabindex="-1"></a><span class="co">        geo_df: GeoDataFrame</span></span>
+<span id="cb22-8"><a href="#cb22-8" aria-hidden="true" tabindex="-1"></a><span class="co">            The GeoDataFrame containing the geographic data.</span></span>
+<span id="cb22-9"><a href="#cb22-9" aria-hidden="true" tabindex="-1"></a><span class="co">        colour:str</span></span>
+<span id="cb22-10"><a href="#cb22-10" aria-hidden="true" tabindex="-1"></a><span class="co">            The color of the line or marker. Defaults to "blue".</span></span>
+<span id="cb22-11"><a href="#cb22-11" aria-hidden="true" tabindex="-1"></a><span class="co">        marker_type: str</span></span>
+<span id="cb22-12"><a href="#cb22-12" aria-hidden="true" tabindex="-1"></a><span class="co">            The type of marker to use, e.g., "LineString" or "Point". Defaults to "LineString".</span></span>
+<span id="cb22-13"><a href="#cb22-13" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb22-14"><a href="#cb22-14" aria-hidden="true" tabindex="-1"></a><span class="co">    Returns</span></span>
+<span id="cb22-15"><a href="#cb22-15" aria-hidden="true" tabindex="-1"></a><span class="co">    -------</span></span>
+<span id="cb22-16"><a href="#cb22-16" aria-hidden="true" tabindex="-1"></a><span class="co">        feature_list: list</span></span>
+<span id="cb22-17"><a href="#cb22-17" aria-hidden="true" tabindex="-1"></a><span class="co">            A list of GeoJSON features, each representing a timestamped marker or line.</span></span>
+<span id="cb22-18"><a href="#cb22-18" aria-hidden="true" tabindex="-1"></a><span class="co">    """</span></span>
+<span id="cb22-19"><a href="#cb22-19" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb22-20"><a href="#cb22-20" aria-hidden="true" tabindex="-1"></a>    df <span class="op">=</span> geo_df.copy()</span>
+<span id="cb22-21"><a href="#cb22-21" aria-hidden="true" tabindex="-1"></a>    x_coords <span class="op">=</span> df[<span class="st">"geometry"</span>].x.to_list()</span>
+<span id="cb22-22"><a href="#cb22-22" aria-hidden="true" tabindex="-1"></a>    y_coords <span class="op">=</span> df[<span class="st">"geometry"</span>].y.to_list()</span>
+<span id="cb22-23"><a href="#cb22-23" aria-hidden="true" tabindex="-1"></a>    coordinate_list <span class="op">=</span> [[x, y] <span class="cf">for</span> x, y <span class="kw">in</span> <span class="bu">zip</span>(x_coords, y_coords)]</span>
+<span id="cb22-24"><a href="#cb22-24" aria-hidden="true" tabindex="-1"></a>    times_list <span class="op">=</span> [i.isoformat() <span class="cf">for</span> i <span class="kw">in</span> df.index.to_list()]</span>
+<span id="cb22-25"><a href="#cb22-25" aria-hidden="true" tabindex="-1"></a>    </span>
+<span id="cb22-26"><a href="#cb22-26" aria-hidden="true" tabindex="-1"></a>    feature_list <span class="op">=</span> [</span>
+<span id="cb22-27"><a href="#cb22-27" aria-hidden="true" tabindex="-1"></a>       {</span>
+<span id="cb22-28"><a href="#cb22-28" aria-hidden="true" tabindex="-1"></a>           <span class="st">"type"</span>: <span class="st">"Feature"</span>,</span>
+<span id="cb22-29"><a href="#cb22-29" aria-hidden="true" tabindex="-1"></a>           <span class="st">"geometry"</span>: {</span>
+<span id="cb22-30"><a href="#cb22-30" aria-hidden="true" tabindex="-1"></a>               <span class="st">"type"</span>: marker_type,</span>
+<span id="cb22-31"><a href="#cb22-31" aria-hidden="true" tabindex="-1"></a>               <span class="st">"coordinates"</span>: coordinate_list,</span>
+<span id="cb22-32"><a href="#cb22-32" aria-hidden="true" tabindex="-1"></a>           },</span>
+<span id="cb22-33"><a href="#cb22-33" aria-hidden="true" tabindex="-1"></a>           <span class="st">"properties"</span>: {</span>
+<span id="cb22-34"><a href="#cb22-34" aria-hidden="true" tabindex="-1"></a>               <span class="st">"times"</span>: times_list,</span>
+<span id="cb22-35"><a href="#cb22-35" aria-hidden="true" tabindex="-1"></a>               <span class="st">"style"</span>: {</span>
+<span id="cb22-36"><a href="#cb22-36" aria-hidden="true" tabindex="-1"></a>                   <span class="st">"color"</span>: colour,</span>
+<span id="cb22-37"><a href="#cb22-37" aria-hidden="true" tabindex="-1"></a>                   <span class="st">"weight"</span>: <span class="dv">3</span>,</span>
+<span id="cb22-38"><a href="#cb22-38" aria-hidden="true" tabindex="-1"></a>               },</span>
+<span id="cb22-39"><a href="#cb22-39" aria-hidden="true" tabindex="-1"></a>           },</span>
+<span id="cb22-40"><a href="#cb22-40" aria-hidden="true" tabindex="-1"></a>       }</span>
+<span id="cb22-41"><a href="#cb22-41" aria-hidden="true" tabindex="-1"></a>    ]</span>
+<span id="cb22-42"><a href="#cb22-42" aria-hidden="true" tabindex="-1"></a>    </span>
+<span id="cb22-43"><a href="#cb22-43" aria-hidden="true" tabindex="-1"></a>    <span class="cf">return</span> feature_list</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Obtaining features from the <code>GeoDataFrame</code> so that we can plot them in <code>folium</code> directly.</p>
-<div id="cell-34" class="cell" data-execution_count="20">
-<div class="sourceCode cell-code" id="cb20"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb20-1"><a href="#cb20-1" aria-hidden="true" tabindex="-1"></a>features <span class="op">=</span> geo_df_to_timestamped_geojson(gdf, colour<span class="op">=</span><span class="st">"blue"</span>, marker_type<span class="op">=</span><span class="st">"LineString"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div id="cell-35" class="cell" data-execution_count="20">
+<div class="sourceCode cell-code" id="cb23"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb23-1"><a href="#cb23-1" aria-hidden="true" tabindex="-1"></a>features <span class="op">=</span> geo_df_to_timestamped_geojson(gdf, colour<span class="op">=</span><span class="st">"blue"</span>, marker_type<span class="op">=</span><span class="st">"LineString"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>This <code>TimestampedGeoJson</code> plugin allows us to draw data on an interactive folium map and animate it depending on a time variable (year, month, day, hour, etc) and supports any kind of geometry (points, line strings, etc). The only thing you have to do is to make sure to “feed” it with a geojson in the right format.</p>
-<div id="cell-36" class="cell" data-execution_count="36">
-<div class="sourceCode cell-code" id="cb21"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb21-1"><a href="#cb21-1" aria-hidden="true" tabindex="-1"></a>xy <span class="op">=</span> gdf.unary_union.centroid</span>
-<span id="cb21-2"><a href="#cb21-2" aria-hidden="true" tabindex="-1"></a><span class="bu">map</span> <span class="op">=</span> folium.Map(location<span class="op">=</span>[xy.y, xy.x], zoom_start<span class="op">=</span><span class="dv">10</span>)</span>
-<span id="cb21-3"><a href="#cb21-3" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb21-4"><a href="#cb21-4" aria-hidden="true" tabindex="-1"></a>TimestampedGeoJson({<span class="st">"type"</span>: <span class="st">"FeatureCollection"</span>, <span class="st">"features"</span>: features},</span>
-<span id="cb21-5"><a href="#cb21-5" aria-hidden="true" tabindex="-1"></a>    period<span class="op">=</span><span class="st">"PT1S"</span>,</span>
-<span id="cb21-6"><a href="#cb21-6" aria-hidden="true" tabindex="-1"></a>    add_last_point<span class="op">=</span><span class="va">True</span>,</span>
-<span id="cb21-7"><a href="#cb21-7" aria-hidden="true" tabindex="-1"></a>    transition_time<span class="op">=</span><span class="dv">10</span></span>
-<span id="cb21-8"><a href="#cb21-8" aria-hidden="true" tabindex="-1"></a>).add_to(<span class="bu">map</span>)</span>
-<span id="cb21-9"><a href="#cb21-9" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb21-10"><a href="#cb21-10" aria-hidden="true" tabindex="-1"></a><span class="bu">map</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div id="cell-37" class="cell" data-execution_count="36">
+<div class="sourceCode cell-code" id="cb24"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb24-1"><a href="#cb24-1" aria-hidden="true" tabindex="-1"></a>xy <span class="op">=</span> gdf.unary_union.centroid</span>
+<span id="cb24-2"><a href="#cb24-2" aria-hidden="true" tabindex="-1"></a><span class="bu">map</span> <span class="op">=</span> folium.Map(location<span class="op">=</span>[xy.y, xy.x], zoom_start<span class="op">=</span><span class="dv">10</span>)</span>
+<span id="cb24-3"><a href="#cb24-3" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb24-4"><a href="#cb24-4" aria-hidden="true" tabindex="-1"></a>TimestampedGeoJson({<span class="st">"type"</span>: <span class="st">"FeatureCollection"</span>, <span class="st">"features"</span>: features},</span>
+<span id="cb24-5"><a href="#cb24-5" aria-hidden="true" tabindex="-1"></a>    period<span class="op">=</span><span class="st">"PT1S"</span>,</span>
+<span id="cb24-6"><a href="#cb24-6" aria-hidden="true" tabindex="-1"></a>    add_last_point<span class="op">=</span><span class="va">True</span>,</span>
+<span id="cb24-7"><a href="#cb24-7" aria-hidden="true" tabindex="-1"></a>    transition_time<span class="op">=</span><span class="dv">10</span></span>
+<span id="cb24-8"><a href="#cb24-8" aria-hidden="true" tabindex="-1"></a>).add_to(<span class="bu">map</span>)</span>
+<span id="cb24-9"><a href="#cb24-9" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb24-10"><a href="#cb24-10" aria-hidden="true" tabindex="-1"></a><span class="bu">map</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="36">
 <div style="width:100%;"><div style="position:relative;width:100%;height:0;padding-bottom:60%;"><span style="color:#565656">Make this Notebook Trusted to load map: File -&gt; Trust Notebook</span><iframe srcdoc="<!DOCTYPE html>
 <html>
@@ -1508,10 +1526,10 @@ <h3 data-number="8.2.1" class="anchored" data-anchor-id="animating-gps-tracks"><
 <h3 data-number="8.2.2" class="anchored" data-anchor-id="other-animations-with-folium"><span class="header-section-number">8.2.2</span> Other Animations with Folium</h3>
 <p>This part of the notebook has been readapted from this <a href="https://www.linkedin.com/pulse/visualizing-nyc-bike-data-interactive-animated-maps-folium-toso/">post</a>.</p>
 <p>We are going to use the data of New York’s Citi Bike-sharing service. This is publicly available and can be downloaded from <a href="https://s3.amazonaws.com/tripdata/index.html">here</a>. In this notebook, we used the data for the month of March 2024. Place it in your data folder, you can download it from <a href="https://github.com/GDSL-UL/wma/blob/main/data/NY_march_bikeData.csv">here</a>.</p>
-<div id="cell-40" class="cell" data-execution_count="23">
-<div class="sourceCode cell-code" id="cb22"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb22-1"><a href="#cb22-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Import the file as a Pandas DataFrame</span></span>
-<span id="cb22-2"><a href="#cb22-2" aria-hidden="true" tabindex="-1"></a>nyc <span class="op">=</span> pd.read_csv(<span class="st">"../data/NY_march_bikeData.csv"</span>)</span>
-<span id="cb22-3"><a href="#cb22-3" aria-hidden="true" tabindex="-1"></a>nyc.head()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div id="cell-41" class="cell" data-execution_count="23">
+<div class="sourceCode cell-code" id="cb25"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb25-1"><a href="#cb25-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Import the file as a Pandas DataFrame</span></span>
+<span id="cb25-2"><a href="#cb25-2" aria-hidden="true" tabindex="-1"></a>nyc <span class="op">=</span> pd.read_csv(<span class="st">"../data/NY_march_bikeData.csv"</span>)</span>
+<span id="cb25-3"><a href="#cb25-3" aria-hidden="true" tabindex="-1"></a>nyc.head()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="23">
 <div>
 
@@ -1629,8 +1647,8 @@ <h3 data-number="8.2.2" class="anchored" data-anchor-id="other-animations-with-f
 <li><code>start_station_id</code>, <code>start_station_name</code>, <code>start_lat</code>, and <code>start_lng</code>: all the useful info to identify and locate the start station.</li>
 <li><code>end_station_id</code>, <code>end_station_name</code>, <code>end_lat</code>, and <code>end_lng</code>: all the useful info to identify and locate the end station of the trip.</li>
 </ul>
-<div id="cell-42" class="cell" data-execution_count="24">
-<div class="sourceCode cell-code" id="cb23"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb23-1"><a href="#cb23-1" aria-hidden="true" tabindex="-1"></a><span class="bu">type</span>(nyc.loc[<span class="dv">0</span>, <span class="st">'started_at'</span>])</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div id="cell-43" class="cell" data-execution_count="24">
+<div class="sourceCode cell-code" id="cb26"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb26-1"><a href="#cb26-1" aria-hidden="true" tabindex="-1"></a><span class="bu">type</span>(nyc.loc[<span class="dv">0</span>, <span class="st">'started_at'</span>])</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="24">
 <pre><code>str</code></pre>
 </div>
@@ -1643,16 +1661,16 @@ <h3 data-number="8.2.2" class="anchored" data-anchor-id="other-animations-with-f
 <li>Define <code>started_at</code> as the data frame’s index. This will allow us to pivot (group) values easier by time of day.</li>
 <li>Create a new column called <code>ty</code> to help us in the pivoting.</li>
 </ul>
-<div id="cell-44" class="cell" data-execution_count="25">
-<div class="sourceCode cell-code" id="cb25"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb25-1"><a href="#cb25-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Setting the right format for started_at and ended_at</span></span>
-<span id="cb25-2"><a href="#cb25-2" aria-hidden="true" tabindex="-1"></a>nyc[<span class="st">'started_at'</span>] <span class="op">=</span> pd.to_datetime(nyc[<span class="st">'started_at'</span>])</span>
-<span id="cb25-3"><a href="#cb25-3" aria-hidden="true" tabindex="-1"></a>nyc[<span class="st">'ended_at'</span>] <span class="op">=</span> pd.to_datetime(nyc[<span class="st">'ended_at'</span>])</span>
-<span id="cb25-4"><a href="#cb25-4" aria-hidden="true" tabindex="-1"></a>nyc[<span class="st">'duration'</span>] <span class="op">=</span> (nyc[<span class="st">'ended_at'</span>] <span class="op">-</span> nyc[<span class="st">'started_at'</span>]).dt.total_seconds()</span>
-<span id="cb25-5"><a href="#cb25-5" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb25-6"><a href="#cb25-6" aria-hidden="true" tabindex="-1"></a><span class="co"># Define the startime as index and create a new column</span></span>
-<span id="cb25-7"><a href="#cb25-7" aria-hidden="true" tabindex="-1"></a>nyc <span class="op">=</span> nyc.set_index(<span class="st">'started_at'</span>)</span>
-<span id="cb25-8"><a href="#cb25-8" aria-hidden="true" tabindex="-1"></a>nyc[<span class="st">'ty'</span>] <span class="op">=</span> <span class="st">'station'</span></span>
-<span id="cb25-9"><a href="#cb25-9" aria-hidden="true" tabindex="-1"></a>nyc.head(<span class="dv">1</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div id="cell-45" class="cell" data-execution_count="25">
+<div class="sourceCode cell-code" id="cb28"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb28-1"><a href="#cb28-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Setting the right format for started_at and ended_at</span></span>
+<span id="cb28-2"><a href="#cb28-2" aria-hidden="true" tabindex="-1"></a>nyc[<span class="st">'started_at'</span>] <span class="op">=</span> pd.to_datetime(nyc[<span class="st">'started_at'</span>])</span>
+<span id="cb28-3"><a href="#cb28-3" aria-hidden="true" tabindex="-1"></a>nyc[<span class="st">'ended_at'</span>] <span class="op">=</span> pd.to_datetime(nyc[<span class="st">'ended_at'</span>])</span>
+<span id="cb28-4"><a href="#cb28-4" aria-hidden="true" tabindex="-1"></a>nyc[<span class="st">'duration'</span>] <span class="op">=</span> (nyc[<span class="st">'ended_at'</span>] <span class="op">-</span> nyc[<span class="st">'started_at'</span>]).dt.total_seconds()</span>
+<span id="cb28-5"><a href="#cb28-5" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb28-6"><a href="#cb28-6" aria-hidden="true" tabindex="-1"></a><span class="co"># Define the startime as index and create a new column</span></span>
+<span id="cb28-7"><a href="#cb28-7" aria-hidden="true" tabindex="-1"></a>nyc <span class="op">=</span> nyc.set_index(<span class="st">'started_at'</span>)</span>
+<span id="cb28-8"><a href="#cb28-8" aria-hidden="true" tabindex="-1"></a>nyc[<span class="st">'ty'</span>] <span class="op">=</span> <span class="st">'station'</span></span>
+<span id="cb28-9"><a href="#cb28-9" aria-hidden="true" tabindex="-1"></a>nyc.head(<span class="dv">1</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="25">
 <div>
 
@@ -1719,17 +1737,17 @@ <h3 data-number="8.2.2" class="anchored" data-anchor-id="other-animations-with-f
 </div>
 </div>
 <p>Now, we need to feed TimestampedGeoJson plugin with the right data format. To do so, we will transform the data frame. First, we count the number of trips per hour that start at each station</p>
-<div id="cell-46" class="cell" data-execution_count="26">
-<div class="sourceCode cell-code" id="cb26"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb26-1"><a href="#cb26-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Aggregate number of trips for each start station by hour of the day</span></span>
-<span id="cb26-2"><a href="#cb26-2" aria-hidden="true" tabindex="-1"></a>start <span class="op">=</span> nyc.pivot_table(<span class="st">'duration'</span>, </span>
-<span id="cb26-3"><a href="#cb26-3" aria-hidden="true" tabindex="-1"></a>                     index <span class="op">=</span> [<span class="st">'start_station_id'</span>, </span>
-<span id="cb26-4"><a href="#cb26-4" aria-hidden="true" tabindex="-1"></a>                              <span class="st">'start_lat'</span>, </span>
-<span id="cb26-5"><a href="#cb26-5" aria-hidden="true" tabindex="-1"></a>                              <span class="st">'start_lng'</span>, </span>
-<span id="cb26-6"><a href="#cb26-6" aria-hidden="true" tabindex="-1"></a>                              nyc.index.hour],</span>
-<span id="cb26-7"><a href="#cb26-7" aria-hidden="true" tabindex="-1"></a>                     columns <span class="op">=</span> <span class="st">'ty'</span>,</span>
-<span id="cb26-8"><a href="#cb26-8" aria-hidden="true" tabindex="-1"></a>                     aggfunc<span class="op">=</span><span class="st">'count'</span>).reset_index()</span>
-<span id="cb26-9"><a href="#cb26-9" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb26-10"><a href="#cb26-10" aria-hidden="true" tabindex="-1"></a>start.head()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div id="cell-47" class="cell" data-execution_count="26">
+<div class="sourceCode cell-code" id="cb29"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb29-1"><a href="#cb29-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Aggregate number of trips for each start station by hour of the day</span></span>
+<span id="cb29-2"><a href="#cb29-2" aria-hidden="true" tabindex="-1"></a>start <span class="op">=</span> nyc.pivot_table(<span class="st">'duration'</span>, </span>
+<span id="cb29-3"><a href="#cb29-3" aria-hidden="true" tabindex="-1"></a>                     index <span class="op">=</span> [<span class="st">'start_station_id'</span>, </span>
+<span id="cb29-4"><a href="#cb29-4" aria-hidden="true" tabindex="-1"></a>                              <span class="st">'start_lat'</span>, </span>
+<span id="cb29-5"><a href="#cb29-5" aria-hidden="true" tabindex="-1"></a>                              <span class="st">'start_lng'</span>, </span>
+<span id="cb29-6"><a href="#cb29-6" aria-hidden="true" tabindex="-1"></a>                              nyc.index.hour],</span>
+<span id="cb29-7"><a href="#cb29-7" aria-hidden="true" tabindex="-1"></a>                     columns <span class="op">=</span> <span class="st">'ty'</span>,</span>
+<span id="cb29-8"><a href="#cb29-8" aria-hidden="true" tabindex="-1"></a>                     aggfunc<span class="op">=</span><span class="st">'count'</span>).reset_index()</span>
+<span id="cb29-9"><a href="#cb29-9" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb29-10"><a href="#cb29-10" aria-hidden="true" tabindex="-1"></a>start.head()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="26">
 <div>
 
@@ -1793,22 +1811,22 @@ <h3 data-number="8.2.2" class="anchored" data-anchor-id="other-animations-with-f
 </div>
 </div>
 <p>In the code above we use the pandas <code>pivot_table()</code> to group the data. Note that the last index we use is “nyc.index.hour”. This takes the index of the data frame and, since it is of the datetime format, we can get the hour of that value like shown above. Similarly, we could get the day or month. Thus, to get a daily average we will divide by the numbers of days.</p>
-<div id="cell-48" class="cell" data-execution_count="27">
-<div class="sourceCode cell-code" id="cb27"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb27-1"><a href="#cb27-1" aria-hidden="true" tabindex="-1"></a>days <span class="op">=</span> nyc.index.day.<span class="bu">max</span>()</span>
-<span id="cb27-2"><a href="#cb27-2" aria-hidden="true" tabindex="-1"></a>start[<span class="st">'station'</span>] <span class="op">=</span> start[<span class="st">'station'</span>]<span class="op">/</span>days</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div id="cell-49" class="cell" data-execution_count="27">
+<div class="sourceCode cell-code" id="cb30"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb30-1"><a href="#cb30-1" aria-hidden="true" tabindex="-1"></a>days <span class="op">=</span> nyc.index.day.<span class="bu">max</span>()</span>
+<span id="cb30-2"><a href="#cb30-2" aria-hidden="true" tabindex="-1"></a>start[<span class="st">'station'</span>] <span class="op">=</span> start[<span class="st">'station'</span>]<span class="op">/</span>days</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Now, we will change the name of the columns and define the color we want the points to have on the map.</p>
-<div id="cell-50" class="cell" data-execution_count="28">
-<div class="sourceCode cell-code" id="cb28"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb28-1"><a href="#cb28-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Rename the columns</span></span>
-<span id="cb28-2"><a href="#cb28-2" aria-hidden="true" tabindex="-1"></a>start.columns <span class="op">=</span> [<span class="st">'station_id'</span>, <span class="st">'lat'</span>, <span class="st">'lon'</span>, <span class="st">'hour'</span>, <span class="st">'count'</span>]</span>
-<span id="cb28-3"><a href="#cb28-3" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-4"><a href="#cb28-4" aria-hidden="true" tabindex="-1"></a><span class="co"># Define the color</span></span>
-<span id="cb28-5"><a href="#cb28-5" aria-hidden="true" tabindex="-1"></a>start[<span class="st">'fillColor'</span>] <span class="op">=</span> <span class="st">'#53c688'</span></span>
-<span id="cb28-6"><a href="#cb28-6" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb28-7"><a href="#cb28-7" aria-hidden="true" tabindex="-1"></a><span class="co"># The stops where less than one daily trip</span></span>
-<span id="cb28-8"><a href="#cb28-8" aria-hidden="true" tabindex="-1"></a><span class="co"># will have a different color</span></span>
-<span id="cb28-9"><a href="#cb28-9" aria-hidden="true" tabindex="-1"></a>start.loc[start[<span class="st">'count'</span>]<span class="op">&lt;</span><span class="dv">1</span>, <span class="st">'fillColor'</span>] <span class="op">=</span> <span class="st">'#586065'</span></span>
-<span id="cb28-10"><a href="#cb28-10" aria-hidden="true" tabindex="-1"></a>start.head(<span class="dv">1</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div id="cell-51" class="cell" data-execution_count="28">
+<div class="sourceCode cell-code" id="cb31"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb31-1"><a href="#cb31-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Rename the columns</span></span>
+<span id="cb31-2"><a href="#cb31-2" aria-hidden="true" tabindex="-1"></a>start.columns <span class="op">=</span> [<span class="st">'station_id'</span>, <span class="st">'lat'</span>, <span class="st">'lon'</span>, <span class="st">'hour'</span>, <span class="st">'count'</span>]</span>
+<span id="cb31-3"><a href="#cb31-3" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb31-4"><a href="#cb31-4" aria-hidden="true" tabindex="-1"></a><span class="co"># Define the color</span></span>
+<span id="cb31-5"><a href="#cb31-5" aria-hidden="true" tabindex="-1"></a>start[<span class="st">'fillColor'</span>] <span class="op">=</span> <span class="st">'#53c688'</span></span>
+<span id="cb31-6"><a href="#cb31-6" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb31-7"><a href="#cb31-7" aria-hidden="true" tabindex="-1"></a><span class="co"># The stops where less than one daily trip</span></span>
+<span id="cb31-8"><a href="#cb31-8" aria-hidden="true" tabindex="-1"></a><span class="co"># will have a different color</span></span>
+<span id="cb31-9"><a href="#cb31-9" aria-hidden="true" tabindex="-1"></a>start.loc[start[<span class="st">'count'</span>]<span class="op">&lt;</span><span class="dv">1</span>, <span class="st">'fillColor'</span>] <span class="op">=</span> <span class="st">'#586065'</span></span>
+<span id="cb31-10"><a href="#cb31-10" aria-hidden="true" tabindex="-1"></a>start.head(<span class="dv">1</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="28">
 <div>
 
@@ -1842,32 +1860,32 @@ <h3 data-number="8.2.2" class="anchored" data-anchor-id="other-animations-with-f
 </div>
 </div>
 <p>After transforming the data frame to the format we need, we have to define a function that takes the values from it and creates the geojson with the right properties to use in the plugin.</p>
-<div id="cell-52" class="cell" data-execution_count="29">
-<div class="sourceCode cell-code" id="cb29"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb29-1"><a href="#cb29-1" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> create_geojson_features(df):</span>
-<span id="cb29-2"><a href="#cb29-2" aria-hidden="true" tabindex="-1"></a>    features <span class="op">=</span> []</span>
-<span id="cb29-3"><a href="#cb29-3" aria-hidden="true" tabindex="-1"></a>    </span>
-<span id="cb29-4"><a href="#cb29-4" aria-hidden="true" tabindex="-1"></a>    <span class="cf">for</span> _, row <span class="kw">in</span> df.iterrows():</span>
-<span id="cb29-5"><a href="#cb29-5" aria-hidden="true" tabindex="-1"></a>        feature <span class="op">=</span> {</span>
-<span id="cb29-6"><a href="#cb29-6" aria-hidden="true" tabindex="-1"></a>            <span class="st">'type'</span>: <span class="st">'Feature'</span>,</span>
-<span id="cb29-7"><a href="#cb29-7" aria-hidden="true" tabindex="-1"></a>            <span class="st">'geometry'</span>: {</span>
-<span id="cb29-8"><a href="#cb29-8" aria-hidden="true" tabindex="-1"></a>                <span class="st">'type'</span>:<span class="st">'Point'</span>, </span>
-<span id="cb29-9"><a href="#cb29-9" aria-hidden="true" tabindex="-1"></a>                <span class="st">'coordinates'</span>:[row[<span class="st">'lon'</span>],row[<span class="st">'lat'</span>]]</span>
-<span id="cb29-10"><a href="#cb29-10" aria-hidden="true" tabindex="-1"></a>            },</span>
-<span id="cb29-11"><a href="#cb29-11" aria-hidden="true" tabindex="-1"></a>            <span class="st">'properties'</span>: {</span>
-<span id="cb29-12"><a href="#cb29-12" aria-hidden="true" tabindex="-1"></a>                <span class="st">'time'</span>: pd.to_datetime(row[<span class="st">'hour'</span>], unit<span class="op">=</span><span class="st">'h'</span>).<span class="fu">__str__</span>(),</span>
-<span id="cb29-13"><a href="#cb29-13" aria-hidden="true" tabindex="-1"></a>                <span class="st">'style'</span>: {<span class="st">'color'</span> : <span class="st">''</span>},</span>
-<span id="cb29-14"><a href="#cb29-14" aria-hidden="true" tabindex="-1"></a>                <span class="st">'icon'</span>: <span class="st">'circle'</span>,</span>
-<span id="cb29-15"><a href="#cb29-15" aria-hidden="true" tabindex="-1"></a>                <span class="st">'iconstyle'</span>:{</span>
-<span id="cb29-16"><a href="#cb29-16" aria-hidden="true" tabindex="-1"></a>                    <span class="st">'fillColor'</span>: row[<span class="st">'fillColor'</span>],</span>
-<span id="cb29-17"><a href="#cb29-17" aria-hidden="true" tabindex="-1"></a>                    <span class="st">'fillOpacity'</span>: <span class="fl">0.8</span>,</span>
-<span id="cb29-18"><a href="#cb29-18" aria-hidden="true" tabindex="-1"></a>                    <span class="st">'stroke'</span>: <span class="st">'true'</span>,</span>
-<span id="cb29-19"><a href="#cb29-19" aria-hidden="true" tabindex="-1"></a>                    <span class="st">'radius'</span>: row[<span class="st">'count'</span>] <span class="op">+</span> <span class="dv">5</span></span>
-<span id="cb29-20"><a href="#cb29-20" aria-hidden="true" tabindex="-1"></a>                }</span>
-<span id="cb29-21"><a href="#cb29-21" aria-hidden="true" tabindex="-1"></a>            }</span>
-<span id="cb29-22"><a href="#cb29-22" aria-hidden="true" tabindex="-1"></a>        }</span>
-<span id="cb29-23"><a href="#cb29-23" aria-hidden="true" tabindex="-1"></a>        features.append(feature)</span>
-<span id="cb29-24"><a href="#cb29-24" aria-hidden="true" tabindex="-1"></a>   </span>
-<span id="cb29-25"><a href="#cb29-25" aria-hidden="true" tabindex="-1"></a>    <span class="cf">return</span> features</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div id="cell-53" class="cell" data-execution_count="29">
+<div class="sourceCode cell-code" id="cb32"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb32-1"><a href="#cb32-1" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> create_geojson_features(df):</span>
+<span id="cb32-2"><a href="#cb32-2" aria-hidden="true" tabindex="-1"></a>    features <span class="op">=</span> []</span>
+<span id="cb32-3"><a href="#cb32-3" aria-hidden="true" tabindex="-1"></a>    </span>
+<span id="cb32-4"><a href="#cb32-4" aria-hidden="true" tabindex="-1"></a>    <span class="cf">for</span> _, row <span class="kw">in</span> df.iterrows():</span>
+<span id="cb32-5"><a href="#cb32-5" aria-hidden="true" tabindex="-1"></a>        feature <span class="op">=</span> {</span>
+<span id="cb32-6"><a href="#cb32-6" aria-hidden="true" tabindex="-1"></a>            <span class="st">'type'</span>: <span class="st">'Feature'</span>,</span>
+<span id="cb32-7"><a href="#cb32-7" aria-hidden="true" tabindex="-1"></a>            <span class="st">'geometry'</span>: {</span>
+<span id="cb32-8"><a href="#cb32-8" aria-hidden="true" tabindex="-1"></a>                <span class="st">'type'</span>:<span class="st">'Point'</span>, </span>
+<span id="cb32-9"><a href="#cb32-9" aria-hidden="true" tabindex="-1"></a>                <span class="st">'coordinates'</span>:[row[<span class="st">'lon'</span>],row[<span class="st">'lat'</span>]]</span>
+<span id="cb32-10"><a href="#cb32-10" aria-hidden="true" tabindex="-1"></a>            },</span>
+<span id="cb32-11"><a href="#cb32-11" aria-hidden="true" tabindex="-1"></a>            <span class="st">'properties'</span>: {</span>
+<span id="cb32-12"><a href="#cb32-12" aria-hidden="true" tabindex="-1"></a>                <span class="st">'time'</span>: pd.to_datetime(row[<span class="st">'hour'</span>], unit<span class="op">=</span><span class="st">'h'</span>).<span class="fu">__str__</span>(),</span>
+<span id="cb32-13"><a href="#cb32-13" aria-hidden="true" tabindex="-1"></a>                <span class="st">'style'</span>: {<span class="st">'color'</span> : <span class="st">''</span>},</span>
+<span id="cb32-14"><a href="#cb32-14" aria-hidden="true" tabindex="-1"></a>                <span class="st">'icon'</span>: <span class="st">'circle'</span>,</span>
+<span id="cb32-15"><a href="#cb32-15" aria-hidden="true" tabindex="-1"></a>                <span class="st">'iconstyle'</span>:{</span>
+<span id="cb32-16"><a href="#cb32-16" aria-hidden="true" tabindex="-1"></a>                    <span class="st">'fillColor'</span>: row[<span class="st">'fillColor'</span>],</span>
+<span id="cb32-17"><a href="#cb32-17" aria-hidden="true" tabindex="-1"></a>                    <span class="st">'fillOpacity'</span>: <span class="fl">0.8</span>,</span>
+<span id="cb32-18"><a href="#cb32-18" aria-hidden="true" tabindex="-1"></a>                    <span class="st">'stroke'</span>: <span class="st">'true'</span>,</span>
+<span id="cb32-19"><a href="#cb32-19" aria-hidden="true" tabindex="-1"></a>                    <span class="st">'radius'</span>: row[<span class="st">'count'</span>] <span class="op">+</span> <span class="dv">5</span></span>
+<span id="cb32-20"><a href="#cb32-20" aria-hidden="true" tabindex="-1"></a>                }</span>
+<span id="cb32-21"><a href="#cb32-21" aria-hidden="true" tabindex="-1"></a>            }</span>
+<span id="cb32-22"><a href="#cb32-22" aria-hidden="true" tabindex="-1"></a>        }</span>
+<span id="cb32-23"><a href="#cb32-23" aria-hidden="true" tabindex="-1"></a>        features.append(feature)</span>
+<span id="cb32-24"><a href="#cb32-24" aria-hidden="true" tabindex="-1"></a>   </span>
+<span id="cb32-25"><a href="#cb32-25" aria-hidden="true" tabindex="-1"></a>    <span class="cf">return</span> features</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>The function:</p>
 <ul>
@@ -1881,9 +1899,9 @@ <h3 data-number="8.2.2" class="anchored" data-anchor-id="other-animations-with-f
 </ol></li>
 </ul>
 <p>Once the function is defined, we can use it on our data frame and get the geojson.</p>
-<div id="cell-54" class="cell" data-execution_count="30">
-<div class="sourceCode cell-code" id="cb30"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb30-1"><a href="#cb30-1" aria-hidden="true" tabindex="-1"></a>start_geojson <span class="op">=</span> create_geojson_features(start)</span>
-<span id="cb30-2"><a href="#cb30-2" aria-hidden="true" tabindex="-1"></a>start_geojson[<span class="dv">0</span>]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div id="cell-55" class="cell" data-execution_count="30">
+<div class="sourceCode cell-code" id="cb33"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb33-1"><a href="#cb33-1" aria-hidden="true" tabindex="-1"></a>start_geojson <span class="op">=</span> create_geojson_features(start)</span>
+<span id="cb33-2"><a href="#cb33-2" aria-hidden="true" tabindex="-1"></a>start_geojson[<span class="dv">0</span>]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="30">
 <pre><code>{'type': 'Feature',
  'geometry': {'type': 'Point',
@@ -1898,18 +1916,18 @@ <h3 data-number="8.2.2" class="anchored" data-anchor-id="other-animations-with-f
 </div>
 </div>
 <p>With this, we can now create the animated map.</p>
-<div id="cell-56" class="cell" data-execution_count="31">
-<div class="sourceCode cell-code" id="cb32"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb32-1"><a href="#cb32-1" aria-hidden="true" tabindex="-1"></a>nyc_map <span class="op">=</span> folium.Map(location <span class="op">=</span> [<span class="fl">40.71958611647166</span>, <span class="op">-</span><span class="fl">74.0431174635887</span>],</span>
-<span id="cb32-2"><a href="#cb32-2" aria-hidden="true" tabindex="-1"></a>                    tiles <span class="op">=</span> <span class="st">"CartoDB Positron"</span>,</span>
-<span id="cb32-3"><a href="#cb32-3" aria-hidden="true" tabindex="-1"></a>                    zoom_start <span class="op">=</span> <span class="dv">14</span>)</span>
-<span id="cb32-4"><a href="#cb32-4" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb32-5"><a href="#cb32-5" aria-hidden="true" tabindex="-1"></a>TimestampedGeoJson(start_geojson,</span>
-<span id="cb32-6"><a href="#cb32-6" aria-hidden="true" tabindex="-1"></a>                  period <span class="op">=</span> <span class="st">'PT1H'</span>,</span>
-<span id="cb32-7"><a href="#cb32-7" aria-hidden="true" tabindex="-1"></a>                  duration <span class="op">=</span> <span class="st">'PT1M'</span>,</span>
-<span id="cb32-8"><a href="#cb32-8" aria-hidden="true" tabindex="-1"></a>                  transition_time <span class="op">=</span> <span class="dv">1000</span>,</span>
-<span id="cb32-9"><a href="#cb32-9" aria-hidden="true" tabindex="-1"></a>                  auto_play <span class="op">=</span> <span class="va">True</span>).add_to(nyc_map)</span>
-<span id="cb32-10"><a href="#cb32-10" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb32-11"><a href="#cb32-11" aria-hidden="true" tabindex="-1"></a>nyc_map</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div id="cell-57" class="cell" data-execution_count="31">
+<div class="sourceCode cell-code" id="cb35"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb35-1"><a href="#cb35-1" aria-hidden="true" tabindex="-1"></a>nyc_map <span class="op">=</span> folium.Map(location <span class="op">=</span> [<span class="fl">40.71958611647166</span>, <span class="op">-</span><span class="fl">74.0431174635887</span>],</span>
+<span id="cb35-2"><a href="#cb35-2" aria-hidden="true" tabindex="-1"></a>                    tiles <span class="op">=</span> <span class="st">"CartoDB Positron"</span>,</span>
+<span id="cb35-3"><a href="#cb35-3" aria-hidden="true" tabindex="-1"></a>                    zoom_start <span class="op">=</span> <span class="dv">14</span>)</span>
+<span id="cb35-4"><a href="#cb35-4" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb35-5"><a href="#cb35-5" aria-hidden="true" tabindex="-1"></a>TimestampedGeoJson(start_geojson,</span>
+<span id="cb35-6"><a href="#cb35-6" aria-hidden="true" tabindex="-1"></a>                  period <span class="op">=</span> <span class="st">'PT1H'</span>,</span>
+<span id="cb35-7"><a href="#cb35-7" aria-hidden="true" tabindex="-1"></a>                  duration <span class="op">=</span> <span class="st">'PT1M'</span>,</span>
+<span id="cb35-8"><a href="#cb35-8" aria-hidden="true" tabindex="-1"></a>                  transition_time <span class="op">=</span> <span class="dv">1000</span>,</span>
+<span id="cb35-9"><a href="#cb35-9" aria-hidden="true" tabindex="-1"></a>                  auto_play <span class="op">=</span> <span class="va">True</span>).add_to(nyc_map)</span>
+<span id="cb35-10"><a href="#cb35-10" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb35-11"><a href="#cb35-11" aria-hidden="true" tabindex="-1"></a>nyc_map</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="31">
 <div style="width:100%;"><div style="position:relative;width:100%;height:0;padding-bottom:60%;"><span style="color:#565656">Make this Notebook Trusted to load map: File -&gt; Trust Notebook</span><iframe srcdoc="<!DOCTYPE html>
 <html>
@@ -2055,44 +2073,44 @@ <h3 data-number="8.2.2" class="anchored" data-anchor-id="other-animations-with-f
 </div>
 <p>It might be a good idea to analyze where trips end at different times of the day. Even more, it could be interesting to see both maps side by side and try to identify a pattern.</p>
 <p>The DualMap plugin can help us achieve this. To use it, we will first run a similar script to the one we did above to get the number of trips that end at each station by time of the day.</p>
-<div id="cell-58" class="cell" data-execution_count="32">
-<div class="sourceCode cell-code" id="cb33"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb33-1"><a href="#cb33-1" aria-hidden="true" tabindex="-1"></a>nyc1 <span class="op">=</span> nyc.reset_index().set_index(<span class="st">'ended_at'</span>)</span>
-<span id="cb33-2"><a href="#cb33-2" aria-hidden="true" tabindex="-1"></a>end <span class="op">=</span> nyc1.pivot_table(<span class="st">'duration'</span>, </span>
-<span id="cb33-3"><a href="#cb33-3" aria-hidden="true" tabindex="-1"></a>                     index <span class="op">=</span> [<span class="st">'end_station_id'</span>, </span>
-<span id="cb33-4"><a href="#cb33-4" aria-hidden="true" tabindex="-1"></a>                              <span class="st">'end_lat'</span>, </span>
-<span id="cb33-5"><a href="#cb33-5" aria-hidden="true" tabindex="-1"></a>                              <span class="st">'end_lng'</span>, </span>
-<span id="cb33-6"><a href="#cb33-6" aria-hidden="true" tabindex="-1"></a>                              nyc1.index.hour],</span>
-<span id="cb33-7"><a href="#cb33-7" aria-hidden="true" tabindex="-1"></a>                     columns <span class="op">=</span> <span class="st">'ty'</span>,</span>
-<span id="cb33-8"><a href="#cb33-8" aria-hidden="true" tabindex="-1"></a>                     aggfunc<span class="op">=</span><span class="st">'count'</span>).reset_index()</span>
-<span id="cb33-9"><a href="#cb33-9" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb33-10"><a href="#cb33-10" aria-hidden="true" tabindex="-1"></a>end[<span class="st">'station'</span>] <span class="op">=</span> end[<span class="st">'station'</span>]<span class="op">/</span>days</span>
-<span id="cb33-11"><a href="#cb33-11" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb33-12"><a href="#cb33-12" aria-hidden="true" tabindex="-1"></a>end.columns <span class="op">=</span> [<span class="st">'station_id'</span>, <span class="st">'lat'</span>, <span class="st">'lon'</span>, <span class="st">'hour'</span>, <span class="st">'count'</span>]</span>
-<span id="cb33-13"><a href="#cb33-13" aria-hidden="true" tabindex="-1"></a>end[<span class="st">'fillColor'</span>] <span class="op">=</span> <span class="st">'#e64c4e'</span></span>
-<span id="cb33-14"><a href="#cb33-14" aria-hidden="true" tabindex="-1"></a>end.loc[end[<span class="st">'count'</span>]<span class="op">&lt;</span><span class="dv">1</span>, <span class="st">'fillColor'</span>] <span class="op">=</span> <span class="st">'#586065'</span></span>
-<span id="cb33-15"><a href="#cb33-15" aria-hidden="true" tabindex="-1"></a>end_geojson <span class="op">=</span> create_geojson_features(end)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div id="cell-59" class="cell" data-execution_count="32">
+<div class="sourceCode cell-code" id="cb36"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb36-1"><a href="#cb36-1" aria-hidden="true" tabindex="-1"></a>nyc1 <span class="op">=</span> nyc.reset_index().set_index(<span class="st">'ended_at'</span>)</span>
+<span id="cb36-2"><a href="#cb36-2" aria-hidden="true" tabindex="-1"></a>end <span class="op">=</span> nyc1.pivot_table(<span class="st">'duration'</span>, </span>
+<span id="cb36-3"><a href="#cb36-3" aria-hidden="true" tabindex="-1"></a>                     index <span class="op">=</span> [<span class="st">'end_station_id'</span>, </span>
+<span id="cb36-4"><a href="#cb36-4" aria-hidden="true" tabindex="-1"></a>                              <span class="st">'end_lat'</span>, </span>
+<span id="cb36-5"><a href="#cb36-5" aria-hidden="true" tabindex="-1"></a>                              <span class="st">'end_lng'</span>, </span>
+<span id="cb36-6"><a href="#cb36-6" aria-hidden="true" tabindex="-1"></a>                              nyc1.index.hour],</span>
+<span id="cb36-7"><a href="#cb36-7" aria-hidden="true" tabindex="-1"></a>                     columns <span class="op">=</span> <span class="st">'ty'</span>,</span>
+<span id="cb36-8"><a href="#cb36-8" aria-hidden="true" tabindex="-1"></a>                     aggfunc<span class="op">=</span><span class="st">'count'</span>).reset_index()</span>
+<span id="cb36-9"><a href="#cb36-9" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb36-10"><a href="#cb36-10" aria-hidden="true" tabindex="-1"></a>end[<span class="st">'station'</span>] <span class="op">=</span> end[<span class="st">'station'</span>]<span class="op">/</span>days</span>
+<span id="cb36-11"><a href="#cb36-11" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb36-12"><a href="#cb36-12" aria-hidden="true" tabindex="-1"></a>end.columns <span class="op">=</span> [<span class="st">'station_id'</span>, <span class="st">'lat'</span>, <span class="st">'lon'</span>, <span class="st">'hour'</span>, <span class="st">'count'</span>]</span>
+<span id="cb36-13"><a href="#cb36-13" aria-hidden="true" tabindex="-1"></a>end[<span class="st">'fillColor'</span>] <span class="op">=</span> <span class="st">'#e64c4e'</span></span>
+<span id="cb36-14"><a href="#cb36-14" aria-hidden="true" tabindex="-1"></a>end.loc[end[<span class="st">'count'</span>]<span class="op">&lt;</span><span class="dv">1</span>, <span class="st">'fillColor'</span>] <span class="op">=</span> <span class="st">'#586065'</span></span>
+<span id="cb36-15"><a href="#cb36-15" aria-hidden="true" tabindex="-1"></a>end_geojson <span class="op">=</span> create_geojson_features(end)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>We first create the dual map and then we add start_geojson to the left map (m1) and the end_geojson to the right map (m2).</p>
-<div id="cell-60" class="cell" data-execution_count="33">
-<div class="sourceCode cell-code" id="cb34"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb34-1"><a href="#cb34-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> folium.plugins <span class="im">import</span> DualMap</span>
-<span id="cb34-2"><a href="#cb34-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb34-3"><a href="#cb34-3" aria-hidden="true" tabindex="-1"></a>dualmap <span class="op">=</span> DualMap(location <span class="op">=</span> [<span class="fl">40.71958611647166</span>, <span class="op">-</span><span class="fl">74.0431174635887</span>],</span>
-<span id="cb34-4"><a href="#cb34-4" aria-hidden="true" tabindex="-1"></a>                 tiles <span class="op">=</span> <span class="st">'cartodbpositron'</span>,</span>
-<span id="cb34-5"><a href="#cb34-5" aria-hidden="true" tabindex="-1"></a>                 zoom_start <span class="op">=</span> <span class="dv">14</span>)</span>
-<span id="cb34-6"><a href="#cb34-6" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb34-7"><a href="#cb34-7" aria-hidden="true" tabindex="-1"></a>TimestampedGeoJson(start_geojson,</span>
-<span id="cb34-8"><a href="#cb34-8" aria-hidden="true" tabindex="-1"></a>                  period <span class="op">=</span> <span class="st">'PT1H'</span>,</span>
-<span id="cb34-9"><a href="#cb34-9" aria-hidden="true" tabindex="-1"></a>                  duration <span class="op">=</span> <span class="st">'PT1M'</span>,</span>
-<span id="cb34-10"><a href="#cb34-10" aria-hidden="true" tabindex="-1"></a>                  transition_time <span class="op">=</span> <span class="dv">1000</span>,</span>
-<span id="cb34-11"><a href="#cb34-11" aria-hidden="true" tabindex="-1"></a>                  auto_play <span class="op">=</span> <span class="va">True</span>).add_to(dualmap.m1)</span>
-<span id="cb34-12"><a href="#cb34-12" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb34-13"><a href="#cb34-13" aria-hidden="true" tabindex="-1"></a>TimestampedGeoJson(end_geojson,</span>
-<span id="cb34-14"><a href="#cb34-14" aria-hidden="true" tabindex="-1"></a>                  period <span class="op">=</span> <span class="st">'PT1H'</span>,</span>
-<span id="cb34-15"><a href="#cb34-15" aria-hidden="true" tabindex="-1"></a>                  duration <span class="op">=</span> <span class="st">'PT1M'</span>,</span>
-<span id="cb34-16"><a href="#cb34-16" aria-hidden="true" tabindex="-1"></a>                  transition_time <span class="op">=</span> <span class="dv">1000</span>,</span>
-<span id="cb34-17"><a href="#cb34-17" aria-hidden="true" tabindex="-1"></a>                  auto_play <span class="op">=</span> <span class="va">True</span>).add_to(dualmap.m2)</span>
-<span id="cb34-18"><a href="#cb34-18" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb34-19"><a href="#cb34-19" aria-hidden="true" tabindex="-1"></a>dualmap</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div id="cell-61" class="cell" data-execution_count="33">
+<div class="sourceCode cell-code" id="cb37"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb37-1"><a href="#cb37-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> folium.plugins <span class="im">import</span> DualMap</span>
+<span id="cb37-2"><a href="#cb37-2" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb37-3"><a href="#cb37-3" aria-hidden="true" tabindex="-1"></a>dualmap <span class="op">=</span> DualMap(location <span class="op">=</span> [<span class="fl">40.71958611647166</span>, <span class="op">-</span><span class="fl">74.0431174635887</span>],</span>
+<span id="cb37-4"><a href="#cb37-4" aria-hidden="true" tabindex="-1"></a>                 tiles <span class="op">=</span> <span class="st">'cartodbpositron'</span>,</span>
+<span id="cb37-5"><a href="#cb37-5" aria-hidden="true" tabindex="-1"></a>                 zoom_start <span class="op">=</span> <span class="dv">14</span>)</span>
+<span id="cb37-6"><a href="#cb37-6" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb37-7"><a href="#cb37-7" aria-hidden="true" tabindex="-1"></a>TimestampedGeoJson(start_geojson,</span>
+<span id="cb37-8"><a href="#cb37-8" aria-hidden="true" tabindex="-1"></a>                  period <span class="op">=</span> <span class="st">'PT1H'</span>,</span>
+<span id="cb37-9"><a href="#cb37-9" aria-hidden="true" tabindex="-1"></a>                  duration <span class="op">=</span> <span class="st">'PT1M'</span>,</span>
+<span id="cb37-10"><a href="#cb37-10" aria-hidden="true" tabindex="-1"></a>                  transition_time <span class="op">=</span> <span class="dv">1000</span>,</span>
+<span id="cb37-11"><a href="#cb37-11" aria-hidden="true" tabindex="-1"></a>                  auto_play <span class="op">=</span> <span class="va">True</span>).add_to(dualmap.m1)</span>
+<span id="cb37-12"><a href="#cb37-12" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb37-13"><a href="#cb37-13" aria-hidden="true" tabindex="-1"></a>TimestampedGeoJson(end_geojson,</span>
+<span id="cb37-14"><a href="#cb37-14" aria-hidden="true" tabindex="-1"></a>                  period <span class="op">=</span> <span class="st">'PT1H'</span>,</span>
+<span id="cb37-15"><a href="#cb37-15" aria-hidden="true" tabindex="-1"></a>                  duration <span class="op">=</span> <span class="st">'PT1M'</span>,</span>
+<span id="cb37-16"><a href="#cb37-16" aria-hidden="true" tabindex="-1"></a>                  transition_time <span class="op">=</span> <span class="dv">1000</span>,</span>
+<span id="cb37-17"><a href="#cb37-17" aria-hidden="true" tabindex="-1"></a>                  auto_play <span class="op">=</span> <span class="va">True</span>).add_to(dualmap.m2)</span>
+<span id="cb37-18"><a href="#cb37-18" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb37-19"><a href="#cb37-19" aria-hidden="true" tabindex="-1"></a>dualmap</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="33">
 <div style="width:100%;"><div style="position:relative;width:100%;height:0;padding-bottom:60%;"><span style="color:#565656">Make this Notebook Trusted to load map: File -&gt; Trust Notebook</span><iframe srcdoc="<!DOCTYPE html>
 <html>
@@ -2346,39 +2364,39 @@ <h3 data-number="8.2.2" class="anchored" data-anchor-id="other-animations-with-f
 <p>Another interesting plugin, <code>HeatmapWithTime</code>, allows animating heatmaps. According to the documentation, the input needs to be a list of lists:</p>
 <p>“data (<em>list of lists of points of the form [lat, lng] or [lat, lng, weight]</em>) – The points you want to plot. The outer list corresponds to the various time steps in sequential order. (weight is in (0, 1] range and defaults to 1 if not specified for a point)”</p>
 <p>Therefore, we need to create this list of points for each hour of the day and then put all those lists in a new one.</p>
-<div id="cell-62" class="cell" data-execution_count="34">
-<div class="sourceCode cell-code" id="cb35"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb35-1"><a href="#cb35-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Create an empty list</span></span>
-<span id="cb35-2"><a href="#cb35-2" aria-hidden="true" tabindex="-1"></a>df_hour_list <span class="op">=</span> []</span>
-<span id="cb35-3"><a href="#cb35-3" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb35-4"><a href="#cb35-4" aria-hidden="true" tabindex="-1"></a><span class="co"># Create a series with the different hours of the day</span></span>
-<span id="cb35-5"><a href="#cb35-5" aria-hidden="true" tabindex="-1"></a>hours <span class="op">=</span> pd.Series(nyc.index.hour.unique().sort_values())</span>
-<span id="cb35-6"><a href="#cb35-6" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb35-7"><a href="#cb35-7" aria-hidden="true" tabindex="-1"></a><span class="co"># Create a list of points for each hour of the day</span></span>
-<span id="cb35-8"><a href="#cb35-8" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> create_list(hour):</span>
-<span id="cb35-9"><a href="#cb35-9" aria-hidden="true" tabindex="-1"></a>    df_hour_list.append(nyc.loc[nyc.index.hour <span class="op">==</span> hour,</span>
-<span id="cb35-10"><a href="#cb35-10" aria-hidden="true" tabindex="-1"></a>                                [<span class="st">'start_lat'</span>,</span>
-<span id="cb35-11"><a href="#cb35-11" aria-hidden="true" tabindex="-1"></a>                                 <span class="st">'start_lng'</span>]].</span>
-<span id="cb35-12"><a href="#cb35-12" aria-hidden="true" tabindex="-1"></a>                        groupby([<span class="st">'start_lat'</span>, </span>
-<span id="cb35-13"><a href="#cb35-13" aria-hidden="true" tabindex="-1"></a>                                 <span class="st">'start_lng'</span>]).<span class="bu">sum</span>().reset_index().values.tolist())</span>
-<span id="cb35-14"><a href="#cb35-14" aria-hidden="true" tabindex="-1"></a>hours.<span class="bu">apply</span>(create_list)<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</div>
-<div id="cell-63" class="cell" data-execution_count="35">
-<div class="sourceCode cell-code" id="cb36"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb36-1"><a href="#cb36-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> folium.plugins <span class="im">import</span> HeatMapWithTime</span>
-<span id="cb36-2"><a href="#cb36-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb36-3"><a href="#cb36-3" aria-hidden="true" tabindex="-1"></a>location <span class="op">=</span> [<span class="fl">40.71958611647166</span>, <span class="op">-</span><span class="fl">74.0431174635887</span>]</span>
-<span id="cb36-4"><a href="#cb36-4" aria-hidden="true" tabindex="-1"></a><span class="co"># Add trip events to the map</span></span>
-<span id="cb36-5"><a href="#cb36-5" aria-hidden="true" tabindex="-1"></a>map_time <span class="op">=</span> folium.Map(location<span class="op">=</span>location, </span>
-<span id="cb36-6"><a href="#cb36-6" aria-hidden="true" tabindex="-1"></a>                      tiles<span class="op">=</span><span class="st">"CartoDB Positron"</span>, </span>
-<span id="cb36-7"><a href="#cb36-7" aria-hidden="true" tabindex="-1"></a>                      zoom_start<span class="op">=</span><span class="dv">12</span>)</span>
-<span id="cb36-8"><a href="#cb36-8" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb36-9"><a href="#cb36-9" aria-hidden="true" tabindex="-1"></a>HeatMapWithTime(df_hour_list, </span>
-<span id="cb36-10"><a href="#cb36-10" aria-hidden="true" tabindex="-1"></a>                auto_play<span class="op">=</span><span class="va">True</span>, </span>
-<span id="cb36-11"><a href="#cb36-11" aria-hidden="true" tabindex="-1"></a>                max_opacity<span class="op">=</span><span class="fl">0.5</span>, </span>
-<span id="cb36-12"><a href="#cb36-12" aria-hidden="true" tabindex="-1"></a>                gradient <span class="op">=</span> {<span class="fl">0.2</span>: <span class="st">'#FBD973'</span>, </span>
-<span id="cb36-13"><a href="#cb36-13" aria-hidden="true" tabindex="-1"></a>                            <span class="fl">0.4</span>: <span class="st">'#fa782f'</span>, </span>
-<span id="cb36-14"><a href="#cb36-14" aria-hidden="true" tabindex="-1"></a>                            <span class="fl">0.75</span>: <span class="st">'#F16578'</span>, </span>
-<span id="cb36-15"><a href="#cb36-15" aria-hidden="true" tabindex="-1"></a>                            <span class="dv">1</span>: <span class="st">'#782890'</span>}).add_to(map_time)</span>
-<span id="cb36-16"><a href="#cb36-16" aria-hidden="true" tabindex="-1"></a>map_time</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div id="cell-63" class="cell" data-execution_count="34">
+<div class="sourceCode cell-code" id="cb38"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb38-1"><a href="#cb38-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Create an empty list</span></span>
+<span id="cb38-2"><a href="#cb38-2" aria-hidden="true" tabindex="-1"></a>df_hour_list <span class="op">=</span> []</span>
+<span id="cb38-3"><a href="#cb38-3" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-4"><a href="#cb38-4" aria-hidden="true" tabindex="-1"></a><span class="co"># Create a series with the different hours of the day</span></span>
+<span id="cb38-5"><a href="#cb38-5" aria-hidden="true" tabindex="-1"></a>hours <span class="op">=</span> pd.Series(nyc.index.hour.unique().sort_values())</span>
+<span id="cb38-6"><a href="#cb38-6" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb38-7"><a href="#cb38-7" aria-hidden="true" tabindex="-1"></a><span class="co"># Create a list of points for each hour of the day</span></span>
+<span id="cb38-8"><a href="#cb38-8" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> create_list(hour):</span>
+<span id="cb38-9"><a href="#cb38-9" aria-hidden="true" tabindex="-1"></a>    df_hour_list.append(nyc.loc[nyc.index.hour <span class="op">==</span> hour,</span>
+<span id="cb38-10"><a href="#cb38-10" aria-hidden="true" tabindex="-1"></a>                                [<span class="st">'start_lat'</span>,</span>
+<span id="cb38-11"><a href="#cb38-11" aria-hidden="true" tabindex="-1"></a>                                 <span class="st">'start_lng'</span>]].</span>
+<span id="cb38-12"><a href="#cb38-12" aria-hidden="true" tabindex="-1"></a>                        groupby([<span class="st">'start_lat'</span>, </span>
+<span id="cb38-13"><a href="#cb38-13" aria-hidden="true" tabindex="-1"></a>                                 <span class="st">'start_lng'</span>]).<span class="bu">sum</span>().reset_index().values.tolist())</span>
+<span id="cb38-14"><a href="#cb38-14" aria-hidden="true" tabindex="-1"></a>hours.<span class="bu">apply</span>(create_list)<span class="op">;</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</div>
+<div id="cell-64" class="cell" data-execution_count="35">
+<div class="sourceCode cell-code" id="cb39"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb39-1"><a href="#cb39-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> folium.plugins <span class="im">import</span> HeatMapWithTime</span>
+<span id="cb39-2"><a href="#cb39-2" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb39-3"><a href="#cb39-3" aria-hidden="true" tabindex="-1"></a>location <span class="op">=</span> [<span class="fl">40.71958611647166</span>, <span class="op">-</span><span class="fl">74.0431174635887</span>]</span>
+<span id="cb39-4"><a href="#cb39-4" aria-hidden="true" tabindex="-1"></a><span class="co"># Add trip events to the map</span></span>
+<span id="cb39-5"><a href="#cb39-5" aria-hidden="true" tabindex="-1"></a>map_time <span class="op">=</span> folium.Map(location<span class="op">=</span>location, </span>
+<span id="cb39-6"><a href="#cb39-6" aria-hidden="true" tabindex="-1"></a>                      tiles<span class="op">=</span><span class="st">"CartoDB Positron"</span>, </span>
+<span id="cb39-7"><a href="#cb39-7" aria-hidden="true" tabindex="-1"></a>                      zoom_start<span class="op">=</span><span class="dv">12</span>)</span>
+<span id="cb39-8"><a href="#cb39-8" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb39-9"><a href="#cb39-9" aria-hidden="true" tabindex="-1"></a>HeatMapWithTime(df_hour_list, </span>
+<span id="cb39-10"><a href="#cb39-10" aria-hidden="true" tabindex="-1"></a>                auto_play<span class="op">=</span><span class="va">True</span>, </span>
+<span id="cb39-11"><a href="#cb39-11" aria-hidden="true" tabindex="-1"></a>                max_opacity<span class="op">=</span><span class="fl">0.5</span>, </span>
+<span id="cb39-12"><a href="#cb39-12" aria-hidden="true" tabindex="-1"></a>                gradient <span class="op">=</span> {<span class="fl">0.2</span>: <span class="st">'#FBD973'</span>, </span>
+<span id="cb39-13"><a href="#cb39-13" aria-hidden="true" tabindex="-1"></a>                            <span class="fl">0.4</span>: <span class="st">'#fa782f'</span>, </span>
+<span id="cb39-14"><a href="#cb39-14" aria-hidden="true" tabindex="-1"></a>                            <span class="fl">0.75</span>: <span class="st">'#F16578'</span>, </span>
+<span id="cb39-15"><a href="#cb39-15" aria-hidden="true" tabindex="-1"></a>                            <span class="dv">1</span>: <span class="st">'#782890'</span>}).add_to(map_time)</span>
+<span id="cb39-16"><a href="#cb39-16" aria-hidden="true" tabindex="-1"></a>map_time</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-display" data-execution_count="35">
 <div style="width:100%;"><div style="position:relative;width:100%;height:0;padding-bottom:60%;"><span style="color:#565656">Make this Notebook Trusted to load map: File -&gt; Trust Notebook</span><iframe srcdoc="<!DOCTYPE html>
 <html>
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diff --git a/docs/search.json b/docs/search.json
index 675e5de..0ee507a 100644
--- a/docs/search.json
+++ b/docs/search.json
@@ -484,7 +484,7 @@
     "href": "labs/w09_advanced.html",
     "title": "8  GPS Traces and Animations",
     "section": "",
-    "text": "8.1 Getting GPS trajectories from OpenStreetMap Data\nThe OSM API provides access to GPS traces contributed by the OpenStreetMap community. Users can query and retrieve traces based on parameters like geographical bounding box, time range, and user ID. The API supports output formats such as GPX for easy integration into applications or analysis. You can have a look at the last GPS traces uploaded here. Each trace entry likely includes:\nIt’s not possible to download traces from OSM using OSMnx. One has to script their own code\nThis first part of the notebook has been readapted from this notebook created by Anita Graser.",
+    "text": "8.1 Getting GPS trajectories from OpenStreetMap Data\nThe OSM API provides access to GPS traces contributed by the OpenStreetMap community. Users can query and retrieve traces based on parameters like geographical bounding box, time range, and user ID. The API supports output formats such as GPX for easy integration into applications or analysis. You can have a look at the last GPS traces uploaded here. Each trace entry likely includes:\nIt’s not possible to download traces from OSM using OSMnx. One has to script their own code\nParts of this section of the notebook have been readapted from this notebook created by Anita Graser.",
     "crumbs": [
       "<span class='chapter-number'>8</span>  <span class='chapter-title'>GPS Traces and Animations</span>"
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@@ -494,7 +494,7 @@
     "href": "labs/w09_advanced.html#getting-gps-trajectories-from-openstreetmap-data",
     "title": "8  GPS Traces and Animations",
     "section": "",
-    "text": "Trace ID (unique identifier for the trace)\nTrace Name (optional name provided by the user)\nUploader Username\nUpload Date\nTrace Summary (e.g., distance, duration)\n\n\n\n\n8.1.1 Downaloding GPS traces from the OpenStreetMap\ndef bbox_to_osm_format(bbox): ““” Convert bounding box coordinates to OSM API format.\nParameters\n----------\nbbox: tuple\n    A tuple containing the bounding box coordinates in the order (north, south, east, west).\n\nReturns\n-------\nbbox_str: str\n    A string representing the bounding box in the format \"west,south,east,north\".\n\"\"\"\nnorth, south, east, west = bbox\nbbox = f\"{west},{south},{east},{north}\"\nreturn bbox\nWe can try to get OSM traces around the Uni of Liverpool Campus. Feel free to change the area.\n\nimport osmnx as ox\n# Define the place name\nplace_name = \"University of Liverpool, UK\"\n\n# Get the latitude and longitude\nlatitude, longitude = ox.geocoder.geocode(place_name)\n\n# Create the bbox\nbbox = ox.utils_geo.bbox_from_point((latitude, longitude), dist = 1500)\nbbox = bbox_to_osm_format(bbox) # needs to be {west},{south},{east},{north} for OSM Api\n\nThe get_osm_traces function below retrieves GPS traces from OpenStreetMap within a specified bounding box, processing up to a user-defined maximum number of pages. It uses a while loop to fetch and parse GPS data into GeoDataFrames, querying the OSM API by incrementally updating the page number until no more data is available or the maximum page limit is reached.\nUpon fetching the data, the function concatenates these individual GeoDataFrames into a single comprehensive GeoDataFrame. Before returning the final GeoDataFrame, it cleans the dataset by dropping a predefined list of potentially irrelevant or empty columns.\n\ndef get_osm_traces(max_pages = 2,  bbox='16.18, 48.09, 16.61, 48.32'):\n    \"\"\"\n    Retrieve OpenStreetMap GPS traces within a specified bounding box.\n\n    Parameters\n    ----------\n    max_pages: int, optional\n        The maximum number of pages to retrieve. Defaults to 2.\n    bbox: str, optional\n        The bounding box coordinates in the format 'west, south, east, north'. Defaults to '16.18, 48.09, 16.61, 48.32'.\n\n    Returns\n    -------\n    final_gdf: GeoDataFrame\n        A GeoDataFrame containing the retrieved GPS traces.\n    \"\"\"\n    \n    all_data = []\n    page = 0\n    \n    while (True) and (page &lt;= max_pages):\n        # Constructing the URL to query OpenStreetMap API for GPS trackpoints within a specified bounding box and page number\n        url = f'https://api.openstreetmap.org/api/0.6/trackpoints?bbox={bbox}&page={page}'\n        \n        # Sending a GET request to the constructed URL\n        response = requests.get(url)\n        \n        # Checking if the response status code is not 200 (indicating success) or if the response content is empty\n        # If either condition is met, the loop breaks, indicating no more data to retrieve\n        if response.status_code != 200 or not response.content:\n            break\n        \n        # Reading the content of the response, which contains GPS trackpoints data, into a GeoDataFrame\n        # The 'layer' parameter specifies the layer within the GeoDataFrame where trackpoints will be stored\n        gdf = gpd.read_file(BytesIO(response.content), layer='track_points')\n\n        if gdf.empty:\n            break\n            \n        all_data.append(gdf)\n        page += 1\n        \n    # Concatenate all GeoDataFrames into one\n    final_gdf = gpd.GeoDataFrame(pd.concat(all_data, ignore_index=True))\n    \n    # dropping empty columns\n    columns_to_drop = ['ele', 'course', 'speed', 'magvar', 'geoidheight', 'name', 'cmt', 'desc',\n                       'src', 'url', 'urlname', 'sym', 'type', 'fix', 'sat', 'hdop', 'vdop',\n                       'pdop', 'ageofdgpsdata', 'dgpsid']\n    \n    final_gdf = final_gdf.drop(columns=[col for col in columns_to_drop if col in final_gdf.columns])\n    return final_gdf\n\nWe initially download 2 pages of GSP traces. We can try with higher numbers to get more data. More recent traces are downloaded first.\n\nmax_pages = 2 # pages of data, \ngps_track_points = get_osm_traces(max_pages, bbox)\n\n\ngps_track_points.head()\n\n\n\n\n\n\n\n\ntrack_fid\ntrack_seg_id\ntrack_seg_point_id\ntime\ngeometry\n\n\n\n\n0\n0\n0\n0\n2023-09-24 10:41:39+00:00\nPOINT (-2.98825 53.40690)\n\n\n1\n0\n0\n1\n2023-09-24 10:41:46+00:00\nPOINT (-2.98793 53.40708)\n\n\n2\n0\n0\n2\n2023-09-24 10:41:47+00:00\nPOINT (-2.98408 53.40931)\n\n\n3\n0\n0\n3\n2023-09-24 10:41:49+00:00\nPOINT (-2.98391 53.40927)\n\n\n4\n0\n0\n4\n2023-09-24 10:41:50+00:00\nPOINT (-2.98378 53.40933)\n\n\n\n\n\n\n\n\ngps_track_points.plot(markersize = 0.5) # still are trackpoints still\n\n\n\n\n\n\n\n\n\n\n8.1.2 Transform the Point GeoDataFrame in a LineString GeoDataFrame with MovingPandas\nMovingPandas is a Python library designed for analyzing movement data using Pandas. It extends Pandas’ capabilities to handle temporal and spatial data jointly. It is the ideal library for trajectory data analysis, visualization, and exploration. MovingPandas provides functionalities for processing and analyzing movement data, including trajectory segmentation, trajectory aggregation, and trajectory generalization.\nSee examples and documentation at https://movingpandas.org\nThe following code creates a TrajectoryCollection object by providing the GPS track points data, specifying the column that identifies trajectories, and indicating the column containing timestamps.\n\nosm_traces = mpd.TrajectoryCollection(gps_track_points, 'track_fid', t='time')\nprint(f'The OSM traces download contains {len(osm_traces)} tracks')\n\nThe OSM traces download contains 14 tracks\n\n\n\nfor track in osm_traces: \n    print(f'Track {track.id}: length={track.get_length(units=\"km\"):.2f} km')\n\nTrack 0: length=9.96 km\nTrack 1: length=3.02 km\nTrack 2: length=2.84 km\nTrack 3: length=2.72 km\nTrack 4: length=2.92 km\nTrack 5: length=3.29 km\nTrack 6: length=4.95 km\nTrack 7: length=4.12 km\nTrack 8: length=4.87 km\nTrack 9: length=1.21 km\nTrack 10: length=0.03 km\nTrack 11: length=0.00 km\nTrack 12: length=0.70 km\nTrack 13: length=0.36 km\n\n\nBelow we set up some visualization options for holoviews. Holoviews is a library for creating interactive visualizations of complex datasets with ease. Holoviews provides a declarative approach to building visualizations, allowing you to express your visualization intent concisely. Holoviews combines the usage of Matplotlib, Bokeh, and Plotly and supports creating interactive visualizations. You can easily add interactive elements like hover tooltips, zooming, panning, and selection widgets to your plots. Compared to folium, however, it does not allow for as much freedom and development outside the box. Folium is primarily designed for creating web-based maps with features like tile layers and GeoJSON overlays, offering a high degree of customization for map-based visualizations. Holoviews, on the other hand, focuses more on general-purpose interactive visualizations beyond just maps, offering a broader range of visualization options.\nSee examples and documentation at https://holoviews.org.\n\nfrom holoviews import opts, dim\nimport hvplot.pandas \n\nplot_defaults = {'linewidth':5, 'capstyle':'round', 'figsize':(9,3), 'legend':True}\nopts.defaults(opts.Overlay(active_tools=['wheel_zoom'], frame_width=500, frame_height=400))\nhvplot_defaults = {'tiles':None, 'cmap':'Viridis', 'colorbar':True}\n\n\n\n\n\n\n\n\n\n\n\n  \n\n\n\n\nTraces generalisation\nThe MinTimeDeltaGeneralizer class from MovingPandas is applied to the osm_traces object. This class is responsible for simplifying or generalizing the GPS traces based on a minimum time delta. In this case, traces are being generalized to a tolerance of one minute.\n\nfrom datetime import datetime, timedelta\nosm_traces = mpd.MinTimeDeltaGeneralizer(osm_traces).generalize(tolerance=timedelta(minutes=1))\nosm_traces.hvplot(title='OSM Traces', line_width=3, width=700, height=400)\n\n\n\n\n\n  \n\n\n\n\nVisualising by speed\n\nosm_traces.get_trajectory(0).add_speed(overwrite=True, units=(\"km\",\"h\"))\nosm_traces.get_trajectory(0).hvplot(\n    title='Speed (km/h) along track', c='speed', cmap='RdYlBu',\n    line_width=3, width=700, height=400, tiles='CartoLight', colorbar=True)\n\n\n\n\n\n  \n\n\n\n\nOne can also convert the TrajectoryCollection (MovingPandas’ object) to a LineString GeoDataFrame. This would allow us to take advantage of what we learnt across the previous sessions. For example, we can plot through GeoPandas and matplotlib after having projected the layer.\n\ncrs = 'EPSG:27700'\ngps_tracks = osm_traces.to_traj_gdf()\ngps_tracks = gps_tracks.set_crs(\"EPSG:4326\").to_crs(crs)\ngps_tracks.plot()\n\n\n\n\n\n\n\n\nAs you can see, from both visualisations, OSM traces may include also odd traces that jump from different locations of the city.\n\nExercise: Explore the gps_tracks dataset and try to compute the average speed of each of the tracks. Try to understand, also using the gps_track_points (do not forget to project it, in case), if you can find a way to filter out traces that follow very odd routes.",
+    "text": "Trace ID (unique identifier for the trace)\nTrace Name (optional name provided by the user)\nUploader Username\nUpload Date\nTrace Summary (e.g., distance, duration)\n\n\n\n\n8.1.1 Downaloding GPS traces from the OpenStreetMap\nWe can try to get OSM traces around the Uni of Liverpool Campus. Feel free to change the area.\n\nimport osmnx as ox\n# Define the place name\nplace_name = \"University of Liverpool, UK\"\n\n# Get the latitude and longitude\nlatitude, longitude = ox.geocoder.geocode(place_name)\n\n# Create the bbox\nbbox = ox.utils_geo.bbox_from_point((latitude, longitude), dist = 1500)\nbbox\n\n(53.420732655032396,\n 53.39375304496761,\n -2.9432044446907044,\n -2.988462877347038)\n\n\nWe need the bbox_to_osm_format function below to convert our bounding box coordinates into the format required by the OpenStreetMap (OSM) API. The input tuple contains coordinates in the order (north, south, east, west). The function rearranges these values into the string “west,south,east,north”, which is the format expected by OSM for API queries.\n\ndef bbox_to_osm_format(bbox):\n    \"\"\"\n    Convert bounding box coordinates to OSM API format.\n\n    Parameters\n    ----------\n    bbox: tuple\n        A tuple containing the bounding box coordinates in the order (north, south, east, west).\n\n    Returns\n    -------\n    bbox_str: str\n        A string representing the bounding box in the format \"west,south,east,north\".\n    \"\"\"\n    north, south, east, west = bbox\n    bbox = f\"{west},{south},{east},{north}\"\n    return bbox\n\n\nbbox = bbox_to_osm_format(bbox) # needs to be {west},{south},{east},{north} for OSM Api\nbbox\n\n'-2.988462877347038,53.39375304496761,-2.9432044446907044,53.420732655032396'\n\n\nThe get_osm_traces function below retrieves GPS traces from OpenStreetMap within a specified bounding box, processing up to a user-defined maximum number of pages. It uses a while loop to fetch and parse GPS data into GeoDataFrames, querying the OSM API by incrementally updating the page number until no more data is available or the maximum page limit is reached.\nUpon fetching the data, the function concatenates these individual GeoDataFrames into a single comprehensive GeoDataFrame. Before returning the final GeoDataFrame, it cleans the dataset by dropping a predefined list of potentially irrelevant or empty columns.\n\ndef get_osm_traces(max_pages = 2,  bbox='16.18, 48.09, 16.61, 48.32'):\n    \"\"\"\n    Retrieve OpenStreetMap GPS traces within a specified bounding box.\n\n    Parameters\n    ----------\n    max_pages: int, optional\n        The maximum number of pages to retrieve. Defaults to 2.\n    bbox: str, optional\n        The bounding box coordinates in the format 'west, south, east, north'. Defaults to '16.18, 48.09, 16.61, 48.32'.\n\n    Returns\n    -------\n    final_gdf: GeoDataFrame\n        A GeoDataFrame containing the retrieved GPS traces.\n    \"\"\"\n    \n    all_data = []\n    page = 0\n    \n    while (True) and (page &lt;= max_pages):\n        # Constructing the URL to query OpenStreetMap API for GPS trackpoints within a specified bounding box and page number\n        url = f'https://api.openstreetmap.org/api/0.6/trackpoints?bbox={bbox}&page={page}'\n        \n        # Sending a GET request to the constructed URL\n        response = requests.get(url)\n        \n        # Checking if the response status code is not 200 (indicating success) or if the response content is empty\n        # If either condition is met, the loop breaks, indicating no more data to retrieve\n        if response.status_code != 200 or not response.content:\n            break\n        \n        # Reading the content of the response, which contains GPS trackpoints data, into a GeoDataFrame\n        # The 'layer' parameter specifies the layer within the GeoDataFrame where trackpoints will be stored\n        gdf = gpd.read_file(BytesIO(response.content), layer='track_points')\n\n        if gdf.empty:\n            break\n            \n        all_data.append(gdf)\n        page += 1\n        \n    # Concatenate all GeoDataFrames into one\n    final_gdf = gpd.GeoDataFrame(pd.concat(all_data, ignore_index=True))\n    \n    # dropping empty columns\n    columns_to_drop = ['ele', 'course', 'speed', 'magvar', 'geoidheight', 'name', 'cmt', 'desc',\n                       'src', 'url', 'urlname', 'sym', 'type', 'fix', 'sat', 'hdop', 'vdop',\n                       'pdop', 'ageofdgpsdata', 'dgpsid']\n    \n    final_gdf = final_gdf.drop(columns=[col for col in columns_to_drop if col in final_gdf.columns])\n    return final_gdf\n\nWe initially download 2 pages of GSP traces. We can try with higher numbers to get more data. More recent traces are downloaded first.\n\nmax_pages = 2 # pages of data, \ngps_track_points = get_osm_traces(max_pages, bbox)\n\n\ngps_track_points.head()\n\n\n\n\n\n\n\n\ntrack_fid\ntrack_seg_id\ntrack_seg_point_id\ntime\ngeometry\n\n\n\n\n0\n0\n0\n0\n2023-09-24 10:41:39+00:00\nPOINT (-2.98825 53.40690)\n\n\n1\n0\n0\n1\n2023-09-24 10:41:46+00:00\nPOINT (-2.98793 53.40708)\n\n\n2\n0\n0\n2\n2023-09-24 10:41:47+00:00\nPOINT (-2.98408 53.40931)\n\n\n3\n0\n0\n3\n2023-09-24 10:41:49+00:00\nPOINT (-2.98391 53.40927)\n\n\n4\n0\n0\n4\n2023-09-24 10:41:50+00:00\nPOINT (-2.98378 53.40933)\n\n\n\n\n\n\n\n\ngps_track_points.plot(markersize = 0.5) # still are trackpoints still\n\n\n\n\n\n\n\n\n\n\n8.1.2 Transform the Point GeoDataFrame in a LineString GeoDataFrame with MovingPandas\nMovingPandas is a Python library designed for analyzing movement data using Pandas. It extends Pandas’ capabilities to handle temporal and spatial data jointly. It is the ideal library for trajectory data analysis, visualization, and exploration. MovingPandas provides functionalities for processing and analyzing movement data, including trajectory segmentation, trajectory aggregation, and trajectory generalization.\nSee examples and documentation at https://movingpandas.org\nThe following code creates a TrajectoryCollection object by providing the GPS track points data, specifying the column that identifies trajectories, and indicating the column containing timestamps.\n\nosm_traces = mpd.TrajectoryCollection(gps_track_points, 'track_fid', t='time')\nprint(f'The OSM traces download contains {len(osm_traces)} tracks')\n\nThe OSM traces download contains 14 tracks\n\n\n\nfor track in osm_traces: \n    print(f'Track {track.id}: length={track.get_length(units=\"km\"):.2f} km')\n\nTrack 0: length=9.96 km\nTrack 1: length=3.02 km\nTrack 2: length=2.84 km\nTrack 3: length=2.72 km\nTrack 4: length=2.92 km\nTrack 5: length=3.29 km\nTrack 6: length=4.95 km\nTrack 7: length=4.12 km\nTrack 8: length=4.87 km\nTrack 9: length=1.21 km\nTrack 10: length=0.03 km\nTrack 11: length=0.00 km\nTrack 12: length=0.70 km\nTrack 13: length=0.36 km\n\n\nBelow we set up some visualization options for holoviews. Holoviews is a library for creating interactive visualizations of complex datasets with ease. Holoviews provides a declarative approach to building visualizations, allowing you to express your visualization intent concisely. Holoviews combines the usage of Matplotlib, Bokeh, and Plotly and supports creating interactive visualizations. You can easily add interactive elements like hover tooltips, zooming, panning, and selection widgets to your plots. Compared to folium, however, it does not allow for as much freedom and development outside the box. Folium is primarily designed for creating web-based maps with features like tile layers and GeoJSON overlays, offering a high degree of customization for map-based visualizations. Holoviews, on the other hand, focuses more on general-purpose interactive visualizations beyond just maps, offering a broader range of visualization options.\nSee examples and documentation at https://holoviews.org.\n\nfrom holoviews import opts, dim\nimport hvplot.pandas \n\nplot_defaults = {'linewidth':5, 'capstyle':'round', 'figsize':(9,3), 'legend':True}\nopts.defaults(opts.Overlay(active_tools=['wheel_zoom'], frame_width=500, frame_height=400))\nhvplot_defaults = {'tiles':None, 'cmap':'Viridis', 'colorbar':True}\n\n\n\n\n\n\n\n\n\n\n\n  \n\n\n\n\nTraces generalisation\nThe MinTimeDeltaGeneralizer class from MovingPandas is applied to the osm_traces object. This class is responsible for simplifying or generalizing the GPS traces based on a minimum time delta. In this case, traces are being generalized to a tolerance of one minute.\n\nfrom datetime import datetime, timedelta\nosm_traces = mpd.MinTimeDeltaGeneralizer(osm_traces).generalize(tolerance=timedelta(minutes=1))\nosm_traces.hvplot(title='OSM Traces', line_width=3, width=700, height=400)\n\n\n\n\n\n  \n\n\n\n\nVisualising by speed\n\nosm_traces.get_trajectory(0).add_speed(overwrite=True, units=(\"km\",\"h\"))\nosm_traces.get_trajectory(0).hvplot(\n    title='Speed (km/h) along track', c='speed', cmap='RdYlBu',\n    line_width=3, width=700, height=400, tiles='CartoLight', colorbar=True)\n\n\n\n\n\n  \n\n\n\n\nOne can also convert the TrajectoryCollection (MovingPandas’ object) to a LineString GeoDataFrame. This would allow us to take advantage of what we learnt across the previous sessions. For example, we can plot through GeoPandas and matplotlib after having projected the layer.\n\ncrs = 'EPSG:27700'\ngps_tracks = osm_traces.to_traj_gdf()\ngps_tracks = gps_tracks.set_crs(\"EPSG:4326\").to_crs(crs)\n\n\n\n\n\n\n\n\nAs you can see, from the map above, OSM traces may include also odd traces that jump from different locations of the city.\n\nExercise: Explore the gps_tracks dataset and try to compute the average speed of each of the tracks. Try to understand, also using the gps_track_points (do not forget to project it, in case), if you can find a way to filter out traces that follow very odd routes.",
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       "<span class='chapter-number'>8</span>  <span class='chapter-title'>GPS Traces and Animations</span>"
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@@ -504,7 +504,7 @@
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     "title": "8  GPS Traces and Animations",
     "section": "8.2 Animations in Folium",
-    "text": "8.2 Animations in Folium\n\n8.2.1 Animating a GPS track\n\nfrom folium.plugins import TimestampedGeoJson\n\nDownload this file in your data folder.\n\ngdf = gpd.read_file('../data/geolife_small.gpkg')\ngdf['t'] = pd.to_datetime(gdf['t'])\ngdf = gdf.set_index('t').tz_localize(None)\ngdf.index.name = None\ngdf.plot(column = 'trajectory_id', markersize = 1, cmap = 'Set1')\n\n\n\n\n\n\n\n\n\ngdf['t'] = pd.to_datetime(gdf['t']): This converts the values in the ‘t’ column of gdf into datetime objects using pd.to_datetime(). This is useful for time-series data where ‘t’ likely represents time information.\ngdf = df.set_index('t').tz_localize(None): This sets the index to the column t and removes the timezone information using tz_localize(None)\n\n\ndef geo_df_to_timestamped_geojson(geo_df, colour=\"blue\", marker_type=\"LineString\"):\n    \"\"\"\n    Convert a GeoDataFrame to a list of timestamped GeoJSON features.\n\n    Parameters\n    ----------\n        geo_df: GeoDataFrame\n            The GeoDataFrame containing the geographic data.\n        colour:str\n            The color of the line or marker. Defaults to \"blue\".\n        marker_type: str\n            The type of marker to use, e.g., \"LineString\" or \"Point\". Defaults to \"LineString\".\n\n    Returns\n    -------\n        feature_list: list\n            A list of GeoJSON features, each representing a timestamped marker or line.\n    \"\"\"\n\n    df = geo_df.copy()\n    x_coords = df[\"geometry\"].x.to_list()\n    y_coords = df[\"geometry\"].y.to_list()\n    coordinate_list = [[x, y] for x, y in zip(x_coords, y_coords)]\n    times_list = [i.isoformat() for i in df.index.to_list()]\n    \n    feature_list = [\n       {\n           \"type\": \"Feature\",\n           \"geometry\": {\n               \"type\": marker_type,\n               \"coordinates\": coordinate_list,\n           },\n           \"properties\": {\n               \"times\": times_list,\n               \"style\": {\n                   \"color\": colour,\n                   \"weight\": 3,\n               },\n           },\n       }\n    ]\n    \n    return feature_list\n\nObtaining features from the GeoDataFrame so that we can plot them in folium directly.\n\nfeatures = geo_df_to_timestamped_geojson(gdf, colour=\"blue\", marker_type=\"LineString\")\n\nThis TimestampedGeoJson plugin allows us to draw data on an interactive folium map and animate it depending on a time variable (year, month, day, hour, etc) and supports any kind of geometry (points, line strings, etc). The only thing you have to do is to make sure to “feed” it with a geojson in the right format.\n\nxy = gdf.unary_union.centroid\nmap = folium.Map(location=[xy.y, xy.x], zoom_start=10)\n\nTimestampedGeoJson({\"type\": \"FeatureCollection\", \"features\": features},\n    period=\"PT1S\",\n    add_last_point=True,\n    transition_time=10\n).add_to(map)\n\nmap\n\nMake this Notebook Trusted to load map: File -&gt; Trust Notebook\n\n\nThe arguments of the TimestampedGeoJson plugin are:\n\nThe geojson with the data.\nperiod: It is the time step that the animation will take starting from the first value. For example: “P1M” 1/month, “P1D” 1/day, “PT1H” 1/hour, and ‘PT1M’ 1/minute.\nduration: Period of time which the features will be shown on the map after their time has passed. If None, all previous times will be shown.\n\n\n\n8.2.2 Other Animations with Folium\nThis part of the notebook has been readapted from this post.\nWe are going to use the data of New York’s Citi Bike-sharing service. This is publicly available and can be downloaded from here. In this notebook, we used the data for the month of March 2024. Place it in your data folder, you can download it from here.\n\n# Import the file as a Pandas DataFrame\nnyc = pd.read_csv(\"../data/NY_march_bikeData.csv\")\nnyc.head()\n\n\n\n\n\n\n\n\nride_id\nrideable_type\nstarted_at\nended_at\nstart_station_name\nstart_station_id\nend_station_name\nend_station_id\nstart_lat\nstart_lng\nend_lat\nend_lng\nmember_casual\n\n\n\n\n0\n9F5EEFB7CF78C6EA\nelectric_bike\n2024-03-21 13:32:42\n2024-03-21 13:35:42\nStevens - River Ter & 6 St\nHB602\nCity Hall - Washington St & 1 St\nHB105\n40.743143\n-74.026907\n40.737360\n-74.030970\nmember\n\n\n1\n01E2E1F218F9D303\nelectric_bike\n2024-03-16 08:26:15\n2024-03-16 08:47:53\nPershing Field\nJC024\nPershing Field\nJC024\n40.742379\n-74.051996\n40.742677\n-74.051789\nmember\n\n\n2\nC2914D662B33AA55\nelectric_bike\n2024-03-15 15:17:36\n2024-03-15 15:32:12\nPershing Field\nJC024\nPershing Field\nJC024\n40.742480\n-74.051942\n40.742677\n-74.051789\nmember\n\n\n3\n6723F7CE51888493\nelectric_bike\n2024-03-13 08:34:26\n2024-03-13 08:50:08\nPershing Field\nJC024\nPershing Field\nJC024\n40.742400\n-74.051963\n40.742677\n-74.051789\nmember\n\n\n4\nEAB7E29AAB546941\nelectric_bike\n2024-03-15 15:37:58\n2024-03-15 16:19:32\nPershing Field\nJC024\nPershing Field\nJC024\n40.742459\n-74.051973\n40.742677\n-74.051789\nmember\n\n\n\n\n\n\n\nFor every trip made we have:\n\nstarted_at: start date and time of the trip.\nended_at: end date and time the trip.\nstart_station_id, start_station_name, start_lat, and start_lng: all the useful info to identify and locate the start station.\nend_station_id, end_station_name, end_lat, and end_lng: all the useful info to identify and locate the end station of the trip.\n\n\ntype(nyc.loc[0, 'started_at'])\n\nstr\n\n\nTransformation\nThe variables started_at and ended_at were imported as text (string). we are going to do the following:\n\nChange the type of these variables to datetime.\nCompute the duration of the trip in seconds.\nDefine started_at as the data frame’s index. This will allow us to pivot (group) values easier by time of day.\nCreate a new column called ty to help us in the pivoting.\n\n\n# Setting the right format for started_at and ended_at\nnyc['started_at'] = pd.to_datetime(nyc['started_at'])\nnyc['ended_at'] = pd.to_datetime(nyc['ended_at'])\nnyc['duration'] = (nyc['ended_at'] - nyc['started_at']).dt.total_seconds()\n\n# Define the startime as index and create a new column\nnyc = nyc.set_index('started_at')\nnyc['ty'] = 'station'\nnyc.head(1)\n\n\n\n\n\n\n\n\nride_id\nrideable_type\nended_at\nstart_station_name\nstart_station_id\nend_station_name\nend_station_id\nstart_lat\nstart_lng\nend_lat\nend_lng\nmember_casual\nduration\nty\n\n\nstarted_at\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n2024-03-21 13:32:42\n9F5EEFB7CF78C6EA\nelectric_bike\n2024-03-21 13:35:42\nStevens - River Ter & 6 St\nHB602\nCity Hall - Washington St & 1 St\nHB105\n40.743143\n-74.026907\n40.73736\n-74.03097\nmember\n180.0\nstation\n\n\n\n\n\n\n\nNow, we need to feed TimestampedGeoJson plugin with the right data format. To do so, we will transform the data frame. First, we count the number of trips per hour that start at each station\n\n# Aggregate number of trips for each start station by hour of the day\nstart = nyc.pivot_table('duration', \n                     index = ['start_station_id', \n                              'start_lat', \n                              'start_lng', \n                              nyc.index.hour],\n                     columns = 'ty',\n                     aggfunc='count').reset_index()\n\nstart.head()\n\n\n\n\n\n\n\nty\nstart_station_id\nstart_lat\nstart_lng\nstarted_at\nstation\n\n\n\n\n0\n6786.02\n40.714002\n-74.093255\n12\n1\n\n\n1\n6839.10\n40.714002\n-74.093255\n17\n1\n\n\n2\n6948.10\n40.714002\n-74.093255\n14\n1\n\n\n3\n6955.05\n40.714002\n-74.093255\n12\n1\n\n\n4\n7141.07\n40.714002\n-74.093255\n16\n1\n\n\n\n\n\n\n\nIn the code above we use the pandas pivot_table() to group the data. Note that the last index we use is “nyc.index.hour”. This takes the index of the data frame and, since it is of the datetime format, we can get the hour of that value like shown above. Similarly, we could get the day or month. Thus, to get a daily average we will divide by the numbers of days.\n\ndays = nyc.index.day.max()\nstart['station'] = start['station']/days\n\nNow, we will change the name of the columns and define the color we want the points to have on the map.\n\n# Rename the columns\nstart.columns = ['station_id', 'lat', 'lon', 'hour', 'count']\n\n# Define the color\nstart['fillColor'] = '#53c688'\n\n# The stops where less than one daily trip\n# will have a different color\nstart.loc[start['count']&lt;1, 'fillColor'] = '#586065'\nstart.head(1)\n\n\n\n\n\n\n\n\nstation_id\nlat\nlon\nhour\ncount\nfillColor\n\n\n\n\n0\n6786.02\n40.714002\n-74.093255\n12\n0.032258\n#586065\n\n\n\n\n\n\n\nAfter transforming the data frame to the format we need, we have to define a function that takes the values from it and creates the geojson with the right properties to use in the plugin.\n\ndef create_geojson_features(df):\n    features = []\n    \n    for _, row in df.iterrows():\n        feature = {\n            'type': 'Feature',\n            'geometry': {\n                'type':'Point', \n                'coordinates':[row['lon'],row['lat']]\n            },\n            'properties': {\n                'time': pd.to_datetime(row['hour'], unit='h').__str__(),\n                'style': {'color' : ''},\n                'icon': 'circle',\n                'iconstyle':{\n                    'fillColor': row['fillColor'],\n                    'fillOpacity': 0.8,\n                    'stroke': 'true',\n                    'radius': row['count'] + 5\n                }\n            }\n        }\n        features.append(feature)\n   \n    return features\n\nThe function:\n\nTakes a data frame and iterates through its rows.\nCreates a feature and defines the geometry as a point, taking the lat and lon variables from our data frame.\nDefines the rest of the properties taking the other variables:\n\nTakes the hour variable to create a time property. ** This one is the most important step to animate our data. **\nTakes fillColor to create the fillColor property.\nDefines the radius of the point as a function of the count variable.\n\n\nOnce the function is defined, we can use it on our data frame and get the geojson.\n\nstart_geojson = create_geojson_features(start)\nstart_geojson[0]\n\n{'type': 'Feature',\n 'geometry': {'type': 'Point',\n  'coordinates': [-74.0932553, 40.714001716666665]},\n 'properties': {'time': '1970-01-01 12:00:00',\n  'style': {'color': ''},\n  'icon': 'circle',\n  'iconstyle': {'fillColor': '#586065',\n   'fillOpacity': 0.8,\n   'stroke': 'true',\n   'radius': 5.032258064516129}}}\n\n\nWith this, we can now create the animated map.\n\nnyc_map = folium.Map(location = [40.71958611647166, -74.0431174635887],\n                    tiles = \"CartoDB Positron\",\n                    zoom_start = 14)\n\nTimestampedGeoJson(start_geojson,\n                  period = 'PT1H',\n                  duration = 'PT1M',\n                  transition_time = 1000,\n                  auto_play = True).add_to(nyc_map)\n\nnyc_map\n\nMake this Notebook Trusted to load map: File -&gt; Trust Notebook\n\n\nIt might be a good idea to analyze where trips end at different times of the day. Even more, it could be interesting to see both maps side by side and try to identify a pattern.\nThe DualMap plugin can help us achieve this. To use it, we will first run a similar script to the one we did above to get the number of trips that end at each station by time of the day.\n\nnyc1 = nyc.reset_index().set_index('ended_at')\nend = nyc1.pivot_table('duration', \n                     index = ['end_station_id', \n                              'end_lat', \n                              'end_lng', \n                              nyc1.index.hour],\n                     columns = 'ty',\n                     aggfunc='count').reset_index()\n\nend['station'] = end['station']/days\n\nend.columns = ['station_id', 'lat', 'lon', 'hour', 'count']\nend['fillColor'] = '#e64c4e'\nend.loc[end['count']&lt;1, 'fillColor'] = '#586065'\nend_geojson = create_geojson_features(end)\n\nWe first create the dual map and then we add start_geojson to the left map (m1) and the end_geojson to the right map (m2).\n\nfrom folium.plugins import DualMap\n\ndualmap = DualMap(location = [40.71958611647166, -74.0431174635887],\n                 tiles = 'cartodbpositron',\n                 zoom_start = 14)\n\nTimestampedGeoJson(start_geojson,\n                  period = 'PT1H',\n                  duration = 'PT1M',\n                  transition_time = 1000,\n                  auto_play = True).add_to(dualmap.m1)\n\nTimestampedGeoJson(end_geojson,\n                  period = 'PT1H',\n                  duration = 'PT1M',\n                  transition_time = 1000,\n                  auto_play = True).add_to(dualmap.m2)\n\ndualmap\n\nMake this Notebook Trusted to load map: File -&gt; Trust Notebook\n\n\nAnother interesting plugin, HeatmapWithTime, allows animating heatmaps. According to the documentation, the input needs to be a list of lists:\n“data (list of lists of points of the form [lat, lng] or [lat, lng, weight]) – The points you want to plot. The outer list corresponds to the various time steps in sequential order. (weight is in (0, 1] range and defaults to 1 if not specified for a point)”\nTherefore, we need to create this list of points for each hour of the day and then put all those lists in a new one.\n\n# Create an empty list\ndf_hour_list = []\n\n# Create a series with the different hours of the day\nhours = pd.Series(nyc.index.hour.unique().sort_values())\n\n# Create a list of points for each hour of the day\ndef create_list(hour):\n    df_hour_list.append(nyc.loc[nyc.index.hour == hour,\n                                ['start_lat',\n                                 'start_lng']].\n                        groupby(['start_lat', \n                                 'start_lng']).sum().reset_index().values.tolist())\nhours.apply(create_list);\n\n\nfrom folium.plugins import HeatMapWithTime\n\nlocation = [40.71958611647166, -74.0431174635887]\n# Add trip events to the map\nmap_time = folium.Map(location=location, \n                      tiles=\"CartoDB Positron\", \n                      zoom_start=12)\n\nHeatMapWithTime(df_hour_list, \n                auto_play=True, \n                max_opacity=0.5, \n                gradient = {0.2: '#FBD973', \n                            0.4: '#fa782f', \n                            0.75: '#F16578', \n                            1: '#782890'}).add_to(map_time)\nmap_time\n\nMake this Notebook Trusted to load map: File -&gt; Trust Notebook\n\n\n\nExercise:\nExplore the other folium plugins and think which of them could be useful for your last assignment. See: https://python-visualization.github.io/folium/latest/user_guide/plugins.html\nIf you have time also have a look at PyDeck.\nPydeck is a Python library used for creating interactive maps and data visualizations. It can be exploited to generate appealing maps with features like scatter plots, line plots, choropleths, and, more importantly, 3D visualisations. Pydeck is built on top of Deck.gl, a WebGL-powered library for rendering large-scale data visualizations. It offers various customization options for styling and configuring the visualizations and supports integration with Pandas and GeoPandas.\nPydeck installation is not trivial and can be result in conflicts with other libraries/versions, see: https://deckgl.readthedocs.io/en/latest/installation.html\nIts usage for the final assignment is optional.",
+    "text": "8.2 Animations in Folium\n\n8.2.1 Animating GPS tracks\n\nfrom folium.plugins import TimestampedGeoJson\n\nDownload this file in your data folder.\n\ngdf = gpd.read_file('../data/geolife_small.gpkg')\ngdf['t'] = pd.to_datetime(gdf['t'])\ngdf = gdf.set_index('t').tz_localize(None)\ngdf.index.name = None\ngdf.plot(column = 'trajectory_id', markersize = 1, cmap = 'Set1')\n\n\n\n\n\n\n\n\n\ngdf['t'] = pd.to_datetime(gdf['t']): This converts the values in the ‘t’ column of gdf into datetime objects using pd.to_datetime(). This is useful for time-series data where ‘t’ likely represents time information.\ngdf = df.set_index('t').tz_localize(None): This sets the index to the column t and removes the timezone information using tz_localize(None)\n\n\ndef geo_df_to_timestamped_geojson(geo_df, colour=\"blue\", marker_type=\"LineString\"):\n    \"\"\"\n    Convert a GeoDataFrame to a list of timestamped GeoJSON features.\n\n    Parameters\n    ----------\n        geo_df: GeoDataFrame\n            The GeoDataFrame containing the geographic data.\n        colour:str\n            The color of the line or marker. Defaults to \"blue\".\n        marker_type: str\n            The type of marker to use, e.g., \"LineString\" or \"Point\". Defaults to \"LineString\".\n\n    Returns\n    -------\n        feature_list: list\n            A list of GeoJSON features, each representing a timestamped marker or line.\n    \"\"\"\n\n    df = geo_df.copy()\n    x_coords = df[\"geometry\"].x.to_list()\n    y_coords = df[\"geometry\"].y.to_list()\n    coordinate_list = [[x, y] for x, y in zip(x_coords, y_coords)]\n    times_list = [i.isoformat() for i in df.index.to_list()]\n    \n    feature_list = [\n       {\n           \"type\": \"Feature\",\n           \"geometry\": {\n               \"type\": marker_type,\n               \"coordinates\": coordinate_list,\n           },\n           \"properties\": {\n               \"times\": times_list,\n               \"style\": {\n                   \"color\": colour,\n                   \"weight\": 3,\n               },\n           },\n       }\n    ]\n    \n    return feature_list\n\nObtaining features from the GeoDataFrame so that we can plot them in folium directly.\n\nfeatures = geo_df_to_timestamped_geojson(gdf, colour=\"blue\", marker_type=\"LineString\")\n\nThis TimestampedGeoJson plugin allows us to draw data on an interactive folium map and animate it depending on a time variable (year, month, day, hour, etc) and supports any kind of geometry (points, line strings, etc). The only thing you have to do is to make sure to “feed” it with a geojson in the right format.\n\nxy = gdf.unary_union.centroid\nmap = folium.Map(location=[xy.y, xy.x], zoom_start=10)\n\nTimestampedGeoJson({\"type\": \"FeatureCollection\", \"features\": features},\n    period=\"PT1S\",\n    add_last_point=True,\n    transition_time=10\n).add_to(map)\n\nmap\n\nMake this Notebook Trusted to load map: File -&gt; Trust Notebook\n\n\nThe arguments of the TimestampedGeoJson plugin are:\n\nThe geojson with the data.\nperiod: It is the time step that the animation will take starting from the first value. For example: “P1M” 1/month, “P1D” 1/day, “PT1H” 1/hour, and ‘PT1M’ 1/minute.\nduration: Period of time which the features will be shown on the map after their time has passed. If None, all previous times will be shown.\n\n\n\n8.2.2 Other Animations with Folium\nThis part of the notebook has been readapted from this post.\nWe are going to use the data of New York’s Citi Bike-sharing service. This is publicly available and can be downloaded from here. In this notebook, we used the data for the month of March 2024. Place it in your data folder, you can download it from here.\n\n# Import the file as a Pandas DataFrame\nnyc = pd.read_csv(\"../data/NY_march_bikeData.csv\")\nnyc.head()\n\n\n\n\n\n\n\n\nride_id\nrideable_type\nstarted_at\nended_at\nstart_station_name\nstart_station_id\nend_station_name\nend_station_id\nstart_lat\nstart_lng\nend_lat\nend_lng\nmember_casual\n\n\n\n\n0\n9F5EEFB7CF78C6EA\nelectric_bike\n2024-03-21 13:32:42\n2024-03-21 13:35:42\nStevens - River Ter & 6 St\nHB602\nCity Hall - Washington St & 1 St\nHB105\n40.743143\n-74.026907\n40.737360\n-74.030970\nmember\n\n\n1\n01E2E1F218F9D303\nelectric_bike\n2024-03-16 08:26:15\n2024-03-16 08:47:53\nPershing Field\nJC024\nPershing Field\nJC024\n40.742379\n-74.051996\n40.742677\n-74.051789\nmember\n\n\n2\nC2914D662B33AA55\nelectric_bike\n2024-03-15 15:17:36\n2024-03-15 15:32:12\nPershing Field\nJC024\nPershing Field\nJC024\n40.742480\n-74.051942\n40.742677\n-74.051789\nmember\n\n\n3\n6723F7CE51888493\nelectric_bike\n2024-03-13 08:34:26\n2024-03-13 08:50:08\nPershing Field\nJC024\nPershing Field\nJC024\n40.742400\n-74.051963\n40.742677\n-74.051789\nmember\n\n\n4\nEAB7E29AAB546941\nelectric_bike\n2024-03-15 15:37:58\n2024-03-15 16:19:32\nPershing Field\nJC024\nPershing Field\nJC024\n40.742459\n-74.051973\n40.742677\n-74.051789\nmember\n\n\n\n\n\n\n\nFor every trip made we have:\n\nstarted_at: start date and time of the trip.\nended_at: end date and time the trip.\nstart_station_id, start_station_name, start_lat, and start_lng: all the useful info to identify and locate the start station.\nend_station_id, end_station_name, end_lat, and end_lng: all the useful info to identify and locate the end station of the trip.\n\n\ntype(nyc.loc[0, 'started_at'])\n\nstr\n\n\nTransformation\nThe variables started_at and ended_at were imported as text (string). we are going to do the following:\n\nChange the type of these variables to datetime.\nCompute the duration of the trip in seconds.\nDefine started_at as the data frame’s index. This will allow us to pivot (group) values easier by time of day.\nCreate a new column called ty to help us in the pivoting.\n\n\n# Setting the right format for started_at and ended_at\nnyc['started_at'] = pd.to_datetime(nyc['started_at'])\nnyc['ended_at'] = pd.to_datetime(nyc['ended_at'])\nnyc['duration'] = (nyc['ended_at'] - nyc['started_at']).dt.total_seconds()\n\n# Define the startime as index and create a new column\nnyc = nyc.set_index('started_at')\nnyc['ty'] = 'station'\nnyc.head(1)\n\n\n\n\n\n\n\n\nride_id\nrideable_type\nended_at\nstart_station_name\nstart_station_id\nend_station_name\nend_station_id\nstart_lat\nstart_lng\nend_lat\nend_lng\nmember_casual\nduration\nty\n\n\nstarted_at\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n2024-03-21 13:32:42\n9F5EEFB7CF78C6EA\nelectric_bike\n2024-03-21 13:35:42\nStevens - River Ter & 6 St\nHB602\nCity Hall - Washington St & 1 St\nHB105\n40.743143\n-74.026907\n40.73736\n-74.03097\nmember\n180.0\nstation\n\n\n\n\n\n\n\nNow, we need to feed TimestampedGeoJson plugin with the right data format. To do so, we will transform the data frame. First, we count the number of trips per hour that start at each station\n\n# Aggregate number of trips for each start station by hour of the day\nstart = nyc.pivot_table('duration', \n                     index = ['start_station_id', \n                              'start_lat', \n                              'start_lng', \n                              nyc.index.hour],\n                     columns = 'ty',\n                     aggfunc='count').reset_index()\n\nstart.head()\n\n\n\n\n\n\n\nty\nstart_station_id\nstart_lat\nstart_lng\nstarted_at\nstation\n\n\n\n\n0\n6786.02\n40.714002\n-74.093255\n12\n1\n\n\n1\n6839.10\n40.714002\n-74.093255\n17\n1\n\n\n2\n6948.10\n40.714002\n-74.093255\n14\n1\n\n\n3\n6955.05\n40.714002\n-74.093255\n12\n1\n\n\n4\n7141.07\n40.714002\n-74.093255\n16\n1\n\n\n\n\n\n\n\nIn the code above we use the pandas pivot_table() to group the data. Note that the last index we use is “nyc.index.hour”. This takes the index of the data frame and, since it is of the datetime format, we can get the hour of that value like shown above. Similarly, we could get the day or month. Thus, to get a daily average we will divide by the numbers of days.\n\ndays = nyc.index.day.max()\nstart['station'] = start['station']/days\n\nNow, we will change the name of the columns and define the color we want the points to have on the map.\n\n# Rename the columns\nstart.columns = ['station_id', 'lat', 'lon', 'hour', 'count']\n\n# Define the color\nstart['fillColor'] = '#53c688'\n\n# The stops where less than one daily trip\n# will have a different color\nstart.loc[start['count']&lt;1, 'fillColor'] = '#586065'\nstart.head(1)\n\n\n\n\n\n\n\n\nstation_id\nlat\nlon\nhour\ncount\nfillColor\n\n\n\n\n0\n6786.02\n40.714002\n-74.093255\n12\n0.032258\n#586065\n\n\n\n\n\n\n\nAfter transforming the data frame to the format we need, we have to define a function that takes the values from it and creates the geojson with the right properties to use in the plugin.\n\ndef create_geojson_features(df):\n    features = []\n    \n    for _, row in df.iterrows():\n        feature = {\n            'type': 'Feature',\n            'geometry': {\n                'type':'Point', \n                'coordinates':[row['lon'],row['lat']]\n            },\n            'properties': {\n                'time': pd.to_datetime(row['hour'], unit='h').__str__(),\n                'style': {'color' : ''},\n                'icon': 'circle',\n                'iconstyle':{\n                    'fillColor': row['fillColor'],\n                    'fillOpacity': 0.8,\n                    'stroke': 'true',\n                    'radius': row['count'] + 5\n                }\n            }\n        }\n        features.append(feature)\n   \n    return features\n\nThe function:\n\nTakes a data frame and iterates through its rows.\nCreates a feature and defines the geometry as a point, taking the lat and lon variables from our data frame.\nDefines the rest of the properties taking the other variables:\n\nTakes the hour variable to create a time property. ** This one is the most important step to animate our data. **\nTakes fillColor to create the fillColor property.\nDefines the radius of the point as a function of the count variable.\n\n\nOnce the function is defined, we can use it on our data frame and get the geojson.\n\nstart_geojson = create_geojson_features(start)\nstart_geojson[0]\n\n{'type': 'Feature',\n 'geometry': {'type': 'Point',\n  'coordinates': [-74.0932553, 40.714001716666665]},\n 'properties': {'time': '1970-01-01 12:00:00',\n  'style': {'color': ''},\n  'icon': 'circle',\n  'iconstyle': {'fillColor': '#586065',\n   'fillOpacity': 0.8,\n   'stroke': 'true',\n   'radius': 5.032258064516129}}}\n\n\nWith this, we can now create the animated map.\n\nnyc_map = folium.Map(location = [40.71958611647166, -74.0431174635887],\n                    tiles = \"CartoDB Positron\",\n                    zoom_start = 14)\n\nTimestampedGeoJson(start_geojson,\n                  period = 'PT1H',\n                  duration = 'PT1M',\n                  transition_time = 1000,\n                  auto_play = True).add_to(nyc_map)\n\nnyc_map\n\nMake this Notebook Trusted to load map: File -&gt; Trust Notebook\n\n\nIt might be a good idea to analyze where trips end at different times of the day. Even more, it could be interesting to see both maps side by side and try to identify a pattern.\nThe DualMap plugin can help us achieve this. To use it, we will first run a similar script to the one we did above to get the number of trips that end at each station by time of the day.\n\nnyc1 = nyc.reset_index().set_index('ended_at')\nend = nyc1.pivot_table('duration', \n                     index = ['end_station_id', \n                              'end_lat', \n                              'end_lng', \n                              nyc1.index.hour],\n                     columns = 'ty',\n                     aggfunc='count').reset_index()\n\nend['station'] = end['station']/days\n\nend.columns = ['station_id', 'lat', 'lon', 'hour', 'count']\nend['fillColor'] = '#e64c4e'\nend.loc[end['count']&lt;1, 'fillColor'] = '#586065'\nend_geojson = create_geojson_features(end)\n\nWe first create the dual map and then we add start_geojson to the left map (m1) and the end_geojson to the right map (m2).\n\nfrom folium.plugins import DualMap\n\ndualmap = DualMap(location = [40.71958611647166, -74.0431174635887],\n                 tiles = 'cartodbpositron',\n                 zoom_start = 14)\n\nTimestampedGeoJson(start_geojson,\n                  period = 'PT1H',\n                  duration = 'PT1M',\n                  transition_time = 1000,\n                  auto_play = True).add_to(dualmap.m1)\n\nTimestampedGeoJson(end_geojson,\n                  period = 'PT1H',\n                  duration = 'PT1M',\n                  transition_time = 1000,\n                  auto_play = True).add_to(dualmap.m2)\n\ndualmap\n\nMake this Notebook Trusted to load map: File -&gt; Trust Notebook\n\n\nAnother interesting plugin, HeatmapWithTime, allows animating heatmaps. According to the documentation, the input needs to be a list of lists:\n“data (list of lists of points of the form [lat, lng] or [lat, lng, weight]) – The points you want to plot. The outer list corresponds to the various time steps in sequential order. (weight is in (0, 1] range and defaults to 1 if not specified for a point)”\nTherefore, we need to create this list of points for each hour of the day and then put all those lists in a new one.\n\n# Create an empty list\ndf_hour_list = []\n\n# Create a series with the different hours of the day\nhours = pd.Series(nyc.index.hour.unique().sort_values())\n\n# Create a list of points for each hour of the day\ndef create_list(hour):\n    df_hour_list.append(nyc.loc[nyc.index.hour == hour,\n                                ['start_lat',\n                                 'start_lng']].\n                        groupby(['start_lat', \n                                 'start_lng']).sum().reset_index().values.tolist())\nhours.apply(create_list);\n\n\nfrom folium.plugins import HeatMapWithTime\n\nlocation = [40.71958611647166, -74.0431174635887]\n# Add trip events to the map\nmap_time = folium.Map(location=location, \n                      tiles=\"CartoDB Positron\", \n                      zoom_start=12)\n\nHeatMapWithTime(df_hour_list, \n                auto_play=True, \n                max_opacity=0.5, \n                gradient = {0.2: '#FBD973', \n                            0.4: '#fa782f', \n                            0.75: '#F16578', \n                            1: '#782890'}).add_to(map_time)\nmap_time\n\nMake this Notebook Trusted to load map: File -&gt; Trust Notebook\n\n\n\nExercise:\nExplore the other folium plugins and think which of them could be useful for your last assignment. See: https://python-visualization.github.io/folium/latest/user_guide/plugins.html\nIf you have time also have a look at PyDeck.\nPydeck is a Python library used for creating interactive maps and data visualizations. It can be exploited to generate appealing maps with features like scatter plots, line plots, choropleths, and, more importantly, 3D visualisations. Pydeck is built on top of Deck.gl, a WebGL-powered library for rendering large-scale data visualizations. It offers various customization options for styling and configuring the visualizations and supports integration with Pandas and GeoPandas.\nPydeck installation is not trivial and can be result in conflicts with other libraries/versions, see: https://deckgl.readthedocs.io/en/latest/installation.html\nIts usage for the final assignment is optional.",
     "crumbs": [
       "<span class='chapter-number'>8</span>  <span class='chapter-title'>GPS Traces and Animations</span>"
     ]
diff --git a/docs/sitemap.xml b/docs/sitemap.xml
index dff5784..0c79580 100644
--- a/docs/sitemap.xml
+++ b/docs/sitemap.xml
@@ -50,10 +50,10 @@
   </url>
   <url>
     <loc>https://gdsl-ul.github.io/wma/labs/w09_advanced.html</loc>
-    <lastmod>2024-04-16T11:25:57.632Z</lastmod>
+    <lastmod>2024-04-16T11:56:14.857Z</lastmod>
   </url>
   <url>
     <loc>https://gdsl-ul.github.io/wma/Web-Mapping-and-Geovisualisation.pdf</loc>
-    <lastmod>2024-04-16T11:30:52.435Z</lastmod>
+    <lastmod>2024-04-16T12:00:53.580Z</lastmod>
   </url>
 </urlset>