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commit 9d1b4d5ef7
7 changed files with 75 additions and 62 deletions
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dev/change-log/index.html
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@ -1624,6 +1624,7 @@
<li>Add logo</li>
<li>Move Citation page to the Setup section</li>
<li>Add <code>config.yaml</code> validation schema and documentation.</li>
<li>Add time at home Doryab location feature and home coordinates to location file</li>
</ul>
<h2 id="v043">v0.4.3<a class="headerlink" href="#v043" title="Permanent link">&para;</a></h2>
<ul>

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dev/features/fitbit-data-yield/index.html
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@ -1503,9 +1503,9 @@
<p>We recommend using <code>ratiovalidyieldedminutes</code> on time segments that are shorter than two or three hours and <code>ratiovalidyieldedhours</code> for longer segments. This is because relying on yielded minutes only can be misleading when a big chunk of those missing minutes are clustered together. </p>
<p>For example, let&rsquo;s assume we are working with a 24-hour time segment that is missing 12 hours of data. Two extreme cases can occur: </p>
<p><ol type="A">
<li>the 12 missing hours are from the beginning of the segment or </li>
<li>30 minutes could be missing from every hour (24 * 30 minutes = 12 hours).</li>
</ol></p>
<li>the 12 missing hours are from the beginning of the segment or </li>
<li>30 minutes could be missing from every hour (24 * 30 minutes = 12 hours).</li>
</ol></p>
<p><code>ratiovalidyieldedminutes</code> would be 0.5 for both <code>a</code> and <code>b</code> (hinting the missing circumstances are similar). However, <code>ratiovalidyieldedhours</code> would be 0.5 for <code>a</code> and 1.0 for <code>b</code> if <code>[MINUTE_RATIO_THRESHOLD_FOR_VALID_YIELDED_HOURS]</code> is between [0.0 and 0.49] (hinting that the missing circumstances might be more favorable for <code>b</code>. In other words, sensed data for <code>b</code> is more evenly spread compared to <code>a</code>.</p>
</li>
<li>

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dev/features/phone-data-yield/index.html
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@ -1526,9 +1526,9 @@
<p>We recommend using <code>ratiovalidyieldedminutes</code> on time segments that are shorter than two or three hours and <code>ratiovalidyieldedhours</code> for longer segments. This is because relying on yielded minutes only can be misleading when a big chunk of those missing minutes are clustered together. </p>
<p>For example, let&rsquo;s assume we are working with a 24-hour time segment that is missing 12 hours of data. Two extreme cases can occur: </p>
<p><ol type="A">
<li>the 12 missing hours are from the beginning of the segment or </li>
<li>30 minutes could be missing from every hour (24 * 30 minutes = 12 hours).</li>
</ol></p>
<li>the 12 missing hours are from the beginning of the segment or </li>
<li>30 minutes could be missing from every hour (24 * 30 minutes = 12 hours).</li>
</ol></p>
<p><code>ratiovalidyieldedminutes</code> would be 0.5 for both <code>a</code> and <code>b</code> (hinting the missing circumstances are similar). However, <code>ratiovalidyieldedhours</code> would be 0.5 for <code>a</code> and 1.0 for <code>b</code> if <code>[MINUTE_RATIO_THRESHOLD_FOR_VALID_YIELDED_HOURS]</code> is between [0.0 and 0.49] (hinting that the missing circumstances might be more favorable for <code>b</code>. In other words, sensed data for <code>b</code> is more evenly spread compared to <code>a</code>.</p>
</li>
</ol>

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dev/features/phone-locations/index.html
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@ -1611,6 +1611,7 @@ For a detailed description of how this is calculated, see <a href="../../citatio
<div class="highlight"><pre><span></span><code>- data/raw/<span class="o">{</span>pid<span class="o">}</span>/phone_locations_raw.csv
- data/interim/<span class="o">{</span>pid<span class="o">}</span>/phone_locations_processed.csv
- data/interim/<span class="o">{</span>pid<span class="o">}</span>/phone_locations_processed_with_datetime.csv
- data/interim/<span class="o">{</span>pid<span class="o">}</span>/phone_locations_processed_with_datetime_with_home.csv
- data/interim/<span class="o">{</span>pid<span class="o">}</span>/phone_locations_features/phone_locations_<span class="o">{</span>language<span class="o">}</span>_<span class="o">{</span>provider_key<span class="o">}</span>.csv
- data/processed/features/<span class="o">{</span>pid<span class="o">}</span>/phone_locations.csv
</code></pre></div>
@ -1672,6 +1673,10 @@ For a detailed description of how this is calculated, see <a href="../../citatio
<td><code>[CLUSTERING_ALGORITHM]</code></td>
<td>The original Doryab et al implementation uses <code>DBSCAN</code>, <code>OPTICS</code> is also available with similar (but not identical) clustering results and lower memory consumption.</td>
</tr>
<tr>
<td><code>[RADIUS_FOR_HOME]</code></td>
<td>All location coordinates within this distance (meters) from the home location coordinates are considered a home stay (see <code>timeathome</code> feature).</td>
</tr>
</tbody>
</table>
<p>Features description for <code>[PHONE_LOCATIONS][PROVIDERS][DORYAB]</code>:</p>
@ -1784,6 +1789,11 @@ For a detailed description of how this is calculated, see <a href="../../citatio
<td>nats</td>
<td>Shannon Entropy computed over the row count of each cluster (significant location) divided by the number of clusters, it will be higher the more rows belong to a cluster (i.e. the more time a participant spent at a significant location).</td>
</tr>
<tr>
<td>timeathome</td>
<td>minutes</td>
<td>Time spent at home (see Observations below for a description on how we compute home).</td>
</tr>
</tbody>
</table>
<div class="admonition note">
@ -1796,6 +1806,8 @@ Note Feb 3 2021. It seems the implementation of this feature is not correct, we
Based on an experiment where we collected fused location data for 7 days with a mean accuracy of 86 &amp; SD of 350.874635, we determined that <code>EPS/MAX_EPS</code>=100 produced closer clustering results to reality. Higher values (&gt;100) missed out some significant places like a short grocery visit while lower values (&lt;100) picked up traffic lights and stop signs while driving as significant locations. We recommend you set <code>EPS</code> based on the accuracy of your location data (the more accurate your data is, the lower you should be able to set EPS).</p>
<p><strong>Duration Calculation</strong>
To calculate the time duration component for our features, we compute the difference between the timestamps of consecutive rows to take into account sampling rate variability. If this time difference is larger than a threshold (300 seconds by default) we replace it with a maximum duration (60 seconds by default, i.e. we assume a participant spent at least 60 seconds in their last known location)</p>
<p><strong>Home location</strong>
Home is calculated using all location data of a participant between 12 am and 6 am, then applying a clustering algorithm (<code>DB_SCAN</code> or <code>OPTICS</code>), and considering the center of the biggest cluster as the home coordinates for that participant.</p>
</div>

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