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CHIPS_ExperienceLab_NL.html
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<!doctype html>
<html lang="en">
<!-- This is a generated file. Do not edit. -->
<head>
<meta charset="utf-8">
<title>CHIPS Experience Lab</title>
<meta name="description" content="Slides for CHIPS Experience Lab (NL) Presentation">
<meta name="author" content="Garrett C. Millar">
<meta name="apple-mobile-web-app-capable" content="yes" />
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<style>
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<!-- Title slide -->
<section data-transition="slide">
<h5 class="shadow" style="color:#fff">Mapping the Emotional Dimension: </h5>
<center>
<img height="450px" src="img/space_time_cube.gif">
<!-- <video data-autoplay align="center" height="480" controls muted loop>
<source src="video/space_time.mp4" type="video/mp4">
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</video> -->
</center>
<h6 class="shadow" style="font-size:1.1em; color:#fff"> Measuring Cyclists’ Physiological Responses <br>Across Space & Time to Inform Urban Planning</h6>
<h6 style="margin-top: 0.5em;color: #F0F0F0; font-size: 24px">Garrett C. Millar</h6>
<img height="30px" style="margin-top: 0.2em" src="img/logos/cga&ncstate_white.png">
<aside class="notes">
Good morning everyone, and thank you for coming to my talk.
Hi everyone, my name is Garrett Millar, and I'm a PhD student at North Carolina State University. I'll be talking about my recent work which uses cyclists' physiological data collected from the CHIPS work (specifically skin conductance) to see how stress can be affected by our environment.
We'll then get into how I plan to improve upon this work while I'm here, and how I can apply similar techniques and approaches to similar data such as those collected for Nuenen.
Before we start, I'd like to briefly explain why I'm doing what I'm doing, by mentioning my background in Cognitive Science, specifically Human-Computer Interaction. I began my graduate research assessing how humans interact with computers, and how computers can be best designed to facilitate users' interaction and experience with them. Here, I apply a similar research approach using geospatial analytics, specifically analyzing how humans interact with the environment, what sort of affects it can have on them, and how it can be best configured for its users.
To do so, we must begin exploring emotional experiences using a spatial analytical framework.
</aside>
</section>
<!-- Methodology: GPS Data -->
<section data-transition="slide">
<h6 style="color: #909090">
Methodology</h6>
<h3 style="color: #F0F0F0">
GPS Data</h3>
<img class="stretch" src="img/GPS_charts_all.png">
<aside class="notes">
. . . . . NOTES HERE . . . . . .
</aside>
</section>
<!-- Methodology: Skin Conductance -->
<section data-transition="slide">
<h6 style="color: #909090">
Methodology</h6>
<h3 style="color: #F0F0F0">
Skin Conductance</h3>
<img class="stretch" src="img/Conductance_charts.png">
<aside class="notes">
. . . . . NOTES HERE . . . . . .
</aside>
</section>
<!-- Methodology: Viewshed Buffers -->
<section data-transition="slide">
<h6 style="color: #909090">
Methodology</h6>
<h3 style="color: #F0F0F0">
Buffers as an Environment Interaction Metric</h3>
<img class="stretch" src="img/100_yd_buffer.png">
<aside class="notes">
. . . . . NOTES HERE . . . . . .
</aside>
</section>
<!-- Methodology: Framework for Cyclists' Experiences -->
<section data-transition="slide">
<h6 style="color: #909090">
Methodology</h6>
<h3 style="color: #F0F0F0">
Framework for Cyclists' Emotional Experiences</h3>
<img class="stretch" src="img/cyclist_exp_framework_no_descrip_2.png">
<aside class="notes">
. . . . . NOTES HERE . . . . . .
</aside>
</section>
<!-- Methodology: Mapping Emotion -->
<section data-transition="slide">
<h6 style="color: #909090">
Methodology</h6>
<h3 style="color: #F0F0F0">
Mapping Emotion</h3>
<img class="stretch" src="img/conduct_landuse_roads_map_grey_pts1-6.png">
<aside class="notes">
. . . . . NOTES HERE . . . . . .
</aside>
</section>
<!-- Methodology: Mapping Emotion -->
<section data-transition="slide">
<h6 style="color: #909090">
Methodology</h6>
<h3 style="color: #F0F0F0">
Mapping Emotion cont'd</h3>
<img class="stretch" src="img/conduct_landuse_roads_map_grey_pts7-11.png">
<aside class="notes">
. . . . . NOTES HERE . . . . . .
</aside>
</section>
<section data-transition="slide">
<h6 style="color: #909090">
Results</h6>
<h3 style="color: #F0F0F0">
Descriptive Statistics: Land Use by Type</h3>
<table style="font-size: 14px; float: left; height:10px;" class="table_6144" border="2">
<thead>
<tr>
<th colspan="1" id="tableHTML_second_header_1" style="background-color:#C0C0C0;color:black;height:10px;">Landcover Class</th>
<th colspan="3" id="tableHTML_second_header_2" style="background-color:#C0C0C0;color:black;height:10px;text-align: center">Statistic</th>
</tr>
<tr style="background-color:#DBDCDE">
<th id="tableHTML_header_1"> </th>
<th id="tableHTML_header_2">Sampled Points (N)</th>
<th id="tableHTML_header_3">Skin Conductance (M)</th>
<th id="tableHTML_header_4">SD</th>
</tr>
</thead>
<tbody>
<tr style="background-color:#f2f2f2;">
<td id="tableHTML_rownames">camp_site</td>
<td id="tableHTML_column_1">69</td>
<td id="tableHTML_column_2">0.11</td>
<td id="tableHTML_column_3">0.91</td>
</tr>
<tr style="background-color:#DBDCDE">
<td id="tableHTML_rownames">cemetery</td>
<td id="tableHTML_column_1">42</td>
<td id="tableHTML_column_2" style="background-color:lightsteelblue;">-0.46</td>
<td id="tableHTML_column_3">0.72</td>
</tr>
<tr style="background-color:#f2f2f2;">
<td id="tableHTML_rownames">christian</td>
<td id="tableHTML_column_1">2</td>
<td id="tableHTML_column_2" style="background-color:lightsteelblue;">-1.31</td>
<td id="tableHTML_column_3">0.31</td>
</tr>
<tr style="background-color:#DBDCDE">
<td id="tableHTML_rownames">college</td>
<td id="tableHTML_column_1">582</td>
<td id="tableHTML_column_2" style="background-color:lightsteelblue;">-0.23</td>
<td id="tableHTML_column_3">0.8</td>
</tr>
<tr style="background-color:#f2f2f2;">
<td id="tableHTML_rownames">farm</td>
<td id="tableHTML_column_1">388</td>
<td id="tableHTML_column_2">0.03</td>
<td id="tableHTML_column_3">0.77</td>
</tr>
<tr style="background-color:#DBDCDE">
<td id="tableHTML_rownames">forest</td>
<td id="tableHTML_column_1">8466</td>
<td id="tableHTML_column_2">0.1</td>
<td id="tableHTML_column_3">0.88</td>
</tr>
<tr style="background-color:#f2f2f2;">
<td id="tableHTML_rownames">fuel</td>
<td id="tableHTML_column_1">54</td>
<td id="tableHTML_column_2" style="background-color:lightsteelblue;">-0.52</td>
<td id="tableHTML_column_3">0.5</td>
</tr>
<tr style="background-color:#DBDCDE">
<td id="tableHTML_rownames">grass</td>
<td id="tableHTML_column_1">39869</td>
<td id="tableHTML_column_2">-0.09</td>
<td id="tableHTML_column_3">0.84</td>
</tr>
<tr style="background-color:#f2f2f2;">
<td id="tableHTML_rownames">graveyard</td>
<td id="tableHTML_column_1">42</td>
<td id="tableHTML_column_2" style="background-color:lightsteelblue;">-0.46</td>
<td id="tableHTML_column_3">0.72</td>
</tr>
<tr style="background-color:#DBDCDE">
<td id="tableHTML_rownames">hotel</td>
<td id="tableHTML_column_1">67</td>
<td id="tableHTML_column_2">-0.09</td>
<td id="tableHTML_column_3">0.69</td>
</tr>
<tr style="background-color:#f2f2f2;">
<td id="tableHTML_rownames">industrial</td>
<td id="tableHTML_column_1">2600</td>
<td id="tableHTML_column_2" style="background-color:lightsteelblue;">-0.23</td>
<td id="tableHTML_column_3">1.01</td>
</tr>
<tr style="background-color:#DBDCDE">
<td id="tableHTML_rownames">muslim</td>
<td id="tableHTML_column_1">27</td>
<td id="tableHTML_column_2">-0.13</td>
<td id="tableHTML_column_3">0.56</td>
</tr>
<tr style="background-color:#f2f2f2;">
<td id="tableHTML_rownames">orchard</td>
<td id="tableHTML_column_1">481</td>
<td id="tableHTML_column_2">0.12</td>
<td id="tableHTML_column_3">1.26</td>
</tr>
</tbody>
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<thead>
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<th colspan="1" id="tableHTML_second_header_1" style="background-color:#C0C0C0;color:black;height:10px;">Landcover Class</th>
<th colspan="3" id="tableHTML_second_header_2" style="background-color:#C0C0C0;color:black;height:10px; text-align: center">Statistic</th>
</tr>
<tr style="background-color:#DBDCDE">
<th id="tableHTML_header_1"> </th>
<th id="tableHTML_header_2">Sampled Points (N)</th>
<th id="tableHTML_header_3">Skin Conductance (M)</th>
<th id="tableHTML_header_4">SD</th>
</tr>
</thead>
<tbody>
<tr style="background-color:#DBDCDE">
<td id="tableHTML_rownames">park</td>
<td id="tableHTML_column_1">1408</td>
<td id="tableHTML_column_2">0.19</td>
<td id="tableHTML_column_3">0.71</td>
</tr>
<tr style="background-color:#f2f2f2;">
<td id="tableHTML_rownames">parking</td>
<td id="tableHTML_column_1">940</td>
<td id="tableHTML_column_2">-0.06</td>
<td id="tableHTML_column_3">0.92</td>
</tr>
<tr style="background-color:#DBDCDE">
<td id="tableHTML_rownames">parking_bicycle</td>
<td id="tableHTML_column_1">38</td>
<td id="tableHTML_column_2" style="background-color:lightcoral;">0.52</td>
<td id="tableHTML_column_3">1.25</td>
</tr>
<tr style="background-color:#f2f2f2;">
<td id="tableHTML_rownames">parking_multistorey</td>
<td id="tableHTML_column_1">80</td>
<td id="tableHTML_column_2">0.16</td>
<td id="tableHTML_column_3">1.03</td>
</tr>
<tr style="background-color:#DBDCDE">
<td id="tableHTML_rownames">pitch</td>
<td id="tableHTML_column_1">41</td>
<td id="tableHTML_column_2">-0.18</td>
<td id="tableHTML_column_3">0.87</td>
</tr>
<tr style="background-color:#f2f2f2;">
<td id="tableHTML_rownames">playground</td>
<td id="tableHTML_column_1">87</td>
<td id="tableHTML_column_2" style="background-color:lightcoral;">0.29</td>
<td id="tableHTML_column_3">0.58</td>
</tr>
<tr style="background-color:#DBDCDE">
<td id="tableHTML_rownames">residential</td>
<td id="tableHTML_column_1">3710</td>
<td id="tableHTML_column_2" style="background-color:lightsteelblue;">-0.22</td>
<td id="tableHTML_column_3">1</td>
</tr>
<tr style="background-color:#f2f2f2;">
<td id="tableHTML_rownames">scrub</td>
<td id="tableHTML_column_1">237</td>
<td id="tableHTML_column_2">0.19</td>
<td id="tableHTML_column_3">1.14</td>
</tr>
<tr style="background-color:#DBDCDE">
<td id="tableHTML_rownames">swimming_pool</td>
<td id="tableHTML_column_1">41</td>
<td id="tableHTML_column_2">-0.01</td>
<td id="tableHTML_column_3">0.91</td>
</tr>
<tr style="background-color:#f2f2f2;">
<td id="tableHTML_rownames">theme_park</td>
<td id="tableHTML_column_1">347</td>
<td id="tableHTML_column_2" style="background-color:lightcoral;">0.21</td>
<td id="tableHTML_column_3">0.7</td>
</tr>
<tr style="background-color:#DBDCDE">
<td id="tableHTML_rownames">wastewater_plant</td>
<td id="tableHTML_column_1">422</td>
<td id="tableHTML_column_2" style="background-color:lightcoral;">0.22</td>
<td id="tableHTML_column_3">0.69</td>
</tr>
<tr style="background-color:#f2f2f2;">
<td id="tableHTML_rownames">water</td>
<td id="tableHTML_column_1">2291</td>
<td id="tableHTML_column_2">-0.05</td>
<td id="tableHTML_column_3">0.92</td>
</tr>
<tr style="background-color:#DBDCDE">
<td id="tableHTML_rownames">wetland</td>
<td id="tableHTML_column_1">306</td>
<td id="tableHTML_column_2" style="background-color:lightcoral;">0.27</td>
<td id="tableHTML_column_3">0.68</td>
</tr>
<tr style="background-color:#f2f2f2;">
<td id="tableHTML_rownames">zoo</td>
<td id="tableHTML_column_1">92</td>
<td id="tableHTML_column_2" style="background-color:lightsteelblue;">-0.2</td>
<td id="tableHTML_column_3">0.78</td>
</tr>
</tbody>
</table>
<aside class="notes">
. . . . . NOTES HERE . . . . . .
</aside>
</section>
<!-- Results: Land Use Bar Charts -->
<section data-transition="slide">
<h6 style="color: #909090">
Results</h6>
<h3 style="color: #F0F0F0">
Descriptive Statistics: Land Use by Type</h3>
<img class="stretch" src="img/conductance_by_land_type.png">
<!-- <center><p style="color: #F0F0F0"> By Individual Land Use Types </p> </center> -->
<aside class="notes">
. . . . . NOTES HERE . . . . . .
</aside>
</section>
<!-- Results: Land Use Tables -->
<section data-transition="slide">
<h6 style="color: #909090">
Results</h6>
<h3 style="color: #F0F0F0">
Descriptive Statistics: Land Use by Group</h3>
<table style="border-collapse:collapse;" class="table_5452" border="2">
<thead>
<tr>
<th colspan="1" id="tableHTML_second_header_1" style="background-color:#C0C0C0;color:black;height:50px;">Landcover Class</th>
<th colspan="3" id="tableHTML_second_header_2" style="background-color:#C0C0C0;color:black;height:50px;text-align: center">Statistic</th>
</tr>
<tr style="background-color:#DBDCDE">
<th id="tableHTML_header_1"> </th>
<th id="tableHTML_header_2">Sampled Points (N)</th>
<th id="tableHTML_header_3">Skin Conductance (M)</th>
<th id="tableHTML_header_4">SD</th>
</tr>
</thead>
<tbody>
<tr style="background-color:#f2f2f2;">
<td id="tableHTML_rownames">urban</td>
<td id="tableHTML_column_1">9019</td>
<td id="tableHTML_column_2">-0.17</td>
<td id="tableHTML_column_3">0.96</td>
</tr>
<tr style="background-color:#DBDCDE ">
<td id="tableHTML_rownames">green_areas</td>
<td id="tableHTML_column_1">51084</td>
<td id="tableHTML_column_2">-0.05</td>
<td id="tableHTML_column_3">0.85</td>
</tr>
<tr style="background-color:#f2f2f2;">
<td id="tableHTML_rownames">water</td>
<td id="tableHTML_column_1">2626</td>
<td id="tableHTML_column_2">-0.02</td>
<td id="tableHTML_column_3">0.9</td>
</tr>
</tbody>
</table>
<aside class="notes">
. . . . . NOTES HERE . . . . . .
</aside>
</section>
<!-- Results: Land Use Bar Charts -->
<section data-transition="slide">
<h6 style="color: #909090">
Results</h6>
<h3 style="color: #F0F0F0">
Descriptive Statistics: Land Use by Group </h3>
<img class="stretch" src="img/conductance_by_land_group.png">
<aside class="notes">
. . . . . NOTES HERE . . . . . .
</aside>
</section>
<!-- Results: Road Type Tables -->
<section data-transition="slide">
<h6 style="color: #909090">
Results</h6>
<h3 style="color: #F0F0F0">
Descriptive Statistics: Road Types</h3>
<table style="font-size: 14px; float: left; height:10px;" class="table_6144" border="2">
<thead>
<tr>
<th colspan="1" id="tableHTML_second_header_1" style="background-color:#C0C0C0;color:black;height:10px;">Road Type</th>
<th colspan="3" id="tableHTML_second_header_2" style="background-color:#C0C0C0;color:black;height:10px; text-align: center;">Statistic</th>
</tr>
<tr style="background-color:#DBDCDE ">
<th id="tableHTML_header_1"> </th>
<th id="tableHTML_header_2">Sampled Points (N)</th>
<th id="tableHTML_header_3">Skin Conductance (M)</th>
<th id="tableHTML_header_4">SD</th>
</tr>
</thead>
<tbody>
<tr style="background-color:#f2f2f2;">
<td id="tableHTML_rownames">cycleway</td>
<td id="tableHTML_column_1">22124</td>
<td id="tableHTML_column_2">-0.15</td>
<td id="tableHTML_column_3">0.94</td>
</tr>
<tr style="background-color:#DBDCDE ">
<td id="tableHTML_rownames">footway</td>
<td id="tableHTML_column_1">2146</td>
<td id="tableHTML_column_2">-0.01</td>
<td id="tableHTML_column_3">0.93</td>
</tr>
<tr style="background-color:#f2f2f2;">
<td id="tableHTML_rownames">motorway</td>
<td id="tableHTML_column_1">339</td>
<td id="tableHTML_column_2" style="background-color:lightsteelblue;">-0.31</td>
<td id="tableHTML_column_3">1.05</td>
</tr>
<tr style="background-color:#DBDCDE ">
<td id="tableHTML_rownames">motorway_link</td>
<td id="tableHTML_column_1">723</td>
<td id="tableHTML_column_2" style="background-color:lightsteelblue;">-0.42</td>
<td id="tableHTML_column_3">1.02</td>
</tr>
<tr style="background-color:#f2f2f2;">
<td id="tableHTML_rownames">path</td>
<td id="tableHTML_column_1">319</td>
<td id="tableHTML_column_2">0.04</td>
<td id="tableHTML_column_3">0.8</td>
</tr>
<tr style="background-color:#DBDCDE ">
<td id="tableHTML_rownames">pedestrian</td>
<td id="tableHTML_column_1">238</td>
<td id="tableHTML_column_2" style="background-color:lightcoral;">0.31</td>
<td id="tableHTML_column_3">1.05</td>
</tr>
<tr style="background-color:#f2f2f2;">
<td id="tableHTML_rownames">primary</td>
<td id="tableHTML_column_1">643</td>
<td id="tableHTML_column_2">-0.18</td>
<td id="tableHTML_column_3">0.79</td>
</tr>
<tr style="background-color:#DBDCDE ">
<td id="tableHTML_rownames">residential</td>
<td id="tableHTML_column_1">4813</td>
<td id="tableHTML_column_2" style="background-color:lightsteelblue;">-0.35</td>
<td id="tableHTML_column_3">0.92</td>
</tr>
<tr style="background-color:#f2f2f2;">
<td id="tableHTML_rownames">secondary</td>
<td id="tableHTML_column_1">4853</td>
<td id="tableHTML_column_2" style="background-color:lightsteelblue;">-0.31</td>
<td id="tableHTML_column_3">0.95</td>
</tr>
</tbody>
</table>
<table style="font-size: 14px; float: right; height:10px;" class="table_6144" border="2">
<thead>
<tr>
<th colspan="1" id="tableHTML_second_header_1" style="background-color:#C0C0C0;color:black;height:10px;">Road Type</th>
<th colspan="3" id="tableHTML_second_header_2" style="background-color:#C0C0C0;color:black;height:10px; text-align: center;">Statistic</th>
</tr>
<tr style="background-color:#DBDCDE ">
<th id="tableHTML_header_1"> </th>
<th id="tableHTML_header_2">Sampled Points (N)</th>
<th id="tableHTML_header_3">Skin Conductance (M)</th>
<th id="tableHTML_header_4">SD</th>
</tr>
</thead>
<tr style="background-color:#DBDCDE ">
<td id="tableHTML_rownames">service</td>
<td id="tableHTML_column_1">3668</td>
<td id="tableHTML_column_2">0.02</td>
<td id="tableHTML_column_3">1.03</td>
</tr>
<tr style="background-color:#f2f2f2;">
<td id="tableHTML_rownames">steps</td>
<td id="tableHTML_column_1">246</td>
<td id="tableHTML_column_2">-0.07</td>
<td id="tableHTML_column_3">0.93</td>
</tr>
<tr style="background-color:#DBDCDE ">
<td id="tableHTML_rownames">tertiary</td>
<td id="tableHTML_column_1">12524</td>
<td id="tableHTML_column_2">-0.07</td>
<td id="tableHTML_column_3">0.99</td>
</tr>
<tr style="background-color:#f2f2f2;">
<td id="tableHTML_rownames">track</td>
<td id="tableHTML_column_1">117</td>
<td id="tableHTML_column_2">-0.02</td>
<td id="tableHTML_column_3">0.88</td>
</tr>
<tr style="background-color:#DBDCDE ">
<td id="tableHTML_rownames">track_grade1</td>
<td id="tableHTML_column_1">93</td>
<td id="tableHTML_column_2">0.1</td>
<td id="tableHTML_column_3">0.65</td>
</tr>
<tr style="background-color:#f2f2f2;">
<td id="tableHTML_rownames">track_grade3</td>
<td id="tableHTML_column_1">30</td>
<td id="tableHTML_column_2">0.11</td>
<td id="tableHTML_column_3">0.63</td>
</tr>
<tr style="background-color:#DBDCDE ">
<td id="tableHTML_rownames">track_grade4</td>
<td id="tableHTML_column_1">28</td>
<td id="tableHTML_column_2" style="background-color:lightcoral;">0.23</td>
<td id="tableHTML_column_3">0.86</td>
</tr>
<tr style="background-color:#f2f2f2;">
<td id="tableHTML_rownames">trunk</td>
<td id="tableHTML_column_1">2656</td>
<td id="tableHTML_column_2">0.09</td>
<td id="tableHTML_column_3">0.78</td>
</tr>
<tr style="background-color:#DBDCDE ">
<td id="tableHTML_rownames">trunk_link</td>
<td id="tableHTML_column_1">531</td>
<td id="tableHTML_column_2">-0.02</td>
<td id="tableHTML_column_3">0.98</td>
</tr>
<tr style="background-color:#f2f2f2;">
<td id="tableHTML_rownames">unclassified</td>
<td id="tableHTML_column_1">12236</td>
<td id="tableHTML_column_2">-0.18</td>
<td id="tableHTML_column_3">0.92</td>
</tr>
</tbody>
</table>
<aside class="notes">
. . . . . NOTES HERE . . . . . .
</aside>
</section>
<!-- Results: Road Types Bar Charts -->
<section data-transition="slide">
<h6 style="color: #909090">
Results</h6>
<h3 style="color: #F0F0F0">
Descriptive Statistics: Road Types</h3>
<img class="stretch" src="img/conductance_by_road_types.png">
<!-- <center><p style="color: #F0F0F0"> By Individual Road Types </p> </center> -->
<aside class="notes">
. . . . . NOTES HERE . . . . . .
</aside>
</section>
<!-- Results: Road Type Tables -->
<section data-transition="slide">
<h6 style="color: #909090">
Results</h6>
<h3 style="color: #F0F0F0">
Descriptive Statistics: Road Groups</h3>
<table style="border-collapse:collapse;" class="table_2961" border="2">
<thead>
<tr>
<th colspan="1" id="tableHTML_second_header_1" style="background-color:#C0C0C0;color:black;height:50px;">Road Class</th>
<th colspan="3" id="tableHTML_second_header_2" style="background-color:#C0C0C0;color:black;height:50px;text-align: center;">Statistic</th>
</tr>
<tr style="background-color:#DBDCDE ">
<th id="tableHTML_header_1"> </th>
<th id="tableHTML_header_2">Sampled Points (N)</th>
<th id="tableHTML_header_3">Skin Conductance (M)</th>
<th id="tableHTML_header_4">SD</th>
</tr>
</thead>
<tbody>
<tr style="background-color:#f2f2f2;">
<td id="tableHTML_rownames">small_roads</td>
<td id="tableHTML_column_1">29916</td>
<td id="tableHTML_column_2">-0.17</td>
<td id="tableHTML_column_3">0.94</td>
</tr>
<tr style="background-color:#DBDCDE ">
<td id="tableHTML_rownames">med_roads</td>
<td id="tableHTML_column_1">21283</td>
<td id="tableHTML_column_2">-0.1</td>
<td id="tableHTML_column_3">0.99</td>
</tr>
<tr style="background-color:#f2f2f2;">
<td id="tableHTML_rownames">large_roads</td>
<td id="tableHTML_column_1">17128</td>
<td id="tableHTML_column_2">-0.15</td>
<td id="tableHTML_column_3">0.91</td>
</tr>
</tbody>
</table>
<aside class="notes">
. . . . . NOTES HERE . . . . . .
</aside>
</section>
<!-- Results: Road Types Bar Charts -->
<section data-transition="slide">
<h6 style="color: #909090">
Results</h6>
<h3 style="color: #F0F0F0">
Descriptive Statistics: Road Groups</h3>
<img class="stretch" src="img/conductance_by_road_groups.png">
<!-- <center><p style="color: #F0F0F0"> By Group </p> </center> -->
<aside class="notes">
. . . . . NOTES HERE . . . . . .
</aside>
</section>
<!-- Results: Road Types Bar Charts -->
<section data-transition="slide">
<h6 style="color: #909090">
Results</h6>
<h5 style="color: #F0F0F0">
Regressions: Ordinary Least Squares & Spatial Autoregressive Modeling</h5>
<table style="border-collapse:collapse; font-size: 24px; height:20px; width:60px;background-color:#f2f2f2;" class="table_3914" border="0">
<caption style="color: #F0F0F0;text-align: left">Comparing coefficient values and standard errors between regression models.</caption>
<caption id="footer" align="bottom" style="text-align:left; font-size: 20px; color: #F0F0F0;margin-top:3px;"><b>Note.</b> Values highlighted in yellow significant at p < 0.01 level. <br>
Any value shown as 0 was too small to be reported in table (surpassed 10th decimal place).<br>
SE = Standard Errors. OLS = Ordinary Least Squares.
</caption>
<thead>
<tr style="border-top:3px solid black;font-weight:bold;font-size: 24px ">
<th colspan="1" id="tableHTML_second_header_1" style="border-top:3px solid black;"></th>
<th colspan="2" id="tableHTML_second_header_2" style="border-top:3px solid black;border-bottom:3px solid black;text-align:center;">OLS Model</th>
<th colspan="2" id="tableHTML_second_header_3" style="border-top:3px solid black;border-bottom:3px solid black;text-align:center;">Spatial Lag Model</th>
<th colspan="2" id="tableHTML_second_header_4" style="border-top:3px solid black;border-bottom:3px solid black;text-align:center;text-decoration:">Spatial Error Model</th>
</tr>
<tr style="border-bottom:2px solid black;">
<th id="tableHTML_header_1"> </th>
<th id="tableHTML_header_2">Coefficients</th>
<th id="tableHTML_header_3">SE</th>
<th id="tableHTML_header_4">Coefficients</th>
<th id="tableHTML_header_5">SE</th>
<th id="tableHTML_header_6">Coefficients</th>
<th id="tableHTML_header_7">SE</th>
</tr>
</thead>
<tbody>
<tr>
<td id="tableHTML_rownames">Altitude</td>
<td id="tableHTML_column_1" style="text-align:center;background-color:#FAFFB8 ">0.031</td>
<td id="tableHTML_column_2" style="text-align:center;background-color:#FAFFB8 ">0.001</td>
<td id="tableHTML_column_3" style="text-align:center;background-color:#FAFFB8">0.01</td>
<td id="tableHTML_column_4" style="text-align:center;background-color:#FAFFB8">0.00</td>
<td id="tableHTML_column_5" style="text-align:center;background-color:#FAFFB8">0.028</td>
<td id="tableHTML_column_6" style="text-align:center;background-color:#FAFFB8">0.001</td>
</tr>
<tr>
<td id="tableHTML_rownames">Speed</td>
<td id="tableHTML_column_1" style="text-align:center;background-color:#FAFFB8">0.009</td>
<td id="tableHTML_column_2" style="text-align:center;background-color:#FAFFB8">0.01</td>
<td id="tableHTML_column_3" style="text-align:center;">0.001</td>
<td id="tableHTML_column_4" style="text-align:center;">0.002</td>
<td id="tableHTML_column_5" style="text-align:center;">0.001</td>
<td id="tableHTML_column_6" style="text-align:center;">0.014</td>
</tr>
<tr>
<td id="tableHTML_rownames">Urban Areas</td>
<td id="tableHTML_column_1" style="text-align:center;background-color:#FAFFB8">0.008</td>
<td id="tableHTML_column_2" style="text-align:center;background-color:#FAFFB8">0.002</td>
<td id="tableHTML_column_3" style="text-align:center;background-color:#FAFFB8">-0.169</td>
<td id="tableHTML_column_4" style="text-align:center;background-color:#FAFFB8">0.001</td>
<td id="tableHTML_column_5" style="text-align:center;background-color:#FAFFB8">-0.428</td>
<td id="tableHTML_column_6" style="text-align:center;background-color:#FAFFB8">0.002</td>
</tr>
<tr>
<td id="tableHTML_rownames">Green Areas</td>
<td id="tableHTML_column_1" style="text-align:center;">0.081</td>
<td id="tableHTML_column_2" style="text-align:center;">0.019</td>
<td id="tableHTML_column_3" style="text-align:center;background-color:#FAFFB8">0.008</td>
<td id="tableHTML_column_4" style="text-align:center;background-color:#FAFFB8">0.016</td>
<td id="tableHTML_column_5" style="text-align:center;">0.004</td>
<td id="tableHTML_column_6" style="text-align:center;">0.024</td>
</tr>
<tr style="border-bottom:3px solid black;">
<td id="tableHTML_rownames">Water</td>
<td id="tableHTML_column_1" style="text-align:center;background-color:#FAFFB8">-0.48</td>
<td id="tableHTML_column_2" style="text-align:center;background-color:#FAFFB8">0.012</td>
<td id="tableHTML_column_3" style="text-align:center;background-color:#FAFFB8">0.032</td>
<td id="tableHTML_column_4" style="text-align:center;background-color:#FAFFB8">0.01</td>
<td id="tableHTML_column_5" style="text-align:center;">0.023</td>
<td id="tableHTML_column_6" style="text-align:center;">0.025</td>
</tr>
</tbody>
</table>
<aside class="notes">
“Spatial is special”;
Spatial data exhibits spatial dependence (also known as spatial autocorrelation) and spatial heterogeneity (spatial non-stationarity) that make it difficult to meet the assumptions and requirements of traditional OLS regression and can bias OLS results.Spatial dependence is the extent to which the value of an attribute in one location is more likely to be similar to the value of the attribute in a nearby location than in a distant location.
Spatial dependence is a function of Tobler’s (1970) First Law of Geography, which stated that “everything is related to everything else, but near things are more related than distant things” (p. 236). Spatial dependence “is determined both by similarities in position,
and by similarities in attributes” large residuals are likely to occur if geographic features are spatially autocorrelated when using nonspatial statistical methods such as OLS regression.
And this is exactly why you see here the OLS demonstrating large effect sizes.
</aside>
</section>
<!-- Space-Time Cube -->
<!-- <section data-transition="slide">
<h6 style="color: #909090">
Methodology</h6>
<h3 style="color: #F0F0F0">
Mapping Emotion over Space /& Time</h3>
<video autoplay muted loop id="space_time" height = "570px">
<source src="video/space_time.mp4" type="video/mp4">
</video>
<aside class="notes">
. . . . . . . . BS TEXT HERE . . . . . . . . . . .
</aside>
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Why do all of this?</h3> -->
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<li style="font-size: 30px"> Why am I even here?</li>
<li style="font-size: 30px"> What else can be done?</li>
<li style="font-size: 30px"> Why is it important?</li>
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<img style="right: -25em; padding-right: 0em; width: 500px" src="img/homer_thinking.png">
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Why do all of this?</h3> -->
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<h5 style="color: #F0F0F0; font-size: 22px;">
Interactive Application Development</h5>
<iframe style="width:1600px; height: 650px;padding: 0em " src="https://gcmillar.github.io/stress3d/"></iframe>
<aside class="notes">
the main motivation for this application is the hopes that urban and city planners can use it to gain valuable insight into which spatial configurations contribute to both favorable (and unfavorable) physiological states of those who use that space. From here, we hope it can help urban planners develop high-quality urban spaces through a more evidence-based design, in comparison to traditionally subjective methods.
On the left, we can toggle back and forth between these different cyclists, which is an important thing to note due to the variability in skin conductance that can be exhibited across multiple people.
So, starting with Cyclist # 1, and looking at the colored markers displayed on the map (blue = low arousal, red = high arousal), we can see that they were experiencing some high levels of arousal in this area.
We can also coordinate what we're seeing on the map, with the skin conductance chart in the top-right corner.
[HOVER MOUSE OVER CHART TO DISPLAY NUMERICAL DATA]
This chart will graphically display all data that is currently loaded into the map frame. As I zoom in and out, and pan around in the map, it will automatically update (as you can see here).
Now lets throw a couple more cyclists into the mix to see if others are experiencing similar states of arousal, or if Cyclist 1 is just an overall anxious person.
[CLICK CYCLIST 3]
So here, it appears these two are experiencing similar levels of high physiological arousal. Lets zoom out a bit and add another cyclist to get a better idea of the bigger picture.
[ZOOM OUT]
[CLICK CYCLIST 10]
So this area [MOVE OVER WITH MOUSE], seems to be causing some heightened states of physiological arousal. Seeing this, we of course would like to explore what is present in the environment that might be causing such reactions. To do so, I can first click one one of the markers ... [CLICK POINT RIGHT BEFORE WATER] ...
to view additional information such as the speed the cyclist was going at that specific point, as well as the current elevation (NL is extremely flat), the total distance travelled to that point, specific skin conductance levels, and which cyclist it is.
But, as you may have noticed, a street view image is also concurrently and automatically displayed in the bottom right corner. With this, it is easier to understand what sort of environment the cyclist is interacting with and experiencing while exhibiting high (or low) levels of stress.
[ZOOM TO SELECTED POINT] --> [ROTATE MAP VIEW, TILT, etc]
There seemed to be another stretch of cycleway provoking some high levels of arousal...