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<title>G.C. Millar Preliminary Oral Examination Presentation </title>
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<br><br> <h3 style="line-height: 1%;font-size:33px"> Maps & Minds: Geospatial Analytics for Studying Human Cognition</h3>
<h6 style="line-height:50%;color:grey;font-size:4px"></h6>
<!-- <br> -->
<h6 style="line-height: 120%;color:grey;font-size:28px">Garrett C. Millar</h6>
<br>
<img style="line-height: 6%;" width="19%" src="img/ncstate_seal.png">
<br> <br>
<h3 style = "line-height: 56%;font-size: 26px">Preliminary Oral Examination</h3>
<h6 style = "line-height: 56%;color:grey;font-size:24px">November 4th, 2020 </h6>
<br>
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<td><img src="img/helena_mitasova.jpg" style="width: 100%"></td>
<td style = "border-bottom: 0px; vertical-align: middle">Helena Mitasova <br>
Committee Chair<br>
<highlight style="background-color: #b4b4b4 ; font-size:.7em">Geospatial Analytics</highlight>
<td><img src="img/ross_meentemeyer.jpg" style="width: 100%"></td>
<td style = "border-bottom: 0px; vertical-align: middle">Ross Meentemeyer<br>
Committee Member<br>
<highlight style="background-color: #b4b4b4 ; font-size:.7em">Geospatial Analytics</highlight>
<td><img src="img/laura_tateosian.jpg" style="width: 100%"></td>
<td td style = "border-bottom: 0px; vertical-align: middle">Laura Tateosian <br>
Committee Member <br>
<highlight style="background-color: #b4b4b4; font-size:.7em">Geospatial Analytics</highlight></font>
<td><img src="img/aaron_hipp.jpg" style="width: 100%"></td>
<td td style = "border-bottom: 0px; vertical-align: middle">Aaron Hipp <br>
Committee Member <br>
<highlight style="background-color: #b4b4b4; font-size:.7em">Geospatial Analytics</highlight></font>
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<aside class="notes">
<ul>
<li > Hi everyone, thank you very much for being here, I really couldnt be more excited about presenting to you all today. With that said, lets go ahead and jump right in
<li> This presentation (as Helena mentioned) serves as a dissertation proposal for a doctorate in the field of Geospatial Analytics
<li> The proposed title: Maps & Minds: Geospatial Analytics for Studying Human Cognition
<li> by me: :D
<li > To start off, I thought it best to bring it all back a bit, and give a brief background on my overall research career.
<li > As you all know, I started my research career in cognitive science (or psychology).
<li > I specifically started out in Human Factors & Applied Cognition (or Human Computer Interaction)
<li > The lab I was involved with researched how computers can help people learn better.
<li > I stuck with this research for 2 years as an undergraduate, and then continued it for 2 years as a graduate student.
<br> <b> NEXT STEP ⇒ </b>
</ul>
<!--
<li > Hi everyone, thank you very much for being here today, it means a lot. I really couldnt be more excited about presenting to you all today. With that said, lets go ahead and jump right in eh??
<li > This presentation serves as a disseration proposal for a Doctorate degree in the field of Geospatial Analytics.
<li > And that dissertation's title?
<ul>
<li ><i>MAPS & MINDS: GEOSPATIAL ANALYTICS FOR STUDYING HUMAN COGNITION" </i><br> <br>
</ul>
<li > To begin my presentation, and introduce you all to my dissertation work as a whole, I thought it best to bring it all back a bit, and give a brief background on my overall research career.
<li > As you all know, I started my research career in psychology, or “cognitive science”.
<li > I specifically started out in a Human Factors (HCI) research lab (in the psychology department) my junior year of undergrad [the SMART lab].
<li > This lab was devoted to the study of learning, wanting to see how computers can help people learn better.
</ul> -->
</aside>
</section>
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<h3 >Background & Motivation</h3>
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<aside class="notes">
<ul>
<li > In those 4 years, I became very well-versed in 3 Cog Sci topics in particular :
<ol>
<li > emotion, <b> NEXT STEP ⇒ </b>
<li>learning, <b> NEXT STEP ⇒ </b>
<li> AND human–computer interaction. <b> NEXT STEP ⇒ </b>
</ol>
<!-- <li > I also picked up extensive technical knowhow for collecting, processing, and analyzing human biometric data; as well as people’s self-report data, and learning outcome measures(← what about administering???) -->
<li > I ended up finding the field of Geospatial Analytics and from which I ended up becoming a Geospatial Scientist,
<li > As a Geospatial Scientist, I saw extremely exciting opportunities to continue researching these Cognitive Science topics of emotion, learning and Human Computer Interaction,, but approach them <b>from a Geospatial Science Perspective instead</b>
<li > The resulting research is what has formed my dissertation I am proposing today
<li > In this dissertation, I
<ol>
<li > <b> NEXT STEP ⇒ </b>combine my experience with emotion research with geo-statistics and -modeling techniques to explore interactions between the environment and human emotions / behavior ( this formed my CH 1);
<li > <b> NEXT STEP ⇒ </b>I then leverage my background in learning research with theories of embodied cognition to facilitate more natural ways to learn about landscapes (which makes up CH 2);
<li > <b> NEXT STEP ⇒ </b>and for my last Chapter, Chapter 3, I propose using Human Computer Interaction principles to evaluate and improve geospatial technologies for better decision-making (CH 3)
</ol>
<!-- <li > These are what have formed the three chapters of my dissertation, and is what we will be talking about today d:-D -->
<li > we'll be discussing each of these in more detail today, so LETS GO AHEAD GET STARTED
</ul>
</aside>
</section>
<section style="font-size:32px;" data-background="white">
<h3 style="font-size:36px"> Maps & Minds: Geospatial Analytics for Studying Human Cognition</h3>
<br>
<ul class="fragment fade-in" style = "margin-left:12%;margin-right:12%">
<p ><strong>Completed Work</strong>
<hr>
<p2 class="fragment fade-in" style="font-size:26px">
<highlight style = "background-color:lightgray ;font-size:26px;" >Chapter 1</highlight> Space-time Analytics of Human Physiology for Urban Planning
</p2> <br> <br>
<p2 class="fragment fade-in" style="font-size:26px">
<highlight style = "background-color:lightgray;font-size:26px;">Chapter 2</highlight> Tangible Landscape: A Hands-on Method for Teaching Terrain Analysis
<br> <br><br>
</p2>
<p class="fragment fade-in"><strong>Ongoing & Proposed Work </strong>
<hr>
<p2 class="fragment fade-in"style="font-size:26px">
<highlight style = "background-color:lightgray;font-size:26px;">Chapter 3</highlight> Usability Evaluation of Web-Based SDSS for Collaborative Management of Biological Invasions</p2>
<br><br>
</p2>
</ul>
<aside class="notes">
<!-- Outline and bullshit. -->
<ul>
<li>just real quick as a formal outline of my dissertation:
<li> For my completed work: <b> NEXT STEP ⇒ </b>
<ul>
<li>there is chapter 1, titled: <b> NEXT STEP ⇒ </b>,
<ul>
<li> <i>Space-time Analytics of Human Physiology for Urban Planning</i>
</ul>
<li>and there's chapter 2 <b> NEXT STEP ⇒ </b>:
<ul>
<li> <i> Tangible Landscape: A Hands-on Method for Teaching Terrain Analysis</i>
</ul>
</ul>
<li>and for my ongoing and proposed work, :<b> NEXT STEP ⇒ </b>
<ul>
<li>we have chapter 3: <b> NEXT STEP ⇒ </b>
<ul>
<li> <i>Usability Evaluation of Web-Based SDSS for Collaborative Management of Biological Invasions</i>
</ul> </ul>
</ul>
<br>
<li>so let's jump into chapter one<br>
<b> NEXT STEP ⇒ </b>
</aside>
</section>
<section data-background="img/getting-around-loop-1MB_orig.gif" data-background-size="108%" >
<h4 style="color: white; text-shadow: 4px 4px 0px #000;" class="shadow" > Space-time Analytics of Human Physiology for Urban Planning
</h4>
<aside class="notes">
<ul>
<li> In our daily commutes between home, work and school, the streets and bike paths we take are also an emotional map of our commute.
<li> Where do we consistently experience frustration with traffic?
<li> Or anxiety from a tricky intersection?
<li> These were the questions i was interested in exploring for the research in Chapter 1.
<li> Well, wearable technologies can actually give us the opprtunity to answer these types of questions, and in result, help us understand how people experience their environment.
<!-- <li> This understanding can inform urban design decisions -->
</ul>
</aside>
</section>
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<div style="font-size:26px;">
<h3 style="font-size:36px;"> <highlight style = "background-color:lightgray;font-size:36px;">Chapter 1</highlight> Space-time Analytics of Human Physiology for Urban Planning </h3>
<hr>
<h6 style="color: #888;font-size:32px;">
Background</h6>
<ul style="font-size:30px;">
<li class="fragment fade-in"><b>Research Objective</b>: Use mobile sensing to improve the understanding of how people experience their environment. <br>
<li class="fragment fade-in">How?
<ul style="font-size:27px;">
<li style="font-size:27px;" > Combine location and wearable emotion data to inform urban design decisions.
<li style="font-size:27px;" class="fragment fade-in"> Use visual stimuli derived from viewsheds to provide more accurate environment interaction metrics.
<li style="font-size:27px;" class="fragment fade-in"> Develop web-based mapping system to explore complex human physiology data across urban landscapes.
</ul>
<br>
</ul>
<hr>
<p style="color:grey;font-size:22px;text-align: left;"class="hangingindent fragment fade-in ">
<strong >Millar, G. C.</strong>, Mitas, O., Boode, W., Hoeke, L., de Kruijf, J., Petrasova, A., & Mitasova, H. (2020).
<strong>
Space-time Analytics of Human Physiology for Urban Planning.
</strong> <i> Computers, Environment and Urban Systems, 85, 101554.</i> <a target="_blank" href= "https://doi.org/https://doi.org/10.1016/j.compenvurbsys.2020.101554"> https://doi.org/10.1016/j.compenvurbsys.2020.101554
</a>
</p>
<aside class="notes">
<ul>
<li > So, this research's main objective was to: <b> NEXT STEP ⇒ </b> <br><b> <i>Use mobile sensing to improve our understanding of how people experience their environment.</b></i>
<!-- <li > Currently, an important urban design issue is the adaptation of infrastructure to increasing cycle and e-bike use. -->
<li > We did this by <b> NEXT STEP ⇒ </b> using location and wearable emotion data collected at a high spatiotemporal resolution to calculate viewsheds of what cyclists saw along a cycle highway.
<li> <b> NEXT STEP ⇒ </b> These viewsheds were then used to link cycling experiences to visual stimuli that were actually present at specific locations along particpants ride.
<li> <b> NEXT STEP ⇒ </b>A dynamic web-based mapping system was also developed to help us and researchers alike better explore complex moment-by-moment human physiology data across urban landscapes.
<br><br>
<li > <b> NEXT STEP ⇒ </b><b><i>mention newly published CEUS article!!!</i> </b> - <br>
i also just wanted to quickly mention this research was recently published in the elsevier journal of computers environment and urban systems<br>
</ul>
</aside>
</section>
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<h6 style="color: #888">
Data & Methods</h6>
<h3> CHIPS: Cycle Highway Project </h3>
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<li class="fragment fade-in"><b>Study purpose: </b>Adapt existing cycling infrastructure for increasingly <br>heavy <i>e-bike </i>use.
<li class="fragment fade-in"><b>Participants</b>: 12
<li class="fragment fade-in"><b>Cycle track</b>: 18 km cycle highway
<li class="fragment fade-in"><b>Type of bike</b>: e-bike
<br><br>
</ul>
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<aside class="notes">
<ul>
<li> My research on this topic stems from a much larger project, the CHIPS project which stands for "Cycle Highways Innovation for smarter People Transport and Spatial Planning" spanning Belgium, Germany, the Netherlands and the UK.
<!-- <li> And it aims to develop and promote cycle highways as an effective and cost efficient low carbon solution for commuting towards and from work (in urban areas specifically). -->
<li> this project involves <b> NEXT STEP ⇒ </b> adapting cycling infrastructure to better account for increasingly heavy e-bike use.
<li> think how much faster an e-cyclist is in comparison to your standard manual bike.
<li> the signs (directions) are too small,
<li>AND cycle paths cant properly account for the presence of both regular and e-bike use (e-bikes passing by fast)
<li> To begin exploring how cycle highways can be adapted for increasing e-bike use, researchers at Breda University of Applied Sciences (in NL_ ran a small human-subject study
<li> in this study : they collected data on <b> NEXT STEP ⇒ </b> 12 participants,<b> NEXT STEP ⇒ </b> who were asked to cycle down an 18km stretch of cycling highway between Tilburg and Waalwijk in NL <b> NEXT STEP ⇒ </b> and these participants were using an e-bike.
</aside>
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<h6 style="color: #888">
Data & Methods</h6>
<!-- <h3> Mobile Data Collection </h3> -->
<h3> Human Movement & Physiology </h3>
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<ul>
<li> to improve the understanding of how people experience their environment, we need to collect and use human data and environmental data? makes theoretical sense right?
<li> well, to use needed human data, this study collected GPS information such as location and speed.
<li> Then, the Empatica E4 was used, which is device similar to a smartwatch (in its mobile sensing abilities) to measure changes in a small electric current passing through the sweat on particpants skin.
<li>This is known as "skin conductance"
<li> These measurements of “skin conductance” are commonly used in psychological research to detect spikes in sweat production and, by proxy, stress / excitement.
</ul>
</aside>
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Data & Methods</h6>
<h3> Environmental Location </h3>
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<aside class="notes">
<!-- using geospatial data to derive -->
<ul>
<li>Because we wantwed to determine effects of environmental stimuli / features on these cyclists emotional experiences, we then used environmental location data and geospatial methods to supplement the human data so that we could then derive environmental interaction metrics to go into our statistical models <b>as predictors</b>
<li> Our main geosptaial data sets were:
<ul>
<li> land classification data categorized into seven land use groups:
<ul>
<li>1) natural, 2) developed, 4) water, 3) recreation, 5) business, 6) agriculture, and 7) forest.
</ul>
<li>we also used 0.5 m resolution digital surface model (DSM) interpolated from first-return lidar points to provide input for computing viewsheds along the cyclists' path
</ul>
</ul>
</aside>
</section>
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Data & Methods</h6>
<h3> Environmental Location </h3>
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<aside class="notes">
<ul>
<li> High resolution digital surface model (DSM) was interpolated from first-return lidar points at 0.5 m resolution to provide input for computation of viewsheds along the cyclists path while capturing the impact of buildings and other structures
</ul>
</aside>
</section> -->
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Data & Methods </h6>
<h3>Dynamic Visualization</h3>
<hr>
<img style = "width:76%" src="img/stress3d.gif">
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<p3>
<highlight style="background-color: lightgray ; font-size:.7em;text-align: left;">Geospatial Application</highlight> <a target="_blank" href = "https://gcmillar.github.io/stress3d/">E-Motion - </a>
Uses cyclists' physiological data to see how emotions can be affected by our environment. </p3>
<aside class="notes">
<ul>
<li>Our first attempt at combining human and environmental data resulted in this dynamic viz tool being shown right now
<li>This application allows you to dive into the detail of spatiotemporal physiological data
<br>
<li> So, just to quickly demonstrate . . .
<ul>
<li> On the left, we can toggle back and forth between different participants's collected data,
<li> We can coordinate the data seen on the map, with the skin conductance chart in the top-right corner.
<li> by clicking on one of the points, we'll receive additional GPS location, but also recieve an automatically displayed street view image (1st person)
<ul>
<li> With this, it is easier to understand the specific environmental features someone likely interacted with at that particilar location
</li>
</ul>
</ul>
<li>Overall, by allowing us to explore the location and emotion data on a moment-by-moment basis, this tool helped inform where we should direct further investigative efforts <!-- and how statistical analyses should be appraoced (such as our methodolgical / statistical approach)> -->
<li>For instance, it helped us realize that the cyclists line of sight needed to be considered in our statistical analyses
</ul></ul>
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Analysis </h6>
<h3 style="font-size:36px;"> Environment Interaction Metrics </h3>
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<li class="fragment fade-in"><b><b><b>Speed
<li class="fragment fade-in">Distance to Roads
<li class="fragment fade-in">Direction
<li class="fragment fade-in">Direction Change (Turning)
<li class="fragment fade-in">Viewable Land Cover Area:</b></b></b> <br>
<ul style="line-height: 40px;">
<li style="font-size:26px;">Developed
<li style="font-size:26px;">Natural
<li style="font-size:26px;">Recreation
<li style="font-size:26px;">Water
<li style="font-size:26px;">Business
<li style="font-size:26px;">Agriculture
<li style="font-size:26px;">Forest
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<aside class="notes">
<ul>
<!-- <li ><b>Statistics</b>:<br> -->
<!-- <ul> -->
<li >Because our main research question was to determine effects of environmental stimuli on experience, as I mentioned we used geospatial data and methods to combine and relate environmental data with the collected human data
<li>this allowed us to derive the parameters used <b>as predictors</b> in our statistical models
<li>These predictors were:
<ul>
<li ><b> NEXT STEP ⇒ </b> speed
<li ><b> NEXT STEP ⇒ </b> distance to road <!-- <ul> <li> We used v.distance, a GRASS GIS module to map the distances between each cyclist’s locational point and the closest roads. These were stored for later statistical analyses to determine possible relationships between how far away cyclists were from roads and observed levels of emotional arousal </ul> -->
<li > <b> NEXT STEP ⇒ </b> direction
<li > <b> NEXT STEP ⇒ </b> Change in direction (or turning) <!-- to our statistical model which significantly improved the model’s fit. -->
<li >AND <b> NEXT STEP ⇒ </b> viewable area in different land cover types
</ul>
</ul>
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Analysis </h6>
<h3> Viewsheds </h3>
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<aside class="notes">
<ul>
<li> Presents overview of Procedure for computing maps of viewable land cover areas
<li> The generated viewshed maps were then intersected with our land cover data layer to extract visible land cover classes.
<li> These viewable land cover maps were then used later on in our statistical analyses to empirically associate
variations in cyclists' perceptual and related mental states as they experience their environment.
<!-- observed relationships between cyclists' perceptual / related mental states with their environmental experiences . -->
</ul>
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<h3>Results</h3>
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<td style="border-bottom: 1px solid black; border-top: 1px solid black;text-align: left;;font-size:21pt"> <b>Descriptive Statistics</b></td>
<td style="border-bottom: 1px solid black; border-top: 1px solid black;font-size:21pt"><b>Multi-level Statistical Modeling</b></td>
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<br><br><br>
<li class="fragment fade-in"><a style= "color: black;font-size:28pt;">☝︎</a> — <u>More emotional</u> in large, viewable <u>natural</u> areas.
<br><br><br>
<li class="fragment fade-in"><a style= "color: black;font-size:28pt;">☟</a> — <u>Less emotional</u> in large, viewable <u>developed</u> areas.
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<aside class="notes">
<ul>
<li>DESCRIPTIVE STATISTICS :
<ul>
<li> For our results <b> NEXT STEP ⇒ </b> we first wanted to provide summary statistics:
<ul>
<li>To do so, we used a <b> NEXT STEP ⇒ </b> hexagonal binning process to summarize and graphically represent summary statistics of the measured data
<li> in the graphic shown now, figure A is showing: Phasic means; and figure B shows: Phasic Standard deviations
<li> These spatially distributed summary metrics show prevailing lower values of phasic skin conductance at the start / end of route in more urban areas, while higher phasic skin conductance values are seen midway along the route in more rural settings
</ul>
<br>
</ul>
<li>MIXED-EFFECT LINER MODELS:
<ul>
<li> Then, to empirically examine and determine environmental influences on cyclists' emotional experience, we used a MIXED-EFFECT LINEAR MODEL
<!-- these specto empirically examine observed connections between viewable land cover, speed, direction, distance to roads, direction changes, and human emotion data (skin conductance). -->
<!-- <li><i>This allowed us </i> -->
<li > Our multi-level model demonstrated two main findings:
<ul>
<li> First, <b> NEXT STEP ⇒ </b> participants became more emotional in large viewable areas managed for natural resource uses (these were less-developed areas, containing more, Recreational, Agricultural, & Forest Land Cover in View. )
<li > Oppositely, particpants <b> NEXT STEP ⇒ </b> became relatively less emotional in large viewable human-built areas (extents of developed and business areas in view).
<li > we also found that the extent participants were turning was negatively related to their emotional excitement
<li> In other words, the more participants were turning, the less excited they were.
</ul>
</ul>
<br>
<li>Lastly, I just wanted to quickly note the most IMPORTANT ENVIRO INTERACTION METRICS / PREDICTORS used in our model:
<ul>
<li> Developed in View −6.229⁎⁎⁎ <br>
<!-- Natural in View −1.170 <br> -->
Recreation in View 3.921⁎⁎⁎ <br>
<!-- Water in View −0.004 <br> -->
<!-- Business in View 1.182 <br> -->
Agriculture in View 2.531⁎<br>
Forest in View 20.361⁎⁎⁎ <br>
Speed −47.916⁎⁎⁎ <br>
<!-- Direction −1.210 <br> -->
Distance to Road 21.767⁎⁎⁎ <br>
Direction Change −28.494⁎⁎⁎ <br>
</li>
</ul>
</ul>
<br>
</ul>
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<h3>Importance & Contributions</h3>
<hr>
<ul style="font-size:30px;line-height: 28pt;">
<li class="fragment fade-in"> Presents novel approach for linking biosensing with location data to examine how the visible environment affects peoples' experiences. <br> <!-- to reflect what people actually see, how fast they go and things like that as they experience their environment.-->
<li class="fragment fade-in"> Presents a first-of-its-kind dynamic visualization tool.<br>
<li class="fragment fade-in"> Reproducibility and applicability of methods and visualization tool. <br>
<!-- <li class="fragment fade-in"> -->
</ul>
<br>
</ul>
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<!-- <h4 class="shadow"> Importance