Mapping the Emotional Dimension:
Measuring Human Behavior Across Space & Time
Garrett C. Millar
Hi everyone, thank you for coming today, and more importantly thank you for your contributions to my last 3 weeks here. My name is Garrett Millar, I'm a PhD student in Geospatial Analytics at North Carolina State University. Geospatial Analytics, also called Spatial Analytics, is the science of WHERE things HAPPEN.
My Netherlands Experience
Stroopwafel & analysis experience rambles . . .
My Netherlands Experience
2 OF THE BAGS WERE EATEN THAT SAME NIGHT.
Wanted to give overview of presentation . . .
2 projects to be discussed, and if time, talk about the next steps.
We first have CHIPS, a project that I've been working on to study and map the emotional arousal of cyclists while biking down a cycling highway between Tilburg & Waalwijk.
Then, there is Nuenen, a project measuring the emotional arousal and locations of its visitors, for the both indoor, and outdoor exhibits.
And last hopefully we have some time to talk about what all this work will look like further down the road.
Emotions
Why do emotions matter?
Measuring emotion
Mapping emotional experiences
Lets start from the top: why emotions? And why are we trying to map them?
Look at these videos. How do they make you feel? Look at the differences in the places. How do the differences in the places make you feel?
Look at these videos. How do they make you feel? Look at the differences in the places. How do the differences in the places make you feel?
Look at these videos. How do they make you feel? Look at the differences in the places. How do the differences in the places make you feel?
Methodology
Study Area — Netherlands
These are all scenes from the Tilburg Waalwijk cycling highway.
Methodology
Framework for Cyclists' Emotional Experiences
Then, lets look at what kind of places they went through, where were they?
Methodology
Buffers as an Environment Interaction Metric
But before we can analyze their experiences statistically, we need to consider how they experience the environment, specifically, how they felt going through those places.
Methodology
Mapping Emotion
And an inital exploration of that statistical process is shown here. We can put on top of the map we just discussed, each participants' range of emotional arousal.
Methodology
Mapping Emotion cont'd
Overall, showing how emotional each person was at each individual point on the map. You cant see very much from that, so whats a way to find out more?
Results
Descriptive Statistics: Road Types
Well, we can statistically average their emotional experiences different kinds of land.
Results
Descriptive Statistics: Land Use by Type
As well as each type of land . . . BUT... problem with # of sampled points (Christian)
Results
Descriptive Statistics: Land Use by Group
So, its better to group these into larger land use categories. For instance: green, urban, and water.
Results
Regressions: Ordinary Least Squares & Spatial Autoregressive Modeling
Comparing coefficient values and standard errors between regression models.
Altitude
0.031
0.001
0.01
0.00
0.028
0.001
Speed
0.009
0.01
0.001
0.002
0.001
0.014
Urban Areas
0.008
0.002
-0.169
0.001
-0.428
0.002
Green Areas
0.081
0.019
0.008
0.016
0.004
0.024
Water
-0.48
0.012
0.032
0.01
0.023
0.025
So here is a bunch of numbers representing the statistical analysis. And what these numbers specifically represent are the statistical relationships between different kinds of land use, namely urban, green, and water. And how emotional people were in those types of land.
If you haven't already begun thinking this yourself, I wanted to use a visualization to demonstrate just how complex this data we are working with is.
Interactive Application Development
Due to these issues,
This brings us to our next project in Nuenen at the Vincent Van Gogh Musuem.
Web Mapping
Here is the outdoor musuem data.
Outdoors: Methodology
Spatial Gridding & Averaging
The grand average I just showed can be done in a more spatial way, where a spatial grid is first overalaid on the map (you can see blended pixels or squares now which represent this grid).
Outdoors: Methodology
Spatial Gridding & Averaging
Road Map
SpatialLY averaged emotional arousal on a road map
Outdoors: Methodology
Spatial Gridding & Averaging
Road Map
Satellite
SpatialLY averaged emotional arousal with a satellite view
Outdoors: Methodology
Spatial Gridding & Averaging
Road Map
Satellite
Topographical
SpatialLY averaged emotional arousal on a topographical map
Spatial Gridding & Averaging
and the interactive version
Indoors: Methodology
Beacon Location Processing
Generate Beacon Coordinates
Now, lets talk about the indoor portion of the Van Gogh musuem.
Indoors: Methodology
Beacon Location Processing
Generate Beacon Coordinates
Generate Buffers
I then performed a similar spatial technique we discussed for the cycling data, generating buffers around each beacon.
Indoors: Methodology
Beacon Location Processing
Generate Beacon Coordinates
Generate Buffers
Crop Buffers by Floor Plans
The overall area of these buffers were then corrected (or cut) by accounting for the musuem's floor plans.
Indoors: Methodology
Indoor Beacon Heatmap: Ground Floor
Finally, we can take these created buffers, associate them with the visitors' collected emotional data, and colorize them by the intensity of their emotional experiences throughout the musuem.
Indoors: Methodology
Indoor Beacon Heatmap: First Floor
(SAME FOR FIRST FLOOR)
These are the closets (blue circles), and these are the hats (red circles).
Looking at this, for this data, we can conclusively say that closets are not really making people emotional, but that hats are.
Indoors: Methodology
What Exhibits are Visitors Responding to Emotionally?
You can see those two exhibits here.
Interactive Application Development
Wrapping Up
What have we learned?
Mapping emotions is useful
Spatially analyzing and visualizing emotional data is a tough nut to crack
Whats next? Where do we go from here?
So, to go over what we've talked about and hopefully learned today . . .
Next Steps?
More robust statistcal analysesWhat would you all like to see?
Cycling: Methodology
GPS Data
. . . . . NOTES HERE . . . . . .
Cycling: Methodology
Skin Conductance
. . . . . NOTES HERE . . . . . .
Cycling: Results
Descriptive Statistics: Road Groups
Nuenen Outdoors: Methodology
GPS Data
. . . . . NOTES HERE . . . . . .
Nuenen Outdoors: Methodology
Skin Conductance
. . . . . NOTES HERE . . . . . .
Mapping the Emotional Dimension:
Measuring Human Behavior Across Space & Time to Inform Tourism & Leisure Management
Garrett C. Millar
Hi everyone, thank you for coming today, and more importantly thank you for your contributions to my last 3 weeks here. My name is Garrett Millar, I'm a PhD student in Geospatial Analytics at North Carolina State University. Geospatial Analytics, also called Spatial Analytics, is the science of WHERE things HAPPEN.
LETS GET STARTED!!
First, I wanted to share my research background and previous research work. I got my Bachelors in Psychology, and then went straight into a PhD in Human-Computer Interaction. During this time, I researched how people use technology, how they experience it, and how they learn from it.
When I transitioned to the Geospatial Analytics program, I took with me this same sort of research ideal, but instead of how people interact with and experience technology, I wanted to find out how people interact with and experience space. With what we'll be discussing today, that space is experienced in the context of tourism and leisure, which I study to help manage those experiences.
Specifically, when people experience tourism and leisure, they feel something, and they feel something in a particular place. And to manage these experiences, you have to know not only that they felt something, but where in their experience they felt it. And SO, I'll be showing you today, where tourism and leisure participants FELT something.
