Hi, today we're going to be learning how to analyze our StreetSeen surveys.
So it's been really great to see so many people developing so many different,
interesting surveys. Asking about all kinds of interesting
things from where do you think it's safer for our children to play to the busyness
of a street. Which street would you like to walk down?
which park would you prefer to play in? Lots of different questions about our
cities. So, I'm going to walk you through step by
step how to analyze StreetSeen. And I'll give you one option.
Which is the straight forward simple way to analyze StreetSeen and draw some basic
conclusions. And then I'm going to explain how to,
conduct a more detailed discrete choice model way of analyzing this.
And this requires a statistical background.
So if you know how to use statistics, then I'm going to be showing you some
ways that you can dig deeper and create statistically significant results.
Many of you participated in this simple survey.
Bicycling preferences in Columbus Ohio, so we wanted to know which street would
you prefer to ride a bicycle down 1 or 2? And so one of the key things in designing
a StreetSeen survey is that you want to consider what is it that your asking
people about. So in this case which street would you
prefer to ride a bicycle on and then, we want to understand what are the
differences between these two images. And amongst the images that you're
particularly interested in. So a discrete choice model is a method of
describing, explaining, and predicting choices between two or more discrete
alternatives. So for example, in the picture that you
saw before we had two discrete choices that you can choose between, the picture
on the right or the picture on the left. And what we want to be able to do is just
statistically relate the choices made by each person to the attributes of the
alternatives available to the person. So in each case, you were given two
choices and you had to make a choice between each one of those.
And so we want to know which people chose which images and what are attributes of
those alternatives. So what's the difference between image
one and image two? And when we're doing a discrete choice
model we want the set of alternatives to be exhaustive.
And we want it to be mutually exclusive and we want it to be a finite number of
alternatives. So, for example, you could only choose
one of the two images. And that the alternatives, there were
finite set of alternatives. So, for example, you selected a number, a
fixed number of images. In my case, there were 60 different
images in that image set. And so, you were voting between two
images, and there were only a certain number of images that were in the set.
Although, you could vote infinitely if you wanted to.
You were limited to those 60 images that were randomly shown in pairs.
Now in this case, we want a defined set of variables.
So when you look at the images that you've selected, you've identified key
characteristics, okay? So in my example I selected ten variables
that were different between the various images.
So the amount of traffic. So there might be zero cars shown in a
particular image, up to ten or more cars. And so I just classify in different
segment the number of vehicles that were seen on the street.
Whether or not there was a sidewalk, whether there was a sidewalk on one side
or whether there was a sidewalk on both sides.
I looked at the type of parking. Was there no parking on the street?
Parallel parking, pull-in parking or a parking lot.
I looked at the character of the street surface.
So was it a well maintained street? Was it a poorly maintained street?
Was it a brick street? An asphalt street?
A concrete street? I looked at the number of lanes.
Is it a simple one lane alley? Is it a two lane street?
A six or more lane street? And I characterized it, each image by the
number of lanes. I identified whether or not any
pedestrians could be seen in the image, or whether any bicyclists could be seen
in the image. I look at the grade of the street so, for
example, is it a flat street? Is it a curve street?
Is it a hilly street? And then I looked at the different kinds
of land uses. So is this a street where there are
individual houses? Are there apartments?
Or, is it a downtown with office buildings, or what are, what is the
character of the land use? I looked at trees, so are the trees along
the street? Are they set back from the street?
Or, is it a heavily forested area? And then I looked at street calming
measures. So for example were there pedestrian
crossings? Were there bumps in the road to calm
traffic? And so these were the ten variables that
I looked at. In your case, you'll have your own set of
variables that you were thinking about. So when you, if you're thinking about
parks, or is this safe for children, what are the things that are different between
the images? And so you may not have thought about
this in this type of detail when you created the survey.
But you can just as easily go back now, and think through, what are the
differences between these different images?
In my own case, I looked at literature around streets and bicycling.
To better understand what are the characteristics that are in the research
that identify. What makes people want to bicycle or not
bicycle on a particular street. No, no, I'm not asking you to go into
that level of detail for this particular exercise.
But it's something you can keep in mind, and certainly you can use your own
intuition about what are the differences between the images.
And how do you understand the differences.
Now, you get your results. So when you go to StreetSeen, you can
simply click on the Analyze button for your survey.
And it's going to pull up these results. And so you'll see, in this case, that
this partic, these series of images were favored 75% of the time with the top
image and it goes down from there. Now, I'm just going to to take two
images, the image that was most preferred and the image that was least preferred.
And so when you look at these two images, what are the things that you notice that
are different? And so we can think back to the variables
that we were referencing. So in this case, the top image, there's
no traffic. And in the bottom image, there's a lot of
traffic. In the top image, this is a two-lane
street. In the bottom image this looks like it's
a four-plus-lane street. When we look at the top image, there's a
lot of landscaping. That there are streets along the tree the
road, as well as trees that are set back from the road.
In the bottom image yes, there are trees. They're set back in the distance.
we can see that in this case there are sidewalks on both side of the road, and
in the bottom case there are no sidewalks that are visible, right?
So you can continue to go through this process of looking at each variable.
And so for the simple analysis, I would ask for you to look at the top five
images, and the bottom five images. And understand what are the differences.
So using your ability to simply measure, what's different about these different
images, and how do they compare? So why are people voting for certain
images over other images? So this is just a really simple analysis
for you to be able to explain what the differences were between these various
images. Some of you may have wanted to compare
different areas. So, you might've compared different
cities, or you might've compared different neighborhoods.
And, so you'll want to report out which areas were favored more than others.
In my own case, this is irrelevant for me.
I did wind up picking some additional areas of the city, but it's because I
wanted to get different types of streets to be included.
But all of them for from within Columbus, and I don't see that it's particularly
valid to compare between areas, and the percent favored is quite small.
Then we have the heat map, for most of you the heat map is going to be
irrelevant. This really depends on having a very
large number of votes that are included in your study.
So if you have an extremely high number of votes, then colors of the map will
change and what will be shown. Is that where there are areas that are
most favored then the heat maps are going to grow.
And you'll see higher concentrations of the heat that is shown on these
individual maps. But for most of you, you have a small
number of votes and so it's just going to show each individual location as a dot.
And that's not going to be very helpful to you.
So, for most of you, you don't need to worry about this heat map.
Okay, now those of you who want an extra challenge, you know something about
statistics you want to take on and really understand things in a more significant
way. I'm going to walk you through how to do a
discrete choice model. The first thing you're going to do is
you're going to go the results and you're going to download the full results.
You can download it as a CSV or an XLS file, it's really up to you.
Now, I don't have time in this class to teach you how to use statistical
programs. But I am going to offer you some simple
instructions. So first of all, you can use a
statistical program, if you already have one, you're welcome to use whatever you'd
like. Or you can use a free statistical program
and the one I'm going to recommend is R. R is available at r-project.org's
website. And then one of my faculty colleagues
Professor Brighton at Ohio State University has created a How to Use a
Discrete Choice Model. And so I'll be providing that link up on
the website as well. And he walks you step by step, how to run
a discrete choice model using R, okay? So this is a free, simple way, and if you
want this challenge you can walk through it and see how it works.
I ran the discrete model, and I got the results.
And so in a very simple explanation, the good news is that the model results match
my hypotheses about what I thought was going to happen.
So for example, I believed that the larger the number of vehicles that are
seen. The less likely people would be to choose
that particular image when paired with another image.
And that's in fact what I found, is that the larger the number of vehicles and the
larger the number of lanes, the less likely an image is to be selected.
On the positive side, where you have sidewalks, where you have pedestrians and
bicyclists and where you have, trees along the, the side of the road.
Then you are more likely to choose that particular image.
And what was interesting to me is that I expected that parking would be negative.
That if you had parallel parking, that you would be less likely to choose the
image. Because there's a danger to bicyclists
from opening the door, and so you can run into the car door and be injured.
Or if it's pull-in parking, then people can't see you as easily when they're
trying to back out of that parking space. Or, in a parking lot that you could have
cars that are pulling out from a parking lot.
But in my model this was not statistically significant.
Alright?, so, that was an interesting finding for me.
So, those were the variables that I was looking at and I was happy to see that
the model matched the results that I, I thought about.
Now, the next step. Is, well, are there differences between
people who are in Columbus or another area?
Are there differences between the different countries that people are from?
And one of the things that's captured by StreetSeen is the voter latitude and the
voter longitude. And so what you'll be able to do is use
the voter latitude and longitude to determine the location of an individual
that took your survey. So, you can go to any number of free
websites that provide the latitude and longitude.
So what you can do at latlong.net, is you can click the LatLong to Address button,
and then you can enter that latitude and longitude.
And then you can find out where people are from.
From and you can enter that country or city or whatever it is that you're
particularly interested in. So in this example this is the latitude
and longitude for the Taj Mahal. And so you can go in and as you saw in
this list, I have created the gone in and filled in the latitude and longitude.
And I can see that somebody completed the survey from Buenos Aires in Argentina
from a number of locations in Australia. In my particular case, what I'm really
interested in is comparing the results for Columbus.
So, for example, many of the people who took my survey may recognize the
individual streets. And so may have different opinions
because they have a user experience from bicycling on the streets.
And so I want to understand whether or not what they've said is different.
From what the people who are not in Columbus have said.
And then I want to understand some of the cultural differences so are there
differences between what people in Asia might think.
From what people in South America might think and have preferences around
bicycling. So that's the next step that I'll be
undertaking is integrating that into my discrete choice model to understand if
there are differences. And this is fairly simple to be able to
set up. I can fragment my samples and you're
welcome to do this in your own studies as well.
so I encourage you to experiment, to play with the data and to just have some fun.
Seeing how you can use discrete choice models in a visual survey setting.
And the last thing I would say is if you're not a statistical person, or
somebody that has these statistical skills, you're welcome to try them using
the R and the walkthrough, but if you're not that's okay.
Stick with some of the simple analysis that I've showed you how to use, by just
understanding and comparing the the images that are in your data set.
Good luck. I look forward to seeing your results.