Learn how advances in geospatial technology and analytical methods have changed how we do everything, and discover how to make maps and analyze geographic patterns using the latest tools. The past decade has seen an explosion of new mechanisms for understanding and using location information in widely-accessible technologies. This Geospatial Revolution has resulted in the development of consumer GPS tools, interactive web maps, and location-aware mobile devices. These radical advances are making it possible for people from all walks of life to use, collect, and understand spatial information like never before. This course brings together core concepts in cartography, geographic information systems, and spatial thinking with real-world examples to provide the fundamentals necessary to engage with Geography beyond the surface-level. We will explore what makes spatial information special, how spatial data is created, how spatial analysis is conducted, and how to design maps so that they’re effective at telling the stories we wish to share. To gain experience using this knowledge, we will work with the latest mapping and analysis software to explore geographic problems.
Lesson 4 - Lecture 1
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From the course by The Pennsylvania State University
Maps and the Geospatial Revolution
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From the lesson
Doing Spatial Analysis
Learn how Geographers solve problems using spatial analysis techniques.
Meet the Instructors
Dr. Anthony C. RobinsonLead Faculty for Online Geospatial Education
Department of Geography
This is Lecture 1 for Lesson 4. So, once you have spatial data, you have
to do something with it. Simply showing that you know something
exists in a certain place isn't enough. You gotta go beyond just visual
inspection. And geographic science uses and develops
a wide range of analytical techniques to take measurements, make comparisons and
detect anomalies in spatial data. One of the most fundamental and basic
spatial analysis method is to just overlay analysis.
You put this, on top of that and you see what happens.
You look at the overlapping territories. This was originally proposed by Ian
McHarg's in his book, Design with Nature in 1969.
And what McHarg proposed to do was to take mylar, so somewhat transparent
plastic overlays looking at different environmental factors, societal concerns,
economic development plans and whatnot. And try to determine which places based
on overlay, looking at all these factors together, would be the, make the most
sense for further development in urban environments.
And this basic principle is now carried out today in all kinds of digital mapping
systems. Another really important and basic
spatial analysis method is buffer analysis.
So, buffering identifies areas of interest around a location based on
distance or time. Let's look at an example here.
Here, I've got a really serious emergency situation in, let's say a mid-sized town
in the United States. I've got a terrible, nasty, deadly diaper
biohazard. And on the left-hand side, you can see
what would happen if I drew a 20-mile buffer outward from this spot.
And maybe I need to give this map to local authorities, so they could evacuate
people away from this horrible biohazard. On the right-hand side, though, you see
something different. This is if I used time to make, to make a
drive time buffer analysis, see how far people could get away in 20 minutes.
What you can do now with modern technology is you can figure out what's
the capacity of different types of roads. How fast do people normally drive on each
type of road? And then, you can figure out which
directions might you be able to get away from this terrible biohazard more
efficiently. In this example, if I tried to go to the
northeast, I wouldn't get very far away, right?
But if I want to the south, I'd probably get out of there quickly and I would
avoid this horrible, horrible hazard. Another major method of spatial analysis
is surface analysis. So, surface analysis and interpolation is
when you have lots of individual observations and you want to make an
overall map that shows trends. So, temperature readings from towns
scattered across your state or region might be a good example here.
Usually see weather maps that show what looks like continuous data, right?
You can see high and low temperatures all over the map and there's color that fills
in the gaps. Well, you actually need Math.
You need interpolation in order to make estimates where you have gaps in coverage
like that. These types of maps are frequently also
called heat maps now because they use a crazy rainbow color scheme, and they look
hot where there's more stuff. They should actually be called density
surface maps, because that's actually what they're measuring, but cartographers
have kind of lost that battle. Let's look at an example here.
I'm going to go to the temperature one again, but here's one that I created.
I've got a bunch of towns and the fake geography that I've, I've created here.
we've got places like Hoegaarden and Niceburb that are in the lowest category
where it's balmy. And we have a place like Ghost Chili
which is in the Wish I Was Dead category. And what you can see on the left there
is, this is the network of sensors for example, that we might have to detect
temperature. These are readings we have.
But what we want to make is like something on the right hand side of this
image where you can actually see what the temperature might be like between these
spots, right? That's what we actually want to get.
That's what the results of surface interpolation can look like.
This is obviously a fake example that I created, but you know, it's something
that demonstrates the point, I think. Another thing you can see here, what
might be problematic, right? Is what happens with these places in the
southeast part of this particular map, where we don't have very many data
points. What if there's a mountain between
Caterwaul and Ghost Chili? Or between Caterwaul and Dulles A-Gates.
And Dulles A-Gates, I can tell you, are actually hotter than hell.
If you had a mountain or a high elevation point here it could very well be that
it's actually lower temperature between these two spots, right?
So, here's a, a, a place where the uncertainty associated with geographic
data kind of comes into play. So, cluster detection is another type of
spatial analysis that's really common in geography.
And a cluster is a spatial pattern that appears distinct from what's expected in
terms of geographic variation. So, if I see ten white minivans at the
supermarket in the parking lot, that's completely expected.
In fact, I would almost guarantee that would happen where I live.
If I see ten white minivans in the same driveway in my neighborhood, that's a
possible cluster. That's unexpected.
That's a space that's occupied by a lot of cars.
And then, they're all the same kind of car, same color?
That's pretty unusual. So cluster detection is all about
detecting the unexpected from the expected and trying to measure that
difference. And one of the most famous geographic
examples is John Snow's map of a cholera outbreak in London.
And what John Snow realized, which transformed epidemiology ever since then,
was that the location of certain types of risk factors related to location of water
wells. And in particular there was one water
well where more people seemed to be sick nearby.
So, the spatial expectation there was being broken.
He had a lot of people who were all over the city were sick.
But in one spot, there were more sick people than others and that suggested one
particular water well that turned out to be the culprit.
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