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 3 - Lecture 1
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Из курса от партнера The Pennsylvania State University
Maps and the Geospatial Revolution
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Understanding Spatial Data
Examine the key elements of spatial datasets.
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Dr. Anthony C. RobinsonLead Faculty for Online Geospatial Education
Department of Geography
This is Lecture 1 for Lesson 3. So, where are we now?
Locating things used to be really difficult.
And it was done using time intensive methods that weren't very accurate.
You probably associate location technology today with GPS.
GPS stands for the Global Positioning System which is a network of satellites
built by the US military beginning in the 1970s.
It's been extended and enhanced a whole bunch since then.
It was not invented by Apple or Google, believe it or not, but GPS is just one
example of a global navigation satellite system, a GNSS.
And others include the Russian GLONASS system, which has the worst acronym ever
and the EU Gaileo System. And it's already common for consumer
devices that you and I both own to use a GNSS to drive locations.
Most of them use the GPS system. This location signal from space is often
augmented by WiFi hot spot signals and cellphone tower signals that combine that
can provide much better accuracy and coverage inside buildings and urban
environments, for example. Consumer grade stuff that you and I both
own can locate positions within a few meters, but they can be off by hundreds
of meters in poor conditions. So, there are professional systems that
use fancy hardware and software. They're used for surveying property lines
and other serious geotasks. Those professional systems can get you
down to just a few centimeters, which is pretty good.
A GPS enabled device can give a point location defined by latitude and
longitude coordinates. So right now, where I'm sitting, I'm at
40.77 degrees north and 77.89 degrees west.
Now, if I walked around my yard collecting multiple points, I can create
a polygon that represents the footprint of the property I own.
I could define that whole entire space. If I collected points in a row between my
couch and fridge, I would have a line feature and I would have a snack.
So, points, lines, and polygons are the primary forms of spacial vector data.
And vector data is one of the two primary spacial data types.
The second data type I want you to know about is raster data.
When you think about raster data, I want you to think first about virtual globes.
You've probably used stuff like Google Earth a whole bunch.
That's a virtual globe. You might have taken this class because
you had an experience using something like that.
And virtual globes like Google Earth have made images of the earth really easily
accessible and fascinating to millions of people.
Most geographic image data comes from satellites and airborne sensors.
That's what populates stuff like Google Earth.
You can even use your own DIY drone now and strap a phone onto a quadcopter and
fly it around, and make pictures of your neighbors tanning if you want.
And geographic image data like this is raster data, the second major data type I
want you to know about. Raster data captures information by
assigning values to cells in a grid. And the size of those raster grid cells
determines how much resolution you have for the image.
Most of you have digital, have digital cameras and they have a certain megapixel
sized sensor. That's the same exact principle.
Here you can see a graphic showing sort of a fake example here, of vector data
versus raster data for the same place. I've concocted a fake geography with
Fancy City and Less Fancy City, and Slinkyhead Lake, and Big Green Forest and
Wheat Fields, and all that kind of stuff. And you can take a look at that.
What you'll see here on the left is, it's got rather big regular-size grid cells
with values assigned to them. Right?
That's what would happen if you take an image.
In this case, a very coarse resolution image of the fake landscape I've created.
On the right, you've got much more detailed vector geometry.
Vectors allow you to, to be much more precise about the boundaries of, of
certain locations. Because you're not limited to the
resolution of a sensor. And there are trade offs for using both
of these different data models. But they're both extremely important to
geography. Now, the science and technology
associated with imaging the Earth from above is called remote sensing.
And it's a huge discipline that's quite big, involves a lot of engineering,
geography, and analytical methods, a lot of Math too.
And it's not just photographs. It can also involve the use of lasers and
infrared sensors. Other forms of life that are not visible
to the human eye. Here's an example of an infrared image.
This is a pretty interesting one, I think.
it was taken in 2011 after a major tornado hit Tuscaloosa, Alabama.
Now, because of infrared light is not visible to the human eye, we have to add
false colors to this image in order to make it visible to us.
So, in this case, the stuff on the map that's red is the vegetated area that's
alive, and the blue stuff includes impervious surfaces dead areas.
places that are urban for example, water bodies, things that are not alive,
essentially. And you can see this huge swath across
this map, right? Of where the tornado caused a huge amount
of damage. This is a good example of the use of an
infrared detection method for imaging the earth, that provides some sort of
different information that we would get from a photograph alone.
And here are a couple of examples using lidar, which is a laser based method for
measuring elevation on the ground. It's extremely precise, you can get down
to just a couple inches of accuracy with this stuff.
And what I'm showing you here are two images before and after Hurricane Sandy
in 2012. This is part of the New Jersey Coast
Line. The top image shows before the storm hit
what was the elevation on that coast line.
And you can see little footprints of houses sticking up above the sandy base
and the roads and stuff like that. And in the second image in the bottom,
you can see that there's this brand new channel where there wasn't anything
before, that's been gauged out. And a lot of the beach has eroded away.
So, laser detection, through lidar, is a great way to map some of these very, very
precise differences in the actual landscape topography.
Here is a map showing the difference between the pre and post images using
lidar. And here you can see with the red areas,
where the elevations has actually decreased since the first image to the
last image. And there are some places on the north
side of the image where sand has actually been deposited and added since the
hurricane hit. So, images like these show that now not
only do we have visible stuff. just straight up, normal aerial
photography we can work with, and satellite imagery.
But we also have invisible things we can make visible using lidar and infrared
sensors, for example. Radar is another option.
To make the environment visible to us and to use it to make maps.
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