- All right.
Well, good afternoon everyone.
I am Alex Suarez with Texas Digital Library.
We'll go ahead and get started.
So please make sure that those mics and videos are muted.
Going to start recording, well, Leah just did,
thank you so much, Leah .
So yes, good afternoon from Austin Texas.
Thank you everyone for joining us today
and welcome to today's webinar, which is Intro' to GIS.
This is our first webinar in TDL's new GIS webinars series.
So I'm really excited.
And thank you all for being here.
Like I said before my name is Alex Suarez
and I am the administrative associate
with Texas Digital Library.
So let me go ahead and get onto that next slide.
All right, so some Zoom stuff,
I'm sure by this point, everyone's very familiar with Zoom,
but just in case for those of us who are new,
you want to make sure
that your microphone and video are muted.
Presenters will also keep their mics and video muted
until it is their turn to present.
If you want to see who's in attendance,
click on the participants icon
in the bottom center of the screen.
And if you have a question or comment,
please type it into chat.
We have myself, Leah and Cynthia Henry from Texas Tech
kind of running referee for that chat.
You can access chat by clicking on the thought bubble
at the bottom right of your screen.
All right, so some good old Bill and Ted,
we at Texas Digital Library are big fans of Bill and Ted
because they're most triumphant
and especially because they believe in being excellent
to each other.
We at TDL ask that you also be excellent to each other.
We are dedicated to providing an experience for everyone
that is free from all forms of harassment
and inclusive of all people.
We ask that everyone here today
be considerate and respectful and speech and action,
attempt collaboration before conflict,
refrain from demeaning,
discriminatory or harassing behavior and speech,
and be mindful of your environment and all
and of your fellow participants.
Please use chat for questions to comments
pertaining to the panelists, presentations
and topics at hand.
We will save all your questions
for a Q and A session at the end of the webinar.
And finally, this meeting is being recorded
we will publish slides and recordings
and share them with you all in attendance today,
as well as our members, partners, and colleagues
in libraries and archives.
So today's presenters,
we're really excited to welcome Michael Shensky,
the GIS and geospatial data coordinator here at UT Austin,
as well as Nathaniel Dede-Bamfo,
the GIS services specialist at Texas State University.
And so with that, I'll go ahead and stop sharing my screen
and I will pass it off to Michael.
- Hi, good morning, everyone.
Thanks for joining us
for our first TDL- GIS Interest Group webinar.
And I'm really excited to be presenting to you this morning.
I'm actually going to be handling kind of
a later part of our webinar today.
And Nathaniel is actually going to be leading us off
with kind of an introduction to core GIS concepts
that it'll be helpful for us to be familiar with
before we jump into the demonstration
where I'll be going through,
some common GIS workflows and processes using QGIS.
So without further ado,
I'll go ahead and turn it over to Nathaniel.
- Alrighty, can everyone hear me?
Michael can you hear me?
- Yep.
- Excellent, all right.
Thank you all for joining us this morning.
I hope you're enjoying the cold weather already,
had some warm weather but suddenly we have the cold,
so enjoy it while it lasts.
So I'm so thankful for this opportunity,
it's an honor to be with you all.
And the goal for me here is basically
to introduce you to some basic concepts of GIS,
just to get you into the whole field of GIS.
It's a huge one, but we'll try as much as possible
to make it easy and simple as possible.
So in case I'm using too many jargons,
please chime in and I'm going to take some time
and they explain certain things.
So with that said, let me share my screen with you all.
Okay, Michael can you see my screen?
- Yep, looks good.
- Okay, all right.
So, I'm joining you from Texas State University,
home of the Bobcat.
And today we're going to talk about an introduction to GIS.
So for the outline we'll start with what is GIS?
We'll move on to talk about some basic GIS data models,
types of GIS data we have.
And what are some benefits of GIS?
You might be wondering, "what am I going to use GIS for?"
And then we'll talk about some examples of GIS software.
Introduce you to some basic functions in GIS
and then turnover.
Okay, before that let me put on my closed captions
and I believe it's on now.
So, that's the goal for today.
And then I would turn over to Michael
to basically take us through some you know,
GIS functionalities in one of the software.
All right, so what is GIS?
Anytime time I throw this question out there
people are like,
"I don't know, maybe I have heard about it."
But I believe a lot of people have
done some aspect of GIS without knowing.
So examples include using Google Maps, Google Earth,
going to, you know, the field
where the GPS unit's in your hand,
or just going online, searching for locations
what's the closest coffee shop or what's the closest mall?
So any of these aspects,
if you have done any of these things,
then you have done some aspect of GIS.
So when you are looking for,
let's say the closest restaurant, the closest coffee shop,
you are applying something in advanced GIS,
we call network analysis.
So you're trying to find that the closest facility.
In Google earth if you're looking at
okay, where things are located,
all these are some basic aspect of GIS.
So GIS in general stand for,
the letter stands for geographic information system.
And when we talk about geographic,
we are simply referring to
things related to our planet earth.
So we tend to use the terms spatial, geographic, geospatial,
they're all synonymous.
But when we refer to geographic,
we are simply talking about things
pertaining to our planet earth.
It comes from the Greek word geo, which is earth
and then graphein, which is description of our planet earth.
So geographic pertains to the earth.
When you use the term spatial, it's a more broader term.
It refers to any location, any locations,
be it on earth, or be it on, like extraterrestrial location.
So it could be on the moon,
it could be in the sun, anywhere.
That's what spatial means.
So, sometimes we use the term interchangeably,
geographic, spatial, and then geospatial.
Information, we are referring to data
and the meanings behind it.
And then system simply a collection of computer software,
hardware, the procedures, structures, applications,
and all that.
So putting all these together,
we can define GIS as a computerized system
which we use simply to capture, manage, analyze,
and then eventually display geographic related data.
And what's the goal?
The goal is simply to promote understanding
and then assist us in making very informed decisions.
So let me break it down.
So GIS simply we are trying to find data
from our environment but the data that we want to capture
has to be located.
And most of the data that we collect
are actually located in space.
So let's say I want to find data,
let me use the example of coronavirus,
where our coronavirus most clustered?
Is it in the South, is it in the midwest,
is it in the Pacific areas,
is it in the Northeast?
So whilst we call it data
that we can pinpoint to exact location,
we referred that as geographic data.
When we collect it, we then ask questions
and that is where the analysis come in.
We ask questions like,
okay, why do we have more clusters over here
as opposed to over there?
Or we can ask questions like,
okay, what if we don't do anything about this virus,
how is it going to spread,
is it going to spread eastward or westward?
And then when we ask those questions, we visualize them,
basically display them.
And I'm going to show you some examples.
And the goal finally is to make decisions out of them.
Okay, these are the components of GIS.
So we have the hardware component
comprising of the computers,
we have software, which are applications.
We collect the data and then the approaches.
So approaches, basically procedures
that have been laid down to help us analyze the data.
And of course, without you and I, there cannot be any GIS.
So people are very important.
The organizations behind it, those who designed the software
and then those who learn it.
In GIS we have two basic data models.
We have what we call vector data and raster data.
When we talk about vector data
we are simply referring to models
that I use to capture discrete data
in the form of points lines, and polygons.
So another term for polygons is what we call the area.
So, in vector, we are simply starting from you know,
a basic understanding of X and a Y coordinates.
So where the X and a Y coordinates meet
we can capture a point.
If the feature is as a very simple feature
like a fire hydrant, or let's say even schools
we can just capture the center of the school as a point.
But if the feature has a linear shape,
like a river, roads or electric lines,
then we can collect those features with series of point
and that forms a line.
So series of points connected together with a segment
forms the line.
In the case of an area, let's say a lake
or county boundaries, state boundaries, a country
then we collect the shape of the area
using series of points that are closed up.
So we collect the edges of the feature.
We close it up with line segment, and that forms a polygon.
So these are the three main ways of capturing features
in our environment using vectors.
Rasters on the other hand,
we assumed that the earth is covered with gridded cells.
So we put sort of like a net on the surface of the earth.
If a feature falls within a cell of the net,
then we associate that cell with the feature.
So rasters represent features using gridded cells
as we have over here.
So let's say a fire hydrant within a cell,
then the entire sale is associated with a fire hydrant.
If it's a linear feature,
then all the cells that intersect with the feature
are considered as part of that linear feature
and the same thing applies to the area.
Having captured all this information,
we move on to the next stage, which is simply layers.
So, if we collect several features
that share the same geometry or, you know,
like location dimensions
then together we call it one layer, a layer.
So we can collect layers of like city boundaries
or county boundaries and then we can call it another layer
of similar features like streets across Texas,
and then the same time parcels,
we can go on and then use raster to capture land usage
and then elevation.
I want to point out rasters
are useful for capturing continuous data set
whereas vectors are useful for discrete data sets.
So, in continuous data sets,
usually we don't have very,
we cannot determine where the boundary begin and end,
that's the name continuous.
So things like elevation, things like our climatic data,
rainfall, pressure and all of that
are captured using elevation data.
So, if we capture all these information
then we can ask questions
like how many counties across Texas
are intersected or crossed by IH- 35, interstate 35?
Let's say we are interested in measuring noise pollution,
and we know that 35 has a lot of traffic.
So one find out which counties are intersected by 35.
In that case, we have to capture the data
get the layer for the counties
and then the layer for the road vectors.
And then we can do a simple geoprocessing
to review that.
And I believe Michael is going to point out
some of these things .
So, under vectors, we have different GIS data types.
Vectors, we can capture vectors as in a format called
shapefile which is very popular,
or we can capture them using KML
which is the format usually presented in Google Earth
and in the Google Maps.
under rasters is we can capture data in the format, TIFFs,
JPEG, IMG, PNG, and so forth and so on.
There are talk about tables.
Any of these data sets come with associated information.
So for example, fire hydrant has a location
but it has a name or an ID associated with it.
We can also associate other information like,
okay the age of the fire hydrant,
what is the current status,
is it working or it's not working?
All those associated information
are captured in what we call an attribute table.
So we can catch up attribute tables using CSVs or DBFs.
With that said, now let's see some benefits of GIS.
Probably you have already recognized some.
One of the first things we see GIS is very helpful
with managing our data and integrating data.
We can bring in data like the boundaries of cities
and counties, and then we can bring in data from the census
associated with that.
So data management and integration is very helpful.
Enhance data visualization.
As we know, pictures are wet more than a thousand words.
So in statistics, we have numbers and charts and all that,
but GIS will provide you with a visualization
of the location,
where are things clustered based on the maps?
Better capabilities to answer questions
like where are things located?
What is there?
How many?
How much?
What has changed this time?
What spatial patterns exist and why?
And what if nothing happens,
how is it going to transition?
So let's see some examples.
This is a basic map showing distribution
of grad students at Texas State from across the world,
where are they coming from?
So in 2014 when the data came in,
where our most grad student coming from?
Of course the U.S. but followed by India China.
So this is where I earth is.
So we can make a simple map like this.
We can also make a map like this showing total enrollments,
which counties a student coming from?
So as you can see, the three main metropolitan areas
Dallas -Fort Worth Austin, San Antonio area,
and then the Houston area.
And this map is just showing
the average age of students, okay?
So where are most students, you know, the age of students,
most of them more, all the counties have the younger age
but there are some counties that are sending, you know,
students who are quite old.
So non-traditional students are coming from certain areas.
Another example is simply map in Twitter.
You know, so tweets of people during disaster
where are most tweets coming from?
We can just implement that
and then correlate it with places that got flooded
and look at certain things.
What is the correlation between the amount of tweets
and then the impact of the disaster?
Regarding GIS software.
We have proprietary ones and then open-source ones.
So under the proprietary ones
the common or the most popular is ArcGIS.
So ArcGIS now has two versions,
We have the old version which is the ArcMap,
or sometimes called ArcGIS Desktop.
And then the current version, which is the ArcGIS Pro.
So depending on your institution,
a lot of institutions have site licenses
but some institutions have limited licenses.
So that's one of the most popular software.
But if you don't have that,
your institutions can still go open.
And these are all forms of open-source GIS software
out there.
The most popular amongst them is QGIS.
And it's very popular in the Houston, San Antonio corridor,
a lot of companies free
like startup companies are using that,
but there are a bunch of them.
So we can explore as much as possible.
And I'm going to wrap up
by talking about some basic GIS functions.
You can do things like adding data filtering.
And it's not going to be important if I just mentioned them.
These are some of the topics Michael
is going to demonstrate.
So with that I'm going to sign off here
and turn over to Michael.
But before Michael jumps in,
I want to ask if anybody has a question,
I know it was just breeze through.
So I'm going to pause here and see if anybody has a question
about these basic concepts.
- We don't have any questions in the chat
as of yet, but it was very informative.
Oh here comes in.
- All right.
- There is just a comment saying,
"thanks for the overview,
"it was very clear and helpful for us newbies."
- Okay, I appreciate that, thanks.
Any other question?
- No, it doesn't look like it.
- Okay, alright.
I believe more questions will come
after Michael does his demonstration.
So you can hold on tight and see GIS in action.
All right, over to you, Michael.
- All right, thank you Nathaniel,
for that excellent introduction.
I'm going to go ahead and share my screen
and we can take a look at a demonstration
of many of the things that Nathaniel was describing
in QGIS software.
All right, so I'm going to go ahead and share my screen.
Can everybody see
it looks like an application window up here?
- Yes - So this is, excellent.
So this is QGIS, I have it installed on my computer
as Nathaniel mentioned it is a free
and opensource application,
so anybody could go and download it and install it.
It's cross platform compatible,
meaning it will work with MacOS computers,
Windows computers, even Linux computers
without any problems, and it's really easy to get set up.
Really, it only takes a few minutes usually
to get it installed and up and running.
So when you open QGIS, you'll be presented with
this blank window.
And that's an indication that what GIS (inaudible)
need to come prepared to supply,
you know, the data that you want to work with.
And so what I'll be showing you today
is how to go through some really common workflows
and hopefully that'll help,
you know, give everyone a better understanding
of how GIS software works
and what we might be able to use it for.
So I've actually downloaded quite a few datasets
in advance to prepare for this,
but I also, before we even jump into QGIS,
I want to show you what that process looks like
'cause that's really important, right?
That's something that everybody is pretty much going to
need to go through at some point, if doing GIS work
'cause they'll need to be able to find data online
and download it for them to be able to use it
in software like QGIS
or you know, an alternative like ArcGIS Pro.
So what I'll do is really quickly,
I'm going to go to a new browser tab
and I'm going to search a city of Austin GIS.
And you could do this for just about any major city
in the United States and you'll likely find
some sort of geospatial data portal.
So when I click on that,
it takes me to the city's GIS website.
And --
one thing to note is that some cities
are going to have multiple pages about GIS.
So this is actually not the page I wanted to go to,
I wanted to go to the GIS data page.
So keep that in mind,
if you're looking for any geospatial data from a major city
it's probably out there.
And the same thing goes for,
you know, fairly populous counties and states,
just about every state is going to have a geo data portal
that you could visit.
And all right,
so now I have found the city of Austin geo data portal.
And I'll just download a random data set here.
So in the city of Austin geo-data portal
there are quite a few datasets
that we could potentially draw from here,
I'm just going to go ahead and download railroads
since that's the first one I happened
to stumble upon some. So I'm going to click on this,
it's going to give me a spatial preview
so I can see what that data looks like.
And then if that looks like,
you know, information that I'm interested in working with
I can go ahead and click the export button
to download that data.
And there we go, great.
So I see some railroads, that's what I expected to see.
We can see it's geospatial legacy presented
in this map preview.
So I'm going to click export
and I'm going to decide to download that as a shapefile
which is one of the file formats that Nathaniel mentioned.
So that's downloading vector data
like data in the shapefile format
usually is fairly small and manageable,
or that's going to download really quickly.
Let me go ahead and extract it
'cause just about all geospatial data
that you download from the internet
is going to be compressed usually into a zip file.
And here it is now on my desktop,
I'm going to go ahead and copy that
to another folder on my computer,
where I store a lot of my data
and I'm just going to paste that in there,
and there we see that railroads folder.
And if I look in the railroads folder here,
we see that shapefile.
So this is what it looks like,
it's comprised of a few different component files,
one of which you'll notice, Nathaniel had also mentioned
in his portion of our presentation today,
the DBF file which contains the attributes
for that vector dataset.
So I'm going to go ahead and go back to QGIS,
hold that back up as well.
All right, so now back in QGIS,
what we're looking at here is our main application window
and there are a few different panels
that it's important to take note of.
So the first one over here on my top right,
is my browser panel.
And this is where I'm able to navigate to datasets
that live on my file system on my computer.
And also to to even access data
that might be available on a server,
on the internet or to access data
that might be like on a shared drive,
but this is how you navigate
the information you want to load into your document.
So I'm going to go ahead and bring in that railroads data
that I downloaded from the city of Austin's website.
And there we go.
So it looks pretty similar to what I saw
in that spatial preview.
You will notice that there is no background information.
So I can't really tell that this is,
you know, railroads in the city of Austin.
So maybe I want to add some additional layers of data.
So I'm going to go ahead and do that.
And this data set that I'm going to add next
is from a shapefile representing the county boundaries
for all the counties in the United States.
So kind of hard to see with the colors
that it's just randomly selected here.
So I'm going to go ahead
and change the colors of these counties
to maybe a light gray.
So what I'm doing is changing the symbology here,
this is a really common operation in GIS software.
So symbology refers to the collection of symbols
that we use to represent our data.
Now it's like gray.
I'm going to make my railroads a little bit thicker
also by adjusting their symbology.
And we'll make those maybe like,
let's do a bright kind of burnt orange color.
There we go.
All right, so now I can see a clear view
of my railroads on top of Travis County right?
So this is Travis County behind me
or behind the railroads, I should say.
And I can highlight Travis County and select it
using the tools that are available here in my top toolbar.
So if I wanted to select Travis County,
I would go ahead and click the selection cursor
and I could click on it and there we go.
Now I've highlighted it.
And so selecting features
is really useful for a number of operations
as we'll see here as I go on.
So now that you know,
I've introduced you to the process of finding data online
there are all kinds of great websites
that you can do that from many of them,
are operated by government agencies ,
it could be local government,
state government, county government,
federal government agencies, like the USGIS
the United States, geological survey,
NOAA, the `National Oceanic, Atmospheric Administration,
the U.S. Census Bureau.
There's plenty of great websites available online
for downloading data.
I'm going to go ahead and jump back quickly
to at Nathaniel's slide
so I can kind of go over things in the order
that he mentioned them.
So first up, I'm going to try filtering data.
And so if I zoom out far enough here
you'll notice that I do indeed have data
for all of the counties in the United States
but let's say I only want to look at Texas counties.
So maybe one of the first things I want to do
is filter my data to only show the Texas counties
and get rid of or at least hide the other counties
in the United States.
So what I'm going to do is
well, first let me look at my attribute table.
So the attribute table is something
that Nathaniel had mentioned
where this is where the information
about each of these spatial features
that we see in our dataset.
This is where that descriptive information
is contained right?
And this looks pretty similar to a spreadsheet
like what we would see an Excel,
and this is what we refer to as the attribute table
in GIS software.
So what I'm looking for here
is where the state information is contained.
And unfortunately it looks like,
in this particular data set that I downloaded
from the Census Bureau,
the state information is contained in the state FP.
field or column.
And so what I need to do is identify
which one of these numbers here whether it's 47 or 48
represents the state of Texas, right?
So we can see that the numbers here range from one
all the way up to 78.
And so each one of these numbers
represents a particular state or U.S territory.
So what I want to do is I want to filter out
just the Texas counties and it turns out Texas,
the code here for Texas is 48.
So that's what I'm going to do.
So I'm going to establish a filter on the county's layer,
I'm going to right click on it, I'm going to click filter,
And I'm going to say that I want the state FP number
to equal 48.
And then I'm going to click the all button here,
and it's going to tell me all possible values
that I could use to filter out features,
and then I'm going to click "OK"
And there we go.
So now we only see those Texas counties.
So that is how we would use GIS software
to filter features in a dataset.
So those features are still part of the dataset,
they're just being temporarily hidden.
If I removed that filter, they'd all pop back up.
So that's different than actually deleting data.
If I did want to delete data, I could do that too.
Just really quickly I could toggle editing,
and I could say, I want to delete a particular county here
in Texas, so I could select it
and hit the delete button on my keyboard,
and now we notice that's not there, that's permanently gone,
if I saved my edits.
If I toggle editing again, it's going to ask me,
"Do you really want to save that change permanently,
"if I click save?"
Yes, that data is, has gone forever.
I'm going to actually discard that
because I don't want to to permanently delete that county.
So we can also use GIS software to edit data.
Alright, so now if I want to sort information,
I could do that as well.
So if I look at my attribute table again,
for the U.S. counties,
so remember I've already filtered it
to only the Texas counties right now,
the other counties are hidden,
so my attribute tables only
going to show me the Texas County information.
Maybe I want to figure out which county
has the greatest land area?
So what I'm going to do is I'm going to double click
on that field, so this here, A-land stands for area of land.
And then I'm going to scroll back over here,
you can see that Rockwall county
is the smallest county in Texas by area.
I'm going to double click on this again and it resorts it
and I see that Brewster County
has the largest area of any county in Texas.
So there's Brewster County,
if I do a reverse sort again
and I click on Rockwell County, that comes up,
you can see Rockwell County is really tiny right here
just Northeast of Dallas.
All right, so now I've covered sorting and filtering.
Measuring distances in area.
So measuring distances and area can also be really useful.
in this case for our U.S counties
we happen to already have the area data
in the attribute table,
so that's something that's already basically pre-calculated
for us in this particular data set
but that's not always the case.
You might download data for, you know, city boundaries
or park boundaries and it might not tell you exactly
what the area of those features is.
That's something you might need to calculate.
So I'm going to bring in a different data set
that doesn't have that information
so that we can do that analysis.
So let's see here if I have a good one,
so I'm going to bring in data from a different source.
I'm going to bring in data for all the countries
in the world here.
And you'll notice as soon as I bring that in
our Texas counties get covered up
and that's because the order of the layers
in your layers panel over here on the left,
which lists all the layers in your dataset,
that's really important.
So this controls, you know,
the order in which they are drawn.
And so if I have the countries on top of the counties,
the counties get hidden,
if I have the counties on top of the countries,
the county show up.
But if I want to calculate things like distance
and area I need to be really careful
about the units of measurements,
the units of measurement that my QGISproject,
is going to be using.
And so what I would want to do in advance,
of doing any of those calculations
is make sure to select a projection
and coordinate reference system
that is going to be using the unit of measurement
that I'm interested in seeing my data presented it.
So what I'm going to do is I'm going to go to the properties
of my QGIS project and I'm going to look
for a specific coordinate reference system,
this is called the WGS 84 /Pseudo Mercator
coordinate reference system, it's really common.
It's the same one that Google Maps uses
or OpenStreetMap uses, and I'm going to click "OK."
The reason I'm selecting this one
is because it uses meters as the unit of measurement.
So what this is doing is it's changing a few things,
one, it changes the way in which my data is displayed,
'cause it's changing the way our 3D coordinates
for the world are being transformed
to two-dimensional coordinates
in order to work to display them on my flat computer screen.
It's also like I mentioned, changing the unit of measurement
that our project is utilizing.
So here are the unit of measurement
for this particular coordinate reference system is meters.
If I change it back to a different corner reference system
like the WGS 84 coordinate reference system,
we'll notice the world looks very different,
Greenland looks much smaller now,
and this is using degrees of latitude and longitude
as the linear unit of measurement.
So, it's really easy to change back and forth.
So it's not actually changing the underlying data,
it's just temporarily changing how that data is displayed
in my project.
So we see again Greenland only gets much larger
in this view.
So this controls how our data is being distorted, right?
Greenland, it has a fixed size as do all of our continents
and countries but depending on the projection
and corner reference system we select,
they will look different in our maps.
So what I'm going to do here now that I've selected that
is I'm going to go to a vector.
So these are tools that I would use to look at vector data
and analyze vector data.
And we could use the, let's see, where is it in here?
- The measure tool?
- Well, I'm looking for the calculate area tool,
which is in one of these basic statistics geometry tools,
add geometry attributes.
So we use this tool to say I want to calculate the area
of all the countries in the world
using the project coordinate reference system
which is going to use meters as the unit of measurement.
And I'm going to plug in my admin country's layer
and it's going to create a new temporary layer
to hold this information.
So it doesn't like the geometry here, one of the features.
So, occasionally you'll run into errors with these things,
let's see if I can just do it for the counties,
'cause it doesn't like that particular data sets.
Let me go back, try this on the counties,
and it runs successfully.
So there was some issue with
the country data that I was working with.
So I'd want to go in and examine
which one of those features caused the problem.
But now if I open the data set here
we already had that information in there,
you can see it recalculated it for me,
using meters as the unit of measurement
from both area and perimeter.
If I want to measure distance,
so I'm going to zoom in a little bit tighter here,
I'm also going to bring in a different data set.
So I'm going to add populated places, all right.
So let's say I want to calculate the distance
between Austin here in Travis County and San Antonio
and Bayer County,
and I want to look at the straight line distance
between the two.
I could use the measuring tool in QGIS
which is inconveniently, hidden behind my zoom controls,
so I'm going to shift the position in my window a little bit
to get around that.
I'm going to say, I want to make my measurement and miles.
So we're got to pick our unit of measurement here,
and I'm going to select a new measurements
and I'm going to basically connect the dots here,
from San Antonio to Austin.
We'll see it estimates that the straight line distance
is about 70 miles, right?
And it's about 100 miles or so if we were to drive it.
So that seems about right, approximately.
And so we can use this to calculate distances.
Again the coordinate reference system here
can impact the accuracy of these distances
that are being calculated,
so you want to make sure that you're using
an appropriate coordinate reference system
for the data you're working with.
All right, I'm going to jump back to the slides again
for a quick second and see what else we need to cover.
Selecting features and queries.
So in order to select features,
I mentioned we can use the the selection tool right here,
in the toolbar to click on features here,
we can select Travis County first,
do you want to highlight the layer
that you want to select under your layers panel,
So here, now that I've selected that layer,
I can click on Travis County or Hayes County
or Williamson County, and it selects them.
If I want to see particular information
about Williamson County using the selection tool's helpful
because if I open my attribute table
here there's a lot of counties in Texas, right?
We have over 250, but where's Williamson County, right?
I can find it really easily
by clicking this button down here on the bottom left
of my attribute table and say,
I only want to see the selected features.
And they're now only Williamson County shows up
in the attribute table.
I want to see all the features again I would say,
show me all the features and there we go.
We select multiple counties, I could go back here and say,
show me only the select the features
and it would show me Travis and Williamson County.
So that is one way to select features.
I'm going to go ahead and clear my selection
if I wanted to select features by location.
So let's say I want to
Nathaniel had a good example in his presentation,
he mentioned that we could try
and find all the counties in Texas that I-35 runs through.
So I'm going to go ahead and actually run that analysis.
So I'm going to bring in the data
from the Texas department of transportation
for our national highway system
those are really light color,
so I'm going to make those a little bit thicker
by editing the symbologies,
we'll make those kind of a bright red,
all right there, we see our highway network.
And what I'm going to do here is try and identify just
where I-35 is.
So again I'm going to use a filter,
I'm going to say route number equals and I'm going to find
route number equals,
there it is 35, okay great.
Now I was able to select just I-35 so I filtered that out
and now I want to figure out which counties
it intersects with.
So what I'm going to do is go to my vector tools
up here on my top toolbar,
and I'm going to go to research tools,
I'm going to say, I want to select by location.
So I want to select all of the counties in Texas
that intersect with the features
in the national highway system layer
which right now has been filtered down to only I-35.
So I'm going to go into run that.
It'll take just a few seconds here to complete.
And I'm going to go ahead and close that, and there we go.
So interestingly, we see some other counties
that are definitely not being intersected
by I-35 showing up here,
that's due to an issue with our dataset
where these are probably state highway-35s
or some other, we can actually even take a look at them.
So in any situation, when you run an analysis
you always need to think critically
about the results you've obtained
and, you know, think does this actually make sense,
and in this case what we're looking at
is an issue with our data.
So we want to go through and actually de-select these
'cause these shouldn't have been highlighted.
- You want to check from the attribute table from the highway?
- Yeah, I'll need to look at it.
So right now, what we're looking at in the highway's table
these are only the filtered segments of highway.
And if I sort by route number
we'll see that they all are listed as 35.
And some of these attributes are not super detailed.
So you can see that a, IH stands for interstate highway,
SH stands for state highway.
So let's say I want to identify the state highway 35s.
And,
yep, so that's the issue.
You'll notice those are highlighted in yellow here,
the issue is that we have not just an interstate 35
we also have state highway 35s
that run through some of these other counties
closer to the coast.
And so what I can do is refine my filter
and say, I want where the only see features
where the route number is 35
and the route prefix equals IH for interstate highway.
Once I do that those disappear,
I'm going to turn my counties back on,
I'm going to rerun that vector select by location analysis.
So I want to select counties that intersect with highways
and there we go, so now my analysis is correct.
And so that was a spatial query,
we can also do attribute queries.
So let's say I want to go into,
I'm going to remove that selection.
And I want to go into counties
and open the attribute table here,
and let's say, I want to find a particular county in Texas
and I don't know, for instance where it's located.
So I'm going to do a attribute query
or select by attribute actually in this situation.
And I want to say show me all of the fields
in this particular data set.
And I want to find the one where the county name equals,
and let's say, I want to find Milam County.
And maybe I don't know where that is in Texas.
So I'm going to type in the name of the county Milam,
and I'm going to click select features, didn't find it.
So that's because the county name let's see here
if I can find Milam County might have miss-typed it.
There it is.
Let's see, well,
let's say I want to find a different county,
I want to find it's a county,
I really don't know where it's located.
Let's say Briscoe County.
No matching features found.
- We changed the case.
- Let's try this again, Calhoun County,
select features, no matching features found.
Interesting.
That's probably it's the case, I bet.
So now if I select features, nope, still not finding it.
Let me try a different field, let's say,
let's do the full name.
Still no matching features found it.
Interesting, let me try it on a different dataset.
All right, let's say I want to find the country of Brazil.
Hopefully this works without any issues.
So here's view of the world, right,
and we want to find out where Brazil is,
let's assume we don't know that.
I'm going to open the attribute table
and I'm going to select by expression.
And again, let's try looking at the fields
and I want to find where the admin information equals,
there's something I'm forgetting here
in this particular expression.
That's because it's not in single quotes.
Here we go.
So you do have to be careful
with the syntax you're using here
in the select by expression
in a live demo that slipped my mind,
and we can see that now that I'm using single quotes
instead of double quotes and the issue is resolved.
So we can do the same thing with the counties in Texas
if we try that example again.
Turning it back on
and we'll see that, that wasn't fact the issue,
me, absentmindedly using the double quotes
instead of single quotes.
Sorry for any confusion on that.
So now if I want to find Milam County,
like features, there it is, there's Milam County.
Was there anything else we were going to cover?
And map projections, which we had already talked about.
So one last thing I do want to show,
if we wanted to let's say, make a map of Milam County
in relation to I-35,
I could quickly do that by creating a new print layout
and I'll call this map 1.
And this takes me to the map composition window,
I'm going to basically determine
where I want the map to be drawn on the page.
There's my map.
And it's showing I-35 running through Texas
and I could give it a name or title.
So in this case, I want to add a label
and I'll add that up here
and call it
access.
And we can include a scale bar down here for reference,
and we can include a legend
showing all of the different datasets
that we have loaded in our document,
and we can take these out if we want to.
So I could say, you know, I don't want to show
the the railroad, which I decided not to have
in this map view or the populated places.
So we have control over what's in the legend,
we can change the units on the scale bar too
if I wanted this to be miles instead of kilometers,
we could change the size of the font and the title
so we can make it larger,
there we go.
And so we can compose a nice looking map.
And so then we could export it let's say as a PDF,
and that's a really common workflow
for creating modern digital maps.
So, sorry, I took up a little bit of extra time there,
but I do want to stop here, so we have time for questions.
- Do you want to show the PDF?
- Yeah, sure.
- And while he's getting that up,
there was some talk about it,
if this is going to be recorded,
it's going to be recorded ad Alex was kind enough to drop
the YouTube channel that is going to be posted on
in the chat,
so if you haven't seen that, you might want to do that.
- And here is the PDF that was saved.
So are there any questions?
- For about five minutes if you have any question
on the concepts or the software demonstration, please?
- If nobody else was going to ask a question
this is something that Henry, I was wondering
if you all would talk about the open source platforms,
there are so many on your slide Nathaniel.
I wonder if either one of you all have used,
I clearly Michael you've been in QGIS,
but are there other ones that you all have used
from that side?
- Personally I've used CrimeStats,
so QGIS is the main thing I use.
Have you used CrimeStats, I've used GeoDa.
I've used SAM and then MGWR.
Pretty much all the ones that have shown on the screen
due to my job, I had to test them
so that whenever somebody shows up, you know.
But amongst them, all CrimeStats GeoDa, SAM, and MGWR
are very useful when you are doing point pattern analysis
like you, you want to do specious statistics
like analyzing clusters, where things clustering,
where things dispersed and all kinds of stuff?
If you want to run things like spatial regression, yes.
Those tools are very, very helpful for that.
Yeah, another thing I want to point out,
some of the tools don't have the visualization component.
They will do the analysis
but they will not visualize for you.
So, it's always useful, you know,
if you have those kinds of tools,
you get another app that can visualize the data for you,
for example, QGIS.
So you can combine them.
- Yeah Nathaniel, and for, you know, my side of it,
I, on the open source side of things,
pretty much exclusively use QGIS
like 'cause it's so powerful and multifaceted
and there's such great documentation available online.
And it integrates really well with, you know, the other
you know, geospatial workflows that I have set up.
But yes, there are definitely some other niche applications
out there that are, you know
things that are perhaps worth looking into
in certain situations.
- Okay, I do think we have one question.
I don't know if Nathaniel was reading that.
So this is from Brian Thomas.
In addition to the actual file
is the creating application for a GIS file
important enough to try to keep
or can we just use the open software
digital preservation question?
- That's an excellent question
and something I did not mention
so thank you for bringing that up.
So this document that we had been working
in this QGIS project, I have not yet saved.
And so it's definitely something that could be worth saving.
And so what I'll do here to actually save the QGIS file
is go to project and I'll save it and I'll call it,
I'll save it to my desktop
and I'll call it a webinar example.
All right, and once I've done that
we'll see that this QGIS file
and it shows up on my desktop.
And what is actually being saved here,
it's a few things, it's not saving the underlying data.
So the data and the map project exists independently.
So whenever you're trying to preserve something
you always want to keep that in mind.
You can't just save them out by itself
and remove it from, you know where the data is stored,
because then what we'd end up with is a completely
empty map, right?
It would look just like this,
it wouldn't be able to find the sources of the data.
So you need to make sure that you're always packaging
and saving the data in and map document together.
In the map document what we're actually saving
are things like the symbology right?
So if I were to reopen this later
I would still see that I-35 is red,
the background counties are, are white gray.
The countries, the world are light green.
Whereas if I were to open up
a brand new QGIS document every time
and load these same data layers in there
the colors would be randomly selected each time
I added the dataset right?
I'd have to refilter the the national highway system
to only show I-35, that filter is being saved
in the map project.
My print composer window
and that layout that I set up to create my map example,
this is also saved in the map project.
So it's definitely worth considering
saving your map projects
before you close out of the application.
- Great, that's so informative
and you all can not even imagine
how many compliments you got and the job,
everybody is just very thankful
that you gave such a well in depth overview of GIS.
Clearly there's a lot to talk about.
So that's exciting. (laughing)
- Yeah I want to thank both Michael and Nathaniel
and Cynthia, everybody that was involved today
it looks like we're right at 12:01,
so I'm going to have to put a cap
on this very exciting conversation
and there's going to be more webinars coming soon
as well as more activity coming
from our GIS Interest Group here at TDL.
So if anybody has any questions
don't hesitate to contact me
a.suarez@Houstondotutexas.edu.
Feel free to check us out on tdl.org.
And yeah we look forward to seeing everybody
for the next one.
Thank everybody so much.
- Sure.