So in the previous module, we talked about the specifics of raster data,
when you might want to use it, and some of the idiosyncracies of the file formats.
In this module, we're gonna focus more on the vector data model.
Now the vector data model, as we've talked about in the first module is,
made up of three different types of geometries, points, lines and polygons.
And then usually, associated data tables with each one of the features,
whether it be a point, a line or a polygon in the data set itself.
So there are other more complicated types of geometries that are available,
things like three-dimensional surfaces.
But we're really just gonna focus on the base three geometries,
zero-dimensional points, one-dimensional lines, and two-dimensional areas.
Now it's important to know that the basic unit of all of these is a point and
its coordinates.
But the actual unit that you're going to deal with,
if it's polygon file, is the polygon, it's not the individual points on it.
Now again, Raster data can be sort of a basis for vector data as we see here.
We can do some sort of outline of, of vector data,
vector data from Raster data itself.
And what this allows us to do us,
find a geometric or geographic entity, and isolate it in some way.
And there, from that, we can begin to get any sort of calculations of distance,
of area, things like that, that might be very difficult to do if we simply tried to
find those pixels in our Raster image.
But it is important to note that there is some schematization that
happens here as well.
We're never gonna get the boundaries 100% perfect.
Now Raster resolution as we talked about in module two changes or
is going to lead to a pixelization of the image as you zoom in and out.
Raster data are not particular flexible with regard to scale.
However vector data, conversely, are very flexible with regard to scale.
So we can see here, as we zoom in and out,
further in on this image, with three vector data layers in here.
We have states, we have highways and we have cities.
You can see that the individual points
that are being sort of rendered here as little circles stay the same size.
The width of the highway stays the same size no matter how far zoomed in or
out we are.
The edges of the polygons of the states stay the same size as well.
Now, regardless of whether we're dealing with points, lines, or polygons they're
all made up of points so the points, a point shaped file, or a point file,
a point data layer, would be individual points each with a record somewhere else.
Lines are gonna be a string of points sort of with a star and
an end and polygons are going to be a stream of points where the start and
the end are the same and the computer knows to render what is in the middle
as somehow different from what is outside of that polygon.
But there are all sorts of xy coordinates within some particular coordinates system.
Now we could have points an example would be the location of libraries.
Here which we see for South Bend, Indiana.
Lines could be things like highways.
It could be things like rivers.
It could be things like a track through a forest.
But each one of those lines is gonna be made up of individual points.
We might never see these under the hood, in the files themselves but
there are a series of points that make up these poly lines.
And in fact we call these individual points that make it up the vertices.
It's just the plural of the word vertex.
It means the points that make up that feature.
We can also have polygons, in this case we're seeing soil types so
this is something that's continuous across the area.
But we could have polygons that weren't continuous that had sort of
areas that they didn't cover.
If we had say the footprints of buildings or
the location of a particular type of forest.
And again, somewhere in the files themselves we have a series of vertices
athat make up the points, rather to make up the polygon.
Now, I already talked a little bit about the concept of schematization but
I want I wanna just address this in a little bit more detail.
Because there's really very little in the real world that's perfectly precise.
The different types of vector data are in fact choices that we're making to
representative different geographic features.
So something is always gonna be lost.
Boundaries are rarely ever a very distinct one thing or another.
Imagine a forest we can draw the boundary of a forest on a map but
if we zoomed in far enough if we went there and looked at it.
Its gonna be some sort of gradation between whatever is on
the outside somewhere where the forest itself is.
So they're not a lot of things that have perfect boundaries on them.
So using a vector data model we should always be aware of this schematization.
It's not usually a problem just because of the detail that we want to
use in our analysis.
And using a vector model is gonna allow us to do a much quicker type of analysis and
render these things much much more effectively.
However it is something we should be aware of when we're working with To rest or
it will affect your data.
Now one thing that is very important in GIS,
particularly in web GIS and systems where we are rendering lots and lots of data,
all at the same time is to be able to display things at different scales.
And one of the advantages of the vector data model is this is easier to do
with vector data.
We can simply say if you're zoomed out really far don't display all of
the points.
Or don't display things if you're after if you're zoomed in past a certain scale.
Raster data, this is much more complicated.
We have to create a different layer that was sort of the zoomed out version to be
able to show all of those pixels.
But really with vector data,
it's much more easy to create these kind of dynamic zooming scales.
So just to review some of the advantages and
disadvantages of the vector data model.
In contrast to raster data,
vector data take up a lot less room on your hard drive.
The file sizes tend to be a lot smaller,
and that's simply because it's storing individual points and
how they're related together, instead of every single pixel in that image.
Because of there nature because of there being points, and being very distinctive
boundarys between things they're very good for discreet features.
Locations, boundaries, roads things like that.
They don't tend to do quite as well with things that grade from one thing
into another.
Stuff like topography or density those are things that might be
better represented with the raster data model.
Vector data are much more flexible with regards to scale.
It's easier to set rules for what happens when you zoom in and out on the map,
it's easier to be able to zoom into a higher level of detail with them.
And one of the big advantages that we'll talk about in just a moment here.
Is that we can store a lot of different attributes associated with each feature.
Whereas with Raster data often you would have to have an entirely separate layer
for a different data thing to be in the same layer.
In the cases of Vector data it's very easy to just add another field to the table.
Now some of the disadvantages we have already addressed a little bit.
Vector data tend to be very schematized view of reality,
they tend to be maybe artificial boundaries placed in there sometimes.
And because of this they tend to be less suited for continuous phenomena.
So lets turn to just talk a little bit about attribute data.
And about those tables that intend to be associated with Vector data.
Now if we wanted to look at one of these tables it should look
remarkable like what you might think of as a data table in Excel or
in some other spreadsheet program.
We have our rows that are individual records and
we have our columns that are the fields.
So every feature, whether it be a point, line or polygon, has a record and
then it has a value for each one of those fields.
That value might be blank but it has to have something in there for
each one of the records, for each one of the features that we have.
Now in contrast to a regular spreadsheet,
it's better to think about these attribute tables as kind of a simple database.
And the big difference between a spreadsheet and
a database is usually that a database, for
every field that we have, we have one type of data that's gonna go in there.
So if we have a numeric field, we have to have numbers in there.
Whereas in something like Excel or Open Office,
a more conventional spreadsheet program.
If I were to type in for an elevation field or something, I could type in
values 3.6, 8.4, and then I could type high if I wanted to as well, or large.
I can type words in a numeric field without any real repercussions for it.
And that's prevented from happening in these attribute tables.
But the attribute tables themselves are very important to GIS analysis.
There a kind of fundamental strength of the GIS system and
can really could be more accurately viewed as part of a larger spatial database.
So the structure of these is kind of crucial to the success of whatever
analysis we're gonna be working with.
And we can tie these tables together, either between each other based
on spatial location or based on some sort of common or indexed view between them.
We'll talk about this in a lot more detail in one of the other modules on tables and
databases.
So as far as physically storing that extra data it is a little bit different than
we might think about from a more conventional way
of thinking about how we store files on a computer.
So we store vector data usually in what we call shape files.
So each shapefile is one layer and that layer is either point that shapefile
is either a point shape file, a poly line shapefile or a polygon shapefile.
And it's actually physically on the hard drive made up of several different files.
So it's important to remember that we might have three to six different
files all with the same name and with a different extension, so after the period.
So we'd have one that would be the file name .shp.
That's the main shape file.
We'd have one that's .shx, that's an index file.
And then we'd actually have the attribute table stored separately from the rest on
the hard drive.
So it would be the name of the file .dbf.
Now we've talked a little bit about the strengths of Vector Data as far as being
able to do selections, being able to do querying based on locations.
And I wanna just go into a little bit more detail on when we might want to
do some of these different interactions with Vector Data.
So we have two main ways that we can query vector data effectively in ArcGIS and
in most GIS programs.
One is by selecting by attributes, so
by selecting by some part of the attribute table.
And the other is selecting by location, so selecting geographically
between different layers based on their proximity to each other.
So just as an example of how we would do a select by attributes.
And we'll do more of these in the demonstration for this module as well.
But we might want to go through, in this case we have a shape file representing all
the states of the US and their associated attribute table.
So we might go through and select those features where the name is Arkansas.
And you can see that the feature has been, Arkansas has been selected here.
We know this because of that cyan color,
that sort of indicates that it's been selected.
It's now in both in the data table and in the actual geography as well.
But we're not limited to just doing one at a time,
we could select all of those states what have a population above five million.
You can see all of those have been selected here.
But we can also do select by locations, so we can go through and
select individual things based on their proximity or
based on the spacial relationships between one layer and another.
So here we have shape files representing US cities, lakes, and
rivers in addition to the actual states themselves.
And if we just zoom in a little bit, we can do a selection where we might wanna
query what cities are within ten kilometers of a lake.
We can do this using Select by Location.
You can see that all of those particular cities have been selected here.
We could also look at how things intersect.
So we could say, if we wanted to find out what polygons or
polylines features intersect
on the longer edges Here we have my home state of South Carolina that we can quite
easily query what rivers intersect the boundaries of South Carolina.
We can also look at distances so we can query any features
that are within a given distance from the edges of another one.
We can do this by looking at what lakes are within 300 miles of Chicago.
And here it pops up most of the Great Lakes.
We can also look at what cities are within 10 miles of any river, so
we can start with a polyline and query whatever points or
anything else are within a proximity of that particular polyline.
And we can do the same thing with polygons, so
if we have these polygons representing the lakes,
we can select all the cities that are within ten miles of any one of them.
Now, there are other more complicated ones,
we can look at completely within relationships.
So if I select Utah here, I can select afterwards I can query what
lakes are completely within the geography of geometry of Utah.
And you see The Great Salt Lake comes up here, it's being selected.
But Lake Powell down in the south there does not because there's a part of it that
goes outside of the boundaries of Utah.
Now there's some other kind of complex ones in here as well that can do things
like query, between different polygon layers,
where the center point of something is.
So in this case, if I wanted to query what lakes have their center
point within either South Carolina or Georgia.
I can look at Clarks Hill Lake here, and if I do this for
South Carolina, it's not selected.
But, if I do it for Georgia it is.
And that's because we're looking at the center point, or the centroid of that
polygon that represents Clarks Hill Lake, and what you notice is that in this case,
the centroid, the center point of that polygon is not actually in that polygon.
So it is important to kind of keep in mind that some of these more complex spatial
relationships might be a little more difficult to run the analysis on.
So that's really the basic of the vector data model.
And what we're going to do next is within the following module we're gonna talk
a little bit about how we do projections.
How we take three dimensional data from both Raster and data models and
actually flatten them out and create a formal map out of them.