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- [Brian] In part two of our lecture,

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I'll demonstrate some
of the lecture concepts

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that we saw in the last slide deck.

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We'll look at both vector

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and raster geo processing operations.

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On the vector side,

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we'll use some parcel data
to explore the union, merge

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and append geo processing tools.

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On the raster side,

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we'll work with some
digital elevation models

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to create a mosaic and add rasters.

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We'll also explore the append
tool for raster data as well.

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If we switch gears here and
look at the ArcGIS Interface,

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we see we've got two parcel datasets,

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Waitsfield Parcels Standardized
shown in light green,

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and Warren Fayston Parcels
shown in a darker green.

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Remember the end goal here is to produce

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a single unified parcel dataset

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that we could use for our analysis.

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Let's first look at the attribute tables

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of the two different datasets we have,

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Our Waitsfield Parcels
Standardized dataset

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contains a number of different attributes

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related to the grand list,

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Vermont's tax rolls
collected at the town level.

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If we look at our Warren
Fayston Parcels attributes

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we see a significantly shorter list.

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Looks like just the key
items out of that grand list.

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Now this won't affect our union operation,

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but think about how choosing

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the appropriate right first input dataset

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might affect the merge
and append operations.

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And we'll take a look at
those both in a minute.

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Let's start with the union.

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Again, think about the fact
that we want to combine

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our parcels data into a
single cohesive parcel dataset

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where all the attributes match up.

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And there are no repeat attributes
represented in the table.

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If I union my two datasets,

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we can look at the output.

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We've got a dataset now
called parcels union,

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and if we look at that
attribute table, what do we see?

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Well, we've got the FID from
the Warren Fayston Parcels,

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and anywhere that value
is greater than zero

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indicates that it comes
from the original dataset,

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that Warren Fayston Parcels,

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and anything with a
negative value indicates

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that that comes from

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the Waitsfield Parcel
Standardized dataset.

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If I continue to scroll over,

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we see now that we've got
quite a few attributes.

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And in fact, we see if we
were to interrogate slowly,

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we could see the repeat
values in the attribute list.

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Let's take another look
from the field interface

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to see if it's a bit easier
to see what I'm talking about.

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If we scroll down,

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we see we've got all these
value related-attributes:

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Real, Hstead, NRES, Land, Improvements.

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And if I continue to scroll
down here through the list,

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I see I've also got these values,

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but they all have underscore one.

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This is exactly why the
union tool does not work

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for assembling these datasets.

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We were not able to do any field mapping.

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So all of the attributes
from the Waitsfield dataset

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are written to the end
of that attribute table.

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In other words, additional
attributes were created

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in the output dataset.

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This is pretty inefficient

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and won't really work for
what we're trying to do.

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Let's take a look at
another possibility here,

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and we'll use the merge command this time.

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So remember how I mentioned previously

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that there were different attributes

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in each one of the datasets.

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Remember also that it really matters

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which dataset we select first

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because with the merge tool, as we know,

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we're going to write
out a new feature class,

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and that new feature class,

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the structure of that attribute table

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will be based on the information

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that's drawn from the first
of your input datasets.

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Well, I like the format of the
Warren Fayston Parcels data.

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So I'm gonna enter that one first.

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And now we see all the attributes

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in the output fields list here

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in the lower left portion
of the merge dialog box.

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Now, if I add the Waitsfield
Parcel Standardized,

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I can scroll down the list,

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and I see a bunch of twos
next to all of my categories.

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That's a good sign.

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That means that those fields
have mapped from both datasets

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onto that single Warren Fayston dataset.

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If I scroll down further, I can also see

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a list of other attributes
with no numbers next to it.

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All of these came from the
second dataset that I entered

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into my input list,
the Waitsfield Parcels.

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We can just get rid of those,

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so they don't get written to the output.

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If I click on the first one

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and click on the last one
that I want to get rid of,

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click that red X,

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and those will not be
in the output dataset.

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Remember, I didn't delete
anything from a dataset.

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I just told ArcGIS not to
include those attributes

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in my output dataset.

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Let's call this one parcels merge.

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Click run and evaluate our result.

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Well, that doesn't look
exactly like I was hoping.

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It appears that none of
the Waitsfield parcels

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were written to the output dataset.

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Let's investigate to see
what we can figure out here.

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I received a warning that indicates

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that ArcGIS is not able to
write the Waitsfield value

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to the town attribute.

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Let's check out why that might be.

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If I click on the town attribute

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and the properties of
that particular attribute,

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I notice that it's a text
field, which is good,

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but it's only got a length of seven.

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Try it in your head to
spell out Waitsfield

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or count the number of
letters, and you'll notice

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that it's definitely more than seven.

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It's actually 10.

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That wasn't a problem for
either Fayston or Warren,

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but for Waitsfield, the
length limit on the text field

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means that the whole name of
the town can't be written.

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ArcGIS gets confused, and as a result,

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none of those records were added.

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Let's try this again, but this time,

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we'll set our length to 10.

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Click run.

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So far so good with no warnings.

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And look here, we've
produced a new dataset

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with all the parcels for
the matter of Valley towns.

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If we open the attribute
table, we can scroll through

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and see that short list of
attributes that was included

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in that original Warren
Fayston Parcels dataset.

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So one last thing to check here.

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We've got 5,474 records in
our parcels merged dataset.

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Let's just do a quick little bit of math

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to see that we've got 4,471 parcels

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in our Warren Fayston parcels dataset

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and 1,003 parcels in our
Waitsfield parcels dataset.

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So if we add those two together,

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we get that 5,474 records in the dataset.

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We can interpret that to mean

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that all of the individual features

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of the two datasets
were successfully added

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to our new output dataset,

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the parcels merged that we just created.

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Okay, let's look at one more example here

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before we move on to our vector analysis.

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In this last example, we'll
look at the difference

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between the merge and the append tool.

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So we just ran the merge

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and successfully created
a unified parcel dataset.

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Remember we had two input datasets

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and used the merge tool to
produce a new output dataset.

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What about the append tool?

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Locate that in my geo processing pane,

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and now I need to add
my input datasets here.

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Just a couple quick
notes before we proceed

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with the append tool.

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Note that this tool will
modify the input data.

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We've also got two required parameters,

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an input dataset and a target dataset.

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Our input dataset contains
the data to be appended

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to the target dataset.

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So that means that in this case,

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our Waitsfield parcel standardized

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would be our input dataset
and that target dataset,

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the dataset where the data of
the inputs will be appended

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is going to be our Warren Fayston Parcels.

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With that, I can click run.

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Notice that that failed
almost immediately.

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If I look at the details here,

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it indicates that the parcels,

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the schema for the attribute
tables do not match

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between the two datasets,

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but that's what I specified
for my field matching type.

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Let's change that to use the field map

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to reconcile my differences.

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That takes us back into our
familiar field map list,

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Where we can see all of
the different attributes

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from before.

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Click run again,

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and this time the tool runs to completion

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but with some errors.

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It looks like it's that same
issue that we had before

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where we can't write the town output,

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town name into the output table.

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Let's try to fix that one more time

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Remembering that we needed 10
characters, not just seven.

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And click run one more time.

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It looks like we're still
suffering from that same problem.

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Surprising that the append operation,

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let me change the length of the field,

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but it does not appear that that actually

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that change actually
took hold in the dataset.

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Okay, we got one more thing to try.

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It's not quite time to give up just yet.

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Let's look at our Warren
Fayston Parcels dataset,

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and check out that attribute table.

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If I go into my fields view,

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I can see that my town is a text data type

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with a length of seven.

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Let's change that length
here and save those changes.

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This doesn't do anything to the data

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other than make the container,

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that text container for the town attribute

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a little bit bigger.

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Okay, let's try that
merge just one more time

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or the append just one more time.

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I've made changes to the data.

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So it's always good to back

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all the way out of the tool
and reset your parameters.

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Once again, I've got my Waitsfield
parcels for my input data

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and my Warren Fayston
parcels for my target data.

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Let's use that field map to
reconcile any differences,

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and now when I scroll down to town

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and switch over to the properties tab,

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I see it's got that length of 10.

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It seems like things are looking up.

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I'll click run, and it looks
like my append is completed.

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If I open the attribute table
for my Warren Fayston Parcels,

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I see I've got 5,474 records.

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That's the same number that we achieved

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when we used the merge tool.

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Now takeaway message here,

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remember that union, not
the right tool for the job.

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If you use the merge tool,

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that's going to produce a
new output feature class.

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And if you use the append tool,

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that's going to modify
one of the input datasets

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that you supply to the append tool itself.

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So that's it for the
vector side of things.

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Let's switch over to raster

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and take a look there real quick.

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To explore the raster example,

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I downloaded some data
from the USGS national map

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one meter digital elevation model.

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We can see the two different slices

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of the data on the screen here.

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So, what are we gonna do here?

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We want to put these two pieces of the DEM

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into a single piece that we
could use in our analysis.

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The equivalent of the
merge on the vector side

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is what's known as
creating a Mosaic dataset.

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So I need to specify an output location,

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and I'll write that information

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to my geo database for the project.

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I need to give my Mosaic dataset a name.

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And lastly I need to
give my Mosaic dataset

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a coordinate system.

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I'm going to use the
input coordinate system

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of one of my datasets, which I
see is the UTM Zone 18 North.

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That's it for creating the Mosaic dataset.

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Just click run, and ArcGIS will add that

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to the database and to the project.

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Note, we've got the boundary,

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the outer bounds of my
Mosaic, the footprints,

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the boundary of the individual tiles

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that go into the Mosaic,
and then the image itself.

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Right now the image
doesn't show us anything

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because we haven't added
any data to the Mosaic.

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Let's add some data now.

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I'll use the add rasters
to Mosaic dataset,

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specify the Mosaic dataset here

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as the one that we just created.

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And the last thing we need to
do is supply some input data.

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If we hover over that information,

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we see that we have three
different options here,

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four different options, actually.

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And that the right one for
us will be the dataset option

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because we're using TIFF files.

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So let's locate those files
and add them to the Mosaic.

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So I add the two TIFF files

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to my list of rasters
that I want to include

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in my Mosaic dataset and click run.

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And now we can see our result.

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We've got our footprints
that define those two slices

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of the digital elevation
model and our resulting image.

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Note the values there in the image itself.

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Let's fix that.

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If we go back to our geo
processing interface,

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we can calculate statistics
on our raster dataset.

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In this case, the Mosaic.

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We don't need to skip anything,

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and we're not going to
specify an area of interest.

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We'll just click run.

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We note that the software
locks down that Mosaic dataset

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while the statistics are being computed.

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We can see those lock icons,

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and once the calculate statistics is done

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that lock icon will disappear.

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Okay, well, calculate
statistics is done running,

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and now we see from our image

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that the symbology looks much better.

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Now, if I had other data

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that I wanted to add into my Mosaic,

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I could certainly do that at this time.

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And I'll note that I
have two other tiles here

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that are not directly
connected to the data

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that I've already built into the mosaic.

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But instead of using that
add rasters to the Mosaic,

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I wanna show you one other thing.

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I'm going to export

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the raster dataset that we just created

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and then we'll use the append tool

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to actually add those other
two slices into the dataset.

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I've set up the export raster tool.

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And I'll just click export
to produce that output.

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All right.

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The export image ran successfully,

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and now I've got that
standalone raster dataset

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that we see up on the top.

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One last thing before we're done.

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Let's add in these other two pieces

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using the append tool.

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Remember that append
tool is going to modify

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my target dataset.

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In this case, the export from
the Mosaic I just created.

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I've added the other two
input datasets from the list.

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And now I'll click run to
see what I can produce.

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Looks like my append operation
concluded successfully.

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So now let's look over at the
map pane to check our results.

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And sure enough, I've got
those two other pieces

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that I appended to the
exported Mosaic dataset.

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That's it for this demo.

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Remember one of the key takeaways here

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is the difference between
the union and the merge tool

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when you're trying to combine vector data.

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And that when we're
working with raster data,

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we take a slightly different approach,

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but note that the append tool

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can work for both raster and vector data.

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The primary difference between
the append and the merge

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is that the merge tool
will produce a new dataset,

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and the append will modify
an existing dataset.

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Keep all these factors in mind

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as you think about ways
to assemble and overlay

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your data for analysis.