EXERCISE 4 – Keeping your feet dry
Environmental Resources 372:362
Intermediate Environmental Geomatics
Prologue - Data
Scale
As an example of how the
scale at which data is produced affects its accuracy and utility, let's take a
quick look at the
Now load Z:\databank\njdep2\admin\state
into the data frame. Next load Y:\intgeo\avdata\usa\states and restrict this
data layer just to display
Water, Water . . .
One of the most frequent
uses of GIS is to find areas on the earth’s surface that meet certain
conditions. For example, you want to buy a house – where would be the best
place to look? Well, you might start with a few simple criteria to start
narrowing down the possibilities. Such things as distance to work, location of
good schools and avoiding congested roads seem a reasonable place to begin. You
can make the treasure hunt for the ‘right house’ as complex as you want as long
as you have access to the data. (Remember acquisition of data can be the most
time-consuming part of any GIS endeavor).
Today you are going to do a
similar hunt – right in your back yard. We are going to work with the Lawrence
Brook watershed to see what types of landuse tend to
be found in floodprone areas. We might be interested
in this type of question for a number of reasons – you want a house with a dry
basement (good luck), you want to reduce your flood insurance payments or you
are a city manager who is interested in long term management of flood prone
areas to minimize flood damage downstream.
Oh, Where to Begin…..
Saving the Selected:
Add landuse and flood from \\ad-rsc\data\teach\intgeo\Classwork\avdata\lbwpuse and become familiar with
these layers and their attributes. We want to create a separate layer of water
and floodprone areas in the Lawrence Brook watershed.
Select those polygons that are water or flood prone.
Once you have created your
selection, export this as a new layer (right-click on layer name,
Data->Export Data) - it will contain just the portion of the flood layer
that meets the conditions you specified. To keep things straight, rename the
new layer to something you will remember.
Buffer for safety:
Generally, one would be
interested in completely avoiding flood prone areas which can be accomplished
by having a buffer around these areas (say 100 feet). To buffer the coverage
we've just created we'll venture into ArcToolBox and use the Buffer Tool (under the Analysis Tools/Proximity Menu). Fill in the
appropriate boxes, saving the output to your own directory (fill in the
appropriate buffer distance, set the dissolve
type to "all" and keep the rest of the defaults).
From the original flood
layer, we now have not only flood prone areas but also buffers around them. It
might be interesting to know what is the current landuse of those areas inside the buffer.
Again to ArcTool Box. Under Analysis Tools/Extract select Clip and then clip
the landuse by the buffered flood prone areas you
created – careful, be sure you have the right order of the layers in the wizard.
Again to keep things tidy and so as not to get confused, be sure to give your
new layer a logical name. Clipping extracts features from one layer using the
features from another layer as a cookie cutter.
After you clip, open up
your output table. Sort by area, then select the polygon
with the largest area. Now examine the selected feature closely. Notice
something odd? There's more than one polygon associated with that feature. How
can this be? Somewhat confusingly, a single feature (represented by a single
record in the attribute table) can be made up more than one polygon. The clip
creates one feature in the output layer for each clipped feature in the
original layer. Depending on precisely how the layer to be clipped overlaps
with our cookie cutter layer, there might one or there might be many polygons
for each feature in the output. This isn't a problem for us, but it is
something we should be aware of.
What is a problem for us is
that the areas in that you see in that column are from the polygons in the
original landuse coverage. You can verify this by
checking the area of the corresponding polygon in the original landuse layer.
What to do? You’re lucky
we’re using ArcGIS 9.2, this used to be a more annoying to fix. Clear any selected features. Add a field called Acres. Make it type double with a precision and
scale of 0. Then right-click on the
field name and select Calculate Geometry.
Set the property you wish to calculate to Area. Use the coordinate system of the data
source. Set your units to acres. Hit Ok.
Now you’ve got the real area of your polygons measured in acres. If you don’t believe me, repeat the process,
but calculate the units as square feet.
By comparing the original area field in your clipped output to the new
sq. footage field, you can then see which polygons in the clipped output differ
in area from the original land use layer, and get some idea of how much ArcGIS
would have overestimated the area if you didn’t fix this significant problem.
Creating a tally:
There is a lot of
information just in this new layer and while you can get a general sense of
what the predominant landuse is, let’s be a little
more specific. In the attribute table of landuse in floodprone areas, right-click on the field LUCODE and
summarize the different landuses by the sum of the
areas. This will create a table that shows the cumulative acreage for each unique value in LUCode.
Homework:
a. Create a map of the
above (land use in the buffered floodprone areas)
following the rules of map-making. Use the LUCODE for your map, but replace the
numbers in the legend with appropriate verbal descriptions of the land use
class. Use the following descriptions (it's up to you to match them with the
appropriate LUCODE values): barren, water, forest/shrub, wetland, developed,
agriculture. Be sure to use these descriptions instead of the numerical code on
the maps and questions that follow as well.
b. Which landuse (using LUCODE) has the most features in floodprone areas? Which landuse
(again using LUCODE) has the largest area inside the buffer? Answer in acres,
please.
c. Create a separate map
using the same information, but this time let’s assume we really don’t care
about buffers and we are only interested in what the landuse
is in the original, unbuffered floodprone
areas. Answer the questions in (c) above with this new condition. In addition,
which landuse changed the most when we did not
consider a buffer? Remember to convert your areas to the units you used in (b).
d. Do another map, but this
time let's be really floodprone sensitive and include
a buffer three times the original buffer. Which landuses
are now most affected compared to no buffer and the 300 foot buffer?
Homework is due before
start of class on Monday, February 25th.