EXERCISE 5 – Keeping your feet dry

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 New Jersey coast.

First, we need to establish contact with the network drive that hosts databank:

Now load Z:\databank\njdep\admin\state into the data frame. Next load Y:\intgeo\avdata\usa\states and restrict this data layer just to display New Jersey. Make it transparent. Compare the outline of the coasts between the two data sets. You'd get quite a different measure of the length of the coast from each of these data sets. Think a second about the coast of New Jersey. What's missing from both of these data sets? Add Z:\databank\njdep\hyd\coastply. Ah, that's right. The state outline from the countrywide layer may be good for continental applications, but you wouldn't want to use it to determine sampling sites based on distance from Barnegat Bay, for instance.

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…..

Add landuse and flood from Y:\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. Notice that the flood coverage has a code (the "flood" field) but doesn't have an description for the codes. We need to use a look-up table that contains descriptions of the code values. Load Z:\databank\njdep\flood\flood.lut and join it to the flood coverage attribute table. After joining, select those polygons that are water or flood prone.

Once you have created your selection, save this as as a new layer (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.

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 ArcTool Box and use the Buffering Wizard (under the Analysis Tools/Proximity Menu). Fill in the appropriate boxes, saving the output to your own directory (keep the defaults but fill in the appropriate buffer distance).

From the original flood layer, we now have not only flood prone areas but also buffers around them. But what we really want to know is what is the current landuse of those areas inside the buffer. Again to ArcTool Box. Under Extract select Clip the landuse by the flood prone areas you created – careful, be sure you have the right order of the files 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.

After you clip, open up your output. Sort by the area field. Notice something strange? What are the chances that all of those polygons would have exactly the same area? Not likely. This is one of the limitations of using shapefiles for analysis. The areas in that you see in that column are from the polygons in the original landuse coverage. One of the original landuse polygons can be split into any number of clipped polygons depending on how the two layers overlap.

What to do? Well, it's a bit ugly, but we can get the appropriate areas into the clipped layers. Add a field (type double) called realarea to the clip output. We need to do calculation the likes of which you haven't seen in this class yet. In the Field Calculator, click on the Advanced check box. Cut and paste the following Visual Basic script from the web version of this document:

Dim dblArea as double
Dim pArea as IArea
Set pArea = [shape]
dblArea = pArea.area

Set realarea equal to dblArea and perform the calculation. If all went well you should have the real area of the clipped polygons in the realarea field.

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, click on the field for LU Code and summarize the different landuses by the sum of the areas. This will create a database file that gives you a better sense of what the actual areas are.

Homework:

a. Create a map of the above (land use in the buffered floodprone areas) following the rules of map-making . (Hint: make your scale bar a reasonable unit of measurement.)

b. When you look at the attribute table, the numbers are very large. What are units of measurement here? (Hint: what were the units in the original landuse coverage?) To make these numbers more reasonable to understand, puts these numbers into a unit that makes more sense (for example, if it is feet turn the large numbers into miles.) Use these new, more sensibly expressed areas to answer the area questions below. Also remember you are working with area so provide your answers in the appropriate notation for area.

c. Which landuse (using LUCODE) has the most polygons in floodprone areas? Which landuse (again using LUCODE) has the largest area?

d. 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).

e. Do another map, but this time let's be really area sensitive and include a buffer three times the original buffer. Which landuses are now most affected compared to no buffer and the 50 meter buffer?

Homework is due before start of class on Monday, February 23rd.