The town of Stowe, Vermont, USA, has experienced a substantial increase in population. Demographic data suggests this increase has occurred due to families with children moving to the region, taking advantage of the many recreational facilities located nearby. it has been decided that a new school must be built to take the strain off the existing schools, and as a town planner you have been assigned the task of finding the potential sites.
In this exercise you will find suitable locations for a new school. The four steps to produce such a suitability map are outlined below.
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1. Click the Add Data button and add the
landuse, rec_sites, elevation and schools layer.
2. Now, we are going to set up the spatial extend for our project to the landuse
layer. Click the Spatial dropdown arrow and click Options. Specify a
working directory on your local drive in which to place your analysis results
(chose My Documents and create a folder with your name). Click the Extent
tab. Click the Analysis Extent dropdown arrow and click Same as Layer 'landuse'.
Click the Cell Size tab. Click the Analysis Cell Size dropdown arrow and
click Same as layer 'elevation' since this is the layer with the lowest
resolution of the whole project.
3. Go to File > Map Properties, click on Data Source Options
and check Store relative Path names to save files in a relative path
structure.
4. Save the project as NewSchool.mxd into your folder.
Deriving data from your input datasets is the
next step in the suitability model. You will derive the following:
a) Slope from elevation
b) Distance from recreation sites
c) Distance from existing schools
a) Deriving Slope
Since the area is mountainous, you need to find areas of relatively flat land to build on, so you will take into consideration the slope of the land.
1. Click the Spatial Analyst dropdown
arrow, point to Surface Analysis, and click Slope.
2. Click the Input surface dropdown arrow and click elevation.
3. Type Slope in the Output raster text box to permanently save your
output slope dataset to your folder.
4. Click OK to create the raster. High values (red areas) indicate
steeper slopes.
b) Deriving distance from recreation sites
In this model, it is preferable that the school be built near recreational facilities, so you will now calculate the straight-line distance from Recreation Sites.
1. Go to Spatial Analyst > Distance
> Straight Line.
2. Choose rec_sites in the Distance to dropdown menu.
3. Save the file as DistRecSites and click OK to create the
raster. Values of zero indicate the location of a recreation site, with values
(distances) increasing as you move away from each of these sites.
4. Uncheck the box next to schools to turn off this layer so you
only see the location of the recreation sites and the distance to them.
c) Deriving distance from schools
You will now derive a dataset of distance from existing schools. It is preferable to locate the new school away from existing schools to spread out their locations through the town.
1. Go to Spatial Analyst >
Distance > Straight Line.
2. Choose schools in the Distance to dropdown menu.
3. Save the file as DistSchools and click OK to create the
raster.
4. Turn off the recreation sites layer and turn back on the schools layer for
further examination.
You now have the required datasets to find the
best location for the new school. The next step is to combine them to find out
where the potential location can be found. In order to combine the datsets, they
must first be set to a common scale. That common scale is how suitable a
particular location (each cell) is for building a new school. You will
reclassify each dataset to a common scale, within the range 1-10, giving higher
values to attributes within each dataset that are more suitable for location the
school:
a) Reclassify slope
b) Reclassify Distance to recreation sites
c) Reclassify Distance to schools
d) Reclassify landuse
a) Reclassifying slope
It is preferable that the new school site be located on relatively flat ground. You will reclassify the Slope layer, giving a value of 10 to the most suitable slopes (those with the lowest angle of slope) and 1 to least suitable slopes (those with the steepest angle of slope).
1. Go to Spatial Analyst toolbar and
click Reclassify.
2. Choose Slope from the Input raster dropdown arrow.
3. Click Classify.
4. Choose Equal Interval as the classification method. Choose 10
classes. Click OK.
5. You want to reclassify the Slope layer so steep slopes are given low values,
as these are least suitable for building on. Click the first New value
record in the Reclassify dialog box and change it to a value of 10.
Give a value of 9 to the next New value, 8 to the next, and so on. Leave
NoData as NoData.
6. Save it as ReclassSlope.
7. Click OK to create the raster. Locations with higher values
(less-steep slopes) are more suitable than locations with lower values (steeper
slopes).
b) Reclassifying distance to recreation sites
The school should be located near recreational facilities. You will reclasify this dataset, giving a value of 10 to areas closest to recreational sites (the most suitable locations) giving a value of 1 to areas far from recreation sites (the least suitable locations) and ranking the values in between. By doing this you will find out which areas are near and which areas are far from recreation sites.
1. Go to Spatial Analyst > Reclassify.
2. Click the Input raster dropdown arrow and click Distance to rec_sites.
3. Click Classify.
4. Make sure Equal Interval is set up as the classification method and
10 classes are chosen. Click OK.
5. You want to locate the schools near recreational facilities, so you will give
higher values to locations close to recreational facilities, as these are the
most desirable. As you did when reclassifying the Slope layer, click the
first New value record in the dialog box and change it to a value of
10. Give a value of 9 to the next, and so on. Leave NoData as NoData.
6. Save it as ReclassRec.
7. Click OK to create the reclassification raster. It shows locations
that are more suitable for locating another school. High values indicate more
suitable locations.
c) Reclassifying distance to schools
It is necessary to locate the new school away from existing schools in order to avoid encroaching on their catchment areas. You will reclassify the Distance to schools layer, giving a value of 10 to areas away from existing schools (the most suitable locations) giving a value of 1 to areas near existing schools (least suitable locations) and ranking the values in between. By doing this you will find out which areas are near and which areas are far from existing schools.
1. Go to Spatial Analyst > Reclassify.
2. Click the Input raster dropdown arrow and click Distance to schools.
3. Click Classify.
4. Make sure Equal Interval is set up as the classification method and
10 classes are chosen. Click OK.
5. You want to locate the school away from existing schools, so you will give
higher values to locations farther away, as these locations are most desirable.
As the default gives high New values (more suitable) to high Old values
(locations farther away from existing schools) you do not need to change any
values this time!
6. Save it as ReclassSchoo.
7. Click OK to create the reclassification raster. It shows locations
that are more suitable for locating another school. Higher values indicate more
suitable locations.
d) Reclassifying landuse
At a town planners meeting it was decided that certain landuse types were better for building on than others, taking into consideration the costs involved in building on different landuse types. You will now reclassify landuse. A lower value indicates that a particular landuse type is less suitable for building on. Water and Wetlands will be given NoData as they cannot be built on and should be excluded.
1. Go to Spatial Analyst > Reclassify.
2. Click the Input raster dropdown arrow and click
landuse.
3. Choose landuse in the Value field.
4. Type the following values in the New values column:
- Agriculture: 10
- Barren land: 6
- Brush/transitional: 5
- Built up: 3
- Forest: 4
5. You will now remove the Water and Wetland attributes and change their values
to NoData. Select the rows for Water and Wetlands (shift
key for multiple selections) and click Delete Entries.
6. Check Change missing values to NoData. All values for Water and
Wetlands will be changed to NoData.
7. Save it as ReclassLand. Click OK.
8. The output reclassified landuse dataset will be added to your ArcMap session
as a new layer. It shows locations that have landuse types that are considered
to be better than others for locating the school (higher values indicate more
suitable locations).
9. Bring up the Properties of the reclassified landuse layer. Click the
Symbology tab and click the Display NoData as dropdown arrow and
choose Arctic White to show NoData (Water and Wetlands) in this color.
Click OK.
After applying a common scale to your datasets,
where higher values are given to those attributes that are considered more
suitable within each dataset, you are ready to combine them to find the most
suitable locations. If all datasets were equally important, you could simply
combine them at this point. however, you have been informed that it is
preferable to locate the new school close to recreational facilities and away
from other schools. You will weight all the datasets, giving each a percentage
influence. The higher the percentage, the more influence a particular dataset
will have in the suitability model. You will give the layers the following
percent influence: (Each percentage is divided by 100 to normalize the values):
- Reclass of Distance to rec_sites: 0.5 (50%)
- Reclass of Distance to schools: 0.25 (25%)
- Reclass of landuse: 0.125 (12.5%)
- Reclass of slope: 0.125 (12.5%)
1. Click the Spatial Analyst dropdown
arrow and click Raster Calculator.
2. Create following expression:
[Reclass of Distance to rec_sites] * 0.5 + [Reclass of Distance to schools] *
0.25 + [Reclass of landuse] * 0.125 + [Reclass of Slope of elevation] * 0.125
(double-click on the layer names to bring them into the expression box)
3. Click Evaluate to perform the weighting and combining of the datasets.
The output raster dataset shows you how suitable each location is for locating the new school, according to the criteria you set in the suitability model. Higher values indicate locations that are more suitable. The suitable locations are those areas that are close to recreation sites, away from existing schools, on relatively flat land, and on certain types of landuse. The higher weightings set for Distance to schools and Distance to rec_sites have a powerful influence on deciding which areas are more suitable than others.
4. Bring up the Properties for the
new layer. Click the Symbology tab and click Classified from
the Show list.
5. Setup 10 classes. Scroll to the last three classes, select
them, right-click the highlighted classes and click Properties for
selected colors.
Give them a bright color.
6. Click the Display NoData as dropdown arrow and click the color
black. This displays values of NoData (Water and Wetlands) in black. Click
OK.
You decide that there are three main potential areas for locating the school. They are labeled in the diagram below.
7. We did not save the Raster Calculation to
our folder. To save the file, right-click the new layer in the table of
contents and click Make Permanent. Save it as Suitability.
8. Also rename the layer in the table of contents to Suitability.
Create a layout of the suitability raster
(uncheck all other layers to make them invisible) and add a title, your name,
scale bar, and other common items you think are appropriate. Export it as NewSchool.pdf
and submit it to your Webfolder.
Lab created by Eva Grund, February 2005, based on ESRI's ArcGIS 9 - Using ArcGIS Spatial Analyst manual, Chapter 2, Exercise 2.