Description
the lab question is
Q1: On your final map, identify which layers are vector layers and which layers are raster layers?
Q2: What information do your data need to have in order to use “Display XY Data” tool to display your data on the map?
the assignment question is
2. What is the highest cell value for the Agriculture_Suitability layer and how many raster cells in the map have this highest value? (5 points)
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GEOG/ESE 379: Intro to GIS
Lab Assignment 4 (40 Points)
There are two tasks in this assignment. Both tasks should be completed after completing the
tutorials and lab exercise of this week (Week 13). Submit them in a single pdf file. Name the file
as “LastNameFirstName.pdf” giving your last name and first name. Submit the pdf file on
Assignment 4 submission page.
Task1: Identifying new developments in the University of Illinois between 1973 and 2011
(20 Points)
In Tutorial 2 of this week you have georeferenced 1973 and 2011 aerial photos of the University
of Illinois. After georeferencing both of the aerial photos, compare the changes of structures
between 1973 and 2011. What changes can you identify?
1. Create a new polygon feature (Refer to Chapter 11; Catalog> Right-click your Tutorial
2_data folder > New > Shapefile)
2. Digitize/add at least two buildings that were built between 1973 and 2011. Consult Chapter
12 of tutorial book if you need any help for creating this new feature. After digitizing the
building polygon features, save your edits and stop editing.
3. Add a new field named “BldName” in your polygon map to store the names of new buildings
you identified (To fill in the field, you may need to start editing again).
4. Label the buildings with this field to show their names on the map.
5. Create two maps: one with 1973 aerial photo, and another with 2011 aerial photo, both with
only the two new buildings on top of their respective aerial photos. Make the building
polygons hollow fill with yellow outline color and give a distinct color to the labels so that
they are clearly visible in both of the maps.
Task 2: Urban Agriculture Suitability Analysis in St. Louis (20 Points)
You have to complete this task after completing the lab exercise of this week (Week 13). Give
your response to following two questions:
1. Create a map showing urban agriculture suitability in St. Louis that you calculated in lab
exercise. Insert the map into your assignment document. The map must incorporate the
following elements:
a. A title (e.g., Suitability for Urban Agriculture in St. Louis)
b. Your name beneath the title (1 points)
c. The Agriculture_Suitability layer symbolized according to the three classifications
applied in step 14 of lab exercise. Use green for high suitability areas, red for low
suitability areas, and yellow for moderate suitability areas. (3 points)
d. The Vacant layer symbolized with a purple color and with transparency for this layer
set to 50% (hint: Properties > Display tab). Place this layer on top of all the other
layers in the table of contents. (2 points)
GEOG/ESE 379: Intro to GIS
e. The StLouisCity layer symbolized with no fill color and a black outline of width 4.
Place this layer just below the Vacant layer in the table of contents. Zoom your map
to the StLouisCity layer. (2 point)
f. A legend that identifies the symbology for the Vacant, StLouisCity, and
Agriculture_Suitability layers. (2 point)
2. What is the highest cell value for the Agriculture_Suitability layer and how many raster cells
in the map have this highest value? (5 points)
GEOG 379
Intro to GIS
Lab Exercise: Urban Agriculture in St. Louis
The City of St. Louis is implementing an urban agriculture policy to help revitalize vacant land
within the municipal boundary. The Director of the Planning Department has asked you to do a
suitability analysis of urban agriculture that will consider soil types, contaminated sites, and
population in poverty by census block group. You have decided a multi-criteria raster analysis
of these three components is the best method for conducting a suitability analysis. You will
convert vector data into raster data, reclassify the data according to its suitability for agriculture,
and, finally, use the raster calculator to determine which sites are the most suitable for
agricultural land uses.
GIS Techniques
Vector data conversion, reclassification, raster calculation
Lab steps
1. Create a new folder for this in-lab exercise.
2. Download and extract this week’s lab data and move them to the in-lab exercise folder.
Open a new blank map document and add all of the layers in the provided file geodatabase.
Turn off the STL_Existing_LU layer.
3. Open the attribute table for the MO_County_Boundaries layer and select the row where
“NAMELSAD10” is equal to “St. Louis City” (Hint: using Select by Attribute). Be sure you do
not select St. Louis County, as this is not the incorporated City of St. Louis. Close the
attribute table.
4. Right-click MO_County_Boundaries in the table of contents and choose Data and Export
Data to save a new feature class named StLouisCity. Make sure you save it to the provided
file geodatabase. Remove the MO_County_Boundaries layer and zoom to the StLouisCity
layer.
5. In the Geoprocessing menu, choose Environments. Be sure to set the workspace (both
Current Workspace and Scratch Workspace) to this lab’s geodatabase. Set the
Processing Extent to be the same as the layer StLouisCity. Under Raster Analysis set the
cell size to 0.018. Since this data is in Geographic Coordinate System, we are assigning the
cell size to be 0.018 decimal degree which is equal to 1 mile distance in this area. Click OK
to apply these environment settings.
6. In the Customize menu, choose Extensions, make sure that Spatial Analyst is enabled.
7. Next you will prepare data to be converted from vector datasets into raster datasets. Notice
that there are two potential datasets for contaminated sites:
STL_2010_Below_Ground_Tanks and STL_2010_Superfund_Sites. You only want one
dataset so you do not have to repeat your work, so you will merge these two datasets first.
8. Select the Merge tool within the Geoprocessing menu. Under Input Datasets select
STL_2010_Below_Ground_Tanks and STL_2010_Superfund_Sites. Assign the Output
Dataset to your file geodatabase and rename it STL_Brownfields, then click OK to create
this merged layer. Remove the two datasets you used to create the STL_Brownfields layer.
9. Now you will use this new layer to create buffers around the brownfield sites and then to
convert it into raster layers. Areas far away from brownfield sites will get higher preference
for urban agriculture.
a. Within ArcToolbox, Analysis Tools, and Proximity, choose the Multiple Ring
Buffer tool.
b. Set STL_Brownfields as the input features and create the new feature class
STLBF_Buffer within the lab geodatabase. Since you are not sure of the nature
of each site’s contamination, you will give a liberal estimate of a safe distance
from the site for agricultural use. Set the buffer distances to 0.25 and 2 and set
the units to miles. Make sure ALL is selected under Dissolve Option. Click OK
to create this buffer then remove the STL_Brownfields layer.
10. Next you will convert each vector layer to be included in the suitability analysis into a raster
layer.
a. Within ArcToolbox, Conversion Tools, and To Raster, choose the Polygon to
Raster tool. Set STLBF_Buffer as the Input Feature, distance as the value field,
and make sure the MAXIMUM COMBINED AREA is selected under Cell
assignment type. This will ensure that the distance value that makes up the
majority of the cell will be attributed to the cell when it is converted. Name the
output raster STLBF_Buffer_Raster and click OK to create this raster layer to
your file geodatabase. Remove the STLBF_Buffer layer.
b. Repeat Step 10a for the STL_Soil_Types_Dissolve layer, but this time set the
Value Field as the field that ends in “Map Unit Name”. Import the cell size from
the STL_BF_Buffer raster layer. It should be 0.018. Also, make sure the
MAXIMUM COMBINED AREA is selected under Cell assignment type. Name
this output raster, STL_Soil_Types_Raster, click OK, and remove the
STL_Soil_Types_Dissolve layer.
c. Turn off both of the new raster layers. Now we will convert the poverty data
(StL_Pov_00 layer) to raster format. For the StL_Pov_00 layer, you will use the
Polygon to Raster tool again, but this time you will utilize the CELL CENTER
method under Cell assignment type since there is less variation across the
census tracts. Also, set “STL_Pov_Lev_CT_Data_00__Below_Pov_Lev_Pop”
as the Value Field (this field represents population below poverty level in each
census tract). Name the output raster STL_Pov_00_Raster, click OK, and the
remove the STL_Pov_00 layer.
11. Now we must reclassify each raster layer according to how suitable each category and
subcategory is for urban agriculture.
a. First, find the Reclassify tool under the Reclass Toolset in the Spatial Analyst
Tools section of ArcToolbox. Set STLBF_Buffer_Raster as the Input Raster and
then click the Classify button.
b. Under break values, set the first value as 0.26 and leave the other at 2. Click
OK. Reclassify values between 0.25 – 0.26 as 1 and those between 0.26 – 2 as
5 (click in the new values column to change these values as needed). This
reclassification defines sites farther away from brownfields as more suitable for
agriculture. Specify the name BF_Reclass for the output raster, click OK, and
then remove the STLBF_Buffer_Raster dataset.
c. Use the Reclassify tool again and set the Input Raster as STL_Pov_00_Raster.
Click the Classify button and notice that the values range from 0 to 1999 people
in poverty per cell. Set the number of classes to three and manually input each
class break value as 500, 1000, and 2000 (if you can’t edit the number of
classes, change the classification method, then change the number of classes
and go back to manual method).
d. In the new values column, set 0-500 as 1, 500-1000 as 3, and 1000-2000 as 5.
This reclassification defines cells with more people in poverty as more preferred
for the location of an urban agriculture program. Specify the name Pov_Reclass
as the output raster, click OK, and remove the STL_Pov_00_Raster dataset
from your map.
e. Use the Reclassify tool again and set the Input Raster as
STL_Soil_Types_Raster. Set the Reclass Field as the field ending in “Map Unit
Name.”
f. Reclassify the Urban land bottomland and upland values as 1, the three Urban
Land Harvester values as 3, the Menfro silt loam and Nevin-urban land values
as 5, and the Water value as 0. This reclassification assigns soil types to groups
ranging from least to most suitable for agriculture. Name the output raster
Soil_Reclass, click OK, and remove the STL_Soil_Types_Raster dataset.
12. Now choose the Raster Calculator tool in ArcToolbox, Spatial Analyst Tools, and Map
Algebra. Double-click each of the reclassified datasets and click on the plus sign to
construct the following equation: “Soil_Reclass” + “BF_Reclass” + “Pov_Reclass”
Name the output raster Agriculture_Suitability, click OK, and then turn off all the raster
datasets besides this newly created one.
13. From the STL_Existing_LU layer, open the attribute table, in “LAND_USE” field, select all
the areas with a “VAC/UNDEV” value (two rows in the attribute table) and export this data to
be a new feature class named Vacant and add this data to your map and make it visible.
14. Open the Agriculture_Suitability attribute table and add a new text field named
“Classification”. Conduct one or more field calculations (or start an edit session) so that the
value for the “Classification” field is “Low Suitability” where the cell values range from 2 to 5,
“Moderate Suitability” where the cell values range from 6 to 10, and “High Suitability” where
cell values are 10 or higher. You can apply select by attribute tool to select the cells based
on their cell values and then calculate values for the “Classification” field. Since it is a text
field and you are also assigning text value to it, you have to use double quotation marks
(e.g. “Low Suitability”) while calculating this field.
15. Save the map document to continue working on it for Lab Assignment 4.
GEOG 379
Intro to GIS
Lab Exercise: Urban Agriculture in St. Louis
The City of St. Louis is implementing an urban agriculture policy to help revitalize vacant land
within the municipal boundary. The Director of the Planning Department has asked you to do a
suitability analysis of urban agriculture that will consider soil types, contaminated sites, and
population in poverty by census block group. You have decided a multi-criteria raster analysis
of these three components is the best method for conducting a suitability analysis. You will
convert vector data into raster data, reclassify the data according to its suitability for agriculture,
and, finally, use the raster calculator to determine which sites are the most suitable for
agricultural land uses.
GIS Techniques
Vector data conversion, reclassification, raster calculation
Lab steps
1. Create a new folder for this in-lab exercise.
2. Download and extract this week’s lab data and move them to the in-lab exercise folder.
Open a new blank map document and add all of the layers in the provided file geodatabase.
Turn off the STL_Existing_LU layer.
3. Open the attribute table for the MO_County_Boundaries layer and select the row where
“NAMELSAD10” is equal to “St. Louis City” (Hint: using Select by Attribute). Be sure you do
not select St. Louis County, as this is not the incorporated City of St. Louis. Close the
attribute table.
4. Right-click MO_County_Boundaries in the table of contents and choose Data and Export
Data to save a new feature class named StLouisCity. Make sure you save it to the provided
file geodatabase. Remove the MO_County_Boundaries layer and zoom to the StLouisCity
layer.
5. In the Geoprocessing menu, choose Environments. Be sure to set the workspace (both
Current Workspace and Scratch Workspace) to this lab’s geodatabase. Set the
Processing Extent to be the same as the layer StLouisCity. Under Raster Analysis set the
cell size to 0.018. Since this data is in Geographic Coordinate System, we are assigning the
cell size to be 0.018 decimal degree which is equal to 1 mile distance in this area. Click OK
to apply these environment settings.
6. In the Customize menu, choose Extensions, make sure that Spatial Analyst is enabled.
7. Next you will prepare data to be converted from vector datasets into raster datasets. Notice
that there are two potential datasets for contaminated sites:
STL_2010_Below_Ground_Tanks and STL_2010_Superfund_Sites. You only want one
dataset so you do not have to repeat your work, so you will merge these two datasets first.
8. Select the Merge tool within the Geoprocessing menu. Under Input Datasets select
STL_2010_Below_Ground_Tanks and STL_2010_Superfund_Sites. Assign the Output
Dataset to your file geodatabase and rename it STL_Brownfields, then click OK to create
this merged layer. Remove the two datasets you used to create the STL_Brownfields layer.
9. Now you will use this new layer to create buffers around the brownfield sites and then to
convert it into raster layers. Areas far away from brownfield sites will get higher preference
for urban agriculture.
a. Within ArcToolbox, Analysis Tools, and Proximity, choose the Multiple Ring
Buffer tool.
b. Set STL_Brownfields as the input features and create the new feature class
STLBF_Buffer within the lab geodatabase. Since you are not sure of the nature
of each site’s contamination, you will give a liberal estimate of a safe distance
from the site for agricultural use. Set the buffer distances to 0.25 and 2 and set
the units to miles. Make sure ALL is selected under Dissolve Option. Click OK
to create this buffer then remove the STL_Brownfields layer.
10. Next you will convert each vector layer to be included in the suitability analysis into a raster
layer.
a. Within ArcToolbox, Conversion Tools, and To Raster, choose the Polygon to
Raster tool. Set STLBF_Buffer as the Input Feature, distance as the value field,
and make sure the MAXIMUM COMBINED AREA is selected under Cell
assignment type. This will ensure that the distance value that makes up the
majority of the cell will be attributed to the cell when it is converted. Name the
output raster STLBF_Buffer_Raster and click OK to create this raster layer to
your file geodatabase. Remove the STLBF_Buffer layer.
b. Repeat Step 10a for the STL_Soil_Types_Dissolve layer, but this time set the
Value Field as the field that ends in “Map Unit Name”. Import the cell size from
the STL_BF_Buffer raster layer. It should be 0.018. Also, make sure the
MAXIMUM COMBINED AREA is selected under Cell assignment type. Name
this output raster, STL_Soil_Types_Raster, click OK, and remove the
STL_Soil_Types_Dissolve layer.
c. Turn off both of the new raster layers. Now we will convert the poverty data
(StL_Pov_00 layer) to raster format. For the StL_Pov_00 layer, you will use the
Polygon to Raster tool again, but this time you will utilize the CELL CENTER
method under Cell assignment type since there is less variation across the
census tracts. Also, set “STL_Pov_Lev_CT_Data_00__Below_Pov_Lev_Pop”
as the Value Field (this field represents population below poverty level in each
census tract). Name the output raster STL_Pov_00_Raster, click OK, and the
remove the STL_Pov_00 layer.
11. Now we must reclassify each raster layer according to how suitable each category and
subcategory is for urban agriculture.
a. First, find the Reclassify tool under the Reclass Toolset in the Spatial Analyst
Tools section of ArcToolbox. Set STLBF_Buffer_Raster as the Input Raster and
then click the Classify button.
b. Under break values, set the first value as 0.26 and leave the other at 2. Click
OK. Reclassify values between 0.25 – 0.26 as 1 and those between 0.26 – 2 as
5 (click in the new values column to change these values as needed). This
reclassification defines sites farther away from brownfields as more suitable for
agriculture. Specify the name BF_Reclass for the output raster, click OK, and
then remove the STLBF_Buffer_Raster dataset.
c. Use the Reclassify tool again and set the Input Raster as STL_Pov_00_Raster.
Click the Classify button and notice that the values range from 0 to 1999 people
in poverty per cell. Set the number of classes to three and manually input each
class break value as 500, 1000, and 2000 (if you can’t edit the number of
classes, change the classification method, then change the number of classes
and go back to manual method).
d. In the new values column, set 0-500 as 1, 500-1000 as 3, and 1000-2000 as 5.
This reclassification defines cells with more people in poverty as more preferred
for the location of an urban agriculture program. Specify the name Pov_Reclass
as the output raster, click OK, and remove the STL_Pov_00_Raster dataset
from your map.
e. Use the Reclassify tool again and set the Input Raster as
STL_Soil_Types_Raster. Set the Reclass Field as the field ending in “Map Unit
Name.”
f. Reclassify the Urban land bottomland and upland values as 1, the three Urban
Land Harvester values as 3, the Menfro silt loam and Nevin-urban land values
as 5, and the Water value as 0. This reclassification assigns soil types to groups
ranging from least to most suitable for agriculture. Name the output raster
Soil_Reclass, click OK, and remove the STL_Soil_Types_Raster dataset.
12. Now choose the Raster Calculator tool in ArcToolbox, Spatial Analyst Tools, and Map
Algebra. Double-click each of the reclassified datasets and click on the plus sign to
construct the following equation: “Soil_Reclass” + “BF_Reclass” + “Pov_Reclass”
Name the output raster Agriculture_Suitability, click OK, and then turn off all the raster
datasets besides this newly created one.
13. From the STL_Existing_LU layer, open the attribute table, in “LAND_USE” field, select all
the areas with a “VAC/UNDEV” value (two rows in the attribute table) and export this data to
be a new feature class named Vacant and add this data to your map and make it visible.
14. Open the Agriculture_Suitability attribute table and add a new text field named
“Classification”. Conduct one or more field calculations (or start an edit session) so that the
value for the “Classification” field is “Low Suitability” where the cell values range from 2 to 5,
“Moderate Suitability” where the cell values range from 6 to 10, and “High Suitability” where
cell values are 10 or higher. You can apply select by attribute tool to select the cells based
on their cell values and then calculate values for the “Classification” field. Since it is a text
field and you are also assigning text value to it, you have to use double quotation marks
(e.g. “Low Suitability”) while calculating this field.
15. Save the map document to continue working on it for Lab Assignment 4.
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