Module 8 Urban Planning, Location Decsions - Welcome to the SWAMP!!!

This week’s module focused on making locations decisions based on using basemap layers, Euclidean Distance analysis, Reclassifying Data, Weighted Overlays, attribute table field calculator, Page layouts and model builder.  Since multiple data frames were needed for each map, it was important to create some base map layers and to be sure that when adding data frames the correct coordinate system was chosen.  With this lab we continued to practice reviewing our metadata and the appropriately organizing both provided data and created data New this week was the use of Euclidean (as the crow flies) Distance analysis, raster data reclassification and weighted overlays.  Blending these tools gave us the ability to make location decisions based on criteria provided by a client.  Creating a model for the weighted overlay portion allowed us to save time as we could simply tweak our parameters rather than starting from scratch with each raster each time.  Because of the various steps involved in this week’s map production, I felt it useful to outline my steps.  Below you will find my outline and the two resulting maps.

Organize and Document WorkA.                 Examine the Data
1.                  The data has already been downloaded and clipped to Alachua County
2.                  It is all in GCS NorthAmerica 1983 Projection NAD 1983 UTM Zone 17N, Meter
3.                  See separate table within this document containing Metadata information.
B.                 Organize and Document your Work
1.                  Make a new folder in Lab08_Location Analysis named Results.
2.                  Within the results folder create a new geodatabase named results.  This is where analysis results  (data files) will be saved.
3.                  Open ArcMap and save document as Location1_rh
4.                  From File Menu, choose Map Document Properties
a)                  Add a title, author and descriptive text
b)                  Set default geotabase to results.gdb
c)                   Always check box “Store relative pathnames to data sources”
C.                 Set the Environments
1.                  View Menu/Data Frame Properties/Coordinate System tab/Projected Coordinate Systems/UTM/NAD193/Zone 17N
2.                  Geoprovessing menu/Environments/expand workspace, output coordinates and processing extent
a)                  Current workspace: Data\Location.gdb
b)                  Scratch workspace: Results\results.gdb
c)                   Output Coords:  Same as Display
d)                  Processing Extent: choose Location.gdb and select cns_tracts
e)                   Raster Analysis settings: limit to cns_tracts by using a mask
(1)                Cell size “as Specified Below”, 300
(2)                Mask: Cns_tracts
3.                  Select OK and save map
D.                 Conduct Analysis
1.                  Add cns_tracts, county, publands, places and roads from arc catalog
2.                  Symbolize layers; right click data frame and select New Basemap Layer and drag all layers under this group layer
3.                  Right click on Basemap layer and select Analyze Basemap Layer
a)                  Review error report to identify potential drawing performance issues and how to address them.
4.                  Using the effects toolbar, adjust the Dim Level
a)                  If at a later time you need to make data edits or layer updates, drag the map layer out of the basemap, edit it and then return it to the basemap layer group.
E.                  Calculate Distance from North Florida Regional Medical Center
1.                  Insert a new data frame named Hospital Distance
a)                  Set the coordinate system in the data frame properties
2.                  Create a selection to select North Florida Regional medical Center
3.                  Export as NFRMC to results.gdb, use same coordinate system as data frame and make sure to export as feature clas.
4.                  Remove the original hospitals layer
5.                  Symbolize and label the NFRMC.
a)                  Add field Abbrev to attribute table, text
b)                  Use field calculator, string, “NFRMC”
c)                   Use this field as the label field
6.                  Spatial Analyst Tool/Distance/Euclidean Distance
a)                  Input: NFRMC
b)                  Output raster: dist_hosp_rh in results.gdb
c)                   Cell size: 300
7.                  Use the reclassify tool to edit proximity zones to whole number values and 9 defined intervals.
a)                  Spatial analyst/reclass/reclassify
b)                  Input rater: dist_hosp
c)                   Reclass field: Value
d)                  Output raster: results.gdb\reclass_hosp_rh
8.                  Classify, Defined Interval, interval size 5000
9.                  Click Reverse New Values to flip the value order so that 9 is the most favorable and 1 is the least favorable.
10.              Remove the dist_hosp_rh once the reclass_hosp_rh is complete.
11.              Select an appropriate color scheme for reclass_hosp_rh
12.              Make the cns_tracts hollow and save the map
F.                  Calculate Distance from UF (University of Florida) CHOMP CHOMP
1.                  Insert new data frame named College Distance and set the coordinate system in the data frame properties
2.                  Add cns_tracts and schools
3.                  Isolate UF from the schools layer and export as a feature class.  Label and symbolize appropriately and then remove the schools layer. UF_rh in Results.gdb
4.                  Perform Euclidean Distance Analysis for UF.  Export as dist_uf_rh
5.                  Reclassify the output
a)                  Defined interval 5000 which will generate 8 values
b)                  Reverse new values so that 8 equates to the most favorable location closest to UF and 1 the least favorable.
c)                   Name the output reclass_UF_rh
d)                  Remove dist_UF upon completion
6.                  Choose an appropriate colorscheme for reclass_UF_rh and save the map.
G.                 Calculate Percentage of Population Aged 40-49
1.                  Insert a new data frame named 40-49 and set the coordinate system in the data frame properties
2.                  Add cns_tracts
3.                  Open the cns-tracts attribute table and click options/add field
a)                  Name: perc_pop
b)                  Field type: float
c)                   Click ok
4.                  Right click the new perc_pop field and select Field Calculator.  Click yes to edit outside of a session.  Perform the following calculation:
a)                  [AGE_40_49]/[POP2010]*100 click ok
5.                  Display cns_tracts by Quantities, Graduated colors, Value: perc_pop and save yor map.
H.                 Convert Tracts to a Raster for Age Range and reclassify
1.                  Conversion tools/To Raster/Feature to Raster
a)                  Input cns_tracts
b)                  Field: perc_pop
c)                   Output raster: results.gdb\IdealAge_rh
2.                  Once IdealAge loads, turn off cns_tracts
3.                  Reclassify as whole number values.  Use the default of 9 classes, reclass_age_rh
4.                  Once reclass_Age_rh loads, remove IdealAte
5.                  Select appropriate color scheme for reclass_Age_rh
6.                  Turn on cns_tracts and make hollow
7.                  Add Uf and NFRMC, symbolize and layer each feature.  Save your map
I.                    Calculate Percentage of Population of Homeowners
1.                  Insert a new data frame called Percent Homeowners, set coordinate system
2.                  Add Census Tracts
3.                  Add a new field called perc_own use the field calculator to generate percent homeowners (owners/(owners+renters))*100
4.                  Convert cns_tracts Feature to Raster based on perc_own field; output = Perc_Own_rh
5.                  Reclassify the outut, 9 natural break classes where nine is the most suitable class/value. Reclass_Own_rh
6.                  Symbolize and display cns_tracts as hollow
7.                  Add UF and NFRMC, symbolize and label.
J.                   Creating Weighted Overlays
1.                  Save Location1_rh as Location2_rh
2.                  Insert new data frame named Weighted Analysis and set coordinate system.
3.                  Add reclass_hosp_rh, reclass_Uf_rh, reclass_age_rh and reclass_own_rh
a)                  Symbolize appropriately
b)                  Remove all other data frames except for basemap data frame which will be used as an inset.
4.                  ArcCatalog/results.gdb, New Toolbox named Weight_Analysis
5.                  Right click Weight_Analysis toolbox and create new model
a)                  From the TOC add the four “reclass” rasters into the model
(1)                Use the auto layout and full extent icons to focus your model
b)                  Drag the Spatial Analyst/Weighted Overlay tool into the model
c)                   Use the connect tool to connect the rasters to the Tool; choose “weighted overlay table from the pop up menu
d)                  Double click the Weighted Overlay tool to open
e)                   Confirm the scale values match the fields in your map
f)                    Choose set equal influence so that each % influence equals 25%
g)                  Run the model and save as weight1_rh in the results.gdb
(1)                Model errored out within ArcDesktop but ran perfectly via ArcCatalog
6.                  Create a second weighted overlay
a)                  Follow the steps above, but this time adjust scale values so that the primary focus becomes proximity to the workplace.
b)                  You will want to restrict a portion of the age and ownership values (lower end of the criteria) so that the weighted analysis shifts to workplace proximity.
K.                  Compile and Present Results
1.                  Make sure to appropriately symbolize both weighted analyses
2.                  Add places and label the ones closest to suggested areas
3.                  Be sure to turn on the cns_tracts, but make them hollow
4.                  Add NFRMC and UWF and symbolize/label appropriately
5.                  Choose three census tracts from each weighted overlay
a)                  Symbolize and label appropriately
b)                  These are the focus of the map and should be easy to interpret
6.                  Make sure to summarize your findings and explain your weighting process so that your clients not only understand the values but also feel confident in your suggestions
a)                  I restricted the lowest 4 values for proximity to NFRMC and UF and weighed each of these at 40%
b)                  I did not restrict values for the ownership or age category, but weighted them at 10%.