Map Document 1: Comparison of Data Classification Methods
Map Document 2: Natural Break Classification of Blacks – Escambia County, FL
Process Summary Description
Map Document 1: Comparison of Data Classification Methods
File: Data Classification_Map1_AK.mxd
1. Log into eDesktopGIS.uwf.edu
2. Copy and paste file (Data Classification.7z) from R: drive to S:drive (S:\Cartography\WEEK 3_DATA CLASSIFICATION\Data Classification)
a. Start Menu > Computer > R:\Cartography\Data Classification\ Data Classification.7z
3. Unzip file – right click > Extract here.
4. Open up ArcGIS – ArcMap10
5. Add Data (escambiscensus.shp) – select the add data button and choose location of shape file (S:\Cartography\WEEK 3_DATA CLASSIFICATION\Data Classification) and click ADD.
6. Map with layer, “escambiscensus” is displayed.
7. Format layer name to “Escambia County Census”
a. Right click on layer > properties > General > Layer Name : here type new name and hit OK
8. To Classify data – right click Escambia County Census > properties > Symbology >Quantities >Graduated Colors.
a. Will do one map with all 4 data classification methods; equal interval, quantile, standard deviation and natural breaks.
b. Fields > Value> p_blacks> Classify (under classification) >Method (list each one method at a time for each of the 4 maps to be created on one map ex. Equal Interval) > Class = 4 >OK >OK
9. Map with Equal Interval classification is created.
10. File > Page and Print Setup > Landscape > check box - Use Printer Paper Settings > Show Printer Margins > Show Printer Margins on Layout > OK
11. Minimize view of Map on Page to be able to fit all 4 Maps > Pointer > drag to reduce size.
12. Add another Frame and repeat for a different classification. Insert> Data Frame>.
13. Rename Frame>Right click >Properties>General>Name>type in name of data classification that shown map will represent, ex. Quantile >OK.
14. Do same for previous Layer > Name that, Equal Interval
15. Copy data to New Frame. Right click Escambia County Census under Equal Interval Layer >Copy >right click 2nd layer = Quantile >Paste Layers.
16. Create 2nd classification map – repeat step 8 above.
17. Add 3rd Frame (Standard Deviation) and again copy and paste data and insert 3rd classification method. Do so until have 4 frames with 4 different classifications on same map.
18. Resize maps to fit.
19. Add essential map elements to each individual map from the “Insert Men” add: title, legend, scale, author Name, date, Source.
20. Title Main page showing the 4 classifications. Insert > Title > type: COMPARISON OF DATA CLASSIFICATION METHODS
21. Format text. Right click on text box >Properties > Text (type name if not already done so) > change symbol > select ARIAL font, 16, Bold >OK>OK
22. Center Title on Page.
23. Format Author name, Source in same way.
24. Save Periodically.
25. Export File . File >Export Map >Save as a JPEG ( Data Classification Map 1_AK.Jpg) to Data Classification folder on s drive (S:\Cartography\WEEK 3_DATA CLASSIFICATION\Data Classification).
26. Archive Work. Right click on WEEK 3_DATA CLASSIFICATION FOLDER > Copy and paste to S:Drive Folder titled Archive to GIS. Then again copy same folder to External Hard Drive, never on the c:drive under eDeskTopGIS.uwf.edu.
Map Document 2: Natural Break Classification of Blacks – Escambia County, FL
File: Data Classification_Map2_AK.mxd
1. Create only one map this time of the preferred data classification method = Natural Breaks using the steps show while creating Map 1 with the exception of adding additional data frames. Limit it to just one data frame.
2. Add all essential map elements.
3. Save file and Export Map as illustrated in step 25 above. Call this Data Classification Map 2_AK.Jpg
4. Also Archive this as illustrated above.
Total Lab: About 3 hours to Complete
Laboratory Question:
Which classification do you think best represents the data and why?
I thought that the data was best represented using the natural breaks data classification
which was easier to comprehend than all other 3. Additionally, this classification method
maximizes the similarity of values within each class and also increases the precision of the
map given the number of classes (used 4 classes in this assignment).
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