Choropleth Maps

Data Selection

1.    Downloaded the following:

http://www.fgdl.org/metadataexplorer/explorer.jsp 

qd24 – Quad index

cntbnd – County boundaries

cities_feb04 - Cities

publicland_sep10 – Public land

majrds_feb10 – Major roads

hy24p18 – Hydrologic pologons

hy24i18 – Hydrologic lines

gap_lcov18 – Land Cover

fnaiip_jun10 – Invasive plants

http://seamless.usgs.gov/website/seamless/viewer.htm  (DEM)

Flagler county

http://data.labins.org/imf3/LABINS2B/labins.jsp  (DOQQ)

Quad #4411 (Bunnell)

2.    Made sure in ArcCatalog all files were using Albers projection, and reprojected all files originally in another coordinate system.

3.    Added cntbnd shapefile to map document in ArcMap.

4.    Using Attribute table, selected Flagler county in cntbnd shapefile, and added layer from selection.  Exported this layer to shapefile, and removed cntbnd.

5.    One by one, added all other layers.  After clipping them to just Flagler county, removed original layer.

6.    Created three data frames.

7.    Switched to layout view.

8.    Arranged data frames, and added applicable layers to each frame.

9.    Created legend, added title to map, added scale bar and text, added north arrow and prepared by/data source text.

10. Exported to JPG.

Map Composition

Other GIS data sources

Out of curiosity, I looked up GIS data for my home state of New Mexico.  I found the New Mexico Resource Geographic Information System (RGIS) clearinghouse.  It is a repository of digital geospatial data from local and national public agencies, and is publically available.  The data can be downloaded for free, except for a handful of large data sets which can be ordered on CD.

Projections Part 2

Process Summary (Practice assignment):

1.    Went to Labins.org.  Clicked on DOQQ.

2.    Downloaded “5359” files from 2004 RGB Albers Units:MT Mr. SID and saved to H: drive.

3.    Opened ArcMap, and added 5359 aerial data.

4.    Closed ArcMap without saving and opened ArcCatalog.

5.    Selected properties of 5359 file in ArcCatalog and changed spacial reference to provided ALBERS_FL.prj file.

6.    Repeated this process for all four 5359 files, and reopened in ArcMap to see the files now had a useable scale.

7.    Closed ArcMap.

8.    Downloaded DRG (digital raster graphic) for Pace quad (mrg5358.exe) from Labins. (State Plane NAD83 Collars Removed).

9.    Edited undefined spacial reference for this file in ArcCatalog to NAD 1983 StatePlane Florida North FPS 0903 (US Feet).prj.

10. Opened blank ArcMap document and added this file to verify useable scale.

11. Downloaded different data sources from FGDL (Florida Geographic Data Library) Metadata Explorer. (All Albers projections!)

“cntbnd” – County boundary

“USGS 24k Quarter Quad Index) – Quad index

“Major Roads”

12. Created excel file with 3 given locations of eagle’s nests in Santa Rosa County, FL.

13. Converted given latitude and longitude in degrees-minutes-seconds to decimal degrees using these formulas:

xcoor = degrees + (minutes/60) + (seconds/3600)

ycoor = (degrees + (minutes/60) + (seconds/3600))*(-1)

Y coordinates are negative because we are dealing with longitude west of the Prime Meridian.

14. Saved excel sheet and imported into ArcMap.

15. Right clicked on file in table of contents and clicked on “Display XY Data”.

16. Selected longitude as the x field, and latitude for y field.

17. Edited Geographic Coordinate system to “WGS 1984”.

18. Points are imported as an Event; exported data to make it a shapefile.

Process summary (Graded assignment):

1.    Using the footprint layer (qd24.shp), picked two adjacent quads (5560 and 5660) in Escambria County, Florida.  Downloaded these quads as 2004 RGB StatePlane FT Mr. SID aerials from Labins.org.

2.    Defined aerials in ArcCatalog as: NAD 1983 StatePlane Florida North FIPS 0903 *US Feet).prj.

3.    Reprojected Quad Index, County Boundary, and major roads shapefiles downloaded before to same State Plane.

4.    Edited Escambria_tanks.xls file as with the practice exercise to have decimal degree values for x and y coordinates.

5.    Imported this excel file into ArcMap like the practice excel file, and exported the event to make it a shapefile.

6.    Reprojected shapefile in ArcCatalog to match State Plane projection, and used NAD1983 to WGS1984 option 5 geographic transformation.

7.    Created map including this Tanks shapefile and DOQQ aerial images.  Added essential map elements and exported to JPG.

8.    Took screen shots of Data Frame Properties/Coordinate System tab to prove all required data sets and layers are in proper coordinate system.

Typography

Data Classification

Q: Which classification do you think best represents the data and why?

I think the Natural Breaks classification fits the data best.

Equal Intervals doesn’t make sense because the distribution of African Americans should vary percentage-wise across the county. Some geographical areas will have a higher percentage of African Americas.

Quantile classification also fails to consider how the data (percentage of African Americans) is distributed. The enumeration units will not be of the same size for percent of population data, and although the map looks great, it is stretching and squeezing the data to give equal map area.

Standard deviation only works when the data is normally distributed.  This data is not.

Natural Breaks classification works well to represent this data.   Determining the breaks is fairly arbitrary (what really differentiates high percentage from medium-high percentage in practical, street-level terms?) but this classification is sensitive to distribution.

Queensland Flooding Task 3

Inspired by this article, I found this map, which is an experiment in crowd mapping. The goal was to combine verified reports of flood events and subsequent damage from government agencies and news outlets with user submitted reports of "incidents (things that “happen”) or situations which provide useful information to those affected by this disaster. This includes situations such as: Property Damage; Road and Bridge Closures;  Evacuations; Injuries and Electricity Outages." (reference)

Reports could be submitted using email, text message, or twitter using hashtag #qldfloodsmap. There was also a free iPhone app.


The most obvious caveat to displaying this kind of data on a map however, is verifying user reports.

Projections Part 1

Process Summary:

1. Opened cntbnd shapefile properties in ArcCatalog.  Layer is projected in Albers type projection.
2. Added layer to ArcMap.
3. Added two data frames into document.  Names: Albers, UTM, and State Plane N.
4. Reprojected cntbnd shapefile to NAD 1983 UTM-16 coordinate system (NAD_1983_To_HARN_Florida geographic transformation), and dragged to UTM data frame in table of contents.
5. Reprojected cntbnd shapefile to NAD 1983 HARN StatePlane Florida North FIPS 0903 Feet coordinate system, and dragged to State Plane N data frame in table of contents.
6. Added “Area” field to attribute tables of all three data layers.
7. Used Calculate Geometry option to calculate area of counties in all three layers.
8. Created map showing each projected layer.  Scale: 1:10,000,000.
9. Generated table comparing areas for each projection for Alachua, Escambia, Miami-Dade, and Polk counties in excel, added to map.
10. Added essential map elements, exported to JPG.
11. Added UWF_N.jpg to Albers data frame.  Photo appears as island in Atlantic. Edited spacial reference to NAD 1983 HARN StatePlane Florida North FIPS 0903 Feet. Image appears in correct spot.
12. Reprojected raster image to UTM. Added to Albers data frame. Raster image appears in correct spot.

GIS Cartography

Process summary (Map 1):

1.     Used ArcCatalog to look over the contents of the GISCartography data folder.

2.     Used navigation tools to look closer at mex_elev layer.

3.     Accessed properties dialogue for mex_elevation layer.

4.     Accessed the properties for Mex_Roads layer.

5.     Americas_Admin layers from ArcCataloge to ArcMap window.

6.     Zoomed to Mex_boundary layer.

7.     Used selection menu to select by attributes. In Layer dropbox of this menu, selected Americas_Admin. Under Method dropbox, left as Create New Selection. “CNTRY_NAME” = ‘Mexico’

8.     Exported selected features to new shapefile and added as layer to map document.

9.     Unchecked Americas_Admin layer in table of contents to hide it.

10.  Applied labels to map. Picked symbol style “Country 2” and reduced font size to 12.

11.  Changed symbol properties for  World_Countries by picking new fill color and changing outline width to 2.

12.  Changed symbology of Mex_States layer to a graduated color ramp by population quantity.

13.  Switched to Layout view, changed to landscape orientation.

14.  Added North arrow, scale bar and text, title and legend. Edited legend.  Added labels showing names of individual Mexican states.

15.  Exported to JPG. 

 Process summary (Map 2):

1.     To Map 1, added layers: mex_rails, mex_rivers, mex_roads, and mex_urban.  Unchecked Mex_states in table of contents.

2.     Opened symbology properties for mex_rivers, and changed the layer to show based on Categories and Unique Values. Value field: RANK. Edited symbology of Primary and Major values. (Unchecked box next to “<all other values>”).

3.     Displayed only Federal roads, changed symbology for mex_rails to predefined railroad symbol style, and changed symbol color for urban areas.

4.     Edited labels properties for mex_urban layer to include all areas with more than a million people in the label class.  Turned on only this new label class.

5.     Changed to layout view, changed scale to 1:5000000.

6.     Switched back to data view, converted labels to annotations.  Changed size of symbol.

7.     Created inset map to indicate where this map is zoomed to.

8.     Rearranged and fiddled with map elements.

9.     Exported as JPG.

Process summary (Map 3):

1.     Using Map 2, removed every layer but World_countries from Layers data frame.

2.     Added mex_elev raster layer.

3.     Experimented with classified and stretched symbologies for raster layer.

4.     Selected stretched symbology with BlueàYellow color ramp.

5.     Rearranged map elements.

6.     Exported as JPG.