Geocoding Process Summary

EXERCISE 1

Opened ArcCatalog.

Examined attributes of RedStreets.shp and RedRestaurants table.

Right-clicked Redlands in table of contents.

Selected style “US Address – Dual Ranges” with the Redlands Streets layer as the reference and named locator.

Opened ArcMap and the Gourmetmap.mxd.

Right-clicked RedRestuarants in table of contents and picked ‘Geocode Addresses’.

Added Redlands Locator and named output: GeocodedRestuarants.shp.

EXERCISE 2

Opened ArcMap and added Redstreets.shp.

Selected ‘Find’ tool and selected “locations” tab.

Chose 10.0 US Streets Geocode Service (ArcGIS Online) (Not 9.3.1)

Entered

380 New York Street, Redlands CA 92373

Right-clicked the top result and added labeled point.

Selected Graphic & Text and selected text and changed to ESRI Café.

Repeated this process with the RedlandsLocator.

EXERCISE 3

Opened Customers.mxd in ArcMap

Opened attribute table for “Customers” and added address text field.

Right-clicked [Address] field and selected Field Calculator.                 

Used Field Calculator to concatenate address fields.

Opened ArcToolbox > Geocoding Tools > Standardize Addresses.

Saved file and closed ArcMap.

EXERCISE 4

Opened ArcCatalog.

Right clicked on Atlanta.gdb  and selected New Address Locator.

Chose US Address – Dual Ranges for the style and Streets.shp for the reference.

Named new locator AtlantaLocator. Adjusted locator properties.

Opened ArcMap and added Streets.shp and Customers table.

Added the just created AtlantaLocator.

Set output to Atlanta.gdb and saved as AtlantaCustomers.shp. Geocoded addresses.

Reviewed results.

Opened Streets_Addresses attribute table  and summarized the street name field.

Opened the summarized table and adjusted similar street names to eliminate unmatched addressed.

Saved document and closed ArcMap.

Raw Data

Process summary:

Opened blank map document in ArcMap.

Added Gulf_Pin shapefile.

Joined shapefile to raw tax roll data in provided .csv file.

Created new shapefile from original layer joined to .csv information.

Opened attributes table for this new layer, and used Calculate Geometry on the acres field.

Generated table with owner names, number of parcels, and total parcel acreage.

Exported this table to Excel to view top 4 landowners in the county.

Back in ArcMap, created an attribute query to select the parcels of the top 4 parcel owners.

Created new layer from this selection.

Under symbolization options, symbolized each landowner by unique values.

Added essential map elements, exported to JPG.

Isometric Maps

I had trouble tinting the areas between isolines, so I regretfully omitted this from my map.  

Vectors 2

1.          Loaded roads data layer into new map document in ArcMap.

2.          Displayed the ArcToolbox, and selected Buffer tool (Under Analysis -> Proximity)

3.          Performed a buffer on roads layer, with a linear distance of 300 meters, dissolving all buffer borders.

4.          Added water shapefile to map document.

5.          Added new field (long integer) to water attribute table: buffdist

6.          Used the field calculator to assign a value of 150 to lakes and 500 to rivers for buffdist field.

7.          Opened ArcToolbox again, and selected Buffer tool. Input feature: Water, Field: buffdist, Dissolve type: List, Dissolve field: buffdist

8.          Added attribute fields to water_buffer and road_buffer layers. (insd_wbuf and insd_rbuf)

9.          In ArcToolbox, selected overlay toolset (Under Analysis tools)

10.      Selected Union, with these parameters: Input layers: water_buffer, roads_buffer, Join Options: all.

11.      Opened attribute table for buffers_union layer, and selected records with a value of 1 in both insd_wbuf and insd_rbuf fields.  Exported these records to new shapefile (buffers_union_export).

12.      Used Multipart of Singlepart tool in ArcToolbox (Data Management -> Features) to make it possible to select individual features.

13.      Repeated steps 9-12 with a different overlay tool.

14.      Added conservation_areas layer to map document.

15.      Used an overlay tool to isolate the buffer areas outside of the conservation areas on the map, with output: possible_sites.

16.      Added new field to possible_sites attribute table for area.  Used calculate geometry function to calculate area of each site feature.

Flow Maps

Vector Analysis 1 process summary

1.    Opened lab7.mxd (Not the VectorAnalysis_1.mxd listed in the lab file, because this was the only file available for this lab on R: drive.)

2.    Using the Select by Location tool under the Selection menu, selected Pensacola land parcels within 2000 feet of a school, which are also within 1 mile of a hospital, not within 1 kilometer of a railroad, and are within 3000 feet of a cell tower.  This required changing the selection methods from “select features from” to “select features from selected features” to “remove from currently selected features” (for railroads) and back to “select features from selected features”.  The spatial selection method was “Target layer(s) features are within a distance of the Source layer feature.”

3.    Still using the Select by Location tool, selected features within Enterprise Zones from the features previously selected.  The spatial selection method was “Target layer(s) are within source layer features.”

4.    Changed the target layer in Search by Location to Superact_risk_features, and changed the source to selected features within the Pensacola parcels layer. There are 100 features selected in ArcMap after this step, NOT ONE.   It’s really frustrating to repeat queries four times because it isn’t clear there will be features selected on the Pensacola parcels layer AND on the risk features layer.

5.    Using the Select by Location tool, selected all parcels intersecting the flood_Zones polygon layer.  Then, using the Select by Attributes tool, selected only residential parcels within the flood zones.  Using the attribute table, and right clicking on the Market_value column heading, and choosing statistics, found the total market value of these parcels.

6.    Displayed layers under the Water group in the table of contents.  Right clicked on superact_risk_features and clicked on Join under Join and Relates menu. Joined Drainage points layer to this layer, giving all the points the value of the attributes of the polygon they fall inside of.  A new layer is created.

7.    Opened the attribute table for the new layer.  Right clicked on the heading Basin and selected Summarize.  Clicked ok on this new window.

8.    Output table was added to table to contents.  Opened this table to see list of basins with associated number of risk points for contamination.

9.    Repeated Join process for Superact_wells_esc and Superact_risk_features layers.  Looked at the output table for answer to Q6.

Dot Map

Georeferencing

Process summary:

1.     Opened blank map in ArcMap, and added buildings and roads shapefiles.  Zoomed to full extent.

2.     Added uwf_n.jpg.  Displayed Georeferencing toolbar and clicked fit to display for this layer.

3.     Added control points scattered over the map.  Adjusted control points with RMS much higher than other control points.  Ended with RMS of 2.19 for a 1^st order polynomial transformation.

4.     Added uwf_s1.jpg to map.  Used fit to display again.  Added control points, and finished with RMS of 0.32 for a 3^rd order polynomial transformation.

5.     Hid black edges of uwf_s1.jpg.

6.     Created new (polygon) shapefile named Athletic_fields.  Went ahead and created shapefiles for (polygon) New_buildings and (polyline) New_roads too, because I wanted these elements to appear differently on my map from the rest of the roads and buildings.

7.     Displayed Editor toolbar to start editing.

8.     Digitized yellow athletic fields with line tool.

9.     Digitized green dot for the new campus building with circle tool.

10. Digitized the new road with white with freehand tool.

11. Clicked stop editing on Editor toolbar and Saved edits.

12. Switched to Layout view, added essential map elements and rearranged elements.

Proportional Symbols

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.