Final Project

Our final project was to use out GIS skills developed over the course of the class to determine if a potential electrical transmission line meets its various goals.  These goals included minimal impact on the environment, homes, schools, and the overall length of the transmission line.  I made a different map for each of these criteria as well as a basemap for the region.  The created story map and transcript for the presentation can be viewed below:

Bobwhite-Manatee Transmission Line Story Map

Bobwhite-Manatee Transmission Line Transcript


Study area of FPL's proposed Bobwhite-Manatee transmission line that will connect Manatee and Sarasota county.

Georeferencing, Digitizing, and 3D Scenes

Georeferencing 

This week's lab was georeferencing - the process of taking an unknown (without coordinate data) raster image and placing it correctly on the map by using vector data of known provenience.  I took satellite images of the North and South campuses and georeferenced them using common points from the shapefiles of the campus buildings and roads such as intersections.  This process creates a stitched together map of the two places that has a placement accurate to the real world.

The more links (control points) between the unknown raster and the known shapefiles the more data ArcGIS Pro has to work with.  The method to evaluate the quality of an individual control point is to look at the residual values of the control points.  If a point has a high residual value it may be misplaced - or if it seems perfectly accurate than the other points may be the source of the inaccuracy.  It is also important that the points are evenly spaced around the image and not clustered in a certain area or all in a line.

Root mean square error (RMSE) is a means of measuring the accuracy of the georeferenced image as a whole.  A lower RMSE means an image is more accurately georeferenced.  Another way to get a an image more accurate is to have enough control points to have a 2nd or 3rd order polynomial transformation.  This gives ArcGIS Pro a greater ability to flex and bend the image it is working with so it can better distort to the control points.  Though sometimes this can distort an image too much so even if the RMSE is low it is important for a human to evaluate the final look of the geoferenced image.

Digitizing

Digitizing is the process of drawing in new features on the map.  In this part of the lab I drew in a polygon for the gymnasium that did not already exist as a building as well as Campus Lane based off of aerial photography.  When making the road it was important to activate edge snapping so that the new road connected to the other roads.

This lab also provided an opportunity to use buffers again as we had to put a buffer around a bald eagle's nest so that future campus construction can be planned around the protected area.  I made a multi-ring buffer - one ring at 330 feet for the conservation easement placed around the nest, and 660 feet protection area that is required by the FWC for certain kinds of development.  Ultimately, the map produced for this part of the lab shows where the eagle's nest in relation to current campus structures so that developers can work around it in the future.

A map of UWF's campus buildings and roads in relation to a known eagle's nest.

3D Scene

The final part of this lab was to make a 3D scene.  This means overlaying the roads, buildings, and georeferenced images over a digital elevation model (DEM).  To create the DEM I took a LiDAR layer and used the geoprocessing tool "LAS Dataset to Raster."  Using this created DEM as a ground for the scene superimposes the layers over it to create the 3D effect.  To make it more striking, I also made the buildings 3D by setting the building height provided by their attribute table to their maximum height in the appearances tab.

A 3D map of UWF's campus

XY Coordinates and Geocoding

XY Coordinates

This section of the lab served as a way to learn how to take known coordinates and import them as points into ArcGIS.  I started by making an excel spreadsheet with known locations of eagle nests in Santa Rosa County, Florida.  I used the spreadsheet to convert the coordinates from the degree, minutes, seconds format to the decimal degrees format that works better with ArcGIS.  I then added this new data into ArcGIS by using the "XY Point Data" tool.  I then reprojected the data to the NAD 1983 (2011) StatePlane Florida North FIPD 0903 (US Feet) projection.

Location of Eagle Nests in Santa Rosa County, Florida

Geocoding

In this lab we generated our own data from addresses pulled off of the Florida Department of Education website.  I took the list of schools located in Brevard county and pasted them into a excel spreadsheet.  From there, I had to clean up the data so that individual pieces of information were parsed out into their own cells.  To do this, I used excel formulas such as =offset, =right, and =mid to pull out parts of the addresses.  I saved this cleaned up file as a .csv.

I then got all lines shapefile Brevard county that I would later use to find addresses from the US Census Bureau.  I also got a shapefile for the county so that I could show the county's outline on the map.  I reprojected these files to the to NAD1983 Harn State Plane Florida East FIPS
0901 Feet projection.

The next step was to set up the address locator using the "Create Address Locator" tool with the all lines file for the reference data.  After that, I geocoded the imported table with the addresses of the schools.  Most of the schools matched automatically, but there were a handful that did not.  For these I took the street name and searched the attributes table of the all lines file for a match.  Then I would also put the address in to Google Maps to see the exact location of the school.  From there I could find where the point for the school should be and added it into my own map.

Location of Schools in Brevard County, Florida

The completed version of this map can be view here.

Vectors and Buffers

Vectors

This week's lab was all about understanding vectors and how to use them in spatial analysis.  I learned how to preform attribute and spatial queries to answer questions about data.  This process is useful for determining how close certain types of features are to other types (for example: how many schools in a county are within a mile of the nearest hospital.  Or what land parcels are more than a mile away from a railroad).  Multiple queries can be combined to make a more sophisticated question.  It is also possible to run queries for features that are completely within or only partially within other features, such as properties that are within a flood zone.

Additionally, it is possible look at the attribute tables from these selections to learn other things, such as the sum of the property values of the buildings within flood zones.  The spatial join tool takes this even further by taking attributes from one layer and appending them to the corresponding attribute from another layer.  For this lab we used the spatial join tool to find out which drainage basins have the most contamination risk features within them by joining a risk features layer to a drainage basins layer.

Buffers

In the second half of the lab we learned about buffers.  A buffer is an area of space around a point, line, or polygon.  Buffers can be variable, such as giving one type of body of water a larger buffer than another type of body of water.  Buffers can also be multilayered when it is important to see multiple distances around a particular feature.

For this lab we used buffers to determine ideal campsites in the De Soto National Forest near Hattiesburg, Mississippi.  We were provided with a layer for the roads, and another with lakes and rivers.  The best campsite has to be within 300 meters of a road and also within 150 meters of a lake or 500 meters of a river so that it is easy to get to but also near enough to water for activities such as fishing and swimming.

The first step was to create a variable distance buffer around the water features.  I added a new field to the water features table called "buffdist" with set the appropriate buffer distance for the body of water.  Then, I used the buffer tool to draw the buffer using "buffdist" to set the distance.  At this point I also created a buffer around the roads.

The next step was to create a union between these two layer to see the overlap between them.  This was done using the union tool to determine the intersect between the two of them.  To ensure that campers are discouraged from staying in areas with rare plants and animals with used a layer with the conserved areas and the overlay tool set to "erase" to eliminate the protected areas.

At this point, I now want to change are multipart polygons to single polygons so we can better assess each camping area.  The multipart to singlepart tool is designed for exactly this.  By adding a new field called area and using the calculate geometry tool I added the area of each feature in hectares.  I also split the polygon symbology into three equal intervals to help highlight the largest suitable campsite polygon.

Suitable Camping areas in the De Soto National Forest