The Analysis of Bobwhite-Manatee Transmission Line Preferred Corridor

        

Introduction
This is an analysis for Bobwhite -Manatee transmission line location beginning at the FPL (Florida Power and Light) Manatee Energy Center in Parish running south to the site of the proposed Bobwhite station. The location was selected after evaluating 1,272 alternative routes. It offers several advantages, including a balance of community, environmental, and engineering factors, a direct route, few homes in close proximity, and avoiding schools and school sites. The route serves new customers in growing areas of Manatee and Sarasota Counties east of I-75, follows property lines or co-locates with road rights-of-way, provides a new right-of-way, crosses the Manatee River at an existing bridge, avoids environmentally sensitive lands, consolidates the new line with existing FPL distribution lines, and can be built at a reasonable cost.

Analysis objectives
Evaluate the project to assess the environmental impacts of the Bobwhite-Manatee Transmission Line on residential areas, land parcels, educational institutions, and environmental sensitive lands, while also considering the comprehensive scope and financial implications of the entire project.

• Homes within proximity of the transmission line.
• Schools within proximity of the transmission line.
• Imposition of the transmission line on; communities, land owners or parcels, and environmentally sensitive lands.
• Length of the transmission line (related to engineering/cost).

Available layers
The data layers in this project were initially in the Albers Conical Projection, which is ideal for large regions with significant east-west expanse. These layers were then reprojected to the State Plane Florida West coordinate system, which uses a projection optimized for high accuracy in Florida's specific region. This makes the State Plane system more precise for smaller-scale mapping within Florida compared to the broader Albers projection.

Project Location
The project is located in two counties on the west coast of the State of Florida. The preferred corridor, shown in orange on the map, runs south from Manatee Energy Center in Parish to the site of the proposed Bobwhite station.

Criteria #1: Homes within proximity of the Transmission Line

Process

Created 400 ft buffer around the preferred corridor.

• Digitized homes within the preferred corridor and 400 ft buffer using provided aerial photos.
• Created a field in the data table attributed digitized homes by inside the corridor or inside the buffer.

Results
Out of the 73 homes that had been digitized, 24 homes inside the preferred corridor and 49 homes inside the 400 ft buffer.

 

 Criteria #2: Schools within proximity of the Transmission Line

Process

• Obtain a list of schools in Manatee County and Sarasota County.
• Geocoded the list.
• Used analysis tool to select by location schools that intersect the preferred corridor and the 400 ft buffer.

Results
Neither the transmission line preferred buffer route nor the 400 ft buffer had any schools. This ensures the safety of the children attending these schools and was a high priority for Florida Power and Light.

     

Criteria #3: Imposition of the transmission line on: communities, land owners or parcels and environmentally sensitive lands

Imposition on land owners or parcels:

Process

• Created a new parcel layer from both Manatee County and Sarasota County parcels layers
• Using geoprocessing tools to select all parcels that intersects the preferred corridor and the 400 ft buffer, and created a new layer.
• From the new layer I did some analysis to find the number of parcels completely within the corridor, intersects the buffer, and intersects the corridor and the buffer.

Results

The result is 255 parcels is in proximity to the corridor and the buffer. 26 parcels within the corridor, 93 intersects the buffer, and 136 intersects both the corridor and the buffer.

 

 Criteria #3: Imposition of the transmission line on: communities, land owners or parcels, and environmentally sensitive lands

Imposition on environmentally sensitive lands (Conservation lands)

Process

• Using geoprocessing tool intersect to create a new layer from the conservation lands that intersects the preferred corridor.

Results

The total conservation area impacted by the new transmission is about 163 Acres which is approximately 2.48 % of the total area of the preferred corridor.

Criteria #3: Imposition of the transmission line on: communities, land owners or parcels, and environmentally sensitive lands

Imposition on environmentally sensitive lands (Wetlands)

Process

• Using the intersection tool to create a new layer from the wetlands layer that intersects the preferred corridor.
• Symbolized the new layer based on wetlands description
• Using excel program to calculate the total areas and percentage for each category and then creating a chart.

Results

A total of 914 Acres of wetlands, approximately 13.92 % of the total area of the preferred corridor, is impacted by the new transmission line construction.

Criteria #4: Length of the transmission line (related to engineering/cost)

Process

• Create a centerline that runs parallel to the the preferred corridor north to south.
• Calculate the length of the centerline in miles
• Developing a cost estimate for the new transmission line using the formula:

Unit Cost per Mile x 1.5 (Urban Factor) x 2 (Line Length Factor)

Results
The total length of the new transmission line is about 25 Mile.
The cost for double circuit, strung on one side, tubular steel pole, engineering and construction cost only = $108,750,000

Module 6 - Georeferencing, editing and 3D Scene

This week lab consists of three parts. We learned georeferencing in part one, editing in part two and 3D scene in part three.

In part one, I georeferenced two raster image files using control points. First, I created a new project and added two layers that were provided, UWF campus buildings and road layers. Subsequently, I added the first jpg file uwf_n.jpg, zooming in to the buildings and roads layers and using Fit to Display feature to display the jpg file to fit the viewing area. After I identified common points from jpg file to buildings and roads layers, I proceeded to add control points. To do so, I first picked a point from the jpg file such as a corner of a building and then clicking on the point for the same building corner from the buildings layer. Adjusting layer transparency allowed me to view features on both layers. Repeating the same steps to add 10 control points and checking Root Mean Square (RMS) error. The RMSE is used as an indicator of the accuracy of the spatial analysis and/or remote sensing. Revising the control point table allowed me to be sure the RMS error as low as possible. I added the second jpg file uwf_s.jpg and following the same process to add control points. This time, I used 3rd Order Polynomial from the transformation drop down box. The second raster image was distorted; to correct this distortion, I used the higher order transformation which allows the raster image to bend and warp.

In part two, I used the editing tool to add a building polygon feature by tracing the building from the raster image that was georeferenced earlier. Following the same process to add a road feature to the road layer. I added the eagle nest layer and a hyperlinked picture to the attribute data. I proceeded to create two conservation easement 330 Ft and 660 FT around the eagle nest using the Multiple Ring Buffer tool. Lastly, I created a layout showing UWF campus and eagle nest location.

In part three I created 3D scene for UWF Campus using Lidar data. First, I need to create a DEM layer from Lidar data that was provided. I used LAS Dataset to Raster conversion tool to convert Lidar data to DEM. Adding UWF campus buildings layer, roads layer, north and south raster images.

 

Module 5 - Geocoding Manatee County Schools, Florida

 

This week’s lab project was an introduction to geocoding tools in ArcGIS Pro. We learned how to geocode a list of schools addresses from an excel sheet to create a point layer showing the schools location in a map.

I started with copying and pasting the schools list data from Florida Department of Education to a new excel sheet. I then cleaned the data, so that only the school’s name, type, address, city, and zip code fields populated and saved the excel sheet as .csv file. I downloaded Manatee County Street layer and boundary shapefiles from US Census Bureau geographic program TIGER/Line Shapefiles. Subsequently, I created a new ArcGIS Project “Geocoding” and added the two shapefiles; Manatee County streets centerlines and boundary shapefiles. I also added the new school list .csv which I created earlier.

The next step was to create an address locator from the streets centerline layer using Create Locator tool and named it ManateeAddressLocator as the output name. At this stage, I was ready to geocode the schools list against the new created address locator. Running geocode table from the geoprocessing pane, the result was 6 records not been geocoded out of the 84 records I had. Thus, I must manually geocode these records. To do that, I used the Rematch Addresses tool to manually geocoded these records, using various sources such as locating the address in ArcGIS Pro or using Google map search.

Three records out of six required additional investigation. Two of these records contained P.O. Box numbers in the address field. I successfully identified one school by searching Google Maps for the school's name, while the other remained untraceable. The third record had an address situated in a different county, which led me to exclude it from the layer.

Upon reviewing the result layer, I observed that nearly all points were positioned along the road centerline rather than on the actual buildings or parcels, with a few located incorrectly. To rectify this, I adjusted the points to their appropriate locations.

To further validate my findings, I downloaded two additional layers: the parcels layer and the address points layer from Manatee County's open data website. Subsequently, I created two new address locators, one based on the parcels layer and the another on the address points layer. I was uncertain about the effectiveness of this approach; nonetheless, I proceeded forward. I then executed a new geocoding process on the .csv file, first utilizing the parcels layer address locator and then the address points locator. Both methods yielded identical results and locations for the school points. Out of curiosity, I also conducted a new geocoding using ArcGIS World Geocoding Service and the results were comparable to those obtained with the parcels and address points locators.

In summary, it is my belief that utilizing the parcels or the address points layer to create an address locator yielded greater accuracy in locating schools compared to the street center line method. Furthermore, employing ArcGIS World Geocoding Service proved to be the most effective option, although it does require the consumption of ArcGIS credits. Moreover, data quality remains a critical concern in the realm of geocoding, as inaccurate or incomplete addresses can result in significant and time-consuming difficulties. The concluding task involved the development of a web map application that displays the locations of schools in Manatee County.

Web map app link: 

https://pns.maps.arcgis.com/apps/instant/interactivelegend/index.html?appid=f72f2b0aae334eae823655499c99150a

Module 4 - Vector Analysis

 Vector Analysis lab was divided into two parts. In part one, I learned about geodatabases and the differences between a shapefile and a Feature Class in a geodatabase. I created a new geodatabase and imported Feature Class(es). We also imported mxd file, which is another ESRI ArcGIS data file, to the project and used query expression to select features based on spatial relationship, such as distance, containments, intersection, and adjacent.

In part two of the lab, I used ArcGIS modeling tools, such as buffer, union and erase, to create a map that will highlight potential campground sites in DeSoto National Forest, located in southeastern of Mississippi State. For a site to be considered suitable, it must be within 300 meters of a road for easy vehicle access, 500 meters from a river, and 150 meters from any lakes. Lastly, all proposed sites must avoid conservation areas, as these are designated to protect plants and wildlife.

First, I used the buffer tool to perform a 300 meters buffer on the Roads feature layer, and selected Dissolve All as the Dissolve Type, to dissolve the buffer into a single feature. This made it easy to visualize; otherwise, I would have had multiple buffer borders overlap.

Second, I created a variable distance buffer, 150 meters for lakes and 500 meters for rivers, from the water layer feature class. In order to do that, I created a new field “buffdist” in the water layer attribute table and populated the field with the appropriate attribute; 150 for lakes and 500 for rivers. To achieve that, I used Select by Attribute tool to select the lakes and assigned the value 150 to the selected records. I performed the same process to add 500 to the lakes’ records. Clearing all selection and using the buffer tool from the geoprocessing pane, I performed a new buffer analysis based on the new criteria, using “buffdist” as the field for Distance [value or Field] and Dissolve features using the listed fields for the Dissolve type with the “buffdist” as the Dissolve Field(s).

Subsequently, I performed a Union overlay on both Roads_Buffer and Water_Buffer to create a new layer that will include both buffers, which represents the best places suitable for campground sites. Only places that falls within the two buffers are considered as the best places.

To conduct this analysis, it is necessary to prepare both buffer layers. I created a new field in the attribute table for each layer Roads_Buffer and Water_Buffer. The new field is designated as insd_rbuf for the Roads_Buffer layer and insd_wbuf for the Water_Buffer layer, with both fields populated with the value 1, indicating that the records are located within the buffer. Subsequently, I used the Union analysis tool, with Roads_Buffer and Water_Buffer as inputs, resulting in an output layer named Buffers_Union. Since we are focusing on regions that fall within the Roads and Water buffers, records that show a value of 1 in both insd_rbuf and insd_wbuf fields are identified as potential campground locations. I applied Select by Attribute query to select records with a value of 1 in both fields, then exported the selected records to a new layer. Lastly, I applied the erase tool to the newly created layer to eliminate conservation areas from the layer indicating potential campground sites.

The resulting feature class was classified as a multipart layer, indicating that although the different polygons seemed distinct, they were collectively associated with a single attribute record or a single feature. The Multipart to Singlepart tool was utilized to divide the data into individual features. The final step required the addition of the Area field to the attribute table, followed by the calculation of the area for each site feature in hectares.

For symbology, I utilized Area for the field, selected manual intervals for the method, and organized the data into 5 classes to represent the areas in hectares. I then manually adjusted the values to present whole numbers rather than fractions, making them easier to comprehend. I chose red for the largest features and yellow for the smallest, which prioritizes attention on the most significant potential sites.

Module 3 - Introduction to Projections

In this lab titled “Introduction to Projections”, we covered different map projections and how these projections will cause various types of distortion.

First, we downloaded Florida County Boundaries shape file from FGDL.org, extracted the data and inserted it in a new map. From the layer properties, we noted that the layer is projected to Albers Conical Equal Area. Using the Project tool under Projections and Transformations in the toolbox, we reprojected Florida boundaries layer to a new projection, NAD 1983 UTM Zone 16N, and renamed the new output layer utm16. Following the same process, we reprojected the original layer to NAD 1983 HARN State Plane North with a new output name, StatePlaneN. We copied and pasted the new projected layers, each in a new map, and renamed the maps appropriately. After creating the new maps, we compared the different projections visually and noticed Albers and State Plane map projections were very similar, while the UTM projection was noticeably tilted more counterclockwise.

The next step was to compare the three different projections quantitatively. To do that, we used Calculate Geometry Attribute tool to calculate areas in US miles for all Florida counties in the three different projected layers. Following this, using select by attribute, we selected 4 counties (Alachua, Escambia, Miami-Dade, and Polk counties) and created a new layer from the selected features. This layer is a temporary layer to show only the four counties of study. Repeating the same process to create a new layers for the same counties for the other two maps, UTM16 and State Plane N.

Finally, we created a layout and inserted the three different projected maps. We then exported the data for the three different projected layers to an Excel sheet and created a new table showing areas in square miles for the four different projected counties, Following that, we inserted the table in the layout. We finished by adding all essential map element (Title, North arrow, Scale bar, legend, etc...) to the layout and exporting it to PNG file.

We also tested a raster projection by adding a .jpg file to an Albers map, but the image appeared in the wrong location. However, when we inserted the same image into a State Plane N map, it displayed correctly. This happens because the image is using the State Plane coordinate system. Now that we know the image's coordinate system, we can reproject it to the correct system for the Albers map.

Results and Conclusions

A map projection is a systematic transformation of the 3-dimensional Earth into a 2-dimensional flat map (Iliffe, 2008; Garnett, 2009). This transformation will create some distortion. The four spatial properties subjected to distortion are shape, area, distance, and direction. In this lab, we demonstrated how various projections of the same feature layer can distort characteristics of the feature. We noticed that UTM 16 projection differs the most in Alachua, Miami-Dade, and Polk counties. A GIS professional should always select the appropriate map projection for the project. For example, Florida Geographic Data Library (FGDL) map Projection is a custom map projection based on the Albers Conic Equal-area map projection, which is the best for the entire state of Florida map projection.