Bobwhite Manatee Transmission Line Analysis - Final Project

The final project for GIS4043/Intro to GIS conducts analysis on the Bobwhite Manatee Transmission Project in Southwest Florida. Part of the Florida Power & Light (FPL) infrastructure, the 24.5 mile long transmission corridor was developed to serve growing areas of eastern Manatee and Sarasota Counties, including Lakewood Ranch. Additionally the new line offers redundancy during hurricanes, something tested since it was completed with Hurricane Irma in 2017 and Hurricane Ian in 2022.

GIS analysis was used in part to determine the optimal location for the proposed transmission corridor. The design of the route took considerations for reducing impacts on sensitive or protected conservation land, avoiding schools and daycares, and providing a buffer from existing homes. Community input factored heavily with the corridor ultimately selected. FPL also worked with Schroeder-Manatee Ranch (SMR), the developer of the Lakewood Ranch community, to select a route that preserves the natural beauty of the area.

The study area was 273 square miles wide, mostly spread across central Manatee County along with a portion of northern Sarasota County. The project was announced by FPL in June 2006. With input from a community advisory panel, open house events and surveys mailed to area residents, FPL developed formal plans, which were unveiled in October 2006.

The Bobwhite Manatee Transmission Line project was eventually certified by the Florida Department of Environmental Protection. It subsequently cleared the Transmission Line Siting Act and was approved by the Florida Cabinet and Governor on October 28, 2008. Construction was anticipated to begin in 2010. It ultimately did in 2013, following additional compromises made between FPL, SMR, area homeowners and Taylor & Fulton, an area agricultural group.

Our project looks at four criteria analyzed by GIS for the selection of a preferred corridor for the transmission line. The first objective considered the number of homes and overall properties within proximity of the corridor.

Using the buffer geoprocessing tool, a 400-foot buffer was created around the preferred corridor of the planned transmission line. A feature class locating all homes within the corridor and associated buffer was next created with heads-up digitizing using 2006 aerial photography. With all visible homes added to GIS, running the "select by attribute" geoprocessing tool on created fields that indicated if a home was either within the corridor or within the 400-foot buffer, provided the totals. A map output of the homes and parcels intersecting the corridor:

The transmission line that was eventually built comes no closer to 600 feet from an existing home. No doubt GIS aided in achieving this buffer.

The second objective of GIS with the Bobwhite Manatee Transmission Line project was a simple one. Are their any schools or daycare centers within the preferred corridor, or the associated 400-foot buffer? Some work outside GIS was required to analyze this, as point feature classes for schools and daycares did not exist.

Researching area schools with the Department of Education website, and other websites for daycares falling within zip codes that crossed the preferred corridor, lists were compiled in Excel. These were in turn geocoded into GIS, using more recent street centerline files to complete address matching for automating the location process.

With school layers compiled, the select by attributes geoprocessing tool determined that no schools or daycare centers were within the preferred corridor or buffer:

That fact that FPL avoided all schools and daycares certainly reduced community opposition to the overall project.

Moving on with the analysis, environmental impacts to both conservation areas and wetlands was considered. National Wetlands Inventory (NWI) and Florida Managed Lands data were provided. The question to be answered is how many acres of each land type was within the preferred corridor?

For wetlands, the NWI feature class was clipped within the preferred corridor polygon. The result were records for uplands and two wetland types, with the Shape Area field providing the areas in square meters. After converting the values into acres, calculating the total acres of uplands and wetlands was easily achieved using the Summary Statistics geoprocessing tool.

Conducting spatial analysis on the conservation areas, a different approach was taken using the Select by Location geoprocessing tool with the Intersect relationship. This extracted all polygons in the conservation land feature class that were within the preferred corridor into a new feature class. The resulting data revealed that relatively small portions of a conservation easement, watershed and state park were in the preferred corridor:

The final objective analyzed by GIS was to estimate the total length of the then-future transmission line, and to use that figure in an equation to estimate construction costs. This was a straightforward process using the Polygon to Centerline geoprocessing tool.

However one data discrepancy occurred with the creation of the centerline feature class. The centerline split into separate branches within the triangular shaped wedge at the south end of the preferred corridor. I considered these to be outliers when it came to the determining the overall length of the transmission line.

One option was to take an average of the length between the two and consider adding that to the main centerline vector. Another option was to omit them entirely, as the project included constructing the Bobwhite Substation within that wedge shaped area.

GIS analysis determined an estimated total length of 24.76 miles. An East County Observer news article on the Bobwhite Manatee Transmission Line in 2013 referenced the line being built at the time as 24.5 miles in length. So this was a pretty good result from GIS.

Living in Bradenton from June 2013 to April 2015, I drove by this project several times without knowing much about it. While doing photography for AARoads, I captured work in progress along State Road 64. Looking back at the photos, what was built was a 230kV single circuit transmission line on a steel tubular pole. Using the equation provided with the GIS project documents, that resulted in a rate of $1.1 million per mile. The $27.236 million I calculated was well above the $20 million cost reported in the East County Observer article.

In conclusion, it appears that FPL designed the Bobwhite Manatee Transmission Line with a priority in the feedback from the community. The route was designed to follow existing right of way for several major highways. Using that space instead of a new corridor, the impacts to protected lands was minimized. Beside one home that was eventually demolished to make way for the Bobwhite Substation, it appears as if most existing homes were avoided by the power line.

Wrapping things up, beyond the deliverables posted above, we were tasked with creating a Power Point Slide Show presentation and accompanying transcript. Both are uploaded to my Google Drive:

• Bobwhite-Manatee Transmission Line Project Power Point slide show
  
  
• Bobwhite-Manatee Transmission Line Project Power Point transcript

Georeferencing UWF aerials for Eagles nest easement analysis

 The sixth lab for Intro to GIS introduces Georeferencing, which transforms raster data such as an aerial photo or a scan of a subdivision plat, to closely match vector data within GIS. I am familiar with Georeferencing from my cartography jobs, where we often acquired subdivision and other development plats and digitized them for updating map products.

Georeferencing utilizes control points, which match features on the raster image without any location data (coordinate system, latitude/longitude), to an existing vector data set in GIS. There are multiple methods of transformation with Georeferencing. The type of transformation used determines the correct map coordinate location for each cell within the raster data.

A lower order transformation, such as Zero Order Polynomial is used to simply shift data to better line a raster image with vector data. This is often the case if the spatial accuracy of the raster is similar to the vector data, or if the raster image was previously georeferenced and needs slight tweaking of its location to better match vectors. Correcting more complex distortion, higher order transformations allow raster data to bend and warp more.

This week's lab georeferences two aerial photographs of the University of West Florida (UWF) campus to feature layers for campus roads and buildings. I utilized a First-Order Polynomial transformation on the north aerial of the campus. First-Order is used to affine transformation to shift, scale or rotate raster data. Affine is defined in mathematics as "allowing for or preserving parallel relationships", so it more or less in this case scaled the raster image down to match the vector data without much in the way of distortion.

The south aerial of the UWF campus utilized a Second-Order Polynomial transformation. This was due to the aerial being intentionally distorted. Needing at least six control points, second order transformation is used where a raster dataset must be bent or curved to line up with vector data. We used ten control points.

Root Mean Square (RMS) Error was also introduced in this week's lab. RMS calculates the difference between where the from point was ultimately located opposed to the actual location specified. The value describes how consistent transformation is between the different control points. We were looking for RMS values of less than 15, any points exceeding that were beyond the accuracy tolerance.

With georeferencing completed, I added a line feature for Campus Lane, a new road constructed since the original data was processed in 2010. I also digitized the new UWF gym, Building 72. With these features added, our next step was to introduce a multi-ring buffer around a nearby Eagles nest, a protected species. Located in an undeveloped area within UWF land to the east, our analysis added 330 foot and 660 foot buffers around the nest location representing two levels of conservation easement. These easements could affect construction of new buildings as part of planned expansion of the UWF campus.

The finished aerial map juxtaposes the existing UWF campus building structures with the conservation easement around the known Eagles nest location.

Geocoding Data - Manatee County Schools

This week's lab project introduced me to Geocoding within ArcGIS Pro and some Excel spreadsheet tactics used to prepare the data for it. The focus of this project is to extract the geographic location for Manatee County Schools from the list posted on the Florida Department of Education web site.

Started the lab with a simple copy and paste of the schools list, which includes 84 entries ranging from Charter Schools to Colleges. Added these to an Excel spreadsheet and proceeded to format the data for processing by ArcGIS Pro. The end result were data columns including the school's name, street address, city and zip code.

With the Excel spreadsheet saved as a .CSV file, proceeded into ArcGIS Pro with downloaded TIGER line shapefile data for Manatee County, Florida. The schools list data was then imported into a table.

Geocoding in ArcGIS Pro utilizes either an X/Y location using latitude/longitude, or in the case here, with an address location. First, we needed to compile an address locator file for Manatee County. Using the Create Locator tool, parameters on the TIGER line fields were set for the street name, zip codes right and left for the side of a street segment, and similarly left and right house number ranges. The process ran on the Manatee County line file, creating the Address Locator file needed for the Geocode Table tool.

The Geocode Table tool cross references the Schools input table with the Address Locator file. With our three columns of location data from the original Excel sheet, selected Address, City and Zip as the Data for the Locator Field within the tool. The process creates a geocoded point file of all school locations.

Of the 83 entries within the Schools table, all but four were matched. Those needed further analysis to be located.

One of the entries was located simply by abbreviating "Street" in the address field to "St". Based upon Florida State Road addresses, the remaining three were manually placed by researching their location.

The data entry for Carlos E. Haile Middle School used Fl 64 as the address street name. A problem with this is that often numbered routes have formal names that are used for postal addressing. Furthermore, while FL 64 implies Florida State Road 64, FDOT and other government agencies often instead use "SR" on signing and for addressing.

Two of the schools were addressed as "State Route 70" or "SR 70". The TIGER line data uses 53rd Avenue E in the FULLNAME field for the segment of SR 70 where the schools are actually located. So those schools are misplaced. Contrary to that, two of the original unmatched schools are located along SR 64 in Lakewood Ranch, which the TIGER line data references incorrectly as Manatee Avenue. Manatee Avenue is the formal name for SR 64 within Bradenton. East of there, State Road 64 is the formal name for SR 64 in unincorporated Manatee County.

Data quality is definitely a concern for when it comes to Geocoding. Whether it be an inaccurate address or missing data, these can result in potentially time consuming problems.

Published the final geoprocessed data, showing Manatee County populated with the various school locations, on the webmap at https://pns.maps.arcgis.com/home/item.html?id=c66a66146c024cec9064604c851b0f23. Edited the webmap on September 26 to show a second point layer including the geocoded school list with all locations corrected.

Buffer Analysis with Desoto National Forest data

This week's GIS Lab introduced spatial analysis and more specifically, the concept of buffers. Buffers in GIS can be applied to spatial data to conduct analysis on a specifically defined area surrounding the data in question. My last experience with buffers was back in 1998 at the University of Delaware. Our final project then used buffers as part of the analysis for the White Clay Creek Watershed in northern Delaware. Still having the paperwork from my GIS work back then, I scanned some of the maps to show buffers that we used:

Please ignore the lack of a North Arrow and Scale Bar, that was a long time ago!

The map to be produced for our Vector analysis lab will show potential campground sites within DeSoto National Forest in southeastern Mississippi. The criteria for a suitable campsite includes locations no more than 300 meters from a road, so that they are accessible by car. Sites should also be within 500 meters from a river for recreational opportunities. However sites should be at least 150 meters from a lake, to offset potential flooding concerns from larger bodies of water. Possible sites also need to exclude areas within conservation areas where plants and wildlife are protected.

Vector data acquired for this analysis includes feature classes for roads across the National Forest, hydro data including both lakes and rivers, and a polygon layer for conservation areas. GIS analysis utilizes a series of buffers on both the roads and hydro data to find areas within optimal range from roads and rivers, but a safe distance from lakes and wholly outside conservation areas.

Initial analysis utilized a fixed distance buffer including all areas within 300 meters from area roads. Dissolving the output feature class for the roads buffer resulted in one seamless polygon in place of separate abutting polygons. Followed this with a variable buffer on the hydro data. First adding a field for buffer distance (buffdist) to the attribute table, I used the Calculate Field function to set a buffdist of 500 meters for rivers and a buffdist of 150 meters for lakes. 

Next ran the Geoprocessing buffer tool with the buffdist Field as the distance parameter and Planar as the method since projected coordinate systems were used for the data. The output feature class used dissolved features based upon the buffdist Field. This produced separate polygons for areas within 150 meters of a lake and 500 meters from a river.

With feature classes for roads buffer and water buffer compiled, analysis shifted to overlaying the two. Continued by adding a new field "insd_wbuf" for inside water buffer and "insd_rbuf" for inside road buffer on the respective layers. Both fields were set to short integers, instituting a Boolean expression where 1 equals yes or is True, and 0 is no or is False. Ran the Union (Analysis) Tool on the buffers, producing a new feature class. Polygons varied from "areas within the lake buffer, but outside the road buffer" to "areas within both the river and road buffer."

The resulting layer from the Union Tool assigned areas within both the roads and water buffers as 1 (True) for the insd_wbuf and insd_rbuf fields. Selected those attributes where both fields were set to 1 and exported the features into a new feature class.

Lastly, the conservation areas were excluded from the feature class containing areas within falling within both roads and water buffers. With those areas eliminated from consideration, the final layer showing possible campground sites was nearly ready to be reviewed.

The resulting feature class was a multipart layer, meaning that while the various polygons appeared separate, they were all part of a single attribute record. The Multipart to Singlepart tool partitioned the data into single sites. One last step involved adding the field Area to the final attribute table and calculating the area of each site feature in hectares.

All geoprocessing completed, the data showing areas that fit our criteria for optimal campground locations was ready for presentation. How big is a hectare? 1 hectare is 2.47 acres. So for the symbology, I color coded areas based upon size, with larger areas represented with darker shades of green. Reading that RV parks often require 5 to 20 acres of land, I excluded all areas that were below 2 hectares.

Following a series of several revisions, the final map:

 

Map Projection Variation with Florida County data

This week's GIS lab delved into map projections, the manipulation of attribute data and the creation of a feature class out of selected data. We were tasked with taking a polygon shapefile of Florida's County Boundaries and reprojecting it from the original Albers Conical Equal Area (Albers) coordinate system to both the State Plane Coordinate System (State Plane Florida North) and the Universal Transverse Mercator (UTM 16 North) system.

Following the completion of that task, we were asked to compare the three maps to note any differences. The Albers and State Plane map projections were very similar, while the UTM projection was noticeably tilted more. Further analysis involved extracting the area in square miles in tables for four select counties across the state. When comparing them, Escambia was nearly identical in area on all three. Alachua was only four square miles larger on UTM 16 N as compared to the other two. More substantial differences were noted in Miami-Dade and Polk Counties, where the square mile numbers for UTM 16 N remained the highest.

Final map showing Florida with a selected Counties layer in Albers, FL State Plane N and UTM 16 N

Our process summary included a section to report my results and conclusions. While I knew of the concept of projections from previous cartography work, I really did not know much about the origins of their geographic coordinate systems. So I devoted several hours of research, composing a word document on them for future reference. Followed this by practicing reprojection more and creating overview maps showing each of the three projections nationally with graticules.

With an understanding of their methodology, the differences in the three Florida County map projections become more clear. The original shape file uses the Albers coordinate system, which it turns out is optimal to use for east to west geographical areas located in the mid latitudes. More specifically, the coordinate system uses a two standard parallels to minimize distortion in the region between them. This results in spatial accuracy along the parallels and minimum distortion between them. However scale along the lines of longitude does not match the scale along the lines of latitude, resulting in the final projection not being conformal. Albers is best suited for projecting the entire state of Florida.

There are 60 UTM zones per hemisphere, each spanning 6° longitude with a central meridian located around 180 kilometers between the edges. Best suited for north-south regions, the scale error does not exceed 0.1% per zone. Error and distortion increases for regions that span more than one UTM zone. A UTM zone is not designed for areas spanning more than 20 degrees of latitude.

Within this lab, Florida was projected using UTM 16 N, with a central meridian crossing the Florida Panhandle. The entire Florida Peninsula is within UTM 17N. Therefore while the scale error is still minimal, county areas are larger than with Albers or State Plane.

The State Plane Coordinate System divides the 50 U.S. states and associated U.S. territories to over 120 numbered zones. An assigned code for each zone defines the projection parameters using one of three conformal projections. The system is designed with a maximum scale distortion of one part in 10,000 per zone, with a central meridian or standard parallels maintaining this level of accuracy.

The Florida County boundaries layer projected in the lab uses Florida State Plane North, one of three zones assigned to the Sunshine State. This zone uses Lambert Conformal Conic projection, which is best for middle latitudes, as long as the range does not exceed 35°. Similar to Albers, Lambert portrays shape more accurately than area. Latitude spacing increases beyond the standard parallels, with minimal distortion near them. Scale is correct along the standard parallels, but reduced between them and increased beyond them.

The Florida Peninsula generally falls within Florida State Plane West or East. Alachua County represents the eastern extent of the North zone, so the area is accurate. Polk and Miami-Dade are located in the other zones, and therefore are scaled slightly higher due to being beyond the standard parallels for Florida State Plane North.

Florida State Plane East and West both use  Transverse Mercator projection, as they have long north to south axes.

ArcGIS Field Maps - Tampa area route markers

Three weeks into GIS4043, we were introduced to a package including data collection, ArcGIS Online, Story Maps and ArcGIS Field Maps. While I have worked with ArcGIS Online creating state maps with data from various Departments of Transportation for research and write-ups for pages on AARoads, a lot of this was new to me.

The purpose of this week's lab is to create a Feature Class with data collected with a mobile unit using ArcGIS Field Maps. Then with that data, creating a web layer to be shared online by multiple methods.

The initial steps in the lab were to create an empty project and utilize Domains. Domains are predetermined options for data collection in the field. For this project, we were to select an aspect of Public Safety, either within a building complex or in a geographical area.

Seeing the opportunity to couple the data collection with an aspect of AARoads, I chose Route Sign Markers as the Public Safety aspect to collect. The clear and consistent marking of numbered routes not only aids in motorist navigation but also indicates that a street or highway is part of a larger system maintained by either the county or state. Clearly marked routes reduce motorist confusion, allowing more attention to be devoted to driving in place of navigation. Poorly marked routes, or ones not marked at all can lead to last minute decisions as far as where to turn or frustration with taking the wrong road altogether.

Florida State Road 597 marker in excellent condition

Faded CR 582 marker in North Tampa

 

Furthermore, GPS based navigation systems such as Google Maps and TomTom devices tend to favor referencing numbered routes over named streets. Therefore having consistently signed route markers working in tandem with street names is better from a motorist perspective. Well marked routes can also aid in hurricane evacuations, keep trucks on designated routes, and provide signed alternates due to congestion on primary routes.

With that in mind, the Domain set up for this lab is "condition", with the field providing options (Code) for excellent, fair and poor. The field type was set to text, and a short description of each Code was entered. This set of criteria works out well with route sign markers, as part of my inspiration for what to collect is because FDOT similarly does field collection where every sign within a district is cataloged in the field and rated based upon its condition.

Following the Domains creation, next was the creation of a new Feature Class that will be populated with the data collected in the field. Having some knowledge of MySQL databases made understanding the need for the Data Type of "Condition" to be set as Text, so it matched the Domain field type set earlier. Other fields added included Photo with the Data Type of Raster and Notes with the Data Type of Text.

With the new Feature Class added to our project Geodatabase, I applied the Condition Domain to the Condition Field. This allows the predetermined Conditions to be used in the field. I then shared the empty Feature Class as a Web Layer with it configured to allow editing. Following a hiccup where the unique numeric ID was not preset (another aspect of MySQL coming into play), the page was published to the Content section of my ArcGIS Online account.

Added the Field Maps application to my Samsung phone via the Google Play Store. Once logged in, data collection could commence! My initial point was a test in the office of a prototype road sign made for me. With the success of that, the next point was within walking distance along U.S. 41.

For the remainder of the points, I wanted a variety of examples allowing me to categorize some signs as fair and others as poor. Knowing where several arrays of such were, my brother, who is a Professional Surveyor, accompanied me as we drove to collect nearly a dozen points. He mentioned Survey Grade collection, which is placing the collection device as close to the data to be cataloged as possible. So I went with that method, placing my phone against each sign support post for the collection.

The various route markers we collected throughout the Lutz and North Tampa area

No major difficulty with collecting the points. The only somewhat confusing aspect was that even after touching the Check button in Field Maps to finalize a data point, occasionally the data entry dialog remained. This initially led me to question whether or not the data I entered went through.

Once back at the PC, verified that the data collection was successful on the Web Map on ArcGIS Online. Opened the map via Portal in ArcGIS Pro as well, noting that the pop up information on the software omits the photo thumbnail for each point. Also created a Map Package from the map layer and KML files for Google Earth.

The finished Web Map was posted at https://pns.maps.arcgis.com/apps/mapviewer/index.html?webmap=acd601fc59ab435391f633fe99df7e66

Cartography Overview - University of West Florida Map

 Following several days of anxiousness following the formation and movement of Hurricane Idalia, I finally was able to resume work on the second Lab assignment for GIS4043. This week's focus is on cartographic basics and design, something I have familiarity with having worked for three map companies. However my experience goes back to ArcMap, and the ArcGIS Pro system definitely requires some time investment to acclimate.

The general purpose of the map is to show the location of the University of West Florida (UWF) Main Campus located north of Pensacola in Escambia County. We were tasked with creating two maps, a small general overview map showing Escambia County's location relative to the whole state of Florida, and a larger map showing Escambia County in more detail with the placement of the campus location.

An early step was to add a folder connection via Arc Catalog. Previously in ArcMap, Arc Catalog was somewhat separate. Now with ArcGIS Pro, catalog is more seamlessly worked into the main functions of the software. A nice improvement.

Using Catalog, the metadata is easily viewed. Collapsible ticks next to section headers aid in better parsing the data for locating desired information. The only drawback is that if you exit from a particular metadata for one file, upon return all closed ticks are again open.

Upon reviewing metadata for the six shape files downloaded for the Lab, discrepancies with how detailed the metadata with each respective file becomes apparent. Some metadata is extremely detailed while other metadata is more limited. Reviewing the metadata for this project, data sources included U.S. Census Bureau Tiger files, the University of Florida GeoPlan Center and FGDL among others.

Moving forward with the Lab, creating and editing the map layouts in ArcGIS Pro was next. The basic elements of this map include Florida County lines, the Interstate Highway system, major rivers and populated cities, towns and census designated places. The idea is to present a simple map conveying where the UWF Main Campus is located without being overly busy with unneeded information.

This Lab involved Clipping Layers, something that I had prior experience with at my cartography job. The clipping aspect was previously located within Arc Catalog on ArcMap. Having it more readily accessible with ArcGIS is convenient. For this map, Escambia County was clipped from the overall state file showing all 67 of Florida's Counties. The Interstates shape file was also clipped to only display I-10 and I-110 within Escambia County, as were the rivers within the Major Rivers shapefile. Lastly the cities shapefile was clipped to only show Pensacola and the census designated place of Ferry Pass, the more specific location of UWF.

Queries were also introduced in the Lab, first with a simple equal statement to select Escambia County from the Florida Counties shapefile. A subsequent query was created to select Pensacola and Ferry Pass from the Cities shapefile. I have previous experience using queries for labeling feature classes, and some knowledge of query structure from managing the mySQL database for AARoads.

Editing the symbology followed and color suggestions were provided. I used green for Escambia County, with a darker shade on the inset. The rest of the counties were set to gray, to minimize their attention. That background could be left white (blank) or a Basemap could be selected from the Layer group. I opted for the World Light Gray Canvas on the UWF Campus Location map. However with selecting that base, a "Reference" entry appeared at the top of the Drawing Order. Unchecking that preventing it from superseding the map.

Labeling included placing a Title, creating a Legend and adding map elements such as a North Arrow, Scale Bar and Source information. There were some differences in the verbiage used to describe actions in the Lab PDF document versus completing those processes in the current version of ArcGIS Pro. This was noted on the Lab discussion board, and previously caused some minor issues with Lab 1.

The final aspect of the map was placing the PNG of the UWF logo. Where to place it and at what size were considerations made as to not have the graphic complete strongly with other map elements. The resulting map in PNG format:

ArcGIS Pro Overview and a basic World Map

Well it's been awhile since I completed a formal assignment such as the Lab 1 for GIS4043! While I have partaken in some ESRI Training, which included exercises, this is the first time working on a GIS project with a grade in mind. 😬

The bulk of this Lab was pretty much a rehash of my existing skill set with GIS from my days using ArcMap... But with that in mind, and as the orientation document suggested, many things with ArcGIS Pro look different or have different names. So getting back to the basics is not necessarily a waste of time as might be imagined.

Working through the Lab, the first step was acquiring the project data for ArcGIS Pro. I have a robust PC that I use for my daily work running AARoads.com, so for class, I choose the option of setting up ArcGIS Pro, Citrix and Google Drive locally. As far as the Lab instructions go, this resulted in a little level of confusion, as I had to keep in mind my local file folder structure versus what is on the Virtual Desktop through ArgoApps. So when checking the project save options on ArcGIS, the default location is obviously different on my PC than the S drive on the UWF server.

Acquiring the data from the R drive was simple, but having both the folder ArcGISOverview with the Data folder containing OverviewArcGIS threw me a little off when it came to zipping up the completed project file and where to place it on my S Drive.

The actual work on Lab 1 involved importing some basic World Map features including polygons for countries and points for major cities. The only hang up for me working with ArcGIS Pro on this was  placing the map in the Layout view. The instructions called for setting up the Layout so that no country was cut off. However my map was slightly zoomed in so that eastern Russia and western Alaska were outside the Layout view.

Watched the Overview video to see if the same scale difference occurred, and it did not. However with my prior knowledge of ArcMap, I recalled changing the Scale Properties to fit the World Map within the Layout. The Overview video also showed the map with the World Cities points reduced in size from 10 point size, so I followed suit and scaled them down on mine to 3 point size.

Spent three and half hours working through the Lab, including a helpful phone call with my wife on how to use the Activate Map Frame option within the Layout. The finished product:

The last aspect was to export out the map as a JPG. The instructions indicated to select the Share tab within the Export group. However, the Share tab is now found within the Output group. Another noted change with ArcPro was that the green Layout arrow button within the Share tab was replaced. Switched in its place is the "Export Layout" button with a smaller green arrow and a dropdown with Export Presets.

ArcPro did prompt me for an update when I started the Lab, so perhaps the upgrade from 3.1 to 3.1.2 resulted in some of these changes.