Friday, April 21, 2017

Final Project - Intro2GIS

The environmentally sensitive lands map


For the final project we were assigned to analyze an actual $20 Million dollar FPL project called the Bobwhite-Manatee Transmission Line. The project was deep in the planning stages in 2006, and was approved by the Governor and the cabinet in 2008. The actual construction for the project began years after the proposed schedule and started in June 2013 due to controversy over the location of the line. 

According to an article from YourObserver.com, "the project was delayed while East County Sarasota homeowners worked with FPL, attorneys, and Lakewood Ranch developer Schroeder-Manatee Ranch to determine a route that would preserve the beauty of lands in eastern Sarasota and Manatee counties." But it takes reading an article from the HeraldTribune.com to understand it wasn't that everybody was against FPL. 


This purpose of this project was to provide additional electrical power resources to the area's customers. It was an above ground project to install a 230-kilovolt transmission line approximately 25 miles.


The line runs from the Manatee Energy Center in Parrish, FL to a station in Sarasota, FL near Fruitville Road called BobwhiteHere is a certified FDEP transmission map that shows the shape of the route as it exists today.


As students we were tasked with repeating real world spatial analysis to experience first hand what tools and skills are required to complete a thorough analysis and presentation. We were to focus on confirming the length of the line to be approximately 25 miles long, the  and that the proposed line met the following criteria:


* It has relatively few homes in close proximity.

* It generally avoids schools and child care sties.
* It avoids large areas of environmentally sensitive lands.

After the analysis that I completed I can report all of the criteria has been met.


Also, the preferred corridor route changed in the controversial route section within Sarasota. The route change appears in the survey map with the approved line that we were given for reference for the project. The articles mentioned above also helped me to determine the community impact and to connect the dots of what happened behind the scenes.

The most difficult part of the assignment for me was subjectively trying to guess what buildings on the raster aerial images for the parcels were actual homes. I made my best guesses but I think that will be the area that most of the students will report different results. 


As requested, please see below:

Link to my written slide-by-slide commentary:

Link to my PowerPoint Presentation.ppsx

This was a very interesting project. There was a lot to be learned. I used to live in Sarasota, many years ago and still have family there. That really helped me stay motivated in the early stages of the work before the natural momentum kicked in.



Saturday, April 15, 2017

Final Project - Cartography

My final project map of Florida's 2010 rural / urban population and a top 12 county ranking of agriculture sold in 2012.
All the information below comes directly from my final written summary.

My final project blends my interest in dot mapping with inspiration from my favorite dot map in our text. (Slocum, McMaster, Kessler, & Howard, 2009, Color Plate 1.2) After we completed lab 10 and 12 using a dot map of urban areas in South Florida, I had an interest in seeing a map of the whole state done in a thematic dot map categorizing both rural and urban populations. During the past year I watched a documentary on Netflix,  Food Chain$ (2014) that focused on farmworkers in Immokalee, Florida and the importance of fair trade. It made an impression on me, and I now look at the price of produce in a new way. While I was getting the idea for my project together I questioned where was the exact location of Immokalee. I looked it up, and it’s in Collier County. I also had a question about what were the top 10 cities that produced the most agriculture in the state. I thought Immokalee may be on the list. I found a single source for this on the web but didn’t believe it was reliable enough to use for the map.

The objectives of my map are to follow the guidelines outlined for the final project and simultaneously produce a map that answers questions for myself and for a larger audience interested in Florida’s agricultural sales and rural / urban populations. There are quite a few stakeholders involved in fair trade and fresh agricultural products. They include farmers, farmworkers, consumers, and the grocery stores and other businesses in between.


First, I began making decisions about the data I would select. I thought using the FGDL geodatabase from module 2 with the counties feature layer and the surface water feature layer would get me off with a head start. I opted to use a beige color for the Marsh or Swamp features since the rural percentage choropleth would be in green. I opened up ArcMap and added the Florida.gdb with the feature layers. After I got started I realized I should have just created a new geodatabase and added the feature layers to it, but I decided to keep moving on. The projection of the feature layers was the Albers Conical Equal Area and I didn’t change it. 


I did a Google search on the urban and rural populations in the U.S. and found the United States Census geography web site (2016) and downloaded the County Classification Lookup Table.pdf. It had everything I needed to get me started with my base map. I did have to scrub the data and create a new Excel table with field names without spaces. I also had to retype the county names exactly to match up with the counties feature layer. Then I could join the table to the feature layer on the name field.


For my first choice of the two thematic map options I chose a choropleth map as my base layer with graduated green color symbology. I used the data classification method of equal interval with 5 classes because I thought it represented the data well and offered nice contrast between the classes. I used the percentage of rural population for the field. No further standardization was necessary as it was already complete as a percentage. After getting that layer created the momentum for the map increased. It was just a series of decisions for the methodology.


Additionally, I created another instance of the counties layer and used the symbology properties and chose the quantities dot density on the field for the raw rural population totals and followed the lessons learned from lab 10. The dot map is my favorite thematic map. I also used the masking and excluded the surface water feature layer. Then I created another instance of the counties layer with the urban population raw total field and decided on the dot size and value. 

Using this choice for mapping really created a slow moving process going towards completion. It took a lot of patience. I must have seen ArcMap not responding messages at least twenty times or more, but I didn’t give up. I thought the dots looked really good overlaid on the choropleth base layer. I didn’t care for the default legend in ArcMap so I did a small legend in AI and imported it back in to ArcMap. I didn’t use the dot density small, medium, and large anchors because although the dots were an integral piece they weren’t the sole focus of the map. I thought showing information just on one dot for each category offered sufficient explanation.


Next to give the map even more attention and some more interest, I found statistics from the Florida Department of Agriculture and Consumer Services (2017) and found a list of Florida County Values of Agricultural Products Sold in 2012. From that list I was able to compile the top 12 ranked counties with the total value sold in an Excel table and add the results to the map for interest and valuable insight. I opted for 12 instead of 10 so I could get Collier County on the map with its rank pointing right up to Immokalee, Florida.

Closing comments for the class: I heard U2's song last week and these words come to mind.

See the world in green and blue
See China right in front of you
See the canyons broken by cloud
See the tuna fleets clearing the sea out
See the Bedouin fires at night
See the oil fields at first light
And see the bird with a leaf in her mouth
After the flood all the colors came out
It was a beautiful day
Don't let it get away
Beautiful day

Good luck everybody! 

Thursday, April 13, 2017

Google Earth

My South Florida Dot Density Map Imported as a KMZ file into Google Earth
The lecture portion of this module focused on neocartographers and the applications that they use. It's people not from a traditional cartography background but citizens that are using in some cases open source map applications and creating maps for their audience. Their work falls under terminology called volunteered geographical information (VGI). We had to complete a class participation assignment and I chose to focus on Open Street Map and the Humanitarian Open Street Map Team and one of their current projects for the 2017 Peru Floods. Open Street has their own wiki. It's a world in itself. 

Some of the supplemental articles for the lesson helped me to pique my interest in doing some neighborhood mapping on Open Street or to volunteer to help map during a crisis somewhere in the world. 

The lab focused on teaching students how to take a map and export it from ArcGIS (ArcMap) using a map to KML tool, and to take an individual layer and export it with the layer to KML tool. In each instance a KMZ file is created which is a zipped KML file.

I think Google Earth is awesome and beautiful. With what I learned in the lesson I could spend hours just sitting at my computer and drilling down to look at new places. The lab guided us through setting up our map, creating place marks, and creating a tour. Then we packaged up all of our layers, place marks, and tour and exported as a KMZ to turn in for our deliverable to be graded. One of the coolest things was looking at the ground floor of the Tampa area where the 3D models are based on LIDAR data so almost any object above the ground is modeled like trees, cars, and bridges.

Saturday, April 8, 2017

3D Mapping

ArcScene Screenshot Crater Lake Raster Elevation Used as Base Height
This module of the course focused on 3D Mapping. The above screenshot is from one of the lessons teaching base height options in ArcScene. We used our Esri accounts to take the Esri - 3D Visualization Techniques Using ArcGIS course. Our lab was in 3 parts. The first part was the Esri course where we used ArcScene to go through training sessions including: setting base heights for raster and feature data; setting vertical exaggeration which allows for a flat surface raster image to gain a dynamic surface by emphasizing small changes in elevation; exploring the scene properties illumination including both sun and shade with a background color on a tin surface elevation layer; exploring extrusion which is the process of stretching 2D features into 3D features, and extruding parcel by their z values set to property monetary values not actual height. The second part of the lab transitioned into a real world example of taking a 2D building layer and converting in to 3D by deriving elevation values for the building footprings using lidar data. Part 3 involved the student by comparing and contrasting Charles Minard's map of Napoleon's Russian Campaign of 1812 in 2D with a 3D map created by Kenneth Field and Nathan Shepherd viewable in Esri's CityEngine.

Below are some of the advantages, disadvantages, and applications of 3D data. As the technology progresses, more and more can be done using 3D data in 3D applications. In my job working in the construction industry, I'm always looking at building information models (BIM) architectural and structural models. A good source to view almost any file format of a model is AutoDesk 360. Other smoking hot apps are sketchup, tekla, and revit. 3D technology is helping urban planners, architects, engineers, fabricators, general contractors, and detailers just to name a few examples in my everyday life. An example is to take the core steel structure of a building with beams and columns and when you see it in 3D, you get to see the vertical extent. The model is present with all of the pieces. When you drill down to the details you get information like dimensions.

I wouldn't want to always read on a computer or a device. Books and paper are nice. A real world example for an advantage of 2D drawings may be a welder. The welder can read the 2D drawing and know where the weld goes as opposed to opening up an 3D app and getting lost in the virtual world losing time. 3D data can be slow to load. I experience it when my model is huge and many megabytes. Waiting for it to load so I can navigate it with the tools takes patience.


Sunday, April 2, 2017

Georeferencing and ArcScene

My georeferencing map of UWF and the Bald Eagles Nest Conservation Area
Our study for this module included georeferencing, editing including digitizing, and ArcScene. In the georeferencing process we used the ArcMap georeferencing tools to mark control points on a raster .jpg image from a reference UWF buildings shapefile. The raster image didn't have any coordinate system or projection defined. This process takes a while to master but the best way I found to complete the task was to put a control point in each corner and then start working through the middle control points. First you pick a control point on the unknown image and then pick a known point. Once you have at least 4 control points you can check each point's RMS or Root Mean Square error. This error corresponds typically to how accurately the  georeferenced point is. Our goal was to have each image completed with a 15 RMS or less. I had to redo mine a few times. By the last time I had learned the tools of the trade and managed to come out with a 2.68 using a 1st order polynomial transformation on the UWF North Image. The South Image I managed a 2.90 with a 2nd order polynomial transformation. My first go around on that task I was about a 27. It wasn't until I saw how off some of my buildings were that I knew it had to be redone.

One of the interesting things about this lab was learning about the Eagle's Nest that was identified by the FFWCC and now has a conservation easement and a protected area. They have a web page here with more information.

To create the map above  I used ArcMap but I struggled with the seam between the two images even after having the georef done about as good as it gets. I tried a mosaic and all kinds of different things. In the end I used what I call a raster patch band-aid. I took a snapshot and then used Preview to crop a thin line of the color I needed. Uploaded it to GIS desktop, copied and pasted it right into my map. It's barely there. You still see a line but it doesn't look ripped or jagged at the seam. I also have a raster patch on my ArcScene Map below. Again it's barely there. Just a cosmetic thing for a little more polish. 

In ArcScene we used a Digital Elevation Model to create a 3D look in a 2D image. 


My UWF Campus Map created in ArcScene

Friday, March 31, 2017

Dot Mapping

My Dot Map of South FL

The work for this module included a study of dot mapping. One of the most interesting parts of this was a supplemental resource YouTube video of a dot map created by Bill Rankin called: Mapping Social Statistics - Race and Ethnicity in Chicago. This video is my favorite part of the module and it really made a connection for me in understanding the beauty of dot mapping.

Dot maps are one type of thematic map that use small dots as symbols to display the spatial distribution. The cartographer decides how much of the phenomena one dot represents. Dot maps are best used when conceptual data has been collected in the form of raw totals for a data collection unit like a county or state and the cartographer wishes to show the data is not uniform throughout the unit. In some cases the dot map is going to be the best way to show variation of data across the particular geographic region being mapped. 


Once again I refer to the figure above from our text on page 78. If the data is discrete (existing at a specific place on the planet and representable by a point like an individual person) and smooth (meaning changing gradually over a spatial distribution) the dot map is the best choice to use in conveying the spatial distribution effectively.

Below are a few of the learning objectives for the module:

• Join spatial and tabular data
• Utilize dot density symbology
• Select suitable dot size and unit value
• Utilize the mask function to manipulate dot placement

In regard to the map I create above. I used ArcMap and used the join feature to join population density statistics from the US Census Bureau in an Excel flat file table to a South Florida shapefile from FGDL. The join was based on the County Name.


Further into the lab from the South Florida layer properties we chose the symbology tab, selected Quantities and chose Dot Density. We selected the population field. After the processing was complete the dots were randomly placed throughout each county.

Next we learned about picking the best dot size in relation to the unit value. We had to have at least one dot in each of the counties being mapped. We learned how to best manipulate the appearance of the counties themselves in order to let the dots be the stars of the map.
Then we applied masking technique and utilized other layers such as surface water and urban land. This gives the analyst control over limiting the attributes so the dots are strategically places to reflect where people actually live.

The next hurdle was adding some finishing touches to create the dot part of the legend. I took a snapshot of my map with only the dot layer showing. Then I opened Preview and cropped 3 squares the same size and saved each file as a .png. This took a few attempts to get a low, medium, and large density example that I thought would be acceptable. I imported these into AI and used the file place option. I created an outline for each rectangle, and added labels. I grouped all of the elements, and used the object transform to reduce the size by 50%. Then I exported a .jpg and imported into the GIS Virtual Desktop. Opened it in Paint and then copied and pasted it into my map. I had a difficult time getting my RGB color to match exactly in transition from AI to ArcMap.




Monday, March 27, 2017

Geocoding & Network Analyst

My Lake County EMS Geocoded Map

I finished this assignment just two days before the due date. It's hard to get two projects done in a weekend because of various challenges which leaves me working on school work most of my time away from work. Now all the work from this class and the Cartography class are converging, so it's tough to keep up the pace when there's no time cushion left and only due dates driving completion.
I'm definitely feeling the toll, and I'll be glad when I get into the final projects phase.

The other students have been and continue to be a huge inspiration for me, and I check out the UWF GIS Blog all the time to see the current and past work.

The studies and lab for this assignment included geocoding, using the ArcGIS Network Analyst Tool, and the Model Builder. It was divided into three parts. We downloaded the Lake County TIGER/Line shapefile data set from the U.S. Census Bureau. Then we imported it into a geodatabase along with an Excel Table of Lake County EMS Station address data. We setup an address locator and used the U.S.  Census shapefile as our reference geocoding dataset. A few steps later working in ArcMap I was able to right click the imported table and utilize the geocode application. My first go around something went wrong and my automatched locations didn't show up correctly. They all pointed down past south Florida. I had to start from scratch again. The second time went well. I learned how to manually geocode the addresses that didn't have a match.

Next I used the Network Analyst tool to design an optimum route layer. I added three stops to the route and added parameters to the layer properties and computed the best route. My map above is the result of geocoding, and network analysis. I also independently learned how to edit an Excel table in ArcMap. I used the Geocoding Result layer to add a station field so I could efficiently add those labels to my map.

In part 3 I learned about Model Builder. I was really impressed with the power and efficiency this feature offers. Students were directed to complete a free ESRI class exercise but run the downloadable data in the Virtual GIS Desktop. This entire project was interesting.