Final Project - GIS 4043

The final presentation for GIS4043 is the final culmination of three-part project: data analysis of the Bobwhite-Manatee 230 kV Transmission Line Right-of-Way.

Step 1 was to review the data, and create a tentative workflow model.
Step 2 was to analyze the data.
Step 3 was to present the data as a powerpoint presentation, and to provide slide notes to go along with it.

These I present here:
http://students.uwf.edu/ear25/Intro2GIS/FINAL_ER.pptx
http://students.uwf.edu/ear25/Intro2GIS/SlideNotes_ER.pdf

This class has been very informative, and I know I learned quite a bit (particularly about those tricky projections!). I enjoyed all the learning challenges, and I know that I will be drawing on lessons presented within this course in the future.

Week 13 - Georeferencing, Editing, & ArcScene

This week marks the last official lab assignment before the final. For this lab we georeferenced aerial photos to buildings and roads associated with the UWF campus, created building polygons and road polylines, and created multiple ring buffers around an eagle's nest. To top it all off, we overlaid data on a DEM in ArcScene - which is a 3D mapping program offered by ESRI.

Georeferenced aerial view of the UWF Campus - and the Eagle Nest location!

Notes on Map 1

The first map shows two separate data frames that contain aerial views of the UWF Campus. The aerial image on the left was georeferenced, and the items in red indicate those features that were digitized during the lab. I've decided to separate out the digitized data from the non-digitized data by color, so that both of my maps sort of flow together.

The data frame on the lower right shows the location of the eagle's nest in relation to the overall boundary of the UWF Campus. The aerial image in the background was provided by ESRI. During the lab I was wondering who in their right mind would ever think building student housing in a wilderness sanctuary would be a great idea... and then I saw that the eagle nest location is right on the university boundary. It sort of makes sense now (but not entirely - I mean, who couldn't see that roadblock coming?).

3D view of a select portion of the UWF Campus.

Notes on Map 2

The second map shows a 3D snapshot view of the UWF Campus, with the digitized buildings 'highlighted' in red. I kept the symbolization and overall map appearance the same as the first map. The buildings were extruded off the surface, and all visible layers are resting on top of a DEM (not immediately visible). Due to the way ArcScene works it was not possible to provide a scale or north arrow... it's almost kind of liberating! Technically the map above is a screenshot of what was visible in ArcScene, with the finishing touches made in ArcMap.

Week 12 - Geocoding, Network Analysis, and Model Builder

This week in GIS4043 we covered geocoding addresses, used the network analyst extension to create a route, and edited a model in Model Builder. It was quite the whirlwind week!

The map below shows the results of the geocoding and network analyst exercises. The addresses were geocoded from a table of EMS locations within Lake County, Florida. To get the geocoding down we first created an address locator. The points on the map below are the results of the geocoding process (and the subsequent address matching process - which basically involved matching problem addresses using Google Earth and the Lake County EMS provider website).

The route was created from various stop points created at random using the network analyst tool. Basically what is shown below is the optimal route based on pre-assigned conditions (in my case, the best route at 4:30 p.m. on a Monday with U-Turns allowed). The network analyst tool navigated through all of the road data to find the quickest travel times and came up with the route below. If you're interested, the estimated travel time is 11 minutes (stopping at both locations), and the total distance traveled is 8.3 miles.

Geocoded EMS Locations and Optimal Route generated by Network Analyst.

For me the most difficult part of the exercise was the geocoding. It seems so simple - match an address with a location, right? Well, not entirely. The unmatched areas didn't really line up well with reality, and what was even more disturbing is that both Google and Bing maps had serious placement discrepancies. In the end I was left with using Google aerial map view (to look for possible EMS-like buildings) and the Lake County, Florida EMS location webpage.

Both options really messed with my comfort level concerning data accuracy because, well, it's not accurate. However I do realize that if that were my job, I may not EVER be able to go out into the field to ground-truth those address locations. The bigger lesson here was that data entry problems really do create bigger problems down the road...

Week 11 - Vector Analysis 2

This week in GIS4043 we wrapped up a two part series on working with vector data. Our deliverable for this week is a map of potential campsites. These potential areas were located using the following modeling tools: buffer analysis, union, and erase. The final map product needed to show the result from applying the modeling tools, using these parameters:

• must be within 300 meters from a road
• must be within 150 meters from a lake or 500 meters from a river
• must not be within a conservation area

View of potential camping areas, as derived from applying various overlay tools.

 Technical Details

The potential camping area polygon is the result of several overlays. First, a buffer analysis was run on the roads and the water layers (300 meters for the roads, and between 150 meters for the lakes and 500 meters for the rivers within the water layer). Once I had my buffer zones identified, I then merged the data together using the union tool. From this I was then able to remove the conservation area locations from the union output by using the erase tool. The result of all this data processing is what you see above.

Within the lab we also ran an additional tool to separate out the polygons - specially, the multipart to singlepart tool. For analysis of each individual area it would be helpful to have the camping area polygons split up within the attribute table, but for visualization purposes I found it more useful to use the multipart polygons. I suppose I could have displayed the singlepart data, but just changed the outline of the polygon to none in order to present a seamless appearance.

The basemap above is ultimately from USGS, as provided via the ESRI basemap service. It's not the best basemap ever as it has labels I don't necessarily want, as well as its own roads and water layers - but it does show a nice hillshade effect and gives a general sense of where the forest is in relation to non-forest land. The basemap I had wanted to use from USGS came in a geo.pdf format, and at this point in time I do not know how to convert that for use within ArcMap (if such a thing is possible) - it's main selling point was that it came with contours. Perhaps for next time...

Week 7/8 - GIS Data Search

This lab represents our mid-term exam! For this particular project we were each assigned a specific Florida county to map in order to highlight our map creating skills learned thus far. From internet sources we were to gather data for nine required layers:

•  5 vector files (county boundary, cities, public lands, roads, and surface water)
•  2 'environmental' files (choice of either invasive plants, Strategic Habitat Conservation Areas, land cover, or wetlands)
• 2 raster datasets (just one DOQQ, and a DEM file showing our entire county).

From this data we had the option to create up to three maps, as long as the data was displayed appropriately... and all of the data needed to be in the same coordinate system (our choice of UTM or Albers). I settled on two maps, shown below.

Overview of Okeechobee County, Florida.

Conservation efforts in Okeechobee County, Florida.

The process

For me, deciding how to depict the data was the hardest part. I needed a story to put these maps together, so I created one: the maps represent areas of environmental concern in Okeechobee County, particularly around the Kissimmee Prairie Preserve State Park. To keep the flow going between the two maps I used the same types of symbolization for layers that appear on both.

The first map is an overview of Okeechobee County - basically what the county looks like, where the cities and towns are, the major roads, the surface water, where the state park is, and where the county is within the state of Florida. The second map is more of a detailed environmental map, showing the overall location of Strategic Habitat areas, and where the invasive plants are in relation to the Kissimmee Prairie Preserve State Park.

Both maps contain clipped vector data, which were clipped using the Clip (analysis) tool. I had to clip my raster files to the data frame itself (on the first map it was the DEM, and on the second map it was the Strategic Habitat areas)... I had tried to use the Clip (Data Management) tool, which says it's for raster graphic files, but ArcMap flipped out on me and I had to give it up. Oh well, maybe next time!

The labeling within the map frames are a combination of automated labels, labels converted to annotation, and labels I put on the map myself. There was no middle ground with the automatic label wizard, but not having to label everything individually was still nice. 

 My second map was the most complicated in terms of design, as it contains four data frames. In order to get items to stand out I made dropped backgrounds sort of a running theme. While creating the second map it occurred to me that maybe the first overview map was unnecessary... but trying to fit that roads layer somewhere on second map was a challenge I wasn't willing to take on.

So the big project is finished. This is the state of my map making capabilities thus far... I wonder what the final will bring!

Week 6 - Projections Part 2

 ... or was it Data Search Part 1?

This week's lab had us gathering spatial information off the internet, reprojecting it, and then putting it all together into a final cohesive whole (the map). We also created spatial data from excel spreadsheets, and converted coordinates from degrees minutes seconds to decimal degrees using a formula entered on the excel spreadsheet. The results of this lab can be seen below.

The majority of the data on this map came from internet derived sources.

Technical Notes

While there is quite a bit going on with the visual part of the map, such as the two insets to show the map location, the real story took place behind the scenes.

My first step was to import an excel spreadsheet of the petroleum storage tank locations. I ran into some snafu's when it came time to put a projection on it, and if it wasn't for the class discussion board I'm not sure how long it would have taken me to treat this as a two-step process. My issue was with defining a correct initial projection - I was trying to fit data from a lat./long. source into a State Plane grid! That won't happen (well, correctly anyway) without first taking the proper steps. Since I had converted degrees minutes seconds into decimal degrees, I needed to assign a lat./long. projection - in this case, WGS84. Only after I set up the GCS to an appropriate system could I then reproject to State Plane Florida North US Feet (the projection required for this lab).

After viewing the storage tank data, I decided to focus my map on the Bratt and Century quads... but it quickly became very clear that in order to have some semblance of consistency, I needed to add more aerials! So the majority of my time was spent downloading aerials from Labins.org (a website of survey data for Florida, hosted by the Florida Department of Environmental Protection), then extracting them. In all there are 8 quads represented on my map - only 2 of which were absolutely required.

The roads, quad index, and county boundary data were also obtained online... this time from the Florida Geographic Data Library (FGDL). It's a source that had already been used several times over in this class. All of the data had been converted from Albers to State Plane Florida North Zone NAD 1983 US Feet, using the project tool.

In all I found this lab very useful - and I've already started to bookmark credible sites with free spatial data downloads for the western U.S.!

Week 5 - Projections Part 1

This week's lab covered the difference between map projections and geographic coordinate systems, which tool to use under the Projections and Transformations toolbox to either re-define or assign a map projection, and touched briefly on geographic transformations. Our map for this week shows the state of Florida, set to three different projections.

The state of Florida, shown with three different map projections.

Technical Details

To create the three different views of Florida, I inserted three separate data frames. To each data frame a different projection was made to each shapefile that represents the state of Florida (technically the shapefile is a collection of all the counties in Florida, not simply a view of the state only). To highlight the changes that each projection had on the state of Florida, a sample of four counties is provided in the legend with the square miles for each listed. For continuity the symbology, labeling, and map extent used for each frame was made exactly the same. The background for each data frame was set to light blue, in order to make this section of the map stand out even more.

The creation experience, in review

Creating the map was fairly easy, but somewhat tedious - as a lot of map creation can be. I enjoyed using the projection tool with confidence, as in the past I felt like I barely knew what I was doing. To be honest, the majority of my lab experience was spent reading all those ArcGIS Help topics on geographic coordinate systems, map projections, and geographic transformations. My personal favorite topic was the one covering what to do when you don't know the original projection of your data, as I've run into that a few times myself... and hopefully next time around I'll be able to figure it out on my own, instead of running to the GIS folks for help!

Week 4 - ArcGIS Online & Map Packages

This week’s lab in GIS4043 covered how to create and share map packages through ESRI ArcGIS.

A map package is a collection of vector data, along with any supporting .pdf or .txt file documents associated with a map. The design of the map, along with a working copy of the data, is what constitutes a map package.

Screenshot1 for the 'Use and Modify Map' exercise.

For the first exercise we modified an existing map package, then uploaded a new map package to our ESRI ArcGIS accounts. This sounds fancier than it was – the exercise was really about poking around with map and tile packages. The view to the left is a screenshot of my uploaded map package for this portion of the lab.

Screenshot 2a for the 'Optimize a Map Package' exercise.

The second exercise allowed us to add and edit data, as well as specifying certain data spatial extents, layer symbology, etc. The maps were then packed up and shared on the ESRI website as a map package. The views below are screenshots of my second uploaded map package.

Screenshot 2b for the 'Optimize a Map Package' exercise.

I found this exercise to be quite informative – I really had no idea one could send data on like that. I can definitely see the usefulness of this type of data sharing. These packages do not need to be directly uploaded to ESRI ArcGIS online either – I couldn’t upload directly to my ESRI account, so I had to save it locally and add it to my account manually… that’s pretty neat!

On a personal note, I found that old screen names can haunt you… I had an ESRI account many many moons ago, so I didn’t need to create a new one. Unfortunately the screen name wasn’t quite as professional as one would hope… thus the highly edited screenshots. Oh well, you live and learn.

Week 3 - GIS Cartography

This week we covered the history of mapping generally, various mapping techniques, and discussed who has the right to create and analyze maps. The lab for this week covered three different mapping layouts, each using slightly more advanced display and layout techniques. These maps are discussed below.

Map 1 - View of Mexican States by Population

For our first map we were tasked with symbolizing the states of Mexico with an appropriate color scheme and using figure-ground design principles to make the country stand out. I did so with a range of brownish colors – the colors increase in darkness along with the increase in the population of each county. The background of the map was set to a light blue, and the surrounding countries were symbolized in yellow to help further distinguish Mexico. I decided to put a light mask around the state names to help them stand out a bit better (without the mask they seemed to blend in with the background).

Map 2 - View of Major Cities within Central Mexico

The second map shows principal cities within central Mexico. The cities were labelled based on their overall population (greater than 1 million) – this was defined by creating a special label class using an SQL definition under layer properties. The labels were then converted to annotation for greater ease of placement. When making this map I realized that my original color scheme for the Mexican states wasn’t going to quite work, so I changed the entire country to a light tan color. An inset map was placed to show where exactly the detailed view of Mexico is; to further clarify the location I matched the country color for Mexico to that used on my main map view.

Map 3 - DEM View of Mexico

The third and final map is a digital elevation model (DEM) map of Mexico. I went with traditional colors within a stretched color scheme to represent the DEM. I realize that there are some interpretation problems with this type of representation – it seems to suggest that the greener areas are lusher than they actually might be. But since this type of elevation representation has been around so long I think an argument can be made that it also represents low elevation (for certain generations anyway). For the inset map I decided to show all of North America and South America –I think one can get the sense of where Mexico is in the world without having to show the entire globe.

*Originally posted on 1/30/2015 @ 8:50 p.m. CST. Edited on 2/2/2015 at 2:15 p.m. to show the correct label tag (should be GIS4043, was initially mislabeled for GIS3015).

Week 2- Own Your Map Lab

This week in GIS4043 the theme of the map lab was to 'Own Your Map'. I created a basic location map using an inset dataframe as an aid for the map viewer. For this example the main map view is of Escambia County, Florida. The location of the county within the overall state of Florida is shown in red on the small inset map.

I did what I could to 'own my map' by customizing the map legend and adhering to the same map text font and scale bar styles throughout. The symbols used on the map below had not been changed much from the lab example mainly because I thought that they already conveyed the point of the map quite well... and in terms of published maps similar to this the main location always seems to be a big fat star. Why reinvent the wheel?

The main map view also contains interstates and rivers that were clipped to the extent of Escambia County. I really enjoyed completing this part of the lab. Previously I had done clips using ArcToolbox and multiple copies of the data... frankly I had found clips to be somewhat of a labor intensive, migraine inducing experience. I had no idea clipping could be as easy as following a few steps within the overall dataframe view - and it's easily reversible too! As ridiculous as it sounds this new knowledge has opened up whole new worlds for me!

Completed map for the 'Own Your Map' lab.

Week 1 - ArcGIS Overview Map Exercise

Orientation to online learning and ArcMap

The first week of class centered around an orientation to the online learning format and an overview exercise showing what ArcMap is all about. 

Various tutorials covered how to create a blog, set up your personal file system, and how to create a basic map using ArcMap.  Some of the information was a bit new to me, such as creating a blog.  The basics of ArcMap were not new to me, but I did learn a few things, such as what each file extension stands for within a single shapefile, and the difference between a raster and a vector graphic.

The lab for Week 1 involved the creation of a basic map.  I added two shapefiles to the map, then oriented and centered the view to one that was more visually pleasing (in this case, a landscape view).  I used the layout view to add additional elements to my map, such as a north arrow, scale bar, a symbol legend, and descriptive text.  After re-arranging these items in a way that I thought looked good, I further tweaked the view by customizing the properties of some of the map elements (specifically the map legend and the scale bar).

I exported my map as a .jpg file.  My final map is shown below.