Module 2 - Python Fundamentals Part 1

This week we created a basic script, the end of result of which shows my last name and the number of letters in my last name times three.

To write the script I first had to define what my name was... this then was split into a list. From this I was then able to separate out my last name, and derive the total count of letters in my last name using the len function. Once I had this information I was able to write a statement multiplying the total last name letter count - the results of which you can see below.

The script, while short, was really the result of several false starts. After reviewing the textbook and the lab exercise several times I was able to finally produce something that should work with names other than mine, and can actually be run in PythonWin. This was quite the learning experience, and I am very happy with my results.

Screenshot of script results.

Lab 1 - Suitability Analysis

The first official lab of the class involved running suitability analysis models. First we focused on simple Boolean analysis (models that answer yes/no type questions), then we covered weighted overlays (applying a ranking to possible suitability results).

Two weighted overlay results.

Discussion

The above map shows the results of two different weighted overlay results. Both maps were derived from the same input data (ranked highway, river, slope, soil, and landcover type) but the overall importance each dataset was weighted differently using the Weighted Overlay tool. The map on the left shows the model results if all criteria are given the same weight (in this case, each criteria weighted 20% out of 100), and the map on the right shows the results where some criteria (such as slope at 40%) are given more weight than others (such as roads at 10%).

As you can see, with the variable weight scenario map on the right the resulting classes were not enough to fill all 5 categories. What the category range means is that areas classed as being 1 are least suited (in terms of the overall criteria and their given weights) and areas classed as being 5 are most suited. Under the alternative scenario the best results are average suitable locations only.

Quite a bit of data processing went into the production of the above maps. The above maps show raster data, not vector data - so those layers that were shapefiles were converted using Euclidean Distance, then reclassified to show the desired number of classes/rankings. The raster data was also reclassified the same way.

A benefit of the weighted overlay over simple Boolean analysis is that one can see the shades of gray within the data... although as shown on the map above, it does matter how you initially class and rank your data. The results of a Boolean analysis are very easy to interpret but weighted overlays are a whole other beast - the above results are a bit trickier to understand, and a solid methodology is required to make sense of it all.

Module 1 - Introducing Python

This blog submission marks the start of a new semester and a new class within the UWF GIS Certificate program! For the first module in GIS Programming we ran a Python script.

The purpose of the script was to set up all our folders and sub-folders for the semester, which it did in the blink of an eye. I had double-clicked on the script which ran it immediately - that was something of a mistake on my part, as I had meant to only open the file for viewing/editing. Oops. The script results are shown below.

What scripting can do for you - above is the Module 1 script results.

Once I opened the script I observed some variables I was able to understand right away (like "import os" or "course folder = [location for folders]"). Others, like "def createPath (path):" I'm not so sure on... but I can hazard a guess as to what that's getting at (and that probably is a definition of some kind). The formatting of the script is also a bit more complex than the pseudocode examples we had played around with in the lecture part of the class.

Overall this looks to be an interesting course - I've already picked up little knowledge nuggets, and I'm pleasantly surprised at how seemingly easy scripting can be.

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.

Final Project - GIS 3015

The final task for GIS 3015 was to gather data for 2013 college placement exams (a choice of either ACT or SAT) and present it on a map. The map is intended to be an infographic for a fictional newspaper article in The Washington Post. There were two main objectives with this map. First, the map must be visually appealing, use the best possible thematic data representation, and adhere to basic cartographic design principles. Second, the data presented in the map must be done in the best possible way, with the choice being ours as to what (if any) data classification method to use or which thematic map style(s) to display.

Map infographic depicting 2013 ACT Data.

Technical Details

Graduated picture symbols (representing a mortarboard) were used to depict the composite ACT scores, and a choropleth map was used to represent the percentage of students who took the exam. Since the data focus is mainly on the composite ACT scores, those took first priority (and thus the is the first item visible on the map). The graduated symbols were range graded using manual breaks divided into seven classes – I wanted to preserve the idea of the whole number composite scores, since most people think of the scores in that way. The choropleth data was divided into five separate classes using the quartile method. The percentage dataset was evenly distributed (more or less), with the only ‘outlier’ being the eight states that have mandated 100% participation for high school students. Information on the 2013 ACT was added to an excel table which was later joined to the state shapefile – thus allowing me to use the same shapefile in two different ways on my map.

Since the map is intended to be an infographic, I wanted to catch the reader’s eye and give them something to think about in the few seconds that I have their attention. To do this I steered well clear of any tables on the map – my own eyes glaze over when I see that, and it would require a bit more effort on the reader’s part to make sense of my map (not to impugn the good readers of The Washington Post!). To create visual interest I used a picture symbol for the ACT score data, changed the map neatline to an oval, and arranged my map items accordingly. It is hoped that the deviation from a non-square map might be visually appealing. To further enhance the prominence of the choropleth map (as opposed to the surrounding map text) I used a drop shadow on the contiguous U.S. outline, and also for Alaska and Hawaii.

Final Thoughts on the Class

I found the class to be very beneficial overall. One of the (many) reasons that I had entered into this GIS Certificate program was to learn how to create better maps – what better way to do that than to take on the principals of map design? I found it a relief to learn that I was at least on the right track with my pre-GIS Certificate program maps… and to learn ways to improve on them. It was fun (even in the depths of CorelDraw hell) to try my hand at all those crazy thematic maps – and to pick up some very useful graphic arts knowledge along the way. I know that any maps I will make in the future will be 100% better for having taken this course.

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 - Google Earth

This week in GIS3013 we created maps using Google Earth... or more specifically, created tours using Google Earth.  Data was added from ArcMap by converting our dot map density maps and associated data (from Module 10) to KMZ files.  Afterwards we created a video tour of various urban areas in southern Florida.

A typical street scene of downtown Tampa, as depicted by Google Earth.

Some thoughts...

The main objective with this lab was to familiarize ourselves with Google Earth, and become comfortable adding information generated in ArcMap to the Google Earth program. Converting the maps and individual shapefiles was easy enough, although the difference in output within Google Earth was striking. I found it interesting that there are more options to manipulate the data when it's been converted to KML as an individual layer as opposed to having been imported as group of layers associated with a map.

My biggest struggle with this lab was with the video tour... I mean, that should have been so easy, I'm almost ashamed of how difficult I found it to be! I did my best to keep the view going smoothly - not too fast, not many sudden movements - but that turned out to be for naught when nothing played back the way I thought it would.  During all my video takes I had turned off the southern Florida dot density data (reused from Module 10) during the zoom in to downtown Miami, which was the first stop on our list.  However you'd never know it because that data either wouldn't show up at all during subsequent playbacks, or it would stay turned on the entire time.  What gives?

Another minor snafu was with the importation of the dot density data from Module 10... I had suppressed the mini-dramas involving the dot density mask and so it took a while to figure out why those dots weren't showing up the way I had remembered them.  Plus the legend or the labels wouldn't import either, as they had been converted to graphics/annotation.  Since we had an example dot map included with this lab I used that, but I believe that I could have gotten around my issues by importing the mask as an individual shapefile... and also by completely re-doing the legend and labels on my original map.

Overall I do feel more comfortable adding data to Google Earth and using it generally.  It is an internet needy program though - on my slower connection all the downtown scenes looked like warped views of Gotham city, with small items eventually popping up like bugs once the graphics were loaded... which was kind of neat to me, but probably not in the way the designers intended.  To compensate for this I tried to steer my video tour well clear of too many 'detail' views!