Lahars and Mt. Hood

This week in Applications in GIS we focused on applying our GIS skills to determining how a lahar would affect a population base using different tools within ArcGIS.  For those who don't know what a lahar is, it is a destructive mudflow that is triggered during a volcanic eruption.  When a volcano erupts, the intense heat melts snow packs and glaciers on the volcano slopes and the resulting flows pick up mud and debris that are then carried down slope destroying everything in its path.  Typically lahars will follow existing stream and river beds as they are the paths of least resistance.  Two somewhat recent lahar flows were those resulting from the Mt. St. Helens eruption in 1980 and the 1985 eruption of Nevado del Ruiz in Columbia whose lahar flow killed over 20,000 people in the town of Armero. 

Our lab this week had us replicate a study of the Mt. Hood stratovolcano and how/where the potential lahar flows would affect the surrounding areas.  The first step to this lab was to obtain the geodatabase that we would be working from.  The data provided within this geodatabase would serve as the foundation for all the processes we would complete in this lab.  A main point to using this geodatabase was keeping a naming convention for newly created elements that made sense and ensuring that we didnt keep useless files within the geodatabase.  The picture below illustrates how my geodatabase ended up at the end of the lab. 

The following steps were used within ArcGIS to create the basis for creating the map that you will see below.  First, a study area was created around the Mt. Hood area that encompassed the Multnohmah, Wasco, Clackamas, and Hood River counties.  This study area would serve as a clip feature to remove unnecessary features later on in the map creation.  

The next step was to create a mosaic raster out of the provided rasters files in the geodatabase.  This was done to make analysis easier by analyzing only one raster vice having to complete the analysis on multiple rasters.  Once the raster mosaic was created the following tools from the Spatial Analysis toolset in ArcGIS were used in the following order: the Fill tool, the Flow Direction Tool, and the Flow Accumulation Tool.  Appropriate file names were used for the resulting outputs.  So what did these do?  They basically identified the likely areas where liquid materials will flow to on Mt. Hood.  In essence, this amounted to a stream network flowing from the peak to the base/surround areas of the mountain.  

Next I used the math Int tool to convert our values from the previous steps to integer.  Originally the pixels had floating point values.  We would then determine what 1% of the value of the total number of pixels were in our stream network.  That would then be used in the Con tool to create what was more likely to be the true stream network.  This output was then converted to a geodatabase feature using the Stream to Feature tool.  

The remaining steps involved conducting the actual hazard analysis by creating a 1/2 mile buffer around our stream feature and determining which population blocks and schools would be affected by the 1/2 mile lahar buffer.  The results were then mapped and the output map is below.  Please let me know if this map works for you.  As a colorblind mapper, I always welcome comments and suggestions to make things better.  Cheers!

Python Fundamentals Part 1

This week was a shallow dive into the basics of Python scripting.  We learned about basic variables, objects, functions, methods and how to use these elements to create a basic script.  The script itself only had one input in the beginning.  This input would be a users name.  From there the script would take the variable with the name, turn it into a list for every name in the initial string that was separated by a space, and then print the last name to the screen.  From there the length of the last name was determined and then tripled.  The final part of the script was to print tripled count value of the last name to the screen.  Here is a screen shot of how mine turned out. 

The script was tested using a number of different name strings as the initial variable and the final outcome always printed whatever the last name and triple the number of characters in that last name.  Again, this was all basic scripting to allow us to scratch the surface of script programming and get a feel for the basic elements of Python. 

Let me know what you think in the comments below!  I can also answer any questions on how to achieve this outcome efficiently as well.  

Introduction to Python

Here we are in our first week of GIS Programming.  This week focused on setting up our folders in our S:/ drive and getting a basic understanding of Python scripting and editors.  The editors we looked at were IDLE, PythonWin, and the Python editor which is in ArcMap 10.5.1.  Within these editors, a simple script was run to display the text "Hello World".  Nothing too fancy, just enough to show how different elements are highlighted and displayed in the different editors.  For the most part we will be using PythonWin for this course. 

To set up our folders for the course, we used a python script vice manually creating 3 folders for each of the 12 modules in the course.  The screenshot below shows the outcome of this script.

The overall process summary felt like an introduction to completing process summaries.  This one focused on showing where you store your data, asked some questions from the reading, and then had us explain how a certain step was completed in our own words.  This introduction to process summaries would be beneficial to first time students or those who have been away for a couple of semesters. 

Other than that, this was probably the only simple week I can hope to have during this course.  My coding/scripting skills are extremely rusty, but I am hoping it will all come back quickly and overall I am looking forward to this course.  

Computer Cartography - Finally Done!

This final project was designed to utilize the cartographic skills, methods, and principles that were learned throughout this course.  To illustrate mastery of these skills, a complex map depicting multiple layers of data was created.  The data used to create this map were the 2014 nationwide average SAT scores for each state as well as each state’s student participation percentage in taking the SAT.  By mapping these two pieces of data together on the same map, it could show a correlation between SAT scores and how many students actually participated in the testing. 

To create a map that depicts two different sets of data, two thematic methods were chosen to provide enough distinction between the data sets.  The two thematic methods used were choropleth mapping using graduated colors for the average composite SAT scores in each state and proportional symbol mapping using graduated symbols for the participation percentage. 

            The data for the state average SAT scores needed to be combined into a composite score as it was provided as the three SAT section scores.  No normalization of the composite scores was applied to the data.  Once these scores were combined for each state, the resulting composite scores were used to create a choropleth map of the United States.  Graduated color symbology was used in ArcMap to achieve this.  The data was also divided into five groups, or score ranges, using the quantile breaks method.  This was used to ensure an equal number of states was in each group, providing some color differentiation and even balance to the overall map.  The color ramp for this data series was red (lower SAT scores) to green (higher SAT scores

            The participation percentage data was taken directly from the source document and no manipulation or normalization was required for mapping.  To map this data, proportional symbology, in the form of graduated circles, was used to map the participation percentage on top of the existing choropleth map for the average scores.  A light blue circle with a black border was used to contrast the color scheme of the base map.  The actual percentage was also placed inside the circle to clearly show the data on the map.  For this symbology the data was divided into five groups, however using natural breaks instead of quantile.  

            As one looks at this map, you can quickly see that the states with the higher average composite SAT scores also happen to be the states reporting the lower participation percentage.  One could assume that only smarter students are encouraged to take the SAT’s in these states where as the states with the lower averages and higher participation rates have their average scores brought down by more average students.  One could also assume that if the states with lower participation rates were to encourage more students to take the SAT, their average composite scores would also come down and fall more in line with the higher participating states.  

          So this map brings this course to an end.  I will say that of all the GIS coursework I have done up till now, this has been one of the more frustrating courses for me.  I think that is because a lot of the evaluation has been subjective in interpretation.  That combined with being colorblind, application of certain principles has proven to be quite challenging.  This is definitely hasn’t been as easy as just obtaining data and creating a map from it.  That being said, I have learned a lot and I guess the Marine in me gets enjoyment and fulfillment from challenging and frustrating situations.  

Mapping with Google Earth

Google Earth (GE) is a simple interface that users of all skills can use to view map layers.  GE provides the base with 3D structure layers, aerial landscape photography, borders, rivers, lakes, oceans, and just about anything you can think to want in a map.  What's more is that GE can import additional layers that you map in other programs like ArcGIS. 

This week we used Google Earth to revisit our Dot Density Population map of Southern Florida.  The dot density layers that were created in ArcGIS were converted to the .kmz format that GE uses.  The two layers were: 1.  Entire map containing dots depicting populations, water features were included as well and 2.  Layer that dot density was derived from that showed different information in GE.

The second layer did not show the dot densities in GE.  Instead, when a county was clicked on, like Miami-Dade County in the screenshot below, it would display the information for that country. 

County Information - Google Earth

The next task we completed in this lab was to create a tour using our existing dot density layers and visiting different points around southern Florida.  The map itself with the dot density layer looked like this:

Additional points were added in GE for reference when creating the tour.  The tour started from the view in the screenshot above, depicting the population dot density for the region as well as highlighting the different water features.  It then hops from Miami to Fort Lauderdale and then moves west across the state to St. Petersburg and Tampa.  I tried to give different perspectives throughout the tour and as hard as I tried, things weren't exactly smooth.  Once the tour was completed, it was added to the overall .kmz package in GE to complete the project.  I was even able to convert/export the movie to a .mp4 format and you all get the exclusive viewing, just click to watch below.  

3D Mapping

This weeks lab took us into 3D Mapping and the various aspects of it.  ESRI offers a great online course for 3D Visualization using ArcScene and ArcGlobe.  If you have not taken this course before and 3D mapping is something you are interested in, then I would highly recommend you taking that course.  It gives the quick, down and dirty of how to accomplish these tasks using the ArcGIS suite. 

In this lab we focused on 5 sections using ArcScene.  The first section taught us how to set base heights for raster and feature data.  This was done using Crater Lake as a backdrop and adding the lake, rivers, watch towers, and land use data over the top of elevation data.  Here is how mine turned out:

The second module taught us how to apply vertical exaggeration to a map.  Minnesota is a fairly flat state, especially in the section we worked with.  This is what made it such a great candidate to apply vertical exaggeration to so we could see the features.  The picture below is Minnesota with about 23 times exaggeration to make the features stand out. 

 

The third module showed us how to use illumination to highlight certain features.  Santa Barbara Island was the backdrop and I set the sun to be coming from the south and not much over the horizon, about 9 degrees.  You can see how this highlighted the cliff face in this picture:

The next two exercises focused on using extrusions to create 3D buildings.  The first picture shows buildings and wells.  You could actually see the depth of the wells if you looked underneath the map.  How cool is that?  The second picture uses the dollar value of the specific parcels to illustrate which ones were worth more and whether they were commercial, residential, or industrial lands.

After completing the ESRI lesson, we moved on to making 3D buildings for Boston.  This started off in ArcGIS to create the data we would need to make the building extrusions in ArcScene.  Once that data was created, we moved to ArcScene and made the buildings there using the extrusion process.  Then that data was saved in as a .kml file to later be imported into Google Earth.  The picture below shows the buildings that were created in ArcGIS/ArcScene and then imported into Google Earth. 

Overall this was an interesting lab.  All of the work we have done over the semester has been in 2D and it was somewhat refreshing to take a different look at it this week.  I can definitely see some pros to 3D mapping in the fields of flood modeling and utilities planning.  The ability of seeing how different water levels may affect a 3D modeled city or how/where different utilities are placed in a 3D environment is definitely useful.  A couple things people need to be aware of though are 1. Be aware of where you are facing in your 3D maps and 2. Know that sometimes 3D maps are distorted to show you their message or enhance features.  The data doesn't change, only the perception of what it is.  

Dot Dot Goose! Dot Mapping Southern Florida

Despite what you may be thinking, dot mapping is not a game of connect the dots to make a map.  Dot mapping, in fact, is a way to display conceptual data what is not uniform throughout a given area.  It allows us to to visualize patterns that may occur in the data.  So how does one come up with a schema for using the dots?  Well, in the map below we use dots to represent a set number of people.  In our case, dots equal 10,000 people on the map.  I also bound the dots to only occur within urbanized zones because it doesn't make much sense to see a dot of 10k people in the middle of Lake Okeechobee does it?  So here is the map and I will later discuss how I made it:

So the map in general was pretty easy to make....once I got past ArcGIS constantly crashing.  So here are the steps I took to make it:

1. The majority of the work took place in ArcGIS with some final polishing in Adobe Illustrator.  
2. First I added the south Florida shapefile to my TOC.
3. Next the population data spreadsheet was added to the TOC and then joined to the Florida shapefile.  This allowed me to access the population data I needed to create the dot map you see above.
4. The next step was to create the dot symbology for the map.  This was done in the symbology tab for the Florida shapefile's property window.  Under the Quantities section, Dot density was selected and the Population field was used to create the Dot Map.
5. The Dot Value was set to 10,000 and the Dot Size was set to 2.6.  This took many iterations to figure out what seemed to look best.  
6. Next, other map layers (water and urban) were added along with the essential map elements.  
7. The water layer ended up causing multiple crashes of ArcGIS.  To combat this I turned the Dot layer into its own map file and kept the rest on the original map file.  
8. With both map files ready, I exported them both to a .AI (Adobe Illustrator) format.  Later I would merge these two in Illustrator to finalize the map.  
9. From here work shifted to Illustrator.  Both maps were merged by copying the dot layer and then using a paste-in-place function on the other layers.  This worked out great as you can see above.  
10. The final pieces to this map were creating the legend and adding a simple drop shadow to the map.  

And there you have it.  I hope you enjoyed the map.  What do you think and how else could I have applied the dot size and values to make this map even better?  Let me know in the comments below!