Wednesday, June 21, 2017

Module 5 - Geoprocessing in ArcGIS Lab Assignment

This week's lab was incredibly useful and full of information. It introduced us to utilizing Python scripting and ModelBuilder with Arcmap to run geoprocessing tools. We also learned about toolboxes and how to create new tools and toolboxes. We began by learning more about geoprocessing tools and how they run, as well as how to set the environment and why that is important. We also learned about ModelBuilder and how it can be used to streamline geoprocessing. Another handy trick we learned is how to export a model as a script and how to make the script run independently from ArcMap. We created a model that clipped the soils shapefile to the boundary of the basin shapefile, then had it select and erase any areas that are not suitable for farming. We then exported the model as a script, entered code to let it run independently, and created a zip file of the newly created toolbox and script to be able to share it. Overall I enjoyed this week's lab and am happy that Python is starting to click more for me.

Sunday, June 18, 2017

Module 4 - Natural Hazards - Hurricanes Lab Assignment

This week's lab assignment was interesting and intricate. We learned all about hurricanes and tropical cyclones, and we learned about the devastating Hurricane Sandy that hit the East Coast of the United States in 2012. We had two portions of our lab this week. For the first portion we tracked Hurricane Sandy's path and mapped the FEMA affected states. We created XY data points from an excel file of Sandy's movements and used the point to line tool to create the path. We also learned how to use expressions to create specific label layouts, and learned how to create our own symbols. For the second portion of the lab we tried our hand at damage assessment mapping. We learned how to edit domain properties and coded values, and created new data points assigned with these coded values in an editing session. We compared pre-storm and post-storm aerial imagery and tried to rank the level of damage for each parcel on a street in the damage assessment area. It was much harder than I anticipated to determine how damaged the structure was from the aerials in some cases. Overall it was a very interesting lab and I enjoyed working with raster data more.

Wednesday, June 14, 2017

Module 4 - Debugging and Error Handling Lab Assignment

This week's course materials and lab assignment taught us the important task of debugging a script and handling errors. We first had to find and repair two syntax errors in a script that had a goal of printing out a list of the field names within one of the shapefiles. Next we stepped things up a bit and had to find eight syntax and exception errors and correct them to complete a script that would print out a list of layers for each data frame in our mxd. Finally we had to execute a successful Try-Except statement in a final code to run the full script, but skip the first section and state the exception, then execute the second section, which prints out the name, spatial reference, and scale for the data frames in our mxd. This week's lab was like a puzzle trying to spot and correct all the different errors, and I think that spotting the errors helped me grasp more of what to do and what not to do.

Participation Assignment 1

An automated external defibrillator is strapped to the back of the drone, which can fly at speeds of up to 47 mph. (Andreas Claesson/TNS)

I found this interesting article from the Seattle times that talks about the novel idea of sending a drone equipped with an AED to the site where a victim has collapsed from cardiac arrest. Victims of cardiac arrest have a much greater chance of survival and recovery if an AED is used to shock their heart back into the correct rhythm as soon as possible. Many large public places such as malls, airports, or schools are equipped with AEDs, but if the victim is unluck enough to collapse in a more remote area their chances can be slim depending on the route time of the ambulance. A research group in Stockholm, Sweden has been studying the option and possible success of sending a drone equipped with an AED to the exact location of the victim. to begin testing their hypothesis that a drone could arrive quicker they decided to use the wonderful tool of GIS! They ran models to test out their hypothesis and found that not only did the drones beat the ambulances, but they beat them in a large amount of time shaving crucial wait time off and increasing chances of survival. The models showed that drones would beat an ambulane 93% of the time and by an average of 19 minutes! This reminds me of the lab we completed last semester where we mapped fastest route options for EMS locations. After seeing such positive results in the GIS models, they decided to start ground testing and ran some trials where they would deploy an AED to the scene of a cardiac arrest victim at the same time that an ambulance took off on it's route. Following the GIS models' prediction, the drone beat the ambulance each of the 18 times that it was deployed. It also had a median trip time of 5 minutes and 21 seconds compared to the ambulances' mean trip time of 22 minutes. They are hoping these tests will become a reality in the not so distant future.

Sunday, June 11, 2017

Module 3 - Natural Hazards - Tsunami Lab Assignment

For this week's lab assignment we learned all about the 2011 tsunami and resulting radiation leak at the Fukushima Nuclear Power Plant. We were tasked with creating a map that included the tsunami evacuation zones, nuclear evacuation zones, and affected areas for both. We first created organized file geodatabases, complete with feature datasets and feature classes, for all the radiation data and the runup data. We were able to use many different tools and solidify our knowledge of the spatial analyst toolbox. We created a multiple ring buffer of the radiation evacuation zones including areas 3, 7, 15, 30, 40, and 50 miles from the power plant. Next we clipped it the the boundaries of Japan. We also created a buffer zone of 10,000 meters of the shoreline near the Fukushima area and clipped it to the land mass as well. We used this buffer of the shoreline and elevation data to create an intricate model that gave us the three main evacuation zones for the tsunami runup. The zones 1, 2, and 3 were 0-10 meters, 11-40, and 41-80 meters inland respectively. I did struggle a bit with fitting everything neatly into the data frame because there was so much data and intricate details, but overall this was a very interesting lab assignment that instilled more confidence in my GIS skills.

Friday, June 9, 2017

Module 3 - Data Classification Lab Assignment

This week's lab assignment was harder for me than I would like to admit. We first had to locate and correct two errors in a section of created script. Once the two errors were corrected, the script created a dice game that printed a random integer as the dice roll and printed each player's roll and if they won or lost. Next, we created a script to create an empty list of 20 spaces and to then use a while loop to fill it with 20 random integers. I had a bit of trouble with my statement to end the loop, but worked through that. Finally, we selected an unlucky number (I chose 5) and wrote a script containing a while loop and conditional statements to remove the unlucky number anytime it occurred in the list and then to print how many times it was removed or if it was not in the list, to print that it was not in the list. While this week's lab was tough for me, I am looking forward to learning the practical applications of Python programming.

Sunday, June 4, 2017

Module 2 - Natural Hazards - Lahars Lab Assignment

     This week's lab was our first lab assignment for Applications in GIS. We learned all about lahars, which are destructive debris flows associated with volcanoes. Lahars move quickly and the material moved during the debris flow dries into a substance similar to concrete.
     For this week's lab we learned how to use many of the spatial analyst hydrology tools to determine stream flow and depict hazard areas related to a lahar event at Mount Hood. Attention to detail was crucial in this lab to make sure the correct layer was selected as the input for each tool. We started with two DEM rasters of the study area and created a single mosaic to work with. Next we used the fill tool to remove imperfections in the data and smooth out the elevation in the layer. We then used the resulting raster in the flow direction tool to develop stream channels. After that, we used that raster as the input for the flow accumulation tool, which determined the number of cells that flow into each cell (and required a lot of patience). Finally, we used the stream to feature tool to create the stream channels that the lahar would likely follow.
     Next we had to determine which schools and census block areas would be affected by a lahar event using our created channels. We created a half mile buffer around the channel layer and used the select by location tool to select each census block area that intersected the buffer area and created a layer from the selected blocks. We repeated the steps with the school layer. Finally we had to label cities and rivers, and add road and railroad data. Because there is a large area covered in this map, there were many cities which made labeling tricky. I chose to use the select by location tool again to select cities that were within or touching the affected census block areas.
     This was a very interesting lab that allowed us to get our feet wet in one of the multiple GIS analysis applications. Some tools required a long wait time to get the results, so it took longer to finish the lab. This lab definitely made me interested in what the hydrology tools can do, and I hope to put my new-found knowledge to use at my job.