Visibility Analysis

For Module 3, we had to take 4 training courses in Esri. The training courses were 3D Visualization using ArcGIS Pro, Performing Line of Sight Analysis, Performing Viewshed Analysis in ArcGIS Pro, and Building Models for GIS Analysis Using ArcGIS. All four of the courses trained on how to use geoprocessing tools to manipulate ArcGIS to provide the best output for data analysis. In 3D Visualization, we linked 3D scenes and 2D maps to enable side by side visualization. We also extruded 2D features based on attributes, applied photorealistic symbology, light, and shadow, and shared a 3D scene in a map layout. In the Performing Line of Sight Analysis, we learned the workflow for performing visibility analysis. The exercise had us determine the line of sight for a parade path from a building. The workflow for this was determine observers (top of building) and targets (parade path), construct sight lines, and determine line of sight. The geoprocessing tools used for this were Construct Sight Lines (3D Analyst Tool), Line of Sight (3D Analyst Tool) Tool, Add Z Information (3D Analyst Tool), and Select by Attributes. For Performing Viewshed Analysis in ArcGIS Pro, we learned how to use the Viewshed Tool to model visibility from vantage points. The geoprocessing tool considers the height of the individual and surrounding objects,  and reflects visible light (you can adjust refractivity coefficient). The symbology of the tool results indicates which areas are visible and which are not. For the Building Models for GIS Anallysis Using ArcGIS, we learned how to use Model Builder to create geoprocessing models that form an analysis workflow that can be executed with one click. It teaches that the three elements used to create these models are variables, tools, and connectors. Each have different colors and shapes assigned in the model builder. These are create to build simple to complex workflows that need to be tailored to a specific company or job and can be shared easily.

Forestry & LiDAR

For this week's lab, we learned how to create a DEM and DSM from LiDAR data. The Shenandoah, VA forest area was the region focused on for this assignment. After the DEM and DSM was created, a variety of geoprocessing tools were used to calculate the canopy density and tree height. This was achieved by using the LAS Dataset to Raster for ground points and non-ground points from the LiDAR data to create a DEM and DSM. Then, the minus tool input the DSM followed by the DEM to create the tree height raster. The biomass (canopy) density was constructed through multiple geoprocessing tools. The LAS to Multipoint tool was initiated twice to create a ground and vegetation file. Then these files were created to rasters by using the point to raster tool. Both rasters were input the IS NULL tool to assign the number 1 to every attribute that was not null. The Con Tool was used to identify all zeros as true values and all ones to come from the original raster; this was completed for both ground and vegetation rasters. The Plus Tool combined the ground and vegetation counts and the Float Tool transformed results from integer to float. Finally, the Divide tool set the vegetation count to the float results to return the canopy density. 

Least Cost Path Corridor Analysis

For the least cost path corridor analysis map, I had to locate potential black bear movement between protected areas of the Coronado National Forest. I used the calculate raster tool with the expression 10-weighted overlay raster. I converted the Coronado polygons to rasters. Then, I used the cost distance tool twice to generate two cost surface layers with the Coronado rasters as the inputs, respectively. I then used the corridor tool twice with each Coronado cost distance layers as the input. The corridors that were generated covered the map but I narrowed it down to two layers. Essentially, the first one covered both Coronado polygons on the map and I had a second layer highlight just outside of it to show a variance.

Suitability Analysis

For the suitability analysis, I had to produce a map showing an equally weighted overlay and an unequally weighted overlay map. I worked with five layers: river, roads, elevation, soils and landcover. I reclassified the elevation. soils, landcover layers and assigned suitability ratings. I also had to convert the soil from polygon to raster. For the rivers and roads layers, I used the euclidian distance tool and assigned suitability ratings to the ranges. I used the weighted overlay tool to combine all four of the raster layers and produced two maps: 20% for the equal on each landcover, elevation, rivers, and roads. For the unequal, 40% land cover, 20% soil, 20% elevation, 10% roads, and 10% rivers.

About Me: Sarah Buchanan

My name is Sarah Buchanan and I graduated from the University of Houston in December 2015 with a BS in Geology. Post graduation, I moved out to the hill country in Texas and worked on a Google Maps project. This experience really inspired me to go back to school and get certified in GIS. I chose the University of West Florida for it's completely online program, affordability, and great reviews. I began the Master's Certificate in GIS program in January 2018. I have to complete this course and the Special Topics course and I will be finished. It has taken me longer than I wanted because of some life circumstances and of course, money, but I have been very satisfied with the choice I made. I have worked in three GIS positions since Google and currently work as a GIS Specialist for a great company, CHA Consulting. I love my job-I get to make maps and perform higher level data and spatial analysis than the previous GIS jobs. I am excited to see where my career path takes me and where the advancements in and from GIS take the world! Below is my story map..check it out!



https://arcg.is/0rbf1K0

Writing Geometries

The final module for this course was about writing geometries. We explored using cursors to return point objects (single point features), an array of points (polygons, polylines), and using using for and nested loops to write to text files. I want to note the key issues I had with this module. For the most part, I was able to follow the exercise and extract what I needed from there. My first error encounter had to do with my search cursor statement. I was writing “NAME@” and receiving a column type error. After researching on Esri forums, I decided to take out the @ sign and my code would execute properly after that. The next issue I had was the v += 1 statement was not returning the vertex id’s in sequential numbering. This was because I was not including it in the for loop, so once I indented it, it executed perfectly. The last issue I had was getting everything to write to my .txt file. I was getting 25 lines of random vertices from each OID. This was another indentation issue. Once I included in the for loop, it executed perfectly and my assignment was finished!

Exploring & Manipulating Data

Just like the title implies, this week we explored ways to manipulate spatial data. This assignment tasked us to work with search cursors, lists, dictionaries, and display correct usage of update and insert cursors. The most challenging aspect in this lab was creating a for loop on the search cursor and dictionary. I had to correct many mistakes and try different ways until I finally had a script I felt good about. I guess that's the entire point of the assignment-learning how to manipulate data to return the desired output. There are multiple ways to do this and I actually discovered two ways to return the same statement on the dictionary by using two different scripts. Now, that was discovered through trial and error, not first hand knowledge on my part. But I appreciate challenges like that because I feel like it helps build stronger technical skills. Below are screenshots of the script I created and a flowchart!