Module 6: Scenario 4 - Suitability and Least-Cost Analysis

Last, but not least, the final post for Applications in GIS. This scenario (2nd deliverable for Mod 6) was a on your own corridor analysis. As a park ranger in the Coronado National Forest we were asked to carry out a corridor analysis using the two National Forest polygons to create a meaningful corridor between them taking into consideration roads, landscape and elevation. Tools to accomplish this were Reclassify, Weighted Overlay, Cost Distance, and Corridor Tool in that order respecfully. Once the suitability output was achieved, and the corridor analysis completed a meaningful symbology was applied based on the minimum values. See layout below.

Module 6: Analysis C

This module is a multi-part analysis through various scenarios. This is blog post #1. For this analysis we were tasked to estimate how much land would be suitable to build on. The analysis itself was based on land cover, soils, slopes, streams and roads. Every item was reclassified into suitability classes using tools: Reclassify, Euclidean Calculator, Polygons to Raster, Slope, and finally Weighted Overlay to get the final result image below. The Weighted Overlay tool was used twice for 2 different scenario with different weights adjusted or being equal.

Module 5: Damage Assessment

This week we explored the damage assessment aspect of Hurricane Sandy's landfall near Atlantic City, New Jersey. We tracked Sandy, created and designed attribute geodatabases for editing purposes. As well as exploring imagery effects tools to visualize pre/post Sandy imagery. We used parcel data, the imagery, and the crewly created damage assessment files to catelog a street on the New Jersey coastline. General steps to accomplish this included, creating line features from points for the track, adjusting the symbology to look like a hurricane graphic, creating a mosaic dataset for pre and post imagery rasters to be. Then created more data using new feature classes and domains and finally filling in those attribute tables with the points/data for our structures in the study area. Below there is a screenshot of the structural damage points created and a table of the distance to coastline to examine patterns.

Module 4: Coastal Flooding

Well we can't win them all! That is the major lesson from this lab. Attempts to follow the directions were fraught with misaligned data and things that just did not make sense. Luckily I was not alone in my efforts coming to bitter ends. The assignment was to conduct a analysis on storm surge in Florida, by comparing a traditional USGS DEM and a DEM derived from LiDAR. We were to compare the differences in results between the two elevation models assuming a storm surge of 1 meter. The layout below is my best attempt to show this.

Module 3: Visibility Analysis

This week we were tasked to complete 4 Esri courses through ArcGIS Online. By passing the quizzes at the end of the courses the certificate was awarded. These exercises were intended to give us more knowledge on visibility analysis. The first course was Introduction to 3D Visualization and I learned navigate 3D scenes, manipulate height variables in a data visualization, how to convert a 2D map in to a 3D local scene, applying 3D symbology and applying realistic enhancements to local and global scenes. The second course was Performing Line of Sight Analysis where I used tools Construct Sight Lines, Line of Sight, and Add Z Information to see parade routes through a city to determine the lines of sight for observers and visibility between observer points and the parade route. The third course was Performing Viewshed Analysis, where I modeled new lighting in a campground. Tools to accomplish this were Viewshed, and we modified field values, and modeled the light coverage until the light coverage was improved. The fourth and last course was Sharing 3D Content from data in the city of Portland, Oregon. To accomplish this we displayed the 2D data as 3D layers, and converted the 2D data to 3D data using the Layer 3D to Feature Class and Feature to 3D By Attribute tools.

As you can see numerous tools were implemented and I learned a great deal from the Esri courses. They have so much information packed into not only the online portion, but the directions for the exercises are full of tips and tricks that I will use in the future. For instance, many items were manipulated in just the properties of the item. Tools were used here and there to accomplish tasks, however it was interesting to see more ways data can be changed using only the properties dialog box. Another trick I learned was in the symbology of 3D items and how to configure the properties to better reflect the environment, including sunlight shadows and the ways water can move.

Here are my resulting certificates to make this post more appealing to the eye.

Module 2: Forestry and LiDAR

This week we were given a .las LiDAR file with data from the Big Meadows area of the Shenandoah National Park in Virginia. We learned how to decompress .las files, create DEMs from LiDAR, create DSMs from Lidar, and calculating forest height from LiDAR. Tools used were LAS Dataset to Raster, Minus, LAS to Multipoint, Point to Raster, IS NULL, Con, Plus, Float, and Divide. From there, the data gained was used to display 3 maps as seen below.

The first map shows tree heights with a distribution bar graph below it.

The second map is of canopy density.

The third map is a LiDAR elevation map with a LiDAR_Derived DEM below it.

Comments: This week ran smooth with almost no issues that I could tell and becoming more aquainted with everything ArcGIS can do is a plus that will only help me in the future.

Module 1: Crime Analysis

For this lab the task was to show hotspot analysis using 3 methods (Grid-Based Thematic Mapping, Kernel Density, and Local Moran's I) The below screenshots show the hotspots of homicides from 2017 in Chicago, IL.

First up is Grid-Based: Based on a grid of 1/2 mile cells clipped to the Chicago city boundary, the end result is the grids with the top 20% overall highest count of homicides. Utilizing Spatial Join, Select by Attributes, and Dissolve tools.

Second is Kernel Density: This map shows the density of homicides that are three times the mean of the data. Utilizing Kernel Density, Reclassify, and Select by Attributes tools.

Last is Local Moran's I: This map uses crime counts and number of homicides per 1,000 housing units for each census block group. The result is spatial clusters of the high-high clusters that are in close proximity to other areas with a high homicide rate. Utilizing Spatial Join, Cluster and Outlier Analysis, and SQL Query tools.

Comments: Other than input and output data any remaining parameters in the tools used were left as default per lab instructions.

About Me

My name is Jenna Clevinger, and I joined the UWF GIS Certificate Program in hopes of combining my BA in Anthropology with GIS for an exciting new career. I gained an interest for maps while I was enlisted in the U.S. Navy for 8 years as a Quartermaster. During my time I learned all about navigation and was able to have some interesting land and underwater archaeology volunteer opportunities in my travels. I hope to gain enough knowledge from the program to get my foot in the door of a surveying or related GIS career. I have drive, passion and willingness to learn. Having a GIS related career my goal, but you never know where life will take you, so I am open to exploration along the way. My Story Map is here.