G.Hudson Mastering GIS

 The first module of GIS 59395 - Special Topics in GIS taught students how to calculate metrics, such as accuracy and precision, as it relates to spatial data quality. We were tasked with calculating the precision and accuracy of waypoints collected using a GPS device. The following image is a map I produced displaying the waypoints and some off my results.  As included in the map, the horizontal precision based on the 68th percentile is 4.6m and the vertical precision based on the 68th percentile is 5.88m. The horizontal accuracy is 3.3 meters, while the vertical accuracy is 5.96m.  For the purpose of this blog I will focus more on the horizontal accuracy and precision. Horizontal precision takes into consideration the distance between the waypoints and the average waypoint, which was determined based on the waypoints. Once the distances are determined, we can determine how many waypoints fall within each percentile. Horizontal accuracy takes into consideration the dista...

 This is my second blog post for Module 6 and the focus is Least-Cost analysis. We were tasked with performing an analysis and producing a map that models a corridor that black bears would use to travel between two protected areas in Coronado National Forest. To do this we had to take into consideration criteria that suits black bear habitats. As such the following is the criteria: Criteria 1: Landcover best suited for black bear habitats, such as forested areas. Criteria 2: Distance from roadways. The farther the better. Criteria 3: Mid elevations. Ideally, 1200 to 2000 meters.  My process included the following steps. To model the corridor that black bears would use to travel between the two protected areas in Coronado National Forest I first created a ranked raster for elevation and landcover using the reclassify tool, followed by performing a Euclidean distance on the roads and reclassification to rank them as well. The ranked rasters were then combined using the Weighted ...

 Module 6 is here! For the 6th and final module students have been tasked with creating two blog posts. This is my first blog post for this module and the focus is Suitability Analysis. We were tasked with creating a suitability map which models and rates the best locations for development. To do this we had to take into consideration criteria that best determines suitable locations for development. As such, the following are the criteria: Criteria 1: Landcover best suited for development, such as agricultural or meadows. Criteria 2: Soil best suited for development. Criteria 3: Gentle slopes . Criteria 4: Areas more than 1000 ft away from streams. Criteria 5: Areas that are near to roads.  For each of the above criteria rasters were ranked or created then ranked via reclassification. This step was followed by performing a weighted overlay analysis, which combined the criteria at various weights to determine the most suitable areas for development. The weighted analysis was pe...

 As a follow up to the last module on Coastal Flooding, this module focuses on the damage assessment after a coastal flooding event, such as a hurricane and it's associated storm surge. In 2012 hurricane Sandy impacted the north eastern coast of the US and left behind monumental damage. This week's module tasked students with performing their own analysis of a portion of the area impacted by hurricane Sandy. In the first instance we were required to create a map of hurricane Sandy's track. The following is my result:  We were then asked to mosaic imagery of the New Jersey shoreline pre hurricane Sandy and post hurricane Sandy, followed by doing a comparative desktop study to assess the damage caused by the storm. To assess the damage a point was created for each building in the study area and the structure damage, wind damage and inundation was determined and ranked based on FEMA standards. The following is an image of the building points based on structural damage:  To f...

During this week's module, module 4, we learned about Coastal Flooding, which can take place at various speeds, including rapidly via surge or slowly via sea level rise. This week we were tasked with performing analysis on the New Jersey shoreline and the coast of Collier County in Florida. In New Jersey we used LiDAR data to evaluate change post hurricane Sandy. This analysis included observing the raw LiDAR data, followed by the creation of Digital Elevation Models (DEMs) of pre and post hurricane Sandy for comparison. A more recent building vector file was also used to assist in the assessment of change, although using more recent data should be used with caution as it does not always accurately depict change as a result of a disaster event. This is particularly true as more time passes. The following map displays elevation change on the New Jersey Shoreline.  Map depicting elevation change post hurricane Sandy.   For Collier County, Florida, we were tasked with determ...

For module 3 we continued learning about LiDAR data and analysis with the introduction of visibility analysis. The visibility analysis introduced included Line of Sight (LOS) analysis and View Shed analysis. The lab assignment supplemented the lectures by requiring the completion of four exercises. The following are the exercises and a bit of information about each:  Exercise 1: Introduction to 3D Visualization As the title suggests, this exercise introduced students to 3D visualizations. It began highlighting how 3D can be used and quickly moved into providing hands on experience in displaying and navigating 3D data in ArcGIS Pro, displaying 2D layers as 3D by extruding the 2D feature, and applying effects to the 3D data such as various types of illumination.  Exercise 2: Performing Line of Sight Analysis According to ESRI, line of sight analysis determines if two points in space are intervisible. Intervisibility can be impacted by and obstruction, such as a building, which i...

The second module of the Applications in GIS course introduced students to Watersheds and LiDAR. While I have previously been exposed to LiDAR, I had not had the opportunity to work with LiDAR data and manipulate it until this module. As such, it was exciting to be able to get this opportunity to learn about it. This week's lecture focused more on watershed analysis, while the lab focused on using LiDAR. The lab tasked students with creating a Digital Elevation Model (DEM) and Digital Surface Model (DSM), calculating forest height and calculating biomass. Below are maps that were produced as a result of the lab assignment.  Map of Canopy Density in the Big Meadows Area of Shenandoah National Park Map of Tree Heights in the Big Meadows Area of Shenandoah National Park LiDAR Scene and LiDAR Derived DEM in the Big Meadows Area of Shenandoah National Park While I enjoyed working with LiDAR data, exploring data in 3D and learning of its power in analysis, this type of data requires hard...