GIS Portfolio: Ross Wygmans

A crime hotspot is an area with higher than average crime or risk of victimization. There are several methods used to determine crime hotspots areas. In this week's lab, we looked at three of the most common methods - grid overlay, kernel density, and local Moran's I.   Below is the analysis process implemented for each of these three methods.  Here are the resulting hotspot maps produced by each method.  Grid Overlay Kernel Density Local Moran's I

Within ArcGIS Pro, there are two options for viewing 3D content -- global scenes and local scenes. Global scenes are most suitable when working with large extent content or when the curvature of the earth is an important factor, while local scenes are better for smaller extent content in a projected coordinate system (PCS). Another difference between these two scene options has to do with available visual enhancements. Basic illumination properties, such as ambient occlusion and light source azimuth and altitude, can be adjusted within local scenes. However, global scenes contain one additional feature where the position of the sun can be set to a specific date and time, adding more realism to the scene.  Local scene Global scene One interesting feature I learned about this week in ArcGIS Pro is the ability to link 2D and 3D views within the same project. Oftentimes, viewing a scene in 3D can reveal spatial patterns and relationships that may otherwise be difficult or impossible to ide

LiDAR, or light detection and ranging, is a common technology used in remote sensing. Aerial LiDAR systems mounted on aircraft or satellites send pulses of laser light to the surface of the Earth, and the reflected energy is recorded. When used over vegetation, LiDAR signals will typically return multiple values representing the top of the canopy and the ground. These values can be used by foresters, scientists, and other natural resource professionals to calculate variables such as canopy height, canopy density, and canopy structure.  In this lab, our task was to create layers showing canopy density and tree height with LiDAR data from a small section of the Shenandoah National Park in Virginia. This data was originally captured by the U.S. Geological Survey. The first step was to convert the ground and vegetation data from point cloud (.las) to raster, using a multipoint layer as an intermediary step. We then used these rasters in conjunction with several other geoprocessing tools to

For another scenario in this week's lab, I was playing the role of a Park Ranger in the Coronado National Forest. My task to model the potential movement of black bears between two protected areas. This potential corridor was based upon three criteria associated with bear habitat suitability:  Distance to Roads Elevation  Land cover type  In order to complete this task, I first used the Euclidean Distance tool on the roads layer to calculate the shortest distance from each cell to the nearest road. I then reclassified each of the three layers (distance to roads, elevation, and land covery) based on a suitability scale of 0 - 10, 0 being the least suitable for bear habitat and 10 being the most suitable for bear habitat.  I then combined these three reclassified layers into a single habitat suitability model based on the following relative weights:  Land cover (60%) Elevation (20%) Distance to Roads (20%)  Next, I created a cost surface layer by "inverting" the habitat sui

For one of the scenarios in this week's lab, I was playing the role of a GIS Analyst for a property developer. My task was to create a map showing suitable land for development based on the following criteria:  Land Cover Soils Slopes Streams Roads  My general process for this analysis was to first convert all of these layers into rasters if they weren't already. For the rivers and roads layers, I used the Euclidean Distance tool to calculate the shortest distance from each cell to the nearest river or road. I then reclassified each of these layers based on a suitability scale of 1 - 5. Lastly, I combined the results of each of these five criteria using the Weighted Overlay tool. Below are my results.  The first map is an equally weighted average where each of the five criteria have an influence of 20% (or 1/5th). The second map shows an alternatively weighted output where each of the five criteria were weighed as follows:  Land cover (20%)  Soils (20%) Slope (40%) Distance to

My name is Ross Wygmans, and I have a B.S. in Forest Resources and Conservation and M.S. in Ecological Restoration from the University of Florida. My current position is with Alachua County's Office of Land Conservation as a Conservation Forester. I dabble with GIS in my current role, but 90% of the time I'm working in the field helping to manage the nearly 24,000 acres of conservation lands owned and/or managed by the Alachua County.  My goal for the Applications course is to tie together many of the concepts and techniques I've been learning in previous courses and applying them to real world scenarios.  Check out my Story Map below to see all the major moves I've made in my life.  My Story Map