Michaela's GIS Journey

Exploring GPS Data Accuracy and Precision: A Brief Analysis Numerical Results for Horizontal Accuracy and Precision Horizontal Accuracy: 3.24 meters Horizontal Precision (68%): 4.47 meters Understanding Horizontal Accuracy and Precision Horizontal Accuracy: This is the measure of how close the average of the collected waypoints is to the true location (not shown in the map). In this case, the average waypoint is 3.24 meters away from the true reference point, indicating reasonably accurate measurements. Horizontal Precision: Precision refers to the spread or variability of the GPS points around the average. Here, a precision of 4.47 meters suggests that the individual measurements are spread out, meaning that while the average position is accurate, the data points themselves are not consistent. This difference between accuracy and precision highlights the GPS unit's performance: it provides a generally accurate estimate of the location, but has low precision with variability in

  Habitat Suitability and Corridor Analysis Introduction In this post, I’ll share the process and results of a habitat suitability and corridor analysis aimed at identifying the most suitable areas for conservation and the optimal paths between protected regions. This analysis involved creating a habitat suitability model based on key environmental factors, followed by a corridor analysis to determine the best routes for wildlife movement between two National Forests. Analysis Steps The first step in the analysis was to create a habitat suitability model using three criteria: land cover, elevation, and proximity to roads. Each criterion was reclassified on a scale of 1 to 10 to reflect its relative suitability for wildlife habitat. I combined these reclassified layers using the Weighted Overlay tool, with land cover given a weight of 60%, elevation 20%, and distance to roads 20%. This produced a suitability raster highlighting the most appropriate areas for conservation. Next, I invert

  Introduction In this blog post, I present the outcomes of a development suitability analysis, focusing on a comparison between two distinct weighting scenarios. The objective was to identify the most favorable areas for development by considering five essential criteria: land cover, soils, slopes, proximity to streams, and proximity to roads. Analysis Steps The analysis started by reclassifying each of the five criteria into a suitability scale from 1 (least suitable) to 5 (most suitable). Initially, all criteria were assigned equal weights of 20%, ensuring an unbiased approach in combining the factors for the suitability model. Then, an alternative scenario was explored, where slopes were given a greater emphasis with a 40% weight, while the remaining criteria—land cover and soils—were weighted at 20% each, and proximity to streams and roads at 10% each. These criteria were integrated using the Weighted Overlay tool, resulting in two final suitability maps. The first map displays th

  Harnessing GIS for Coastal Flooding Analysis: A Deep Dive into Technology and Techniques Introduction Coastal regions, increasingly vulnerable in the face of climate change, are finding Geographic Information Systems (GIS) to be essential tools for managing the devastating impacts of flooding and erosion. This blog post explores the utilization of GIS technologies in assessing coastal flood risks, focusing on erosion analysis and storm surge predictions. Understanding Erosion Through GIS Analyzing coastal erosion involves multiple sophisticated steps that leverage advanced mapping and data collection via GIS. In places like Mantoloking, New Jersey, detailed assessments can capture significant landform changes before and after events such as hurricanes, thanks to high-resolution LiDAR data. This kind of detailed analysis is crucial for effective coastal management. Analyzing Storm Surge Impact In hurricane-prone areas, the potential damage from storm surges can be modeled extensively

Introduction After Hurricane Sandy made landfall, conducting a precise damage assessment was crucial for effective disaster response and recovery. Utilizing Geographic Information System (GIS) technology, we systematically analyzed the damage to structures along the affected coastlines. Steps to Complete the Damage Assessment To carry out the damage assessment, we followed a structured approach using GIS tools for data visualization and analysis: Data Preparation: We started by importing both pre-storm and post-storm imagery into the GIS platform, ensuring that all necessary layers, such as parcel boundaries and the study area polygon, were correctly set up. Digitizing Points on Structures: Next, using the pre-storm imagery as a reference, points were placed on every visible structure within the study area. This step was crucial for ensuring that no buildings were overlooked. Comparative Analysis: By utilizing the swipe tool, we compared pre- and post-storm imagery to visually assess