Alec's GIS Blog

In this module we covered damage assessments in ArcGIS Pro. The lab started by creating a map showing the path of hurricane Sandy. We mapped not only the path of Sandy but also the changes it made from a tropical depression to a hurricane and the locations where that took place.  In this section of the lab, I used ArcGIS Pro to analyze structural damage caused by Hurricane Sandy, focusing on how distance from the coastline influenced the severity of damage. The process began by overlaying pre- and post-storm imagery to visually inspect damage, using swipe and flicker tools for quick comparisons. I then calculated the distance of each damaged structure from a digitized coastline using the Near tool. To explore patterns, I grouped structures into distance bins (0–100 m, 100–200 m, and 200–300 m) and summarized the number of structures in each damage category within those ranges. The results showed a strong trend: structures closer to the coast were more likely to be destroyed or seve...

 In this lab, I explored how GIS can be used to model and analyze the impacts of coastal flooding caused by storm surge, using real-world data from Hurricane Sandy in New Jersey and simulated flooding in Collier County, Florida. By working with elevation models (DEMs) and building footprint data, I was able to map potential flood zones and estimate which buildings would be affected by a 1-meter surge. The process involved reclassifying elevation rasters to identify low-lying areas, filtering out isolated depressions not connected to open water, and using spatial joins to determine which buildings intersected the predicted flood areas. One major takeaway was understanding how different data sources (like high-resolution LiDAR vs. traditional USGS DEMs) can lead to different impact estimates—and how those differences can be measured through errors of omission and commission. This lab highlighted how GIS is not just about mapping, but also about making data-driven decisions in emergen...

In module 3 we used ESRI Academy to complete 4 trainings. Those trainings were Introduction to 3D Visualization, Preforming Line of Sight Analysis, Preforming Viewshed Analysis in ArcGIS Pro, and Sharing 3D Content Using Scene Layer Packages. In these trainings not only do you gain a basic understanding of the topics, but you also get to interact with different tools and learn some tips for practicing the concepts.  Introduction to 3D Visualization In this training, I learned the basics of working with 3D data in ArcGIS Pro. It showed how to create a 3D scene, set up elevation surfaces, and apply 3D symbols to features so they appear realistically in space. The course helped me understand how to view and explore geographic data in three dimensions, which can reveal patterns and relationships that aren’t as obvious in 2D maps. One of the main tools used was the 3D Scene Viewer, which lets you build local or global 3D scenes and visualize how features interact wit...

  In Module 2 we covered the use of LiDAR in ArcGIS Pro. Some of this module served as a refresher from a previous class in the certificate course called remote sensing. This lab gave me a much better understanding of how elevation data is used in forest analysis. The main goal was to process a LAS file to create a Digital Elevation Model (DEM), Digital Surface Model (DSM), and a Canopy Height Model (CHM). Once the CHM was built, I created a 3D view to visualize tree canopy density across the landscape. What stood out most was seeing how LiDAR data can clearly show the height of trees and other features compared to the bare earth surface. The 3D model included things like roads, rivers, and dense forest areas, with visible buildings. This was a enjoyable lab for several different reasons but primarily due to the 3D aspect. It was also helpful seeing a practical use for LiDAR in this setting. I hope to use it more as this class progresses. 

 Crime analysis in ArcGIS Pro involves using spatial data and geoprocessing tools to identify patterns, trends, and hotspots of criminal activity. By integrating crime incident data with demographic and geographic layers, analysts can perform operations like spatial joins, density mapping, and statistical analysis to better understand where and why crimes are occurring. Tools such as Kernel Density, Local Moran’s I, and attribute-based symbology allow users to visualize high-crime areas and evaluate relationships with social or environmental factors. This type of analysis supports informed decision-making for law enforcement. Below are a few of the maps we completed in the crime analysis lab. I used a spatial join to calculate homicide counts within a half-mile grid. The top 20% of grid cells with the most homicides were selected and dissolved into a single hotspot polygon. This method is simple and straight forward but may not capture subtler patterns. The Kernel Density analysis ...