GIS5050: Module 4 - Coastal Flooding

In this storm surge analysis lab, A key learning outcome was understanding how elevation data impacts flood modeling when comparing LiDAR DEMs to USGS DEMs. In this lab I learned to model flooding using raster analysis to identify areas suceptable to a 1-meter storm surge. I used tools including Raster Calculator, Region Group, and Extract by Attributes, to isolate connected flood zones. The lab required using Select by Location and Field Calculator to identify buildings affected by flooding. Using Symbology and Map Layout tools, I communicated the high risk zones, and the importance of identifying flood zones.

 

GIS5100: Module 3 - Visibility Analysis

 

For this week's lab, we were tasked with completing learning exercises that consisted of modules and seven quizzes. Each exercise focused on different aspects and concepts concerning 2D and 3D maps and scenes. One essential new tool I learned about during this lab was Construct Sight Lines, which generates 3D lines between the observer and the target features to analyze visibility. This tool is ideal for fire tower coverage or urban planning. We also learned about the Line of Sight tool, which provides visibility along those lines. This identifies whether targets are visible or obstructed. I really liked going through the exercises and being able to learn about the software and geo-processing methods, and then being given walkthroughs on how to do things in ArcGIS simultaneously. This week's lab was a refreshing exercise that I think helped me a lot.

GIS: 5100 - Module 2 - LiDAR

In this lab, we saw how LiDAR can be used to analyze forest structure using ArcGIS Pro. The lab focused on a forested area in Virginia, where we downloaded and processed LiDAR data to model terrain, vegetation height, and canopy density.

The above maps are as follows. Map 1- Canopy Density Map. Map 2- Tree Heights Map. Map 3 - Terrain Map.

We started the lab by downloading a compressed .laz file from the Virginia LiDAR application. We then converted it into usable data in ArcGIS Pro. I create a DEM and DSM using the data. We subtracted the DSM from the DEM to determine tree height. We calculated the canopy density by comparing the vegetation and ground LiDAR points. Finally, we created a histogram and our maps.

The key takeaways from this lab were that LiDAR can be a handy tool for the analysis of forest canopies and ground points. Another is that this information can be very important to helping foresters do their job. Overall, I think I learned a lot from this lab, and it was very useful information.

GIS5100: Module 1 - Crime Analysis

 

For this lab, I analyzed the spatial distribution of homicides in Chicago for the year 2017 using three different techniques. Grid-based mapping, Kernel Density Estimation, and Local Moran's 1. The ultimate goal was to determine the patterns of crime in the location and evaluate which method would best predict the locations of homicides in 2018. 

The maps are in order as follows. Map 1 - Grid-based mapping. Map 2 - Kernel Density Estimation. Map 3 - Local Morans 1. Each method highlighted crime concentrations but differed in a few ways. The grid-based mapping was the simplest and relied heavily on the position of the grids. The kernel density provided good patterns but covered large areas and was generalized. The best, in my opinion, was Local Morans 1. This technique used real-world boundaries to identify clusters and where they intersected. 

I think this lab was very useful for understanding how maps can be used and for understanding different ways of displaying information. The hotspot analysis showed that using GIS techniques can have real-world applications, reducing crime and improving policing by showing crime hotspots.