Coastal Flooding

This week’s lab involved analyzing coastal flooding from Hurricane Sandy in New Jersey and a storm surge event in Florida, using both LiDAR-derived and traditional USGS DEMs. The main goal was to understand how differences in elevation data quality affect flood mapping and building impact assessments.

Using pre- and post-storm LiDAR data, I created detailed elevation models to map how the shoreline changed. By subtracting the pre-storm elevation from the post-storm elevation, I identified areas of erosion and deposition. The results clearly showed the extent of damage along barrier islands like Mantoloking, NJ (Figure 4).

Figure 4

I also examined storm surge impacts by mapping areas that would be flooded by a 2-meter rise in sea level, as occurred during Sandy. This helped us estimate the percentage of vulnerable land and buildings, such as those in Cape May County, that might have been affected.

Comparing USGS elevation models with LiDAR-based models in Florida showed that the higher resolution LiDAR data revealed many small, low-lying areas vulnerable to flooding that coarser models missed. 

GIS Applications: Visibility Analysis

This week, I completed four Esri courses centered on 3D visualization and visibility analysis. These courses gave me methods to better visualize and share spatial information.

The line of sight and viewshed analysis lessons showed me how to assess what can be seen from specific locations. These techniques are useful in fields like urban planning, environmental studies, and archaeological research to identify visible and hidden areas.

I also learned how to build 3D scenes with multipatch features, which add depth and detail to projects and can make spatial data easier to interpret.

I also practiced packaging and publishing 3D scene layers as scene layer packages. This makes sharing interactive 3D maps with coworkers and the public much easier through web platforms like ArcGIS Online.

GIS Applications: Forestry and LiDAR

This week, lab focused on working with LiDAR data to understand forest structure in the Shenandoah region of Virginia. After decompressing the .laz file, I loaded the LAS dataset into a Local Scene in ArcGIS Pro. The result was a dense floating point cloud showing elevation returns, styled in red-yellow-blue based on height in feet (Figure 1, top). While visually striking, the terrain wasn’t fully clear until I generated a DEM to create a ground surface from the filtered LiDAR ground points (Figure 1, bottom).

Figure 1

The canopy density calculation seemed to have been affected by processing limitations within the virtual workspace, possibly due to the size and complexity of the LiDAR data. This likely caused the layer to render mostly low-density values, with the high-density canopy areas not fully represented. Despite this, the resulting map still highlights key features like roads and clearings where vegetation is sparse (Figure 2). This experience underscores the importance of system resources and careful setup when working with large 3D datasets in ArcGIS.

Figure 2

I also calculated canopy height by subtracting the DEM from the DSM, producing a height raster with a maximum tree height of 163 ft and some negative values down to -5 ft. The cells with values under 0 feet are primarily located along roads and slopes, with fewer than expected near streams, likely due to LiDAR return errors or edge effects. The histogram of vegetation heights shows a roughly normal distribution, peaking around 50–60 feet with a mean of 54.4 feet and a standard deviation of 20.4 feet. The data generally stops around 110–120 feet, making the 163-foot tree an outlier. This distribution illustrates the forest’s vertical structure and variation in canopy height (Figure 3).

Figure 3

GIS Applications: Chicago Homicide Crime Analysis Hotspot Techniques

This week explored how different spatial analysis techniques can identify homicide hotspots in Chicago, using 2017 data to evaluate how well they predict 2018 incidents. Three methods were applied: grid overlay, kernel density estimation, and Local Moran’s I. Each of these methods offer a unique view of crime area in the city.  

The grid overlay method divided Chicago into half-mile squares and selected the top 20% with the highest homicide counts. This approach was simple and direct, producing clean polygons ideal for operational planning (Figure 1).

Figure 1

The kernel density technique produced a surface showing where homicides were most concentrated, based on how closely the incidents were located to each other. This method helps highlight areas with higher crime intensity without breaking the map into fixed units, giving a clearer view of crime clusters (Figure 2).

Figure 2

The Local Moran’s I method identified clusters of high homicide rates by finding areas where high rates were near other high rates. Although it covered the largest area, it also captured the most 2018 homicides (Figure 3).

Figure 3

I looked at each map to see how well it predicted the 2018 homicides by counting how many incidents fell inside each hotspot and checking how big those zones were. Local Moran’s I caught the most homicides, but its hotspots covered a lot of ground and weren’t very precise. The grid and kernel density methods showed smaller, tighter hotspots that would be easier to focus on for real-world action.

The lab showed there can be a trade-off between the size of a hotspot and how accurately it predicts crime. Bigger zones catch more incidents but can be too broad to really target resources. Smaller hotspots might miss some crimes but give clearer, more practical areas to work with. In the end, this lab clarified how different mapping techniques can actually help shape crime prevention strategies in the real world.

 

GIS Applications: Orientation Story Map

Hi, I’m April Holmes, a full-time Research Associate and Archaeologist at the University of West Florida’s Archaeology Institute. I earned both my B.A. and M.A. in Anthropology from UWF, with a focus on terrestrial archaeology. While I was first introduced to GIS through foundational courses during my academic studies, I’ve continued to build on those skills professionally, using ESRI’s ArcMap and ArcGIS Pro for archaeological mapping and analysis.

There is always more to learn, which is why I’m currently enrolled in UWF’s Graduate GIS Certificate program. Through this program, I’ve expanded my knowledge in cartography and Python, further developing the technical skills needed to support archaeological research and data visualization.

For this week’s assignment, I created a simple Story Map Tour to introduce myself, using the cities I’ve lived in as a thematic guide. While I am new to working with ArcGIS Online (AGOL), I learned a great deal through trial and error, exploring the tools, and drawing inspiration from other examples.

Throughout the process, I also began to see the broader potential of Story Maps, particularly as an outreach tool for cultural resources. For sites that are open to visitors, they can provide an engaging, interactive platform to share archaeological context, enhance public education, and promote preservation.

If you'd like to take the tour, you can check it out here: April's Story Map