Module 3 - Coastal Flooding

 Coastal flooding is one of the most destructive hazards associated with hurricanes, making accurate flood modeling an important component of emergency planning and disaster response. In this lab, I used ArcGIS Pro and LiDAR-derived elevation data to evaluate the potential impacts of Hurricane Sandy and model storm surge flooding in coastal communities.

The project began by comparing pre- and post-Hurricane Sandy LiDAR data to identify changes in elevation caused by erosion and deposition. Elevation models were created from the LiDAR point clouds and compared to produce an elevation change raster. This provided a visual representation of how the coastline changed as a result of the storm and demonstrated the value of high-resolution elevation data for identifying subtle landscape changes.

The next phase focused on modeling a potential 2-meter storm surge. Using raster reclassification and polygon conversion tools, I identified low-lying areas that could potentially be inundated during a coastal storm. The results were clipped to Cape May County and analyzed to estimate the percentage of the county that could be affected by flooding. During this process, I also compared calculated areas with published county statistics, reinforcing the importance of understanding dataset sources, metadata, and how different boundary definitions can influence GIS results.

The final analysis compared building impacts using a LiDAR-derived DEM and a lower-resolution USGS DEM. The comparison clearly demonstrated that elevation data resolution has a significant effect on flood modeling results. The LiDAR model produced a more realistic estimate of impacted structures, while the lower-resolution DEM substantially overestimated flooding in several building categories.

Overall, this exercise demonstrated that GIS is much more than producing maps. Reliable hazard analysis depends on selecting appropriate datasets, understanding the assumptions built into analytical models, validating results, and recognizing the limitations of spatial data. These skills are essential for supporting informed decision-making before, during, and after coastal disasters.

 

Module 2 Blog: Exploring Forestry and LiDAR in ArcGIS Pro

This week's Forestry and LiDAR lab gave me a much better understanding of how LiDAR data can be transformed into meaningful information about a forest. Before this exercise, I understood that LiDAR produced millions of elevation points, but I had not fully appreciated how those points could be processed into different datasets that each answer a unique question about the landscape.

One of the most interesting parts of the lab was comparing the Digital Elevation Model (DEM) and the Digital Surface Model (DSM). Although they are created from the same LiDAR dataset, they represent two very different views of the landscape. The DEM shows the bare-earth terrain after vegetation has been removed, while the DSM represents the highest surfaces, including trees and other objects. Creating the Height raster by subtracting the DEM from the DSM made it much easier to visualize vegetation height across the entire study area.

Another valuable aspect of the lab was calculating canopy density. The workflow required separating ground and vegetation returns, converting them to raster datasets, and using several raster analysis tools to calculate the proportion of vegetation within each cell. This process demonstrated how relatively simple geoprocessing tools can be combined to produce a useful management product. The canopy density map clearly identified dense forest stands, roads, and open areas, illustrating how LiDAR can support forestry applications such as habitat assessment, forest inventory, and monitoring disturbance.

One challenge I encountered was learning how to present the results effectively. Choosing appropriate symbology required some experimentation because the default display settings did not always highlight the differences I wanted to emphasize. Adjusting color ramps, transparency, and stretch settings significantly improved the readability of the Height, DEM, and canopy density maps. This reinforced the importance of cartographic design, since even accurate analyses can be difficult to interpret if they are not displayed effectively.

Overall, this lab strengthened both my technical and analytical GIS skills. I gained experience working with LiDAR datasets, raster processing, and 3D visualization while also learning how to communicate results through professional map layouts. I can see how these techniques would be valuable in forestry, environmental management, emergency management, and natural resource planning, where understanding terrain and vegetation patterns is essential for informed decision-making.

 

Module 1 Blog Post: Crime Analysis and Hotspot Mapping in ArcGIS Pro

This week's lab provided an excellent opportunity to compare several commonly used crime mapping techniques while reinforcing many of the spatial analysis tools available in ArcGIS Pro. Although each exercise built on concepts introduced earlier in the course, this lab demonstrated how different analytical approaches can influence the way crime patterns are interpreted and how those results might be used to support decision-making.

The first part of the exercise focused on creating a choropleth map displaying burglary rates throughout Washington, DC. Rather than simply symbolizing raw crime counts, this map represented burglary rates by census tract, making it easier to compare areas of different population sizes. I spent time adjusting the classification method, color ramp, and map layout to create a product that was both accurate and visually balanced. Adding the required cartographic elements—including the title, legend, north arrow, scale bar, projection, author information, and data sources—reinforced the importance of creating maps that effectively communicate information to an audience instead of simply displaying data.

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The second portion of the lab introduced Kernel Density analysis using assault incidents. This technique creates a continuous surface that estimates where crime events are most concentrated by assigning greater influence to incidents located near the center of the search radius. After generating the density raster, I experimented with several classification methods and color ramps before selecting a six-class Natural Breaks (Jenks) classification that clearly emphasized the highest-density areas. This process demonstrated how cartographic choices can significantly improve the readability and interpretation of a map.

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The final portion of the lab compared three different hotspot analysis techniques using Chicago homicide data. While all three methods attempted to identify areas with elevated concentrations of crime, each approached the problem differently.

The Grid Overlay method divided the study area into uniform grid cells and identified the cells containing the highest number of incidents. This approach was straightforward and easy to understand, but it also highlighted one of its limitations: the location of the grid boundaries can influence the results. Even small changes in grid placement could potentially change which locations are identified as hotspots.

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The Kernel Density hotspot method produced a smooth surface that highlighted general areas of concentrated homicides rather than individual hotspot polygons. During this portion of the lab, I encountered a few challenges while converting the raster output into polygons and distinguishing between raster and vector datasets. Working through these issues improved my understanding of raster workflows and reinforced the importance of carefully managing intermediate outputs during spatial analysis.

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The Local Moran's I analysis was particularly interesting because it identified statistically significant High-High clusters instead of simply locating areas with many incidents. After selecting only the High-High clusters and dissolving the results into a single hotspot feature, I compared this method with the Grid Overlay and Kernel Density approaches. Although all three methods identified a similar proportion of the 2018 homicides, the hotspot areas differed considerably.

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To compare the effectiveness of each hotspot technique, I calculated the total hotspot area, determined how many 2018 homicides occurred within each hotspot, calculated the percentage of all homicides captured, and computed homicide density. The Grid Overlay method produced the smallest hotspot area and the highest homicide density, while the Kernel Density method identified a larger hotspot area with a lower density. Local Moran's I produced results that balanced hotspot size with statistically significant clustering.

From a practical standpoint, I found Local Moran's I to be the most useful method for supporting law enforcement decision-making. Rather than simply identifying areas with many incidents, it identifies clusters that are statistically significant, making it a stronger analytical tool for directing limited enforcement resources. While Kernel Density remains an excellent visualization technique and Grid Overlay provides a simple summary of crime concentration, Local Moran's I offers the strongest foundation for strategic planning because it accounts for the spatial relationship between neighboring locations.

Overall, this lab strengthened both my technical skills and my understanding of spatial crime analysis. In addition to practicing raster analysis, polygon processing, spatial selection, and geometry calculations, I gained a better appreciation for how analytical methods influence the interpretation of crime patterns. It also reinforced that effective GIS work involves more than running tools—it requires understanding what each method measures, recognizing its limitations, and selecting the approach that best answers the question being asked.