Module 4 - Hazards: Coastal Flooding

 

This week’s GIS assignment walked us through a complete spatial analysis pipeline, starting with post-disaster building damage detection and finishing with coastal flood modeling using LiDAR data.

We began by creating a Spatial ETL Tool, which allowed us to automate data translation and transformation between different formats. Using this, we compared pre- and post-disaster building data to detect structural changes after a hurricane event. The differences were clear: some buildings were missing in the post-disaster file, which likely indicates they were damaged or destroyed. Visual comparison of layers helped us identify red polygons representing possible destruction zones.

The second half of the assignment focused on coastal flooding risk in Florida using DEM rasters. We used both LiDAR and USGS elevation data to identify areas at or below 1 meter above sea level. 

We also performed a building analysis on the flooding areas, which revealed key differences between the two elevation models. While the USGS DEM showed a similar total number of impacted buildings compared to the LiDAR DEM, it produced both errors of omission (missed flooded buildings) and errors of commission (buildings incorrectly marked as flooded). These differences highlight how relying on less accurate elevation data can misrepresent actual flood risk, even if the overall counts appear close.

This assignment was a great example of how multiple tools — from ETL and raster analysis to logical extraction and polygon conversion — can work together to build a realistic spatial model of both damage and risk. It also emphasized the importance of careful interpretation, especially when modeling disaster impacts or flood exposure.

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