Internship Progress and LinkedIn Update

This week, I’m continuing my GIS internship at the UWF Archaeology Institute, where I’m updating county-level archaeological predictive models using ArcGIS Pro. I’m working with environmental, historical, and LiDAR data to improve the model’s accuracy and reliability. Alongside that, I’m developing metadata templates and a standardized folder structure to keep data organized and make the workflows easier to replicate.

I’m also drafting technical documentation to guide future users through these processes, aiming to make it as clear and useful as possible. Balancing this internship with my full-time role has been a challenge, but it’s helping me build stronger geospatial skills.

To reflect this growing expertise, I recently updated my LinkedIn profile to highlight both my archaeology background and new GIS skills. It’s been a good exercise in clearly showing how these areas connect and complement each other.

Surface Interpolation

This week, I worked on creating surfaces of water quality in Tampa Bay using different interpolation methods. Interpolation helps estimate values at unsampled locations based on known sample points, which is key for understanding spatial patterns in environmental data like Biochemical Oxygen Demand (BOD) concentrations.

I compared four methods: Non-spatial averaging, Thiessen polygons, Inverse Distance Weighting (IDW), and Spline interpolation. Non-spatial simply averages all sample values without considering location, so it lacks any spatial detail. Thiessen divides the area into polygons where each sample controls its own zone, assuming sharp boundaries that don’t usually exist in natural systems. IDW improves on this by weighting nearby points more heavily, producing a smoother surface that respects distance relationships. Finally, Spline fits a smooth surface passing exactly through the points, but it can exaggerate values in areas with sparse data, sometimes creating unrealistic peaks.

Overall, IDW gave the most balanced and realistic surface for Tampa Bay’s water quality, avoiding extremes while capturing spatial trends. Figure 1 shows the IDW surface I generated for BOD concentrations.

Figure 1. IDW interpolation surface.