Jennifer's GIS Blog

 Understanding water quality across an area as dynamic as Tampa Bay often requires transforming discrete sample points into a continuous surface. This process, known as interpolation , allows us to visualize trends and identify potential areas of concern. However, not all interpolation methods are created equal, and their underlying approaches can significantly impact the resulting surface. The inclusion of a mask improves results significantly as seen in the spline examples below Technique Minimum Maximum Average Standard Deviation Non-spatial 0.8 3.5 1.81 0.567 Thiessen 0.8 3.5 1.80 0.46 IDW 0.8 3.5 1.80 0.32 Spline - Regularized -5.5 25.8 1.82 1.88 Spline - Tension -2.7 3.9 1.80 0.50 Spline-Regularized Masked -2.7 3.9 1.80 0.5 Spline - Tension Masked -1.3 3.8 1.66 0.70 Based on these metrics the Spline tension masked gives the best result.

This week's lab focused on exploring various elevation tools, covering everything from visualizing elevation to generating contours and analyzing elevation data. The ski slope suitability map was a particularly relevant exercise for archaeology. The methodology for creating this map is very similar to how we perform archaeological site modeling. While the variables we use differ—we typically incorporate factors like proximity to fresh water sources and soil preferences—the underlying analytical process is essentially the same. This type of analysis allows us to predict the location of potential archaeological sites by mapping areas that meet specific criteria known to have been important to past human populations.

The goal of the accuracy assessment is to provide an easy comparison of the completeness and accuracy of the two different road networks. Clip - remove data outside of the grids Calculate Geometry Attribute - length in KM Summarize within - to calculate the length of the road within each grid square Join- join two grids created by summarize within to create a comparable datatable & make it possible to do the required calculations compare_networks(!Grid_Centerlines_Summary.sum_Length_KILOMETERS!, !Grid_TigerSummary.sum_Length_KILOMETERS!) def compare_networks(x, y): if x > y: return "Centerlines" elif y > x: return "Tiger" else: # This will handle the case where x and y are equal return "Equal" !Grid_Centerlines_Summary.sum_Length_KILOMETERS! - !Grid_TigerSummary.sum_Length_KILOMETERS! ((!Grid_Centerlines_Summary.sum_KM_Length!-!Grid_TigerSummary.sum_KM_Length! )/ !Grid_Centerlines_Summary.sum_KM_Length!)*100

  To complete this exercise, I first created three shape files. I then digitized the intersection of 60 test points, 20 for ABQ Streets, 20 for Street Map USA, and 20 for reference derived from the provided aerial imagery. I used a batch process to add XY coordinates to all three shapefiles simultaneously. Rather than exporting to DBF, I selected the columns as needed and copied and pasted them into the correct spot in the workbook I set up in Google Sheets. You can access a template workbook with the formulas added here Street Map- Tested 211.41 ft horizontal accuracy at 95% confidence level ABQ Streets- Tested 26.11 ft horizontal accuracy at 95% confidence level

 Medium-sized image of your map layout (Deliverable 2)  The numerical results for horizontal accuracy and precision (Deliverable 4)  Report your results for horizontal accuracy.  3.3m How does this compare to your results for horizontal precision?  This result is less than the anticipated 68th percentile precision of 4.5m Report your results for vertical accuracy.  6.0m How does this compare to your results for vertical precision?  This is almost 2 more meters above the vertical precision measurement of 4m In general, is there any evidence of bias in your results? If so, where?  There is evidence of bias in the vertical results because the vertical accuracy of 6m is significantly worse than the calculated vertical precision.  A very brief statement on how horizontal accuracy and precision are measured (so readers know the difference between the two) Horizontal accuracy and precision are measured by comparing a set of measurements to a known t...

Modeling Black Bear Connectivity: A Least-Cost Corridor Analysis This project focused on identifying optimal connectivity pathways for Black Bear (Ursus americanus) movement between two designated park areas. Utilizing a least-cost path analysis framework, we modeled a potential travel corridor by integrating ecological principles with advanced spatial analysis techniques in ArcGIS Pro. While some traditional tools are being superseded by newer, more efficient geoprocessing functions in modern GIS software, the core methodology remains robust. The analysis workflow, conceptualized below, systematically progressed through the following stages: 1. Habitat Suitability Assessment: The foundational step involved creating a habitat suitability model , quantifying the relative "goodness" of the landscape for Black Bear movement. This was achieved by: Reclassifying key environmental variables: We transformed raw data for factors like land cover, elevation, and proximity to human in...