Jennifer's GIS Blog

 For my internship, I will focus on updating the GIS data for St. Michael's Cemetery to create a publishable dataset, as well as developing an updated GIS Web Application. Currently, the Cemetery GIS map is accessible in a webmap builder that has ended support, so the internship effort will feature moving the map to the new Experience Builder. This work will be done as part of my current job with the University of West Florida Archaeology Institute I'm a member of the Women in GIS user group, which has had excellent learning opportunities for members, from preparing for GISP certification. They offer virtual office hours as well as standing community of practice meetings across multiple industry sectors.  Membership is geared towards women and allies of women in the GIS workplace.  

  Vector data is generally not inherently tied to a scale, but it can be affected by the scale and resolution of the raster data it is derived from. It is important also to consider how data is going to be displayed when determining what kind of vector data (point, line & polygon) is appropriate for displaying a certain type of scale. This may involve simplification, aggregation or omission of data depending on the purpose of the map. Scale is much more significant when it comes to raster data where the resolution of the raster is predetermined by the size of the cell. The lower the resolution the larger the cell size and the less detailed a raster may be. For Blog question 2, please see the submitted document, not posted publicly for professional reasons.

 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