Internship Highlights

I'm almost halfway through my internship and am excited to share what I've been up to. My main goals for the semester were to create a geodatabase with historic resources and imagery for a large site recording project at my organization, create a geodatabase to use to create maps and shapefiles for recording sites, and conduct several GPR studies and organize the findings in GIS.

So far, I've been able to pull USGS maps for our project area spanning from the 1910s through the 1980s. I found a lot of these already georeferenced through the USGS topoView, so it was a matter of figuring out which quad maps I needed. I scoured these maps for cemeteries and churches, creating separate feature layers for each and digitizing the location of these sites on the old maps. I have more recent data to compare these locations to to ensure that all have been recorded in the Florida Master Site File (FMSF). I've gone through the cemeteries and have not found any new sites, but have not yet sorted through all of the churches.

I have also started to pull historic aerials from UF's Aerial Photography collections for the project area from 1943 and 1960. These I've had to georeference myself using the modern aerial imagery. It's easier in areas with roads and streets, as many of these haven't change very much in the past 80 years. However, it's been more difficult in the marshy areas of the project area. But these areas have also been the most interesting to see change through time, as you can see oxbows become cut off from the main channel, or new flow paths develop into bigger channels.

The geodatabase for recording and updating sites has been far easier and straightforward. I downloaded the current FMSF data to use in updating site boundaries and also added layout templates from my colleague for creating pdfs of the maps. I download our data layer from ArcGIS online every so often to get the points and polygons to aid in redrawing old boundaries and drawing new boundaries. I've completed at least a dozen shapefiles and maps this fall already.

As for the GPR work, I've conducted several surveys starting back in the late summer and continuing into the fall. I've created online maps from three of the sites in order to share with our community partners as well as to be able to pull them up in Field Maps while on site. We marked all of the areas with high potential for burials in two of the cemeteries. I still have another set of survey data to process, and have one more day of field work on my calendar. 

Here's a photo of our intern, Maria, working with a community partner to collect GPR data.

Over all, I wish I had more time to spend on all of these projects! I'm finding more and more how much I love GIS. I'm also thinking of all the ways we can deploy it in other projects. 

Surfaces: Interpolation

In this lab, we learned about the different methods for interpolating surfaces using several data sets. In the first part, we compared the interpolation between Spline and IDW for elevation data from Kansas. In general, the difference between the two techniques was not that great. Over half of the pixels fell within 3 m of each other in the models.

For the second part, we used water quality data from the Tampa Harbor to compare Thiessen, IDW, and two different methods for Spline interpolations. 

Thiessen uses the exact values from the points, creating large blocks of the study area through a nearest neighbor technique and assigning them these values. This create a not very nuanced surface model with abrupt changes between the areas.

IDW, or inverse distance weighted, compares each point to the five closest points around it, working under the assumption that things closer together are more similar. This resulted in a surface with circles around many of the individual points, with areas disconnected by slopes rather than flowing into each other.

Spline runs through each point to create a smooth surface while minimizing the total curvature. The regularized method generally creates more elastic surface that will frequently have values that lie outside of the sample data range. The tension method will create a surface that is flatter and force the estimated values to stay closer to the known sample points. Because spline tries to make nice smooth surfaces, we had to fix the data where several points were clustered together with varying BOD measurements.

In the end, I think I was happiest with the Spline interpolation using the tension method. It was a great exercise to see the results from each.

Here's an image of the Spline interpolation using the tension method.

Surfaces: TINs and DEMs

This week we played with various types of and ways to visualize surface elevation data. We looked at TINs and DEMs, creating models and contour lines as well as playing with symbology settings. This is the first time I've encountered TINs but they are oddly familiar to me after doing so much work in 3D modeling. It's interesting that they are still treated as 2D data, though you can use them as your elevation source within the scene.

One of the tasks was to create a suitability model for ski runs from a DEM. This allowed us to explore the various aspects of surface data including elevation, slope and aspect. We learned about the weighted overlays in the Applications class so I was very happy to revisit this process, now with added 3D visualization!

Areas of darker green are more suitable for skiing!

We also created a TIN and DEM from a layer of point data. From this, we visualized the results with contour lines and compared the two sets of data. While the DEM is much more pleasing to look at, the TINs have less extrapolation and are in theory more accurate. 

GIS Job Search

For my internship, I had to spend some time looking at job postings to check out the options and see what kinds of GIS skills postings list. I looked in a few different places at potential jobs. First, I just searched Google for "GIS Archaeology jobs." I got quite a few postings, but unsurprisingly, most of the postings were for GIS positions in the private sector at CRM or environmental firms. I did find a few positions with States - Florida and California are apparently looking for archaeologists that explicitly have GIS skills and experience. I also found what I'm sure is a lot of people's dream job - archaeologist at Gettysburg. This position required a lot of GIS skills. Alas, I'm just not a big enough Civil War buff so it's all lost on me.

From the USA Jobs website, I searched for GIS related jobs and found quite a few with multiple agencies. A lot of the positions were focused more on natural resources, or on infrastructure and planning. I did see a few archaeology jobs including another with the NPS in Alaska and a few with the BLM in California. These positions required specialized experience to be hired on at a higher GS rank, some of which could include knowledge of GPS and GIS systems to record archaeological resources and maintain databases of the known resources. 

While I was searching, I found plenty of blogs and organizational websites discussing how important GIS skills are in the field. It's interesting to see it hyped up but not see it listed out in very many job postings. I searched for just "archaeology jobs" and of course found a ton of other positions. Most of these job postings do not explicitly list GIS but include tasks that will involve collecting and analyzing spatial data. Maps are a crucial part of any archaeological endeavor, but I think the field is still catching up to the technology. It will be interesting to see if these things get included in future job postings, or remain vague tasks that employers assume potential employees have some experience with.

Data Quality Assessment

 For this lab, we had to assess the completeness of a network of roads from Jackson County, Oregon, comparing the County's data to that of TIGER data from the US Census. Because we're always building more in this country, it's assumed that more roads mean more complete data. As such, the datasets were compared by the metric of total road length.

For the analysis, we first looked at the county as a whole. The TIGER total length was 11382.7km while the County dataset was 10805.8km. By our metrics, the TIGER dataset appears to be more complete and more accurate.

To get a better look at different parts of the county, we used a 5x5km grid, clipping each dataset to this and comparing individual grids. To accomplish this, I used the split tool on each road network and then used Python script to calculate and sum the road segments for each grid. Finally, I calculated the percentage difference for each grid using the County data as the base and created a map to visualize this information.