Special Topics in GIS, Module 1.3

In this week's lab, we compared to street network datasets to determine which was more complete (based on the total length of road segments) overall and for each grid square within the study area. One of the datasets contained TIGER road data and the other contained street centerlines maintained by the county. On the basis of overall length, the TIGER data was more complete.

To compare completeness by grid square, I first split the street data along the grid and then merged the resulting smaller feature classes back into one large feature set in order to have a single layer containing all the roads segmented by grid square. I then attached the grid information to each of the road datasets using a Spatial Join, which allowed me to calculate the sum of the road segment lengths for each grid square. That data could then be brought into Excel to calculate the difference in length between the two datasets for each grid square.  

Special Topics in GIS, Module 1.2

This week's lab assignment was to assess the accuracy of two different sets of street data against reference points taken from high-resolution orthoimagery. The first step was to establish test points according to the National Standard for Spatial Data Accuracy (NSSDA): at least 20 points at well-defined locations (in this case, street intersections) with at least 20% of the points located in each quadrant of the study area and a distance between points of at least 10% the diagonal length of the study area.

Screenshot of test point distribution, with street data:

XY coordinates were obtained at each test point for each of the datasets to be assessed as well as for the actual location of the intersection based on the orthoimagery. Then the errors statistics for each point and the RMSE and NSSDA accuracy statistic were calculated for each dataset.

Result for the first dataset, from the City of Albuquerque: Tested 23.94516 feet horizontal accuracy at 95% confidence level.

Result for the second dataset, from StreetMap USA: Tested 184.40877 feet horizontal accuracy at 95% confidence level.

In other words, 95% of the data is expected to fall within 23.94516 feet or 184.40877 feet, respectively, of its true location.

Special Topics in GIS, Module 1.1

This week's lab was about accuracy and precision of data and related error metrics, using a set of GPS points as a case study. Accuracy measures how close a feature in the data is to the real-life location of that feature and is measured by finding the difference between the data being assessed and a reference dataset known to be more accurate. Precision refers to the consistency of repeated measurements and can be measured by taking their mean and comparing the individual points to that mean. The map below illustrates the GPS data from the lab along with an average of all the points and buffers encompassing 50%, 68%, and 95% of the data points relative to the mean. The distance in meters for each interval is 3.1, 4.5, and 14.8, respectively. The accuracy of the dataset was later evaluated by comparing the average point to a reference point; the difference was 3.2 meters.

GIS for Archaeology, Final Project

The final project assignment for GIS Applications for Archaeology dealt with cost analysis. The ultimate outcome for both parts of the project was a least cost path, which is essentially a route from point A to point B that GIS has calculated as being easier to travel than any other possible route, based on the data provided and the parameters set by the user. In this case, we used slope (i.e., it's generally easier to cross flat terrain than to climb up a steep hill) and land cover (i.e., forest vs. agricultural fields vs. the ocean) to determine how hard each pixel would be to cross in the real world--the "cost" of traveling that way. Slope and land cover data are classified according to difficulty of passage and then used to create a weighted overlay raster which then serves as the basis for the cost distance and cost path calculations, which ultimately result in ArcGIS's idea of the optimal route between two points. (For a route with multiple points, you have to run each leg as a separate analysis.) Because this is a somewhat complex, multi-step process, we used Model Builder to set up the analysis rather than running the tools one at a time.

For the first part of the project, in which we learned how to perform the analysis, we examined routes between three prehistoric archaeological sites in Panama: 

For the second part, we had a choice of subjects but were on our own to acquire the necessary data and set up the model. I chose to try to reconstruct the route of the Camino de Mulas, a historic mule trail in Costa Rice and Panama. I had to run the model one or two steps at a time rather than all at once because the processing times were extremely long, probably because of the size of the raster files I needed to cover the whole study area. In the end, it looks like some of the trail may have been where the Pan-American Highway is now, but other sections of it may have escaped destruction. That's making a number of assumptions about how "right" the least cost path is, though. It would take a lot more research to figure out if this route actually makes sense (especially since the analysis was performed with modern land cover data that ignored rivers), let alone to determine whether it's the right one. Still, it's a starting point and an interesting way of looking at history.

GIS for Archaeology, Module 9

For this week's lab, focusing on remote sensing, we worked with an aerial photograph of the area around Cahokia. Cahokia is a large Mississippian site in what is now southern Illinois. Constructed and occupied from about the 800s to about 1300, the city originally covered about 6 square miles and contained some 120 earthen mounds. Part of this area, including about 80 mounds, are now preserved as a National Historic Landmark and state historic site. It is the largest pre-Columbian archaeological site north of Mexico.

For the lab assignment, we obtained an aerial photo from the USGS database and performed two different land cover classifications in ArcMap, one unsupervised (classified entirely by the program's algorithms) and one supervised (classified with the guidance of a set of points with user-assigned values). The resulting maps are below. The unsupervised classification resulted in only three easily defined land cover classes, while I was able to create five using the supervised method and produce a more detailed image, but both contain many errors resulting from the algorithm being unable to tell the difference between similar pixels representing different land cover types (e.g. water and dark-colored trees, or pavement vs. a barren agricultural field). However, it might be possible to refine the supervised classification by adding additional control points. 

GIS for Archaeology, Module 8

This week the topic was 3D modeling, which I was excited about as there are numerous ways to use 3D modeling in archaeology. For this assignment, we used data from a shovel test survey to predict and model the stratigraphy across the entire study area, using ArcMap for analysis and ArcScene for visualization. I did my best to describe the size and orientation of these views, since ArcMap can't create an accurate scale bar or north arrow for a 3D scene. I forgot to note, however, that the shovel tests were dug to a depth of one meter, and these surfaces represent the top of each stratum.

We also used ArcScene to create a 3D visualization of the stratigraphy within the shovel tests themselves, and ArcScene's Fly tool to explore it. My video is a little awkward because I'm still getting the hang of using the Fly tool, but here it is:

Finally, we used the interpolated stratigraphic surfaces to predict the stratigraphy of a cross section of the site, using points along a line through the study area. Below you can see these points, with predicted stratum depths, fitted into the interpolations from before.

GIS for Archaeology, Module 7

This week was all about interpolations, and I ran many of them, comparing the results of different interpolation methods and different parameters for some of them. We used two different datasets for this lab, one for shovel test results from a site in Panama and one for population estimates for a regional survey in Ecuador. For the latter, we then used the interpolations to consider settlement patterning for a particular time period, having read an interesting article that described several case studies using a similar methodology. There are more complete descriptions on the map posters below.