Special Topics in GIS, Module 3.1

The topic of this lab assignment was scale and spatial data aggregation. Scale affects the level of detail present in both vector and raster data. The lab demonstrated that the number and size/length of features in vector data is influenced by scale, which can in turn affect any calculations performed using a given dataset. Raster resolution also impacts analysis; the example used in the lab assignment was calculation of slope from DEMs of varying resolutions. Lower resolutions had a smoothing effect on the slope calculations, resulting in decreasing average slope values. 

The second part of the lab dealt with the Modifiable Areal Unit Problem (MAUP), which is the phenomenon where different approaches to data aggregation (e.g. ZIP codes vs. census tracts) results in statistical differences, and gerrymandering, which is the practice of drawing legislative boundaries in a way that intentionally favors one group over another. There are several ways to measure gerrymandering, including by evaluating the compactness of a district, its contiguity, and/or the demographic makeup of its constituents.

The lab assignment involved calculating a compactness statistic (the Polsby-Popper score), which showed North Carolina's Congressional District 12 to the be the least compact in the contiguous US:

Special Topics in GIS, Module 2.2

This week's lab assignment was about interpolation. In the first portion of the lab, we worked with DEMs, and in the second portion we used several different interpolation methods to create surface rasters depicting water quality in Tampa Bay. The methods used were Thiessen, inverse distance weighting (IDW), and spline. Spline additionally has two possible techniques, regularized and tension.

Thiessen interpolation first requires Thiessen polygons, which are geometrically calculated "neighborhoods" where each polygon contains one input point and any location within the polygon is closer to that input point that to any other in the dataset. To create a raster from this, each cell is assigned the same value as the input point in its neighborhood. IDW calculates each cell value based on the values of the closest X number of input points, and gives more weight to points that are closer to the cell being calculated. In the spline methods, smooth curves are drawn to connect the input points and cell values are based on the cell's position on the curve. Tension spline is more constrained by the input values than regularized spline, though both methods preserve the input values at their individual locations.

Here is the output of the tension spline interpolation for the Tampa Bay data (with the original sample points classified similarly to the surface raster):

Special Topics in GIS, Module 2.1

In this week's lab assignment, we explored TIN (triangulated irregular network) datasets containing terrain data. Unlike typical raster DEMs, TINs consist of a patchwork of triangles using the input sample points as vertices. Thus, they retain the exact input values at the vertices, which is not always the case with interpolated raster DEMs. That means they can easily accommodate an adaptive sampling strategy in variable terrain. Although TINs are more complex than raster DEMs, they can also be better for some applications because of that added precision. However, their angularity means they do not always capture detailed features accurately, as illustrated by the screenshots below of a TIN from Bear Lake, CA. The outline of the lake was not clear in the original TIN, and had to be incorporated by using a shapefile of the lake to create a hard edge in the TIN surface at the correct elevation. 

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.

GIS for Archaeology, Module 6

Over the last two weeks, we were tasked with a large multi-step project using data from the archaeological survey of the Valley of Oaxaca, which is not available in digital form. First, we needed to georeference a topo map showing the survey grid in order to tie the data to the correct locations in GIS. Each student was assigned two or three grid squares to digitize. Here is my section of the survey grid, digitized and overlaid on a topographic base map. 

For me this was the most challenging part of the lab--I was not able to get the survey grid georeferenced as well as I would have liked, which unfortunately means that the rest of my digitized data is also less accurate than it could be since it's based on those early errors.

The next task was to georeference and digitize maps of the land cover types and survey collection sites for each individual grid square. The sites are assigned numbers for each time period represented by the artifacts collected. My maps are a bit simplistic, but they do what they need to do.

Finally, graduate students had an extra task, to join a data table containing more information about the various sites to the collection sites shapefile. One type of data included in the table is population estimates for each site, so the final map shows one of my grid squares with sites classified by population size. (Note that this only represents one of the time periods included in the survey.)

Population size does not seem to necessarily correlate well with productivity of the land at/around the site, as most of my study area is characterized by relatively unproductive areas and it still contains at least one high-population site. And although many of the others are small, there are still quite a few of them.

GIS for Archaeology, Module 5

This week was all about georeferencing, specifically of historic maps, and the lab assignment was to georectify an image of Captain Cook's 1785 map of Macau and its surroundings. I found this really challenging because the area has changed so dramatically due to development that it was hard to see how the historic map should fit onto the modern one. It was also tough to find useful control points--the historic map just doesn't give us much to work with if we can't tell where the historic landforms are on the modern landscape.

Anyway, here's what I came up with (after more or less giving up after a certain point):

GIS for Archaeology: Module 4

This week's topic was historical imagery/maps, which can be incorporated into GIS and used to study a particular moment in the past or to look at changes over time. This can be particularly helpful with regard to changing landscapes or human activities that could impact archaeological sites. Historical data can also be used to identify potential archaeological sites that aren't yet recorded, including those which may have already been destroyed in some way.

Below is my lab assignment for the week, a poster about Paul Revere that includes a historic map. It also includes an image of a census record; historical census data is also a useful resource that can easily be incorporated into GIS.

GIS for Archaeology: Module 3

This week's lab assignment utilized data from the Middle Eastern Geodatabase for Antiquities (MEGA), an online database of archaeological sites in Jordan that includes site information such as location and condition (for monitoring purposes as well as research). It was developed as a way to help keep track of sites threatened by looting or other forms of destruction, and different users have varying levels of access to the data depending on their position and needs, although site locations are visible even to the general public, which is often not the case with databases like this. Below is a map created in ArcMap illustrating a selection of the sites for which data is available through MEGA.

The reason site locations are often not publicized, particularly in areas where looting and vandalism are common or in remote areas where it is difficult to monitor the condition of sites, is, of course, the need to protect those sites for the future. The safeguarding of archaeological resources, and the maintenance and preservation of records created and artifacts recovered through excavation, is a key principle of archaeological ethics. While there is sometimes a balancing act between the need to protect archaeological sites and the need to share data with fellow researchers and engage with the general public about the importance of archaeology, archaeologists must always keep in mind our responsibility to the archaeological record. Once a site damaged or destroyed, valuable information is lost forever. Thus ethical conduct by archaeologists is of the utmost importance--as is reaching out to educate the public (even if we sometimes have to withhold site locations in the process) so that people understand the significance of archaeological sites. 

GIS For Archaeology: Module 2

For the first lab assignments for Applications in Archaeology, we worked with data related to the Great Chicago Fire of 1871, comparing the city just prior the fire and approximately 20 years later. The map below illustrates the boundaries of the city in both 1869 and 1890, along with the origin and extent of the fire, landmarks constructed prior to the fire, and landmarks constructed after the fire but before 1890. This allows us to see both the expansion of the city and the concentrated construction of new landmarks within the damaged area and its surrounding wards.

I didn't have enough time this week to make the map look as good as I would have liked (especially those labels--yikes, ArcMap is bad at automatically labeling!), but I think it turned out okay, albeit unimpressive. The data for this week was very interesting, even though the lab itself was mostly a review of things I've done before.

GIS Portfolio

Below is the link to my GIS portfolio showcasing some of my projects and assignments from the last few semesters. I'm considering this a work in progress; since I haven't yet taken the final two advanced courses for the GIS certificate, and I'm doing GIS work outside of class as well, I will be able to add more examples in the future, or replace these with better ones. I will probably make a website at some point, too. In the meantime, here is the "paper" version.

The second link is to an audio file with my [awkward] answers to two mock interview questions. I get kind of self-conscious about recording myself talking to no one rather than to an actual person, but I didn't want to write out my answers beforehand because that seemed like cheating!

Portfolio
Interview Questions

GIS Day!

As part of the internship seminar, we were to celebrate GIS Day (which is technically in November, but that doesn't mean we can't observe it at another time of year) by sharing GIS with others. Conveniently, I had already planned to attend the Middle Atlantic Archaeological Conference in Virginia Beach last weekend, where I gave a presentation about some of the work I'm doing for my internship project--specifically, georeferencing several historic maps of the region where our archaeological site is located. Unfortunately, since I'm still in the process of georeferencing, we haven't been able to do much with them in GIS yet, but I spoke about the benefits of eventually being able to overlay them with other GIS data and use them to digitize features like historic road networks and the locations of other plantations, mills, stores, and other sites that are now long gone, all in the interest of helping to contextualize our site.

The conference is typically attended by professional archaeologists in academic, government, and private sector positions, along with many students. My presentation was in a session organized around a regional theme, but the following day I was able to attend another session that was all about applying GIS to archaeology. All of the papers in that session were great, but one that I found particularly interesting (and important) was about creating detailed predictive models to assess the risk sea level rise poses to coastal sites and estimate how long it will take for those sites to be destroyed. All in all, it was a great conference. It's exciting to hear what other people are working on and that they're interested in my work, too.

Photo Interpretation and Remote Sensing Module 10

This module's focus was the last method of image classification, supervised classification, which involves identifying known examples of different LULC classes so that the software can associate a spectral signature with each one and classify the remaining pixels based on their statistical similarity to the training classes. It's more work upfront than unsupervised classification, but there's the advantage of not having to go back and figure out what the classes are after the fact.

This was another fun lab assignment, but also kind of frustrating. Despite several attempts, I had a very hard time establishing a unique spectral signature for the road class, and as you might be able to see in my map below, even the final output has some confusion between the road and urban classes, and possibly also road and grass in some places.

You can also see from the distance output image (in which brighter areas correspond to areas that are more different from the established spectral signatures and thus more likely to end up misclassified) that there might be some issues with some of the agricultural areas and along the edges of certain features like the lakes. If this map were for a real-world application, those areas might need to be ground truthed or compared with other imagery to see if they need to be reclassified.

Photo Interpretation and Remote Sensing Module 9

This week we're back to land use land cover classification from aerial imagery, this time using automated methods. In this lab, we learned how to perform unsupervised classification in both ArcMap and ERDAS Imagine, culminating in a classified image of the UWF campus. For this assignment, the image was automatically classified into 50 different classes based on pixel characteristics, then manually simplified into five classes by identifying the broad LULC category each of the original 50 classes. My output is below; I ended up with a significant portion of the image falling into the "Mixed" class, which consists of pixels whose automatically assigned spectral class spanned multiple LULC classes (most of the confusion seemed to be between grass and buildings/roads--when I started out by assigning classes to the grass category, I suddenly had a number of green roofs!). 

Photo Interpretation and Remote Sensing Module 8

Another fun lab this week! After learning about thermal energy, how to work with thermal infrared imagery in both ArcMap and Erdas (as well as how to compile multiple image bands into a single image in both programs), and some applications for thermal image analysis, such as identifying forest fires, we were asked to use the thermal band in one of the lab images to identify a feature of our choice.

I looked at the image from Ecuador using a panchromatic display of the thermal band in comparison to a true color composite and a panchromatic display of the near-infrared band. There were several things in the image that intrigued me (some of which I actually wasn't able to identify with certainty), but I eventually noticed this pair of bridges that only popped out at me when looking at the thermal band--in the visible and near-infrared bands, the one on the left is subtle and the one on the right is almost invisible, because their coloring isn't that far off from the water and both pavement and water absorb strongly in the near-infrared. But pavement certainly gives off thermal energy after warming up in the sun!

Anyway, I thought it was really interesting, because I wasn't expecting such a stark difference in what seems like it should be a prominent feature. There are certainly benefits to comparing multiple image bands.