Portfolio

This portfolio presents most of the projects completed during coursework for the Graduate Certificate in GIS for Archaeology at the University of West Florida.  These courses included Cartography, Introduction to GIS, Remote Sensing, GIS for Archaeology, and Special Topics in Archaeology.  It also includes a project begun during the final semester, Spring 2017, when I worked as an intern at the New Bedford Whaling Museum in Massachusetts.  I created an Esri Story Tour based on a logbook of a voyage taken by Horatio Hathaway from New York to China and back in 1850-1851. This project is ongoing.

A link to my portfolio can be found here, and a short video explaining my favorite project can be found here.  The Story Tour, although incomplete, can be viewed here.

Creating this portfolio required me to reflect back on how far I've come and how much I've learned over the past two years.  The maps show steady progress in my GIS skills and abilities, and the variety of topics and purposes for which these maps were made is quite impressive, looking back.  I feel like I have accomplished what I set out to learn and more.  I also realize where my strengths and weaknesses exist as they relate to GIS.  Overall, the process of creating the portfolio has left me feeling proud and hopeful that I can get meaningful, satisfying work in this field in the future.

Final Project: Predictive Modeling of native sites in Dartmouth and Westport, MA

The study area for this project, located in southeastern MA, was home to native Wampanoags during the Archaic, Woodland, and Contact Periods. By 1800, their permanent and seasonal settlements along the shores of the Slocums, Little, Apponagansett, and Westport Rivers had disappeared.

Seasonal and permanent settlements had been reported along the shores of rivers from 1602 until 1800.  Trails and waterways were transportation routes used first by natives and later by colonial settlers.

By 1856, natives were no longer living in settlements but were dispersed throughout the community.

The Digital Elevation Model (DEM) is a starting point for creating a predictive model.

Elevation is divided into classes to show areas of similar values.  

A contour map is created so that slope and aspect maps can be derived from it.  

The slope map shows areas where the terrain is steep near the rivers.  These areas would not have been ideal for settlements.

Aspect illustrates places where the land faces N,S,E, or W.  Those areas facing south were often favored for settlements because of their warmer climate and protection from harsh winds.  Eastern and western facing slopes were the next most desirable.  North facing land was least desirable.

The resources found in rivers, streams, lakes, ponds, marshes and coastal areas were used by native people both seasonally and year-round.  The study area shows a far-reaching network of these environments.

Surface geology could have played a role in site selection.  However, more work needs to be done to identify characteristics that would have been favorable or not favorable, based on what is known about identified native sites.

To predict the location of unknown sites, environmental variables such as Elevation, Slope, and Aspect can be weighted and combined into a Weighted Overlay map which creates a pictures of  areas where sites are more or less likely to be found.

Several types of analysis can be done using known sites and random points to see if the predictive model is useful, and how the variables relate to one another and to the sites.  Elevation was a particularly useful variable in this model.

Biscayne Shipwrecks: Analysis

The sea-bed attributes most commonly associated with shipwrecks are used to create a map showing where wrecks are likely to be found.  The first step is to reclassify this data, giving more weight to these classes, and less to attributes were shipwrecks are absent.

The benthic map shows areas in red are most likely to have shipwrecks.  The bathymetric map is based on sea depth.

The two classified maps are combined to create a predictive map.  The benthic map has a greater influence (70%) on the overlay; the bathymetric map has less (30%), reflecting the greater importance of sea-bed type over sea depth in the location of existing shipwrecks.

Biscayne Shipwrecks Week 1

Shipwrecks are common in this area of Florida where the Gulf Stream hugs the coast.  Reefs and storms were two common perils that claimed ships over the last several centuries.  Some sites are known, but others remain uncharted.  By examining bathymetric maps of the sea floor, combined with historic and modern charts and data on characteristics of the bottom type (reef, pavement, etc.), patterns can be detected, and models created to predict where other sites may exist.  

Scythian Burial Mounds: Report

The process of modeling the spatial distribution of Scythian burial mounds in Tuekta, Siberia, focuses on the relationship of the mounds to their environment.  First, a DEM of the region was clipped to the study area, and three secondary surfaces were created from it – slope, aspect, and elevation.  Slope was reclassified to weigh more heavily in favor of flat or gently sloping terrain.  Aspect was reclassified to favor southern facing areas.  Elevation was reclassified to emphasize areas with a similar elevation range.  The Tuekta mounds sites were digitized into a point shapefile to be used in the OLS regression analysis, and then an additional 100 random points were created.  These were merged, creating a new shapefile for the dependent variable – the presence or absence of sites.  The data was edited to reflect the slope, aspect, and elevation of each point, and to populate each point with XY data.  The data could now be used in the OLS Regression.

The OLS Regression was done on the model to identify trends and to see relationships between the dependent and explanatory variables. The results showed that all three explanatory variables were contributing to the model (Aspect = 0.088971, Slope = 0.130735, Elevation = 0.581643).  These positive values are expected with models using reclassified values that weight the higher numbers to aspects of the landscape that are favorable for site location. The Adjusted R-squared value was .718372, indicating a high percentage of site presence or absence would be predicted using a model with these variables. The Spatial Autocorrelation showed the clustering of sites.  The p-value of 0.000 indicates a 100% confidence level that the patterning is completely non-random, and that there is a spatial variable influencing the data.  The z-score of 13.3590348071 indicates that there is a less than 1% likelihood that this clustered pattern could be the result of random chance.  There are areas where the model has under-predicted the presence or absence of sites.  These include areas on the edges of the valleys in areas of transition to higher elevation and increasing slope, and along the valley floor in the eastern part of the study area. Other variables could be included to further refine the model, such as geological features, soil type, and vegetation.  Proximity to rivers could also be included as a variable.  This model is limited because of its use of only three variables, so the addition of these others would make it more precise, but clearly it shows the significance of these variables.  A Geographically Weighted Regression model is another option, because it is suited to a regional scale and clustered data, while the OLS regression model is better for non-clustered data.  However, the dataset used here was not large enough for GWR Regression.  

Scythian Mounds: Analysis

The Tuekta mounds are located in a long valley surrounded by mountains, as can be seen in the contour map and the reclassified Elevation map.  The Slope map is weighted so that the greatest weight (4) goes to  flat or gently sloping terrain, found on the valley floor.  The Aspect map highlights areas in bright green that face in a southerly direction.  Analysis of these maps reveals a pattern of location characteristics favored by the builders of the mounds.  This information can be used to better understand the experience of those who built the mounds and for people for whom the mounds were part of the landscape.  It can also help predict where other mounds may be located.

Scythian Mounds: Analysis

The Tuekta mounds are located in a long valley surrounded by mountains, as can be seen in the contour map and the reclassified Elevation map.  The Slope map is weighted so that the greatest weight (4) goes to  flat or gently sloping terrain, found on the valley floor.  The Aspect map highlights areas in bright green that face in a southerly direction.  Analysis of these maps reveals a pattern of location characteristics favored by the builders of the mounds.  This information can be used to better understand the experience of those who built the mounds and for people for whom the mounds were part of the landscape.  It can also help predict where other mounds may be located.

Scythian Burial Mounds Part I

These Scythian burial mounds are located in a valley in Siberia.  A mosaic DEM illustrates the variation in elevation in this region.  By examining the characteristics of the landscape, it may be possible to understand the reasons why the burial mounds were located here.

The inset map shows a georeferenced image of the mounds, overlaid on a DEM of the study area.

Predictive Modeling

Predictive modeling can be a useful tool for archaeologists trying to narrow down likely areas where cultural resources may be found.  Environmental factors such as proximity to water, soil and vegetation types, elevation, slope, and aspect (which direction a slope faces) are understood to have some value in predicting where settlement and activity occurred in the past.  This type of information is becoming increasingly easy to acquire, and the resulting models can be used to narrow down areas where field survey is more likely to result in finding cultural remains.  The map above shows a weighted overlay map indicating areas where there is a high, medium and low probability of finding archaeological material.  By choosing specific variables, and by weighting them according to their relative importance, archaeologists can guide field survey, saving money and time.  However, this technique has been criticized for being too focused on environmental variables, and not taking into account cultural factors that would likely impact choices made by those who settled in a particular area.  Predictive modeling is currently used in CRM work most often.  When used in an academic situation, it is critical that substantial ground-truthing and field survey are conducted in addition to the use of the predictive model.

Finding Angkor's Hidden Sites

While the use of satellite images for identifying potential archaeological sites is successful in several parts of the world, it is less so in others.  In Cambodia, where many monumental stone structures are hidden in the dense tropical vegetation, and where land mines and unexploded bombs pose a threat to those conducting ground survey, it is possible to use training samples to classify images for the identification of previously unknown sites.  However, it is problematic.  This map shows a supervised classification of the area surrounding the core of Angkor's monumental architecture.  The classification does identify several areas where potential sites may be located, and indeed one area, Phnom Kulen, has recently been identified as a previously undiscovered urban landscape associated with early Angkorian settlement (Evans et al, 2013).  The classification is not good at distinguishing the stone monuments from other classes, such as dense forest and water.  However, patterning that points to hydraulic features and geometric lines associated with Angkorian architectures is visible.

It appears that the use of lidar in this situation is far superior to the results that can be achieved using Landsat imagery, as seen here.  The following website and article provide additional information.

http://angkorlidar.org/publications/

Evans, D. H., R. J. Fletcher, C. Pottier, J.-B. Chevance, D. Soutif, B. S. Tan, S. Im, D. Ea, T. Tin, S. Kim, C. Cromarty, S. De Greef, K. Hanus, P. Bâty, R. Kuszinger, I. Shimoda and G. Boornazian. 2013. “Uncovering archaeological landscapes at Angkor using lidar,” Proceedings of the National Academy of Sciences of the United States of America 110: 12595-12600

Band Combinations, Training Samples, and Supervised Classification

Different band combinations can be used to bring out various characteristics in the environment.  The top two maps here use two different combinations of Landsat satellite imagery.  The NVDI map uses a False Color image composite, which joins bands 2,3, and 4.  When the NVDI process is added, the negative ouputs show up as red.  Bare rocks, sand, and snow have an output close to zero, and those with a higher measure of "greenness" have higher values.  This allows dense vegetation, like tropical rainforest, to show up clearly.  The combination of bands 4,5, and 1 in the second map is used to differentiate vegetation that is stressed and sparse with healthy vegetation.  

The map at the bottom shows a map resulting from a supervised classification,  First a training signature file was created by drawing polygons around samples of each class.  Known Mayan pyramid sites were used to create this class's sample.  The classification shows the locations of possible new sites, and creates a tool to be used in survey and ground-truthing.

Maya Pyramids Part I

This series of maps shows several different band combinations that can be useful in locating potential sites.  Landsat imagery from the USGS can be viewed in ways which reveal certain characteristics depending on the combination of bands.  ArcMap Image Analysis and Processing tools are used to create combinations suitable for different purposes.

For example, the Landsat Band 8 is a high resolution panchomatic view, used here by itself to show the location of the Mirador pyramid.  It can also be used to create a sharper composite image, using the Pan-sharpening option. 

The Natural Color map shows a band combination that displays a color image to visualize data like a color photo using Bands 1,2 and 3, which show visible light.  Band 1 distinguishes soil from vegetation, Band 2 is useful for showing which plants are stressed, and which are more healthy, and Band 3 is also used to highlight vegetation.

The False Color map uses Bands 2,3,and 4, adding the Near Infared which emphasizes biomass content.  This band combination is most useful for the dense jungle where the Mirador pyramid is located because the red band (#3) indicates areas where chlorophyll is being absorbed, and the NIR band (#4) indicates areas of high refelectivity of plant materials.  The NDVI(Normalied Difference Vegitation Index) tool is used to show relative biomass in the image.