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.

GIS at home

Like many other people involved in this GIS Certificate Program, I have explained what GIS is to my children and friends.  I have used examples from watching the local weather forecast to finding the nearest movie theater.  I have helped my boys with homework in social studies and science by providing resources that use GIS, and I explain how the maps were made or how they can be used to answer questions.  Often they get back from school and ask me what I've been doing, and I show them.    So, in this way it is "GIS DAY" every day at my house, it seems.  Probably the thing that interests them the most is 3D modeling.  I showed them some examples I found on YouTube and the lesson we had at the beginning of the first semester.  My older one has expressed some interest in making and using maps and Story Tours in ArcGIS Online for some of his school projects, but it hasn't happened yet.  

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.