Module 8 Biscayne Shipwrecks Preparation

For Module 8 we worked on preparing data for a three part Biscayne Bay Shipwrecks lab.  This module is particularly interesting to me, as I am a maritime studies student. I always enjoy seeing what GIS can do in the maritime field. We collected data through several websites including a DEM and a historical chart map that required digitization.  The map includes a modern-day nautical chart, historical chart, and bathymetry data. You will see additional information regarding shipwreck locations and the Biscayne National Park boundary.

Scythian Landscapes - Report

For the final Scythian mound lab we were tasked with creating a final predictive result based on our previous work over the last two weeks.  We looked at spatial distribution of burial mounds in the nearby region of Tuekta, Russia. Three secondary coverages were created for statistical analysis showing elevation, slope, and aspect. Additionally, we combined a shapefile showing known burial mounds and random points then followed that with an OLS regression analysis.

The results for the analysis showed an R-squared value of 0.714636, which suggests that the three surface variables of the analysis account for roughly 71.4% of the sites in the predictive model. The coefficient for elevation was 0.633116, the coefficient for slope was 0.073404, and the coefficient for aspect was 0.076252.  Because the coefficients for each variable are positive and not near zero, indicates the variables are contributing to the model.   Spatial Autocorrelation results show a z-score of 14.811686 and a p-value of 0.00.  A high z-score indicates a normal distribution of data. The p-score indicates that there is less that 1% likelihood that the clustered patern could be the result of random chance.

Because the results show clusters with valleys, it may be beneficial to add a variable showing access to waterways/hydrography. Further analysis could be done to confirm that the model is producing tangible data by adding additional variables, ground-truthing, and perhaps additional regression models.

http://arcgis.com/explorer/?open=195a401048ad4f51a49970fc5e8938ba&extent=9544873.05706802,6549970.40297947,9624367.56646057,6592469.39069318

Scythian Landscapes - Analyze

This week we continued our work on Scythian landscapes.  We used last weeks primary data and expanded the datasets to show secondary coverages as seen above in the contour, reclassified slope, reclassified elevations, reclassified aspect, and georeferenced map with point files showing locations of mounds.

Model 5: Scythian Landscapes

In model 5 we began our work with modeling of scythian landscapes. We were required to create a mosaic of ASTER images as the DEM background for the study area.  I then created an separate data frame showing the georeferenced aerial basemap. This is just the beginning, as we will continue our work on the scythian landscapes for the next few weeks. 

Module 4 - Predictive Modeling

In module 4 we began with a DEM (digital elevation model) of Tangle Lakes in Alaska.  We used information  such as the ice mass and streams and rivers in order to create additional rasters using several tools in arcmap. The end resulted in finding the slop, aspect, dem and final stream/river and compiling them into a weighted overlay with specific % influence.  The map now highlights areas of greater archaeological interest where habitable areas most likely occurred. Areas of greater interest are shown in green, less likely in yellow, and least likely in red.

This lab was most enjoyable and filled with great tools for future mapping. 

Identifying Maya Pyramids: Analysis and Report

For our final week of Identifying Maya Pyramids our lab involved using different visualizations of aerial images and our supervised classification that we created last week. We used the convert map to KML tool that gave us the ability to transfer the file into Google Maps. This was a pretty challenging lab and I ran into a few difficulties.

Identifying Maya Pyramids: Data Analysis

This week we continued our work on El Mirador and surrounding pyramids.  We started with an NDVI (normal difference vegatation index) which highlights biomass base on NIF and IF spectral bands.  The second map shows a composite band using bands 4, 5, 1 RGB. The last map is a supervised classification of the surround area using the training sample manager (TSM) in the image classification tool.  Polygons were drawn and combined to show each respective class. By utilizing the TSM we are able to highlight different classes of the area given. 

I noticed a few glitches in ArcMap, most likely caused by using large files.  There was periods of lagging and a few where I needed to restart.  Additionally, there may be a need to use higher resolution images. I struggled with locating pyramids and found it to be highly pixelated. With the use of the base map and the swipe feature I was able to narrow it down.