GIS 5265 Module 10: Final Project

For our final project of the course, we were to either choose an independent project or work with the Oaxaca Valley data from module 6 for further catchment analyses.  As I could not find a suitable topic (or at least one for which data were readily available), I went with the Oaxaca Valley data.  Looking through the data provided for the northern portion of the valley (grid rows N11 through N14), there appeared to be oscillations in population density from the south to the north and back again through the time periods.  My project was to calculate maize consumption estimates based on population and compare this with estimated maize yields based on 1 km catchments.  

To create the Thiessen polygons as described in the module video for catchment analysis, I set up a model in ModelBuilder to create the final 1 km catchment buffer using the Buffer, Feature to Point, Thiessen Polygons, and Intersect tools.  I also used the model to create additional buffers of 2 km and 5 km, although I did not end up making use of them for this project.

The population data for my collection units for each period was acquired by joining the Excel sheets provided in our module 6 data.  Using the high and low consumption estimates provided in our readings (.16 metric tons and .29 metric tons respectively), I added four fields to the attribute tables for each period and used the Field Calculator to compute consumption estimates for each collection unit.

Arriving at maize yield estimates for the 1 km catchments was also a multi-step process.  The Identity tool was used to create shapefiles for each land type corresponding to the catchment areas.  A new field as added to calculate the area in hectares (since the estimates used in our readings are based on hectares).  Based upon data provided by Anne Kirkby (1973, The use of land and water resources in the past and present Valley of Oaxaca, Mexico, University of Michigan Museum of Anthropology Memoir 5), the yields of maize through time were multiplied by the area of each land type (again using the Field Calculator) to arrive at potential yields.

Microsoft Excel's Data Analysis correlation tool was used to determine if there was a correlation between population consumption estimates and maize yield estimates.  Unfortunately, no such correlation was seen.  The resulting data does show an oscillation of population from south to north and back, but significant variables remain unknown.  We do not know the fallowing strategy that was applied, nor do we know the potential degradation in catchment yields as the population grew and cultivation was intensified.  We also do not know the details of rainfall variation.  The majority of the study area (excepting one grid square) is considered to feature low average rainfall (below 700 mm), and the potential yields of the land types in the region (Type II and Type III) are highly susceptible to precipitation variation.  While this is suggestive as a potential cause for population movement through time, the lack of more detailed rainfall data prevents me from offering more than a mere suggestion.

GIS 5265 Module 9: Remote Sensing

I was already looking forward to working with remotely sensed data next semester, so I found this module an enjoyable introduction.  Our task this week was to download a DEM of the Cahokia region and produce two raster classifications of that image.  The classification process divides the target image into categories that, ideally, reflect categories of land cover on the ground.  An unsupervised classification creates the classified raster automatically, with the user defining the number of categories and other limited variables.  A supervised classification, on the other hand, bases the output classified raster on a classification scheme provided by the user.  In this case, we created a new point shapefile composed of 30 points placed within our intended land cover categories.  This shapefile functioned as the input for the "Create Signatures" tool to create the classification file used with the "Maximum Likelihood Classification" tool.  If we were successful in our placement of control points, the output classified raster would expand the classification to cover the image.

Both my unsupervised and supervised classified rasters succeeded in some areas of classification while failing in others.  The biggest problem was the expansion of some categories to include areas I would rather be classified differently.  The unsupervised classification captured a wide range of land cover types in the two categories representing the most reflective objects, notably including Monk's Mound.  Perhaps additional classes would have teased out light grass, bare ground and the mound from roads and buildings.

I attempted to create a category specifically for Monk's Mound in the supervised classification; unfortunately, as can be seen above, the mound was still mostly classified along with roads, buildings, and light grass.  This isn't completely surprising since the mound is covered in grass, but I had hoped to capture some difference with the supervised classification.  As in the unsupervised classification, however, Monk's Mound does show up quite clearly as a distinct shape.  More tweaking of the input classification scheme would likely lead to a more accurate output.  Even as they are, both classified rasters could be useful if one were looking for new sites or interested in an overview of the land cover around Cahokia.

GIS 5265 Module 8: 3D Modeling

This week introduced us to the use of ArcScene for creating and displaying archaeological data.  We were given a study area and shovel test data; from this we created three-dimensional layouts showing shovel test depth as well as the relative depths of three soil strata.  We went further by creating interpolated surfaces from the shovel test data depicting the surfaces of each stratum (above).  These surfaces gave us a better idea of the trajectory of the strata than the shovel tests alone.  However, to make the differences in depth stand out, we used a high vertical exaggeration value.
An additional task for grad students was to create another three-dimensional scene with a cross-section of strata depth data derived from the interpolated raster surfaces.  For both layouts, I went with textual scale information rather than a visual scale due to the distortion of the 3D image as well as the vertical exaggeration.

We were also introduced to the Fly tool of ArcScene.  Below is a recording of a fly-through of the shovel test data.  While I can imagine the Fly tool would not be something used on every project, it does allow the user to view data from different perspectives; recording a fly-through gives others the opportunity for multiple perspectives as well.

Fly-through of shovel tests.


While not part of the assignment, a note in the lab instructions briefly described how to incorporate elevation data into ArcScene.  Although I did not have much time to experiment, I was able to successfully incorporate elevation data from a DEM (downloaded from USGS.gov) into our shovel test data and create interpolated surfaces.  For this image I set the vertical exaggeration to 10 rather than 20.
 

GIS 5265 Module 7: Surface Interpolation

The main focus of this week's module was to experiment with a variety of surface interpolation methods in interpreting artifact and population density.  Surface interpolation produces a continuous surface from point data.  This could be useful if, as in our readings, one were attempting to identify large-scale communities or social interaction over distances that may not be obvious from point data.  In the above maps of Volcan Baru, for example, the surface interpolations were derived from shovel test point data, specifically the number of artifacts from each shovel test.  These interpolations may give us some idea of the social interaction of what may at first appear to be separate sites.

In addition to surface interpolation, we also were required to convert text and AutoCAD data into more GIS friendly formats.  This is something GIS specialists deal with quite often, and I was glad to get some practice working with them.

Graduate students were also required to do some more experimentation with the inverse distance weighted interpolation method.  By doing so, we saw first hand that tweaking the parameters of interpolation methods can have a significant effect on the outcome.  By changing the power parameter, the surface interpolation goes from extremely smooth with little detail (power of 0.5) to being little better than the original point data (power of 10).  The default power of 2 seemed the right balance to see potential community interactions without stretching the data too far.

GIS 5265 Module 6: Digitizing and Editing Large Datasets

Module 6, spread out over two weeks, was quite a challenging and time-consuming task.  As in the previous module, we began by georeferencing a map image to an ArcMap basemap.  In this case, the image as a topographic map with a survey grid of the Oaxaca Valley, Mexico.  This was a frustratingly difficult task, as common points of reference between the image and basemap were difficult to locate.  By finding a couple of possible control points and updating the image's position as I went along, I was able to get a reasonably well-positioned image.  I then digitized by assigned three grid squares (first image above) and georeferenced the corresponding collection and land type maps to them.

The next task was to create new shapefiles for the collection units and land type.  While the land type shapefile was easy to complete (especially since I only have Type III land in my squares), digitizing the collection units was more tedious.  Square N9E8 in particular took some time to digitize properly.  The task was made more difficult by the presence of multiple units with separate components and the asymmetrical overlapping of units from difference periods.  As an example, in the northern portion of square N9E8 there is unit V-58, composed of four separate sections.  I thus created a multipart polygon to represent the unit as a single feature.  However, the westernmost circular component of V-58 also represents unit IIIA-47.  Therefore, I needed to create a new feature exactly corresponding to this component of V-58 (using the 'trace' editing tool) to represent unit IIIA-47.  I also had to interpret the labeling scheme for the dense cluster of units in the center of square N9E8.

Labeling the collection units of N9E8 was also problematic.  The complex combination of different time periods for the same components or portions of components effectively ruled out a color symbology.  Replicating the original labeling scheme was a possibility, but I decided to attempt a simpler scheme of indicator lines and arrows when necessary.  I used Illustrator to create the lines and arrows as well as to fine-tune label placement.

The map below represents an extra task graduate student completed.  We were given an Excel table with data on sites in the Oaxaca Valley.  By using the grid square column, we could isolate the sites located in our squares.  We were also to only use the sites dating to the IIIB Period.  I created a new table containing only the sites dating to the IIIB Period that were in my grids; this resulted in only two sites.  I then joined the table to my collection units layer in ArcMap based on unit name.  My initial join failed due to column names that were incompatible with ArcMap (mostly the presence of periods).  After editing the column names accordingly, the join went smoothly.  I then symbolized the two sites based on the conservative population estimate.  Unfortunately, having only two sites makes the map a bit uninteresting.  It also doesn't allow me to say much in way of interpretation.  Both the sites are relatively small and are located in the same land type (10% arable, not symbolized here).

GIS 5265 Module 5: Georeferencing Historic Maps

This week's assignment continued our investigation and incorporation of historic data into GIS, but this time the data are maps.  We began by exploring the David Rumsey map collection (http://www.davidrumsey.com) and downloading a historic map of Macao based on surveys conducted by Cook and Bligh in the late 18th century.  We then georeferenced this historic map based on imagery and topographic baselayers downloaded from ESRI.  This was done by connecting control points from the historic map to corresponding locations on the baselayers.  This was difficult for a couple of reasons. First, the historic map is of an unknown projection and clearly has errors.  Second, the modern geography of Macao has changed much as a result of urbanization and land reclamation.  It is thus difficult to know where best to place control points.

After placing thirteen control points and switching the transformation from first order polynomial to spline, I began to see a closer correspondence between the historic map and the modern baselayers.  I was tempted to place more control points in the western portion of the historic map after seeing how the spline transformation altered it, but I believe Macao itself, the area of primary interest, is reasonably well-aligned.

This was an enjoyable exercise that made me more comfortable with georeferencing.  I look forward to using these skills in the future.

GIS 5265 Module 4: Historic Records and Documents

Module 4 of GIS Applications in Archaeology introduced us to searching for and incorporating historic documents in ArcMap for research and layout design.  Of course, this includes historic maps and aerial imagery, but it also includes other types of data (such as the census document in the map above).  One source we explored was Ancestry.com, from which the image of the 1790 census was acquired.  While searching for Paul Revere and acquiring the census were the only requirements for using Ancestry.com, I have begun a sample family tree using the site and am amazed at the wealth of data available.  I may not continue with a paid subscription at this time, but I will keep it in mind for the future.

The lab assignment also explored the use of the Swipe, Hyperlink, and HTML pop-up tools.  The Swipe tool in particular is useful for comparing maps of the same area from different times, as we did here with our Boston maps.  The Hyperlink tool is used to link documents (such as the census and portrait images above) to features in a map.  The HTML pop-up tool can be used to display attribute data for features as well as link to a website in a pop-up window.  Unfortunately, I experienced some issues with this tool.  Although the browser pop-up window did appear when I used the tool, the target of the link (a Google Map Streetview of the Revere House) never loaded.  Instead, I was consistently given a script error warning and a pop-up window of a Google Search for the Revere House.  The provided link worked correctly when input in a browser, so I imagine there is a setting in my ArcMap somewhere that is preventing the link from working properly.

To finalize the above map, I exported it from ArcMap into Adobe Illustrator for fine-tuning.  I was skeptical at first in last semester's Cartography class that I would use Illustrator much, but I have been converted on its superior image editing capability.  In particular, the drop-shadows created in Illustrator look much better than those created in ArcMap.

This was a fun lab that expanded our idea of historic data beyond maps and aerial imagery.

GIS 5265 Module 3: Ethics in Archaeological GIS

This week's assignment aimed to place GIS data and research within a discussion of ethics in archaeological research.  How do we conduct research that includes (or is primarily) spatial data when site location is a sensitive issue?  The assignment began with an exploration of the Middle Eastern Geodatabase for Antiquities (MEGA, http://www.megajordan.org/Map), an online geodatabase of archaeological sites in Jordan.  With different levels of access depending on user category, the project aims to facilitate academic research as well as provide information for conservationists and the public to aid in protecting the sites.

The actual lab portion of this week's assignment was largely a review of how to create and use a geodatabase in ArcMap, but it added a discussion of the security benefits of geodatabases over isolated shapefiles in multiple folders.  By collecting all data files in a single database, one has more control over who has access to the data therein.  It is much more difficult to control and protect data spread over multiple locations.

This module also explored methods of creating new point feature classes in our geodatabase.  In the map above, Petra was added as its own layer by directly entering its latitude and longitude coordinates in an ArcMap edit session.  The other sites were imported from an Excel spreadsheet containing each site's name, description, and coordinates.  In completing the above map, my main creative contribution was converting all site labels to annotations stored in the map (after setting the font and halo) for better placement.  I also created a definition query to remove the Petra location that was included in the Excel spreadsheet.

The ethics of sharing archaeological data is a big topic without clear answers.  On the one hand, we would like to educate the public as much as possible, hoping that through education we can minimize the desire to engage in activities that will damage sites.  Unfortunately, there will likely always be a small number of people willing to irreparably damage sites for potential financial gain (or simply to damage sites through inconsiderate land use).  That is why I think the route taken by the State Historic Preservation Offices (at least the ones I am familiar with) in restricting access to detailed location data is prudent.

GIS 5265 Module 2: Chicago Fire of 1871, Queries and Clipping

The first lab of GIS Applications in Archaeology used the topic of the Chicago fire of 1871 to review ArcMap tools that will be important for future projects.  The map above, while relatively simple, conveys multiple layers of information.  We see the geographic footprint of Chicago in 1871 and 1890, thus getting a sense of how quickly the city grew.  We also see the origin and extent of the fire; from this we can see the effects of the wind on the spread of the fire.  Finally, the location of currently extent landmarks built before and after the fire are depicted, showing the destruction caused by the fire as well as the rapid rebuilding in its aftermath.  To produce the final map, the 'clipping' and 'select by attributes' tools were used and the layer selections exported as new layers.  While an inset map was not required, I added one to focus on the landmarks built before the fire; that they are clustered together makes them fit well in an inset map and allows for the main map to remain uncluttered from their labels.

This module was a good refresher and introduction to the course.  The simple tools and techniques of this module could be used for a wide variety of research questions.  I look forward to the next modules and learning more of how to apply GIS to archaeological research.