Oaxaca Survey Grids - Georeferencing and Digitizing

  






































Paper maps and documents are often the primary sources of historical data and site reports.  This week we focused on how to take this type of data and incorporate it into ArcMap documents.  Being able to view and examine landscape data in this way is an important new method for analysis and interpretation in settlement pattern studies.

Maps and site data from a published report of a survey done in Oaxaca, Mexico were available as scanned files in .pdg format.  The maps were made into jpegs and then georeferenced to a topographic basemap.  Then they were digitized: grid squares, soil maps, and site plan maps.  By combining these map layers, archaeologists can make inferences about settlement patterns and how these relate to environmental conditions.

The last step involved joining data in an Excel spreadsheet from the site report with the newly created shapefiles showing the sites in each grid.  In this case, the resulting data shows the low population figures for the period IIIB sites in grid N9E8,

Looking at this map, it is difficult to make a guess about whether or not population size was a function of land type, since only 3 sites are involved.

Georectifying James Cook's 1785 Map of Macao

Historical maps can be fantastic sources of information, but they are rarely accurate enough to layer over current images without going through the process of georeferencing and rectifying.  This map shows a georectified map of Macao in 1785 layered over a recent image.

To do this, there must be at least 2 layers, and control points are added between the historic map and the modern one, using points that can be identified on both maps.  In this lab, we also added a topographic map to better identify specific locations.  As more points are added, the historic map shifts in location so that the 2 maps line up as much as possible.  In this case, 10 points were added before the map was rectified using a type of sampling called Cubic Convolution, which is used for data like images and photographs.

The purpose here is to highlight the changes in landscape and environment over time.  Other data can be examined using this method, such as population trends and settlement patterns. It can be used at a variety of scales, from neighborhoods and cities, to larger regions.

GIS Methods for Incorporating Historic Records and Documents

Historic records and documents can be incorporated into GIS maps, which is incredibly useful for examining and interpreting the past. The example above combines information from a variety of sources - a historic map from the 1890's-1920's, a user-created map of the Freedom Trail from ArcGIS Online, census data, an image, and informational text.  In addition, hyperlinks were created so that when the user clicks on Paul Revere's House using the Hyperlink tool, they can access the census and see the image.  An HTML pop-up was added to the map, so when you open the link it takes the user to the Google Maps Street View scene in front of the house.  It's easy to see how much information can be pulled together this way.

This is a phenomenal tool for organizing, visualizing, and interpreting a huge variety of historical data. Even more can be learned from using it in tandem with data in other fields.  Readings this week focused on two methods for using these methods to identify and document sites that were threatened, and to prioritize urgency and inform policy decisions.  In Peru, aerial photographs from the 1940's and Google Earth images spanning more recent years were used to document looting, and to identify additional cemeteries.  It will continue to be used to monitor sites and to help policy makers decide how to manage the resources to prevent more looting.  In Georgia, GIS methods were used to determine what areas were threatened by erosion, what sites existed there, and which ones were most at risk.  These are just two examples.  The potential for these methods of analysis in archaeology is limitless.

Mapping Jordanian Archaeological Sites

This map of Jordanian Archaeological Sites was made by creating a new file database, opening a new map document, adding a basemap, and creating points to show the location of sites.  The site of Petra was created by creating a new feature class and adding fields for lat/long and notes describing the site.  After the shapefile was added to the map, it was opened for editing so that the lat/long and description information could be manually added.  To do this, you can either click to add the point to the map, or you can enter the "absolute XY"  in decimal degrees.  This is fine if you only have a limited number of features. Another way, better for large numbers of features, is to add points by importing the coordinates from an Excel file. Location information found using the META website was added to the database, and then a new feature class was created from the XY table of the Excel spreadsheet. The output was a new feature class of archaeological sites, saved to my geodatabase and added to the map as a new layer.

Many of the sites shown here have been heavily looted and damaged.  To help reduce these incidents, a system was set up to provide information about the location and current status of each site.  The database, called the Middle Eastern Geodatabase for Antiquities(MEGA), is open-source, and uses Google Earth for imagery, so it is free and user-friendly.  There is even training available.  Users are granted varying degrees of access - Guests have limited query access, and at the top end, Administrators have the ability to add to and edit the information.  By allowing such open access, the greatest number of people can be involved in helping to preserve archaeological resources.  Logistically, it is much more readily updated than if information was gathered by field survey or using aerial photography, both of which are slower and also more expensive methods.

The benefits of having such open access must be weighed against the risks of having the data available to people who may misuse it.  Site locations are seen in the map above, and published material is often quite specific about not only location, but also the numbers and types of material remains which could become the object of looting.  

Decisions about granting public access to data is guided by ethical principles adopted by archaeological organizations including the SAA, AAA, AIA, RPA and SHA that state the responsibilities to the profession, the stakeholders, and the integrity and preservation of resources.   These ethical standards are helpful as archaeologists try to balance the different points of view of various stakeholders who may be working with a different set of ethical standards and goals.

Using Clip and Query to Examine and Interpret Historical Data

 

This lab focused on using the clip and query functions in ArcMap to answer questions about the Chicago Fire of 1871 - where it started, the extent of the fire's destruction, the direction the fire travelled and where the wind was coming from, and how Chicago grew after the fire in terms of geographic expansion, the building of new landmarks, and the restructuring of some of the wards, especially in the fire zone. 

 

Another useful skill we learned this week was how to take an Excel file with Lat./Long. information for all the landmarks in the Chicago area, and create a new shapefile showing their locations as points on the map. That file can then be used to select for various attributes, such as address or year built.

 

More data is available within these layers that would be useful to answer different questions - clearly these tools will be very useful in many situations.

Supervised Classification

This map shows a supervised classification to identify land use and land cover in Germantown, MD for the purposes of tracking changes in population and land consumption.  An image of the area was used to create signatures to be used in classification, and based on those signatures, the image was classified and the signatures merged into 8 classes.  The area for each class was calculated so that, when compared with images from previous or later years, the changes can be calculated.  

Creating signatures requires paying attention to what type of process you use for each signature.  Sometimes it is better to draw a polygon, other times Growing a Seed is the preferred method.  In general, the second method seemed to be best, although for the Fallow Field 1 signature, the polygon was a good choice.  By looking at histograms of each signature, it was possible to see if there was a good enough sample of pixels, and if there was separation in the spectral signature between classes.  By using the Mean Plot it was possible to determine the best band combination to create separation between each spectral signature.

With more time, even better signatures could have been created, but the distance image shows there were not too many poorly classified areas.

Unsupervised Classification

This is an example of unsupervised classification. Here a Landsat image was processed so that clusters of similar pixels were grouped as classes, and then those classes were matched to various feature types in the original image.  The result is a thematic map representing, in this case, 5 classes.  The classes can be analyzed to answer questions; in this case the question was what percentage of the image represented impermeable versus permeable surface.  The necessary information was contained in the Attribute table for the newly created classification image.  One issue that came up was that some pixels were classified in more than one group - for example there were green grass pixels on the roofs of some buildings.  The solution was to create a "Mixed" class that would allow for those situations where classification into a clearly identified group was not possible.