Recent & Regular Courses

Anthropology is at root the study of human variation, and archaeology might be characterized as the study of human variation in the past: how much diversity of human behavior/beliefs was there at any given time and place, and how different was that from the present? Both practically and theoretically, a key component of that variation is spatial variation: for example, in human behavior, material culture, language, demographic attributes, or resources. Archaeological method and theory have to grapple with recording and analyzing spatial variation. As a result, geospatial technologies generally and Geographic Information Systems (GIS) in particular are increasingly recognized as critical tools. This course examines the use of GIS for management and analysis of geospatial data in anthropological contexts, both experimenting with methods and critically examining case studies. It covers both methodological approaches (collecting data in the field and finding publicly-available data as well as integrating and querying it in a GIS) and theoretical implications (the nature of spatial data as well as scales and topics of analysis). It also examines selected case studies and considers critiques that have been leveled at GIS in particular and spatial analysis in general.

Digital tools have become as fundamental to archaeology as the trowel, and are today integral to archaeological practice and knowledge production. Many questions still surround the digital turn in archaeology, however.

This course introduces students to the basic quantitative methods required to describe and analyze archaeological data. Each week, students will be introduced to a new statistical technique and asked to use that technique in order to solve a small research problem. Because many of the research challenges archaeologists face have to do with acquiring and managing data (especially legacy data) and then using it in argumentation, this course will emphasize not only statistical methods, but also 1) how to construct and critically evaluate arguments grounded in quantitative data, and 2) how to explore, analyze, and display data in the open-source R statistical environment.