EASTASN 105:
Digital China: Using computational methods to illuminate society, politics, and history (EASTASN 205)
Any scholar in the humanities and social sciences who studies China would face a wealth of spatial, temporal, and textual data that is beyond any person's capability to digest in a lifetime. What makes the task all the more daunting is the data's variety, which ranges from historical gazetteers and maps to the news, images, and social media posts of our time. This unprecedented volume of information can, however, also be considered a sealed treasure trove that, once opened, has the potential to illuminate past and present in ways hitherto thought closed for lack of practical methods to give the findings shape and meaning. A major purpose of this course is to present some of these methods and see how they can be applied to various questions that arise in the humanities and social sciences. Note that although the course's title is "Digital China," its methods are also applicable to other non-Western countries. Students whose research interest lies in, say, Southeast Asia or Africa are welcome.nThe course has two components: seminar and workshop. The seminar begins with data collection, is followed by data analysis, and will conclude with data visualization. Data collection covers data types, sources, and structure. Data analysis covers spatial analysis, textual analysis, temporal analysis, and network analysis. Data visualization covers cartography and graphing. Also to be examined in the seminar are research projects that have recently emerged in digital form. We explore whether students can turn some of them, perhaps along a slightly different or narrower path, into research ventures of their own. nnWorkshops, which will run alongside seminars, are intended to provide instruction and hands-on guidance on some essential digital techniques. Instruction covers four areas: (1) database (PostgreSQL) and SQL; (2) web scraping and API data collection (using python); (3) spatial digitizing and geocoding (using ArcGIS); (4) textual analysis and visualization (using Google Bigquery, python, Tensorflow, and ArcGIS). Hands-on guidance should give each student the skills to design a digital project that relates to her or his area of specialization.
Terms: Spr
| Units: 3-5