Article: Big Data and Ecological History: The Compilation and Database Construction of the Historical Sources of Chinese Disaster History (2015)

Xia, Mingfang. 2015. “Big Data and Ecological History: The Compilation and Database Construction of the Historical Sources of Chinese Disaster History in an Information Age.” The Qing History Journal 2: 67-82. (夏明方. 2015. 大数据与生态史：中国灾害史料整理与数据库建设. 《清史研究》2: 67-82.)

Historian Xia Mingfang summarizes the range of historical data on disasters in China, focusing on recent efforts and challenges in documenting, standardizing, and digitizing data culled from physical archives. Using the efforts by Renmin University, Beijing Normal University, Nankai University, and several other Chinese universities in Qing disaster history as exemplar, Xia proposes that the way forward is to build a well-validated, multi-source, multi-disciplinary and publicly accessible database that can capitalize on technological advances in data analytics.

Xia’s article is a valuable resource for several reasons. It enumerates the range of data types (e.g., meteorological, types of disaster, geographical) and sources (e.g., imperial memorials, local gazettes, inscriptions on tablets). It also clearly lays out the three main perspectives on studying disasters in China, and articulates their stakes.

Previous perspectives (the “heavenly mandate view” [天命观 tianming guan] and the “scientific view” [科学观 kexue guan]) privileged the supernatural and physical sciences, respectively. In contrast, Xia argues that the current “ecological view” (生态观 shengtai guan) takes into account both the natural and the social environments in which disasters are produced and unfold. This view, he argues, underscores the value and contribution that social sciences and the humanities can bring to bear on understanding disasters in China.

Since the article stops short of defining “big data,” it may be useful for students to take up the task of elaborating on Xia’s thesis by investigating how researchers can productively harness the features of big data analytics. These include volume (number of records), velocity (for example, the speed at which weather data are created), and variety (types of records, such as meteorological reports, eye witness accounts, geospatial data).

Wee-Kiat Lim, Nanyang Technological University