Person:

Bol, Peter

From a historian's perspective, the use of GIScience and technology in the study of history holds the promise of an integration of historical and geographic modes of analysis. The national geographic information systems (GIS) that provide extensive coverage of changes in administrative structures over time provide important support for GIS-enabled historiography. Other parts of the cyberinfrastructure necessary to support collaborative research in a digital environment are now beginning to emerge, but a world-historical gazetteer, an essential tool for linking historical data to mapped places, has yet to be developed. 就史学者的角度而言, 在历史研究中运用地理信息科学与技术, 具有整合历史与地理分析模式的前景。全国地理信息系统 (GIS) 广泛地包覆了行政结构随着时间的变迁, 为由地理信息系统促成的历史地理学提供了重要的支援。信息基础建设中, 支援在数码环境中合作研究的其他必要部分目前正逐渐浮现, 但全球性的历史地名词典——一个将历史数据连结至已绘製于地图上之地方的必要工具——仍然尚未建立。 Desde la perspectiva del historiador, el uso de SIGciencia y tecnología en el estudio de la historia es algo prometedor para la integración de los modos de análisis históricos y geográficos. Los sistemas de información geográfica (SIG) nacionales que dan amplia cobertura a los cambios que ocurren a través del tiempo en las estructuras administrativas, proveen apoyo importante a la historiografía en la que los SIG han sido protagónicos. Otras partes de la ciber-infraestructura que se requiere para la investigación colaborativa en un entorno digital están ahora empezando a aparecer, aunque un diccionario histórico-geográfico mundial—herramienta esencial para enlazar los datos históricos con los lugares cartografiados—todavía está por realizarse.

Without a department of geography, Harvard University established the Center for Geographic Analysis (CGA) in 2006 to support research and teaching of all disciplines across the University with emerging geospatial technologies. In the past four and a half years, CGA built an institutional service infrastructure and unleashed an increasing demand on geographic analysis in many fields. CGA services range from helpdesk, project consultation, training, hardware/software administration, community building, to system development and methodology research. Services often start as an application of existing GIS technology, eventually contributing to the study of geographic information science in many ways. As a new generation of students and researchers growing up with Google Earth and the like, their demand for geospatial services will continue to push CGA into new territories.

Interviewer’s Note: Peter K. Bol is the Charles H. Carswell Professor of East Asian Languages and Civilizations at Harvard University. A leading scholar in the field of Chinese intellectual history, the much-published Professor Bol has long been an active figure in academic conferences and research collaborations. Professor Bol’s influential works on Neo-Confucianism, especially the monograph, “This Culture of Ours”: Intellectual Transitions in T’ang and Sung China1 and his recent book, Neo-Confucianism in History,2 present a compelling delineation of the transformation and magnification of intellectuals’ roles from the seventh century to the seventeenth century. Professor Bol is also a distinguished Harvard faculty member, being its first director of the Center for Geographic Analysis, first director of the China Biographical Database project, and department chair from 1997 to 2002. He won the prestigious honor of College Professor in recognition of his dedication to teaching.

I first met Professor Bol in the spring of 1997 when I participated in a symposium he organized for graduate students in the field of premodern China. During the 2008‐09 academic year, I was a visiting professor at Harvard Divinity School and had the opportunity to tour his Center for Geographic Analysis and meet with him from time to time to discuss the CBDB project. I also visited his class and interacted with his students. I sensed that Professor Bol’s vision and passion as a humanist and his dedication to education are very similar to those of Neo-Confucians of the Song-Yuan-Ming eras, save for his acute interest in global politics and, especially, the state of affairs in the US and China. Inspired by this encounter, I made a request for interviewing him for CHR readers. Professor Bol graciously accepted my request. Here is the interview.

WorldMap is a web-based, map-centric data exploration system built on open-source geospatial technology at Harvard University. It is designed to serve collaborative research and teaching, but is also accessible to the general public. This article explains WorldMap's basic functions through several historical research projects, demonstrating its flexible scale (from neighborhood to continent) and diverse research themes (social, political, economic, cultural, infrastructural, etc.). Also shared in this article are our experiences in handling technical and institutional challenges during system development, such as synchronization of software components being developed by multiple organizations; juggling competing priorities for serving individual requests and developing a system that will enable users to support themselves; balancing promotion of the system usage with constraints on infrastructure investment; harnessing volunteered geographic information while managing data quality; as well as protecting copyrights, preserving permanent links and citations, and providing long-term archiving.

With the growing availability of digitized text data both publicly and privately, there is a great need for effective computational tools to automatically extract information from texts. Because the Chinese language differs most significantly from alphabet-based languages in not specifying word boundaries, most existing Chinese text-mining methods require a prespecified vocabulary and/or a large relevant training corpus, which may not be available in some applications. We introduce an unsupervised method, top-down word discovery and segmentation (TopWORDS), for simultaneously discovering and segmenting words and phrases from large volumes of unstructured Chinese texts, and propose ways to order discovered words and conduct higher-level context analyses. TopWORDS is particularly useful for mining online and domain-specific texts where the underlying vocabulary is unknown or the texts of interest differ significantly from available training corpora. When outputs from TopWORDS are fed into context analysis tools such as topic modeling, word embedding, and association pattern finding, the results are as good as or better than that from using outputs of a supervised segmentation method.