Peter K. Bol

WorldMap – a geospatial framework for collaborative research

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 WorldMaps 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.

On the Cyberinfrastructure for GIS-Enabled Historiography

From a historians 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.

Quantitative historical analysis uncovers a single dimension of complexity that structures global variation in human social organization

Significance Do human societies from around the world exhibit similarities in the way that they are structured and show commonalities in the ways that they have evolved? To address these long-standing questions, we constructed a database of historical and archaeological information from 30 regions around the world over the last 10,000 years. Our analyses revealed that characteristics, such as social scale, economy, features of governance, and information systems, show strong evolutionary relationships with each other and that complexity of a society across different world regions can be meaningfully measured using a single principal component of variation. Our findings highlight the power of the sciences and humanities working together to rigorously test hypotheses about general rules that may have shaped human history. Do human societies from around the world exhibit similarities in the way that they are structured, and show commonalities in the ways that they have evolved? These are long-standing questions that have proven difficult to answer. To test between competing hypotheses, we constructed a massive repository of historical and archaeological information known as “Seshat: Global History Databank.” We systematically coded data on 414 societies from 30 regions around the world spanning the last 10,000 years. We were able to capture information on 51 variables reflecting nine characteristics of human societies, such as social scale, economy, features of governance, and information systems. Our analyses revealed that these different characteristics show strong relationships with each other and that a single principal component captures around three-quarters of the observed variation. Furthermore, we found that different characteristics of social complexity are highly predictable across different world regions. These results suggest that key aspects of social organization are functionally related and do indeed coevolve in predictable ways. Our findings highlight the power of the sciences and humanities working together to rigorously test hypotheses about general rules that may have shaped human history.

GIS, prosopography and history

This study of the early spread of Neo-Confucianism as an intellectual–social movement in southern China in the twelfth century applies geospatial analysis to a prosopographical study of the social networks of leading figures. By analyzing the spatial distribution of intellectual networks, we see that Neo-Confucianism was most successful in areas that had strong traditions of investment in education but that were generally marginal to the commercial economy of the times. The research draws on data from the China Biographical Database, which include data on the social associations, kinship, careers and addresses of over 40,000 Song dynasty figures and the China Historical GIS, a times-series database of administrative units from 221 BCE to 1911 CE. Intellectual history traditionally has focused on the transmission of ideas; network and spatial analysis helps explain why some areas were more receptive to certain ideas than others.

On the unsupervised analysis of domain-specific Chinese texts

Significance We propose top-down word discovery and segmentation (TopWORDS), an unsupervised tool for Chinese word (and phrase) discovery, word ranking, and text segmentation. We show that pipelines formed by combining TopWORDS with context analysis tools can help researchers quickly gain insights into new types of texts without training and recover almost all interesting features of the text that a well-trained supervised method can find. 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.

Embracing Geographic Analysis Beyond Geography: Harvard's Center for Geographic Analysis Enters 5th Year

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.

Mining local gazetteers of literary Chinese with CRF and pattern based methods for biographical information in Chinese history

Person names and location names are essential building blocks for identifying events and social networks in historical documents that were written in literary Chinese. We take the lead to explore the research on algorithmically recognizing named entities in literary Chinese for historical studies with language-model based and conditional-random-field based methods, and extend our work to mining the document structures in historical documents. Practical evaluations were conducted with texts that were extracted from more than 220 volumes of local gazetteers (Difangzhi,

ON SHAO YONG’S METHOD FOR OBSERVING THINGS*

). Difangzhi is a huge and the single most important collection that contains information about officers who served in local government in Chinese history. Our methods performed very well on these realistic tests. Thousands of names and addresses were identified from the texts. A good portion of the extracted names match the biographical information currently recorded in the China Biographical Database (CBDB) of Harvard University, and many others can be verified by historians and will become as new additions to CBDB.1