48 research outputs found
World-Historical Gazetteer
This project will advance work toward creation of a world-historical gazetteer that will provide comprehensive databases of places throughout the world since 1500 CE, including attention to the range of attributes known for each place. To satisfy the needs of all the large-scale historical data resources now being created, there is need for such a comprehensive and general gazetteer system. The convening of a two-day workshop, including leading figures who have developed gazetteers and the datasets in which they are incorporated, will bring about a research design for this world-historical gazetteer system, which can then be implemented in subsequent work. Four small research tasks concerning services, standards, and content will bring immediate advance toward implementation. The project is organized by the Collaborative for Historical Information and Analysis (CHIA), which has a record in sustaining collaborations for large-scale humanities work
Putting the World in World History
vol. 13, no.
Quantitative historical analysis uncovers a single dimension of complexity that structures global variation in human social organization
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
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Quantitative historical analysis uncovers a single dimension of complexity that structures global variation in human social organization.
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
Quantitative Historical Analysis Uncovers a Single Dimension of Complexity that Structures Global Variation in Human Social Organization
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 âSeshat: Global History Databank.â 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
Geospatial Semantics
Geospatial semantics is a broad field that involves a variety of research
areas. The term semantics refers to the meaning of things, and is in contrast
with the term syntactics. Accordingly, studies on geospatial semantics usually
focus on understanding the meaning of geographic entities as well as their
counterparts in the cognitive and digital world, such as cognitive geographic
concepts and digital gazetteers. Geospatial semantics can also facilitate the
design of geographic information systems (GIS) by enhancing the
interoperability of distributed systems and developing more intelligent
interfaces for user interactions. During the past years, a lot of research has
been conducted, approaching geospatial semantics from different perspectives,
using a variety of methods, and targeting different problems. Meanwhile, the
arrival of big geo data, especially the large amount of unstructured text data
on the Web, and the fast development of natural language processing methods
enable new research directions in geospatial semantics. This chapter,
therefore, provides a systematic review on the existing geospatial semantic
research. Six major research areas are identified and discussed, including
semantic interoperability, digital gazetteers, geographic information
retrieval, geospatial Semantic Web, place semantics, and cognitive geographic
concepts.Comment: Yingjie Hu (2017). Geospatial Semantics. In Bo Huang, Thomas J. Cova,
and Ming-Hsiang Tsou et al. (Eds): Comprehensive Geographic Information
Systems, Elsevier. Oxford, U
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An Introduction to Seshat: Global History Databank
This article introduces the Seshat: Global History Databank, its potential, and its methodology. Seshat is a databank containing vast amounts of quantitative data buttressed by qualitative nuance for a large sample of historical and archaeological polities. Thee sample is global in scope and covers the period from the Neolithic Revolution to the Industrial Revolution. Seshat allows scholars to capture dynamic processes and to test theories about the co-evolution (or not) of social scale and complexity, agriculture, warfare, religion, and any number of such Big Questions. Seshat is rapidly becoming a massive resource for innovative cross-cultural and cross-disciplinary research. Seshat is part of a growing trend to use comparative historical data on a large scale and contributes as such to a growing consilience between the humanities and social sciences. Seshat is underpinned by a robust and transparent workflow to ensure the ever growing dataset is of high quality
Quantitative historical analysis uncovers a single dimension of complexity that structures global variation in human social organization
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 âSeshat: Global History Databank.â 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.This work was supported by a John Templeton Foundation Grant (to the Evolution Institute) entitled âAxial-Age Religions and the Z-Curve of Human Egalitarianism,â a Tricoastal Foundation Grant (to the Evolution Institute) entitled âThe Deep Roots of the Modern World: The Cultural Evolution of Economic Growth and Political Stability,â Economic and Social Research Council Large Grant REF RES-060-25-0085 entitled âRitual, Community, and Conflict,â an Advanced Grant from the European Research Council under the European Unionâs Horizon 2020 Research and Innovation Programme Grant 694986, and Grant 644055 from the European Unionâs Horizon 2020 Research and Innovation Programme (ALIGNED; www.aligned-project.eu). T.E.C. is supported by funding from the European Research Council (ERC) under the European Unionâs Horizon 2020 research and innovation programme (Grant Agreement 716212).Peer Reviewe