Cartographies of warfare in the Indian subcontinent: Contextualizing archaeological and historical analysis through big data approaches

IF 8.6 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS
Monica L. Smith, Connor Newton
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Abstract

Some of the most notable human behavioral palimpsests result from warfare and its durable traces in the form of defensive architecture and strategic infrastructure. For premodern periods, this architecture is often understudied at the large scale, resulting in a lack of appreciation for the enormity of the costs and impacts of military spending over the course of human history. In this article, we compare the information gleaned from the study of the fortified cities of the Early Historic period of the Indian subcontinent (c. 3rd century BCE to 4th century CE) with the precolonial medieval era (9-17th centuries CE). Utilizing in-depth archaeological and historical studies along with local sightings and citizen-science blogs to create a comprehensive data set and map series in a “big-data” approach that makes use of heterogeneous data sets and presence-absence criteria, we discuss how the architecture of warfare shifted from an emphasis on urban defense in the Early Historic period to an emphasis on territorial offense and defense in the medieval period. Many medieval fortifications are known from only local reports and have minimal identifying information but can still be studied in the aggregate using a least-shared denominator approach to quantification and mapping.

Abstract Image

印度次大陆的战争地图:通过大数据方法对考古和历史分析进行语境分析
战争及其以防御性建筑和战略基础设施形式留下的持久痕迹是一些最显著的人类行为古迹。对于近代以前的时期,这种大规模的建筑往往研究不足,导致人们对人类历史上军事开支的巨大代价和影响缺乏认识。在本文中,我们将对印度次大陆早期历史时期(约公元前 3 世纪至公元前 4 世纪)的设防城市和前殖民时期的中世纪(公元前 9-17 世纪)的设防城市进行比较研究。我们利用深入的考古和历史研究以及当地目击和公民科学博客,以 "大数据 "方法(即利用异构数据集和存在-不存在标准)创建了一个综合数据集和地图系列,讨论了战争建筑如何从早期历史时期强调城市防御转变为中世纪时期强调领土进攻和防御。许多中世纪防御工事仅从地方报告中得知,识别信息极少,但仍可使用最小公分母方法进行量化和制图,对其进行总体研究。
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来源期刊
Journal of Big Data
Journal of Big Data Computer Science-Information Systems
CiteScore
17.80
自引率
3.70%
发文量
105
审稿时长
13 weeks
期刊介绍: The Journal of Big Data publishes high-quality, scholarly research papers, methodologies, and case studies covering a broad spectrum of topics, from big data analytics to data-intensive computing and all applications of big data research. It addresses challenges facing big data today and in the future, including data capture and storage, search, sharing, analytics, technologies, visualization, architectures, data mining, machine learning, cloud computing, distributed systems, and scalable storage. The journal serves as a seminal source of innovative material for academic researchers and practitioners alike.
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