ArchaeoSRP

IF 1.9 2区 历史学 0 ARCHAEOLOGY
Sean Bergin, Grant Snitker
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引用次数: 0

Abstract

Abstract For much of its history, archaeological research has relied on site-specific projects, regional comparisons, and theory building from case studies. However, recent research themes concerning the emergence of complex social-ecological systems and long-term land-use legacies require a new approach to archaeological data. Large-scale syntheses of archaeological data provide an effective way forward to address these new research themes. In more concise terms, “big questions” require “big data” to help answer them. The archaeological information collected by the USDA Forest Service is one such “big dataset” and represents an incalculable investment in time, resources, and expertise. This article explores this concept and presents an R package (ArchaeoSRP) designed to extract archaeological information from USDA Forest Service site record files. We demonstrate the functionality of this R package through a case study examining the archaeological data for the Cle Elum Ranger District, within Central Washington's Okanogan-Wenatchee National Forest. Our results reveal the efficiency of using automated methods to extract, organize, and synthesize district-level archaeological data, which, in turn, reveal patterns of precontact and historic land use that were otherwise not distinguishable.
ArchaeoSRP
在其历史的大部分时间里,考古研究一直依赖于特定地点的项目、区域比较和案例研究的理论构建。然而,最近关于复杂社会生态系统的出现和长期土地利用遗产的研究主题需要一种新的考古数据方法。大规模综合考古数据为解决这些新的研究主题提供了有效的途径。更简洁地说,“大问题”需要“大数据”来帮助回答。美国农业部林务局收集的考古信息就是这样一个“大数据集”,它代表了在时间、资源和专业知识方面不可估量的投资。本文探讨了这一概念,并介绍了一个R包(ArchaeoSRP),旨在从美国农业部林业局的现场记录文件中提取考古信息。我们通过一个案例研究来展示这个R包的功能,该案例研究检查了位于华盛顿中部奥卡诺根-韦纳奇国家森林的Cle Elum Ranger区的考古数据。我们的研究结果揭示了使用自动化方法提取、组织和综合区级考古数据的效率,这些数据反过来揭示了接触前和历史土地利用模式,否则无法区分。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
3.70
自引率
21.40%
发文量
39
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