区域科学中的新数据革命:对已故斯坦·查曼斯基的致敬

IF 1.8 3区 经济学 Q3 ENVIRONMENTAL STUDIES International Regional Science Review Pub Date : 2021-02-11 DOI:10.1177/0160017621994639
Dani Broitman, K. Kourtit
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引用次数: 0

摘要

区域科学的方法论传统上是基于概念框架和操作(或应用)分析的完美结合。近年来,我们目睹了区域科学研究中使用的大量新的经验数据(如社交媒体、“大数据”、传感器数据、应用程序数据等)。这些新数据尤其是数字技术应用和互动媒体的结果。但在传统的统计领域,我们也观察到区域科学研究的数据库在迅速扩大,从基本思维到应用研究的逐渐转变正在发生。已故的斯坦·查曼斯基(Stan Czamanski,1918-2012)是区域科学的先驱之一,他将自己的大部分科学工作投入到了应用研究中。他对如何以可衡量的方式将空间作为地方一级社会经济研究的一个关键变量或结构组成部分的问题很感兴趣。基于空间经济的坚实概念框架,他能够设计可操作的宏观经济区域账户和区域投入产出表,并将其用作绘制复杂空间经济现象的实用工具。他成长于Walter Isard、Wassley Leontief和Lawrence Klein等人开创的定量研究传统中;他们都试图开发用于研究产业结构和(空间)经济联系的测量模型,特别是通过根据区域经济调整(国家间)数据。一个突出的问题还将是,空间相关数据量的空前增长是否会更好地了解空间经济的复杂性,而不考虑这是否会带来更好的政策决策。为了解决这个问题,家乐福于2019年4月在特拉维夫和海法举办了为期两天的国际先进头脑风暴研讨会,以纪念已故的斯坦·查曼斯基。本次会议由地区科学院(TRSA)组织,特拉维夫大学和以色列理工学院支持。ABC研讨会汇集了来自各大洲的学者,反思后恰曼斯基时代新的数据分析挑战。在这次富有成果的会议上,对解决上述挑战的概念研究和应用研究都表示欢迎。
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The New Data Revolution in Regional Science: A Tribute to the Late Stan Czamanski
The methodology of regional science is traditionally based on a sound mix of conceptual framing and operational (or applied) analysis. In recent years, we have witnessed an avalanche of new empirical data used in regional science research (e.g., social media, ‘big data’, sensor data, App data, etc.). Such new data emerge in particular as a result of digital technology applications and interactive media. But also in traditional statistical domains we observe a rapid expansion of the data base for regional science research, to the extent that a gradual shift from basic thinking towards applied research is taking place.

One of the pioneers in regional science who has devoted a significant part of his scientific work to applied research has been the late Stan Czamanski (1918 - 2012). He was fascinated by the question of how to introduce space in a measurable way as a key variable or a structural component in socio-economic research at the sub-national level. Based on a solid conceptual framework of the space-economy, he was able to design operational macroeconomic regional accounts and regional input-output tables, which he used as practical instruments to map out complex spatial-economic phenomena. He grew up in the quantitative research tradition pioneered by Walter Isard, Wassilly Leontief and Lawrence Klein amongst others; they all tried to develop measurement models for studying industrial structures and (spatial-)economic linkages, inter alia by adjusting (inter-)national data to regional economies.

A prominent question will also be whether the unprecedented rise in spatially relevant data volumes leads to better insights into the complexity of the space-economy, leave aside whether this may lead to better policy decisions. To address this question, a two-day international Advanced Brainstorm Carrefour (ABC) workshop (in April 2019) was held in Tel- Aviv and Haifa in honor of the late Stan Czamanski. This meeting was organized by The Regional Science Academy (TRSA) and supported by the Tel- Aviv University and the Technion – Israel Institute of Technology. The ABC workshop brought together scholars from all continents to reflect on the new data-analytics challenges of the post-Czamanski age. Both conceptual and applied studies addressing the above-mentioned challenges were welcomed at this fruitful meeting.
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来源期刊
CiteScore
4.50
自引率
13.00%
发文量
26
期刊介绍: International Regional Science Review serves as an international forum for economists, geographers, planners, and other social scientists to share important research findings and methodological breakthroughs. The journal serves as a catalyst for improving spatial and regional analysis within the social sciences and stimulating communication among the disciplines. IRSR deliberately helps define regional science by publishing key interdisciplinary survey articles that summarize and evaluate previous research and identify fruitful research directions. Focusing on issues of theory, method, and public policy where the spatial or regional dimension is central, IRSR strives to promote useful scholarly research that is securely tied to the real world.
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