Urban Economic Structures as Multidimensional Networks: A Complex Systems Framework for Analyzing Economic Development Trajectories

IF 1.7 4区 工程技术 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Complexity Pub Date : 2024-01-25 DOI:10.1155/2024/5521625
Shade T. Shutters
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引用次数: 0

Abstract

Urban and regional systems often face the difficulty and necessity of structural transitions. These transitions, which can be imposed by external circumstances or initiated by a city itself, include energy transitions, transitions to a circular economy, transitions following a pandemic or natural disaster, or intentional policies meant to “move” an urban economy toward a desired state. However, what does economic structure mean in these cases? Traditional notions of economic structure are ambiguous and simplistic and typically consist of simple distributions, such as number of workers per industry. Yet to better understand, guide, or respond to system transitions, planners must move beyond these nebulous notions toward a theoretically grounded, quantifiable definition of economic structure. A recent trend emerging from the nexus of complexity science and urban science has been to operationalize urban economic structures as networks of interacting economic components. Typically based on colocation patterns of some type of entity, these networks have previously been constructed using economic entities such as products, occupations, or labor skills. Yet different types of entities also exhibit colocation patterns with each other, such as patent technology classes and industries. Here, those cross-entity colocation patterns are used to merge multiple types of entities into a single network representation of urban economies, offering a granularity not possible using a single node type. Occupations, industries, college degrees, and patent technology codes are merged into one multidimensional or multinodal network. As in previous studies, a dense core of highly connected entities emerges in this network. The network locations of individual cities are contrasted, and community detection algorithms are used to identify clusters of highly connected economic entities, showing that the densely connected network core is associated with science, technology, and business-related economic entities. Proximities between individual cities within the network are also measured revealing that many cities that are close to each other in the network are also close to each other in physical space. This framework offers potential applications including the ability to quantify structural change over time in response to a shock or to assess the relative difficulty of future desirable trajectories. More broadly, this framework might be applied to the study of structural change in other complex adaptive systems from human institutions to ecosystems.

作为多维网络的城市经济结构:分析经济发展轨迹的复杂系统框架
城市和区域系统经常面临结构转型的困难和必要性。这些转型可能是外部环境强加的,也可能是由城市自身发起的,包括能源转型、向循环经济转型、大流行病或自然灾害后的转型,或旨在将城市经济 "推向 "理想状态的有意政策。然而,在这些情况下,经济结构意味着什么?传统的经济结构概念模糊而简单,通常包括简单的分布,如每个行业的工人数量。然而,为了更好地理解、指导或应对系统转型,规划者必须超越这些模糊的概念,对经济结构进行有理论基础的量化定义。最近,复杂性科学与城市科学的结合部出现了一种趋势,那就是将城市经济结构操作化为相互作用的经济成分网络。通常情况下,这些网络是基于某种类型实体的同地定位模式,以前是通过产品、职业或劳动技能等经济实体来构建的。然而,不同类型的实体之间也会表现出同位模式,例如专利技术类别和产业。在这里,这些跨实体的同地模式被用来将多种类型的实体合并到城市经济的单一网络表征中,从而提供单一节点类型无法实现的粒度。职业、行业、大学学位和专利技术代码被合并成一个多维或多节点网络。与以往的研究一样,在这个网络中出现了一个高度关联实体的密集核心。对各个城市的网络位置进行对比,并使用群落检测算法来识别高度连接的经济实体群,结果显示,密集连接的网络核心与科学、技术和商业相关的经济实体有关。此外,还测量了网络中各个城市之间的接近度,发现许多在网络中相互接近的城市在物理空间上也相互接近。这一框架具有潜在的应用价值,包括能够量化结构随时间的变化以应对冲击,或评估未来理想轨迹的相对难度。更广泛地说,这一框架可用于研究从人类机构到生态系统等其他复杂适应系统的结构变化。
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
Complexity
Complexity 综合性期刊-数学跨学科应用
CiteScore
5.80
自引率
4.30%
发文量
595
审稿时长
>12 weeks
期刊介绍: Complexity is a cross-disciplinary journal focusing on the rapidly expanding science of complex adaptive systems. The purpose of the journal is to advance the science of complexity. Articles may deal with such methodological themes as chaos, genetic algorithms, cellular automata, neural networks, and evolutionary game theory. Papers treating applications in any area of natural science or human endeavor are welcome, and especially encouraged are papers integrating conceptual themes and applications that cross traditional disciplinary boundaries. Complexity is not meant to serve as a forum for speculation and vague analogies between words like “chaos,” “self-organization,” and “emergence” that are often used in completely different ways in science and in daily life.
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