Urban systems’ dynamics are the result of two intertwined processes that operate at different rhythms: their physical structure and underlying social processes. This paper suggests a novel approach to the spatial analysis of urban systems, using neighborhoods as a basic building block. Neighborhoods are usually the minimal homogeneous geographical unit in urban areas, both regarding their physical, and social characteristics, and the availability of governmental data. Using unsupervised machine learning algorithm, an extensive real-estate transaction dataset, and census data, a multi-scale analysis of neighborhoods’ dynamics in England and Wales is performed. The spatial and temporal dynamics of the resulting clusters of neighborhoods highlights the urban challenges faced by entire urban systems. The results suggest that processes triggered by urban inequalities may affect not only the social structure of cities (for example, through gentrification and displacement), but also the environmental sustainability of the whole urban system at a much larger scale: Suburbanization pressures seem to threaten rural areas at an unprecedented magnitude. The identification of the areas where this pressure is acute allows for the design of appropriate urban policy responses. The main message for the analysis of urban dynamics is that physical transformations and socio-political rearrangements that are intrinsically intertwined: Therefore, the joint management of the both aspects is the key to a sustainable future. The analysis also highlights the potential of machine learning algorithms for the benefit of urban science in general, and the study of urban dynamics in particular.

中文翻译：

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通过社区动态进行城市系统空间分析的无监督机器学习方法


城市系统的动态是两个以不同节奏运行的相互交织的过程的结果：它们的物理结构和潜在的社会过程。本文提出了一种城市系统空间分析的新方法，使用社区作为基本构建块。就其自然特征、社会特征以及政府数据的可用性而言，社区通常是城市地区最小的同质地理单元。使用无监督机器学习算法、广泛的房地产交易数据集和人口普查数据，对英格兰和威尔士的社区动态进行多尺度分析。由此产生的社区集群的空间和时间动态凸显了整个城市系统面临的城市挑战。结果表明，城市不平等引发的过程不仅可能影响城市的社会结构（例如，通过中产阶级化和流离失所），而且还会在更大范围内影响整个城市系统的环境可持续性：郊区化压力似乎威胁着农村地区。领域达到前所未有的规模。确定压力严重的地区有助于设计适当的城市政策应对措施。城市动态分析的主要信息是，物理变革和社会政治重新安排本质上是相互交织的：因此，这两方面的联合管理是可持续未来的关键。该分析还强调了机器学习算法对于城市科学，特别是城市动力学研究的总体优势的潜力。