Evolution of Urban Patterns: Urban Morphology as an Open Reproducible Data Science
Evolution of Urban Patterns: Urban Morphology as an Open Reproducible Data Science
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DOI：
10.1111/gean.12302
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发表时间：
2021-07
影响因子：
3.6
通讯作者：
Martin Fleischmann;Alessandra Feliciotti;W. Kerr
中科院分区：
文献类型：
--
作者：
Martin Fleischmann;Alessandra Feliciotti;W. Kerr
地理数据科学(GDS)最近的增长受到越来越多可用的开放数据和开放源码工具的推动，已经影响了许多领域的城市科学。然而，城市形态--一门研究城市形态的科学--的应用有限。虽然形态研究的定量方法正在寻找动力，但现有的此类分析工具范围有限，主要是作为独立地理信息系统软件的插件实施的。这在本质上限制了研究的透明度和重复性。同时，GDS的Python生态系统正在走向成熟，能够完全支持高度专业化的形态分析。在这篇文章中，我们在一个工作流程中使用开源的Python生态系统，通过一个案例研究来说明它的能力，该案例研究在42个地点的样本上评估了六个历史时期的城市模式的演变。结果显示，城市形态的规模和结构呈现出从前工业发展到当代街区的变化轨迹，在二战后现代主义时代出现了最大偏差，证实了之前的研究结果。这种完全可重复的方法被封装在计算笔记本中，说明了现代GDS如何应用于城市形态研究，以促进开放、协作和透明的科学，独立于专有或其他有限的软件。
The recent growth of geographic data science (GDS) fuelled by increasingly available open data and open source tools has influenced urban sciences across a multitude of fields. Yet there is limited application in urban morphology—a science of urban form. Although quantitative approaches to morphological research are finding momentum, existing tools for such analyses have limited scope and are predominantly implemented as plug-ins for standalone geographic information system software. This inherently restricts transparency and reproducibility of research. Simultaneously, the Python ecosystem for GDS is maturing to the point of fully supporting highly specialized morphological analysis. In this paper, we use the open source Python ecosystem in a workflow to illustrate its capabilities in a case study assessing the evolution of urban patterns over six historical periods on a sample of 42 locations. Results show a trajectory of change in the scale and structure of urban form from pre-industrial development to contemporary neighborhoods, with a peak of highest deviation during the post-World War II era of modernism, confirming previous findings. The wholly reproducible method is encapsulated in computational notebooks, illustrating how modern GDS can be applied to urban morphology research to promote open, collaborative, and transparent science, independent of proprietary or otherwise limited software.