Building(s and) Cities: Delineating Urban Areas with a Machine Learning Algorithm

Daniel Arribas-Bel, M. García-López, Elisabet Viladecans-Marsal
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引用次数: 60

Abstract

This paper proposes a novel methodology for delineating urban areas based on a machine learning algorithm that groups buildings within portions of space of sufficient density. To do so, we use the precise geolocation of all 12 million buildings in Spain. We exploit building heights to create a new dimension for urban areas, namely, the vertical land, which provides a more accurate measure of their size. To better understand their internal structure and to illustrate an additional use for our algorithm, we also identify employment centers within the delineated urban areas. We test the robustness of our method and compare our urban areas to other delineations obtained using administrative borders and commuting-based patterns. We show that: 1) our urban areas are more similar to the commuting-based delineations than the administrative boundaries but that they are more precisely measured; 2) when analyzing the urban areas’ size distribution, Zipf’s law appears to hold for their population, surface and vertical land; and 3) the impact of transportation improvements on the size of the urban areas is not underestimated.
建筑(s)和城市:用机器学习算法描绘城市区域
本文提出了一种基于机器学习算法描绘城市区域的新方法,该算法将建筑物分组在足够密度的空间部分内。为了做到这一点,我们使用了西班牙所有1200万幢建筑的精确地理位置。我们利用建筑高度为城市地区创造了一个新的维度,即垂直土地,它提供了一个更准确的尺寸测量。为了更好地理解它们的内部结构,并说明我们的算法的额外用途,我们还确定了划定的城市区域内的就业中心。我们测试了我们方法的稳健性,并将我们的城市区域与其他使用行政边界和通勤模式获得的划分进行了比较。我们发现:1)我们的城市区域与基于通勤的划定比行政边界更相似,但它们的测量更精确;2)在分析城市区域规模分布时,Zipf定律适用于城市人口、地表和垂直土地;3)交通改善对城市规模的影响不容低估。
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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