全球智库信息集成服务系统(G2TT): DP14450 Building(s and) cities: Delineating urban areas with a machine learning algorithm

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来源类型	Discussion paper
规范类型	论文
来源ID	DP14450
	DP14450 Building(s and) cities: Delineating urban areas with a machine learning algorithm
	Elisabet Viladecans-Marsal; Miquel-Àngel Garcia-López; Daniel Arribas-Bel
发表日期	2020-02-27
出版年	2020
语种	英语
摘要	This paper proposes a novel methodology for delineating urban areas based on a machine learning algorithm that groups build-ings 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 admin-istrative 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.
主题	International Trade and Regional Economics
关键词	Buildings Urban areas City size Transportation Machine learning
URL	https://cepr.org/publications/dp14450
来源智库	Centre for Economic Policy Research (United Kingdom)
资源类型	智库出版物
条目标识符	http://119.78.100.153/handle/2XGU8XDN/543348
推荐引用方式 GB/T 7714	Elisabet Viladecans-Marsal,Miquel-Àngel Garcia-López,Daniel Arribas-Bel. DP14450 Building(s and) cities: Delineating urban areas with a machine learning algorithm. 2020.