ABSTRACT The spatial pattern of urban growth determines how the physical, socio-economic and environmental characteristics of urban areas change over time. Monitoring urban areas for early identification of spatial patterns facilitates assuring their sustainable growth. In this paper, we assess the use of spatio-temporal metrics from land-use/land-cover (LULC) maps to identify growth patterns. We applied LULC change models to simulate different scenarios of urban growth spatial patterns (i.e., expansion, compact, dispersed, road-based and leapfrog) on various baseline urban forms (i.e., monocentric, polycentric, sprawl and linear). Then, we computed the spatio-temporal metrics for the simulated scenarios, selected the most informative metrics by applying discriminant analysis and classified the growth patterns using clustering methods. Two metrics, Weighted mean expansion and Weighted Euclidean distance, which account for the densification, compactness and concentration of urban growth, were the most efficient for classifying the five growth patterns, despite the influence of the baseline urban form. These metrics have the potential to identify growth patterns for monitoring and evaluating the management of developing urban areas.

中文翻译：

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通过土地利用/土地覆盖时空指标识别城市增长模式：模拟和分析

摘要 城市增长的空间格局决定了城市地区的物理、社会经济和环境特征如何随时间变化。监测城市地区以及早识别空间格局有助于确保其可持续发展。在本文中，我们评估使用土地利用/土地覆盖 (LULC) 地图中的时空指标来识别增长模式。我们应用 LULC 变化模型来模拟各种基线城市形态（即单中心、多中心、蔓延和线性）上的城市增长空间模式（即扩张、紧凑、分散、道路和跨越）的不同情景。然后，我们计算模拟场景的时空指标，通过应用判别分析选择信息量最大的指标，并使用聚类方法对增长模式进行分类。两个指标，尽管受到基线城市形态的影响，但加权平均扩张和加权欧几里得距离是城市增长的致密化、紧凑性和集中度的最有效分类方法。这些指标有可能确定用于监测和评估发展中城市地区管理的增长模式。