The task of identifying similar regions within and between cities is an important aspect of urban data science as well as applied domains such as real estate, tourism, and urban planning. Regional similarity is typically assessed through comparing socio-demographic variables, resource availability, or urban infrastructure. An essential dimension, often overlooked for this task, is the spatiotemporal mobility patterns of people within a city. In this work we present a novel approach to identifying regional similarity based on human mobility as proxied through micromobility trips. We use a dataset consisting of e-scooter trip origins and destinations for two major European cities that differ in population size and urban structure. Three dimensions of these data are used in modeling the spatial and temporal variability in movement between regions in cities, allowing us to compare regions through a mobility lens. The result is a parameterized similarity model and interactive web platform for comparing regions across different urban environments. The application of this model suggests that human mobility patterns are a quantifiable, unique, and appropriate characteristic through which to measure urban similarity.

识别城市内部和城市之间的相似区域的任务是城市数据科学以及房地产、旅游和城市规划等应用领域的一个重要方面。通常通过比较社会人口变量、资源可用性或城市基础设施来评估区域相似性。在这项任务中经常被忽视的一个基本维度是城市内人们的时空流动模式。在这项工作中，我们提出了一种新的方法来识别基于人类移动性的区域相似性，如通过微移动旅行所代表的。我们使用了一个数据集，该数据集由两个人口规模和城市结构不同的欧洲主要城市的电动滑板车旅行起点和目的地组成。这些数据的三个维度用于模拟城市区域之间移动的空间和时间可变性，使我们能够通过移动性镜头来比较区域。结果是一个参数化的相似性模型和交互式网络平台，用于比较不同城市环境中的区域。该模型的应用表明，人类流动模式是衡量城市相似性的可量化、独特和适当的特征。