Abstract This study develops a Geographic Data Science framework that transforms the Foursquare check-in locations and user origin-destination flows data into knowledge about the emerging forms and characteristics of cities' neighbourhoods. We employ a longitudinal mobility dataset describing human interactions with Foursquare venues in ten global cities: Chicago, Istanbul, Jakarta, London, Los Angeles, New York, Paris, Seoul, Singapore, Tokyo. This social media data provides spatio-temporally referenced digital traces left by human use of urban environments, giving us access to the intangible aspects of urban life, such as people behaviours and preferences. Our framework capitalizes on these new data sources, bringing about a novel Geographic Data Science and human-centered methodological approach. Combining network science – a study area with great promise for the analysis of cities and their structure – with geospatial analysis methods, we model cities as a series of global urban networks. Through a spatially weighted community detection algorithm, we uncover functional neighbourhoods for the ten global cities. Each neighbourhood is linked to hyper-local characterisations of their built environment for the Foursquare venues that compose them, and complemented with a range of measures describing their diversity, morphology and mobility. This information is used in a clustering exercise that uncovers a set of four functional neighbourhood types. Our results enable the profiling and comparison of functional neighbourhoods, based on human dynamics and their contexts, across the sample of global cities. The framework is portable to other geographic contexts where interaction data are available to bind different localities into functional agglomerations, and provide insight into their contextual and human dynamics.

中文翻译：

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一个地理数据科学框架，用于对全球城市中的人类动态进行功能和背景分析

摘要 本研究开发了一个地理数据科学框架，将 Foursquare 签到位置和用户来源-目的地流数据转换为关于城市社区的新兴形式和特征的知识。我们采用纵向移动数据集描述人类与全球十个城市的 Foursquare 场所的互动：芝加哥、伊斯坦布尔、雅加达、伦敦、洛杉矶、纽约、巴黎、首尔、新加坡、东京。这些社交媒体数据提供了人类使用城市环境留下的时空参考数字痕迹，使我们能够了解城市生活的无形方面，例如人们的行为和偏好。我们的框架利用这些新数据源，带来了一种新颖的地理数据科学和以人为本的方法论方法。将网络科学——一个在分析城市及其结构方面具有巨大前景的研究领域——与地理空间分析方法相结合，我们将城市建模为一系列全球城市网络。通过空间加权社区检测算法，我们发现了全球十个城市的功能社区。每个街区都与组成它们的 Foursquare 场地的建筑环境的超本地特征相关联，并辅以一系列描述其多样性、形态和流动性的措施。此信息用于发现一组四种功能邻域类型的聚类练习。我们的结果能够根据人类动态及其背景，在全球城市样本中对功能性社区进行分析和比较。