Place is a concept that is fundamental to how we orientate and communicate space in our everyday lives. Crowdsourced social media data present a valuable opportunity to develop bottom‐up inferences of places that are integral to social activities and settings. Conventional location‐led approaches use a predefined spatial unit to associate data and space with places, which cannot capture the richness of urban places (i.e., spatial extents and their dynamic functions). This article develops a name‐led framework to overcome these limitations in using social media data to study urban places. The framework first derives place names from georeferenced Twitter data combining text mining and spatial point pattern analysis, then estimates the spatial extents by spatial clustering, and further extracts their dynamic functions with time, which makes up a complete place profile. The framework is tested on a case study in Camden Borough, London and the results are evaluated through comparisons to the Foursquare point of interest data. This name‐led approach enables the shift from space‐based analysis to place‐based analysis of urban space.

中文翻译：

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以名称为导向的方法，基于经过地理标记的Twitter数据来描述城市地点

位置是我们在日常生活中如何定位和交流空间的基础概念。众包社交媒体数据提供了宝贵的机会，可以对社交活动和环境必不可少的场所进行自下而上的推断。传统的位置导向方法使用预定义的空间单位将数据和空间与位置相关联，而无法捕获城市位置的丰富性（即空间范围及其动态功能）。本文建立了一个以名称为主导的框架，以克服使用社交媒体数据研究城市地点时的这些限制。该框架首先结合文本挖掘和空间点模式分析从地理参考的Twitter数据中获取地名，然后通过空间聚类估计空间范围，然后进一步提取其随时间变化的动态功能，构成了完整的地点资料。该框架在伦敦Camden Borough的一个案例研究中进行了测试，并通过与Foursquare兴趣点数据进行比较来评估结果。这种以名称为主导的方法使城市空间从基于空间的分析转变为基于空间的分析。