由河流、池塘、湖泊及其他水体构成的城市淡水生态系统，对城市居民具有重要的社会经济与生态价值。然而，关于个体如何与湖泊互动的研究仍十分有限，尤其在城市尺度及精细时空分辨率下尤为不足。为弥补这一空白，我们提出一种数据驱动的分析框架，全面感知人湖交互，并刻画城市内部各湖泊的社会人口学特征。“Lakeplace”一词被提出，用以描述包含湖泊及其中人类活动的场所。对于每个湖泊，其lakeplace的地理边界定义为一级行政区划单元，反映湖泊社会经济邻近尺度。利用大规模个体移动定位数据，我们在美国明尼苏达州双城大都市区（TCMA）的2036个主要湖泊及其互动人群上开展了lakeplace感知分析。各lakeplace的受欢迎程度以其时序访问量衡量，并进一步划分为“湖上”与“湖周”两类人类活动。针对高人气lakeplace，我们探究其吸引力主要源于湖泊本体，抑或源于周边社会人口环境。该lakeplace感知框架为刻画人类活动的时空特征、理解人湖系统相关社会人口知识提供了实用方法。本研究通过地理空间大数据实现人类—环境交互的社会感知，为人本导向的可持续城市规划与城市水资源管理提供启示。

Urban freshwater ecosystems, composed of rivers, ponds, lakes, and other water bodies, have essential socioeconomic and ecological values for urban residents. However, research investigating how individuals interact with lakes remains limited, especially within cities and at fine spatiotemporal resolutions. To fill this gap, we propose a data-driven analytical framework that comprehensively senses human-lake interactions and profiles the social-demographic characteristics of intra-city lakes. The term "lakeplace" is proposed to depict a place containing lakes and human activities within it. For each lake, the geographic boundary of its lakeplace refers to the first-order administrative units, reflecting the neighboring scale of lake socioeconomics. Utilizing large-scale individual mobile positioning data, we performed lakeplace sensing on the 2,036 major lakes in the Twin Cities Metropolitan Area (TCMA), Minnesota, and the people interacting with them. The popularity of each lakeplace was measured by its temporal visitations and further categorized as on-lake and around-lake human activities. Popular lakeplaces were investigated to depict whether the attractiveness of a lake is mostly brought by the lake itself, or the social-demographic environment around it. The lakeplace sensing framework offers a practical approach to the spatiotemporal characteristics of human activities and understanding the social-demographic knowledge related to human-lake systems. Our work exemplifies the social sensing of human-environment interactions via geospatial big data, shedding light on human-oriented sustainable urban planning and urban water resource management.