Lakeplace: Sensing interactions between lakes and human activities

Abstract

Urban freshwater ecosystems, composed of rivers, ponds, lakes and other water bodies are important for residents in terms of both their socioeconomic and ecological values. However, research on the interactions between human activities and intra-city lakes remains limited, especially at finer spatiotemporal resolutions and based on individual-level human activity information. To fill this gap, we offer a data-driven analytical framework that senses the interactions between lakes and their associated human activities to profile the socioeconomic characteristics of intra-city lakes. We use the term “lakeplace” to depict the place containing a lake and inner human activities. For each lake, the spatial extent of its lakeplace refers to the first-order contiguous census blocks, as they reflect the neighboring scale of lake socioeconomics. Utilizing large-scale individual mobile positioning data in the Twin Cities Metropolitan Area (TCMA), Minnesota, we analyze human activities on 2036 lakes during July 2021. To evaluate the attractiveness of each lake, we investigate the human activities in the corresponding lakeplaces at multiple temporal scales. As illustrated in the results, the attractiveness of lakes varies from time to time, with most lakes being more popular during non-working hours. The spatial pattern of lake popularity is explored. Furthermore, the most popular lakes are found and classified to depict whether the attractiveness of a lake is mostly brought by the lake itself, or the socioeconomic environment around it. 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.