<b>Planning </b><b>multifunctional urban</b><b>green street </b><b>network</b><b>linking city parks through multisource </b><b>big data</b><b>: a case study in Beijing's built-up urban areas</b>

Urban greenstreets are pivotal in enriching urban qualityand fostering socioecological well-being. Despite China's extensive efforts in green street planning, critiques persistregarding their fragmented and monolithic pattern. In this study, we introduce an innovative approach to planning a multifaceted urban green street networkconnecting city parks, leveraging a theoretical framework grounded in big data analytics. Focusing on Beijing's Built-up Urban Areas (BBUA), we harnessdiverse data sources, including bus swipe cards, public comments,street panoramic images, and area of interest (AOI) analysis of parks. Firstly, we evaluate the green space exposure of 331 parks using an integrated "Availability–Accessibility–Adaptability" assessment framework as potential carriers of green streets. Then, through spatially explicit workflows and the least-cost path methodology, we leverage a vast dataset of 70 million public transportation swipe records to optimize the alignment of multifunctional green streets, prioritizing the criterion of maximizing recreational footfalls. Our planning framework yields a network spanning1,566.36 km in BBUA, encompassing 93.88% of parks and offering diverse functions ranging from ecological to historical significance. Verification of the feasibility of our planning approach is conducted using a green view index derived from over 1,000 street panoramic images. Overall, our study presents a pioneering big data-driven framework for green street planning, with implications for urban planning and regeneration initiatives.