基于多源数据的社区公园游憩规律及其空间特征关联研究——以上海为例  

Research on Correlation Between Recreation Rules and Spatial Features of Community Parks Based on Multi-Source Data:A Case Study of Shanghai

作　　者：邵钰涵 卢慧霖 SHAO Yuhan;LU Huilin(the Department of Academic Development,College of Architecture and Urban Planning(CAUP),Tongji University;Restorative Urbanism Research Centre(RURC)of Joint International Research Laboratory of Eco-Urban Design,Ministry of Education;the College of Architecture and Urban Planning(CAUP),Tongji University)

机构地区：[1]同济大学建筑与城市规划学院学术发展部 [2]教育部生态化城市设计国际合作联合实验室恢复性城市研究分实验中心(RURC) [3]同济大学建筑与城市规划学院

出　　处：《风景园林》2024年第2期32-40,共9页Landscape Architecture

基　　金：国家自然科学基金“城市自然景观视觉舒适度感应机理研究”(编号51808393);国家重点研发计划重点专项“宜居城市环境品质提升关键技术研究与应用”(编号2023YFC3805300);科技部外传项目“恢复性城市景观理论、方法与实践研究”(编号G2022133023L);上海市城市更新及其空间优化技术重点实验室课题“城市街道视觉疗愈与经济活力平衡调控系统研究”(编号CAUP-UD-06)。

摘　　要：【目的】对社区公园进行精细化游憩规律分类,并探索游憩规律与空间特征的关系,对社区公园游憩效率的提升具有指导意义。【方法】对上海市中心城区110个社区公园进行研究,基于手机信令数据,识别社区公园绿地周期游憩活跃规律并进行频谱聚类;结合多源数据,提取区域功能、交通可达性和公园空间特征并与游憩到访率进行关联。【结果】社区公园日周期游憩规律可分为单波峰活跃型和多波峰活跃型2类,包括晨间、午间、晚间波峰活跃型和早晚间、中下午、中晚间波峰活跃型,活跃类型与周边用地功能呈现直接关联。游憩到访率与空间特征要素的关联中,总体与周边空间商业用地占比、公交站点密度、道路密度呈现强相关,不同游憩活跃类型中也各呈现出与公共管理用地占比、绿地可达性、公园面积、硬质景观占比等指标的相关性。【结论】各游憩活跃类型的绿地游憩效率提升应结合游憩规律考虑不同的指标,例如中晚间波峰活跃型绿地宜更加关注绿地可达性、硬质活动场地的面积等。这些发现为研究城市社区公园游憩时空行为、精准提升社区公园服务水准提供了新视角。[Objective]The growing demand for meticulous green space allocation has made the intensive utilization of urban community parks an imperative nowadays.Nonetheless,prevailing community park planning tends to be expansive and lacks the adept integration of efficiency nuances arising from diverse usage behaviors.The classification of recreational patterns within community parks,coupled with an exploration of their correlation with spatial features,presents substantial potential for precisely steering the augmentation of recreational efficiency in community parks[Methods]This research conducts a comprehensive investigation encompassing 110 community parks in Shanghai.Leveraging location based service(LBS)data,the research identifies patterns of recreational activities within community parks across distinct time periods.Employing spectral clustering based on visit frequency,peak counts,and peak time periods,the research amalgamates data from diverse sources,including built environment data and urban transportation data.The primary objective is to extract three pivotal feature categories:regional functionality,transportation accessibility,and green space features.These features undergo meticulous scrutiny for their correlation with recreational visit efficiency.Regional functional features encompass parameters such as proportion of industrial land,public management and service land,residential land,transportation land,and commercial land,and the functional mix of parkland.Transportation accessibility features comprise metrics such as green space accessibility,bus stop density,and road network density.Green space features include park area,green coverage,hard surface ratio,and water area proportion.The synthesis of these features provides a nuanced understanding of the factors influencing recreational visit efficiency in community parks.[Results]The recreational activity patterns in community parks are successfully categorized into single-peak and multi-peak types,including morning,noon,and evening peak activity types,a

关 键 词：风景园林 游憩行为 手机信令数据 社区公园 相关性 

分 类 号：TU986[建筑科学—城市规划与设计]