Abstract Parks and other green spaces are an important part of sustainable, healthy and socially equal urban environment. Urban planning and green space management benefit from information about green space use and values, but such data are often scarce and laborious to collect. Temporally dynamic geographic information generated by different mobile devices and social media platforms are a promising source of data for studying green spaces. User-generated data have, however, platform specific characteristics that limit their potential use. In this article, we compare the ability of different user-generated data sets to provide information on where, when and how people use and value urban green spaces. We compare four types of data: social media, sports tracking, mobile phone operator and public participation geographic information systems (PPGIS) data in a case study from Helsinki, Finland. Our results show that user-generated geographic information sources provide useful insights about being in, moving through and perceiving urban green spaces, as long as evident limitations and sample biases are acknowledged. Social media data highlight patterns of leisure time activities and allow further content analysis. Sports tracking data and mobile phone data capture green space use at different times of the day, including commuting through the parks. PPGIS studies allow asking specific questions from active participants, but might be limited in spatial and temporal extent. Combining information from multiple user-generated data sets complements traditional data sources and provides a more comprehensive understanding of green space use and preferences.

中文翻译：

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从用户生成的地理信息中了解城市绿地的使用

摘要 公园和其他绿地是可持续、健康和社会平等的城市环境的重要组成部分。城市规划和绿地管理受益于有关绿地使用和价值的信息，但此类数据往往稀缺且难以收集。由不同移动设备和社交媒体平台生成的时间动态地理信息是研究绿色空间的有前途的数据来源。然而，用户生成的数据具有平台特定的特征，限制了它们的潜在用途。在本文中，我们比较了不同用户生成的数据集提供有关人们在何处、何时以及如何使用和评价城市绿地的信息的能力。我们比较了四种类型的数据：社交媒体、运动追踪、芬兰赫尔辛基案例研究中的移动电话运营商和公众参与地理信息系统 (PPGIS) 数据。我们的结果表明，只要承认明显的局限性和样本偏差，用户生成的地理信息源就可以提供关于进入、穿过和感知城市绿地的有用见解。社交媒体数据突出了休闲活动的模式，并允许进行进一步的内容分析。运动跟踪数据和手机数据可捕获一天中不同时间的绿地使用情况，包括在公园上下班。PPGIS 研究允许向积极参与者提出具体问题，但可能在空间和时间范围内受到限制。