This special issue of Distributed and Parallel Databases journal covers recent advances in spatio-temporal data analytics in the context of urban computing. It contains 9 articles that present solid research studies and innovative ideas in the area of spatio-temporal data analytics for urban computing applications. All of the 9 papers went through at least two rounds of rigorous reviews by the guest editors and invited reviewers. Location-based recommender systems are becoming increasingly important in the community of urban computing. The paper, by Hao Zhou et al., “Hybrid route recommendation with taxi and shared bicycles,” develops a two-phase data-driven recommendation framework that integrates prediction and recommendation phases for providing reliable route recommendation results. Another paper, by Hao Zhang et al., “On accurate POI recommendation via transfer learning,” proposes a transfer learning based deep neural model that fuses cross-domain knowledge to achieve more accurate POI recommendation. Spatial keyword search has been receiving much attention in area of spatio-temporal data analytics. Xiangguo Zhao et al. develop anindex structure that comprehensively considers the social, spatial, and textual information of massive-scale spatio-temporal data to support social-aware spatial keyword group query in their paper “Social-aware spatial keyword top-k group query.” Jiajie Xu et al. propose a hybrid indexing structure that integrate the spatial and semantic information of spatio-temporal datain their paper “Multi-objective spatial keyword query with semantics: a distance-owner based approach.” Matching of spatio-temporal data is a fundamental research problem in spatiotemporal data analytics. The paper, by Ning Wang et al., “An efficient algorithm for spatio-textual location matching,” targets the problem of finding all location pairs whose spatio-textual similarity exceeds a given threshold. This matching query is useful in urban computing applications including hot region detection and traffic

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时空数据驱动的城市计算简介

本期分布式和并行数据库期刊特刊涵盖了城市计算背景下时空数据分析的最新进展。它包含 9 篇文章，展示了城市计算应用时空数据分析领域的扎实研究和创新思想。9篇论文至少经过了客座编辑和特邀审稿人的两轮严格评审。基于位置的推荐系统在城市计算社区中变得越来越重要。由 Hao Zhou 等人撰写的论文“出租车和共享单车的混合路线推荐”开发了一个两阶段数据驱动的推荐框架，该框架将预测和推荐阶段相结合，以提供可靠的路线推荐结果。Hao Zhang 等人的另一篇论文，“通过迁移学习实现精准POI推荐”，提出了一种基于迁移学习的深度神经模型，融合跨领域知识，实现更精准的POI推荐。空间关键词搜索在时空数据分析领域备受关注。赵相国等。在他们的论文“Social-aware spatial keyword top-k group query”中开发了一种综合考虑海量时空数据的社会、空间和文本信息的索引结构，以支持社会感知空间关键字组查询。徐家杰等。在他们的论文“具有语义的多目标空间关键字查询：基于距离所有者的方法”中提出了一种混合索引结构，该结构集成了时空数据的空间和语义信息。” 时空数据的匹配是时空数据分析的基础研究问题。由 Ning Wang 等人撰写的论文“一种有效的空间文本位置匹配算法”针对的是找到所有空间文本相似度超过给定阈值的位置对的问题。此匹配查询在城市计算应用程序中非常有用，包括热点区域检测和交通