Point of interest (POI) data provide digital representations of places in the real world, and have been increasingly used to understand human-place interactions, support urban management, and build smart cities. Many POI datasets have been developed, which often have different geographic coverages, attribute focuses, and data quality. From time to time, researchers may need to conflate two or more POI datasets in order to build a better representation of the places in the study areas. While various POI conflation methods have been developed, there lacks a systematic review, and consequently, it is difficult for researchers new to POI conflation to quickly grasp and use these existing methods. This paper fills such a gap. Following the protocol of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), we conduct a systematic review by searching through three bibliographic databases using reproducible syntax to identify related studies. We then focus on a main step of POI conflation, i.e., POI matching, and systematically summarize and categorize the identified methods. Current limitations and future opportunities are discussed afterwards. We hope that this review can provide some guidance for researchers interested in conflating POI datasets for their research.

翻译：兴趣点（POI）数据提供了对现实世界地点的数字化表示，并日益被用于理解人类与场所的互动、支持城市管理以及建设智慧城市。目前已开发出多个POI数据集，它们在地理覆盖范围、属性侧重点和数据质量方面往往存在差异。研究者时常需要通过融合两个或多个POI数据集，以构建研究区域内地点的更优表征。尽管已有多种POI融合方法被提出，但缺乏系统性综述，这使得刚接触POI融合的研究者难以快速掌握和应用现有方法。本文填补了这一空白。依据系统综述与荟萃分析优先报告条目（PRISMA）协议，我们使用可复现的检索语法，通过三大文献数据库进行系统检索以识别相关研究。随后聚焦于POI融合的核心步骤——POI匹配，对已识别的方法进行系统归纳与分类。最后讨论了当前方法的局限性及未来研究机遇。希望本综述能为有意在其研究中融合POI数据集的研究者提供参考。