The field of human mobility and Point-of-Interest (POI) recommendation is experiencing significant developments, driven by the increasing importance of understanding human mobility for applications such as urban planning, personalized services, and generative agent simulation. A major direction of research is the creation of large-scale, publicly available datasets that can facilitate reproducible and equitable research in this area. Another noteworthy trend is the exploration of novel methods for generating commuting Origin-destination flows, which are vital for sustainable development across cities. Researchers are also investigating the impact of spatial features and index choice on database performance, aiming to provide practical guidance for developers optimizing spatial storage and querying. Notable papers include: Massive-STEPS, which introduces a large-scale benchmark dataset for POI recommendation, and Satellites Reveal Mobility, which presents a novel data generator for commuting Origin-destination flows using satellite imagery. Evaluating the Impact Of Spatial Features Of Mobility Data and Index Choice On Database Performance is also a significant contribution, as it evaluates the performance impact of index choice, data format, and dataset characteristics on spatial database systems.