In Japan, a new concept of 'Station and Town Special Design’, an urban development that considers the station and the surrounding town block as a whole and revitalises the vitality of the town as a whole, is attracting attention. We hypothesised that people's movement behaviour in the area would become more active via this Station-Town Space Design project. We analysed the effects using movement direction entropy (MDE), which measures the diversity of people's movement directions. The results showed that MDE increased in the project area. Furthermore, MDE tended to be higher in the surroundings of stations. This suggests that the Station-Town Space Design stimulates people's movement behaviour in the station, station square, and surrounding area. These findings have significant implications for urban planners and developers, as they indicate that MDE can be a useful indicator for measuring the liveliness of a town, and that the Station-Town Space Design design can enhance the vitality of urban areas.

Communication regarding data use enhances and sustains trust in the data market. Showing this principle based on the literature on trust, this study proposes a novel method for representing the digest information of datasets to foster the thoughts of potential data users, who attempt to create valuable products, services, or business models using datasets and aid their communication with data providers. Compared with existing metadata, where variable labels in a dataset are listed, the presented metadata, called data leaf (DL), includes events, situations, and/or actions that, as a set, compose scenarios supposed to be active in the target real world of a dataset. This method considers the fitness of metadata corresponding to various datasets to a feature concept (FC), which provides an abstract illustration of the knowledge acquired or expected to be acquired from the data, and plays an essential role in data utilization. In experiments using metadata as elements to be combined in human thought for data federative innovation, DLs significantly outperformed cases using data jackets (DJs) where variable labels were used as attributes of the datasets.