Public bike-sharing is eco-friendly, connects excellently with other transportation modes, and provides a means of mobility that is highly suitable in the current era of climate change. This study proposes a methodology for inferring the bike trip purpose based on bike-share and point-of-interest (POI) data. Because the purpose of a trip involves decision-making, its inference necessitates an understanding of the spatiotemporal complexity of human activities. Thus, the spatiotemporal features affecting bike trips were selected from the bike-share data, and the land uses at the origin and destination of the trips were extracted from the POI data. During POI type embedding, the data were augmented considering the geographical distance between the POIs and the number of bike rentals at each bike station. We further developed a ground truth data construction method that uses temporal mobile and POI data. The inference model was built using machine learning and applied to experiments involving bike stations in Seocho-gu, Seoul, Korea. The experimental results revealed that optimal performance was achieved with the use of decision tree algorithms, as demonstrated by a 78.95% overall accuracy and 66.43% F1-score. The proposed method contributes to a better understanding of the causes of movement within cities.

Public libraries provide equitable access to information for all citizens, and they play an important role in preserving and promoting culture, formal education and self-education, and enriching leisure time. Accordingly, there has been an increasing amount of research on the use factors and accessibility of public libraries, but research on the accessibility of public libraries in non-Western cities is insufficient compared to the corresponding research on other public facilities. In particular, in high-density cities such as Seoul, the Republic of Korea, it may be desirable in terms of sustainability to focus on the qualitative, rather than the quantitative, expansion of public libraries. In previous studies, the attractive factors on the supply side were analyzed using questionnaire surveys, but in this study, the attractive factors for users were quantified in the form of the library attraction index by means of user-generated contents such as location-based social media, and the accessibility was analyzed based on this. The results showed that many public libraries have high accessibility, with a high library attraction index. Therefore, these findings indicate that the qualitative expansion of public libraries is important for information equality. It is meaningful that this study analyzed the attractive factors on the supply side by analyzing the contents generated by users.

Identifying urban functional areas increasingly relies on data-driven approaches that utilize multimodal spatial information. There is a growing focus on purpose-oriented functional area identification with greater policy relevance. This paper proposes a data-driven methodology to identify functional areas from the perspective of bicycle users. To achieve this, line-based road network units were defined around bicycle stations, and spatial–temporal data such as Origin–Destination flows and Point of Interest information were semantically integrated to delineate functional areas. An experiment was conducted on 2628 public bicycle stations in Seoul, Republic of Korea, for May 2022, and a total of five functional areas were identified via a Co-Matrix Factorization-based fusion approach. Additionally, the proposed method was validated through visual evaluation and comparison with actual bicycle usage data. The results demonstrate that by simultaneously incorporating spatial–temporal information and latent connectivity, this approach identifies bicycle-friendly areas, even with low observed usage, highlighting its potential for policy applications.

Spatial question answering requires modeling relations among entities, yet metric georeferencing faces structural constraints in routinely available operational data: two-dimensional coordinates cannot distinguish floors within a building, rule-defined address relations require geometric approximation after coordinate conversion, and unregistered indoor spaces lack official geometry. We propose question answering over a knowledge graph derived deterministically from the assignment rules of the Korean road-name address system, in which number parity, order, and difference encode the opposite-side, same-side successor, and address-order interval relations. Mapping these rules onto graph edges yields a three-layer graph—reference frame, physical entities, and facilities—that resolves relational queries by edge traversal without coordinate computation. On a graph of 191,808 entities and 380,743 relation edges for Jongno-gu, Seoul, 93.9% of the rule-derived opposite edges were geometrically confirmed, and on an independently constructed 400-question benchmark the proposed method reached an F1 score of 0.381—under a structural ceiling imposed by administrative–physical binding coverage—versus 0.018 for relation-ablated retrieval and 0.103–0.119 for relation-verbalized retrieval. Queries on unregistered floors, which lack official geometry, reached an F1 score of 0.647; the advantage persisted through answer generation with three open 7–8B models (0.316–0.359 vs. 0.004–0.017); and applications to Sydney, Seattle, Paris, and Amsterdam yielded 0.521–0.746 across address-assignment schemes.