Geocoding is an essential procedure in geographical information retrieval to associate place names with coordinates. Due to the inherent ambiguity of place names in natural language and the scarcity of place names in textual data, it is widely recognized that geocoding is challenging. Recent advances in deep learning have promoted the use of the neural network to improve the performance of geocoding. However, most of the existing approaches consider only the local context, e.g., neighboring words in a sentence, as opposed to the global context, e.g., the topic of the document. Lack of global information may have a severe impact on the robustness of the model. To fill the research gap, this paper proposes a novel global context embedding approach to generate linguistic and geospatial features through topic embedding and location embedding, respectively. A deep neural network called LGGeoCoder, which integrates local and global features, is developed to solve the geocoding as a classification problem. The experiments on a Wikipedia place name dataset demonstrate that LGGeoCoder achieves competitive performance compared with state-of-the-art models. Furthermore, the effect of introducing global linguistic and geospatial features in geocoding to alleviate the ambiguity and scarcity problem is discussed.

In this paper, we propose the fine-grained geospatial knowledge graph (FineGeoKG), which can capture the neighboring relations between geospatial objects. We call such neighboring relations strong geospatial relations (SGRs) and define six types of SGRs. In FineGeoKG, the vertices (or entities) are geospatial objects. The edges (or relations) can have “sgr” labels together with properties, which are used to quantify SGRs in both topological and directional aspects. FineGeoKG is different from WorldKG, Yago2Geo, and other existing geospatial knowledge graphs, since its edges can capture the spatial coherence among geospatial objects. To construct FineGeoKG efficiently, the crucial problem is to find out SGRs. We improve the existing geospatial interlinking algorithm in order to find out SGRs faster. To answer SGR queries efficiently, we design an index to organize the SGR edges and improve the binary join method for subgraph matching. We conduct experiments on the real datasets and the experimental results show that the proposed algorithm is more efficient than the baseline algorithms. We also demonstrate the usefulness of FineGeoKG by presenting the results of complicated spatial queries which focus on structural and semantic information. Such queries can help researchers (for example, ecologists) find groups of objects following specific spatial patterns.

Studies on disaster countermeasures utilize extensive simulations of earthquake, tsunami, people evacuation, and other targets, generating enormous amounts of data. The continuing development of computational capability has facilitated the increase of the simulation data size and the utilization of such “big data” has become a serious problem. With this background, the present study proposes, from the viewpoint of information science, the simulation data warehouse approach for the interactive analysis of large simulation data and describes a method of realizing a data warehouse. An objective of this study is to integrate different simulation data sets and enable exploratory analysis of multiple accumulated simulation data with high-speed response by data preprocessing. Further, the developed prototype system architecture and a case example of its use are explained.

Ride-hailing platforms allow people to request rides, but sometimes they do not respond quickly enough. Due to the complex urban transportation system and the large geographical area of the city, the imbalance between supply and demand becomes a common problem. To solve this problem, many studies have proposed taxi repositioning methods. However, each of two mainstream methods (region-based and taxi-based) has its own advantages and disadvantages. In this paper, we propose the RSTR model, which is a two-layer model that takes advantages of the two mainstream methods. We model the problem as a partially observable Markov decision process and design the optimization objective. To generate more accurate repositioning strategies and improve model training outcomes, we propose a many-to-many scheduling mode and demonstrate its effectiveness. Extensive experiments on real-world datasets show that RSTR can effectively balance supply and demand and outperform other baseline methods.

In analyzing observation data and simulation results, there are frequent demands for comparing more than one data on the same subject to detect any differences between them. For example, comparison of observation data for an object in a certain spatial domain at different times or comparison of spatial simulation data with different parameters. Therefore, this paper proposes the difference operator in spatio-temporal data warehouses, which store temporal and spatial observation data and simulation data. The requirements for the difference operator are summarized, and the approaches to implement them are presented. In addition, the proposed approach is applied to the mass evacuation of simulation data in a tsunami disaster, and its effectiveness is verified. Extensions of the difference operator and their applications are also discussed.