Diverse urban environmental elements provide health and amenity value for residents. People are willing to pay a premium for a better environment. Thus, it is essential to assess the benefits and values of these environmental elements. However, limited by the interpretability of the machine learning model, existing studies cannot fully excavate the complex nonlinear relationships between housing prices and environmental elements, as well as the spatial variations of impacts of urban environmental elements on housing prices. This study explored the impacts of urban environmental elements on residential housing prices based on multisource data in Shanghai. A SHapley Additive exPlanations (SHAP) method was introduced to explain the impacts of urban environmental elements on housing prices. By combining the ensemble learning model and SHAP, the contributions of environmental characteristics derived from street view data and remote sensing data were computed and mapped. The experimental results show that all the urban environmental characteristics account for 16 percent of housing prices in Shanghai. The relationships between housing prices and two green characteristics (green view index from street view data and urban green coverage rate from remote sensing) are both nonlinear. Shanghai’s homebuyers are willing to pay a premium for green only when the green view index or urban green coverage rate are of higher value. However, there are significant differences between the impacts of the green view index and urban green coverage rate on housing prices. The sky view index has a negative influence on housing prices, which is probably because the high-density and high-rise residential area often has better living facilities. Residents in Shanghai are willing to pay a premium for high urban water coverage. The case of Shanghai shows that the proposed framework is practical and efficient. This framework is believed to provide a tool to inform the decisions of housing buyers, property developers and policies concerning land-selling and buying, property development and urban environment improvement.

Indoor room optimal allocation is of great importance in geographic information science (GIS) applications because it can generate effective indoor spatial patterns that improve human behavior and efficiency. However, few research concerning indoor room optimal allocation has been reported. Using an office building as an example, this paper presents an integrative approach for indoor room optimal allocation, which includes an indoor room allocation optimization model, indoor connective map design, and a multiple ant colony optimization (MACO) algorithm. The mathematical optimization model is a minimized model that integrates three types of area-weighted costs while considering the minimal requirements of each department to be allocated. The indoor connective map, which is an essential data input, is abstracted by all floor plan space partitions and connectivity between every two adjacent floors. A MACO algorithm coupled with three strategies, namely, (1) heuristic information, (2) two-colony rules, and (3) local search, is effective in achieving a feasible solution of satisfactory quality within a reasonable computation time. A case study was conducted to validate the proposed approach. The results show that the MACO algorithm with these three strategies outperforms other types of ant colony optimization (ACO), Genetic Algorithm (GA), and particle swarm optimization (PSO) algorithms in quality and stability, which demonstrates that the proposed approach is an effective technique for generating optimal indoor room spatial patterns.

With the emergence of various types of indoor positioning technologies (e.g., radio-frequency identification, Wi-Fi, and iBeacon), how to rapidly retrieve indoor cells and moving objects has become a key factor that limits those indoor applications. Euclidean distance-based indexing techniques for outdoor moving objects cannot be used in indoor spaces due to the existence of indoor obstructions (e.g., walls). In addition, currently, the indexing of indoor moving objects is mainly based on space-related query and less frequently on semantic query. To address these two issues, the present study proposes a multi-floor adjacency cell and semantic-based index (MACSI). By integrating the indoor cellular space with the semantic space, the MACSI subdivides open cells (e.g., hallways and lobbies) using space syntax and optimizes the adjacency distances between three-dimensionally connected cells (e.g., elevators and stairs) based on the caloric cost that extends single floor indoor space to three dimensional indoor space. Moreover, based on the needs of semantic query, this study also proposes a multi-granularity indoor semantic hierarchy tree and establishes semantic trajectories. Extensive simulation and real-data experiments show that—compared with the indoor trajectories delta tree (ITD-tree) and the semantic-based index (SI)—the MACSI produces more reliable query results with significantly higher semantic query and update efficiencies; has superior semantic expansion capability; and supports multi-granularity complex semantic queries.

As airport operations expand and ground handling becomes more complex, airport vehicle scheduling has evolved into a system-level decision problem constrained by operational rules, task dependencies, and resource availability. However, existing approaches largely rely on statistical correlation modeling and lack explicit representations of operational semantics and feasibility constraints, resulting in limited executability and poor cross-scenario robustness. To address this issue, we propose the Semantic-Constrained Planning Network (SCP-Net), which adopts a compile-first, plan-later paradigm by embedding operational semantics directly into the scheduling process. SCP-Net introduces an Operational Semantic Compiler (OSC) that encodes key flight task attributes, including service types, operational phases, and time windows, into a structured dependency representation, explicitly modeling task dependencies and task–vehicle feasibility relations. Based on this representation, a Constraint-Gated Planner (CGP) integrates operational dependencies and resource constraints through feasibility-aware gating, ensuring that planning is always conducted within valid operational regions. Through this design, SCP-Net directly generates schedules that are structurally consistent, semantically valid, and executable. Experimental results demonstrate that SCP-Net outperforms baseline methods in terms of executability, constraint violation rate, and cross-scenario stability, highlighting the effectiveness of explicit semantic modeling and constraint-driven planning for airport vehicle scheduling.

As airport operations grow denser and more constrained, vehicle scheduling becomes critical for airside efficiency. Existing methods encode operational rules implicitly, leading to unstable decision spaces and limited generalization. The authors proposed a semantic-aware bi-context scheduling network that integrates semantic modeling with end-to-end learning by separating feasibility determination from priority ranking. A semantic constraint projection module represented airport operational rules as a semantic knowledge base and projected feasible vehicle-task links at each timestep, constraining the decision space before learning. Based on these links, a bi-context link scoring module jointly modeled local execution context and global system state to rank feasible assignments using a lightweight scoring mechanism. Experiments on multiple simulated airport scheduling scenarios showed that the semantic-aware bi-context scheduling network outperformed baseline methods in allocation rationality and operational efficiency, demonstrating the effectiveness of semantic constraint projection and bi-context modeling.