geographic information systems

This study investigates the spatial evolution of the U.S. transportation cybersecurity ecosystem and the geographic distribution of skilled professionals, elucidating how socioeconomic factors shape industry clustering and dynamic visitor flows across automotive, logistics, transportation, and cybersecurity sectors. Method: We propose BiTransGCN—a novel hybrid framework that jointly integrates attention-based Transformers with Graph Convolutional Networks (GCNs) to model and forecast spatiotemporal visitor mobility and industrial clustering simultaneously. Contribution/Results: BiTransGCN achieves a 23.6% reduction in mean absolute error (MAE) over baseline models and identifies five high-potential regional industry clusters. The findings provide data-driven decision support for strategic investment in transportation critical infrastructure, cross-sector workforce planning, and evidence-based regional policy formulation.

This paper addresses critical challenges in urban network modeling—inefficient data acquisition, insufficient multimodal integration, and limited openness—by proposing a systematic solution built upon OSMnx. Methodologically, it extends OSMnx to enable automated downloading and joint modeling of multimodal transportation networks (e.g., walking, cycling, bus), integrates high-fidelity geometric parsing, dynamic attribute embedding, and graph-theoretic spatial analysis, and establishes a reproducible, extensible open-science framework. Contributions include: (1) the first deep integration of open science principles into urban computing infrastructure design; (2) substantial improvements in street-network modeling accuracy and cross-scale analytical capability; and (3) robust support for interdisciplinary research in geography, transportation engineering, and computer science. The resulting tool has become a mainstream infrastructure in open urban analytics, adopted in hundreds of empirical studies worldwide.

This study identifies a tripartite inequity in San Diego’s multimodal transportation system: (1) a core–periphery structural disconnection; (2) a transit accessibility gap—30.3% of points of interest lack bus coverage within walking distance; and (3) excessive highway dependence, resulting in low overall walkability, especially in suburban and rural areas. To address this, we construct an integrated multilayer network incorporating driving, walking, and public transit, fusing OpenStreetMap and Metropolitan Transit System data. We propose a novel walkability metric grounded in path connectivity and service radius, and conduct comprehensive diagnostics via centrality analysis, community detection, and multiscale accessibility assessment. Results show that travel-scale characteristics are governed by the dominant transport mode, while system resilience is disproportionately determined by critical highway nodes. The framework delivers a transferable methodology for assessing transportation equity and resilience in urban contexts.

This work addresses the performance optimization challenge of spatial databases in mobile Internet-of-Things (IoT) environments. We systematically investigate the coupled effects among spatial indexing strategies (e.g., R-tree, GiST), mobile object data representations (trajectory vs. snapshot), and intrinsic spatial characteristics of datasets—specifically, geographic overlap degree and distribution skewness. We propose, for the first time, approximate quantification methods for overlap and skewness, and design a comprehensive PostGIS benchmark covering diverse real-world datasets and mixed read/write workloads. Experimental evaluation uncovers scenario-specific optimal combinations of index structures and data formats under varying spatial characteristics. The results yield actionable, deployment-ready guidelines for storage layout and query optimization in spatiotemporal applications; performance differences for key operations reach up to 5.3× across configurations.

In transportation geography research, street intersection counts are frequently overestimated, introducing bias into network analyses. This paper systematically identifies three primary causes of such overcounting—dangling edges, acute angles below detection thresholds, and pseudo-bifurcations—and proposes a topology-driven graph simplification framework. The method integrates spatial node merging, edge contraction, and robust metric validation to achieve street network simplification while preserving topological fidelity and computational efficiency. Evaluated across 100+ global cities, the algorithm reduces the median intersection overestimation rate from 14% to less than 0.5%. Consequently, downstream analyses exhibit significantly improved memory efficiency and runtime performance without compromising geographic modeling accuracy. This work establishes a reproducible, scalable, and standardized preprocessing paradigm for quantitative urban network analysis.

This study evaluates the impact of random and targeted perturbations on the structural connectivity and freight functionality of the U.S. rail–water intermodal transport network. By developing a simulation framework that integrates graph-theoretic metrics with flow-weighted centrality measures and incorporates a dynamic update mechanism to model progressive degradation and complete failure of nodes or edges, the research reveals that partial functional degradation (e.g., reduction to 60% capacity) of high-freight-volume hubs inflicts significantly greater damage on system performance than the total removal of an equivalent number of high-betweenness or randomly selected nodes. This finding underscores the critical role of freight flow dynamics in resilience assessment and challenges conventional analysis paradigms that rely solely on topological structure.

Existing approaches to comparing road network datasets predominantly emphasize topological structure while neglecting traffic flow dynamics, thereby limiting their utility for traffic-oriented data selection. This study proposes a macroscopic quantitative evaluation framework that explicitly incorporates traffic flow into the comparison process. The method generates traffic-weighted spatial distributions through static traffic assignment and quantifies their spatial discrepancies using the two-dimensional Wasserstein distance. By integrating traffic flow as an explicit component, this approach enables functional assessment of road network datasets from a transportation perspective. Case studies demonstrate that the framework effectively discriminates between datasets of varying sources and levels of simplification, offering a novel and practical tool for informed dataset selection in traffic analysis.

This study investigates the interdependent mechanisms between energy and transportation infrastructure within urban metabolic systems under perturbations. Treating the city as an organism, it pioneers the integration of real-world, city-scale power distribution and road network geometry to construct an electricity–road interdependent network model. Combining graph-theoretic connectivity analysis with empirical data-driven robustness assessment, the work introduces both weighted and unweighted metrics to quantitatively evaluate system resilience. The research uncovers cascading failure pathways triggered by disturbances in a single infrastructure network, offering an innovative modeling framework and quantitative foundation for understanding the coupled vulnerability of urban multi-infrastructure systems.

This study addresses the growing demand for mobility insights across urban planning, transportation, and retail by proposing an end-to-end urban mobility analytics framework. Built upon a reusable modular architecture, the framework integrates high spatiotemporal resolution mobility modeling with multi-scenario business applications. It establishes a closed-loop pipeline—from raw geolocation data to strategic insights—through anonymization, ETL workflows, BigQuery-based data management, Vertex AI–driven model training, and Power BI visualization. The system effectively supports diverse analytical tasks, including traveler profiling, trajectory mining, catchment area analysis, traffic anomaly detection, and origin–destination pattern recognition, thereby delivering scalable and efficient decision support for both urban governance and commercial strategy.

This study addresses the overlooked spatial coordination mechanisms in critical infrastructure investment, aiming to identify and quantify their impact on equipment replacement decisions—thereby preventing mistimed interventions and loss of coordination benefits arising from neglecting spatial dependence. Method: We propose a hybrid estimation framework integrating nested fixed-point algorithms with simulated method of moments (SMM), enabling computationally tractable identification of spatial coordination while preserving structural model interpretability. Contribution/Results: Empirical analysis using GPU replacement data reveals two dominant coordination patterns: sequential replacement cascades and simultaneous failure batch processing. Spatial interdependence accounts for 5.3% of residual variation unexplained by an independent model; sequential coordination is three times stronger than batch processing; and “hotspot” effects in high-risk regions are tenfold those in low-risk areas. A chi-square test strongly rejects the spatial independence hypothesis (p < 0.001).