Observation-based sensor deployment problems require facility configurations to satisfy geometric, topographic, and redundancy constraints in addition to the coverage objectives that directly influence the quality of the observations. In many real-world monitoring systems, effective service areas are shaped by terrain-constrained visibility, distance-dependent signal attenuation, overlapping coverage requirements, and separation between facilities. These characteristics are not explicitly considered in widely used continuous maximal covering formulations, which commonly assume simplified circular service regions, homogeneous demand distribution, and uniform coverage strength. This study develops the Obstacle- and Geometry-Constrained Continuous Coverage Model (OG-CCM), an extension of the continuous maximal coverage problem that incorporates distance-decay coverage attenuation, obstacle-constrained visibility, redundant coverage requirements, admissible beam intersection geometry, and inter-facility separation constraints within a unified optimization framework. The proposed model is solved using heuristic methods based on swarm and evolutionary intelligence, and we demonstrate the applicability of the proposed model through a case study on optimizing dual-Doppler mobile radar configuration to observe severe weather events in Central Oklahoma. The results demonstrate that the proposed model effectively captures coverage attenuation, redundant coverage requirements, geometric constraints, and topography-dependent service areas within a unified optimization framework. By doing so, it provides a flexible and robust methodology for sensor facility deployment planning where conventional models fall short, enabling more reliable and operationally feasible deployment decisions.
Global maritime networks are highly complex and dynamic systems, organized around hubs and corridors that form hierarchical structures essential to international trade. Hubs and corridors are conceptually interconnected; however, their identification has often relied on distinct analytical approaches, with hubs typically defined by connectivity-based metrics and corridors by patterns of spatial interaction and flow concentration. Although resilience studies recognize the interdependence of hubs and corridors, existing approaches often rely primarily on hub-centered data, offering limited empirical insight into how corridor-level flow dynamics evolve under external disruptions such as pandemics or economic shocks. To bridge this gap, this study introduces a Maritime Hub–Corridor framework that integrates hubs and their inter-hub linkages into a hierarchical network system, developed through a structured, sequential analytic procedure. Applying this framework to Port-of-Call data, we use three complementary indices—connectivity, freight capacity, and spatial interaction—to capture distinct dimensions of hub functionality. Rather than treating these as isolated computed measures, we use them to screen candidate hubs, evaluate them against a Cumulative Flow Contribution Curve (CFCC) criterion across all dimensions, identify the resulting hub set and hierarchy, and grouped interconnected hubs into corridor clusters. Findings reveal that maritime networks organize as differentiated hub-corridor systems across four spatial types, exhibiting multi-scale hierarchies. Additionally temporal analysis reveals the adaptability and contrasting resilience mechanisms of hub-corridors to socio-economic disruptions: Suez consolidation versus Panama fragmentation. This research contributes a novel analytical approach for identifying hub-corridors structures systematically, characterizing multi-scale maritime network organization, and understanding resilience mechanisms under major disruptions.
The science output of a region may depend on various intraregional and interregional factors. A Spatial Durbin Model was used to estimate the impacts of regional factors as well as spillovers from other regions on science output in European NUTS2 regions. Three different weightings of interregional impact were tested to capture geographical proximity: the inverse distance, the inverse distance squared, and a binary distance-threshold specification that excluded distant regions. The main findings are that research and development (R D) employment in higher education institutions in the region and in surrounding regions has a positive impact on science output, while R D employment in high-tech industries in the region has a negative impact. There are also intraregional and interregional effects of disciplinary specialization, with less specialized regions exhibiting higher science output. Regions with older universities exhibit higher within-region scientific output, although the total spatial effect of university age is not statistically robust once spillovers are accounted for.
Urban planners are increasingly tasked with strengthening neighborhood economies and encouraging more connected communities. Central to this objective is ensuring equitable access to essential amenities such as grocery stores, parks, and hospitals. In recent years, shared e-micromobility services such as publicly available e-bikes and e-scooters have gained popularity as a means of local transport, offering potential to extend access. However, their contribution to the 15-Minute City framework across cities is not well understood. In this study, we assess if shared e-micromobility services expand access to essential amenities (i.e., supermarkets, schools, hospitals, parks, and entertainment venues), across five international cities. Observed e-micromobility trips are used to generate city-level travel time surfaces, which are compared with counterfactual walking times for the same trips. Results suggest access gains from e-micromobility are uneven, largely determined by urban form, distribution of amenities, and distance from city center. Cities and neighborhoods with less compact urban form and more dispersed land use tend to experience the largest gains in access with e-micromobility, increasing the proportion of the city reachable within 15 min by up to threefold in some cities. This work provides evidence to inform policies to better integrate new forms of local-scale transport (i.e., e-micromobility services), to improve access to essential services within the 15-Minute City framework.
This study develops a Fractional-Order Reaction–Diffusion (FO-RD) model to capture the spatiotemporal dynamics of the adoption of Remote Healthcare Systems (RHS) in geographically isolated regions. By integrating Caputo fractional derivatives (CFDs) into a classical RD framework, the model incorporates memory effects and non-Markovian dynamics that characterize human behavioral responses to healthcare interventions. The model comprises three interacting compartments: unaware population, adopter population, and healthcare need, which evolve through socially driven local reactions and spatial diffusion. Theoretical stability analysis establishes conditions for local and global Mittag-Leffler stability of the equilibrium points. Numerical simulations validate the theoretical results and demonstrate how the FO modulates memory strength in adoption dynamics. Specifically, lower values of the fractional order (stronger memory) lead to slower initial adoption but more sustained long-term adoption. Sensitivity analysis highlights the influence of key parameters such as social contagion rate, external influence, adopter mobility, and relapse rate. The model serves as a quantitative tool for predicting adoption patterns, identifying geographical tipping points, and evaluating policy interventions aimed at enhancing healthcare equity in remote areas.
