## 1 Introduction  
Network analysis enriched with geospatial data offers a powerful framework for understanding spatial dynamics and supporting data-driven decision-making. The growing body of literature--reflected in works such as [1, 2, 3]--demonstrates the increasing availability of geospatial datasets and the advancement of analytical techniques, which have enabled widespread applications across diverse real-world domains. For instance, during Thailand's second wave of COVID-19, [PERSON]'s GitHub project addressed the absence of official data by providing provincial adjacency information, thereby enabling more accurate modeling of disease transmission [4, 5]. Adjacency-based networks have proven particularly useful in epidemiological studies. [PERSON] et al. [6] developed the Spatiotemporal Epidemiological Modeler (STEM), an extensible framework for simulating disease spread across spatial and temporal scales. Similarly, [PERSON] et al. [7] applied spatiotemporal analysis to tuberculosis (TB) incidence in Thailand from 2011 to 2020, revealing significant spatial heterogeneity and temporal trends along international borders. Beyond epidemiology, [PERSON] and [PERSON] [8] demonstrated how combining spatial and mobility data--using methods such as eigenvector centrality and Deep Gravity modeling--can enhance insights into flow dynamics and network structures. In a different domain, [PERSON] et al. [9] proposed a GIS-based framework for assessing land-use compatibility, showing improved accuracy and transparency over conventional approaches through a case study in Qaemshahr, Iran. Together, these studies underscore the versatility and impact of geospatial network analysis across various sectors, including public health, transportation, and urban planning. These diverse applications of geospatial network analysis highlight its value in uncovering hidden patterns and guiding data-informed interventions across various domains. Among the many analytical goals within this framework, one particularly critical task is identifying key or influential nodes in a network. These nodes often serve as strategic connectors, control points, or high-impact agents whose presence significantly affects the structure, function, and resilience of the system. Whether in the context of disease transmission, urban mobility, or land-use planning, understanding which nodes exert the most influence can greatly enhance predictive modeling, resource allocation, and policy development. As a result, a wide range of algorithms has been developed to tackle this challenge, increasingly moving beyond simplistic topological measures. Researchers have introduced various techniques for influential node detection, as shown in [10; 11]. In the context of urban studies, where nodes can represent locations, these influential nodes are often referred to as hubs. In general terms, a hub refers to a region or a location that plays a pivotal role within a network by serving as a central point where multiple connections converge. These hubs are critical to the efficiency and functionality of various systems, often acting as integrators of key infrastructure, enablers of high-throughput mobility, or reducers of overall transportation costs. Their strategic position within a network allows them to influence broader spatial patterns, facilitate the flow of goods and services, and support coordinated development. As such, understanding the role of hubs offers valuable insights into spatial organization, functional zoning, and regional growth dynamics. For instance, [PERSON] et al. [12] investigated urban agglomerations along the Yangtze River and found that major hub cities like Wuhan exert substantial spillover effects on land-use intensity, underscoring their impact on interregional development and spatial planning. Given the importance of hubs, numerous studies have proposed methods to identify them within spatial and mobility networks. [PERSON] et al.