Geographical Network Research Articles

Energy distribution in long-range-interacting weighted geographic networks

Energy distribution in long-range-interacting weighted geographic networks

Mitigating an Epidemic on a Geographic Network Using Vaccination

We consider a mathematical model describing the propagation of an epidemic on a geographical network. The size of the outbreak is governed by the initial growth rate of the disease given by the maximal eigenvalue of the epidemic matrix formed by the susceptibles and the graph Laplacian representing the mobility. We use matrix perturbation theory to analyze the epidemic matrix and define a vaccination strategy, assuming vaccination reduces the susceptibles. When mobility and the local disease dynamics have similar time scales, it is most efficient to vaccinate the whole network because the disease grows uniformly. However, if only a few vertices can be vaccinated, then we show that it is most efficient to vaccinate along an eigenvector corresponding to the largest eigenvalue of the Laplacian. We illustrate these results by calculations on a seven-vertex graph and a realistic example of the French rail network. When mobility is slower than the local disease dynamics, the epidemic grows on the vertex with largest number of susceptibles. The epidemic growth rate is more reduced when vaccinating a larger degree vertex; it also depends on the neighboring vertices. This study and its conclusions provide guidelines for the planning of a vaccination campaign on a network at the onset of an epidemic.

Geography of research networks in the Big South of Russia

The geography of research and innovation activity is a traditional topic of research in human geography. Classical studies of the clustering and diffusion of innovation in the 1960s and 70s received a new impetus in the 21st century with the understanding of the openness of innovation and the importance of interregional collaborative networks. Digitalization has played an important role, providing access to new sources of large geocoded data on the movement of key elements of the knowledge economy, including publications as a formalized outcome of scientific activity. The purpose of the article is to identify the center-periphery research ties of one of the largest macroregions of Russia – the Big South of Russia. The aim was to assess the territorial patterns of interregional research networks with the identification of gravity poles and interconnections at the local and national levels. The study uses the method of spatial scientometrics. Geocoded data on publications from the Scopus bibliographic database were used. In the course of the study the hypotheses of “Moscow-centricity” and “St. Petersburg-centricity” of the knowledge domain of the macroregion are tested, the influence of the factor of geographical proximity and the role of the diversity of interregional ties are evaluated, and the “heights-lowlands” of the landscape are revealed. The results of the research have shown a high degree of connectivity between the regions of the Big South of Russia in the research area. The hypothesis about the presence of several centers of gravity of scientific activity in the macroregion was confirmed. First of all, the peaks of the “scientific ridge” of the Big South form Rostov oblast with the largest urban agglomeration in the south of the country, the Rostov urban agglomeration, as well as the Krasnodar krai and Volgograd oblast. The hypothesis of the existence of several centers of scientific activity in the macroregion was confirmed. The influence of the factor of territorial proximity in the formation of research ties is not high and is manifested mainly in the southern Russian regions with relatively low indicators of research output (primarily, it is typical for national republics). Interregional research cooperation between advanced regions is not limited to immediate geographical proximity but is due to a combination of non-territorial factors. The “Moscow-centricity” of the scientific agenda of the regions of the Big South of Russia is true for most of the southern regions of the Russian Federation, which is particularly pronounced in regions with low internal scientific potential and in new regions undergoing a period of transformation of their scientific systems.

GS–CDNet: a remote sensing image cloud detection method with geographic spatial data integration

ABSTRACT In optical remote sensing images, clouds exhibit irregular scales and boundaries that vary with elevation across diverse geographical locations. To accurately capture the diverse visual patterns of clouds, we propose a cloud image segmentation approach named GS-CDNet (Geographic Spatial Data-Cloud Detection Network), which is based on the integration of geospatial data with multifaceted self-attention feature extraction, multi-scale feature aggregation, and boundary clarification techniques.Firstly, we utilize geographical coordinates from optical remote sensing images to extract a raster DEM (Digital Elevation Model) from SRTM3. This process creates a dataset consisting of elevation images, longitude, and latitude maps as geospatial data, enhancing the model’s capability in spatial positioning for cloud detection. Secondly, the proposed method consists of three interconnected modules within the cloud detection network: the Interleaved Self-Attention module(ISAM) utilizes a variety of self-attention mechanisms in an interleaved manner to extract multi-scale feature information.The Bidirectional Multi-Scale Feature Fusion Module(BIMFM) is responsible for integrating features, enabling a more comprehensive contextual understanding. The Boundary Extraction Module(BEM) utilizes a residual structure to generate a boundary cloud mask, effectively addressing the common issue of boundary blurring in multi-scale cloud masks. Finally, we compared and evaluated GS-CDNet with other cloud detection methods and conducted an ablation study on the key components of the method. The validation of generalization performance demonstrates the exceptional performance of the proposed model in cloud mask generation. Geospatial data and the different modules of the method play a significant role in the model.

Supporting Fire Response: Advanced Spatial Data Analytics for Hydrant Access Assessment

ABSTRACTCalifornia faces increasing wildfire frequency and severity, necessitating effective firefighting strategies along with well‐located infrastructure. Hydrants are vital for urban safety and must comply with established standards to adequately protect people and property. One such standard involves hydrant spacing, critical for ensuring effective emergency response in the event of a fire. Traditional geographic information systems techniques are not well suited to fully assess hydrant networks, especially in the context of spacing given complex road network geographies. This paper introduces an innovative approach to assess hydrant spacing. The developed technique is applied in the evaluation of infrastructure in Santa Barbara, California. The results reveal significant code violations along streets throughout the region, suggesting clustered pockets of vulnerability and risk. Potential strategies emerge from this analysis to enhance firefighting preparedness, offering guidance to local agencies regarding hydrant system limitations across this fire‐prone region.

Geography of Research Networks in the South of Russia

Geography of Research Networks in the South of Russia

Socio-spatial dynamics of E-participation: A case study of the Mudamos app in João Pessoa (2017–2020)

Emerging civic technologies can support civil society and the state toward participatory democracy. They offer citizens alternative tools for online participation or e-participation. In this study, we explore the socio-spatial dynamics of an e-participation activity in a city's policy-making process through the artifice of the network analyses. We build upon the idea of a network conceived as an object of human action, considering its social, technical, and spatial elements. We propose a conceptual framework to translate the geographic network of an e-participation activity in the city's policy-making process constituted by six dimensions: vocation, technology, institutional design, spatiality, participation, and the deliberative system. The case study applies the framework to the Mudamos application in the city of João Pessoa, in the State of Paraíba, Brazil. This e-participation tool facilitates digital signatures for Citizen Initiative Draft Bills (CIDB). Despite initial technical, institutional, and financial support, the app did not deliver its potential value. The reasons for low adoption are the lack of economic resources, low levels of civic skills, discontinuation of citizen recruitment, and discontinuation of the partnership between the control agents. Furthermore, socio-spatial inequality was critical in users' access and participation. Our findings suggest that the development of state capacities and participatory literacy are crucial to the success of e-participation initiatives. Consequently, the proposed framework and its application serve as a valuable starting point for researchers and policymakers seeking to understand the socio-spatial relationships involved in this process, offering knowledge to address digital inequalities and increase the effectiveness of e-participation initiatives.

Identifying authorial roles in research: A Kano model-based bibliometric analysis for the Journal of Medicine (Baltimore) 2023.

The landscape of research roles within academic journals often remains uncharted territory, with authorial contributions frequently reduced to linear hierarchies (e.g., professor and assistant professor). The Kano model, traditionally used in customer satisfaction research, offers a nuanced framework for identifying the multifaceted roles of authors in scholarly publications. This study utilizes the Kano model to dissect and categorize the roles of authors in the medicine field. To conform to the hypothesis, China is the research leader while the US is the research collaborator, as reflected in the publications of the journal of Medicine (Baltimore) in the year 2023. We conducted a comprehensive bibliometric analysis of all research articles published in the journal of Medicine (Baltimore) in 2023. The Kano model was applied to classify authors into 5 categories reflective of their research roles: followers, leaders, partners, contributors, and collaborators. Data on author publications and co-authorship networks with multi-author rates (MARs) were analyzed to assign Kano categories based on the authorship positions of first and corresponding authors. Descriptive statistics and network analysis tools were used to interpret the data, including radar plots, geographical maps, and Kano diagrams. The analysis covered 1976 articles, uncovering a complex network of author roles that extends beyond the conventional binary distinction of lead and supporting authors (i.e., leading, and following researchers). A research leader in China and a collaborator in the US were conformed to support the hypothesis, based on their publications (1148 vs 51) and MARs (12.20% vs 19.61%). The Kano classification was visually adapted to classify authors (or entities) into 5 categories. The combined choropleth and geographical network maps were illustrated to identify author roles in research briefly. The Kano model serves as an effective tool for uncovering the diverse contributions of authors in medical research. By moving beyond the lead and follower dichotomy, this study highlights the intricate ecosystem of authorial roles, emphasizing the importance of each in advancing knowledge within the field of medicine. Future application of the Kano model could foster a more collaborative and inclusive recognition of contributions across various disciplines.

Phylogeography of a herbal Pinellia ternata reveals repeated range expansions and inter/postglacial recolonization routes on the fragmented distribution pattern in China.

Most plant phylogeographic studies in China have focused on the importance of genetic divergence and where should the shelter be located. Little attention has been paid to range expansion and recolonization routes in this region. In this study, two cpDNA fragments (psbK-psbI and trnL-F), two pairs of nuclear gene sequences (ITS and ETS), and nine pairs of SSR molecular markers were used, combined with Bayesian Skyline Plot method, gene barrier analysis, and species distribution models to explore the phylogeographical pattern, potential expansion routes and population dynamic history of Pinellia ternata from 22 population. The results showed that phylogeograhical pattern and genetic structure for P. ternata are effected by environmental heterogeneity and climate fluctuation, and it can be divided into two groups (Southwest group, Central and Eastern group) and thus there are at least two glacial refugia in China. Three expanding routes within groups were explored to contribute to the phylogeogrephic pattern of P. ternata based on the geographical distribution and network analysis of haplotypes. In a word, our study reveals repeated range expansions and inter/postglacial recolonization routes on the fragmented distribution pattern in China and resolves the refugia distributing in China and has also certain reference value for the protection of the medicinal plant P. ternata.

Optimization model for urban ecological network connectivity considering geospatial constraints

ABSTRACT Connectivity enhancement of ecological networks is an efficient and advantageous spatial planning strategy, and it is a hot research topic to analyze ecological networks based on complex systems theory and perform model optimization. As a typical geographic network, the evolution process of ecological network, such as node and edge growth, will be subject to geospatial constraints, and the current research lacks optimization models that take the geospatial constraints into account. This study focused on urban ecological network connectivity and the distribution of nodes as well as structural fractures where the connectivity of the ecological network was hindered were analyzed. Then it introduced a local world network model to optimize network connectivity and designed a network growth mechanism with limited added nodes and optimal connections. An ecological network optimization model considering geospatial constraints was constructed, and the network closeness index was used to fit the objective function curve to select optimization schemes. This study used Shenzhen, China as a case study, and the results indicated that the initial ecological network was significantly fractured by urban construction. In the optimization scenario, the network evolution reached its optimal state at the simulation step size of t = 26. At this point, the closeness index of the optimized network was increased by 15%. The optimization model constructed in this paper emphasizes the importance of functional restoration of existing network nodes, and the results of the study can provide support for ecospatial layout optimization and management.

Connecting the dots: Applying multispecies connectivity in marine park network planning

Marine ecosystems are highly dynamic, and their connectivity is affected by a complex range of biological, spatial, and oceanographic factors. Incorporating connectivity as a factor in the planning and management of marine protected areas (MPAs) is important yet challenging. Here, we implemented a novel integrative framework that uses intraspecific genetic and genomic data for multiple marine species to characterise connectivity across a recently established South Australian MPA network. We generated connectivity networks, estimated cross-species concordance of connectivity patterns, and tested the impact of key spatial and oceanographic factors on each species. Connectivity patterns varied markedly among species, but were most correlated among those with similar dispersal strategies. Ordination analyses revealed significant associations with both waterway distances and oceanographic advection models. Notably, waterway distances provided better predictive power in all-species combined analyses. We extended the practical relevance of our findings by employing spatial prioritisation with Marxan, using node values derived from both genetic and geographic connectivity networks. This allowed the identification of several priority areas for conservation, and substantiated the initial decision to employ spatial distance as a proxy for biological connectivity for the design of the South Australian marine park network. Our study establishes a baseline for connectivity monitoring in South Australian MPAs, and provides guidelines for adapting this framework to protected networks elsewhere in the world.

What is the Right Size for Truckload Carrier Alliances?

In spot markets for truckload transportation services, centralized collaboration among carriers is often regarded as an ideal way to determine which load (a.k.a. shipment) is delivered by each carrier. The challenge of getting all competing carriers to collaborate under a centralized system (instead of being rivals for loads) has prompted interest in collaboration modes that are on a smaller scale than complete centralization. In this research vein, this paper answers the following question: for the small-scale collaboration of decentralized load exchange among small alliances of willing carriers, how close do the performance results (profits, etc.) come to the purported ideal results under centralization? Our core finding from extensive computational experiments is that, by collaborating with the right load exchange partners, a carrier in a small and easier-to-manage alliance can achieve financial savings that closely match and sometimes even surpass the per carrier savings from a fully centralized system. This, and some closely related insights, comprise this paper’s main contributions to the literature. The practical relevance of the contributions is in facilitating decisions about how much effort is worth expending on having every carrier within geographic network participate in a centralized system.

Study on the spatial network structure of energy carbon emission efficiency and its driving factors in Chinese cities

How to adapt to climate change while achieving sustainable economic and social growth has become a major topic of concern worldwide. With the constraints of the dual‑carbon strategy, the integration of regional environmental governance and energy carbon reduction governance is a prevalent focus. This study focuses on analyzing 282 prefecture-level cities in China. The SNA approach and modified gravitation model are utilized to estimate the energy carbon emission efficiency of Chinese cities from 2006 to 2021. The spatial correlation network and the QAP model are ultimately utilized to investigate the factors. The study presented the following findings: ① There are notable temporal and regional discrepancies in the energy carbon emission efficacy of Chinese cities. Generally, defined by high values in the east and low values in the west. ② The efficiency of energy carbon emissions in networks connecting urban areas in China is multidimensional, complex, and organic and has improved stability. ③ The developed regions in the east exert a dominant influence on the geographical network, while the central and western parts of the country, which are distant, are considered peripheral. ④ There are few connections within each segment of the geographic correlation network for energy carbon emission efficiency in Chinese cities; however, there are substantial correlations between segments, indicating the presence of a substantial spillover effect. ⑤ The formation of energy‑carbon emission efficiency correlation networks in Chinese cities is significantly influenced by disparities in economic development and government intervention. Conversely, the level of science and education exerts a significantly negative impact on this phenomenon. It is advisable to encourage the development of a spatial correlation network that connects urban energy carbon emission efficiency. This can be achieved through the implementation of specific measures, the establishment of a regional coordination mechanism, leveraging the strengths of energy-efficient regions in the east, maximizing the potential of each sector, and considering the factors that influence the outcomes. Compressing the driving factors and attributes of the spatial correlation network of energy carbon emission efficiency holds substantial practical importance for facilitating the ongoing expansion of the regional low-carbon energy network space and establishing a regional low-carbon synergistic energy governance system.

Electricity market integration in China: The role of government officials’ hometown ties

Market segmentation is common across various sectors, particularly evident in China's electricity market. Meanwhile, officials form a variety of geographic networks due to their innate backgrounds and acquired social connections. An extensive body of studies providing insights into the important role of officials' network ties in resource allocation, but little attention has been paid to the impact of this dynamic process on energy resources. Given that energy plays a significant role in shaping economic growth, our study complements this body of literature by examining how leaders with hometown ties impact inter-provincial electricity trade in China using a gravity model. The gravity model, which has become the traditional framework for estimating the determinants of trade flows, is applicable to the province pairs data structure in our study. Using data on provincial officials' hometown and inter-provincial electricity transactions for 30 provinces in China between 2007 and 2019, the results reveal that a leader whose hometown is i and current jurisdiction is j significantly increases the probability of electricity flowing from i to j, corresponding to a 53.73 % increase, which indicates that they implement a weaker market segmentation on their hometown. Moreover, the results reveal that this effect only occurs in a leader's tenure, and is particularly pronounced when a leader's hometown and current jurisdiction are on the same regional grid, or at least adjacent to each other, or when the current jurisdiction has higher power load levels. The results of mechanism analysis reveal that China's immature electricity market drives officials to leverage hometown ties for market integration, rather than promotion or rent-seeking. Our findings contribute to a deeper understanding of the broader impact of officials' personal ties. It also provides a valuable insight into the factors facilitating the integration of China's electricity market.

Where the money is: branch network structure and bank profitability in Switzerland

This paper investigates the impact of the geographic concentration of branch networks on bank profitability in Switzerland. Leveraging Switzerland's distinctive federalist fiscal structure, where communes set their own income tax rates, we also explore whether the tax burden of branch locations moderates this relationship. Using a hand-collected dataset of branch networks of commercial banks, we find that geographic branch network concentration positively affects bank profitability but only when banks concentrate their branches in low-tax communes. This suggests that the proximity to wealthy clients, who are more likely to reside in low-tax areas, enhances bank profitability.

The geographies and complexities of online networks in the off-street sex market

Exploitation and human trafficking in sex markets tend to include both online and offline spaces. Understanding the scale, complexity and geography of networks is important in policing human trafficking and online escort adverts are often used to identify organised crime in this context. This article aims to make a methodological contribution to how data relating to online networks in the sex market can be collected and analysed. Through the application of web scraping, social network analysis and principal component analysis, the digital traces of 15,016 online networks operating on an adult services website were analysed in relation to their complexity and geographical patterning. The findings suggest that structural and geographical characteristics are useful for understanding the heterogeneity of online networks. Analysing networks, as opposed to assessing escort adverts, offers a more robust approach to understanding the sex market, which is more sensitive to the continuum of experiences encapsulated therein.

Geospatial Structure and Evolution Analysis of National Terrestrial Adjacency Network Based on Complex Network

The first law of geography is one of the most important concepts in geographical analysis, revealing the significant role of spatial proximity. At present, some current international relation studies or geographic network analysis studies tend to build corresponding network models according to different themes, but the most basic level of geographic neighborhoods is intentionally or unintentionally neglected in those processes. Based on the adjacency relationship between the terrestrial countries in the world, the model of the terrestrial adjacency network (TAN) is constructed. The model includes almost all land-based countries and is divided into three main regions, respectively, Eurasia, Africa, and America. On the mathematical model of these regions, we analyze the geospatial structure and network evolution of the adjacent networks utilizing statistical methods and network analysis methods. This study helps to map and understand the geographical attributes and characteristics of countries from the perspective of holistic structure, aiming to provide a quantitative reference for subsequent research on international relations and geographic computing. Moreover, despite some limitations, TAN represents a new advance in geographical network analysis that can be further applied by overlaying more attribute data.

DNA barcoding, phylogeography and evolutionary dynamics of Chrysichthys auratus

This study embarked on an exploration into the genetic structure and evolutionary history of the Chrysichthys auratus species, leveraging PCR amplification, phylogenetic trees, and haplotype networks. Specific DNA segments were successfully amplified and visualized through electrophoresis. Newly obtained sequences were Bank into GenBank and given accession numbers (OR730807-OR730808-OR730809). The Neighbor-Joining method provided insights into the evolutionary relationships among taxa, further augmented by bootstrap values and the Tamura 3-parameter method. A comprehensive geographical haplotype network showcased pronounced genetic differentiation, especially between remote populations. Nonetheless, shared haplotypes between proximate regions indicated either ancestral genetic connections or ongoing gene flow. Employing the COI-DNA barcodes, an in-depth understanding of intra- and inter-populational genetic diversity was achieved. The study's findings unravel the intricate genetic landscape and evolutionary dynamics of C. auratus, offering novel perspectives into its demographic history across its vast native habitat.

Predicting network congestion by extending betweenness centrality to interacting agents.

We present a simple model to predict network activity at the edge level by extending a known approximation method to compute betweenness centrality with a repulsive mechanism to prevent unphysical densities. By taking into account the strong interaction effects often observed in real phenomena, we aim to obtain an improved measure of edge usage during rush hours as traffic congestion patterns emerge in urban networks. In this approach, the network is iteratively populated by agents following dynamically evolving fastest paths who are progressively attracted towards uncongested parts of the network as the global traffic volume increases. Following the transition of the network state from empty to saturated, we study the emergence of congestion and the progressive disruption of global connectivity due to a relatively small fraction of crowded edges. We assess the predictive power of our model by comparing the speed distribution against a large experimental data set for the London area with remarkable results, which also translate into a qualitative similarity of the congestion maps. Also, percolation analysis confirms the quantitative agreement of the model with the real data for London. We perform simulations for seven other topologically different cities to obtain the Fisher critical exponent τ that shows no common functional dependence on the traffic level. The critical exponent γ, studied to assess the power-law decay of spatial correlation, is found to be inversely proportional to the number of vehicles for both real and simulated traffic. This simulation approach seems particularly fit to describe qualitative and quantitative properties of the network loading process, culminating in peak-hour congestion, by using only topological and geographical network features.

An Evaluation of the Spatial Repercussions of Student Mobility Policy in European Higher Education Area Using Network Analysis

The mobility of international students is a crucial tool for the European Union's goal of creating a unified European Higher Education Area. Despite the initial assumption that all European universities and students can benefit equally from cross-university study experiences, certain European regions have become disproportionately favored over time. This has resulted in specific geographical patterns, challenging the principles of equality and openness in the EU's higher education policy. To better understand these spatial effects and enhance the EU's mobility policy effectiveness, this research analyzes the network properties of Erasmus+, comparing it with traditional degree-seeking activities. Utilizing a modularity measure with data from the EU and UNESCO, the study reveals significant sub-regional variations in the Erasmus+ geographical network, posing challenges for policy implementation and limiting mobility alternatives.