Geographical Scenarios Research Articles

Coordination mechanisms applied to logistical systems for local disaster preparedness: a Latin American case

Purpose The study aims to present an agent-based simulation model (ABM) for exploring interorganizational coordination scenarios in local disaster preparedness. This approach includes local actors and logistical processes as agents to compare various strategic coordination mechanisms. Design/methodology/approach The ABM model, developed in the Latin American context, specifically focuses on a case study of Colombia. Three coordination mechanisms (centralized, decentralized and cluster-type) have been evaluated using three performance indicators: effectiveness, efficiency and flexibility. Findings Simulation results show that the decentralized scenario outperforms in terms of efficiency and flexibility. On the contrary, the centralized and cluster-type scenarios demonstrate higher effectiveness, achieving a greater percentage of requirements coverage during the disaster preparedness stage. The ABM approach effectively evaluates strategical coordination mechanisms based on the analyzed performance indicators. Research limitations/implications This study has limitations due to the application of results to a single real case. In addition, the focus of the study is primarily on a specific type of disaster, specifically hydrometeorological events such as flash floods, torrential rains and landslides. Moreover, the scope of decision-making is restricted to key actors involved in local-level disaster management within a municipality. Originality/value The proposed ABM model has the potential as a decision-making tool for policies and local coordination schemes for future disasters. The simulation tool could also explore diverse geographical scenarios and disaster types, demonstrating its versatility and broader applicability for further insights and recommendations.

Design and Implementation of a Geographic Scenario Computation System Based on Knowledge Base

Abstract. The promotion and expansion of Geographic Information System (GIS) applications have increased the complexity of geocomputation. The concept of a geographic scene, which encompasses both geographic entities and complex spatial interaction relationships, serves as a means of organizing and expressing geographic information. Existing methods of organizing and computing geographic scenes often focus on single geometric elements or single-category objects, neglecting the complexity of the geocomputation process and leading to biased results. To address this issue, this study designs and implements a geographic scenario computation system based on WebGIS to overcome the lack of integration of knowledge elements in traditional geographic scenario computation methods. The system consists of a data management module, a geographic scenario visualization module, and a geographic scenario computation module. It not only realizes the visualization of geographic scenarios but also introduces a knowledge base into the geographic scenario computation process, enabling accurate management of geographic scenarios. This paper uses a farmland scenario in Shandong Province, China, as a case study to verify the effectiveness and feasibility of the system. The application of this system provides new technical support for the computation process of geographic scenarios, promoting the refinement and intelligent development of geographic scenario management. This method can also be applied to similar applications of intelligent sensors and the Internet of Things in the organization and management of sensing information in the future.

Evaluation and Ranking of Energy Alternatives for Implementation in Different Geographic Scenarios using Decision Methods: Case Study of Colombia

In this research, several crucial aspects have been explored in the evaluation and ranking of energy alternatives in Colombia using advanced decision methods such as TOPSIS and Diffuse TOPSIS. The study analyzed how these techniques allow to effectively compare different renewable energy sources in various regions of the country, highlighting the importance of considering multiple approaches due to the unique geographical and energy characteristics of each region. Specifically, five regions were evaluated: Caribbean 1 and 2, Pacific 1 and 2, Andean, Amazonian and Orinoquia, observing significant differences in the evaluation of technologies between the models. In the evaluation of energy sources for each region, the potential of photovoltaic solar energy in the Caribbean and Orinoquia regions was emphasized due to the high solar radiation, wind energy in the Caribbean and Pacific regions due to its strong winds, and biomass in the Amazon and Andean regions thanks to the abundance of organic resources. These technologies not only contribute to the reduction of carbon emissions and the diversification of the energy matrix, but also promote a just energy transition by generating employment and improving the quality of life in local communities. The TOPSIS method and its diffuse variant make it possible to weigh criteria with different degrees of importance and manage uncertainty in decision-making, resulting in more precise evaluations adapted to specific contexts. This type of research is vital for decision-making in the Colombian electricity sector, as it provides a solid and replicable methodology for evaluating energy alternatives in an integral way. The results not only contribute to environmental sustainability, but also boost the socio-economic development of the regions, fostering an equitable and fair energy transition.

SelfMixed: Self-supervised mixed noise attenuation for distributed acoustic sensing data

Distributed acoustic sensing (DAS) is an emerging data acquisition technique known for its high sensing density, cost effectiveness, and environmental friendliness, making it a technology with significant future application potential in many fields. However, DAS signals are often contaminated by various types of noise, such as high-frequency, high-amplitude erratic, and horizontal noise, making their processing challenging. Therefore, it is crucial to leverage the physical characteristics of these diverse types of noise in DAS data and effectively attenuate them. In this work, we develop SelfMixed, a novel self-supervised learning method for mixed noise suppression of DAS data. We fully exploit the physical characteristics of different types of noise in DAS data and introduce a physical characteristic-based training strategy. Specifically, we use the [Formula: see text] norm to characterize random noise, the [Formula: see text] norm for erratic noise, and horizontal smoothness and vertical nonsmoothness for horizontal noise. In addition, we use a blind-spot-based training strategy for DAS denoising, relying solely on observed noisy data. To more effectively attenuate horizontal noise, we also introduce a Fourier transform-based parameterization method. By combining self-supervised deep priors with the physical characteristics of mixed DAS noise, our method effectively attenuates complex mixed noise in field DAS data. Extensive experiments on synthetic and field data from various geographic scenarios validate the superiority of SelfMixed over seven state-of-the-art DAS denoising approaches.

Bayesian cluster geographically weighted regression for spatial heterogeneous data.

Spatial statistical models are commonly used in geographical scenarios to ensure spatial variation is captured effectively. However, spatial models and cluster algorithms can be complicated and expensive. One of these algorithms is geographically weighted regression (GWR) which was proposed in the geography literature to allow relationships in a regression model to vary over space. In contrast to traditional linear regression models, which have constant regression coefficients over space, regression coefficients are estimated locally at spatially referenced data points with GWR. The motivation for the adaption of GWR is the idea that a set of constant regression coefficients cannot adequately capture spatially varying relationships between covariates and an outcome variable. GWR has been applied widely in diverse fields, such as ecology, forestry, epidemiology, neurology and astronomy. While frequentist GWR gives us point estimates and confidence intervals, Bayesian GWR enriches our understanding by including prior knowledge and providing probability distributions for parameters and predictions of interest. This paper pursues three main objectives. First, it introduces covariate effect clustering by integrating a Bayesian geographically weighted regression (BGWR) with a post-processing step that includes Gaussian mixture model and the Dirichlet process mixture model. Second, this paper examines situations in which a particular covariate holds significant importance in one region but not in another in the Bayesian framework. Lastly, it addresses computational challenges in existing BGWR, leading to enhancements in Markov chain Monte Carlo estimation suitable for large spatial datasets. The efficacy of the proposed method is demonstrated using simulated data and is further validated in a case study examining children's development domains in Queensland, Australia, using data provided by Children's Health Queensland and Australia's Early Development Census.

Risk identification of public opinion on social media: a new approach based on cross-spatial network analysis

PurposeThis study aims to use geographical information on social media for public opinion risk identification during a crisis.Design/methodology/approachThis study constructs a double-layer network that associates the online public opinion with geographical information. In the double-layer network, Gaussian process regression is used to train the prediction model for geographical locations. Second, cross-space information flow is described using local government data availability and regional internet development indicators. Finally, the structural characteristics and information flow of the double-layer network are explored to capture public opinion risks in a fine-grained manner. This study used the early stages of the COVID-19 outbreak for validation analyses, and it collected more than 90,000 pieces of public opinion data from microblogs.FindingsIn the early stages of the COVID-19 outbreak, the double-layer network exhibited a radiating state, and the information dissemination was more dependent on the nodes with higher in-degree. Moreover, the double-layer network structure showed geographical differences. The risk contagion was more significant in areas where information flow was prominent, but the influence of nodes was reduced.Originality/valuePublic opinion risk identification that incorporates geographical scenarios contributes to enhanced situational awareness. This study not only effectively extends geographical information on social media, but also provides valuable insights for accurately responding to public opinion.

UNDERGROUND POWER LINES: ANALYSIS OF ENVIRONMENTAL, VISUAL, ECONOMIC, AND SAFETY BENEFITS

This study investigates the transition from above-ground to underground power lines in Hawaii, focusing on the severe threats posed by downed lines during storms, particularly on roadways where escape routes are critical. The research uses a comprehensive approach to explore environmental, visual, economic, and safety aspects. The study employed a methodology that involved reviewing powerline undergrounding studies in New York City and Florida, chosen for their geographical resemblance to Hawaii. Legal submissions from activist groups and current event articles discussing safety concerns in comparable geographical scenarios were also analyzed. Findings indicate substantial benefits, including minimized environmental impact on landscapes and ecosystems, heightened visual aesthetics, and economic advantages through reduced maintenance costs. Crucially, the shift to underground power lines significantly enhances safety during storms, preventing life-threatening scenarios on vulnerable roadways. This research underscores the urgency and practicality of adopting underground power lines in storm-prone regions, providing a compelling case for policymakers and stakeholders to prioritize this transition for communities’ overall well-being and resilience.

UNDERGROUND POWER LINES: ANALYSIS OF ENVIRONMENTAL, VISUAL, ECONOMIC, AND SAFETY BENEFITS

This study investigates the transition from above-ground to underground power lines in Hawaii, focusing on the severe threats posed by downed lines during storms, particularly on roadways where escape routes are critical. The research uses a comprehensive approach to explore environmental, visual, economic, and safety aspects. The study employed a methodology that involved reviewing powerline undergrounding studies in New York City and Florida, chosen for their geographical resemblance to Hawaii. Legal submissions from activist groups and current event articles discussing safety concerns in comparable geographical scenarios were also analyzed. Findings indicate substantial benefits, including minimized environmental impact on landscapes and ecosystems, heightened visual aesthetics, and economic advantages through reduced maintenance costs. Crucially, the shift to underground power lines significantly enhances safety during storms, preventing life-threatening scenarios on vulnerable roadways. This research underscores the urgency and practicality of adopting underground power lines in storm-prone regions, providing a compelling case for policymakers and stakeholders to prioritize this transition for communities’ overall well-being and resilience.

Geographical Global Rural Cultures and Cultures in Particular Geographical Realities: Some Particular Ideas for a Global Debate

Scientific contributions to global culture are scarce from a rural geographic perspective. The vision of global rural culture is framed between global as a product of particular cultures or as particular cultures contributed to geographically global rural. Furthermore, the emergence of new politics of rural cultures associated with re- and territorialization processes and small tactics in place is analyzed. Finally, the bases are established for the joint and integrated study of the concept and approaches to global rural and global culture in different geographical scenarios in the global South and North.

Advancing indoor risk mapping for virus transmission of infectious diseases through geographic scenario simulation

ABSTRACT Close-range interpersonal interactions serve as a major channel for virus transmission, with higher infection risks indoors than outdoors. Thus, evaluating indoor infectious disease transmission risks is vital for effective epidemic prevention and control. However, collecting complete individual-level behavioral data faces challenges due to privacy concerns and data acquisition costs, impeding accurate indoor risk mapping. To address this, we propose an individual-centered, scenario-based simulation framework in this paper. This framework incorporates stepwise movement of agents to model crowd interactions in indoor spaces, enabling infection risk mapping through geographic scenario simulation. The simulation model’s core components encompass the generation of indoor environments, formulation of individual behavior rules, establishment of human-environment interaction logic, and simulation of virus transmission processes. Additionally, we outline the implementation algorithm for this simulation model. Lastly, we employ a high-risk university canteen as a case study to demonstrate the model’s capabilities in creating risk maps at different levels: spatial, spatiotemporal, and individual. The proposed framework achieves the construction and process simulation of multi-dimensional geographic scenarios at a microscale, introduces a behavior path model based on spatiotemporal cubes, and enhances the comprehensive analysis and mapping capabilities for indoor infectious disease risk, laying the foundation for precise prevention and control.

Anti-Toxoplasma gondii IgG seroprevalence in the general population in Iran: A systematic review and meta-analysis, 2000-2023.

Toxoplasmosis ranks among the most prevalent parasitic diseases globally. It seems that chronic toxoplasmosis is associated with several neuropsychiatric and other harmful effects in infected people, therefore, there is a need to investigate the prevalence of toxoplasmosis across various world regions. In this study, we conducted a meticulous meta-analysis to estimate the seroprevalence of anti-Toxoplasma gondii IgG antibodies within the general population in Iran (GPI). International and national scientific databases for studies published between January 1, 2000, and September 30, 2023, were searched. Observational studies reporting anti-T. gondii IgG seroprevalence in the GPI was selected/included. The data were synthesized using a random-effects model to calculate with a 95% confidence interval (95% CI) the national and regional anti-T. gondii IgG seroprevalence rates in Iran. Additionally, subgroup analyses were conducted to investigate the frequency of exposition to T. gondii in different socio-demographic, climatic, and geographical scenarios. From 18661 identified studies, 327 were included in the present meta-analysis, encompassing 122,882 individuals across the 31 Iranian provinces. The pooled nationwide anti-T. gondii IgG seroprevalence among the GPI was determined to be 32.9% (95% CI: 30.9-35.1%). The highest anti-T. gondii IgG seroprevalence was observed in Mazandaran province (North of Iran) (61%), whereas the lowest was in Semnan province (12.5%).Anti-T. gondii IgG seroprevalence demonstrated a higher occurrence in provinces characterized by moderate temperatures of 16-21°C, high relative humidity, and annual precipitation. Additionally, a higher anti-T. gondii IgG seroprevalence was identified among individuals with a habit of consumption of undercooked meat, raw fruits or vegetables, and untreated water. Moreover, those reporting direct contact with cats, possessing a lower level of education, residing in rural areas, being engaged in farming occupations, or playing the role of housewives exhibited higher anti-T. gondii IgG seroprevalence figures.The anti-T. gondii IgG seroprevalence within GPI closely aligns with the estimated worldwide average exposition rates. This underscores the imperative for public health policymakers to prioritize educational efforts regarding toxoplasmosis transmission pathways and its link to harmful effects.

Linking Individuals to Areas: Protecting Confidentiality While Preserving Research Utility

Modern computational capabilities have brought about concerns about risks associated with the level of information disclosed in public datasets. A tension exists between making data available that protects the confidentiality of individuals while containing sufficiently detailed geographic information to underpin the utility of research. Our aim is to inform data collectors and suppliers about geographic choices for confidentiality protection and to balance this with reassurance to the research community that data will still be fit-for-purpose. We test this using simple logistic regression models, by investigating the interplay between two geographical entities (points for the observations and polygons for area attributes) at a variety of scales, using a synthetic population of 22,000 people. In an England and Wales setting, we do this for individuals located by postcodes and by postal sector and postal district centroids and link these to a variety of census geographies. We also ‘jitter’ postcode coordinates to test the effect of moving people away from their original location. We find a smoothing of relationships up the geographical hierarchy. However, if postal sector centroids are used to locate individuals, linkages to Lower/Medium Super Output Area scales and subsequent results are very similar to the more detailed unit postcodes. Postcode locations jittered by 500–750 m in any direction are likely to allow the same conclusions to be drawn as for the original locations. Within these geographic scenarios, there is likely to be a sufficient level of confidentiality protection while statistical relationships are very similar to those obtained using the most detailed geographic locators.

Point-of-Interest Recommendations Based on Immediate User Preferences and Contextual Influences

With the development of various location-based social networks (LSBNs), personalized point-of-interest (POI) recommendations have become a recent research hotspot. Current recommendation methods tend to mine user preferences from their historical check-in records but overlook interest deviations caused by real-time geographic environments and immediate interests present in the records, failing to meet users’ real-time and accurate needs. Therefore, this paper proposes a composite preference-based recommendation model (CPRM) for personalized POI recommendation. This method first extracts multi-factor contextual features, constructs a dual-layer attention network (DLAN) to capture long and short-term preferences, combines real-time geographic scenarios to uncover user immediate preferences, and then weights and fuses these three types of preferences to generate user composite preferences. Finally, a prediction function is employed to obtain the Top-N recommendation list. The experiments on two classic datasets, Foursquare and Gowalla, affirm the effectiveness of the model presented in this paper and offer a novel approach for providing personalized POI recommendations to users.

Geospatial Collective Intelligence Approach in the appreciation phase of military planning

Military planning is an essential process by which it is possible to determine the best idea of manoeuver and the most efficient way to use of military power, allowing the commander to make accurate and timely decisions; for this purpose, it is essential to have a deep knowledge of military science and operational factors such as geographical space, force, and time, which allow to have greater probabilities of success in the execution of military operations. This article intends to propose a new approach in the assessment phase of military planning, which allows providing support to the commander in complex geographic scenarios, through which current and prospective geospatial patterns can be established, mainly focused on the planning, organization and use of land resources.

Improving Trust in a (Trans)National Invoicing System: The Performance of Crash vs. Byzantine Fault Tolerance at Scale

Crash fault tolerance describes the capability of a distributed system to maintain its proper function despite the occurrence of crashes or failures in one or more of its components. When a distributed system possesses crash fault tolerance, it can be further fortified to achieve Byzantine fault tolerance. Byzantine fault tolerance empowers a distributed system to establish consensus among participants, even when faced with faulty or malicious behavior. Consensus plays a critical role in various tasks, including determining the accurate value of a shared variable, electing a leader, or validating the integrity of a business transaction. Compared to crash fault tolerance, Byzantine fault tolerance instills greater trust because it enables consensus even in the presence of malicious entities. This paper focuses on the performance evaluation of two blockchain solutions that exhibit Byzantine fault tolerance, in contrast to a blockchain solution that demonstrates crash fault tolerance. Specifically, the paper investigates the additional performance requirements associated with the enhanced trust resulting from Byzantine fault tolerance in e-business trading on both national and transnational scales. We analyze the resources needed to operate a business-to-business/business-to-government (B2B/B2G) compliance framework in two distinct geographic scenarios. The first examines the national scale, using Denmark as an example, which is the eleventh largest European country by GDP. The second scenario considers the scale of the European Union (EU) with its 27 member states (plus the United Kingdom).

Long-Range Lightning Interferometry Using Coherency

Traditional lightning detection and location networks use the time of arrival (TOA) technique to locate lightning events with a single time stamp. This contribution introduces a simulation study to lay the foundation for new lightning location concepts. Here, a novel interferometric method is studied which expands the data use and maps lightning events into an area by using coherency. The amplitude waveform bank, which consists of averaged waveforms classified by their propagation distances, is first used to test interferometric methods. Subsequently, the study is extended to individual lightning event waveforms. Both amplitude and phase coherency of the analytic signal are used here to further develop the interferometric method. To determine a single location for the lightning event and avoid interference between the ground wave and the first skywave, two solutions are proposed: (1) use a small receiver network and (2) apply an impulse response function to the recorded waveforms, which uses an impulse to represent the lightning occurrence. Both methods effectively remove the first skywave interference. This study potentially helps to identify the lightning ground wave without interference from skywaves with a long-range low frequency (LF) network. It is planned to expand the simulation work with data reflecting a variety of ionospheric and geographic scenarios.

Visualization of Environmental Sensing Data in the Lake-Oriented Digital Twin World: Poyang Lake as an Example

Access to real-time environmental sensing data is key to representing real-time environmental changes in the digital twin of lakes. The visualization of environmental sensing data is an important part of establishing a digital twin of lakes. In the past, environmental sensing data display methods were either charts or two-dimensional map-based visualization methods. Breaking through the traditional visualization methods of environmental sensing data and realizing a multi-dimensional and multi-view display of environmental sensing data in a digital twin of lakes is something that this particular paper tries to resolve. This study proposes a visualization framework to integrate, manage, analyze, and visualize the environmental sensing data in the digital twin of lakes. In addition, this study also seeks to realize the coupling expression of geospatial data and long-term monitoring sequence data. Different visualization methods are used to realize the visualization of environmental sensing data in the digital twin of lakes. Using vector and scalar visualization methods to display ambient wireless sensor monitoring data in a digital twin of lakes provides researchers with richer visualization methods and means for deeper analysis. Using video fusion technology to display environmental sensing video surveillance data strengthens the integration of the virtual environment and real space and saves time for position identification using video surveillance. These findings may also help realize the integration and management of real-time environmental sensing data in a digital twin of lakes. The visualization framework uses various visualization methods to express the monitoring data of environmental wireless sensors and increases the means of visualizing environmental sensing data in the world of a lake digital twin. This visualization framework is also a general approach that can be applied to all similar lakes, or other geographical scenarios where environmental sensing devices are deployed. The establishment of a digital twin of Poyang Lake has certain practical significance for improving the digital management level of Poyang Lake and monitoring its ecological changes. Poyang Lake is used as an example to verify the proposed framework and method, which shows that the framework can be applied to the construction of a lake-oriented digital twin.

Indigenous ethnoknowledge about stingless bees (Apidae: Meliponini) in the Brazilian territory: Bibliometrics

This study aimed to analyze the characteristics of scientific productions that address the knowledge of indigenous peoples about stingless bees in different ethnicities, in the Brazilian territory, through bibliometric analysis. Thus, we sought to answer the following question: what are the characteristics of scientific publications with the theme in question found in the databases in the research tools? As descriptors, the following keywords were used: “Brazilian Indigenous Knowledge and Stingless Bees”, in the databases of Google Scholar and Scopus, totaling 18 (eighteen) scientific productions, in Spanish, English and Portuguese, with no year limit, and the survey was conducted in October and November 2022. As a result, we obtained: 10 (ten) (56%) scientific articles, 2 (two) (11%) master’s dissertations, 2 (two) (11%) doctoral thesis and 4 (four) (22%) scientific papers published in annals of events, covering the period from 1982 to 2020, notifying the representativeness of the unconventional literature in this research. The study evidenced the contribution of Higher Education Institutions – federal public HEIs and the little performance of state HEIs with research on the subject, in addition to large gaps with regard to: the time and evolution of publications, the periodicity of journals and, especially, the geographical scenario with the presence of indigenous ethnicities in their relations with stingless bees in the Brazilian territory. The importance of greater openness of databases to the dissemination of knowledge of traditional peoples and communities is highlighted.

Topological visualization and graph analysis of rough sets via neighborhoods: A medical application using human heart data

<abstract><p>In the field of medical applications, graph theory offers diverse topological models for representing the human heart. The key challenge is identifying the optimal structure as an effective diagnostic model. This paper explains the rationale behind using topological visualization, graph analysis, and rough sets via neighborhood systems. We introduce the novel $ 1 $-neighborhood system ($ 1 $-$ NS $) tools, enabling rough set generalization and a heart topological graph model. Exploring minimal and core minimal neighborhoods, vital for classifying subsets and accuracy computation, these approaches outperform existing methods while preserving Pawlak's properties. Multiple topologies are constructed and examined using these systems. The paper presents a real-world example showcasing innovative topological spaces through a human heart's vertex network. These spaces enhance understanding of the heart's structural organization. Two algorithms are introduced for decision-making and generating graph topologies, defining unique spaces. Beyond graph theory, these techniques apply to medical contexts like blood circulation and geographical scenarios such as community street mapping. Implemented using MATLAB, they are valuable tools.</p></abstract>

An equidistance/equiratio method of maritime delimitation on the Earth ellipsoid

The United Nations Convention on the Law of the Sea provides only basic legal principles for maritime delimitation disputes among coastal states. The implementation of maritime delimitation requires the comprehensive use of legal, political, and technical means by relevant states to generate the expected equitable results. Accurate calculation of the maritime delimitation boundary provides the basis for this equitable delimitation. Although the final determination of the maritime boundary is the result of diplomatic agreement, both disputing parties need a reasonable starting line as the point of departure for negotiations. As such, the equidistance/equiratio line can aid in this important task. The traditional three-point methods have many disadvantages, however, including a complex calculation process, dependence on map projections, and difficulty in calculating the equiratio line. Although the elastic method, water-line method, and mesh-points method can be used to calculate the equidistance/equiratio line, the boundary pattern is complex and the number of turning points is large. As a result, a great deal of adjustment and simplification is required, and simplifying the boundary line requires a complex diplomatic negotiation process. To solve these problems, we proposed a new three-point equidistance/equiratio method based on the Earth ellipsoid, which is called pendulum method. By choosing different geographical scenarios, we used the new method and CARIS to calculate the boundary and compared the demarcation results. The results demonstrated that the proposed method could generate an equidistance/equiratio line in various scenes using a unified algorithm. The algorithm was simple and efficient and was not limited by the map projection.