Developing an open geographic data model and analysis tools for disaster management: landslide case

This qualitative study aimed to undertake a critical examination of the models by thematic analysis to determine their contribution to disaster management. A review and analysis of the literature were used. The models were studied to explore their contribution to disaster management and to identify any significant constraints or challenges which could limit the ability of the models to carry out appropriate disaster risk reduction actions. The study found that such models are indispensable because they simplify and improve disaster management. Additionally, they may support planners, managers, and practitioners in reaching proper decisions, making them valuable and necessary decision-making support tools. The study also found that the four key phases of disaster management – mitigation, preparedness, response, and recovery – constitute the basis of the majority of models. The study also showed that each model has an advantage that distinguishes it from the other models. The findings also confirmed the doubts raised about the limitations and concerns associated with the models. Concerns included future disasters’ unpredictability, the models’ prescriptive nature, and the impact on businesses. The findings also indicated that certain planners, managers, and practitioners had a limited understanding of the use of models in disaster management. As such, they appear to have overlooked the use of models while dealing with disasters. Hence, the study recommends that the models should be employed in all disaster management phases. The study also recommends that the findings are utilized as a basis for further research into the potential use of disaster management models.

Abstract: Geospatial intelligence (GEOINT) is a multidisciplinary field that combines geographic information systems (GIS), remote sensing, and data analysis to provide critical insights for decision-making in various sectors. This research article explores the theoretical foundations, methodologies, and practical applications of geospatial intelligence. Through comprehensive analysis, the study identifies key techniques used in GEOINT, significant case studies, and the challenges faced by professionals in the field. The findings highlight the importance of geospatial technologies in enhancing situational awareness, disaster management, urban planning, and national security. Practical recommendations for geospatial practitioners, policymakers, and researchers are provided, along with suggestions for future research to further advance the field. Keywords: Geospatial Intelligence, Mapping the Future, Geospatial Technologies, Geographic Information Systems (GIS), Remote Sensing, Spatial Data Analysis, Geospatial Data, Earth Observation, Geospatial Analytics, Cartography, Spatial Information, Geospatial Applications, Location-Based Services, Spatial Intelligence, Geospatial Innovation, Geographic Data Processing, Spatial Decision Support, Geospatial Solutions, Environmental Monitoring, Urban Planning, Geospatial Big Data, Spatial Visualization, Geospatial Modeling, Geospatial Infrastructure and Future Geospatial Trends.

Networking continues to increase day by day to invest increasingly our daily, creating huge volume of various and precise data: it is not easy to collect content, especially in crisis times. We focus on Smart Education proposed as primary tool of a hybrid of Deep Convolutional Neural Networks (CNN)-Long Short-Term Memory (LSTM)-based model to retrieving content efficiently: CNN is used to extract meaningful features from multiple sources, enabling to have qualitative and sure information, notably with an efficient fake news security, and LSTM is used to maintain long-term dependencies in the extracted features with recurrent connections. This model has been compared to previous approaches to the performance of a publicly available dataset to demonstrate its highly satisfactory performance. This new approach makes it possible to integrate artificial intelligence technologies, deep learning, social media and detecting or avoiding fake news into the crisis management model. It is based on an extension of our previous approach, namely disaster management based on short-term memory and education: this experience constitutes a background for this model. It combines representation training with awareness and education, while retrieving pattern information by combining various search results from multiple sources. We have extended it to improve our disaster management model and evaluate it in the case of Covid-19 while obtaining promising results, through past programs and experience that have shown overwhelmingly positive effects of education for vulnerability reduction and disaster risk management, in the pusuit of Environmental Sustainability

The application of a visual data mining framework to determine soil, climate and land use relationships

A concept of fuzzy three-dimensional Voronoi Diagram is presented for spatial relations analysis of real world three-dimensional geographical data, where it is an extension of well known two-dimensional Voronoi Diagram to three-dimensional representation with uncertain spatial relation information in terms of fuzzy set. It makes possible to analyze quantitatively complex boundaries of geographically intricate areas, to give human friendly fuzzy explanation of determining three-dimensional directions, and to express uncertain spatial relations by precise unified fuzzy description. It is applied to decide spatial direction relations of artificial geographicalmountain data, which includes 8 spatial directions with at most 60 relative direction relations, and it leads to detect threedimensional directions whereas the expression of traditional 4 directions and 12 relative directions indicate two-dimensional directions only. The proposed concept aims to discriminate neighbors’ class relations and spatial-temporal changes of specially appointed objects, and also aims to be a tool to achieve the intellective extraction and analysis of geographical data of a mountainous area located in northeast China.

Purpose The purpose of this paper is to maximize the total demand covered by the established additive manufacturing and distribution centers and maximize the total literal weight assigned to the drones. Design/methodology/approach Disaster management or humanitarian supply chains (HSCs) differ from commercial supply chains in the fact that the aim of HSCs is to minimize the response time to a disaster as compared to the profit maximization goal of commercial supply chains. In this paper, the authors develop a relief chain structure that accommodates emerging technologies in humanitarian logistics into the two phases of disaster management – the preparedness stage and the response stage. Findings Solving the model by the genetic and the cuckoo optimization algorithm (COA) and comparing the results with the ones obtained by The General Algebraic Modeling System (GAMS) clear that genetic algorithm overcomes other options as it has led to objective functions that are 1.6% and 24.1% better comparing to GAMS and COA, respectively. Originality/value Finally, the presented model has been solved with three methods including one exact method and two metaheuristic methods. Results of implementation show that Non-dominated sorting genetic algorithm II (NSGA-II) has better performance in finding the optimal solutions.

This study analyses the harmonisation of customary law, state law, and religio‑magical values in disaster management among coastal communities in Parepare, taking the Mappano/Mappanongnggo/Maccera Tasi’ ritual as the focal point of a socio‑legal inquiry. The analytical framework departs from the growing vulnerability to hydrometeorological hazards and the disjuncture between state disaster regulations and offering‑larung practices, which local communities understand as a collective effort to preserve harmony between humans, nature, and the unseen world. The study employs a socio‑legal legal research design grounded in normative‑juridical analysis, combining statutory, conceptual, and case approaches to disaster regulations, the legal recognition of adat communities, and previous studies on Mappano/Mappanongnggo/Maccera Tasi’ and comparable religio‑magical practices, which are qualitatively examined to map patterns of interaction and tension between normative orders. The findings indicate that the Mappano/Mappanongnggo/Maccera Tasi’ ritual embodies values of reverence for water, awareness of disaster risk, and the strengthening of social solidarity that intersect with the objectives of protecting life, the environment, and public order, while simultaneously revealing ongoing negotiations between customary law, formal religious interpretations, and positive law in local disaster governance. Building on these insights, the article proposes a framework for developing a local‑wisdom‑based disaster management model that integrates the role of adat institutions, recontextualises ritual forms to align with religious principles and environmental protection, and utilises ritual as a medium for disaster education and risk communication in coastal Parepare.

The objective of this project is to evaluate existing and emerging Open Geospatial Consortium (OGC) standards for use in LLNL programs that rely heavily on geographic data. OGC standards are intended to facilitate interoperability between geospatial processing systems to avoid duplication of effort, lower development costs, and encourage competition based on improved capability and performance rather than vendor lock-in. Some of these standards appear to be gaining traction in the geospatial data community, the Federal government, DOE and DHS. A serious evaluation of this technology is appropriate at this time due to increasing interest and mandated compliance in the Federal government in some situations. A subset of OGC standards is identified and reviewed with a focus on applications to LLNL programs. Each standard or recommendation reviewed was evaluated in general terms. In addition, for specific programs such as Gen&SIS and NARAC, a specific evaluation was made of several of the standards and how they could be used most effectively. It is also important to evaluate the acceptance of these standards in the commercial arena. The implementation of OGC standards by the largest GIS vendor (ESRI) was reviewed. At present, OGC standards are primary useful in specific situations. More generally, many of the standards are immature and their impact on the government and commercial sectors is unclear. Consequently, OGC and related developments need to be observed. As specific standards or groups of standards mature and establish their relevance, these can also be incorporated in LLNL programs as requirements dictate, especially if open implementations and commercial products are available.

Interactive visual analysis of geographic data on mobile devices based on dynamic queries

This paper focuses on the interoperable Sensor Web and Web Processing standards defined by the Open Geospatial Consortium. Special attention will be given to the combination of Sensor Web and the Web Processing Service, which opens up new outlooks in the field of disaster management, by having near real-time sensor data flows linked on-demand to different geoprocessing services. Though in an early stage, a web-based prototype will be introduced as a proof of concept of the integration of those two concepts.

In the domain of GIS, large amounts of geographic data are increasingly shared, interchanged and analyzed in many different applications. Mobile GIS technologies bring lots of advantages for field survey and the datasets acquired by field based data collection in Mobile GIS are often employed for many purposes. Therefore, to ensure the quality of the collected datasets and tell the data users information about how reliable the datasets are, more concerns have to be contributed to spatial data quality issues. This paper will describe the open architecture of field based Mobile GIS and show the importance of spatial data quality issues. Then it explains how the spatial data quality concerns are considered for Mobile GIS aligning with international standards in geoinformatics, like ISO and OGC standards. Geographic data modeling with emphasis of spatial integrity constraints will be introduced here. In order that field users can simultaneously receive the warning of errors when they are entering the data in the field and correct them through seeing the reality, the data quality assurance work should be seamlessly integrated into the Mobile GIS. Therewith, the method of applying the spatial integrity constraints into the data collection work will also be explained.

The quite new Web Processing Service (WPS) specification of the Open Geospatial Consortium (OGC) is increasing the value of Spatial Data Infrastructures (SDIs) by offering a standard for the actual processing of spatial data. The interface is already used in a range of projects, but up to now mainly in connection with 2D spatial data. In this paper we want to demonstrate that the standard can also be applied in a 3D environment which increases the potential of such 3DSDIs. We implemented two use cases of disaster management and environmental monitoring and discuss the benefits and drawbacks of applying geoprocessing functionality in 3D. The scenarios were integrated and visualized in a Web3D Server (W3DS) viewer and demonstrate how useful it is to present the outcomes of such simulations in the third dimension.

This paper describes a pilot project effort under the auspices of the Namibian Ministry of Agriculture Water and Forestry (MAWF)/Department of Water Affairs, the Committee on Earth Observing Satellites (CEOS) /Working Group on Information Systems and Services (WGISS) and originally moderated by the United Nations Platform for Space-based Information for Disaster Management and Emergency Response (UN-SPIDER). The effort began by identifying and prototyping technologies which enabled the rapid gathering and dissemination of both space-based and ground sensor data and data products for the purpose of flood disaster management. This was followed by an international collaboration to build small portions of the identified system which was prototyped during the past few years during the flood seasons which occurred in the February through May timeframe of 2010 and 2011 with further prototyping to ongoing in 2012. The pilot effort has been fostered by CEOS to facilitate international efforts to promote satellite sensor data interoperability. In particular, the group has been making use of a technology effort call SensorWeb being developed at NASA which leverages Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) standards to facilitate various satellite and ground sensor interoperability. The group has made use of such satellites such as Earth Observing 1, Terra/Aqua MODIS and the Canadian Space Agency (CSA) Radarsat together with various ground sensors such as river gauges in Namibia and models such as Global Disaster Alert and Coordination System (GDACS) from Joint Research Center (JRC) from the European Commission. Finally, the group has been experimenting with integrating a large Cloud Computing service provided by the Open Cloud Consortium (OCC) with the SensorWeb to provide management and distribution of the large data sets for emergency workers.

This study aims to analyse the model of disaster communication carried out by the government, especially the Karangasem Regency Public Relations, toward the communities impacted by the eruption of Mount Agung to lessen the risk of disaster. This research applied qualitative descriptive research with a case study method. The case study method specifically looked at the context of disaster communication in the Mount Agung eruption disaster in Bali in 2017. To obtain in-depth data, a focus group discussion and in-depth interviews were carried out as data collection techniques and the data analysis technique used the Miles and Huberman model. The results revealed that the government’s communication model of Mount Agung disaster management worked together with the community through PASEBAYA. The synergy communication model had effectively handled the Mount Agung eruption disaster, considering that disaster management should involve communication, information, coordination, and cooperation. The communication synergy model was carried out by delivering messages in disaster communication from the government to the community to quickly and accurately reach disaster victims and information related to the appeal to use traditional and technology media to evacuate in safe areas, with evacuation locations and order to bring only essential items when evacuated.

Bayesian network is a widely used tool for data analysis, modeling and decision support in various domains. There is a growing need for techniques and tools which can automatically construct Bayesian networks from massive text or literature data. In practice, Bayesian networks also need be updated when new data is observed, and literature mining is a very important source of new data after the initial network is constructed. Information closely related to Bayesian network usually includes the causal associations, statistics information and experimental results. However, these associations and numerical results cannot be directly integrated with the Bayesian network. The source of the literature and the perceived quality of research needs to be factored into the process of integration. In this demo, we will present a general methodology and toolkit called AutoBayesian that we developed to automatically build and update a Bayesian network based on the casual relationships derived from text mining.