In 2023, there was a total of *** natural disasters events recorded worldwide, down from *** recorded a year earlier. The Europe, Middle East and Africa region experienced the highest number of natural disasters that year. Deaths and costs of natural disasters Natural disasters affect almost every part of the world. In February 2023, Turkey and Syria were hit by earthquakes that resulted in the highest number of deaths due to natural disaster events that year. In terms of economic damage, Hurricane Katrina remains one of the most expensive natural disasters in the world, topped only by the earthquake/tsunami which hit Japan in 2011. Climate change and natural disasters Climate change has influenced the prevalence of natural disasters. Global warming can increase the risk of extreme weather, resulting in higher risk of droughts and stronger storms, such as tropical cyclones. For instance, higher levels of water vapor in the atmosphere give storms the power to emerge. Furthermore, the heat in the atmosphere and high ocean surface temperatures lead to increased wind speeds, which characterize tropical storms. Areas that are usually unaffected by the sea are becoming more vulnerable due to rising sea levels as waves and currents become stronger.

In 2024, the United States experienced 29 natural disasters, which made it the most natural catastrophe-prone country in the world that year. Indonesia and China came second on that list, with 20 and 18 natural disasters occurring in the same year, respectively. Storms were the most common type of natural disaster in 2024. Types of natural disasters There are many different types of natural disasters that occur worldwide, including earthquakes, droughts, storms, floods, volcanic activity, extreme temperatures, landslides, and wildfires. Overall, there were 398 natural disasters registered all over the world in 2023. Costs of natural disasters Due to their destructive nature, natural disasters take a severe toll on populations and countries. Tropical cyclones have the biggest economic impact in the countries that they occur. In 2024, tropical cyclones caused damage estimated at more than 145 billion U.S. dollars. Meanwhile, the number of deaths due to natural disasters neared 18,100 that year. The Heat Wave in Saudi Arabia had the highest death toll, with 1,301 fatalities. Scientists predict that some natural disasters such as storms, floods, landslides, and wildfires will be more frequent and more intense in the future, creating both human and financial losses.

The risk of natural disasters, many of which are amplified by climate change, requires the protection of emergency evacuation routes to permit evacuees safe passage. California has recognized the need through the AB 747 Planning and Zoning Law, which requires each county and city in California to update their - general plans to include safety elements from unreasonable risks associated with various hazards, specifically evacuation routes and their capacity, safety, and viability under a range of emergency scenarios. These routes must be identified in advance and maintained so they can support evacuations. Today, there is a lack of a centralized database of the identified routes or their general assessment. Consequently, this proposal responds to Caltrans’ research priority for “GIS Mapping of Emergency Evacuation Routes.†Specifically, the project objectives are: 1) create a centralized GIS database, by collecting and compiling available evacuation route GIS layers, and the safety eleme..., The project used the following public datasets: • Open Street Map. The team collected the road network arcs and nodes of the selected localities and the team will make public the graph used for each locality. • National Risk Index (NRI): The team used the NRI obtained publicly from FEMA at the census tract level. • American Community Survey (ACS): The team used ACS data to estimate the Social Vulnerability Index at the census block level. Then the author developed a measurement to estimate the road network performance risk at the node level, by estimating the Hansen accessibility index, betweenness centrality and the NRI. Create a set of CSV files with the risk for more than 450 localities in California, on around 18 natural hazards. I also have graphs of the RNP risk at the regional level showing the directionality of the risk., , # Data from: Improving public safety through spatial synthesis, mapping, modeling, and performance analysis of emergency evacuation routes in California localities

https://doi.org/10.5061/dryad.w9ghx3g0j

Description of the data and file structure

For this project’s analysis, the team obtained data from FEMA's National Risk Index, including the Social Vulnerability Index (SOVI).

To estimate SOVI, the team used data from the American Community Survey (ACS) to calculate SOVI at the census block level.

Using the graphs obtained from OpenStreetMap (OSM), the authors estimated the Hansen Accessibility Index (Ai) and the normalized betweenness centrality (BC) for each node in the graph.

The authors estimated the Road Network Performance (RNP) risk at the node level by combining NRI, Ai, and BC. They then grouped the RNP to determine the RNP risk at the regional level and generated the radial histogram. Finally, the authors calculated each ana...

Extreme weather and climate disaster events caused a total of **** billion U.S. dollars in damages across the United States in 2023. This was some ** billion U.S. dollars less than the previous year. In total, there were ** separate billion-dollar extreme weather and climate events in the United States in 2023. These included severe storms, wildfires, tropical cyclones, flooding, and drought.

Source: The Emergency Events Database (EM-DAT) , Centre for Research on the Epidemiology of Disasters (CRED) / Université catholique de Louvain (UCLouvain), Brussels, Belgium – www.emdat.be.Category: Climate and WeatherData series: Climate related disasters frequency, Number of Disasters: TOTAL  Climate related disasters frequency, Number of Disasters: Drought  Climate related disasters frequency, Number of Disasters: Extreme temperature  Climate related disasters frequency, Number of Disasters: Flood  Climate related disasters frequency, Number of Disasters: Landslide  Climate related disasters frequency, Number of Disasters: Storm  Climate related disasters frequency, Number of Disasters: Wildfire Climate related disasters frequency, People Affected: Drought  Climate related disasters frequency, People Affected: Extreme temperature  Climate related disasters frequency, People Affected: Flood  Climate related disasters frequency, People Affected: Landslide  Climate related disasters frequency, People Affected: Storm  Climate related disasters frequency, People Affected: Wildfire Climate related disasters frequency, People Affected: TOTAL  Disaster IntensityMetadata:EM-DAT: The International Disasters Database - Centre for Research on the Epidemiology of Disasters (CRED), part of the University of Louvain (UCLouvain) www.emdat.be, Brussels, Belgium. Only climate related disasters (Wildfire, Storm, Landslide, Flood, Extreme Temperature, and Drought) are covered. See the CID Glossary for the definitions. EM-DAT records country level human and economic losses for disasters with at least one of the following criteria: i.          Killed ten (10) or more people  ii.         Affected hundred (100) or more people  iii.        Led to declaration of a state of emergency iv.        Led to call for international assistance   The reported total number of deaths “Total Deaths” includes confirmed fatalities directly imputed to the disaster plus missing people whose whereabouts since the disaster are unknown and so they are presumed dead based on official figures. “People Affected” is the total of injured, affected, and homeless people. Injured includes the number of people with physical injuries, trauma, or illness requiring immediate medical assistance due to the disaster. Affected includes the number of people requiring immediate assistance due to the disaster. Homeless includes the number of people requiring shelter due to their house being destroyed or heavily damaged during the disaster. Disaster intensity is calculated by summing “Total Deaths” and 30% of the “People Affected”, and then dividing the result by the total population. For each disaster and its corresponding sources, the population referred to in these statistics and the apportionment between injured, affected, homeless, and the total is checked by CRED staff members. Nonetheless, it is important to note that these are estimates based on certain assumptions, which have their limitations. For details on the criteria and underlying assumptions, please visit https://doc.emdat.be/docs/data-structure-and-content/impact-variables/human/. Methodology:Global climate related disasters are stacked to show the trends in climate related physical risk factors.

In 2018, it was estimated that almost *** thousand people were at risk of displacement per year due to natural disasters in Brazil. Among the countries shown in this graph, Colombia had the second highest annual number of citizens at risk of being displaced for this reason, with more than *** thousand.

Time series data for the statistic Share of transport network exposed to natural disasters (%) and country Malta. Indicator Definition:

Transportation networks play a crucial role in society by enabling the smooth movement of people and goods during regular times and acting as arteries for evacuations during catastrophes and natural disasters. Identifying the critical road segments in a large and complex network is essential for planners and emergency managers to enhance the network’s efficiency, robustness, and resilience to such stressors. We propose a novel approach to rapidly identify critical and vital network components (road segments in a transportation network) for resilience improvement or post-disaster recovery. We pose the transportation network as a graph with roads as edges and intersections as nodes and deploy a Graph Neural Network (GNN) trained on a broad range of network parameter changes and disruption events to rank the importance of road segments. The trained GNN model can rapidly estimate the criticality rank of individual road segments in the modified network resulting from an interruption. We address two main limitations in the existing literature that can arise in capital planning or during emergencies: ranking a complete network after changes to components and addressing situations in post-disaster recovery sequencing where some critical segments cannot be recovered. Importantly, our approach overcomes the computational overhead associated with the repeated calculation of network performance metrics, which can limit its use in large networks. To highlight scenarios where our method can prove beneficial, we present examples of synthetic graphs and two real-world transportation networks. Through these examples, we show how our method can support planners and emergency managers in undertaking rapid decisions for planning infrastructure hardening measures in large networks or during emergencies, which otherwise would require repeated ranking calculations for the entire network.

Network of 45 papers and 73 citation links related to "Towards an understanding of the psychological impact of natural disasters: An application of the conservation resources stress model".

Tracking the occurrence of natural disasters can save lives by helping countries prepare for future ones.

In our work on natural disasters, we visualize data from EM-DAT, the most comprehensive international disaster database. Make a chart of the number of recorded disaster events over time — like the one above — and it looks like the number of disasters rose alarmingly from the 1970s to the millennium. This has led to many media outlets and organizations claiming that the number of disasters has quadrupled over the last 50 years.

However, as EM-DAT itself makes clear, most of this is due to improvements in recording. The Centre for Research on the Epidemiology of Disasters, which builds this database, was not established until 1973, and didn’t start publishing EM-DAT until 1988.

The number of recorded disasters increased due to more focused efforts to obtain globally comprehensive data and improvements in communication technologies, which allowed more events to be included, even in the planet's most remote areas.

EM-DAT suggests that only data from 2000 onwards is relatively complete and comparable. The number of events before 2000 is likely to be underestimated. Note that this data does not tell us anything about the intensity of disasters.

In 2024, the economic losses due to natural disasters worldwide amounted to about *** billion U.S. dollars. Natural disasters occur as a result of natural processes on Earth. Many different types of natural disasters can occur, including floods, hurricanes, earthquakes, and tsunamis. Natural disasters in 2024 Tropical cyclones generated the highest amount of economic losses in 2024 with *** billion U.S. dollars worldwide. Hurricanes Helene and Milton were the most destructive events worldwide that year with over 100 billion U.S. dollars in economic losses. Flooding events ranked second in the costliest events in 2024, with flooding in Valencia, Spain, and South and Central China being the worst examples. Asia hardest hit by natural disasters A highly destructive force, Asia is one of the most susceptible regions to natural disasters. The repercussions of natural disasters are not only physical, but also economic. Costs may be high – depending on the severity – as areas affected by natural disasters might need to be rebuilt. Lower income countries are more likely to be affected by natural disasters for a multitude of reasons, including a lack of developed infrastructure, inadequate housing, and lack of back-resources.

Natural hazards including wildfires, hurricanes, and floods change network topology, which in turn, affect the vulnerability of road network components (e.g., intersections and road segments). Therefore, a dynamic assessment of the road network vulnerability is essential during disruptions to obtain up-to-date information on at-risk components. However, dynamically assessing vulnerability requires repeatedly recalculating centrality measures, which can be computationally expensive and time-consuming. To address this, we propose a machine learning architecture called PathVGAE that leverages the embedding structure of a Variational Graph Auto-Encoder (VGAE) with a path sampling encoder to learn latent representations that capture key topological features for centrality predictions. Our model can accurately identify high importance roads in seconds by leveraging only the static structure of the network. The experimental results demonstrate that PathVGAE outperforms baseline models in accur..., , # Data and code from: PathVGAE: A path-based variational graph autoencoder framework for ranking centrality in road networks

Dataset DOI: 10.5061/dryad.8w9ghx40m

Description of the data and file structure

The data was generated using the OSMnx package in Python. The files are organized into training, testing, and simulation data which can be accessed using the "torch.load" function from the PyTorch package. Further instructions can be found in "main.py" file.

Files and variables

File: layers.py

Description:Â The graph neural network layers used to construct the PathVGAE model, including the layer-wise aggregation mechanism.Â

File: main.py

Description:Â Train PathVGAE on the Los Angeles County road network and test it on various network across the U.S.

File: simulation.py

Description:Â Run a simulated event whereby the network topology changes to test PathVGAE's performance in a dynamic situation.

File: models.py

...,

Network of 42 papers and 76 citation links related to "When Is Exposure to a Natural Disaster Traumatic? Comparison of a Trauma Questionnaire and Disaster Exposure Inventory".

Time series data for the statistic Share of transport network exposed to natural disasters (%) and country Bhutan. Indicator Definition:

Network of 45 papers and 79 citation links related to "Multiple Diagnoses in Posttraumatic Stress Disorder in the Victims of a Natural Disaster".

's No. of persons that suffered due to natural disasters is 14person which is the 38th highest in Japan (by Prefecture). Transition Graphs and Comparison chart between Shizuoka and Fukuoka(Fukuoka) and Ibaraki(Ibaraki)(Closest Prefecture in Population) are available. Various data can be downloaded and output in csv format for use in EXCEL free of charge.

Time series data for the statistic Share of transport network exposed to natural disasters (%) and country Nauru. Indicator Definition:

This is a triplet dataset and code related to the Storm water management model (SWMM), mainly including fields such as UID, theme, subclass of flood disaster, and software tools. This dataset is based on academic literature related to flood disasters, and uses the BiLSTM-CRF model to extract SWMM entities. On this basis, articles that use the SWMM model to study flood disasters were screened, and the corresponding flood disaster subcategories, research themes, and software tools used were extracted with the help of natural language processing technology, thereby obtaining an overall trend of studying flood disasters with the SWMM model.

The field information collection process of flood disaster spatiotemporal process observation is cumbersome, and it is very difficult to unify data from different sources, which cannot effectively support disaster risk assessment and other requirements. The ontology of flood disaster spatiotemporal process observation knowledge graph includes structured data sources such as flood disaster databases, geographic resource databases, and remote sensing satellite resource catalogs, as well as ubiquitous text data such as domain literature and disaster emergency industry websites. The different modules in the observation ontology of flood disaster spatiotemporal processes have different data sources: flood event data source, flood disaster observation task data source, flood disaster data source, flood disaster model method data source, and flood disaster observation resource data source.

The datasets are derived from a detailed case study of the emergency response to the severe rainstorm and geological disaster in Longchuan County, China, in 2019. The whole event is extensively documented in the published monograph, ''Comprehensive Response to Emergencies: Lessons from the 2019 Severe Rainstorm and Geological Disaster in Longchuan County''. The datasets include a comprehensive record of emergency management activities. They are divided into four stages: Monitoring and Early Warning, Prevention and Emergency Preparedness, Emergency Response and Rescue Activities and Recovery and Reconstruction. The data details the collaborative interactions among various human and non-human agents within the disaster response systems at each stage. We provide codes to construct the hypergraph representation of the disaster response system in each stage. The codes include hypergraph clustering and dismantling, which can be used to analyze the robustness of inter-community structure and system reliability. The code used for generating graphics can also be found. The detailed descriptions and step-by-step guidelines can be found in READ_ME.md. About the updates of Version 2: we developed new codes to address the robustness of the disaster response system at a macroscopic scale. These analysis codes are included in the updates. Detailed descriptions can be found in READ_ME_About_Updates.md.