Skip to main content

The global dimensions of disaster

Disasters are not merely natural occurrences but complex interactions between natural hazards and human vulnerabilities. To effectively address disaster risk, we must consider several interconnected dimensions:

1. Vulnerability:

  • Definition: The susceptibility of individuals, communities, or assets to harm from a disaster.
  • Factors: Socioeconomic conditions, geographic location, and environmental factors influence vulnerability.
  • Example: Communities with high poverty rates and limited access to resources are more vulnerable to disaster impacts.

2. Exposure:

  • Definition: The degree to which people, property, and infrastructure are located in hazard-prone areas.
  • Factors: Population density, land use patterns, and infrastructure development influence exposure.
  • Example: Coastal cities with high population density are highly exposed to hurricane and tsunami risks.

3. Capacity:

  • Definition: A community's ability to prepare for, respond to, and recover from disasters.
  • Factors: Strong governance, early warning systems, resilient infrastructure, and community preparedness contribute to capacity.
  • Example: Countries with well-developed disaster management systems and resilient infrastructure can recover more quickly from disasters.

4. Hazard Characteristics:

  • Definition: The nature, intensity, frequency, and duration of a hazard.
  • Factors: Climate change, tectonic activity, and human activities can influence hazard characteristics.
  • Example: Increasing frequency and intensity of extreme weather events due to climate change pose significant risks to communities.

5. Data and Information Management:

  • Definition: The collection, analysis, and dissemination of data to inform decision-making and improve disaster response.
  • Factors: Advanced technologies, effective communication systems, and data-driven approaches are crucial.
  • Example: Early warning systems rely on real-time data to alert communities of impending hazards.

6. Governance:

  • Definition: The institutional framework that coordinates disaster risk reduction efforts.
  • Factors: Strong leadership, effective policies, and public-private partnerships are essential.
  • Example: Well-governed countries with transparent and accountable institutions are better equipped to manage disaster risks.

The Disaster Risk Equation

The interplay of these dimensions can be encapsulated in a simple equation:

Risk = Hazard x Vulnerability x Exposure / Capacity

By reducing vulnerability, exposure, and enhancing capacity, we can significantly mitigate disaster risk.

The Sendai Framework

The Sendai Framework for Disaster Risk Reduction 2015-2030 provides a global blueprint for building resilient societies. It emphasizes:

  • Reducing exposure and vulnerability through sustainable development.
  • Strengthening governance to improve coordination and decision-making.
  • Improving resilience and adaptive capacity to enhance community preparedness and response.




Fyugp note 
Disaster Management 

PG and Research Department of Geography,
Government College Chittur, Palakkad
https://g.page/vineeshvc

Comments

Popular posts from this blog

History of GIS

1. 1832 - Early Spatial Analysis in Epidemiology:    - Charles Picquet creates a map in Paris detailing cholera deaths per 1,000 inhabitants.    - Utilizes halftone color gradients for visual representation. 2. 1854 - John Snow's Cholera Outbreak Analysis:    - Epidemiologist John Snow identifies cholera outbreak source in London using spatial analysis.    - Maps casualties' residences and nearby water sources to pinpoint the outbreak's origin. 3. Early 20th Century - Photozincography and Layered Mapping:    - Photozincography development allows maps to be split into layers for vegetation, water, etc.    - Introduction of layers, later a key feature in GIS, for separate printing plates. 4. Mid-20th Century - Computer Facilitation of Cartography:    - Waldo Tobler's 1959 publication details using computers for cartography.    - Computer hardware development, driven by nuclear weapon research, leads to broader mapping applications by early 1960s. 5. 1960 - Canada Geograph...

Pre During and Post Disaster

Disaster management is a structured approach aimed at reducing risks, responding effectively, and ensuring a swift recovery from disasters. It consists of three main phases: Pre-Disaster (Mitigation & Preparedness), During Disaster (Response), and Post-Disaster (Recovery). These phases involve various strategies, policies, and actions to protect lives, property, and the environment. Below is a breakdown of each phase with key concepts, terminologies, and examples. 1. Pre-Disaster Phase (Mitigation and Preparedness) Mitigation: This phase focuses on reducing the severity of a disaster by minimizing risks and vulnerabilities. It involves structural and non-structural measures. Hazard Identification: Recognizing potential natural and human-made hazards (e.g., earthquakes, floods, industrial accidents). Risk Assessment: Evaluating the probability and consequences of disasters using GIS, remote sensing, and historical data. Vulnerability Analysis: Identifying areas and p...

History of GIS

The history of Geographic Information Systems (GIS) is rooted in early efforts to understand spatial relationships and patterns, long before the advent of digital computers. While modern GIS emerged in the mid-20th century with advances in computing, its conceptual foundations lie in cartography, spatial analysis, and thematic mapping. Early Roots of Spatial Analysis (Pre-1960s) One of the earliest documented applications of spatial analysis dates back to  1832 , when  Charles Picquet , a French geographer and cartographer, produced a cholera mortality map of Paris. In his report  Rapport sur la marche et les effets du cholĂ©ra dans Paris et le dĂ©partement de la Seine , Picquet used graduated color shading to represent cholera deaths per 1,000 inhabitants across 48 districts. This work is widely regarded as an early example of choropleth mapping and thematic cartography applied to epidemiology. A landmark moment in the history of spatial analysis occurred in  1854 , when  John Snow  inv...

Supervised Classification

Image Classification in Remote Sensing Image classification in remote sensing involves categorizing pixels in an image into thematic classes to produce a map. This process is essential for land use and land cover mapping, environmental studies, and resource management. The two primary methods for classification are Supervised and Unsupervised Classification . Here's a breakdown of these methods and the key stages of image classification. 1. Types of Classification Supervised Classification In supervised classification, the analyst manually defines classes of interest (known as information classes ), such as "water," "urban," or "vegetation," and identifies training areas —sections of the image that are representative of these classes. Using these training areas, the algorithm learns the spectral characteristics of each class and applies them to classify the entire image. When to Use Supervised Classification:   - You have prior knowledge about the c...

Representation of Spatial and Temporal Relationships

In GIS, spatial and temporal relationships allow the integration of location (the "where") and time (the "when") to analyze phenomena across space and time. This combination is fundamental to studying dynamic processes such as urban growth, land-use changes, or natural disasters. Key Concepts and Terminologies Geographic Coordinates : Define the position of features on Earth using latitude, longitude, or other coordinate systems. Example: A building's location can be represented as (11.6994° N, 76.0773° E). Timestamp : Represents the temporal aspect of data, such as the date or time a phenomenon was observed. Example: A landslide occurrence recorded on 30/07/2024 . Spatial and Temporal Relationships : Describes how features relate in space and time. These relationships can be: Spatial : Topological (e.g., "intersects"), directional (e.g., "north of"), or proximity-based (e.g., "near"). Temporal : Sequential (e....