Common network representations
Dependency data from different platforms are represented in shared network formats, so cascade methods can be applied consistently.
GCRI · 系统科学与建模
Network science and contagion is a method this platform stewards for the network. It provides common tools for representing dependencies and simulating spread, used across infrastructure, finance, supply chains, health and information.
Network science and contagion study how the structure of connections among entities shapes the spread of failures, information and disease. It provides tools to analyse interdependent infrastructure, supply chains and financial 系统. In methods and modelling, risk management and 系统性风险 and research and evidence, network science identifies critical nodes and pathways through which shocks propagate across sectors. It informs targeted protection and diversification to limit cascades. The canonical description is on STEM Commons.
Dependency data from different platforms are represented in shared network formats, so cascade methods can be applied consistently.
Threshold, load-redistribution and epidemic-type models are compared on the same networks to show how model choice changes results.
Interdependent networks are analysed together to identify cross-layer cascades that a single-layer analysis would miss entirely.
北美的人口密度、洪水水体和夜间灯光,来自 NASA GIBS。每个图层均标有各自日期。
交互式地图需要JavaScript;图层列表和来源见下文。
图层 3
本页面的危害与暴露
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最新可用
NASA GIBS / EOSDIS · ASTER GDEM (METI, NASA) · Blue Marble (NASA) · © OpenStreetMap贡献者 (ODbL) · Natural Earth
正在加载最新的NASA影像。
拖动以移动。按住 Ctrl 或 ⌘ 并滚动、双指缩放或使用按钮缩放;悬停可查看指针下的数值;重置可返回该区域。
危害与风险暴露,以北美为取景。NASA 发布的卫星和模型产品,各带自己的日期。它们是指标,而非对任何地点的评估。
图层与日期
Datasets and records used by network science and contagion on this platform, with the role each plays and the label its values carry. Sources are listed in 来源登记册.
| 数据 | 角色 | 标签 |
|---|---|---|
| Benchmark networks | Method testing | 观测 |
| Platform dependency data | Applications | 已声明 |
| Event records | 验证 | 已报告 |
Assembled from the Nexus Standards area profile 复合与级联危害标准: the hazards it names, the 系统 and sectors that carry the loss, the controls that dampen it and the data that observe it. Choose the components, pick an initiating hazard and step through the cascade. The Map view places the run on an example geography with open data. Every coupling weight is a stated assumption you can change. A teaching model, not a forecast, an assessment or a certification. To model a place of your own choosing, open the full System Builder.
灾害
系统与耦合
控制措施
NS-0 章程与原则NS-3 风险记录 UNI(语法、旧编码对照、分配机构)NS-9 情境 UNI(部署、实例、计划实例)NS-11 验证与监测(证据、证明、零信任符合性观测)NRS-H61 危害族:级联失效(NHS-1 sys.cascade)NRS-H62 危害类别:复合与多危害事件(NHS-1 sys.compound)NRS-H60 危害类别:供应链与流动中断(NHS-1 sys.supply-chain)NRS-H94 危害族:系统性网络事件(NHS-1 cyber.systemic-cyber)NRS-H107 危害族:数字基础设施集中与单一化(NHS-1 tech.digital-concentration)NRS-H7 危害类别:大尺度环流异常(NHS-1 atmo.climate-mode)观测
示例地理范围
Models are checked by reproducing observed cascades and outbreaks. Robustness of results to missing or uncertain links is tested.
| 核查 | 如何开展 |
|---|---|
| Missing link sensitivity | Results are tested under perturbed network structure |
| Model comparison | Several contagion models are applied before conclusions |
| Event validation | Simulated cascades are compared with documented events |
Network data are often incomplete or confidential. Static network models may miss adaptive behaviour during cascades.
模型、其假设、数据和不确定性,以科学家或工程师会核查的形式呈现。结构遵循已发表的方法。所取数值仅为示意,不是研究发现。
The model structure used for network science methods, the assumptions it makes and how each is tested.
Ri = f(Hi, Ei, Vi, Ci)危害、风险暴露、脆弱性和应对能力EAL = ∑k pk · Lk对年概率为 pkxt+1 = A xt + B ut + εtA carries the couplings, u the shock, ε the error| 假设 | 为何制作 | 如何检验 |
|---|---|---|
| 在一次事件中,危害与暴露相互独立 | 使损失积分保持可计算 | 与基于联合记录拟合的 copula 进行比较 |
| 耦合在工作点附近是线性的 | 让冲击通过 A 传播 | 在线性范围之外施加扰动并比较 |
| 脆弱性曲线可以在不同地点之间迁移使用 | 很少有本地曲线可用 | 在有观测损失的地方据此进行验证 |
Process explorer · Process
8 个步骤·4 个阶段
流程
An initial hazard can trigger later risks and events; this illustration draws one cascade across 系统 such as energy, water, ICT, health and transport.
Outline of an external standard or method, from its public sources. It describes what the framework says, not a record of any run. It does not show that Nexus or any partner performs, certifies or approves a step. A gate is a step of the method, never a Nexus approval. The dependencies are examples. A dependency survey for the place sets them.
系统 Science and Modelling stewards network science and contagion for the Consortium and reviews how it is applied across platforms.
报告 is the Consortium’s quarterly publication. What you know about this subject can become evidence, a case or a correction in the next edition, credited to you and published under CC BY 4.0 where it is your own work.
切勿发送个人数据、敏感基础设施细节或您无权分享的任何内容。贡献在使用前会经过审查;发送贡献不赋予署名权或编辑权,赞助也绝不能换取研究结论。
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