Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Applications of GIS: Disaster Management and Emergency Response01:29

Applications of GIS: Disaster Management and Emergency Response

Geographic Information System (GIS) technology is essential for risk identification, action prioritization, and resource optimization in critical situations like flooding and earthquakes. By integrating spatial and demographic data, GIS provides a comprehensive framework for emergency response.GIS integrates data layers, like rainfall intensity, topography, elevation profiles, and river levels, to model high-risk flood zones. These layers assess areas susceptible to flooding based on their...
Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
Hazard Rate01:11

Hazard Rate

The hazard rate, also known as the hazard function or failure rate, is a statistical measure used to describe the instantaneous rate at which an event occurs, given that the event has not yet happened. From a probabilistic perspective, it represents the likelihood that a subject will experience the event in a very small time interval, conditional on surviving up to the beginning of that interval. In terms of frequency, the hazard rate can be viewed as the ratio of the number of events to the...
Interpreting Run Charts01:25

Interpreting Run Charts

Run charts, essentially line graphs plotted over time, serve as fundamental yet effective tools for process analysis. They chronicle data sequentially, facilitating the identification of trends, shifts, or cyclical movements. This graphical representation is instrumental in determining whether a process is stable or exhibits signs of potential instability indicative of special cause variation. In the healthcare domain, run charts depict infection rates over time, enabling hospitals to monitor...
Manipulation and Analysis01:21

Manipulation and Analysis

GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Unlocking Giant Optical Nonlinearity in Rare-Earth MOFs.

ACS applied materials & interfaces·2026
Same author

Model Lineage Analysis: Determination and Closeness Measurement.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Codonopsis pilosula polysaccharides in intestinal flora and intestinal homeostasis: A review.

International journal of biological macromolecules·2026
Same author

Decoding Plant-Microbe Interactions through the Kiwifruit Microbiome in Bacterial Canker Disease.

Journal of agricultural and food chemistry·2025
Same author

Hyperproduction of Rhamnolipid in <i>P. putida</i> by Protein and Metabolic Engineering.

Journal of agricultural and food chemistry·2025
Same author

Development of NAFLD-Specific Human Liver Organoid Models on a Microengineered Array Chip for Semaglutide Efficacy Evaluation.

Cell proliferation·2025
Same journal

Trust-Building Communication for Extreme Heat Preparedness.

Risk analysis : an official publication of the Society for Risk Analysis·2026
Same journal

Spring Broken: A Risk Analysis of Fatal and Nonfatal Traffic Injuries in Florida.

Risk analysis : an official publication of the Society for Risk Analysis·2026
Same journal

Global Sensitivity Analysis of Societal Resilience Using Shapley Values and Polynomial Chaos Expansion.

Risk analysis : an official publication of the Society for Risk Analysis·2026
Same journal

Assessing How Fact-Checks Influence Accuracy and Consensus Judgments: Evidence From the Olympics.

Risk analysis : an official publication of the Society for Risk Analysis·2026
Same journal

Applying the Bow Tie Method to Evaluate Emerging Risk: The Case of Carbon Capture and Water Stress.

Risk analysis : an official publication of the Society for Risk Analysis·2026
Same journal

Quantitative Microbial Risk Assessment of Human H5N1 Infection From Consumption of Fluid Cow's Milk.

Risk analysis : an official publication of the Society for Risk Analysis·2026
See all related articles

Related Experiment Video

Updated: Jun 18, 2026

Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

Identifying Urban Cascading Disaster Risks From the Past Disaster Processes: A Process Mining Approach.

Zhao-Ge Liu1,2, Xiang-Yang Li3, Li-Min Qiao4

  • 1School of Public Affairs, Xiamen University, Xiamen, China.

Risk Analysis : an Official Publication of the Society for Risk Analysis
|June 17, 2026
PubMed
Summary
This summary is machine-generated.

Integrated Process Mining (IPM) effectively models urban disaster patterns, outperforming traditional methods in identifying complex cascading risks even with incomplete data. This approach enhances disaster risk assessment for better urban planning.

Keywords:
disaster processesdisaster risk identificationopen dataprocess miningurban cascading disasters

Related Experiment Videos

Last Updated: Jun 18, 2026

Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

Area of Science:

  • Urban planning and disaster management
  • Data science and artificial intelligence
  • Geospatial analysis

Background:

  • Urban areas face complex cascading disaster risks.
  • Traditional methods struggle to model these intricate disaster patterns.
  • Process mining offers potential for disaster modeling.

Purpose of the Study:

  • To propose and evaluate an Integrated Process Mining (IPM) approach.
  • To assess IPM's effectiveness in generating disaster process models.
  • To determine IPM's utility in identifying urban cascading disaster risks.

Main Methods:

  • Developed an Integrated Process Mining (IPM) approach combining knowledge graphs.
  • Conducted three sets of experiments using real-world data from multiple Chinese cities.
  • Evaluated model generation reliability, risk identification effectiveness, and performance under temporal/spatial constraints.

Main Results:

  • IPM generates reliable disaster process models, even with incomplete data.
  • IPM models outperform traditional methods in identifying diverse cascading disaster risks and their associations.
  • IPM performance is enhanced in cities with similar features and high temporal proximity.

Conclusions:

  • Integrated Process Mining is a robust method for urban disaster risk assessment.
  • Leveraging multi-source data and domain knowledge improves process mining for disaster scenarios.
  • Spatially and temporally precise risk identification frameworks are crucial for urban resilience.