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Related Concept Videos

Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

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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...
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Levels of Use of a GIS

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Geographic Information Systems (GIS) operate across three levels of application, each representing an increasing degree of complexity: data management, analysis, and prediction. These levels reflect the expanding functionality and versatility of GIS technology in handling spatial data for diverse purposes.Data ManagementAt its foundational level, GIS serves as a tool for data management, enabling the input, storage, retrieval, and organization of spatial data. This level is often employed in...
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Applications of GIS: Disaster Management and Emergency Response01:29

Applications of GIS: Disaster Management and Emergency Response

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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...
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Pareto Chart00:52

Pareto Chart

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A Pareto chart is a bar graph or a combination of both line and bar graphs. The bar lengths represent the individual values or the frequency, while the lines represent the cumulative total values. In this chart, the longest bars are arranged on the left and the shortest bars on the right, which makes it easier to read and interpret the data. It can also be called a Pareto diagram or Pareto analysis.
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Disease surveillance is the systematic collection, analysis, and interpretation of health data essential to the planning, implementation, and evaluation of public health practice. This process integrates data dissemination to entities responsible for preventing and controlling disease, injury, and disability. Surveillance systems provide crucial information for action, helping public health authorities make informed decisions to manage and prevent outbreaks, ensure public safety, optimize...
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Related Experiment Videos

Visualizing and assessing US county-level COVID19 vulnerability.

Gina Cahill1, Carleigh Kutac2, Nicholas L Rider1

  • 1Baylor College of Medicine and Texas Children's Hospital, Section of Immunology Allergy and Retrovirology, Houston, TX.

American Journal of Infection Control
|December 22, 2020
PubMed
Summary

COVID19 fatality rates varied by county, linked to age and race. Older populations and higher Caucasian proportions correlated with higher COVID19 case fatality rates (CFR%).

Keywords:
Data visualizationNovel CoronavirusPopulation-healthSARS-CoV-2

Related Experiment Videos

Area of Science:

  • Epidemiology
  • Public Health
  • Data Science

Background:

  • The COVID19 pandemic significantly impacts public health and economies globally and within the United States.
  • Macro-scale data may obscure localized pandemic effects, necessitating county-level analysis.
  • Understanding community-level vulnerability is crucial for targeted interventions.

Purpose of the Study:

  • To visualize the spread of COVID19 at the county level.
  • To identify county-level features associated with pandemic vulnerability.
  • To assess the relationship between COVID19 case fatality rates and demographic, ethnic, and economic factors.

Main Methods:

  • Utilized COVID19 case data from The New York Times GitHub repository.
  • Incorporated 2018 United States Census data for all counties.
  • Merged and filtered datasets for visualization and analysis of case fatality rate (CFR%) and associated features.

Main Results:

  • County-level COVID19 fatality rates correlate with advanced population age (P < .001).
  • Higher proportions of Caucasians in counties were associated with higher CFR% (P < .001).
  • Counties with lower CFR% had a greater proportion of individuals identifying with two or more races (P < .001).
  • No significant differences in mean income or poverty rates were found between high and low CFR% counties.

Conclusions:

  • COVID19 impacts manifest uniquely at the county level.
  • Public datasets, data science, and visualization offer valuable insights into community vulnerability.
  • This approach can enhance understanding and inform public health strategies for pandemic response.