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

z Scores and Area Under the Curve01:17

z Scores and Area Under the Curve

11.0K
z scores are the standardized values obtained after converting a normal distribution into a standard normal distribution. A z score is measured in units of the standard deviation. The z score tells you how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a z score of...
11.0K
Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

84
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...
84
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

442
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
442
Relative Risk01:12

Relative Risk

265
Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...
265
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

616
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
616
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

4.1K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
4.1K

You might also read

Related Articles

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

Sort by
Same author

Low work ability and high disability burden in Cushing's syndrome: a multicenter cohort study.

Journal of endocrinological investigation·2026
Same author

Skipping rope and pamphlet intervention to promote physical activity among young adolescents in South Africa: study protocol for a randomized controlled trial.

Trials·2026
Same author

A Model to Assess the Costs and Consequences of Changes in Diet and Nutrition From Potential Population-Wide Policies: The Microsimulation of Nutrition, Diabetes, and Cardiovascular Disease (MONDAC).

Preventing chronic disease·2026
Same author

The Impact of the COVID-19 Pandemic on Weight Loss and Quality of Life One Year Post Metabolic Bariatric Surgery.

Obesity surgery·2026
Same author

Estimating the causal effect of cardiometabolic conditions on socioeconomic and healthcare outcomes: a scoping review of Mendelian randomization studies.

Health economics review·2026
Same author

ERC-funded grant: the economics of obesity prevention.

European heart journal·2026

Related Experiment Video

Updated: Aug 6, 2025

Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

13.6K

A spatial obesity risk score for describing the obesogenic environment using kernel density estimation: development

Maximilian Präger1,2, Christoph Kurz3, Rolf Holle4

  • 1Department of Sport and Health Sciences, Technical University of Munich, Munich, Germany. maximilian.praeger@tum.de.

BMC Medical Research Methodology
|March 18, 2023
PubMed
Summary
This summary is machine-generated.

Environmental factors contribute to obesity. Researchers developed a spatial obesity risk score (SORS) using OpenStreetMap data and kernel density estimation to map obesity risks, identifying high-risk areas.

Keywords:
Kernel density estimationObesityOpenStreetMapRisk scoreSpatial

More Related Videos

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.7K
Author Spotlight: Advancing Cardiovascular Imaging - Introducing the Spatially Weighted Calcium Score for Early Disease Detection
06:57

Author Spotlight: Advancing Cardiovascular Imaging - Introducing the Spatially Weighted Calcium Score for Early Disease Detection

Published on: September 22, 2023

1.1K

Related Experiment Videos

Last Updated: Aug 6, 2025

Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

13.6K
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.7K
Author Spotlight: Advancing Cardiovascular Imaging - Introducing the Spatially Weighted Calcium Score for Early Disease Detection
06:57

Author Spotlight: Advancing Cardiovascular Imaging - Introducing the Spatially Weighted Calcium Score for Early Disease Detection

Published on: September 22, 2023

1.1K

Area of Science:

  • Environmental Health
  • Geographic Information Systems (GIS)
  • Public Health

Background:

  • Obesity is a global public health issue linked to chronic diseases like type 2 diabetes.
  • Environmental factors significantly influence lifestyle and weight status.
  • Geographic Information Systems (GIS) offer methods to assess environmental risks related to obesity.

Purpose of the Study:

  • To develop and validate a spatial obesity risk score (SORS).
  • To utilize OpenStreetMap (OSM) data and kernel density estimation (KDE) for spatial risk assessment.
  • To visualize and analyze the spatial distribution of obesogenic factors.

Main Methods:

  • Downloaded obesity-related factors from OSM for two Bavarian municipalities.
  • Applied Ripley's K function to test spatial heterogeneity of risk factors.
  • Developed SORS using positive and negative KDE surfaces and spatial bootstrap for uncertainty analysis.

Main Results:

  • Identified significantly clustered patterns of obesogenic and protective environmental factors.
  • Visualized high- and low-risk areas using SORS density maps.
  • Determined that bandwidth and edge correction parameters most impacted SORS outcomes.

Conclusions:

  • SORS effectively visualizes obesity risk patterns across geographic areas.
  • The developed approach is geographically scalable for diverse applications.
  • Careful consideration of parameter selection is crucial for accurate SORS application.