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

Uncertainty: Overview00:59

Uncertainty: Overview

1.6K
In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
1.6K
Modeling and Similitude01:12

Modeling and Similitude

833
Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
833
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

9.9K
The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
9.9K
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

1.4K
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
1.4K
Modeling with Differential Equations01:25

Modeling with Differential Equations

328
Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...
328
Typical Model Studies01:30

Typical Model Studies

805
Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
805

You might also read

Related Articles

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

Sort by
Same author

Why it's so hard to match residence addresses to census blocks - and how to fix it.

Transactions in GIS : TG·2026
Same author

Emissions from burned structures in wildfires as significant yet unaccounted sources of US air pollution.

Nature communications·2025
Same author

Changes in agglomeration and productivity are poor predictors of inequality across the archaeological record.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same author

The fastest-growing and most destructive fires in the US (2001 to 2020).

Science (New York, N.Y.)·2024
Same author

An Integrated Multi-Source Dataset for Measuring Settlement Evolution in the United States from 1810 to 2020.

Scientific data·2024
Same author

Spatially explicit accuracy assessment of deep learning-based, fine-resolution built-up land data in the United States.

International journal of applied earth observation and geoinformation : ITC journal·2023

Related Experiment Video

Updated: Apr 26, 2026

Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

1.9K

Dasymetric Modeling and Uncertainty.

Nicholas N Nagle1, Barbara P Buttenfield2, Stefan Leyk2

  • 1Department of Geography, University of Tennessee, Knoxville, TN 37996 ; Computational Sciences and Engineering Division, Oak Ridge National Laboratory.

Annals of the Association of American Geographers. Association of American Geographers
|July 29, 2014
PubMed
Summary

A new Penalized Maximum Entropy Dasymetric Model (P-MEDM) addresses uncertainty in population data. This method improves spatial resolution and quantifies uncertainty in estimates, unifying data integration for geographers.

Keywords:
dasymetric modelingmaximum entropysmall area estimation

More Related Videos

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations
12:09

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

Published on: January 8, 2013

13.2K
A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump
09:04

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump

Published on: June 1, 2022

2.6K

Related Experiment Videos

Last Updated: Apr 26, 2026

Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

1.9K
Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations
12:09

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

Published on: January 8, 2013

13.2K
A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump
09:04

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump

Published on: June 1, 2022

2.6K

Area of Science:

  • Geographic Information Science (GIS)
  • Spatial Analysis
  • Population Geography

Background:

  • Dasymetric modeling enhances population data spatial resolution using ancillary data.
  • Existing methods inadequately address uncertainty inherent in population data and geographic processes.
  • Difficulty in establishing robust relationships between population distribution and ancillary data layers is a common challenge.

Purpose of the Study:

  • To introduce and evaluate the Penalized Maximum Entropy Dasymetric Model (P-MEDM).
  • To enable the representation and modeling of uncertainty in dasymetric population estimates.
  • To unify the integration of diverse ancillary data within dasymetric models.

Main Methods:

  • Development of the Penalized Maximum Entropy Dasymetric Model (P-MEDM).
  • Propagation of uncertainty through the dasymetric modeling process.
  • Integration of household survey data with census tracts, block groups, and land cover classifications.

Main Results:

  • The P-MEDM successfully represents and models uncertainty in fine-resolution population estimates.
  • The model simplifies the process of incorporating ancillary data, accommodating disparate resolutions and uncertainties.
  • The methodology allows for a more comprehensive characterization of dasymetric estimate quality.

Conclusions:

  • The P-MEDM offers a unified approach to integrating ancillary data in dasymetric modeling.
  • This methodology enhances the types of data usable in population mapping and improves uncertainty assessment.
  • The P-MEDM provides geographers with tools to better understand and communicate the quality of spatial population data.