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

Population Growth00:57

Population Growth

Population size is dynamic, increasing with birth rates and immigration, and decreasing with death rates and emigration. In ideal conditions with unlimited resources, populations can increase exponentially, which plots as a J-shaped growth rate curve of population size against time. This type of curve is characteristic of newly-introduced invasive species, or populations that have suffered catastrophic declines and are rebounding.
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Modeling with Differential Equations01:25

Modeling with Differential Equations

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...
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

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...

You might also read

Related Articles

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

Sort by
Same author

Deep learning four decades of human migration.

Nature·2026
Same author

A review and evaluation of internal migration forecasting models.

Population studies·2026
Same author

Measuring global migration flows using online data.

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

Multiregional Population Forecasting: A Unifying Probabilistic Approach for Modelling the Components of Change.

European journal of population = Revue europeenne de demographie·2025
Same author

The contributions of immigration to demographic change across cities and regions in Australia.

Population studies·2025
Same author

The form and evolution of international migration networks, 1990-2015.

Population, space and place·2024
Same journal

An empirically grounded conceptual framework of the determinants of economic resilience: Insights from seven major Canadian regions.

Environment & planning A·2026
Same journal

Steering FinTech: Techno-industrial policy for the data-driven economy in China's Greater Bay Area.

Environment & planning A·2026
Same journal

'This big shadow that we need to turn into light' - How labour intermediaries moralise commodified domestic care work.

Environment & planning A·2026
Same journal

Towards a radical highway geography: Berlin and the remaking of city logistics in global capitalism.

Environment & planning A·2025
Same journal

Municipal structural adjustment: For an institutional analysis of global development finance.

Environment & planning A·2025
Same journal

Searching for housing in the digital age: Neighborhood representation on internet rental housing platforms across space, platform, and metropolitan segregation.

Environment & planning A·2025
See all related articles

Related Experiment Video

Updated: May 16, 2026

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling
20:36

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Published on: July 4, 2007

Does Specification Matter? Experiments with Simple Multiregional Probabilistic Population Projections.

James Raymer1, Guy J Abel, Andrei Rogers

  • 1ESRC Research Centre for Population Change, University of Southampton, Southampton, SO17 1BJ, United Kingdom.

Environment & Planning A
|December 14, 2012
PubMed
Summary
This summary is machine-generated.

Comparing population projection models reveals that multiregional models offer distinct forecasts and prediction intervals. Model specification significantly impacts population projections and uncertainty estimation.

More Related Videos

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

Related Experiment Videos

Last Updated: May 16, 2026

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling
20:36

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Published on: July 4, 2007

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

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:

  • Demography
  • Statistical Modeling
  • Regional Science

Background:

  • Population projection models are increasingly incorporating uncertainty.
  • Model specification choices significantly influence projection outcomes.
  • Understanding regional population dynamics is crucial for policy and planning.

Purpose of the Study:

  • To compare forecasts and prediction intervals from four distinct regional population projection models.
  • To assess the impact of model specification on population projections in England.
  • To evaluate the influence of international migration assumptions on regional forecasts.

Main Methods:

  • Utilized vector autoregressive (VAR) models to forecast demographic rates.
  • Employed four regional population projection models: overall growth, component (net migration), component (in/out migration), and multiregional.
  • Used annual time-series data (1976-2008) from the Office for National Statistics for England's North, Midlands, and South regions.

Main Results:

  • The multiregional model produced different subpopulation totals and prediction intervals compared to simpler models.
  • Assumptions regarding international migration also led to variations in forecasts.
  • Vector autoregressive models effectively forecasted various demographic rates and migration measures.

Conclusions:

  • Model choice, particularly between multiregional and simpler approaches, substantially affects population projection outcomes and uncertainty.
  • International migration assumptions are critical drivers of regional population forecasts.
  • Further research should explore advanced modeling techniques for improved population projection accuracy.