This model shows how the population of Durham Region could change over 100 years. Population increases through births and immigration and decreases through deaths and emigration. The model also includes a population capacity, which causes the birth rate to decrease as the population becomes larger. This allows the model to show logistic population growth while also accounting for migration.

ASSUMPTIONS: The population capacity of 1.5 million is a modelling value used to demonstrate logistic growth and is not an official maximum population for Durham Region. The birth rate, death rate, immigration, and emigration values are simplified estimates that remain constant during the simulation. The delay and “population X years ago” parts of the example model were not included because I did not have a reasonable Durham-specific delay value, so the current population is used directly to adjust the birth rate.

The Canadian population over time from 2021 initial values onwards. 
This model includes the Addition of Birth rates, Death rates, Immigration and Emigration of people in 2021. Data from statistics Canada.

This model simulates the population of the City of Toronto using a logistic (density-dependent) structure with three flows: births, deaths, and net migration. Births and deaths use roughly balanced constant per-capita rates, because Toronto's population is relatively young, a fact that continual immigration helps sustain. Net migration is the dominant driver of the city's real-world growth, and it's modeled as density-dependent: as the population approaches an estimated housing/land-constrained carrying capacity, net in-migration tapers toward zero, so growth slows and the population levels off rather than growing without bound.

This model looks at how the student population at Ontario Tech University could change over time. I chose Ontario Tech as the local population scale instead of modeling the entire city or Durham Region. The model uses logistic growth to show that the student population can grow when there is available capacity, but that growth will eventually slow down as the university gets closer to the amount of students its infrastructure and resources can support.

The model also includes a time delay because changes in population and university capacity do not happen instantly. For example, if enrollment increases, it can take time for the effects of that increase to show up through things like classroom availability, housing, student services, and other campus resources. The delay is meant to represent this lag in the system.

The goal of this simulation is to model the population growth of Markham, Ontario, Canada. The model begins with the initial population from 2025 statistics and examines how births, deaths, immigration and emigration affect the population. Births and deaths are represented by using the annual birth and death rate applied to the current population. Immigration and emigration are represented as annual values.

The dynamics of a Human population with birth and death rates that are the same year in and year out, as of 2024 (the most recent I could find), in the Oshawa Region. 

The dynamics of a human population with density-dependent birth rate...the birth rate equals the death rate when the population is at carrying capacity; the birth rate is greater than the death rate when the population is below carrying capacity; the birth rate is below the death rate when the population is above carrying capacity.

The dynamics of a moose population with density-dependent birth rate...the birth rate equals the death rate when the population is at carrying capacity; the birth rate is greater than the death rate when the population is below carrying capacity; the birth rate is below the death rate when the population is above carrying capacity.

The dynamics of a moose population with density-dependent birth rate...the birth rate equals the death rate when the population is at carrying capacity; the birth rate is greater than the death rate when the population is below carrying capacity; the birth rate is below the death rate when the population is above carrying capacity.

This model projects the population of Oshawa, Ontario over 100 years, starting from 221,497 people in 2026 (World Population Review). It uses the same logistic structure as the moose model, with two changes that matter for a city: births and deaths are separated into their own flows rather than combined into one net growth rate, and a third flow is added for migration.

This model shows how the population of Ajax, Ontario could change from 2026 to 2051. It starts with Ajax’s projected population of 140,960 people in 2026. The model looks at four main factors that can change the population: births, deaths, immigration, and emigration. Births and immigration add people to the population, while deaths and emigration decrease it. The rates in this model stay the same throughout the simulation and are based on general population data from Ontario. Since population can be affected by many different factors, this model is meant to give a simple estimate of how Ajax’s population could change over time.

The dynamics of the National Capital Region population with birth, death, immigration, and emigration rates that are the same year in and year out.

This model shows how Markham’s population could change over the next 100 years. The population is affected by births, deaths, and net migration. Net migration includes both people moving into and out of Markham. Births and migration increase the population while deaths reduce it. The model also uses a carrying capacity of 500,000 people. The carrying capacity represents an assumed limit of people to how much the city of Markham can support. As Markham’s population gets closer to this limit, the growth rate slows down. Because of this, the population grows faster at the beginning of the simulation and then  slowly as it approaches the carrying capacity.

This model simulates Vancouver's population growth over 50 years using births, deaths, immigration, and emigration. The initial population of 662,248 was based on Statistics Canada Census 2021 data.The model assumes:

• 5,000 immigrants and 2,000 emigrants per year.
  
• A carrying capacity of 1 million.
  
• Birth and death rates are affected by population density.
  
• A 2-year delay represents the time needed for density-dependent effects to influence growth.
  

The graph shows a mostly linear progression rather than a curved growth pattern. This is because the constant net effect of immigration and emigration contributes to steady population growth, while the density-dependent effects gradually reduce the growth rate.After modelling, the population increases from 662,248 to approximately 1.06 million over 50 years. The population slightly exceeds the carrying capacity due to the delayed density-dependent effects, before growth slows.This is a simplified model and does not account for changes in immigration, housing, economic conditions, government policies, unexpected events, or carrying capacity. Therefore, the results represent a model-based projection rather than an exact prediction.

The dynamics of a moose population with  birth and death rates that are the same year in and year out.

A predictive model of Hawaii's human population from the initial 2026 population over the next century, using birth, death & migration rates.

The dynamics of Oshawa's human population with a density-dependent growth rate, projected from 2026 to 2126. Births and in-migration outpace deaths and out-migration while the population is below carrying capacity; the two balance out at carrying capacity; and deaths and out-migration would outpace births if the population overshot it. A five-year delay means crowding slows growth only after a lag, since housing takes years to build, and people move based on conditions they saw in the past.

This model shows fluctuations in Durham Region's population due to factors like births, deaths, immigration, and emigration. The Human Population stock represents the entire population. However, during this 100-year cycle, the total population figure is not fixed and will increase or decrease through the four main types of population flows: two inflow items and two outflow items.

The dynamics of Toronto’s population involve density-dependent growth, immigration, and emigration. The birth and death rates balance when the population is at carrying capacity; births exceed deaths when the population is below carrying capacity; and deaths exceed births when the population is above carrying capacity. Immigration and emigration provide additional population inflows and outflows based on 2025 data.

This model represents the population of Durham Region, Ontario (the eastern GTA, including Oshawa, Whitby, Ajax, Pickering, and Clarington), using the same density-dependent logistic structure as a wildlife population model. Unlike a closed animal population, most of Durham's growth comes from net migration rather than biological births and deaths, so "growth rate" here means the region's overall per-capita population growth (natural increase + net migration combined), not literal fertility/mortality rates. Growth is fastest while the region is well below its land-use/infrastructure capacity and tapers toward a low background rate as the region approaches build-out. A delay is included because housing supply and infrastructure only respond to population pressure after a lag; rezoning and construction take years, not months.

This model represents the dynamics of the human population in Ajax, Ontario, using logistic population growth. The population changes based on birth rate, death rate, carrying capacity, and density dependent effects. When the population is below carrying capacity, the population can continue to grow. As the population approaches carrying capacity, factors such as limited housing, land, infrastructure, and resources can slow population growth. The model also includes a delay to represent the time it can take for the effects of population density to influence population growth. The stock in this model is the Ajax population, which represents the total number of people, while the flow represents the change in Ajax's population over time. 

This system dynamics model simulates the population dynamics of Boston, Massachusetts, starting from a baseline population of 650,000 residents. The model evaluates long-term population change by tracking four simultaneous dynamic flows: natural births, baseline mortality, domestic/international immigration, and emigration.