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.

A model simulating the population growth (Humans) of Oshawa (the city proper) over a 100-year timeline. The models growth is influenced by per capita birth rates, death rates, and migration (in-flows and out-flows). Population growth is determined by a carrying capacity of approx. 400,000 people which reflectsOshawa's (approximate) urban and infrastructure limits. Net growth is positive when the population is below carrying capacity, balanced when at capacity, and negative when it exceeds capacity. All stocks, flows, and variables use standard units (humans, humans/year, and 1/year).

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.

This model shows how the population of Whitby changes over time using logistic growth. Population growth is influenced by birth rate, death rate, migration rate, and an estimated carrying capacity. When the population is well below the carrying capacity, growth is faster. As the population approaches the carrying capacity, the population growth rate decreases.  

This model shows how Toronto's population can change over time while considering the city's limited space and resources. As more people move into the city, resources such as housing, jobs, and food may become more limited, which can slow population growth. The model uses a carrying capacity to represent the population Toronto could reasonably support. It also includes a delay to show that the effects of a growing population may take some time to occur. Overall, I made this model to show how population growth over the span of 100 years can happen quickly, and as Toronto gets closer to its carrying capacity, it will effect many lives of the individuals living there. 

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 is a model demonstrating the scale of growth for the town of Newmarket, located in Canada Ontario, roughly 60km north of Toronto. The total population capacity for Newmarket was based off of York Region Figures of 118,500 people in the year 2051, and the current population was based off a current rough number of around 97,000 people.

This model shows how the population of Durham Region changes over time using a logistic growth model. The Durham Region Population is the main stock and represents the number of people living in the region. The Change in Durham Population is the flow that represents the yearly increase or decrease in the population. The model uses a Human Maximum Birth Rate and Human Minimum Death Rate to determine population growth. The Durham Carrying Capacity represents the population level that the region could reasonably approach as growth slows. The model also includes a delay in the effects of population density, which represents the idea that changes in population density may take time to affect population growth. The starting population is approximately 800,000 people, and the values used in the model are approximate and intended to demonstrate population dynamics rather than make an exact prediction.

This deterministic model explores how Durham Region's human population could change over 100 years from an illustrative baseline of 800,000 residents. Population is the stock, measured in people. Births and Inmigration add people; Deaths and Outmigration remove people. All four flows are measured in people per year. As the population grows, pressure on housing and services reduces inmigration. A two-year response delay represents the time between changing conditions and decisions to move. This creates a balancing feedback that gradually slows growth.

This model simulates the future trajectory of the global human population using a delayed logistic growth framework. Unlike simple exponential growth, this model assumes that Earth has a finite carrying capacity (K) limited by resources, space, and environmental constraints. It incorporates a generation-time delay to represent the lag between an increase in population density and its feedback effects on birth and death rates (e.g., resource depletion, urbanization, and social changes). The model demonstrates how a delay in density-dependent feedback can cause the human population to overshoot the carrying capacity before stabilizing or oscillating.

The population dynamics of Markham, Ontario, appear in this model. Based on carrying capacity, birth rate, and death rate, it models how Markham's population evolves over time. Because the effects of growing population density, such as a demand on housing, infrastructure, land, and services, may take time to impact population increase, a time delay is implemented.

References:
https://www.canada.ca/en/immigration-refugees-citizenship/campaigns/immigration-matters/local-economies/toronto.html
https://www150.statcan.gc.ca/t1/tbl1/en/tv.action?pid=1310042901&pickMembers%5B0%5D=1.7&pickMembers%5B1%5D=4.1&cubeTimeFrame.startYear=2011&cubeTimeFrame.endYear=2021&referencePeriods=20110101%2C20210101

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.

Oshawa population dynamics: 

The dynamics of human population growth across different geographic scales, in the following order, Oshawa, Durham, Ontario, Canada then the Global population. Population size changes over time based on births, deaths, immigration, and emigration. Births and immigration increase the population, while deaths and emigration decrease it. For the global scale, the population is only impacted by births and deaths as there is no meaningful immigration to or emigration from Earth.

This model represents the dynamics of the human population in Vancouver, taking into account births, deaths, immigration, and emigration. Population growth depends on density-dependent birth and death rates. When the population is below its carrying capacity, the birth rate is greater than the death rate, causing the population to grow. When the population reaches carrying capacity, the birth and death rates are equal. If the population exceeds carrying capacity, the death rate becomes greater than the birth rate, causing the population to decrease. Immigration adds people to the population, while emigration removes people from the population.

A graphic model that displays the human population from a global to local scale. The model presents how the population gets smaller as you move from a global scale to a local scale which is Ajax. The city holds approximately 130,000 people and connects to the larger population of Durham Region, Canada, and globally. 

This model simulates long term human population growth in Durham Region, Ontario. It begins with a population of 800,000 people and uses birth and death rates to calculate population change. Population growth is dependent on density, meaning that growth gradually slows as the population approaches an assumed carrying capacity of 1.2 million people. A five year delay is included to represent the time required for population pressures, such as housing availability, infrastructure capacity and resource limitations, to influence population growth. The model is a simplified representation intended to demonstrate logistic human population dynamics rather than provide an exact population forecast.

The dynamics of the human population between the years of 2024-2025 using the statistics of the the population to the city of Halifax.This entails the birth, death, immigration and emigration rates components used to determine population growth. 

In this model I will be showcasing the human population in ajax for the next 100 years. The current population will be set to 127k and the max capacity will be 180k. You can simulate this model and see how the population will grow over 100 years.

This model shows the growth of the human population in Markham. The initial number of population is set to 350k, and the model assumes that the capacity is 500k. The model's purpose is to show how population growth can actually slow down as a population approaches its carrying capacity. 

This model explores human population growth at global and local scales from 2026 onward (within a 100 year period). It explores the global world population while addressing local data for Durham Region and Oshawa, Canada. Each population is represented as a stock, with births acting as an inflow and deaths as an outflow. Birth and death rates are taken from current 2026 official data sources and assumed to remain constant, allowing the model to demonstrate exponential population change. Migration is excluded, so the results represent natural population change rather than a complete population forecast.

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.