This model represents the population of ancient Rome in 200 BC with a density-dependent birth rate...the birth rate does not equal the death rate when the population is at carrying capacity because immigration and emigration are also factors. All values are based on historical estimates, as precise figures are not available.

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.

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

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.

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 moose population with  birth and death rates that are the same year in and year out.

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 system diagram represents population levels for humans ranging from the world population level down to the Oshawa population level. This diagram consists of stocks which represent World, Canada, Ontario, Durham Region, and Oshawa in terms of number of people. The flows in the diagram represent the movement of people between the population levels in terms of people per year.

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).

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 projects the population of the city of Miami, Florida over the next 100 years using a stock and flow structure. The single stock, Miami Population, is increased by the Births and Immigration and decreased by the Deaths and Emigration. Miami's birth and death rates are quite balanced, therefore, the main driving force of Miami's increase in population is migration shaping the city's future population. 

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 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. 

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. 

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.

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.

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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.

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. 

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 shows the global human population over the next X years, with a max global capacity of 12 billion, initial population of 8 billion, birth rate of 0.4, and death rate of 0.2. 

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 model shows the change of Pickering's population by using birth rate, death rate and also how many people move in and out of the city. The population stock shows the number of residents and the flows are based on people per year. The model assumes that as the population grows and housing becomes more limited, the rate at which people move in decreases. Every data value that I entered aside from the population is my own assumption.

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.