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.

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

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 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.

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.

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 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 moose (prey) and wolf (predator) populations

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.

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.  

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 moose (prey) and wolf (predator) populations

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 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.

This model simulates population growth in Toronto using births and deaths. The population increases through births and decreases through deaths. Growth gradually slows as the population approaches the city's carrying capacity, representing limits such as housing availability, land, infrastructure, and other resources.

This model simulates population change in the City of Toronto. Population increases through births and immigration. Population decreases through deaths and emigration. Population is measured in people and rates are measured per year.