SUST1001U Models

These models and simulations have been tagged “SUST1001U”.

Insight diagram
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. 
Ashvitha's - Human Population Deterministic Model
Insight diagram
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.
Clone of Logistic Moose Population Dynamics Deterministic w Delay
Insight diagram
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.
Clone of Logistic Moose Population Dynamics Deterministic w Delay
Insight diagram
This model projects how the population of the Charlottetown Census Agglomeration can change over the next 100 years, starting with 96,516 people, as of January 14, 2026. Population change is based on the annual birth, death, immigration, and emigration rates from Statistics Canada. Births and immigration add people to the population, while deaths and emigration can reduce it. The model also looks at population density, which means the number of people living in each square kilometre. The Charlottetown Census Agglomeration has a land area of 1,112.43 km², which is kept the same throughout our simulation. As the population increases or decreases, the number of people living in each square kilometre also changes. A Population Density Index is included to make this change easier to understand; the density at the beginning of the model is represented by 1. An index of 2 would mean that twice as many people are living in each square kilometre compared with the beginning of the simulation.  

Over the 100-year simulation, the model shows the Charlottetown Census Agglomeration population increasing from 96,516 people to approximately 520,000 people. Since the land area remains the same, population density also increases from about 87 people/km² to approximately 468 people/km² (calculations and data used to determine these values are provided within the corresponding variables). This means the Population Density Index increases from 1 to approximately 5.4, indicating that the population density is about 5.4 times the starting density. These results show what could happen if the birth, death, immigration, and emigration rates used in the model remained constant over the entire 100 years and thus should be viewed as a modelled scenario rather than a prediction of Charlottetown's actual future population.

The simulation results appear in the Simulation Results displays, which provide separate views of the 100-year population projection, population density projection, and a results table showing population, population density, as well as the Population Density Index over time.
Sam's Charlottetown Population Dynamics: A 100-Year Projection of Population Growth and Density
last month
Insight diagram
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.
Clone of Logistic Moose Population Dynamics Deterministic w Delay
last month
Insight diagram
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.
Raymond's Logistic Moose Population Dynamics Deterministic w Delay
last month
Insight diagram
The dynamics of a human population. This shows how the birth rate and death rate interact to form our population, alongside factors such as density and environment.
Human Population Growth in Canada
last month
Insight diagram
This model demonstrates Ontario's population and its projected growth over 100 years. It uses variables such as the birth rate, death rate, immigration rate and emigration rate to predict how Ontario's population will change in coming years. A projected carrying capacity (K) of 25 million people is included to represent the idea that population growth decreases as the population increases. In the simulation, Ontario's population grows from approximately 16 million to approximately 19 million people over 100 years, while the per capita growth rate gradually decreases.
Human Population Dynamics in Ontario
last month
Insight diagram
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.
Clone of Logistic Moose Population Dynamics Deterministic w Delay
last month
Insight diagram
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.
Clone of Logistic Moose Population Dynamics Deterministic w Delay
Insight diagram
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.
Julia's Logistic Edmonton Population Dynamics
Insight diagram
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.
Clone of Logistic Moose Population Dynamics Deterministic w Delay
last month
Insight diagram
The dynamics of moose (prey) and wolf (predator) populations
Clone of Moose (Prey) and Wolf (Predator) Population Dynamics
last month
Insight diagram
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.
Clone of Logistic Moose Population Dynamics Deterministic w Delay
Insight diagram
Simulation of Human population increase in Ajax, Ontario using a 2021 statistic. Factors used are a combination of statistics in Ajax, and assumptions (based off of nearby cities in Durham Region.

*appears to be somewhat accurate as predictions state the current population of Ajax is about 140-150 thousand, which lines up with the year 5 mark on the simulation.

* this is a clone of Logistic Moose Population Dynamics Deterministic w Delay
Ajax Population (Human)
last month
Insight diagram

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.

Durham Region Human Population Growth, Logistic Model with Migration
Insight diagram
The dynamics of moose (prey) and wolf (predator) populations
Clone of Moose (Prey) and Wolf (Predator) Population Dynamics
last month
Insight diagram
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.
Clone of Logistic Moose Population Dynamics Deterministic w Delay
Insight diagram
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.
Clone of Logistic Moose Population Dynamics Deterministic w Delay
last month
Insight diagram
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.
Krishnath Sirivel's Clone of Logistic Human Population Dynamics Deterministic w Delay
Insight diagram
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.
Modeling Human Growth Population in Markham, Ontario
Insight diagram
The dynamics of moose (prey) and wolf (predator) populations
Clone of Moose (Prey) and Wolf (Predator) Population Dynamics
last month
Insight diagram
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.
Clone of Logistic Moose Population Dynamics Deterministic w Delay
last month
Insight diagram
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.
Clone of Logistic Moose Population Dynamics Deterministic w Delay
last month