This simple system displays the dynamics of a monkey population present in the tropical rainforests of Africa, with constant birth and death rates.
Note: As this is an example of a simple system, the model includes one stock component, an inflow, and an outflow, each of which is affected by a variable. The stock within this model is the monkey population, the inflow is the monkey births, which are affected by the monkey birth rate, and the outflow is the monkey deaths, which are affected by the monkey death rate. Exponential and graph population data can be viewed using the "Simulate" feature.
The power dynamics of a fictitious rural community (Bobville). The dynamics illustrate the power needs of Bobville residents and businesses throughout the day and how they change when microgeneration, residential/commercial daytime use, and nighttime use are considered.
The dynamics of Codfish (prey) and Shark (predator)
populations.
The model showcases a connected population growth of Codfish and
Sharks in an ocean. The model has two positive feedback loops being the
birth-rate of codfish and the birthrate of sharks; and two negative feedback
loops being the death-rate of codfish and the death-rate of sharks. The stock
components of the model link to each other through feedback loops. The feedback
loops are the variables of codfish death-rate which depends on the stock of number of sharks, and the variable of shark’s birth-rate which depends
on the stock of number of codfish in the ocean/model.
This model illustrates the flow of water in a reservoir, with inflows from rainfall and outflows from community water usage. It tracks how the amount of water in the reservoir changes over time, depending on the balance between inflow and outflow. In this example, the inflow from rainfall (2,000 liters/day) exceeds the outflow from water consumption (1,500 liters/day + 400 liters/day = 1,900 liters/day), leading to a gradual increase in the reservoir's water level by 100 liters per day. The model demonstrates how fluctuations in rainfall and water usage rates affect the sustainability of water resources, making it useful for understanding water management in changing environmental conditions.
The dynamics of Oshawa's human population are influenced by a density-dependent growth rate. The population growth rate equals the mortality rate when the population reaches the city's carrying capacity, defined by available housing, jobs, healthcare, and other resources. The growth rate exceeds the mortality rate when the population is below carrying capacity, allowing for positive net growth. Likewise, when the population surpasses the city's carrying capacity, growth declines, and the mortality rate exceeds the growth rate due to overburdened resources and infrastructure.
The dynamics of human population in Oshawa are influenced by density dependent growth rates. When the population exceeds carrying capacity, the city's resources become overextended, leading to a decline in growth rates. Death rates and emigration rise, while birth and immigration rates drop, causing a population decrease. When population falls below its carrying capacity, the birth and immigration rates surpass death and emigration, resulting in population growth due to the availability of ample resources.
The changing dynamics of distance and velocity over time according to several variables including, constant acceleration, constant mass, force and work.
This model graphs the general idea of the inflow and outflow of energy in a small town in Ontario's power grid. The main sources of energy come from Nuclear being one of Ontario's biggest sources of energy, Wind, and Solar. The graph shows energy generation vs energy consumption and the demand in energy in the town.
In this model, I will be demonstrating my understanding of Modelling a Human Population by using Oshawa as a current example. For this model, we begin with a current population of 170,000. However, with Oshawa's "theoretically new" (just to demonstrate my understanding) neighbourhoods being built the population will change to reflect that and the city's appropriate carrying capacity! By using variable factors such as "Moving in/inflow rate" and "Moving out/outflow rate" to reflect the number of current residents residing within Oshawa (nResidents).
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 simulates human population growth on both a global scale and a local scale for Ajax, Ontario. The global population starts at 8 billion and Ajax starts at 121,780 Carrying capacities are set to 12 billion for Earth and 150,000 for Ajax. The model demonstrates the growth, showing how populations grow and stabilize as they approach their environmental limits.
The changing dynamics of distance and velocity over time according to several variables including, constant acceleration, constant mass, force and work.
This model analyzes the growth and dynamics of Oshawa’s population using a logistic approach. Starting with an initial population of 170,000 and an increased carrying capacity of 180,000, it evaluates how the addition of new neighbourhoods, planned to accommodate an extra 10,000 residents over the next 10-15 years (or whatever time period) affects population changes. Key factors include the Oshawa Residents Death/Emigration Rate of 0.8% (realistic percent approximation), accounting for natural deaths and emigration, and the Oshawa Residents Birth/Immigration Rate of 2.4% (also a realistic percent approximation), reflecting new residents through births and immigration. The model tracks the net population change, providing insights into how Oshawa's population might grow or stabilize as it approaches its new carrying capacity!
Brooklin is a small town that is developing quickly and has a younger average population than places like Whitby and Oshawa, therefore giving it a slightly higher birth rate. From when I was a child (Around 2008) it had a population of 15,000, and was estimated at >25,000 in 2018. This simple model is specifically for births and deaths, and doesn't take the fast development of neighbourhoods (i.e. people moving in from other places).
This model simulates the global human population's growth and decline over a 50-year timeline, factoring in birth rates (density-dep), death rates, and Earth's carrying capacity (K). The model illustrates how population dynamics shift as the population approaches or moves away from K: when the population reaches K, the birth rate matches the death rate, causing no net change; when the population is below K, births exceed deaths, leading to growth; and when the population exceeds K, deaths surpass births, causing the population to shrink. Through this simulation, the model offers insights into possible population trends like stabilization, growth, or decline, and highlights the relationship between reproduction, mortality, and environmental limits. Clear units reflect these shifts per year.
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.
Star production was greater at the beginning of the universe, and has slowed down as lots of gases are now trapped in living stars. While this isn't true for every galaxy, this is a model simulating a galaxy where star formation rate is decreasing.
Everything is divided by 1 billion as to not have to simulate huge numbers.
This model displays how the population of the Earth changes. With a larger birth rate than death rate the population increases and heads towards K (carrying capacity). If the birth rate is lower than the death rate, the population will slowly diminish and will move away from carrying capacity.
Tanjiopolis is a unique municipality located in northern Canada, known for its extreme seasonal climate where there is six months of very hot summers followed by six months of very cold winters, with no transitional seasons. This distinct environment has driven Tanjiopolis to innovate and thrive, harnessing its natural resources to achieve energy independence.
The municipality has invested heavily in a robust infrastructure of solar and wind generators, complemented by a few nuclear power facilities. The nuclear plants operate at only 10% of their maximum capacity during the summer, as the abundant solar energy meets the municipality's power needs. In contrast, during the winter, the nuclear facilities ramp up to 100% capacity to compensate for the reduced solar output due to limited sunlight.
Tanjiopolis takes pride in its commitment to sustainability, reinforced by a government-mandated policy that requires 2 solar panels per residential building, 4 solar panels per small business building, and 6 solar panels per large business building. This ensures that the municipality can sustain a population of 3 million people entirely through renewable energy sources, maintaining a self-sufficient power grid that operates independently from external systems.
