Land Use Simulation
Land Use Simulation - Related Videos
02:22
The Colonization of Land
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2026
Changes in the environment of the early Earth drove the evolution of organisms. As prokaryotic organisms in the oceans began to photosynthesize, they produced oxygen. Eventually, oxygen saturated the oceans and entered the air, resulting in an increase in atmospheric oxygen concentration, known as the oxygen revolution approximately 2.3 billion years ago. Therefore, organisms that could use oxygen for cellular respiration had an advantage. More than 1.5 years ago, eukaryotic cells and...
10:22
In Situ SIMS and IR Spectroscopy of Well-defined Surfaces Prepared by Soft Landing of Mass-selected Ions
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Cited by 4 •
2014
Soft landing of mass-selected ions onto surfaces is a powerful approach for the highly-controlled preparation of novel materials. Coupled with analysis by in situ secondary ion mass spectrometry (SIMS) and infrared reflection absorption spectroscopy (IRRAS), soft landing provides unprecedented insights into the interactions of well-defined species with surfaces.
01:12
Equilibrium Rent: The Market for Land
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2025
The land is a critical factor of production that is supplied by landowners and rented by producers. For instance, farmers rent land to grow crops. Similarly, a restaurant owner may rent commercial building space to run the restaurant. The price for land is the rental price. Land can be bought, and in that case, the purchase price is the price paid to use that land indefinitely. However, in the current analysis, the rental price of land is used, as it presents a continuous payment similar to the...
09:46
MPI CyberMotion Simulator: Implementation of a Novel Motion Simulator to Investigate Multisensory Path Integration in Three Dimensions
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Cited by 48 •
2012
An efficient way to gain insight into how humans navigate themselves in three dimensions is described. The method takes advantage of a motion simulator capable of moving observers in ways unattainable by traditional simulators. Results confirm that movement in the horizontal plane is underestimated, while vertical movement is overestimated.
08:45
Study of Siphon Breaker Experiment and Simulation for a Research Reactor
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Cited by 2 •
2017
The siphon breaking phenomenon was investigated experimentally and a theoretical model was proposed. A simulation program based on the theoretical model was developed and the results of the simulation program were compared with experimental results. It was concluded that the results of the simulation program matched the experimental results well.
FAQs
Suitability rules determine which mapped cells or parcels are more appropriate for particular uses under specified conditions. They can incorporate spatial data and observed historical patterns, then guide where development, agriculture, conservation, or other changes are assigned. Adjusting these rules changes the location and extent of simulated land-use transitions, allowing researchers to examine how environmental constraints or planning priorities shape landscape outcomes.
Scenario-based drivers represent alternative environmental, social, or economic conditions that may influence future land-use patterns. By changing the assumed drivers, researchers can compare possible development, conservation, or resource-demand trajectories rather than relying on a single projection. This makes the model useful for examining policy options and identifying how different decisions could create competing demands across the landscape.
Updating individual cells or parcels preserves the spatial pattern of change instead of describing only total land-use quantities. Each mapped unit can be evaluated against its location, suitability conditions, historical trends, and scenario drivers. The resulting arrangement helps reveal where urban expansion, agricultural shifts, conservation areas, or fragmented habitats may occur, which is essential for environmental assessment and landscape planning.
A simulation typically draws on spatial data, historical land-use or land-cover trends, suitability rules, and scenario-based drivers. These elements establish current mapped conditions, indicate how patterns have changed, and specify factors that may influence future transitions. Researchers combine them in a computational model that updates cells or parcels over time, producing spatially explicit projections for comparison and analysis.
Researchers can use projected land-use patterns to examine urban growth, habitat fragmentation, resource demand, and climate-related planning. The model helps connect spatial changes with environmental concerns by showing where competing uses may expand or shift. These results support comparison of management strategies and can identify areas where development, conservation, agriculture, or resource needs may conflict.
Policy alternatives can be represented as different scenarios, suitability conditions, or constraints within the modeling framework. Researchers then compare the resulting spatial patterns to determine how each option affects development, conservation, resource demand, or landscape conflicts. Although the model does not select a policy by itself, its projections provide a structured basis for evaluating sustainability strategies and planning trade-offs.