Spatial Data Acquisition

The primary purpose of the project is to use model simulations to forecast spatial patterns among various species in the environment. By comparing current situations with test results, Graniero hopes to have the ability to predict spatial patterns for species in the environment. This will give environmentalists and scientists alike the ability to prevent specie disaster and to study such areas as future habitat.

Graniero’s first step involved measuring the earth’s topography, under the bedrock of the surface. This experiment took place in Newfoundland, Canada. To do this he took a random sampling scheme. These schemes were tested at a density of 40 points per hectare. In order to bring the most precise and comprehensive data to the table, such technologies as mobile computers and GPS systems were used.

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The field in which was being tested proved to be very difficult to measure due to the changing system and the high demand of physical resource. His objective still remained the same though, to take this data and run a model that would enable him forecast spatial data on various species. The model he used was known as Cellular Automation (CA). The models properties were as follows: a finite set of discrete states and a state transition rule where the next state is determined by; current cell state, states of the nearest neighbours, and the state of other layers.

The model worked in specific steps. First, a spatial structure was built. Second, data was collected from it. Third, the simulation of different collection agencies were put forth. Fourth, the model information was compared to the behaviour of actual systems. Fifth, the model was repeated with random initial conditions. Thousands of trials were done at this point. This model is often referred to as a “virtual lab”. When the information was taken at the conclusion of each test, it was sent to processing units where it was studied in the form of a grid. These grids were then used to study the spatial patterns of various species.

Such future models will be more complex and more specific, thus showing species habitats and migratory trends. Adjusting the variables in the model can allow scientists to measure such activities as the population density of a species. Through the experiment there were three experiment sets. These included populations, disturbances, and resource mapping. The resource spatial structure also varied from uniform, smooth, and “patchy” environments (soil and forest types).

This information is very valuable to environmentalists and society in general due to the fact that it “looks-out” for species that may be in danger and monitors the move from one territory to another over a given time frame. Allowing scientists to predict the habitat and density of species in given areas with such models keeps humans aware of the impact they may have. This helps protect the future of species and insures that humans don’t interfere with its habitat as well.

In conclusion, the model is very useful and as it grows and becomes more sophisticated it should prove to be a valuable resource to environmental scientists.


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Spatial Data Acquisition. (2018, Jun 17). Retrieved from