Spatial data acquisition and system modeling: notes from the field and the lab.
Name of Lecturer: Philip Graniero Department of Lecture: Earth Sciences Date andTime of Lecture: January 13th, 2000 at 4:30pm Purpose of Research Project: Theprimary purpose of the project is to use model simulations to forecast spatialpatterns among various species in the environment. By comparing currentsituations with test results, Graniero hopes to have the ability to predictspatial patterns for species in the environment. This will giveenvironmentalists and scientists alike the ability to prevent specie disasterand to study such areas as future habitat. Description of Research/Technologyused: Graniero’s first step involved measuring the earth’s topography, underthe 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 adensity of 40 points per hectare. In order to bring the most precise andcomprehensive data to the table, such technologies as mobile computers and GPSsystems were used. The field in which was being tested proved to be verydifficult to measure due to the changing system and the high demand of physicalresource. His objective still remained the same though, to take this data andrun a model that would enable him forecast spatial data on various species.
Themodel he used was known as Cellular Automation (CA). The models properties wereas follows: a finite set of discrete states and a state transition rule wherethe next state is determined by; current cell state, states of the nearestneighbours, 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, themodel information was compared to the behaviour of actual systems. Fifth, themodel was repeated with random initial conditions. Thousands of trials were doneat this point. This model is often referred to as a “virtual lab”.
When theinformation was taken at the conclusion of each test, it was sent to processingunits where it was studied in the form of a grid. These grids were then used tostudy the spatial patterns of various species. Such future models will be morecomplex and more specific, thus showing species habitats and migratory trends. Adjusting the variables in the model can allow scientists to measure suchactivities as the population density of a species. Through the experiment therewere three experiment sets. These included populations, disturbances, andresource mapping.
The resource spatial structure also varied from uniform,smooth, and “patchy” environments (soil and forest types). Conclusion: Thisinformation is very valuable to environmentalists and society in general due tothe fact that it “looks-out” for species that may be in danger and monitorsthe move from one territory to another over a given time frame. Allowingscientists to predict the habitat and density of species in given areas withsuch models keeps humans aware of the impact they may have. This helps protectthe future of species and insures that humans don’t interfere with its habitatas well. In conclusion, the model is very useful and as it grows and becomesmore sophisticated it should prove to be a valuable resource to environmentalscientists.