3 resultados para location analysis

em Digital Archives@Colby


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Maine has the highest potential for wind energy in New England and falls within the top twenty states in the nation. It falls just behind Wisconsin and California with an estimate electrical output of 56 billion kWhs. The geological makeup of Maine’s mountains in the western part of the state, and the exposed coastline provide opportune areas to capture wind and convert it into energy. The information included in this poster will suggest the most likely areas for wind development based on a number of factors as recommended by the American Wind Energy Association.

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Moose (Alces alces) are a keystone herbivore in Maine. Because of the large number of rural roads in Maine, there is a high rate of moose-vehicle collisions (MVCs), which is increasing. On-road encounters with animals resulted in 231 fatalities in the United States in 1999. Because of the fatality of MVCs, it is important to know where they are most likely to occur. I used GIS analysis to estimate where future MVCs would occur, factoring in the variables of land cover suitability for moose, distance from water bodies, locations of past MVCs, and speed limits on the roads. I ran four different analyses, each one weighting the variables equally. I also ran a regression to determine if increasing road speed was associated with the increase in the number of MVCs per length of road. There was not a strong positive relationship between the number of MVCs per length of road and the speed limit, but it was interesting to note that there were more MVCs per length of road on 35mph and 40mph roads than on 45, 50, 55 or 65mph roads. Future research on MVCs would benefit from the inclusion of include moose population density and road traffic data.

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This map shows one option for a viable energy source that is clean, free and endless: wind power. This map shows that the coast of Maine has the potential space and wind speed to be a location for wind farms. Four NOAA buoys placed in different locations along the Maine coast are the source of the wind speed data for this project. The average wind speed of every ten minutes of every day for the year 2004 were averaged so that each buoy was represented by one number of wind speed measured in meters/ second. The values in between these four buoys were estimated, or interpolated, using ArcGIS. Other factors that I took into consideration during this lab were distance from airports (no wind farm can be with in a three mile radius of an airport ) and distance from counties (no one wants an offshore wind farm that obstructs their view). I calculated the most appropriate locations for a wind farm in ArcGIS, by adding these three layers. The final output shows an area along Mt. Desert to be the most appropriate for development.