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The primary data set used in cost distance analysis is the cost raster, sometimes called the cost-of-passage surface, [9] the friction image, [8] the cost-rate field, or cost surface. In most implementations, this is a raster grid , in which the value of each cell represents the cost (i.e., expended resources, such as time, money, or energy) of ...
Sample build-out analysis. A typical analysis of build-out might start with 10 acres (40,000 m 2) of land with a proposed density of 4 dwelling units per acre. A simple multiplication (10x4) would produce a build-out of 40 units. A more complex analysis might deduct .75 acres (3,000 m 2) for 660 feet (200 m) of 50-foot (15 m) road right-of-way ...
In the United Kingdom and Hong Kong both plot ratio and site ratio are used. [8] [9] In Singapore the terms plot ratio and gross plot ratio (GPR) are more commonly used. In the United States and Canada, floor space ratio (FSR) and floor area ratio (FAR) are both used. [10] Use ratios are used as a measure of the density of the site being developed.
Common synonyms for geomorphometry are geomorphological analysis (after geomorphology), terrain morphometry, terrain analysis, and land surface analysis. Geomorphometrics is the discipline based on the computational measures of the geometry , topography and shape of the Earth's horizons, and their temporal change. [ 2 ]
A benefit–cost ratio [1] (BCR) is an indicator, used in cost–benefit analysis, that attempts to summarize the overall value for money of a project or proposal. A BCR is the ratio of the benefits of a project or proposal, expressed in monetary terms, relative to its costs, also expressed in monetary terms.
In that case one must specify a real parameter , the ratio between the cost of creating or destroying one unit of "dirt", and the cost of transporting it by a unit distance. This is equivalent to minimizing the sum of the earth moving cost plus α {\displaystyle \alpha } times the L 1 distance between the rearranged pile and the second ...
A supervised classification is a system of classification in which the user builds a series of randomly generated training datasets or spectral signatures representing different land-use and land-cover (LULC) classes and applies these datasets in machine learning models to predict and spatially classify LULC patterns and evaluate classification accuracies.
One method to conduct quantitative analysis of this type is to discount (specifically, divide) raw measures of market size or potential with measures of distance, broadly defined. [ 1 ] Ghemawat emphasizes that different types of distance matter to different extents depending on the industry.