An experimental analysis of least-cost path models on ordinal-scaled raster surfaces
| Autor: | Rachel Mundeli Murekatete, Takeshi Shirabe |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Geographic information system
Computer science Geography Planning and Development 0211 other engineering and technologies 0507 social and economic geography 02 engineering and technology Library and Information Sciences Geosciences Multidisciplinary Selection (genetic algorithm) shortest path problem 021101 geological & geomatics engineering Least cost path business.industry Least-cost paths 05 social sciences raster cost surfaces/suitability surfaces Function (mathematics) computer.file_format lexicographic preference scales of measurement Grid Multidisciplinär geovetenskap Level of measurement Shortest path problem Raster graphics business 050703 geography Algorithm computer Information Systems |
| Popis: | Selection of optimal paths or sequences of cells from a grid of cells is one of the most basic functions of raster-based geographic information systems. For this function to work, it is often assumed that the optimality of a path can be evaluated by the sum of the weighted lengths of all its segments–weighted, i.e. by the underlying cell values. The validity of this assumption must be questioned, however, if those values are measured on a scale that does not permit arithmetic operations. Through computational experiments with randomly generated artificial landscapes, this paper compares two models, minisum and minimax path models, which aggregate the values of the cells associated with a path using the sum function and the maximum function, respectively. Results suggest that the minisum path model is effective if the path search can be translated into the conventional least-cost path problem, which aims to find a path with the minimum cost-weighted length between two terminuses on a ratio-scaled raster cost surface. On the other hand, the minimax path model is found mathematically sounder if the cost values are measured on an ordinal scale and practically useful if the problem is concerned not with the minimization of cost but with the maximization of some desirable condition such as suitability. © 2020, © 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. Not duplicate with DiVA 1252643QC 20201130 |
| Databáze: | OpenAIRE |
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