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Fig. 9 Relative surface cost maps of four different perspectives

Figure 9 Relative surface cost maps of four different perspectives

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change in value from that cell to its neighbors. Basically, the maximum change in elevation over the distance between the cell and its eight neighbors identifies the steepest downhill percentage from the cell. Conceptually, the tool fits a plane to the z values of a 3 x 3 cell neighborhood around the pro- cessing or center cell.
change in value from that cell to its neighbors. Basically, the maximum change in elevation over the distance between the cell and its eight neighbors identifies the steepest downhill percentage from the cell. Conceptually, the tool fits a plane to the z values of a 3 x 3 cell neighborhood around the pro- cessing or center cell.
The slope criterion in an Indian Road Congress (IRC) spec- ification should be acceptable in the range 0 to 10% for a flat area, 10 to 25% for a rolling area, 25 to 60% for a mountain- ous area, and more than 60% for the steepest area. In the
The slope criterion in an Indian Road Congress (IRC) spec- ification should be acceptable in the range 0 to 10% for a flat area, 10 to 25% for a rolling area, 25 to 60% for a mountain- ous area, and more than 60% for the steepest area. In the
Fig. 4 Cost criteria maps of physical features
Fig. 4 Cost criteria maps of physical features
Supervised classification method using the maximum likeli- hood classifier was performed for land classification. The sat- ellite image is classified for acquiring the available informa- tion of land use/land cover. The classified map is given in Fig. 6. The various land use classes, delineated from the LISS-II image, such as agriculture land, water body, rivers, trees, ur- ban area, and sandbank, are used for analyzing the study area and for creating the least-cost route alignment.
Supervised classification method using the maximum likeli- hood classifier was performed for land classification. The sat- ellite image is classified for acquiring the available informa- tion of land use/land cover. The classified map is given in Fig. 6. The various land use classes, delineated from the LISS-II image, such as agriculture land, water body, rivers, trees, ur- ban area, and sandbank, are used for analyzing the study area and for creating the least-cost route alignment.
Fig. 7 Cost criteria maps of land use and land cover The task of selecting a particular route alignment for a high- way as an alternative is complex and challenging. MCDA is applicable to solving complex and challenging problems that are characterized as a choice among alternatives (Malczewski, 1999a, b, 2004, 2006). The MCDA provides a rich collection of techniques and procedures for structuring decision prob- lems and designing, evaluating, and prioritizing alternative decisions (Carver 1991). A factor is a criterion that enhances or detracts from the suitability of a specific alternative for the Evaluation of criteria map attributes can only be compared if the measurement units are the same. Because the evaluation of the criteria had their own characteristics and extent and each datum of criteria had its own dimension and distribution, it was difficult to directly compare them or include them togeth- er in an operational system. The process of setting the relative importance of each criterion is known as the standardization of criteria (Feizizadeh and Blaschke 2013). Standardization of the attributes was performed for each of the cost maps in ESRI Spatial Analyst reclassification module. In this process, scales of 0 to 1, 0 to 10, or 0 to 100 are normally used for criteria
Fig. 7 Cost criteria maps of land use and land cover The task of selecting a particular route alignment for a high- way as an alternative is complex and challenging. MCDA is applicable to solving complex and challenging problems that are characterized as a choice among alternatives (Malczewski, 1999a, b, 2004, 2006). The MCDA provides a rich collection of techniques and procedures for structuring decision prob- lems and designing, evaluating, and prioritizing alternative decisions (Carver 1991). A factor is a criterion that enhances or detracts from the suitability of a specific alternative for the Evaluation of criteria map attributes can only be compared if the measurement units are the same. Because the evaluation of the criteria had their own characteristics and extent and each datum of criteria had its own dimension and distribution, it was difficult to directly compare them or include them togeth- er in an operational system. The process of setting the relative importance of each criterion is known as the standardization of criteria (Feizizadeh and Blaschke 2013). Standardization of the attributes was performed for each of the cost maps in ESRI Spatial Analyst reclassification module. In this process, scales of 0 to 1, 0 to 10, or 0 to 100 are normally used for criteria
Fig. 10 Cost distance maps
Fig. 10 Cost distance maps
Fig. 11 Cost direction map
Fig. 11 Cost direction map
Validation and discussion alignment which is near to sum of the weights is the optimum alignment. Alignment 4 is near to sum of the weights, so we can decide that alignment 4 is the optimum alignment. Table | explains these different computed criteria for each suggested alignment and the results.
Validation and discussion alignment which is near to sum of the weights is the optimum alignment. Alignment 4 is near to sum of the weights, so we can decide that alignment 4 is the optimum alignment. Table | explains these different computed criteria for each suggested alignment and the results.
Table 1 Route selection criteria
Table 1 Route selection criteria
Fig. 14 Field survey of some intersection locations
Fig. 14 Field survey of some intersection locations
Related topics:
Multicriteria Decision AnalysisMulticriteria Optimization
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