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| - | {{ :logo_guidebook1.jpg?400 |}} | + | {{ :logo_logo.png?400 |}} |
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| - | =====LESSON 17: Calculating accumulated cost surface and least-cost pathway on Dinamica EGO===== | + | =====LESSON 17: Calculating accumulated cost surface and least-cost pathway===== |
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| * Functors: \\ - //[[:Calc Cost Map]]//\\ - //[[:Calc Pathway Map]]// | * Functors: \\ - //[[:Calc Cost Map]]//\\ - //[[:Calc Pathway Map]]// | ||
| - | This exercise requires the calculation of a friction surface, representing the relative cost of crossing a unit cell depending on the land use. We can express this surface both in terms of distance – no differential cost exits among types of land uses; hence the least-cost pathway will be the shortest route, i.e. the Euclidian distance – time, financial cost or some type of effort. Thus, this value is calculated in relation to some unit (time, transportation cost, etc). | + | This exercise requires the calculation of a friction surface, representing the relative cost of crossing a unit cell depending on the land use. We can express this surface both in terms of distance – no differential cost exits among types of land uses; hence the least-cost pathway will be the shortest route, i.e. the Euclidean distance – time, financial cost or some type of effort. Thus, this value is calculated in relation to some unit (time, transportation cost, etc). |
| In this exercise, we explore the use of the functors //[[:Calc Cost Map]]// and //[[:Calc Pathway Map]]//. To find an optimum solution for the accumulated cost surface, the functor //[[:Calc Cost Map]]// uses a heuristic algorithm that recursively brushes a map until the best cost surface is obtained. As the number of passes increase so does an approximation of an optimum solution. Use “0” for an optimum solution, but in general, only two passes are sufficient to obtain a surface very close to the optimum solution. | In this exercise, we explore the use of the functors //[[:Calc Cost Map]]// and //[[:Calc Pathway Map]]//. To find an optimum solution for the accumulated cost surface, the functor //[[:Calc Cost Map]]// uses a heuristic algorithm that recursively brushes a map until the best cost surface is obtained. As the number of passes increase so does an approximation of an optimum solution. Use “0” for an optimum solution, but in general, only two passes are sufficient to obtain a surface very close to the optimum solution. | ||
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| - | Open Dinamica EGO and load the aforementioned maps from ''lesson4\originals'' on the Map Viewer using the color table “mt”. Open the slope map using “Pseudocolor” as the current color palette and in the Histogram click on Limits to Actual and Histogram Equalize. As a first step, you need to reclassify the land use map to depict the cost of crossing each one of its land uses. Also you need to reclassify the slope map and then combine it with the land use map. | + | Open Dinamica EGO and load the aforementioned maps from ''Guidebook_Dinamica_5\Database\accumulated_cost_surface _least_cost_pathway'' on the Map Viewer. Open the slope map using “Pseudocolor” as the current color palette and in the Histogram click on Limits to Actual and Histogram Equalize. As a first step, you need to reclassify the land use map to depict the cost of crossing each one of its land uses. Also you need to reclassify the slope map and then combine it with the land use map. |
| For the land use map use the following table: | For the land use map use the following table: | ||
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| {{ :tutorial:cost6.1.jpg |}} | {{ :tutorial:cost6.1.jpg |}} | ||
| - | <note>You may feel free to use other software to view the maps.</note> | + | <note>You may feel free to use other software to view the maps, but our viewer is the best.</note> |
| Let’s move on to the second part of this exercise. Load ''town1.tif'' from ''\originals'' using //[[:Load Map]]// and ''railroad.tif'' using //[[:Load Categorical Map]]//. Remember that this functor categorizes a map.\\ | Let’s move on to the second part of this exercise. Load ''town1.tif'' from ''\originals'' using //[[:Load Map]]// and ''railroad.tif'' using //[[:Load Categorical Map]]//. Remember that this functor categorizes a map.\\ | ||
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| Here are some notes on this algorithm:\\ | Here are some notes on this algorithm:\\ | ||
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| - | The algorithm that calculates the cost map is a general type of "Pushbroom". However its spatial performance approximates the so called "Pushgrow" algorithm, especially when using two or more passes.\\ | + | The algorithm that calculates the cost map is a general type of "Pushbroom". However, its spatial performance approximates the so called "Pushgrow" algorithm, especially when using two or more passes.\\ |
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| By default, the cells dimensions (width and height) are not considered in the calculation of cost. To take this into consideration, turn on in Advanced options **Friction is relative**.\\ | By default, the cells dimensions (width and height) are not considered in the calculation of cost. To take this into consideration, turn on in Advanced options **Friction is relative**.\\ | ||
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| {{ :tutorial:cost13.2.jpg |}}\\ | {{ :tutorial:cost13.2.jpg |}}\\ | ||
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| - | If you go to ''Examples\run_lucc_northern_mato_grosso\run_roads_with_comments'' and open the model ''mato_grosso_road.egoml'', you will see how this set of algorithms can be adapted and combined to build a Road Constructor Module, a submodel that simulates the expansion of the road network in an Amazonian frontier region. This model is an example of the ability of Dinamica EGO platform for the ingenious design of spatial models. | + | \\ |
| + | ===Congratulations, you have successfully completed this lesson!=== | ||
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| + | ☞[[lesson_18|Next Lesson]] | ||
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| + | ☞[[:guidebook_start| Back to Guidebook Start]] | ||