The final step in the aptitude model is to consolidate the reclassified Distance\noutputs (aptitude maps) vs. recreational facilities, Distance vs. existing schools,\nSlope and Land Use. <\/p>\n\n\n\n
To take into account that some goals weight more than others in the\naptitude model, you can weight the datasets. You provide to the larger datasets\na higher percentage of weighting. If all datasets are of equal importance, you can\nprovide the same weighting to all of them. <\/p>\n\n\n\n
In our example, you know in advance given the problem examination, that\nthe first objective is to locate the school near recreation facilities, and the\nsecond is to set it apart from existing schools. The following weighting\npercentages will be attributed to the aptitude maps. Values \u200b\u200bin parentheses correspond\nto the percentage divided by 100 to normalize values. These are attributed to\neach aptitude map: <\/p>\n\n\n\n
- Distance to recreational\nfacilities: 50% <\/li>
- Distance to schools: 25% <\/li>
- Slope: 12.5% <\/li>
- Land use types: 12.5% <\/li><\/ul>\n\n\n\n
The Distance<\/em> vs. Recreational Facilities<\/em> aptitude map has\nan weight of 50% (.5) over the final result, and the Distance from Schools<\/em>\nmap has a weight of 25% (.25). Both Slope<\/em> and land use types<\/em> have\na weight of 12.5% \u200b\u200b(0.125). As when assigning aptitude scales, assigning\nweights is a subjective process that depends on the most important goals of\nyour study. <\/p>\n\n\n\n
Weighting analysis<\/strong> <\/p>\n\n\n\n
What you have just read seems logical, and even clever. Do not be\noffended if you’re a fan of weights, but let’s try to see a little deeper. <\/p>\n\n\n\n
What does it mean to say this mathematical transformation of a criterion\nin weight value?
\nAnd more important, remember that the recipient of the result will necessarily\nbe a decision maker to whom we will provide a map to help him make his\ndecision.
\nWe understand that the distance criteria from existing schools and recreation centres\nare more important for the decision-maker than the type of terrain (slope and\noccupancy).
\nBy applying these weights we will (we believe) respect the decision maker\nreasoning. <\/p>\n\n\n\nSo, as an example is better than a long speech, let’s discuss the\nfollowing two examples: <\/p>\n\n\n\n
1- A site that is excellent according to the three criteria: distance to\nschools (value = 10), distance to recreation centres (value = 10) and slope\n(value = 10), will have a cumulative result for the three criteria (criterion\nvalue multiplied by the weight): <\/p>\n\n\n\n
0.5 x 10 + 0.25 x 10 + 0.125 x 10\n= 8.75 <\/p>\n\n\n\n
if land use is the best (agriculture = 10) we will have as final result <\/p>\n\n\n\n
8.75 + 1.25 = 10 <\/p>\n\n\n\n
if land use is the worst (built = 3) we will have as final result <\/p>\n\n\n\n
8.75 + (0.125 x 3) = 9.125 <\/p>\n\n\n\n
Do you reckon the decision-maker can decide between 9,125 and 10? To the\npoint of realizing that the second choice involves major drawbacks? Do you\nthink it appears at first glance? <\/p>\n\n\n\n
So let’s consider an even more obvious example: <\/p>\n\n\n\n
2- A site that is excellent according to the three criteria: distance to\nschools (value = 10), distance to recreation centres (value = 10) and land\noccupation (value = 10), it will have the following cumulative result for the\nthree criteria: <\/p>\n\n\n\n
0.5 x 10 + 0.25 x 10 + 0.125 x 10\n= 8.75 <\/p>\n\n\n\n
if the slope is 30 \u00b0 it will have a value of 7. The final result will be\n<\/p>\n\n\n\n
8.75 + (0.125 x 7) = 9.625 <\/p>\n\n\n\n
Here, we border on perfection. We are, almost, at the ideal site! <\/p>\n\n\n\n
Yet a slope of 30 \u00b0 corresponds to a slope percentage of 57% which is\nmore than double the suitable limit for a construction project! <\/p>\n\n\n\n
How do you think such a proposal will be perceived by the decision\nmaker? <\/p>\n\n\n\n
Then, while crossing the criteria we will exclude values \u200b\u200bfrom the\nSlope criterion, with the Restricted option for the weighted overlay. This will\nprevent proposing sites with slopes of more than 30%. Except that now, the only\nacceptable values \u200b\u200bof the criterion will be 10, 9 and 8, others being\nexcluded. And therefore, all the other criteria being constant, between the\nbest and worst slope, the final result will vary only 0.25 !!! <\/p>\n\n\n\n
Contribution of slope criterion to total if slope = 10 -> 10 * 0.125\n= 1.25 <\/p>\n\n\n\n
Contribution of slope criterion to total if slope = 8-> 8 * 0.125 =\n1.00 <\/p>\n\n\n\n
You can try to change the weights as much as you want, we will always\nfind a mismatch of results with human reasoning. Simply because we do not\nweight criteria in our heads. What makes\nmatters more confusing is the fact that we give different importance to\ncriteria and assign them a ciphered weight. <\/p>\n\n\n\n
We will come back later on. First, let’s finish the exercise with\nSpatial Analyst. <\/p>\n\n\n\n
Weighted Overlay with Spatial Analyst<\/strong> <\/p>\n\n\n\n
Steps<\/strong> :<\/strong> \n\nOpen the Weighted Overlay tool. (Spatial Analyst\nTools -> Overlay -> Weighted Overlay) \n\n\n\n<\/p>\n\n\n\n
![\"\"](\"https:\/\/i0.wp.com\/www.sigterritoires.fr\/wp-content\/uploads\/2019\/04\/301.png?resize=630%2C436&ssl=1\")
<\/figure>\n\n\n\n- Type 1,\n10, and 1 in the From<\/em>, To,<\/em> and By<\/em> text boxes<\/em> . The\ndefault rating scale is 1 at 9 in steps of 1. A scale of 1 \nat 10 was used when reclassifying the datasets, before adding input\nrasters to the Weighted Overlay tool, so you must set the rating scales of 1\n at 10 in steps of 1. This means that you avoid having to update\nthe scale values \u200b\u200bafter adding your input datasets. <\/li>
- Click on\nApply <\/li>
- Add the SlopeReclass<\/em>\nlayer to the Weighted Overlay<\/em><\/strong> tool .
- Click the\nAdd Raster Line<\/em><\/strong> button . <\/li><\/ol>
- For the input\nRaster<\/em><\/strong> , select SlopeReclass<\/em> in the\ndrop-down list and leave the input field set to Value<\/em> . <\/li><\/ol>
- Click OK<\/em><\/strong>\n<\/li><\/ol><\/li><\/ol>\n\n\n\n
\n\n\n\n\n\n\n\n\n\n\n\nThe raster is added to the weighted Overlay\nTable. The Field<\/em> column displays the values \u200b\u200bof the SlopeReclass<\/em>\ndata . The Scale Value<\/em> column replicates the Field<\/em> column because\nthe Evaluation Scale has been set to include the range of values \u200b\u200bfor each\ninput raster. At this point, you can change the Scale Values<\/em> for each\nclass, but for this entry the values \u200b\u200bhave already been properly weighted\nduring the reclassification.
\nRepeat the previous step for each of the reclassified datasets: LanduseReclass<\/em>\n, DistSchoolsReclass,<\/em> and DistRecSitesReclass<\/em> .
\nFor the SlopeeReclass<\/em> entry , in the Scale Value<\/em> column , click\nthe cell that has the value 1.
\nClick the drop-down arrow, scroll down the view, and then click Restricted<\/em>\n. \n\n\n\n<\/p>\n\n\n\n![\"\"](\"https:\/\/i0.wp.com\/www.sigterritoires.fr\/wp-content\/uploads\/2019\/04\/302.png?resize=630%2C566&ssl=1\")
<\/figure>\n\n\n\nIn this way, whatever the values \u200b\u200bof the other layers, the resultant\nvalue will be 0. <\/p>\n\n\n\n
Repeat this for values \u200b\u200b2 and 3. <\/p>\n\n\n\n
For the LandUseReclass<\/em> layer , set the Scale Value<\/em> that\nrepresents Water<\/em> and Wetlands<\/em> (0) to Restricted<\/em><\/strong> . <\/p>\n\n\n\n
Now, you have to assign a weighting percentage to each raster, based on\nthe importance (or weighting) of each in the final aptitude map.
\nIn the % Influence<\/em><\/strong> column , enter the percentages for each of the\ninput rasters: <\/p>\n\n\n\nDistSchoolsReclass out of 25
\nDistRecSitesReclass out of 50
\nSlopeReclass of 13
\nLlandUseReclass of 12 <\/p>\n\n\n\n![\"\"](\"https:\/\/i0.wp.com\/www.sigterritoires.fr\/wp-content\/uploads\/2019\/04\/303.png?resize=630%2C581&ssl=1\")
<\/figure>\n\n\n\nClick OK to run the tool. <\/p>\n\n\n\n
The final result, our aptitude map, appears as follows: <\/p>\n\n\n\n
![\"\"](\"https:\/\/i0.wp.com\/www.sigterritoires.fr\/wp-content\/uploads\/2019\/04\/304.png?resize=630%2C426&ssl=1\")
<\/figure>\n\n\n\nThe blue areas are the excluded areas (from the weighted overlay\nRestricted command). <\/p>\n\n\n\n
Green areas are the most suitable areas. <\/p>\n\n\n\n
In the next article we will perform these operations using the flexible\naggregation command, applying fuzzy logic instead of the weights used in\nSpatial Analyst. <\/p>\n","protected":false},"excerpt":{"rendered":"
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- Click OK<\/em><\/strong>\n<\/li><\/ol><\/li><\/ol>\n\n\n\n
- For the input\nRaster<\/em><\/strong> , select SlopeReclass<\/em> in the\ndrop-down list and leave the input field set to Value<\/em> . <\/li><\/ol>
- Type 1,\n10, and 1 in the From<\/em>, To,<\/em> and By<\/em> text boxes<\/em> . The\ndefault rating scale is 1 at 9 in steps of 1. A scale of 1 \nat 10 was used when reclassifying the datasets, before adding input\nrasters to the Weighted Overlay tool, so you must set the rating scales of 1\n at 10 in steps of 1. This means that you avoid having to update\nthe scale values \u200b\u200bafter adding your input datasets. <\/li>