{"id":5674,"date":"2018-07-16T05:51:43","date_gmt":"2018-07-16T03:51:43","guid":{"rendered":"http:\/\/www.sigterritoires.fr\/?p=5674"},"modified":"2018-10-17T13:00:32","modified_gmt":"2018-10-17T11:00:32","slug":"tutorial-create-an-aptitude-map-with-arcmap-with-and-without-fuzzy-criteria-2","status":"publish","type":"post","link":"https:\/\/www.sigterritoires.fr\/index.php\/en\/tutorial-create-an-aptitude-map-with-arcmap-with-and-without-fuzzy-criteria-2\/","title":{"rendered":"Tutorial : create an aptitude map with ArcMap, with and without fuzzy criteria (2)"},"content":{"rendered":"

Let\u2019s go back literally to the Spatial Analyst tutorial:
\n\u00a0\u00bb \u00a0 …<\/p>\n

Creation of an<\/strong> aptitude<\/strong> map<\/strong><\/p>\n

The<\/em> creation<\/em> an<\/em> aptitude<\/em> map allows<\/em> you to get<\/em> an<\/em> aptitude value <\/em>for<\/em> each<\/em> location on the map.<\/em>
\nOnce<\/em> you<\/em> have<\/em> created<\/em> the<\/em> necessary<\/em> layers<\/em> (<\/em> in<\/em> this<\/em> example<\/em> , the layers<\/em> are<\/em> Slope<\/em> , Distance to<\/em> Recreational<\/em> Facilities<\/em> , Distance to<\/em> Schools<\/em> and Land Use) to<\/em> your<\/em> analysis, how<\/em> do<\/em> these<\/em>\u00a0 created<\/em> layers<\/em> combine<\/em> to<\/em> create<\/em> a<\/em> classified map<\/em> of the<\/em> potential<\/em> surfaces<\/em> to<\/em> locate<\/em> school<\/em> \u00a0<\/em> ?<\/em> You<\/em> must compare the<\/em> values<\/em> of<\/em> classes<\/em> among layers.<\/em> To<\/em> accomplish this task<\/em>, a<\/em> method<\/em> involves<\/em> assigning<\/em> numerical values<\/em> to<\/em> the<\/em> classes included<\/em> in<\/em> each<\/em> layer<\/em> of the map<\/em> or<\/em> to<\/em> re classify them<\/em>.<\/p>\n

Each<\/em> layer<\/em> of the map<\/em> is<\/em> classified according its<\/em> aptitude<\/em> degree<\/em> as location<\/em> for the new<\/em> school<\/em>..<\/em> For<\/em> example<\/em>, you<\/em> can<\/em> assign<\/em> a<\/em> value<\/em> to each<\/em> class<\/em> of<\/em> each<\/em> layer<\/em>, according to<\/em> a<\/em> scale<\/em> from 1 to 10, 10<\/em> being<\/em> the<\/em> best<\/em> ranking<\/em>.
\nThis<\/em> scale is called<\/em> an<\/em> \u201captitude scale\u00a0\u00bb.<\/em> Use<\/em> the<\/em> value<\/em> NoData<\/em> to<\/em> rule out<\/em> areas that<\/em> should<\/em> not<\/em> be<\/em> taken<\/em> into<\/em> consideration.<\/em> If all measures are affected by the same numerical scale, grants the same importance in the determination of the most adequate locations.<\/em> Initially, the<\/em> model<\/em> is<\/em> developed<\/em> this<\/em> way<\/em>. Afterwards,<\/em> when<\/em> you<\/em> test<\/em> other<\/em> scenarios, weighting<\/em> factors<\/em> will<\/em> be<\/em> applied to the layers to<\/em> increase<\/em> the exploration<\/em> of<\/em> data<\/em> and<\/em> their<\/em> relationships.<\/em><\/p>\n

Creation of aptitude scales<\/strong><\/p>\n

As<\/em> shown<\/em> in<\/em> this<\/em> example<\/em>, many<\/em> scales<\/em> are<\/em> synthetic<\/em>. Usually a ranking<\/em>, from the most<\/em> appropriate<\/em> to the<\/em> least<\/em> appropriate<\/em> is used.<\/em> This<\/em> ranking<\/em> is based<\/em> on<\/em> something<\/em> measurable,<\/em> such as<\/em> distance to<\/em> schools,<\/em> but<\/em> in<\/em> fact<\/em>, it<\/em> is<\/em> a<\/em> subjective<\/em> measure<\/em> that determines the<\/em> aptitude degree<\/em> of a<\/em> certain<\/em> distance from<\/em> a<\/em> school<\/em> to place<\/em> a<\/em>nother<\/em> school<\/em>.
\nThere<\/em> are<\/em> natural scales<\/em> that<\/em> are<\/em> commonly<\/em> associated<\/em> with<\/em> certain<\/em> objectives<\/em>. The<\/em> cost<\/em> is<\/em> a good<\/em> example<\/em>, but<\/em> must<\/em> be<\/em> defined<\/em> in detail<\/em> .<\/em> In<\/em> a<\/em> study<\/em> on<\/em> the feasibility<\/em> of a<\/em> building, a<\/em> weak cost<\/em> target<\/em> would be<\/em> \u00a0<\/em> measured<\/em> on<\/em> a dollar scale.<\/em> Be sure<\/em> to<\/em> define<\/em> an<\/em> adequate scale<\/em>. For<\/em> an<\/em> element<\/em> well known<\/em> as the dollar<\/em> other variables must be taken into account, for<\/em> example<\/em> whether<\/em> it<\/em> is<\/em> American<\/em>, Australian<\/em> dollars<\/em> or<\/em> an<\/em> exchange<\/em> rate<\/em> between currencies.<\/em>
\nMany<\/em> scales<\/em> are<\/em> not linear relationships<\/em> nevertheless they<\/em> are<\/em> often<\/em> presented<\/em> as if they were<\/em> to<\/em> save<\/em> time and<\/em> money<\/em> or<\/em> because<\/em> all<\/em> the<\/em> options<\/em> have not<\/em> been<\/em> considered<\/em>. For<\/em> example<\/em>, if<\/em> you<\/em> assign<\/em> a<\/em> scale to<\/em> a<\/em> travel<\/em> distance, a<\/em> displacement<\/em> of 1.5<\/em> or<\/em> 10<\/em> km<\/em> would<\/em> not<\/em> be<\/em> classified<\/em> as<\/em> an<\/em> aptitude of 10, 5 and 1<\/em> if<\/em> the<\/em> movement<\/em> was<\/em> done<\/em> on foot.<\/em> Some<\/em> people<\/em> will<\/em> evaluate that a<\/em> 5<\/em> kilometres walk<\/em> is<\/em> only<\/em> twice<\/em> more<\/em> tiring<\/em> than<\/em> a<\/em> 1<\/em> kilometre walk while others<\/em> will evaluate it as<\/em> tenfold.<\/em>
\nWhen<\/em> you<\/em> are developing<\/em> a<\/em>n aptitude scale,<\/em> ask<\/em> the opinion<\/em> of<\/em> well-informed people<\/em> to identify<\/em> both<\/em> extremes<\/em> of a<\/em> scenario<\/em> and<\/em> as many<\/em> intermediate<\/em> points<\/em> as possible.<\/em> These<\/em> people<\/em> must have a good knowledge of <\/em>the objective<\/em> under<\/em> consideration<\/em>. For<\/em> example<\/em>, it is<\/em> more<\/em> interesting<\/em> to ask<\/em> users<\/em> their<\/em> opinion about their preferences about the travel time<\/em> from<\/em> their<\/em> homes to<\/em> their<\/em> work, than asking an agent the<\/em> time brackets<\/em> when<\/em> traffic<\/em> is<\/em> the worst.<\/em>
\n…<\/em> \u00a0<\/em> \u00ab\u00a0<\/em><\/p>\n

The problem is very well framed. Let’s see how to answer by using the classical tools.<\/p>\n

Ranking<\/strong> of<\/strong> nearby<\/strong> areas<\/strong> to recreational facilities<\/strong> with<\/strong> the<\/strong> Re-classification<\/strong> tool<\/strong><\/p>\n

In order to locate a school near recreational facilities, you must know the distance between them. The Spatial Analyst Euclidean Distance tool creates this type of map by calculating the distance in straight line (Euclidean) between a location and the nearest recreational facility. The result is a raster dataset in which each cell represents the distance to the nearest recreational facilities. To rank the map, use the Reclassification tool. Since it is desirable to locate the school near recreational facilities, assign the value 1 to the distant places from these facilities and the value 10 to closer places. Then rank intermediate distances linearly as shown in the next illustration.<\/p>\n

$\"\"$ <\/p>\n

We use the Re-classification tool from the toolbox Spatial Analyst-> Classify<\/em><\/strong>.<\/p>\n

$\"\"$ <\/p>\n

Steps:<\/p>\n

\n
Open the Re-classification tool<\/li>\n
As layer in the entry indicate DistanceToRecSites<\/li>\n
Accept the default value for the parameter reclassification Field to use as Value field.<\/li>\n
Click<\/li>\n<\/ul>\n
$\"\"$ <\/p>\n
\n
Set the Method to equal Interval (variable number) and the number of Classes out of 10.<\/li>\n
Click<\/li>\n
Click Reverse news values.<\/li>\n<\/ul>\n
The selection of the option Invert<\/em> the<\/em> news<\/em> values<\/em> attribute a new, higher value to close distances to the leisure centres, as these areas are more desirable.
\nGive a name for Raster output parameter: DistRecSitesReclass
\nClick OK.<\/p>\n
The result got is the following:<\/p>\n
$\"\"$ <\/p>\n
<\/p>\n
For the geomaticists that we are, this result appears as quite consistent. We have a study area, concentric distances from our targets and a regular gradient of values as we move away from our targets.<\/p>\n
But the question is: does it answer the question?<\/p>\n
The green areas, i.e. the most suitable areas according to this criterion, depend on the location of the recreational sites. But not only. They depend on our study area: if we would have selected a larger area, they would be bigger, we would have limited our area, they would be smaller. They also depend on our arbitrary choice of 10 zones. If we would have chosen to make 7, they would be bigger, and if we had selected to make 12 of them would be smaller.<\/p>\n
The question asked is to find sites, given that parents want the new school to be near a recreational site. As a parent, I want to find that a recreational site is close or far according to many personal criteria, but most likely not as a function of the total area of the planning project!<\/p>\n
Ranking<\/strong> of<\/strong> nearby<\/strong> recreational facilities areas<\/strong> using<\/strong> the Fuzzy Criterion<\/strong> tool<\/strong><\/p>\n
The satisfaction of the criterion Proximity to a recreational site must be asked to the actors. Therefore we will be able to understand what is considered \u00ab\u00a0Close\u201d, a site within walking distance with children, or by car but with a maximum traject between 5 to 10 minutes.<\/p>\n
If we translate the latter in distance, we can say that the optimum satisfaction is between 0 and 1,000 m (route distance) and, from 7 km (route 10 ‘ by car) is considered as \u00ab\u00a0Far\u00a0\u00bb.<\/p>\n
We use the command Criterion flexible on the layer DistanceRecre<\/p>\n
$\"\"$ <\/p>\n
<\/p>\n
We select the attribute Gridcode and define a maximum satisfaction up to 1000m (a) and a zero satisfaction from 5000 m (b).<\/p>\n
We click Calculate, and obtain the following result:<\/p>\n
$\"\"$ <\/p>\n
<\/p>\n
Of course we could do something similar with the Reclassify tool. For the time being, what is important, is the reasoning used.<\/p>\n
We have two different results, not just because we are using two different tools, but because we have two different reasoning.<\/p>\n
Ranking<\/strong> of<\/strong> remote<\/strong> areas<\/strong> from existing<\/strong> schools<\/strong>, using Spatial Analyst<\/strong><\/p>\n
We have created a raster dataset in which each cell represents the distance to the nearest school (DistToSchools). To rank the map, use the Reclassification tool. Since it is desirable to locate the school away from existing schools, assign the value 1 to the distances close to these schools and the value 10 to the distances far away. Then rank intermediate distances linearly as shown in the next illustration.<\/p>\n
$\"\"$ <\/p>\n
As for the distance to the recreational sites, \u00a0 we use the Re-classify tool of the toolbox Spatial Analyst-> Classify<\/em><\/strong> .<\/p>\n
Steps :<\/p>\n
\n
Open the Re-classification tool<\/li>\n
As layer in entry indicate DistSchoolsReclass<\/li>\n
Accept the default value for the parameter re-classification Field to use the field Value.<\/li>\n
Click<\/li>\n
Set the Method to Equal Interval ( variable number ) and the number of Classes out of 10.<\/li>\n
Click<\/li>\n<\/ul>\n
You want to locate the school away from existing schools; therefore you assign increasing numbers to ranges of values that represent more remote locations, because these locations are most desirable. Since the default affects New high values (more suitable locations) to Old high values (locations further away from existing schools), you do not need to change the values at this stage.<\/p>\n
The result is as follows:<\/p>\n
$\"\"$
\nThe remarks stated for the remoteness of recreational sites are applicable to the result of this criterion. The distance of existing schools is not a concept related to the size of our study area.<\/p>\n
Ranking<\/strong> of<\/strong> remote<\/strong> areas<\/strong> from existing schools<\/strong> \u00a0<\/strong> with<\/strong> the tool<\/strong> \u00a0 Fuzzy<\/strong> Criterion<\/strong> tool<\/strong><\/p>\n
<\/p>\n
If we apply the same reasoning as previously, a user will consider that the school is \u00ab\u00a0Far\u00a0\u00bb if \u00a0it takes more than 10 \u2018 by car and \u00a0 \u201cClose\u201d if can go walking (1000m). $\"\"$ <\/p>\n
<\/p>\n
<\/p>\n
We select the attribute Gridcode and define zero satisfaction \u00a0 up to 1000m (a) and maximum satisfaction \u00a0 from 5000 m (b).<\/p>\n
We click Calculate, and we get the following result:<\/p>\n
$\"\"$ <\/p>\n
<\/p>\n
This result matches better our notion of \u00ab\u00a0Far\u00a0\u00bb: from a certain distance, one is far, not far-far or far- very distance, etc.<\/p>\n
In the next article, we will classify the slopes and the type of land use, before facing the aggregation of all these criteria for obtaining the final result: our aptitude map.<\/p>\n
<\/p>\n","protected":false},"excerpt":{"rendered":"
Let\u2019s go back literally to the Spatial Analyst tutorial: \u00a0\u00bb \u00a0 … Creation of an aptitude map The creation an aptitude map allows you to get an aptitude value for each location on the map. Once…<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_bbp_topic_count":0,"_bbp_reply_count":0,"_bbp_total_topic_count":0,"_bbp_total_reply_count":0,"_bbp_voice_count":0,"_bbp_anonymous_reply_count":0,"_bbp_topic_count_hidden":0,"_bbp_reply_count_hidden":0,"_bbp_forum_subforum_count":0,"sfsi_plus_gutenberg_text_before_share":"","sfsi_plus_gutenberg_show_text_before_share":"","sfsi_plus_gutenberg_icon_type":"","sfsi_plus_gutenberg_icon_alignemt":"","sfsi_plus_gutenburg_max_per_row":"","_monsterinsights_skip_tracking":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_feature_clip_id":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_post_was_ever_published":false},"categories":[1260],"tags":[],"class_list":["post-5674","post","type-post","status-publish","format-standard","hentry","category-non-classe-en"],"aioseo_notices":[],"aioseo_head":"\n\t\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t