{"id":7652,"date":"2019-07-30T17:35:11","date_gmt":"2019-07-30T21:35:11","guid":{"rendered":"http:\/\/consortechsite.wpengine.com\/integrating-geospatial-data-into-your-decision-making\/"},"modified":"2020-03-13T13:29:19","modified_gmt":"2020-03-13T17:29:19","slug":"integrating-geospatial-data-into-your-decision-making","status":"publish","type":"post","link":"https:\/\/www.consortech.com\/fr\/integrating-geospatial-data-into-your-decision-making\/","title":{"rendered":"Int\u00e9grer les donn\u00e9es g\u00e9ospatiales \u00e0 vos d\u00e9cisions"},"content":{"rendered":"\t\t
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\n\t\t\t\t\t\tAccueil<\/a><\/span><\/span>\t\t\t\t\t<\/div> \n\t\t\t\t<\/div> \n\t\t\t<\/div> \n\t\t<\/div>\n\t\t

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Int\u00e9grer les donn\u00e9es g\u00e9ospatiales \u00e0 vos d\u00e9cisions<\/h2>\n

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\u201cThe world\u2019s most valuable resource is no longer oil, but data.\u201d \u2014The Economist<\/em><\/p>\n

Cela fait plusieurs ann\u00e9es qu\u2019on entend ce mantra. La plupart des organismes utilisent diff\u00e9rentes donn\u00e9es afin de prendre des d\u00e9cisions d\u2019affaires. Des donn\u00e9es financi\u00e8res sont utilis\u00e9es pour analyser le march\u00e9, des donn\u00e9es sur la client\u00e8le permettent d\u2019avoir le portrait des consommateurs, etc. Un type de donn\u00e9es dont votre entreprise peut grandement b\u00e9n\u00e9ficier est la donn\u00e9e g\u00e9ospatiale. Dans la plupart des cas, les d\u00e9cideurs ne poss\u00e8dent pas de formation en g\u00e9omatique ou une autre discipline connexe. Ainsi, il est important de rendre les donn\u00e9es g\u00e9ospatiales simples \u00e0 comprendre et \u00e0 utiliser. C\u2019est l\u00e0 que nous, g\u00e9omaticiens, entrons en jeu. Notre r\u00f4le est de manipuler les diff\u00e9rentes donn\u00e9es et de les int\u00e9grer afin qu\u2019elles soient utilisables pour tous. Toute votre organisation en sera gagnante. Voici un exemple simpliste d\u2019utilisation de donn\u00e9es g\u00e9ospatiales dans un contexte d\u2019analyse de march\u00e9 :<\/p>\n

La banni\u00e8re Br\u00fblerie Caf\u00e9Caf\u00e9 (marque d\u00e9pos\u00e9e\u2026) s\u2019explique mal pourquoi son comp\u00e9titeur (situ\u00e9 \u00e0 deux coins de rue) a un plus grand achalandage. Pourtant, le caf\u00e9 de la Br\u00fblerie Caf\u00e9Caf\u00e9 est meilleur et un peu moins cher… Au d\u00e9part, on pourrait analyser les habitudes de consommation des clients ainsi que leur statut socio\u00e9conomique (femme vs homme, \u00e9tudiant vs professionnel\u2026). On aurait alors un portrait global de la client\u00e8le et on pourrait orienter notre strat\u00e9gie marketing de fa\u00e7on \u00e0 attirer plus de clients. Apr\u00e8s quelques mois, on remarque une faible augmentation du nombre de clients. Quelque chose d\u2019autre explique le fait que la comp\u00e9tition a plus de clients\u2026 Et si on analysait le nombre de pi\u00e9tons qui passe chaque jour par l\u2019intersection o\u00f9 se trouve notre br\u00fblerie? Lorsqu\u2019on s\u2019en va au travail, il est beaucoup plus facile d\u2019arr\u00eater prendre un caf\u00e9 \u00e0 pied que lorsqu\u2019on est en voiture. Pour l\u2019analyse, on utilisera les donn\u00e9es de comptage aux feux de circulation, disponibles sur le site de la Ville de Montr\u00e9al<\/a>. On d\u00e9veloppe alors un workspace avec FME pour pr\u00e9parer les donn\u00e9es afin d\u2019\u00eatre en mesure de les visualiser. Voici un aper\u00e7u du processus :<\/p>\n

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Expliquons un peu ce qui se passe dans ce workspace. Premi\u00e8rement, on cr\u00e9e un point repr\u00e9sentant l\u2019emplacement de la br\u00fblerie (notre point d\u2019int\u00e9r\u00eat). Afin de lire les donn\u00e9es de trafic aux coins des rues, une zone tampon de l\u2019emplacement de la br\u00fblerie a \u00e9t\u00e9 cr\u00e9\u00e9e. En utilisant le FeatureReader ainsi que la zone tampon, il est possible de lire seulement les donn\u00e9es \u00e0 l\u2019int\u00e9rieur de la zone d\u2019int\u00e9r\u00eat. Ceci est une bonne pratique lorsqu\u2019il n\u2019est pas n\u00e9cessaire de lire les donn\u00e9es sur l\u2019ensemble de la zone couverte par ces derni\u00e8res. Dans ce cas-ci les donn\u00e9es couvrent toute la ville de Montr\u00e9al, ce qui repr\u00e9sente plus de 800 000 points! Avec la m\u00e9thode pr\u00e9sent\u00e9e, on ne traite qu\u2019un peu plus que 26 000 points. Les traitements qui suivent la lecture seront donc plus rapides. Une fois les donn\u00e9es lues, on s\u2019assure de n\u2019avoir que les donn\u00e9es les plus r\u00e9centes. Ensuite, puisqu\u2019on s\u2019int\u00e9resse aux pi\u00e9tons seulement, on utilisera l\u2019AttributeFilter pour filtrer les diff\u00e9rents moyens de d\u00e9placement. La derni\u00e8re \u00e9tape avant l\u2019exportation est l\u2019agr\u00e9gation des donn\u00e9es de trafic. En effet, les donn\u00e9es dont on dispose sont acquises toutes les 15 minutes aux diff\u00e9rentes intersections. On utilisera l\u2019Aggregator avec un groupement sur le Id_Interse (attribut de l\u2019identifiant de l\u2019intersection). \u00c9galement, on comptera le nombre d\u2019enregistrements pour chaque intersection, nous donnant ainsi le nombre de pi\u00e9tons par intersection. Enfin, on exporte les donn\u00e9es dans le format voulu (ici en Excel).<\/p>\n

* Note: Les r\u00e9sultats obtenus par cette m\u00e9thode sont approximatifs.<\/em><\/p>\n

Une fois les donn\u00e9es pr\u00e9par\u00e9es, il est maintenant temps de les visualiser. Voici un r\u00e9sultat simpliste, mais qui en dit long (la quantit\u00e9 de pi\u00e9tons qui circulent \u00e0 l\u2019heure de pointe est inscrite dans les cercles aux intersections) :<\/p>\n

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Il est \u00e0 noter que l\u2019on ne dispose pas d\u2019informations sur le nombre de pi\u00e9tons pour l\u2019ensemble de la zone d\u2019\u00e9tude, seulement pour les intersections majeures. Ainsi, pour cet exemple, on remarque que la br\u00fblerie est situ\u00e9e sur un axe (avenue Laurier) o\u00f9 il y a moins de pi\u00e9tons qui passent. On remarque \u00e9galement qu\u2019\u00e0 un coin de rue plus bas, sur l\u2019axe Saint-Joseph, il y a beaucoup plus de pi\u00e9tons. Donc, pour le prochain local, il serait judicieux de choisir un emplacement o\u00f9 il y a plus de pi\u00e9tons. Une analyse multi crit\u00e9rielle bas\u00e9e sur des poids aurait \u00e9galement pu \u00eatre faite. Par exemple, puisqu\u2019un pi\u00e9ton a plus de chance de s\u2019arr\u00eater prendre un caf\u00e9 qu\u2019un cycliste, le poids (score) d\u2019un passage de pi\u00e9tons serait plus \u00e9lev\u00e9 que celui d\u2019un passage de cycliste. Il aurait \u00e9t\u00e9 possible d\u2019aller plus en profondeur et d\u2019analyser la proximit\u00e9 avec un stationnement. Ainsi, la circulation par voiture aurait pu \u00eatre \u00e9galement consid\u00e9r\u00e9e. Avec FME, il est facile d\u2019int\u00e9grer diff\u00e9rentes informations de diverses sources et d\u00e9velopper notre algorithme de g\u00e9omarketing.<\/p>\n

Cette situation, bien que simpliste, illustre bien comment vos organisations peuvent b\u00e9n\u00e9ficier de l\u2019utilisation de l\u2019information g\u00e9ospatiale pour diff\u00e9rentes applications. Voici d\u2019autres id\u00e9es : le choix de l\u2019emplacement du prochain poste de police (bas\u00e9 sur la taille du territoire couvert par les postes existants), le suivi en temps r\u00e9el de vos \u00e9quipes sur le terrain pour mieux coordonner vos interventions, le choix de l\u2019emplacement d\u2019une nouvelle tour de t\u00e9l\u00e9communication, etc.<\/p>\n

Les donn\u00e9es g\u00e9ospatiales sont une mine d\u2019information pour vos entreprises, autant publiques que priv\u00e9es.<\/p>\n

\u00a0<\/p>\n

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Vous voulez prendre avantage de vos donn\u00e9es g\u00e9ospatiales?<\/p>\n

[\/et_pb_cta][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":"

Il est important de rendre les donn\u00e9es g\u00e9ospatiales simples \u00e0 comprendre et \u00e0 utiliser. 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