à gauche, la liste des sources extérieurs sur lesquelles on récupÚre les données :
Au milieu, le processus de Conversion des donnĂ©es (dĂ©tails sur le prochain schĂ©ma) A droite, lâaffichage des donnĂ©es converties sur le site de Communecter ainsi que des exemple dâusage de ces donnĂ©es par des sites extĂ©rieurs.
For any city, We retreive main information available on Wikidata The process is the following :
The convert system will interrogate the Wikidata API to get data in JSON. The next exemple is the data for the city of Saint-Denis, capital city of RĂ©union island : And convert this data in the pivot language named âPH onthologyâ
/ph/api/convert/wikipedia?url=https://www.wikidata.org/wiki/Special:EntityData/Q47045.json
[Exemple Wikidata here](/4 - Documentation technique/api.md) Here are the mapping
| Sourceâs data | PH onthology |
|---|---|
| itemLabel.value | name |
| coor.latitude | geo.latitude |
| coor.longitude | geo.longitude |
| item.value | url |
| itemDescription.value | description |
For any city, we retreive main information avaible on OSM The process is the following :
The next exemple is all the OSM data of the city of Saint-Louis :
http://overpass-api.de/api/interpreter?data=[out:json];node[%22name%22](poly:%22-21.303505996763%2055.403919253998%20-21.292626813288%2055.391189163162%20-21.282029142394%2055.381522536523%20-21.256155186265%2055.392395046639%20-21.232012804782%2055.387888015185%20-21.211100938923%2055.390619722192%20-21.199480966855%2055.382654775478%20-21.185882138486%2055.385961778627%20-21.173346518752%2055.389949958731%20-21.16327583783%2055.399563417107%20-21.14709868917%2055.405379688232%20-21.166028899095%2055.414700890276%20-21.184085220909%2055.432085218794%20-21.190290936422%2055.440880800108%20-21.195166490948%2055.462318490892%20-21.237553168259%2055.459769285867%20-21.258726107298%2055.463692709631%20-21.286021128961%2055.455515913879%20-21.294777773557%2055.419916682666%20-21.303505996763%2055.403919253998%22);out%2030;
Here are the mapping
| Sourceâs data | PH onthology |
|---|---|
| tags.name | name |
| lat | geo.latitude |
| long | geo.longitude |
| type | type |
| tags.amenity | tags.0 |
COSM a pour but donner Ă l’utilisateur une meilleur visibilitĂ© des Ă©lĂ©ments OSM d’un territoire, de pouvoir contribuez a enrichir les tags OSM d’un OSM. COSM permet Ă l’utilisateur de pouvoir lister l’intĂ©gralitĂ© des Ă©lĂ©ments OSM pour un scope gĂ©ographique (uniquement les villes pour le moment). Les Ă©lĂ©ments sont afficher en bleu s’il ont un type. C’est Ă dire, soit :
L’utilisateur peut Ă tout moment cliquer sur le bouton "Voir tous les tags" pour … voir tous les tags de l’Ă©lĂ©ment OSM. Dans le cadre listant tous les tags de l’Ă©lĂ©ment l’utiliseur peut en cliquant sur "Modifier/Ajouter un tag" ajouter ou modifier un tag. Pour on ne peut ajouter que les 5 tags citĂ©s plus haut. L’information sera directement pusher vers la page OSM de l’Ă©lĂ©ment en question.
For any city, we retreive main information of the organizations placed in this city The process is the following :
The module will find all the organizations placed in the geographic scope filter and then extract all the data in the differents datasets available. The next exemple is all the data of the different structure of MĂ©to-France, meteorological center of France.
https://www.data.gouv.fr/api/1/datasets/54a12162c751df720a04805a/
Here are the mapping
| Sourceâs data | PH onthology |
|---|---|
| slug | name |
| page | url |
| tags[] | tag[] |
| item.value | url |
| owner | creator |
For any city, we retreive all the job offer. (no exact localisation of the job place) The process is the following :
To get data with the PĂŽle emploiâs API, a token is needed. The next exemple fetch all the job offer of the city of Saint-Louis.
https://api.emploi-store.fr/partenaire/infotravail/v1/datastore_search_sql?sql=SELECT%20%2A%20FROM%20%22421692f5-f342-4223-9c51-72a27dcaf51e%22%20WHERE%20%22CITY_CODE%22=%2797414%27%20LIMIT%2030
For any city, we retreive all the organizations and the association of the SIRENâs database. The process is the following :
The next exemple will fetch all the data in the SIRENE database for the city of Saint-Louis.
https://data.opendatasoft.com/api/records/1.0/search/?dataset=sirene%40public&facet=categorie&facet=proden&facet=libapen&facet=siege&facet=libreg_new&facet=saisonat&facet=libtefen&facet=depet&facet=libnj&facet=libtca&facet=liborigine&rows=30&start=0&geofilter.polygon=(-21.303505996763,55.403919253998),(-21.292626813288,55.391189163162),(-21.282029142394,55.381522536523),(-21.256155186265,55.392395046639),(-21.232012804782,55.387888015185),(-21.211100938923,55.390619722192),(-21.199480966855,55.382654775478),(-21.185882138486,55.385961778627),(-21.173346518752,55.389949958731),(-21.16327583783,55.399563417107),(-21.14709868917,55.405379688232),(-21.166028899095,55.414700890276),(-21.184085220909,55.432085218794),(-21.190290936422,55.440880800108),(-21.195166490948,55.462318490892),(-21.237553168259,55.459769285867),(-21.258726107298,55.463692709631),(-21.286021128961,55.455515913879),(-21.294777773557,55.419916682666),(-21.303505996763,55.403919253998)
Here are the mapping
| Sourceâs data | PH onthology |
|---|---|
| fields.l1_declaree | name |
| fields.categorie | type |
| fields.siret | shortDescription |
| fields.coordonnees.0 | geo.latitude |
| fields.coordonnees.1 | geo.longitude |
| fields.libapen | tags.0 |
[Exemple OpenDataSoft here](/4 - Documentation technique/api.md)
For any city, we retreive main information from the national education of France The process is the following :
The next exemple fetch all the actives research strutures of the city of Bordeaux :
https://data.enseignementsup-recherche.gouv.fr/api/records/1.0/search/?dataset=fr-esr-etablissements-publics-prives-impliques-recherche-developpement&facet=siren&facet=libelle&facet=date_de_creation&facet=categorie&facet=libelle_ape&facet=tranche_etp&facet=categorie_juridique&facet=wikidata&facet=commune&facet=unite_urbaine&facet=departement&facet=region&facet=pays&facet=badge&facet=region_avant_2016&rows=30&start=0&geofilter.polygon=(44.810795852605,-0.5738778170842),(44.817148298105,-0.57643460444186),(44.823910193873,-0.58695822406613),(44.818476638462,-0.60304723869607),(44.822474304509,-0.61064859861704),(44.824937843733,-0.61415033833008),(44.835177466959,-0.61079419661495),(44.841384923705,-0.62771243191386),(44.860667021743,-0.63833642556746),(44.871658097695,-0.63105127891779),(44.86227970331,-0.61630176568479),(44.854215265872,-0.59460939385687),(44.865671076253,-0.57646019656194),(44.869188961886,-0.57608874140575),(44.909402227434,-0.58088555560083),(44.908480410411,-0.57648917779388),(44.916666965125,-0.54773554113942),(44.889099273803,-0.53553255107571),(44.869138522062,-0.54141014437767),(44.868086689933,-0.53680669655034),(44.861267174723,-0.53784686147751),(44.848134506953,-0.53761462401784),(44.842390488778,-0.5422310311368),(44.836291776079,-0.54665943781219),(44.829021270567,-0.53642317794196),(44.822772234064,-0.53766321563778),(44.813135278103,-0.55606047183132),(44.810795852605,-0.5738778170842)
Here are the mapping
| Sourceâs data | PH onthology |
|---|---|
| fields.libelle | name |
| fields.site_web | shortDescription |
| fields.geolocalisation.0 | geo.latitude |
| fields.geolocalisation.1 | geo.longitude |
[Exemple ScanR here](/4 - Documentation technique/api.md)
For any city, we retreive the location of all buildings of La Poste The process is the following :
https://datanova.laposte.fr/api/records/1.0/search/?dataset=laposte_poincont&rows=30&start=0&geofilter.polygon=(-21.303505996763,55.403919253998),(-21.292626813288,55.391189163162),(-21.282029142394,55.381522536523),(-21.256155186265,55.392395046639),(-21.232012804782,55.387888015185),(-21.211100938923,55.390619722192),(-21.199480966855,55.382654775478),(-21.185882138486,55.385961778627),(-21.173346518752,55.389949958731),(-21.16327583783,55.399563417107),(-21.14709868917,55.405379688232),(-21.166028899095,55.414700890276),(-21.184085220909,55.432085218794),(-21.190290936422,55.440880800108),(-21.195166490948,55.462318490892),(-21.237553168259,55.459769285867),(-21.258726107298,55.463692709631),(-21.286021128961,55.455515913879),(-21.294777773557,55.419916682666),(-21.303505996763,55.403919253998)
Here are the mapping
| Sourceâs data | PH onthology |
|---|---|
| fields.libelle_du_site | name |
| recordid | type |
| fields.adresse | address.streetAddress |
| fields.latlong.0 | geo.latitude |
| fields.latlong.1 | geo.longitude |
| fields.libapen | tags.0 |
Accessible depuis l’url suivante : /co2/#interoperability.copedia
Dans CopĂ©dia, le but est de permettre Ă l’utilisateur d’avoir une autre vision d’une page WikipĂ©dia, de faciliter sa recherche et de la rendre plus intuitive.
Copédia permet en selectionnant un scope géographique (uniquement les villes pour le moment) de lister tous les arcticles Wikipédia en liens avec la page Wikipédia de la ville selectionné (tout liens hypertexte renvoyant vers une autre page Wikipédia).
Les liens Wikipédia listés sont ainsi catégorisés parmis 5 grands types :
Il est possible Ă tous moment de pouvoir filtrer parmis ces 6 grands types et ainsi obtenir par exemple uniquement les personnes d’une page WikipĂ©dia.
Copédia met également les dates en relations avec certains éléments, dans une frise chronologique.
Les dates que Copédia affiche sont :
Pour tous les éléments listés il est possible de :
Si un Ă©lĂ©ment ne possĂšde pas de type, il est possible d’ajouter vous mĂȘme un type Ă cet Ă©lĂ©ment en cliquant sur le bouton "Ajouter un type (Wikidata)" qui va permettre Ă l’utilisateur de pusher lui mĂȘme un type directement dans la page Wikidata de l’Ă©lĂ©ment parmis ces 4 grands type :