Interopérabilité

À fusionner avec [[COSM]] et [[Copédia]].

Vue d’ensemble du chantier sur l’interopérabilité

À gauche, la liste des sources extérieurs sur lesquelles on récupère les données :

  • Wikidata
  • Wikipédia
  • OpenStreetMap
  • OpenDataSoft (la base SIRENE)
  • Data.gouv
  • Datanova (les enseignes La Poste)
  • Pôle Emploi
  • SCANR

Au milieu, le processus de Conversion des données (détails sur le prochain schéma)

A droite, l’affichage des données converties sur le site de Communecter ainsi que des exemple d’usage de ces données par des sites extérieurs.

Conversion des données sémantiques

We interoperate with

using their API

Wikidata

For any city, We retreive main information available on Wikidata

The process is the following :

  • We choose a geographic scope (a country) to filter
  • We call our own semantic convert system (doc avaible here) :

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 Réunion island :

And convert this data in the pivot language named “PH onthology”

/ph/api/convert/wikipedia?url=https://www.wikidata.org/wiki/Special:EntityData/Q47045.json

Exemple Wikidata here

Here are the mapping

Source’s data PH onthology
itemLabel.value name
coor.latitude geo.latitude
coor.longitude geo.longitude
item.value url
itemDescription.value description
  • We’ll want to contributing back any extra data we can offer with COpédia (coming soon)

DBpedia

  • For any city, We retreive main information available on Wikipedia

OpenStreetMap

For any city, we retreive main information avaible on OSM

The process is the following :

  • We choose a geographic scope (a country) to filter
  • We call our own semantic convert system (doc avaible here) :

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

Source’s data PH onthology
tags.name name
lat geo.latitude
long geo.longitude
type type
tags.amenity tags.0

Exemple OSM here

  • We’ll want to contributin back any extra data we can offer with COSM (coming soon)

Data.gouv

For any city, we retreive main information of the organizations placed in this city

The process is the following :

  • We choose a geographic scope (a country) to filter
  • We call our own semantic convert system (doc avaible here) :

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 Méto-France, meteorological center of France.

https://www.data.gouv.fr/api/1/datasets/54a12162c751df720a04805a/

Here are the mapping

Source’s data PH onthology
slug name
page url
tags[] tag[]
item.value url
owner creator

Exemple Data.gouv here

Pôle emploi

For any city, we retreive all the job offer. (no exact localisation of the job place)

The process is the following :

  • We choose a geographic scope (a country) to filter
  • We call our own semantic convert system (doc avaible here) :

To get data with the Pôle emploi’s 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

OpenDataSoft (SIREN database)

For any city, we retreive all the organizations and the association of the SIREN’s database.

The process is the following :

  • We choose a geographic scope (a country) to filter
  • We call our own semantic convert system (doc avaible here) :

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

Source’s 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

ScanR ( National Education )

For any city, we retreive main information from the national education of France

The process is the following :

  • We choose a geographic scope (a country) to filter
  • We call our own semantic convert system (doc avaible here) :

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 :

Source’s data PH onthology
fields.libelle name
fields.site_web shortDescription
fields.geolocalisation.0 geo.latitude
fields.geolocalisation.1 geo.longitude

Exemple ScanR here

  • Datasets used :
    • Public or private research and development structures
    • Member of the university institute of France

Datanova ( La Poste )

For any city, we retreive the location of all buildings of La Poste

The process is the following :

  • We choose a geographic scope (a country) to filter
  • We call our own semantic convert system (doc avaible here) :

The next exemple will fetch all La Poste buildings localised in the city of Saint-Louis.

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

Source’s 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

Exemple Datanova here

Smart Citizen (coming soon)

  • onclick : we’ll show all SCK kits for a given city

Umaps (coming soon)

  • POI’s of type geoJson, on click we show the content on our map

WordPress RSS (coming soon)

  • any WP blog’s RSS can be pluggued to an elements wall

using an iframe

FramaPads

  • users can use Framapads from inside CO(simple Iframe)
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