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How to organize the data flow of the City: a case study with the Spatial Data Infrastructure CartoPOLIS

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HAL Id: halshs-01093076

https://halshs.archives-ouvertes.fr/halshs-01093076

Submitted on 10 Dec 2014

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How to organize the data flow of the City: a case study with the Spatial Data Infrastructure CartoPOLIS

Gwendall Petit

To cite this version:

Gwendall Petit. How to organize the data flow of the City: a case study with the Spatial Data Infrastructure CartoPOLIS. La modélisation des flux au service de l’aménagement urbain, Jun 2012, Lille, France. 2012. �halshs-01093076�

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Author : Gwendall Petit (gwendall.petit@ec-nantes.fr) - Atelier SIG - IRSTV - FR CNRS 2488 - 06/2012

Needs Answer

How to organize the data flow of the City: a case study with the Spatial Data Infrastructure CartoPOLIS

Research Institute on Urban Sciences and Technology (IRSTV)

- French Research Federation from CNRS - A common goal: research on the city

- Multi-disciplinary context

> a lot of data produced and used

The response of the IRSTV

The IRSTV SDI www.cartopolis.org

Institut de recherche en sciences et techniques de la ville - FR CNRS 2488

Aims

All the tools are partitioned. So how to

facilitate the communication between these components ?

How to connect results from simulation model to SDIs ?

The OGC must provide solutions.

Catalog

Search, find, list, sort the data Metadata catalog Geonetwork

Store

Organize, structure, backup data DataBase Management System (DBMS) PostgreSQL / PostGIS

Geoserver Distribute

Transform the data via streams Cartographic server

Tools

SQL, SFS

WMS, WFS, WCS

OrbisGIS Analyse

Treat, cross, extract data Geographic Information System (GIS)

SQL, OWS ... and WPS, SE CSW, ISO 19115

Interoperability

Structure information to facilitate the exchange.

Standardization and normalisation organisation : - Open Geospatial Consortium (OGC)

- International Organization for Standardization (ISO)

Context

The multiplication of end users (public authorities, national organizations, companies, associations, ...) and the improvement of production

methods of geographic information (remote sensing imagery, GPS survey, ground sensors, ...) cause the increase of the number of data on city.

Indeed, it is now possible to have data at different time scales (years, months, days, hours, ...) and spatial scales (city, district, building, ...).

This is reinforced by the development of Internet technologies and "Web 2.0" services that facilitate the dissemination of data. The "wave" of open- data initiatives such as the Data Nantes portal and community projects like OpenStreetMap are illustrations of that. It becomes easy for anyone to find geographic information.

Problem

Therefore, this raises the problem of identifying relevant information for an area or topic of study: geometric quality, semantic scope of the dataset, ... are burning issues.

Solution

To answer this question, public institutions, such as [Clinton, 1994] have set up Spatial Data Infrastructures (SDI).

Spatial Data Infrastructure (SDI)

Information system that allows, with different software, to store, catalog and share geographic information, through flow (metadata or data).

> enable the discovery and delivery of spatial data from a data repository [Steiniger & Hunter, 2012]

To be interoperable, IDSs are based on standards of the OGC and ISO.

(example: Web Map Service (WMS) standard for the dissemination of data through image, or ISO 19115 which describes metadata).

INSPIRE

At European scale, the INSPIRE Directive (adopted in 2007) aims at regulating the dissemination of environmental public data , through the

implementation of SDIs. It establish a legal framework for european states.

+ 200 researchers

Services

Geoprocessing

GDMS server

WPS : Web Processing Services

Geonetwork

Geonetwork

SE : Symbology Encoding

Geoservices

Geoserver

WMS : Web Map Services WFS : Web Feature Services WFS : Web Feature Services

In progress OK

But ...

What about ?

Références

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