04 --spatial-data
DESCRIPTION
How to build data model acording INSPIRETRANSCRIPT
Spatial data
Karel Janecka, Karel CharvatDepartment of Mathematics, Faculty of applied Sciences
University of West Bohemia
Pilsen, Czech Republic
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Agenda
• Data models for spatial planning
- Experience from the PLAN4ALL project• Spatial data harmonization• Experience from the HUMBOLDT project
PLAN4ALL project
• PLAN4ALL (Towards Harmonisation of Spatial Planning Data)
• Start: May 2009
• Duration: 30 months
• The harmonisation of spatial planning data according to the INSPIRE Directive based on the existing best practices in EU regions and municipalities and the results of current research projects.
eContentplus project (Best Practice Networks)
Experience from the PLAN4ALL project
• In the scope of PLAN4ALL project will define data models for the following INSPIRE Themes:– Land Cover
– Land Use
– Utility and Government Services
– Production and Industrial Facilities
– Agricultural and Aquaculture Facilities
– Area Management/Restriction/Regulation Zones and Reporting Units
– Natural Risk Zones
Experience from the PLAN4ALL project
• On the basis of experience from single project countries the work will include:– The analyse of data models for these Themes used in single
countries.– The definition of conceptual data models for selected Themes
(platform neutral!)
• Data specifications including UML data models for Annexes II and III are not defined yet. Plan4all will contribute to define them.
• Contribution will be ensured by providing reference materials, leading a specific data theme, providing experts, testing, providing comments, …
Data models for spatial planning
• The process of defining conceptual data models for the selected themes will be based on the INSPIRE document “D2.6 - Methodology for the development of data specifications”
Data models for spatial planning
Data models for spatial planning
• STEP: Identification of user requirements and spatial object types
• RESULTS: – List of spatial object types as proposals for entries in the INSPIRE
Feature Concept Dictionary Register
– “First cut” data specification
– List of requirements
• REFERENCE MATERIALS: – D2.6 (section 6.4), D2.3, – Generic Conceptual Model, ISO 19131, – INSPIRE Feature Concept Dictionary Register, – INSPIRE data specification template, – Consolidated INSPIRE UML Model
Data models for spatial planning
• STEP: Gap analysis
• RESULTS: - Description of data interoperability issues derived from the identified
user requirements and taking the as-is analysis into account (in principle, for every source data set)
- Choice of harmonisation approach
- Updated as-is analyses (additional data sources that have been identified) or updated/reduced user requirements to reduce the identified gaps
• REFERENCE MATERIALS: – D2.6 (section 6.6), – D2.6 Annex A, – D2.6 Annex F
Data models for spatial planning
• STEP: Data specification development• RESULTS:
- Data specification (per spatial data theme) with clauses specified in
ISO 19131 (including application schema in UML as well as the
corresponding feature catalogue and GML application schema)
- Updated Consolidated INSPIRE UML model
- Updated INSPIRE Feature Concept Dictionary Register
- Updated glossary
• REFERENCE MATERIALS: – D2.6 (section 6.7), D2.6 Annex A, Generic Conceptual Model, – ISO/TS 19103, ISO 19109, ISO 19110, ISO 19126, ISO 19131,
INSPIRE data specification template, Consolidated INSPIRE UML Model, INSPIRE Feature Concept Dictionary Register, INSPIRE Glossary, ISO 19136
PLAN4ALL – profit for data holders
• Regions and municipalities will have their data and systems in compliance with INSPIRE after the end of the project
• Regions and municipalities will be able to promote investment possibilities of their region trough European Plan4all portal, which will be managed in close cooperation with ISOCARP
PLAN4ALL – profit for technological partners
• Solutions of technological partners will become INSPIRE compliant through modification, testing and validating.
PLAN4ALL – European Portal for Spatial Planning data
• This portal will be European gate for investors, planners, real estate business, …
• It will allow municipalities and regions to publish their information in central place
Spatial data harmonization
• Providing access to data through network services in a representation that allows for combining it with other INSPIRE data in a coherent way by using within the European Spatial Data Infrastructure (ESDI) a common set of data product specifications.
• This includes agreements about coordinate reference systems, classification systems, application schemes, etc.
Spatial data harmonization
Spatial data harmonization
Components of harmonization:
• INSPIRE principles• Reference model• Data translation model• Portrayal model• Application schemes and
feature catalogues• Dictionaries
• Metadata• Maintenance• Quality• Data transfer• Derived reporting &
multiple representations• Consistency between data• Data Capturing
HUMBOLDT project
• Project HUMBOLDT will contribute to the implementation of an European Spatial Data Infrastructure that integrates all the diversity of spatial data available from the multitude of European organizations.
• It is the aim of this project to manage and advance the implementation process of this ESDI
• Duration: 2006 - 2010
Experience from the HUMBOLDT project
• HUMBOLDT Scenario FOREST– The scenario is focused mainly on land cover and vegetation integration
with data for spatial planning, water resources, risk management and security
– It demonstrates the possibilities of updating international data set Corine Land Cover (CLC) with using Regional Plans of Forest Development (RPFD) data.
Experience from the HUMBOLDT project
• CLC Use Case – source data– RPFD contain data summaries on the state of the forest and the service
needs of the forest as a public interest.
– Geometry types: Data can contain all types of geometry primitives (point, line, polygon) and some texts, too.
– Storage format(s): Database, shapefiles.
– Coordinate reference system: S-JTSK (Czech national system)
– Scale / resolution: from 1:10 000 to 1:50 000
– Language: Czech
– Metadata: Defined by FMI
Experience from the HUMBOLDT project
• CLC Use Case – target data– Geometry types: Data contain polygons.
– Storage format(s): Shapefiles.
– Conceptual model: The CLC data are composed of features with three parameters:
• GEOMETRY: Geometry
• CODE_00: String
• AREA: Double
– Coordinate reference system: ETRS 1989
– Scale / resolution: from 1:100 000 to 1:250 000
– Language: English
Experience from the HUMBOLDT project
• CLC Use Case – harmonization issues
– Data formats conversion (to GML)
– Classification schemes and systems, codelists, terminology and vocabulary (selection of corresponding items)
– Types of geometric primitives (to polygons)
– Metadata profile
– Coordinate system
– Geometry improvement
– Generalisation, multi-linguality…
Experience from the HUMBOLDT project
• CLC Use Case – main steps of data harmonization
– Harmonisation of classifications schemes and systems, codelists, terminology and vocabulary (selection of corresponding items) must be created before the building of harmonisation tools, because there is necessary to define the rules for mapping language.
– Type of geometric primitives (all types to polygons – this harmonisation step is necessary, but the majority of the selected source data (maybe all data) will be in the form of polygons).
– Harmonisation of metadata profile is necessary in the term of INSPIRE directive.
Experience from the HUMBOLDT project
• CLC Use Case – main steps of data harmonization
– Data must be converted from Czech national system S-JTSK (RPFD) to system ETRS 1989 (CLC).
– Because the geometry of source and target data is not the same, there will be necessary the transformation and/or improvement of geometry (e.g. elimination of differences between data providers and adaptation to reference data, edge-matching). It relates with next item – generalisation.
– Because the harmonised data layers are in different level of detail and target data sets have mostly the smaller scale than source data, the generalisation methods could be used.
– Source and target data are coded in the same language. But because the CLC data do not contain any description except codes and these codes are translated into many languages including Czech, the multi-linguality is supported.
Experience from the HUMBOLDT project
The benefits of proposed solution:
• Conformity with standards and major European initiatives
• Reduction of cost and effort of INSPIRE implementation
• Integration of services and data to facilitate decision-making
• Information accessibility and distribution
• Core technology will be available as Open Source
• Information management technology related user-driven operational services
Experience from the HUMBOLDT project
• Harmonization – conclusion
– Harmonization in forestry makes possible better:• Classification schemes and systems, codelists, terminology
and vocabulary (selection of corresponding items)• Combination of internal and external data sets with different
parameters • Using of different IT systems • Cross-border cooperation
Thank you for your attention!
Karel Janecka, Karel CharvatDepartment of Mathematics, Faculty of applied Sciences
University of West Bohemia
Pilsen, Czech Republic
Help Service Remote Sensing