Thinking spatially with your open data

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The Open Data movement is now moving a step forward, many governments, institutions and business have recently started the process of making information available to citizens and customers. Data is now seen as a powerful instrument to increase transparency in public administration and business on policies. About 80% of this information has a spatial component that is not entirely exploited yet. A range of open source solutions are now available to address this challenge, in this session we will explore their potential and possible applications. The so-called “data deluge” is here.. but we can build good umbrellas.

Tecnologia

Thinking spatially with your open data
“Spatial data deluge: please bring me an
umbrella!”
Juan Arévalo, Marco Giacomassi

Inspire Directive
• Infrastructure for spatial
information in Europe to support
Community environmental policies:

- Decreto Legislativo 32/2010 di recepimento
della Direttiva Inspire

- Metadata, interoperability, Web services,
standards, data specifications, etc..

Is it worth it??
Cost:

Total direct cost: 1.5 M € (76%
Human Resources)
Period: 2002-2006

Benefits:

Internal efficiency benefits: 500
hours per month (30€ hour)
Recovery cost: 6 months

Spatial Data Infrastructures
and Open Data

Two sides of the same coin:

- About making data available
- Data should be freely available

Everything can be mapped!!

80% of the data has a spatial
component

Data exploration and analysis
through spatial operations

Data mining tools: Business
Intelligence

Spatial ETL Tools
 Extract, automate and define a data flow process
Translate spatial data into the format that the end
consumer requires
 Load and integrate disparate data into a spatial database
 Document your processing data steps
 Execute process in a Parallel Computing Environment
Restructure the data into the required data model and
coordinate system

Spatial data cubes

- Store geometry in a data cube
- Spatial query language (MDX)
- View agreggated data from different perspectives
- Fast retrieval and anlysis of data

Spatial OLAP & Drupal
data feed workflow management

S. ETL
DB S. CUBE

Spatial OLAP & Drupal
data feed workflow management frontend

S. ETL
DB S. CUBE

geojsp

Conclusions

1. Huge amount of data with a spatial component that needs to
be explored and analysed.

2. Availability and maturity of open source technologies and
Know-how: OLAP and Geographical Information Systems
(GIS).

3. Spatial OLAP merging OLAP and GIS via Open Source Tools.

Links and References

http://www.spatialytics.org/projects/geomondrian/
http://www.spatialbi.org
http://www.spatialytics.com
http://www.geobi.org
http://inspire.jrc.ec.europa.eu/reports/
Study_reports/catalonia_impact_study_report.pdf

Contacts

Marco Giacomassi Juan Arevalo
mail: marco@twinbit.it mail: juan@twinbit.it
twitter: @marcogiaco twitter: NO!

Thank you!
Grazie mille!
Muchas gracias!

“In the middle of
difficulty lies
opportunity.”

Albert Einsten

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Thinking spatially with your open data

1. Thinking spatially with your open data “Spatial data deluge: please bring me an umbrella!” Juan Arévalo, Marco Giacomassi

2. Data is everywhere...

3. Inspire Directive • Infrastructure for spatial information in Europe to support Community environmental policies: - Decreto Legislativo 32/2010 di recepimento della Direttiva Inspire - Metadata, interoperability, Web services, standards, data specifications, etc..

4. Is it worth it?? Cost: Total direct cost: 1.5 M € (76% Human Resources) Period: 2002-2006 Benefits: Internal efficiency benefits: 500 hours per month (30€ hour) Recovery cost: 6 months

5. Open Data Movement

7. Open Data Applications

8. Spatial Data Infrastructures and Open Data Two sides of the same coin: - About making data available - Data should be freely available

9. Everything can be mapped!! 80% of the data has a spatial component Data exploration and analysis through spatial operations

10. Spatial Data Formats

11. Programming languages

12. GIS Desktop Tools

13. Spatial Databases

14. Web Mapping Clients

15. Data processing...

16. Data mining tools: Business Intelligence

17. Spatial ETL Tools  Extract, automate and define a data flow process Translate spatial data into the format that the end consumer requires  Load and integrate disparate data into a spatial database  Document your processing data steps  Execute process in a Parallel Computing Environment Restructure the data into the required data model and coordinate system

18. The umbrella...

19. Spatial data cubes - Store geometry in a data cube - Spatial query language (MDX) - View agreggated data from different perspectives - Fast retrieval and anlysis of data

20. SOLAP Clients

21. SOLAP Clients

22. Spatial OLAP & Drupal data feed

23. Spatial OLAP & Drupal data feed S. ETL

24. Spatial OLAP & Drupal data feed workflow management S. ETL DB S. CUBE

25. Spatial OLAP & Drupal data feed workflow management frontend S. ETL DB S. CUBE geojsp

26. Conclusions 1. Huge amount of data with a spatial component that needs to be explored and analysed. 2. Availability and maturity of open source technologies and Know-how: OLAP and Geographical Information Systems (GIS). 3. Spatial OLAP merging OLAP and GIS via Open Source Tools.

27. Links and References http://www.spatialytics.org/projects/geomondrian/ http://www.spatialbi.org http://www.spatialytics.com http://www.geobi.org http://inspire.jrc.ec.europa.eu/reports/ Study_reports/catalonia_impact_study_report.pdf

28. Questions?

29. Contacts Marco Giacomassi Juan Arevalo mail: marco@twinbit.it mail: juan@twinbit.it twitter: @marcogiaco twitter: NO!

30. Thank you! Grazie mille! Muchas gracias! “In the middle of difficulty lies opportunity.” Albert Einsten

Thinking spatially with your open data

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