Nordic Statistical Meeting 26.-28.8.2019, Helsinki

1. Integrating areal classifications and
geospatial data – case IGALOD
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Statistics Finland: Jennika Leino, Essi Kaukonen, Tuuli Pihlajamaa, Rina Tammisto, Daniel Davis
National Land Survey of Finland: Eero Hietanen, Kai Koistinen 1

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• Jennika Leino, Statistics Finland
• Statistics Finland: Essi Kaukonen, Tuuli Pihlajamaa, Rina Tammisto, Daniel Davis
• National Land Survey of Finland: Eero Hietanen, Kai Koistinen
Geospatial
Classifications
Linked data
Geospatial
data
How to utilise more geospatial data in the statistical production
process?

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Statistical production process Geospatial data

4. 091 Helsinki
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091 Helsinki
Official
Classifications
Areal Classifications as
Linked Open Data
091 Helsinki
Geographic data as
Linked Open Data
C G

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091 Helsinki
178 Juva
186 Järvenpää
FI1B1 Helsinki-Uusimaa
FI1D1 Etelä-Savo
FI1B1 Helsinki-Uusimaa
FI1B Helsinki-Uusimaa
FI1D Northern and Eastern Finland
FI1B Helsinki-Uusimaa

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https://lod-cloud.net/
www.shutterstock.com
• General metadata description
language for web resources
• Relational, graph-based model
• W3C recommendation 1.1
Resource Description Framework,
RDF

7. Benefits from IGALOD project
• A new way to utilise geographical data in statistical production process
• Relevat standards acknowledged (GSBPM, GSIM, INSPIRE, XKOS, etc…)
• Linked Open Data (LOD) for customers
• New opportunities to use our data
• Visualising the data on a map, a pilot app → https://www.ubigu.fi/allusion/
• Lots of new skills and competence
• Close co-operation between Statistics Finland and National Land Survey
of Finland
• New ideas, new ways to work together
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8. Thank you!
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