1. Towards connecting geospatial information and statistical standards in statistical production: two cases from Statistics Finland
Workshop on Integrating Geospatial and Statistical Standards
Stockholm, Sweden 6-8 November 2017
Essi Kaukonen, Standards and methods
Rina Tammisto, ICT Management
Statistics Finland

2. Background: Geospatial production at Statistics Finland
Geospatial-related production, a long tradition at Statistics Finland. Over 25 years as a recognised production branch that has own datasets, production methods, tools and expertise
Point-base foundation is the core of geospatial production (e.g. centroids of buildings), other geographical data is also utilised
Geospatial information can be utilised in the whole statistical production process – it may be the starting point or the end-product of the process
Most recent at Stat Fi: geospatial actions are included in strategic targeting and technological guidelines -> A Geospatial Reference Architecture (2018)
The actions may relate to
Maintenance, production and dissemination of geographical data or geospatial statistics
Maintenance of location information
Visualisation and geospatial analysis
Application development for geospatial-related production and dissemination
7 November 2017
Statistics Finland

3.    
   
   
Towards connecting geospatial information and statistical standards in statistical production: two cases from Statistics Finland
A) Testing the GSBPM in the geospatial-related statistical production process: results
B) Linking GSIM statistical areal classifications and corresponding geographies: future plans
7 November 2017
Statistics Finland

4. A) Testing the GSBPM in the geospatial-related statistical production process: results

5. Objective and approach of the GEOSTAT 2 work at Stat Fi
Evaluate the Generic Statistical Business Process Model (GSBPM) from the point of view of geospatial data involved in the statistical production process
Is the GSBPM usable when geospatial-related work phases are described?
What does using of the model require?
Does the model or its documentation need further development?
Requires understanding the GSBPM and applying it to the production of geospatial statistics
The GSBPM was introduced within the project group at Stat Fi
Our geospatial-related process was described phase by phase (as is)
Phases were reflected against the phases and sub-processes of the GSBPM
7 November 2017
Statistics Finland
Case A) Testing the GSBPM in the geospatial-related statistical production process: results

6. Testing of the GSBPM at Stat Fi
7 November 2017
Statistics Finland
Case A) Testing the GSBPM in the geospatial-related statistical production process: results

7. Findings: Specify Needs
The geospatial dimension of statistical data should be recognised and taken into account when specifying the needs
Statistical outputs should include maps or geospatial statistics
In addition to statistical concepts, other related (e.g. geospatial) concepts should also be taken into account
In the Check data availability sub-process integrability with geospatial data should also be evaluated
7 November 2017
Statistics Finland
Case A) Testing the GSBPM in the geospatial-related statistical production process: results

8. Findings: Design Design Geospatial standards should also be noticed.
When designing geospatial statistical data output, the scalability of disclosure control should be taken into account.
When designing variable descriptions, the existing link between statistical classification and corresponding geographical data should be noted and described.
Design collection should also include the mention of techniques for managing geospatial data, e.g. mobile technology.
When routines to integrate datasets are designed, integration of statistical data and geospatial data should be noted
7 November 2017
Statistics Finland
Case A) Testing the GSBPM in the geospatial-related statistical production process: results

9. Findings: Build
Build
In building and testing the production solution, geospatial data and services that are used should have a general and stable basis, not separate solutions for different production processes
In case of geospatial data, data gathering usually concerns use of an existing web service (WMS, WFS). Direct connections or data storage procedures may be used.
The data collection system should be able to handle geospatial data
7 November 2017
Statistics Finland
Case A) Testing the GSBPM in the geospatial-related statistical production process: results

10. Findings: Collect Collect
Geospatial data may be collected from different data sources, providers and by using several different methods (web services or other services, s, register based data from (other) statistical system
Geospatial data can be collected manually or automatically
7 November 2017
Statistics Finland
Case A) Testing the GSBPM in the geospatial-related statistical production process: results

11. Findings: Process Process Integration Location quality Derivation
The most concrete phase where the integration of statistical and geospatial data is present.
Enriching the statistical data with location information or it may mean integrating the geospatial dataset to the aggregated dataset (integrate statistical data to corresponding boundaries)
Includes maintaining geospatial data with updated areal classifications or vice versa
Includes study of location information and identification of locational errors
Editing of geospatial data may consist of merging or creating new geospatial elements or changing the location information of spatial objects
Aggregation of data by areal classification
Storing aggregated, integrated, edited or conducted data in data warehouses
Integration
Location quality
Derivation
Aggregation
7 November 2017
Statistics Finland
Case A) Testing the GSBPM in the geospatial-related statistical production process: results

12. Findings: Analyse Analyse
Accessibility measures, travel and time distance calculations
Map visualisations
Disclosure control for geospatial data depends on the scale of geospatial data
Compiling metadata - also for geospatial data
7 November 2017
Statistics Finland
Case A) Testing the GSBPM in the geospatial-related statistical production process: results

13. Findings: Disseminate
Geospatial data outputs conform to the geospatial data dissemination standards (i.e. ISO, OGC, INSPIRE)
The creation of geospatial web services (WFS, WMS) and user support
7 November 2017
Statistics Finland
Case A) Testing the GSBPM in the geospatial-related statistical production process: results

14. Findings: Evaluate Evaluate
Gathering information for evaluation of the geospatial process, data or created services
Evaluation of this information
Action plan for further development
7 November 2017
Statistics Finland
Case A) Testing the GSBPM in the geospatial-related statistical production process: results

15. Conclusions, part A
The GSBPM seems to be able to cover geospatial related statistical production; however,
Use of descriptions of the GSBPM requires raising the level of abstraction when interpreting phases, a risk from the coherence point of view
Our opinion is that the GSBPM model does not need new sub-processes for covering geospatiality but the descriptions should be reformed in order to cover the geospatial point of view
It is not necessary to write down the geospatial approach specifically in the descriptions in every case – a wider scope of the meaning of the sub-process could suffice
See also: Geostat 2 final report
7 November 2017
Statistics Finland
Case A) Testing the GSBPM in the geospatial-related statistical production process: results

16. B) Linking GSIM statistical areal classifications and corresponding geographies: future plans

17. Big picture: Logical Data Warehouses at Statistics Finland and Geospatial Information
7 November 2017
Statistics Finland
Case B) Linking GSIM statistical areal classifications and corresponding geographies: future plans

18. Case B) Linking GSIM statistical areal classifications and corresponding geographies: IGALOD project 2018-
For
Instance
7 November 2017
Statistics Finland
Case B) Linking GSIM statistical areal classifications and corresponding geographies: future plans

19. Zooming in… IGALOD project GSIM: Statistical Classification Item
   
   
Zooming in…
GSIM: Statistical Classification Item
The Classification System has been renewed using the GSIM Statistical Classification Model
Areal Classifications are included in the system (municipalities, counties, etc.) as GSIM Statistical Classifications
Classifications and Code Lists are used in production from the new central system since 2016
The Classifications (including areal classifications) will be published on the web as open data in 2018
IGALOD project
091 Helsinki
Geospatial Data: Geographies
Geographies of the most common areal classifications are part of basic geospatial data production
Since 2013 they have been also published as open data using standardised web services (WMS/WFS)
This has been done as part of the INSPIRE implementation at Statistics Finland
Today, there is no way to connect the areal classifications and the corresponding geographies directly
7 November 2017
Statistics Finland
Case B) Linking GSIM statistical areal classifications and corresponding geographies: future plans

20. The expected outcomes of the IGALOD project
New connections between areal classifications and corresponding geographies can be used together throughout the statistical production process whenever needed
The expected outcomes of the IGALOD project
The connections will also be published for our customers as Open Linked Data
7 November 2017
Statistics Finland
Case B) Linking GSIM statistical areal classifications and corresponding geographies: future plans

21. Conclusions
Geospatial information is an important part of the statistical production process.
The GSBPM and the GSIM models, especially their guidelines, should be further elaborated to fully support the understanding and the coherent use of geospatial data in an NSO.
Geospatial information and the direct linkages to areal classifications can have an important role in the development of the statistical production and in the customer-oriented services in the future.
7 November 2017
Statistics Finland

22. essi.kaukonen@stat.fi rina.tammisto@stat.fi
Thank You!