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

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

            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

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

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

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

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

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

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

Findings: Collect Collect Geospatial data may be collected from different data sources, providers and by using several different methods (web services or other services, e-mails, 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

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

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

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

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

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

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

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

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

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

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

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

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