1. Geo-enabling the SDG indicators – experiences from the UN Global Geospatial Management and the GEOSTAT 3 project Agenda item 12
Ekkehard PETRI – Eurostat, E2
Sustainable Development and Europe 2020 Indicators Working Group Luxembourg, March 2019

2. Topics of this presentation
What do we mean by geo-enabling?
Why geo-enable SDG indicators?
Stakeholders and relevant activities
Presentation of selected examples for goal 11
Conclusions and recommendations 2
Eurostat

3. What do we mean by geo-enabling?

4. Definition: geo-enabling of statistics refers to processes that link information or data to a geographical feature
Combination of geospatial and statistical data during the production of statistics
Statistical products where the spatial dimension is one of the key characteristics

5. Example
Use geospatial information during the calculation or dissemination of an SDG indicator
Indicator – Proportion of rural population who live within 2 km of an all-season road
©ONS datasciencecampus

6. Why geo-enable SDG indicators?

7. Because we are told
‘Transforming our world’ resolution Paragraph 74 (g) on the Follow-up and Review processes

8. Because it’s necessary
Geospatial information essential for the calculation or dissemination of an SDG indicator
Indicator Forest area as a proportion of total land area
©UN-GGIM: Europe

9. Because it will result in better quality indicators
Relevance
Accuracy
Coherence and Comparability
Accessibility and Clarity

12. Why geo-enable SDG indicators?
Required (e.g. land extent used in the calculation)
Increase relevance (disaggregation to lower NUTS levels or typologies, priority areas)
Increase accuracy (exposure, accessibility)
Ensure comparability (global satellite data vs. national data)
Longer time series (satellite data replace ad-hoc modules)
Increase timeliness and frequency (satellite data) …

13. Stakeholders and relevant activities

14. sub group geospatial information
The SDG-GEO industry
sub group geospatial information

15. IAEG-SDG indicator geo short list
Look out for geospatial key words in the indicator title or metadata (access to, area, extent, location, geography …)
Check the metadata for issues with geospatial relevance
Select indicators from all tiers (for demonstration purposes)

16. IAEG-SDG WGGI short list

17. Top-candidates in Europe
Indicator relevant and improvements interesting for national reporting and EU indicator set
©UN-GGIM: Europe

18. Two examples from GEOSTAT and UN-GGIM for goal 11 (type 1)

19. Indicator Average share of the built up area of cities that is open space for public use for all, by sex, age, and persons with disabilities (Tier III->II, EU SDG on hold)
Metadata issues
Conceptual
Definition of cities
Definition of built-up
Definition of open space (including car parks, streets, …?)
Data and methodology
Availability of data on public use (ownership)
Requires combination of Earth observation data and in-situ data

20. GEOSTAT choices made Use proxy of green space Minimum size 0.5 ha
Euclidian distance (200 m and 500 m)
Work with cadastre data
©Statistics Sweden

21. Green space vs public green space
©Statistics Sweden

22. Calculation Delineate green space Mask open green space
Geocode urban population
Buffer open green space
Aggregate

23. National experiences Very demanding indicator
Intra-city and intra-country dispersion of results substantial
Main challenge: information on public access/ use
©Statistics Norway

24. Indicator Proportion of population that has convenient access to public transport, by sex, age, and persons with disabilities (Tier II, EU SDG on hold)
Metadata issues
Conceptual
Classification of settlements for aggregations
For stops - convenient access, service frequency, safe and comfortable environment need to be defined.
Data and methodology
Road network
Transport stops
Time table information
Access for disabled persons
Starting points for journeys (other than place of residence)
Buffer distance or road network routing

25. GEOSTAT choices made Home address as starting point for geocoding
Use national data for stops
Between 6h and 20h on a working day
Minimum frequency one service per hour
Euclidian distance (500 m) and network distance
©GEOSTAT 3

26. National experiences Differences due to buffering method significant
Difference between intra-urban and regional transport (purpose and service level)
Main challenge: time table information
©Statistics Norway

27. ©UN-GGIM: Europe

28. General observations Disaggregation by sex not significant
Disaggregation by age groups can be significant (max. 5% points)
Definition of cities has significant impact (national vs. European concepts)

29. Further general observations
Geo-enabling of indicators is possible if
High quality land cover and land use information
Geocoded statistical microdata (persons, businesses, other)
are available.
Some issues:
Concepts alignment pending (forest, built-up, urban)
Complexity of using EO data (size, temporal consistency, conceptual consistency) is a challenge for NSIs
Availability and access to data for statistical production (cadastre, time-tables, disabilities) is another major issue

30. Conclusions
Need for better guidance on geospatial concepts, methods, data => IAEG-SDG, ESS
Need for alignment of area classifications (urban)
=> ESS, NSIs
Need for geocoded statistical data on persons and businesses
=> NSIs, ESS
Need to define characteristics of geospatial reference data for SDG indicators (both Earth Observation and in-situ)
=> ESS, Geospatial agencies
NSIs should ask for more geospatial data and better access conditions
=> NSIs, Geospatial agencies
More cooperation between geospatial agencies and statistical offices for SDGs => NSIs, Geospatial agencies

31. Possibilities for cooperation
Global set (and national indicators)
EU set
| Proportion of population that has convenient access to public transport, by sex, age and persons with disabilities
Share of population with access to public transport by service level (on-hold)
| Average share of the built-up area of cities that is open space for public use for all, by sex, age and persons with disabilities
Share of urban population without green urban areas in their neighbourhood (on-hold)
| Ratio of land consumption rate to population growth rate
Settlement area per capita (LUCAS data) &
Imperviousness change
Rate (Copernicus data)
| Forest areas as proportion of total land area
Share of forest area (LUCAS data)

32. Questions for discussion
Do you work on geo-enabling SDG indicators? Which ones? Are they different from global indicators?
What are your experiences (success stories, issues)?
Do you use Earth Observation data for calculating SDG indicators?
Do you face obstacles in accessing geospatial data needed to calculate SDG indicators?
Have you established specific cooperation mechanisms for SDG monitoring with your national geospatial agency?
What do you expect from Eurostat?

33. References
GEOSTAT projects
IAEG-SDG Work Group Geospatial Information
UN-GGIM: Europe work group on data integration