Geo-enabling the SDG indicators – experiences from the UN Global Geospatial Management and the GEOSTAT 3 project Agenda item 12 Ekkehard PETRI – Eurostat, E2 ekkehard.petri@ec.europa.eu Sustainable Development and Europe 2020 Indicators Working Group Luxembourg, 26-27 March 2019

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

What do we mean by geo-enabling?

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

Example Use geospatial information during the calculation or dissemination of an SDG indicator Indicator 9.1.1 – Proportion of rural population who live within 2 km of an all-season road ©ONS datasciencecampus

Why geo-enable SDG indicators?

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

Because it’s necessary Geospatial information essential for the calculation or dissemination of an SDG indicator Indicator 15.1.1 Forest area as a proportion of total land area ©UN-GGIM: Europe

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

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) …

Stakeholders and relevant activities

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

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)

IAEG-SDG WGGI short list

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

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

Indicator 11.7.1 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

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

Green space vs public green space ©Statistics Sweden

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

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

Indicator 11.2.1 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

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

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

©UN-GGIM: Europe

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)

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

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

Possibilities for cooperation Global set (and national indicators) EU set 11.2.1 | 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) 11.7.1 | 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) 11.3.1 | Ratio of land consumption rate to population growth rate Settlement area per capita (LUCAS data) & Imperviousness change Rate (Copernicus data) 15.1.1 | Forest areas as proportion of total land area Share of forest area (LUCAS data)

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?

References GEOSTAT projects https://www.efgs.info/information-base/case-study/sdg-indicators/ IAEG-SDG Work Group Geospatial Information http://ggim.un.org/UNGGIM-wg6/ UN-GGIM: Europe work group on data integration http://www.un-ggim-europe.org/content/wg-b-data-integration