Vala, Francisco - The integration of geospatial data with statistical data to grasp the territorial dimension in SDG indicators
0 likes132 views
The document discusses the integration of geospatial and statistical data to enhance the understanding of territorial dimensions in Sustainable Development Goals (SDGs) indicators, focusing on a workshop held in Paris in May 2019. It outlines specific SDG indicators that benefit from this integration and highlights the importance of harmonizing data themes and developing spatial data infrastructures. Additionally, it provides recommendations for effective geospatial data integration to support national statistical initiatives and improve the monitoring framework of the SDGs.
Vala, Francisco - The integration of geospatial data with statistical data to grasp the territorial dimension in SDG indicators
- 1. Copyright ©2013 EuroGeographics Working Group on Data Integration PIER-GIORGIO ZACCHEDDU (Chair) FRANCISCO VALA (Subgroup Leader) OECD Workshop: Towards an OECD localised indicator framework for SDGs Paris, 14 May 2019 The integration of geospatial data with statistical data to grasp the territorial dimension in SDG indicators
- 2. UN-GGIM: Europe WG on Data Integration Work plan 2017-2019 – Tasks defined Task 1 Task 2 Policy Outreach Paper – Lead by Eurostat Promote the benefits of the integration of statistical and geospatial data aiming at responsible ministries but also relevant stakeholders Make use of recommendations and findings of WG reports already published Select and analyse SDG indicators – Lead by NSI PT - INE Meet the Sustainable Development goals 2030 Analyse data integration aspects Reflect cross-cutting issues regarding the integration of geospatial and statistical data based on a Global, European and National perspective
- 3. AIM The territorial dimension in SDG indicators: geospatial data analysis and its integration with statistical data To address the territorial dimension of the Sustainable Development Goals indicators by focusing on the contribution of geospatial data analysis and its integration with statistical data based on a global, European and national perspective The work took into consideration, at the global level, the activities of the IAEG-SDG WG GI, and also the background and experiences of European and national initiatives addressing the SDGs from a geospatial perspective
- 4. Background for selecting the SDG indicators IAEG-SDG WG GI Short list of indicators directly or indirectly benefiting from geospatial information GEO list of indicators that can directly or indirectly be supported by earth observations EU SDG Indicator set to monitor EU policies in the perspective of 2030 Agenda Eurostat analysis on the spatial dimension in SDG indicators – present in all the 17 SDG, but especially in goals 6, 11 and 15 National indicators defined within national SDG monitoring strategies EU Urban Audit contributions to UN SDG Agenda by mainly focusing on the scope of goal 11
- 5. Selected SDG indicators 11.2.1 11.3.1 11.7.1 15.1.1 Proportion of population that has convenient access to public transport, by sex, age and persons with disabilities Ratio of land consumption rate to population growth rate 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 II indicator Indicator coordinator: Austria (NSI) Indicator coordinator: Portugal (NSI) tier II indicator tier III indicator (currently tier II) Indicator coordinator: Sweden (NSI) tier I indicator Forest area as a proportion of total land area Indicator coordinator: Italy (e-GEOS) Contributors: Austria (NSI), France (NMCA), Ireland (NSI), Sweden (NSI), Switzerland (NSI) Contributors: Finland (NMCA), Ireland (NSI), Italy (e-GEOS), Portugal (NSI and NMCA) Contributors: Austria (NMCA), Finland (NMCA), France (NMCA), Germany (NMCA), Italy (e-GEOS), Spain (NMCA) Contributors: Ireland (NSI), Sweden (NSI and NMCA), Switzerland (NSI)
- 6. Measuring accessibility has a strong spatial character, since it is intrinsically associated to the physical distance to a place The 11.2.1 tier II indicator showed that geospatial data and modelling is at the core of this indicator Measuring accessibility using spatial modelling and analysis 11.2.1 Proportion of population that has convenient access to public transport
- 8. The geospatial analysis combining land cover and population data provides the possibility of deriving new metrics that are relevant to grasp important dimensions on human settlement planning and management The 11.3.1 tier II indicator was a very straightforward example on this type of data combination Deriving new metrics integrating land cover and population data 11.3.1 Ratio of land consumption rate to population growth rate
- 10. The SDG monitoring framework includes a number of more challenging indicators due to the lack of data availability and existing established methodology The 11.7.1 former tier III (now tier II) indicator showed that land use and cadastral data, obtained using different geospatial based products, can play a significant contribution for its operationalization Addressing challenging indicators based on land use and cadastral data 11.7.1 Average share of built-up area of cities that is open space for public use
- 12. Earth observation data can provide an objective and consistent view of the earth for different periods in time, at different scales and ensuring a coherent basis for comparability between different countries The 15.1.1 tier I indicator was a very good example of how earth observation data can increase the scope of territorial disaggregation Increasing the scope of indicators disaggregation with earth observation data 15.1.1 Forest area as a proportion of total land area
- 14. 12 RECOMMENDATIONS towards a more effective geospatial data integration to address SDG statistical indicators
- 15. 1| Harmonize relevant geospatial data themes 2| Implement Cadastral and Land Cover data as key national authoritative data 3| Use geospatial layers generated from Earth Observation data 4| Create capacity building initiatives for National Statistical Institutes to take full advantage of Earth Observation based data 5| Define and implement National Spatial Data Infrastructures having in mind the requirements for statistical production 6| Implement consistent and stable sub-national spatial units 7| Develop and use population grids and other grid-based statistics 8| Adopt harmonised and comparable concepts, definitions and classifications and build consensus among Geospatial Agencies and National Statistical Institutes 9| Ensure availability and accessibility of processing workflows, including open formats of programming codes 10| Develop initiatives that promote availability, accessibility and usability of geospatial data 11| Increase the collaboration with researchers and data providers 12| Increase cooperation between National Statistical Institutes and Geospatial Agencies
- 16. Copyright ©2013 EuroGeographics Working Group on Data Integration PIER-GIORGIO ZACCHEDDU (Chair) FRANCISCO VALA (Subgroup Leader) OECD Workshop: Towards an OECD localised indicator framework for SDGs Paris, 14 May 2019 The integration of geospatial data with statistical data to grasp the territorial dimension in SDG indicators