A Knowledge Graph-Driven Analysis of the Interlinkages among the Sustainable Development Goal Indicators in Different Spatial Resolutions

Abstract

The way towards sustainable development is paved through the commitment to the 17 Sustainable Development Goals (SDGs), which encompass a wide range of global challenges. The successful progress of these goals depends on the identification and understanding of their interconnected nature. A plethora of data is made available for tracking targets related to the SDGs at country, regional and urban levels. However, various challenges are identified to semantically align and homogeneously represent such data to improve their interoperability, comparability and analysis. In the current work, we provide an innovative solution for analyzing SDG-oriented data based on the development of a Knowledge Graph that provides access to semantically aligned data for the SDGs. We consider Knowledge Graphs as a suitable technology for the representation of data related to the interlinkages among SDGs, since they provide a structured representation of knowledge that incorporates entities, relationships and attributes, organized in a graph format. We examine the interlinkages among indicators of the same SDG, as well as across indicators of the various SDGs. Such interlinkages are further evaluated as synergies or trade-offs. Our analysis is applied in country and regional levels, considering various constraints in terms of data quality and availability. In total 476 synergies are identified at the national level among the SDGs, compared to 140 trade-offs. The SDGs that mostly participate in the synergies are SDGs 17, 10, 9 and 8, while SDGs 7 and 16 participate in most of the trade-offs. At the regional level, SDGs 8, 4 and 9 are more active in terms of interlinkages.