Towards a knowledge base to support global change policy goals

In 2015,it was adopted the 2030 Agenda for Sustainable Development to end poverty,protect the planet and ensure that all people enjoy peace and prosperity.The year after,17 Sustainable Development Goals(SDGs)officially came into force.In 2015,GEO(Group on Earth Observation)declared to support the implementation of SDGs.The GEO Global Earth Observation System of Systems(GEOSS)required a change of paradigm,moving from a data-centric approach to a more knowledge-driven one.To this end,the GEO System-of-Systems(SoS)framework may refer to the well-known Data-Information-Knowledge-Wisdom(DIKW)paradigm.In the context of an Earth Observation(EO)SoS,a set of main elements are recognized as connecting links for generating knowledge from EO and non-EO data–e.g.social and economic datasets.These elements are:Essential Variables(EVs),Indicators and Indexes,Goals and Targets.Their generation and use requires the development of a SoS KB whose management process has evolved the GEOSS Software Ecosystem into a GEOSS Social Ecosystem.This includes:collect,formalize,publish,access,use,and update knowledge.ConnectinGEO project analysed the knowledge necessary to recognize,formalize,access,and use EVs.The analysis recognized GEOSS gaps providing recommendations on supporting global decision-making within and across different domains.

New data sources for social indicators: the case study of contacting politicians by Twitter

Data availability is a persistent constraint in social policy analysis.Web 2.0 technologies could provide valuable new data sources,but first,their potentials and limitations need to be investigated.This paper reports on a method using Twitter data for deriving indications of active citizenship,taken as an example of social indicators.Active citizenship is a dimension of social capital,empowering communities and reducing possibilities of social exclusion.However,classical measurements of active citizenship are generally costly and time-consuming.This paper looks at one of such classic indicators,namely,responses to the survey question‘contacts to politicians’.It compares official survey results in Spain with findings from an analysis of Twitter data.Each method presents its own strengths and weakness,thus best results may be achieved by the combination of both.Official surveys have the clear advantage of being statistically robust and representative of a total population.Instead,Twitter data offer more timely and less costly information,with higher spatial and temporal resolution.This paper presents our full methodological workflow for analysing and comparing these two data sources.The research results advance the debate on how social media data could be mined for policy analysis.