The service for delivering data from official statistics as support to socio-ecological research

Abstract

Statistical analysis plays a key role in the field of economics, enabling scientists to extract meaningful information from large and complex data sets for understanding the quantitative aspects of social and economic phenomena. However, similarly important role they could play also in socio-ecological research for better understanding the socio-economic context influencing LTER sites and processes taking place on LTSER platforms. For site- or platform managers could be important to receive answers to questions related to socioeconomic conditions, type and intensity of human activities influencing their site/platform. It could be important to know how heterogeneous is the LTSER platform from socio-economic aspects, where hotspots of human activities are located or how are developing demographic parameters across the platform. Statistical data could contribute also to cross-site comparisons or upscaling and eLTER network-level analyses. The eLTER Whole System Approach for In-situ Research on Life Supporting Systems (WAILS) recognises sociosphere as one of five spheres that are subject of LTER research. The eLTER Standard Observation process is crucial for harmonising and streamlining research effort and it led to identification of 13 Standard Observations in the Sociosphere domain that are obligatory for eLTER sites and platforms. It was also decided that the Sociosphere Standard Observations on the level of Basic method will be provided by the central service.The eLTER PLUS task 4.2 “Harvesting of official statistics” represents the first step to such service. Its aim was to “develop and test a workflow to retrieve variables available of relevance for the eLTER Standard Observations from European and national statistics which are relevant at LTSER platforms and associated eLTER sites.” We systematically screened almost 30 main European and global data platforms for availability of statistical data related to 179 variables suggested in process of Standard Variables selection. Not surprisingly, Eurostat is the most important source of statistical data for EU Member States and for some other European countries. Other important data portals are operated by the JRC, EEA, DG Agri, and at the global level by the OECD and FAO. We identified 116 datasets that are related to 42 standard observation variables and prepared metadata for them. We found that most data are related to larger administrative units, usually to NUTS2 or NUTS3 regions. Only limited set of variables (mostly demographic ones) are available at local level (LAU – Local Administrative Units). We next developed and tested the workflow of data retrieval with a subset of the variables: population density, employment by sectors, deaths due to COVID-19, and land cover, used DataLabs (Halada et al. 2022).The eLTER PLUS Task 4.2 has prepared a good foundation for developing an eLTER service for providing data from official statistics. In meantime, a set of eLTER Measurement protocols was developed and we decided to develop first part of service for standard variables related to three protocols: SOSOC_042 Economic; SOSOC_043 Demography; and SOSOC_044 Status of Employment.To enhance access and usability of these datasets and variables, we developed an R Shiny application for downloading, processing, and analysing socio-economic datasets from main European and global data platforms via APIs (Fig. 1). The app enables users to combine specific variables from these datasets into a single dataset based on the user-specified administrative unit level (from countries to LAU). Users can filter data by time and dataset-specific variables, allowing for tailored data retrieval. Additionally, users can select areas of interest based on official LTER sites and platforms, administrative units, or custom polygons layer. The application features interactive maps and plots to visualize results and supports multiple download options. Users can export data in long or wide formats, with or without labels, and in various file types, including CSV, GeoPackage, PNG, and HTML.We will continue in the service development by adding further datasets relevant to eLTER Standard Observations. We expect that the service described in this paper will provide data needed for managers of the LTER sites and LTSER platforms to comply with criteria for sites and platforms and for their research. When using them, the researcher plays a significant role in defining questions, selecting relevant data, using them, subsequent processing, and correct interpretation