Rural areas across Europe face multiple demographic, climate, economic, social and environmental challenges. Responding to these challenges demands a clear understanding of what constitutes rural areas, as well as informed insight into the concerns of those living in these areas. Definitions of rural areas tend to emphasise population density or size but lack perception of the dynamics, drivers and fluidity of contemporary diverse rural-urban relations and identities. Despite the increasing acknowledgement that rural areas are diverse and that typologies should better reflect the identities of such territories, this lack of data at a fine scale prevents innovations.

GRANULAR will generate new insights for characterising rural diversity based on a multi-actor and interdisciplinary approach. It will generate novel datasets using a wide range of methods and primary data, such as remote sensing, crowd-sourced data, mobile phone data and web-scraping. These data will be combined with existing institutional data to derive indicators relevant to rural communities – resilience, well-being, quality of life and attractiveness.

This will enable GRANULAR to create a Rural Compass that will integrate the factors affecting rural communities and can be used to inform policymakers and rural actors when considering new rural policies. Datasets, data visualization and other tools will be made directly available on a dedicated platform designed by and for rural actors.

WorldPop joins 22 other European university-based research programmes and institutions in this project, which is being led by the International Center for Advanced Mediterranean Agronomic Studies (CIHEAM). Senior Enterprise Fellow, Dr Jessica Steele is leading our contributions to the project which include:

• Supporting the projects’ open science strategy, intellectual property, ethics and data management plans.
• Supporting research activities that foster the participation of all relevant actors at multiple levels.
• Exploring conventional and unconventional rural data sources and methods (e.g., national surveys, censuses, microdata, earth observation, online text, IoT, crowdsourcing, hybrid and mobility data).
• Adopting a range of methods including statistical methods, demographic analysis, Artificial Reality, Artificial Intelligence, Machine Learning (ML), Deep Learning (DL), sentiment analysis and text mining.
• Designing, validating and implementing new methods.
• Making newly datasets created in GRANULAR available through the Zenodo repository.