Periodic Reporting for period 1 - R-Map (Mapping, understanding, assessing and predicting the effects of remote working arrangements in urban and rural areas)
Reporting period: 2024-02-01 to 2025-04-30
R-Map aims to analyze the impact of remote working arrangements (RWAs) on the disparities between urban & rural regions in Europe. To achieve its goals, R-Map will produce an Integrated Impact Assessment Framework (powered by the R-Map model) that will allow the assessment of individual, social, economic, environmental & spatial impacts of RWAs. A visualization platform will be developed leveraging the R-Map model to provide visualization and interactive services allowing decision-makers to monitor & assess how remote work arrangements affect people, communities, space, economy, & environment in urban and rural regions. The developed tools will be applied at local level to monitor and assess current remote working trends across six representative use-cases in the EU and the AC, in regions within Greece, United Kingdom, Italy, Turkey, Netherlands-Germany, Austria-Switzerland (two cross-border cases). Using scenario building & forecasting methodologies, R-Map will move on to explore the potential future impact of remote work in these regions for the next 5-10 years & formulate policy recommendations on how to create environments conducive to remote work, that are tailored to the needs of local governments in both urban & rural settings. Cross-regional exchanges between stakeholders & policy roundtables will help move beyond the regional focus ensuring the replication of results across areas with different characteristics in Europe & beyond. By taking a comprehensive approach to research on RWAs, R-Map addresses the role & the needs of policymakers, employers, workers, researchers, & civil society, providing capacity to urban & rural regions to seize the opportunities & cope with the challenges brought by remote work.
Exploring the current landscape of Remote Working Arrangements (RWAs): A systematic literature review drawing on two academic databases (Scopus & Web of Science), review of relevant gray literature and available data sources (e.g. broadband quality and availability data, quality of life indicators, etc.), 15 interviews with stakeholders (employers; policy makers; employee representatives) & a survey targeting employees engaged in remote work or relevant work arrangements.
Investigation of the spatial implications of RWAs in Europe and beyond: The study adopted a mixed methods approach, combining a literature review with empirical data gathered through case studies and interviews. It included a systematic literature review for peer-reviewed material, 21 interviews with key local actors (urban planners, regional authorities, real estate professionals, coworking space founders) across eight locations (use cases) in Europe & the US & a cross-case comparative analysis with a synthetic matrix to identify variations in the cause-effect patterns of remote working across Europe & beyond.
Assessing the socio-economic implications of RWAs: An extensive literature review was conducted to identify the key socio-economic themes associated with the effects of RWAs. Eleven themes were identified and categorised as social, economic or socio-economic. Eight case studies focusing on each of the eleven themes were conducted. The case studies were informed by 31 semi-structured interviews with key stakeholders from America, Asia, Australia and Europe.
Conducting a large-scale survey: 20,013 Europeans shared their perceptions of remote work, including its challenges, benefits and needs. Data collection was conducted via Prolific from July to September 2024. All participants answered the same set of survey questions, which were translated into Greek, Dutch, Portuguese, German, and Turkish.
Co-designing and implementing the R-Map model.: An in-person co-design workshop was conducted in Twente. Domain and regional experts, as well as Advisory Board (AB) members, were involved. Through extensive discussions, partners, experts and AB members collaborated to lay the foundations for co-creating the conceptual model, which serves as a framework for assessing the spatial, economic & social impacts of remote work. 3 other technical workshops and 1 validation workshop were conducted online. The latter involved AB members and members of sister projects, who provided valuable input to refine the conceptual model. Finally, the model has been implemented as an operational Bayesian Network in Python programming language. It is an open-source platform that can be deployed as a standalone application to model and understand impacts of remote working.
Developing the R-Map platform: Extensive desk research was conducted to identify & define the specifications of recognised and widely accepted visualisation tools. In addition, an online technical workshop was organised with partners to discuss the platform’s user requirements, and an online validation workshop was held with AB members to review the suggested user interface/user experience of the platform. The architecture of the R-Map platform has been finalised through this process. The first version of the platform is set to launch by M20.
Investigation of the spatial implications of RWAs in Europe and beyond: The study adopted a mixed methods approach, combining a literature review with empirical data gathered through case studies and interviews. It included a systematic literature review for peer-reviewed material, 21 interviews with key local actors (urban planners, regional authorities, real estate professionals, coworking space founders) across eight locations (use cases) in Europe & the US & a cross-case comparative analysis with a synthetic matrix to identify variations in the cause-effect patterns of remote working across Europe & beyond.
Assessing the socio-economic implications of RWAs: An extensive literature review was conducted to identify the key socio-economic themes associated with the effects of RWAs. Eleven themes were identified and categorised as social, economic or socio-economic. Eight case studies focusing on each of the eleven themes were conducted. The case studies were informed by 31 semi-structured interviews with key stakeholders from America, Asia, Australia and Europe.
Conducting a large-scale survey: 20,013 Europeans shared their perceptions of remote work, including its challenges, benefits and needs. Data collection was conducted via Prolific from July to September 2024. All participants answered the same set of survey questions, which were translated into Greek, Dutch, Portuguese, German, and Turkish.
Co-designing and implementing the R-Map model.: An in-person co-design workshop was conducted in Twente. Domain and regional experts, as well as Advisory Board (AB) members, were involved. Through extensive discussions, partners, experts and AB members collaborated to lay the foundations for co-creating the conceptual model, which serves as a framework for assessing the spatial, economic & social impacts of remote work. 3 other technical workshops and 1 validation workshop were conducted online. The latter involved AB members and members of sister projects, who provided valuable input to refine the conceptual model. Finally, the model has been implemented as an operational Bayesian Network in Python programming language. It is an open-source platform that can be deployed as a standalone application to model and understand impacts of remote working.
Developing the R-Map platform: Extensive desk research was conducted to identify & define the specifications of recognised and widely accepted visualisation tools. In addition, an online technical workshop was organised with partners to discuss the platform’s user requirements, and an online validation workshop was held with AB members to review the suggested user interface/user experience of the platform. The architecture of the R-Map platform has been finalised through this process. The first version of the platform is set to launch by M20.
R-Map model serves as a framework for assessing the spatial, economic and social impacts of remote work. It has been implemented in Python as an open source Bayesian Network model, where factors driving and being influenced by the remote working arrangements (RWAs) are included as the nodes of the Bayesian Network, and the links among the factors are the edges of the network. Future works from the successive tasks and workpackages can feed input data into the operational model to derived modelled outcomes of RWAs. User interface are also to be built on top of this operational model, so that it can be used by participants of decision-making process, educators, and also students who would like to explore and study the impacts of RWAs. The target users and markets include urban and rural researchers, governmental research departments, educational institutes, consultants, planners, and policy makers.