DECEMBER 2023
Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of theEuropean Union or the European Research Executive Agency (REA). Neither the European Union nor the granting authority can be held responsible forthem. UK participants in the GRANULAR project are supported by UK Research and Innovation (grant number 10039965 - James Hutton Institute; andUniversity of Southampton).
BENCHMARK OFPERFORMANCE &COSTS ENABLINGIDENTIFICATION OFVIABLE OPTIONSGOING FORWARD
D 3.2
GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
2GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
D3.2 Benchmarking Data Performanceand Costs
Project name GRANULAR: Giving Rural Actors Novel data and re-Useable tools toLead public Action in Rural areas
Project No Horizon Europe Grant Number (101061068); UKRI Grant Numbers James HuttonInstitute (10039965) and University of Southampton (10041831).
Type of fundingscheme Horizon Europe Research and Innovation Action (RIA)- UK Research & InnovationGrant
Call ID & topic HORIZON-CL6-2021-COMMUNITIES-01-01
Website www.ruralgranular.eu
Document type Deliverable
Status [x] Submitted to the European Commission
[ ] Approved by the European Commission
Dissemination level Public
Authors Kull M., Weckroth M., Vihinen H., Hiltunen A., Niskanen L. (Luke);Ysebaert R., Guérois M. (CNRS); Balagué J. (Départ. des Pyrénées-Orientales); Hopkins J., Miller D. (Hutton); Stjernberg M., Tapia C.(Nordregio); Georgieva I., McCallum I., Hofer M. (IIASA); Kurdys-Kujawska A. (TU Koszalin), Chasset L., Depontailler L. (Pays PyrénéesMéditerranée), Voepel H. (UoS), Martins B., Doval Ruiz M. (UVigo), JainP., Berchoux T. (IAMM)
Work PackageLeader International Institute for Applied Systems Analysis (IIASA)
Project coordinator Mediterranean Agronomic Institute of Montpellier (IAMM)
This license allows users to distribute, remix, adapt, and build upon the material in any medium or format fornoncommercial purposes only, and only so long as attribution is given to the creator.
3GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
Table of contents
1. Contents
D3.2 Benchmarking Data Performance and Costs...........................................................................................2
Table of contents.................................................................................................................................................3
2. Executive summary .................................................................................................................................... 5
3. Introduction.................................................................................................................................................6
3.1. Introduction to Task 3.2. and purpose of this deliverable.........................................................................6
3.2. Data cost dimensions and considerations................................................................................................6
3.3. Structure of the Document.......................................................................................................................8
4. Methodology and approach.......................................................................................................................8
4.1. Survey: national and regional statistical offices, authorities and data providers in the EU and beyond..8
4.2. Inspirational examples from Granular partner countries – Data, tools and approaches..........................9
4.3. Data Fiches: From initial datasets to data fiches .....................................................................................9
5. The costs of and for rural data – a journey across regional and national statistical offices andauthorities in the EU and beyond.................................................................................................................... 12
5.1. Main databases openly available, containing data about rural development issues..............................13
5.1.1. Bulgaria..........................................................................................................................13
5.1.2. Croatia ........................................................................................................................... 13
5.1.3. Cyprus............................................................................................................................13
5.1.4. Finland...........................................................................................................................13
5.1.5. France............................................................................................................................14
5.1.6. Greece...........................................................................................................................14
5.1.7. Hungary ......................................................................................................................... 14
5.1.8. Ireland............................................................................................................................15
5.1.9. Italy ................................................................................................................................ 15
5.1.10. Moldova ......................................................................................................................... 15
5.1.11. Poland............................................................................................................................15
5.1.12. Portugal..........................................................................................................................16
5.1.13. Scotland.........................................................................................................................16
5.1.14. Serbia.............................................................................................................................16
5.1.15. Slovakia ......................................................................................................................... 16
5.1.16. Slovenia.........................................................................................................................17
5.1.17. Spain / Galicia................................................................................................................17
5.1.18. Sweden..........................................................................................................................17
5.2. Data types and domains available at no costs.......................................................................................18
4GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
5.3. Customers, costs and revenues.............................................................................................................20
6. Inspirational examples – Data, tools and approaches from Granular partner countries...................23
6.1. Finland: Indicator to follow Subjective Wellbeing (SWB) development of the rural population during the CAPprogramming period – by Mikko Weckroth (Luke) ......................................................................................... 23
6.2. Finland: The Rural Barometer – by Hilkka Vihinen & Michael Kull (LUKE)............................................24
6.3. France: Monitoring mobility and road traffic at local scale – Case Conseil Départemental des Pyrénées-Orientales (CD66) – by Louise Chasset, Lenaïc Depontailler (Pays Pyrénées Méditerranée) & Jean-ClaudeBalagué (Département des Pyrénées-Orientales).........................................................................................26
6.4. Galicia – Spain: Telecare for the elderly at home in the rural areas of Ourense – by María Isabel Doval Ruiz& Breixo Martins (University of Vigo) ............................................................................................................. 28
6.5. Poland: Functional and spatial diagnosis for social revitalization at the local (municipal) level – by AgnieszkaKurdys-Kujawska (TU Koszalin).....................................................................................................................29
6.6. Scotland: Scottish National Islands Plan Survey (2020): results explorer – by Jonathan Hopkins and DavidMiller (Hutton).................................................................................................................................................31
6.7. Nordic Countries: The Nordic Service Mapper - by Mats Stjernberg (Nordregio)..................................33
6.8. EU: Integrating text-to-image and image-to-text techniques to enhance accessibility and understanding ofrural land-use data - Cross Modality Framework – by Pallavi Jain (IAMM) ................................................... 35
6.9. Global: Geo-Wiki Earth Observation & Citizen Science – by Ivelina Georgieva (IIASA) .......................36
6.10.Key lessons learned, cost considerations, policy implications and ways forward..................................38
7. Discussion, conclusions and going forward..........................................................................................42
8. Conclusion.................................................................................................................................................44
9. Acknowledgements .................................................................................................................................. 45
10. References.................................................................................................................................................45
11. Appendix 1 – Overview of national databases openly available, containing data about ruraldevelopment issues..........................................................................................................................................48
12. Appendix 2 - Data Fiches ......................................................................................................................... 53
12.1.Accessibility............................................................................................................................................53
12.2.Agriculture..............................................................................................................................................54
12.3.Climate...................................................................................................................................................57
12.4.Demography...........................................................................................................................................59
12.5.Digitalisation...........................................................................................................................................66
12.6.Economic Development.........................................................................................................................67
12.7.Energy....................................................................................................................................................70
12.8.Health.....................................................................................................................................................71
12.9.Infrastructure..........................................................................................................................................72
12.10. Mobility...........................................................................................................................................75
12.11. Recreation......................................................................................................................................76
12.12. Transversal ....................................................................................................................................77
5GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
2. Executive summary
Through collaboration with and input from regional, national, and international data providers, Living Labs,and statistical offices, we evaluated the performance and costs associated with acquiring data relevant forrural territories at various geographical scales. The resulting document presents a compilation of 27 datafiches, a comprehensive exploration of data costs and dimensions through a survey, and nine inspirationalexamples of data, tools, and approaches. The document seeks to inform GRANULAR's Living and ReplicationLabs, as well as data users and providers from various levels, about practical insights into data collection types andcosts for rural territories, their challenges, and opportunities. The outlined cost categories, spanning datainfrastructure, governance, model training, security, and more, provide a robust framework for understandingand managing the complexities associated with enriching knowledge and advancing data-driven initiativesfor policy making in rural contexts.
The Survey engaged national statistical offices and data providers, yielding 17 responses from 16 differentcountries, and focused on data availability, costs, and user information. The survey delves into the landscape ofdata accessibility applied to rural development across several European countries. The report highlights key opendatabases, data domains, and types of data available at no cost, revealing variations in resolution and thematiccoverage. The majority of surveyed offices make over 500 data files freely accessible, with primary domainsincluding demography, economy, agriculture, and tourism. While most respondents adhere to Eurostat guidelines,data types vary, encompassing tabular, grid-level, vector, and raster data. Notably, the report touches upon thediverse bases for user charges, with some offices levying fees for data compilation or structuring. The analysisunderscores the crucial role of individuals, research organizations, and the private sector as the main users of thesedatasets, emphasizing the multifaceted demand for demographic, economic, and development-focused information.
Data fiches were compiled for 27 datasets that capture a wide range of rural data types, and include a descriptionof the data, including indicator class, data class, spatial and temporal information, a short description and how tocite it. Cost information include data infrastructure costs, e.g. software or hardware needed or costs for datarepositories / storage. Data Governance & Management costs, i.e. costs needed to work with the data e.g. comprisesinformation about staff costs needed to work with / access the data, for data analysis, quality assurance and“cleaning” data. Authors also reflect on data documentation costs. Data covered the 4 rural functions identified inthe Rural Compass (productive, residential, environmental, recreational), with a variety of domains, such asaccessibility, agriculture, climate, demography, digitalization, economic development, energy, health, infrastructure,mobility, recreation, and transversal.
The inspirational examples showcase data collection and provision methodologies that have been implementedat local and international scales. Each example was documented with a structured overview including costs, photos,and a matrix of key insights, policy implications, and future considerations. The aim is to inform actors on the diversityof methods and data that can be collected and the costs that are associated with such initiatives. Examples include:
 Indicator to monitor the subjective well-being of the rural population during the CAP programming period inFinland (National) Rural Barometer Finland (National) Monitoring mobility and road traffic at local scale in France (Local) Telecare for the elderly at home – Galicia / Spain (Local) Functional & spatial diagnosis for social revitalization – Poland (National) Scottish National Islands Plan Survey (Regional) Web-mapping tool to visualize proximity to different services - Nordic countries (International) Enhancing accessibility & understanding of rural land use data – EU (International) Earth Observation & Citizen Science – Geo-Wiki (Locally informed, international coverage)
The holistic approach chosen in this document with three focus areas enables a comprehensive understandingof rural data, costs, and innovative examples for evidence-based policy-making.
Effectively utilizing open data in rural territories within the EU demands strategic investments in capacitybuilding, with an emphasis on training local personnel in Geographic Information System (GIS) and datamanagement. The estimated annual cost of employing entry-level GIS technicians ranges from €30,000 to €40,000,reflecting the necessity to bridge the skills gap for meaningful spatial data analysis. Additionally, ensuring the
6GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
1 The Rural Compass is a tool aimed at orientating and providing policy direction, going from policy design in rural areas to policy
monitoring and evaluation. The Rural Compass is conceived as a multi-dimensional set of indicators and trends. The assessment of rural
areas against those indicators and trends can be used to map and assess rural communities and their functional characteristics. It can be
found here https://www.ruralgranular.eu/tools/.
reliability of open data for decision-making requires rigorous quality assessment processes, including validation withlocal databases and complementary data collection, with associated annual expenses ranging from €25,000 to€35,000.
3. Introduction
3.1. Introduction to Task 3.2. and purpose of this deliverable
Task 3.2. builds on D3.1_Screening_Rural_Data_Sources, where an initial screening of data availability wasconducted for the generation of new and novel datasets to support indicators of rural sustainability for Europe. Morethan 90 existing datasets relevant for rural territories, along with accompanying meta-data have been recorded. Thescreened datasets address the majority of GRANULAR’s Rural Compass1 indicators, with the majority of datasetsrepresenting demography, infrastructure and environment.
T3.2. has prioritised data sources and methods identified in T3.1, considering the performance and costs of acquiringdata at an appropriate geographical scale. Options identified in T3.1 were considered to make them relevant forusers both in the context of the Living Labs and Replication Labs (https://www.ruralgranular.eu/living-labs/).
This document showcases the performance and costs of different data types and tools and discuss viable optionsgoing forward. Different regional, national, and international data providers were consulted, as were the Living Labsas such. Consultation and co-writing took place during various stages of producing this deliverable and alwaysincluded the identification of costs for users and those occurred during the development of a tool or compiling data.We also consulted national and regional statistical offices and authorities to get an overview of available data atdifferent levels of granularity, the costs of and for data and data users. Finally, project partners, including Living Labswere invited to share and write about inspirational examples from their region or country, including on how they haveused and produced data and indicators or tools to work with data.
This document was written to inform and inspire both GRANULAR Living and Replication Labs as well as data usersand providers in rural areas and beyond. It thus responds to one of key aims of GRANULAR, i.e. to enrich knowledgefor rural actors on the diversity of rural areas, their functional characteristics, challenges and opportunities.Consequently, this document contains:
 A journey across national and regional statistical offices and authorities in the EU and beyond to betterunderstand costs of and for rural data,
 9 inspirational examples of data, tools, and approaches and
 Data fiches summarizing key information for 27 different datasets (in the appendix).
These elements all include different cost dimensions as far as they were available.
3.2.Data cost dimensions and considerations
Most of the datasets identified in D3.1_Screening_Rural_Data_Sources contain a free and open license or allowpartial access, often complying with the FAIR Principles and the INSPIRE Directive. Just a few datasets requirepurchase. Likewise, statistical offices at different levels of governance and throughout the EU provide numerousdata, datasets and maps being free of charge for the users (chapter 5). Whilst the inspirational examples in chapter
7GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
6 are also free of charge for different user groups, there are different development costs to consider, particularly ifthey are to be replicated elsewhere.
Overall, there are different cost and performance dimensions that need to be thought about when working with orprocessing data. Whilst we try to specify the particular costs for each dataset and inspirational example as best aspossible and as far as applicable, there are a number of general data costs categories defined in the literature (e.g.Becker 2017, Colas et al 2014, Martinez et al. 2021, Saltz et al. 2017b, Sivarajah et al. 2016), which will also beconsidered where applicable as far as information is available. These include (Figure 1):
 Data infrastructure: including software to work with the data, hardware to process data, data storage, or datarepositories. This also includes processes for storing, retrieving, and sharing data. This also includes “the big
data perspective” with data volume increasing and velocity intensifying computation requirements and
dependence on IT resources (Martinez et al. 2021, Saltz et al. 2017b, Sivarajah et al. 2016). Finally,
 Data governance & management: the needs and capabilities of people to conduct data analysis, trainingneeds of staff, or costs for staff effort for quality assurance. An intermediary (e.g. Martinez et al. 2021) that
understands both the language of data analytics and the domain of application, creating an understanding
between data scientists and stakeholders, customers, businesses etc. This dimension also includes model
training and retraining costs, e.g. for machine learning models, which may be resource intensive and costly.
(Martinez et al. 2021).
 Data quality and scale: there might be a need to clean “dirty” data and reflection on the potential to besuitable. Coordinated data cleaning and quality assurance are needed to assure a robust validation (Martinez et
al. 2021). Data scale implies reflecting complexity, adjust necessary architecture and infrastructure and the
corresponding costs (Becker 2017, Colas et al 2014 & Martinez et al. 2021).
 Data security & privacy including compliance with data protection regulations, encryption methods,anonymization, user education and training (Martinez et al. 2021, Saltz & Shamshurin 2016, Colas et al 2014,
Sivarajah et al. 2016). In this connection, there should be awareness of dependency on legacy systems and
data integration (Colas et al 2014, Becker 2017).
 Creating institutional memory & retaining institutional knowledge: personnel costs associated withknowledge documentation and sharing etc. (Byrne 2017, Martinez et al. 2021, Colas et al 2014, Becker 2017).
Martinez et al. 2021 conclude that in addition to an understanding to what data might be available (see also Saltz etal. 2017a), its representativeness for the problem at hand (Saltz & Shamshurin 2016) and its limitations (e.g. Byrne2017) is critical for the success of data projects.
Figure 1. Data costs categories
8GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
2 All questions are available from the lead author.3 One of the cases was a regional authority from Spain, which was contacted due to the relevance and connection to the Living LabOurense. The other respondent was from Italy.
3.3.Structure of the Document
This document is organised as follows. After this introduction, section 4 explains the methodology used to gatherdifferent insights and lessons learned for this report – for the data fiches, the survey and the inspirational examples.Chapter 5 is a journey throughout Europe and its national and regional statistical offices, all providing insights onaccessibility and availability of data, different costs dimensions and additional costs users must be aware of. Chapter6 provides inspirational examples of data, tools and applications from around the EU. Chapter 7 is a discussion andconclusion of our findings and discusses possible ways forward. The data fiches – 27 different datasets of relevancefor rural areas and actors – are to be found in the appendix.
4. Methodology and approach
Our three inspirational examples were all built on their own particular methodology and approach. Hence, we outlinemethodologies separately for each, starting with the survey, shortly describing how the inspirational examples wereselected, composed and structured and finally explain how and why the data fiches have been chosen andcomposed.
4.1.Survey: national and regional statistical offices, authorities and data providersin the EU and beyond
GRANULAR’s aim is to identify, develop and provide novel data and reusable tools to understand the characteristics,dynamics and drivers of rural areas and hence support place and evidence-based policy making. In connection tothis, we want to better understand and map the performance and costs of acquiring data at different geographicalscales, including from the national level and the statistical offices and authorities from around the EU and beyond.We thus invited national statistical offices and authorities across the EU and beyond, including all countries wherethe GRANULAR Living and Replication Labs are located, to take part in a survey. We inquired about data availability,pricing policies and costs of data as well about customers and user groups and their demands. The survey contains3 sections: 1) data availability, 2) data costs and 3) users. More than 20 questions are both open-ended, selectionand multi-selection, ranking and open-ended questions2. We received 17 answers from both EU and non-EUcountries. Most respondents were national statistical authorities. In two cases respondents answered on behalf ofother authorities3 (Figure 2).
Figure 2. Location of responding organizations.
9GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
4 Data collected by GRANULAR will be made available in the project’s Repository, available athttps://platform.ruralgranular.eu/collection/All/1.
In some cases, respondents sent their replies and additional answers directly to us (e.g. Scotland, Sweden,Moldova). In some cases, such as Spain, also regional offices were included, since they are working closely withLiving Lab actors.
We used the Webropol platform to collect the answers. The survey was open between September and November2023. Survey results are presented and discussed in chapter 5. We also include links to the main databases relevantfor rural development issues, and present the data domains contained in open databases and the finest resolutionof data free of charge in each of the country that participated in the survey.
4.2. Inspirational examples from Granular partner countries – Data, tools andapproaches
To better understand and map the performance and costs of acquiring data at different geographical scales, weinvited project partners including Living Labs, whether they have good examples of data collection or tools that arealready being implemented in their territory and invited them to contribute to a collection of inspirational examplesfrom different scales. This means, examples ranging from an indicator to an international or transboundary mappingtool and from the local level to national level examples and beyond. The idea and motivation were that the exampleswill not only be included in this deliverable, but also published on the GRANULAR website.
All examples follow a similar structure and are composed of a description of the example and costs for developingit. Authors explain the motivation and objective, their experience thus far, future considerations, and who hasdeveloped it.
The cost dimensions covered – and as far as the authors had them available – include:
Budget of the whole initiative, such as:
 Data Governance & Management costs
 Staff costs or PM
 Costs for Data analysis
 Quality assurance, “cleaning data”
Data Infrastructure costs, such as:
 infrastructure needed (software, hardware)
 Data repositories / storage needs
Costs for data processing & visualization
Nice photos from the area and the tool, including maps, add some additional flavor. A matrix will summarize keylearnings, cost dimensions, policy implications and future considerations from the inspirational examples.
4.3.Data Fiches: From initial datasets to data fiches
D3.1_Screening_Rural_Data_Sources developed a Rural Data Table with more than 90 datasets was developed.4Those datasets were initially ranked according to their degree of relevance for GRANULAR, in terms of supportingGRANULAR’s Rural Compass and on a scale from 4 (very relevant) to 1 (lower relevance). For the purpose of thisdocument, those data ranked 4 (very relevant) and 3 (relevant) were selected. The motivation is twofold. First, wewish to provide LL and RL and other rural stakeholders with cost and accessibility information about these relevantdatasets for their work. Second, we want to summarise the current knowledge about these datasets to support thecontinuous work of GRANULAR, its Repository and the Rural Compass.
Datasets that had certain shortcomings, for instance regarding temporality, old data, or quality availability ofmetadata, were replaced. The selected datasets for the data fiches are summarized in table 1.
10GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
5 Relates to the free and open access and availability of the derived datasets, as well as raw datasets (for the analysis) and the model orcode.
Table 1: Datasets selected for analysis by Indicator Class & Rural Compass.
Title Description Author /Producer Free & openAccess5 Rural Compass
Accessibility
Open-sourcerouting machine Accessibility to services (by car and bybike) OSRM Yes Residential
Agriculture
Carbon budgetin the EUagricultural soils
C budget in the EU agricultural soilsincluding lateral C fluxes JRC Yes Productive
Cover Cropsacross Europe Disaggregated map of cover cropsoccurrence for Europe and the UK JRC Yes Productive
EuroCrops -Land ParcelIdentificationSystems
Combines all publicly available self-declared crop reporting datasets EU Yes Productive
Climate
Temperature,Precipitation Climate model data ECMWF Yes Environmental
WorldClim Satellite derived climate data(temperature, rainfall) Worldclim Yes Environmental
Demography
GHSL-POP Distribution of population, expressed asthe number of people per cell JRC Yes Residential
WorldPop High resolution world population weighted-density UoS Yes Residential
WorldPop High resolution world population density UoS Yes Residential
WorldPop High resolution world population (age andsex structures by 5-year classes) UoS Yes Residential
WorldPop High resolution world population(Population Counts) UoS Yes Residential
Data4good Population Meta Partial Residential
11GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
TotalPopulation Population ARDECO Partial Residential
Digitalisation
Ruralhouseholdinternet accessin 2021
Share of rural household with internetaccess in the selected European countries2021
Statista n.a. n.a.
Economic Development
Accommodation Overnight stays Trip Advisor No Productive
Accommodation Overnight stays AirBnB No Productive
EU SILC Longitudinal Household Survey EuroStat Partial Productive
Energy
Suitability mapfor solar energy(PV)s
Suitability for installation of large-scale PVsystems in Europe JRC Yes Productive
Health
Healthcareserviceslocations &number of bedsin Europe
Dataset making centrally, geo-localizedhealthcare information available EuroStat Yes Residential
Infrastructure
OpenStreetMap Specific point locations for potentialdestinations for accessibility indicatorscalculation. Topographic mapping offeatures across the globe; good coverageof Europe; high quality of mapping.
OSM Yes Residential
Mapillary Access street-level imagery & map datafrom all over the world Mapillary Partial Residential
Tourism Tourism capacity and density based onbooking.com, TripAdvisor and Eurostatdata
JRC Yes Recreational
Mobility
Mobility heatmaps, raw data(jogging, biking)
The heatmap shows 'heat' made byaggregated, public activities. Strava No Residential
12GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
6 Appendix 1 contains a summary table providing a quick overview over the results.
Recreation
Potential quietrural areas These spacious areas allow for exampleextensive walks without crossing noisyareas.
Data.Europa.EU n.a. n.a.
Transversal
LocalAdministrativeUnits (LAU)
Smaller official territorial division forEurope GISCO Yes Other
GEOSTAT 1kmpopulation grid EU reference grid for statistics. Onlypopulation currently, but data diversity willcertainly grow in the future. Onlypopulated cells (not a regular grid)
GISCO Yes Other
NUTSgeometries NUTS division GISCO Yes Other
The data fiches (in the appendix) contain general information about the dataset and cost information. The first pageinforms about the data type, spatial and temporal extent, a short description of the data, how to cite it and themethodology used to produce the data. The second page is about the costs. Whilst most datasets are free and openaccess, data analysis, quality assurance, and infrastructure costs need to be considered.
Data domains covered by the fiches are accessibility, agriculture, climate, demography, digitalization, economicdevelopment, energy, health, infrastructure, mobility, recreation, and transversal.
Rural Compass categories included are environmental, productive, recreational, residential, and other.
The data fiches are in the appendix and will be made available online at https://www.ruralgranular.eu/tools/.
5. The costs of and for rural data – a journey across regional andnational statistical offices and authorities in the EU and beyond
This section presents and discusses the survey results6 by focusing on:
- Main databases openly available in the participating countries, containing data about rural development issues.
- Data types and domains available at no costs.
- Customers, costs, and revenues.
Most of the information compiled below was derived from the survey. In some cases, additional research tocomplement missing information was undertaken. In cases no answers were provided to particular questions, weleave the specific category out in the section 5.1. descriptions. This concerns, for instance, and in several cases,data types and domains.
13GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
5.1.Main databases openly available, containing data about rural developmentissues
5.1.1. Bulgaria
Main database openly available, containing data about rural development issues: The IS Infostat platform(https://infostat.nsi.bg) publishes data relevant for rural development. It includes business, demographic social,macroeconomic, environment energy and multi-domain statistics. Data on population and housing census isavailable free of charge. Paid databases provide users access to more detailed data at lower levels afterdisaggregation. Grid data for population (total, age groups and sex) for 2011, 2021 is free of charge.
Data domains contained in open databases: Demography, energy and health.
Finest resolution of data free of charge: Grid data for population: Total population, Age groups 0-14; 15-64 ;65+ andsex, for 2011 and 2021.
5.1.2. Croatia
Main database openly available, containing data about rural development issues: There are several databasesavailable but not strictly connected with rural development issues. The so-called PC-Axis databases are availableat https://web.dzs.hr/PX-Web_e.asp?url=%22Eng/Archive/stat_databases.htm%22. Some data available is atmunicipality level. Grid 1000 is accessible, free of charge, at the GeoSTAT - Web GIS portal of the Croatian Bureauof Statistics (https://geostat.dzs.hr). The available grid-level data is on population – number of populations, numberof population by large age groups, population by educational attainment, population by activity, business register(active business entities). Tourism data on accommodation capacities and tourist arrivals and nights. The PC-Axisdatabases https://web.dzs.hr) has some data by municipalities. Some census data by settlements is available athttps://podaci.dzs.hr/en/statistics-in-line/. All data published online is free of charge. If special data processing isneeded for grid-level data, it is charged according to the subject’s hourly rate.
Data domains contained in open databases: agriculture, demography, economy, energy, environment, tourism /recreation, transport.
Finest resolution of data free of charge: 1km grid. Available data free of charge at the GeoSTAT - Web GIS portalfor the Croatian Bureau of Statistics https://geostat.dzs.hr/?lang=en.
5.1.3. Cyprus
Main database openly available, containing data about rural development issues: The main database by StatisticsCyprus is CYSTAT-DB, available at https://cystatdb.cystat.gov.cy/pxweb/en/8.CYSTAT-DB/. It contains data onagriculture, livestock, fishing, business register, construction, education, energy, environment, external trade, health,industry, information society, innovation, labor market, living conditions, social protection, national accounts,population, price indices, public finance, research and development, services, tourism and trade.
Data domains contained in open databases: Demography, Health, Education, ICT Usage.
Finest resolution of data free of charge: 1000m for grid-level data.
5.1.4. Finland
Main database openly available, containing data about rural development issues: The main database published byStatistics Finland is called StatFin (https://www.stat.fi/tup/statfin/index_en.html). StatFin is freely accessible andincludes data on population, economy, housing, transport, tourism, consumption, prices, wages and salaries,energy, enterprises etc. The Paavo database (https://www.stat.fi/tup/paavo/index_en.html) contains data by postalcode area on the population structure, education, income, housing, workplaces, households' life stage etc. Thereis also a grid-level database with grid sizes of 250 m x 250 m, 1 km x 1 km and 5 km x 5 km(https://www.stat.fi/tup/ruututietokanta). The grids cover the whole of Finland, but this is not for free and chargedbased on number of licenses and grid size. Data is available on the areas' population structure, level of education,income of inhabitants and households, size and stage in life of households, buildings and dwellings, workplaces,and main activities of inhabitants. Population structure grid data 1km x 1km is free of charge.
14GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
Data domains contained in open databases: agriculture, demography, economy, energy, environment, housing,infrastructure, mobility, tourism / recreation, transport.
Finest resolution of data free of charge: Population structure grid data 1km x 1km.
5.1.5. France
The geoservices.ign.fr site (https://geoservices.ign.fr/) and its Géoservices catalogue(https://geoservices.ign.fr/catalogue) by the Institut National de l’Information Géographique et Forestière (IGN) areof relevance for anyone interested in geodata and web services related to rural development. Data on the site is freeof charge and available under an open license and accessible without registration. It contains, vector databases,maps, ortho-images, cadastral parcels, 3D models as well as other applications and services. Information includesthe Common Agricultural Policy, forest geographical reference system, "Land-Sea Boundary" data, a RenewableEnergy Map Portal and Good Agricultural and Environmental Condition.A second data source is by the Centre for Studies on Risks, the Environment, Mobility and Urban Planning(CEREMA). Its catalogue with over 300 datasets, maps and series is open and available athttps://catalogue.cdata.cerema.fr/geonetwork/srv/eng/catalog.search#/home. Data covers such fields like landcover, natural risks, energy use, habitats and biotopes and hydrography.The Office français de la biodiversité with its partners feeds an information system on biodiversity, with 14 datasets,services and maps, incl. characterization of Natura 2000 sites, protected natural areas, inventory of Natural Areasof Ecological, Faunal and Floristic Interest etc. (https://www.ofb.gouv.fr/)Thematic data and datasets on agriculture, culture and heritage, sustainable development and energy, economicsand statistics, eductaion and reserach, international and EU issues, health and social issues, tourism and recreation,territories and transport can be found at https://www.geoportail.gouv.fr/. This is the national portal for territorialknowledge providing open and interoperable data to facilitate the exchange and sharing of data in support of publicpolicies.
Finest resolution of data free of charge: grid data
5.1.6. Greece
Main database openly available, containing data about rural development issues: Currently, there is nodissemination database openly available to the public. However, data files available for the public are uploaded atthe website of the Hellenic Statistics Authority (ELSTAT) in the form of time series, tables and Public Use Files(PUFs). ELSTAT publishes statistical data on its website at a level of analysis, where statistical confidentiality is notviolated, and all users can access them. Most of the data on ELSTAT's website refer to NUTS 2 level. Dependingon the limitations set due to statistical confidentiality, some data refer also to NUTS 3 level and few data (mainlypopulation data) to LAU level. Statistical data that allow the indirect identification of statistical units are provided tousers under certain conditions. Tailor-made data based on user requirements are generally priced. The cost ofproviding these data depends on the number of man-days required for their compilation by ELSTAT staff. The pricingpolicy of ELSTAT is available here: https://www.statistics.gr/en/microdata_pricing
Finest resolution of data free of charge: R&D, innovation, population data at NUTS-2 level.
5.1.7. Hungary
Main database openly available, containing data about rural development issues: There is no dedicated databasefor rural development data, but the main database contains data on agriculture, environment, and many aspects ofterritorial data. The data can be downloaded in csv and xlsx format and are free of charge. The database can beaccessed here: https://statinfo.ksh.hu/Statinfo/themeSelector.jsp?&lang=en. Predefined data tables in the STADATsystem also contain data on the abovementioned topics, however, this is not a database, but ready-made tables areavailable, which can also be downloaded in csv and xlsx, also free of charge. The STADAT-system is available here:https://www.ksh.hu/stadat_eng.The third main resource where users can find territorial data is the Interactive Mapping Application, accessible here:https://map.ksh.hu/timea/?locale=en. Data can be downloaded from this interface, from the attribute table in csv.Unlike the aforementioned two sources, this application also contains grid-level data.
Data domains contained in open databases: agriculture, demography, economy, energy, environment, health,housing, infrastructure, tourism / recreation, transport.
15GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
Finest resolution of data free of charge: Grid-level data. Charging for the data is not determined by the level ofresolution or the theme but by the capacity it requires from the statisticians to prepare the data.
5.1.8. Ireland
Main database openly available, containing data about rural development issues: The Central Statistics Office(CSO) of Ireland produces a wide range of statistics. This includes business sectors including agriculture andfisheries, census data, data on the economy, environment, labour market, people and society as well as otherthemed publications. CSO's PxStat Open Data Platform is available at https://data.cso.ie/#. Data published on theplatform is also provided by other public sector databases, such as by the Department of Agriculture, Food and theMarine, the Department of Housing, Local Government and Heritage, the Sustainable Energy authority etc.
Data domains contained in open databases: see above.
Data types available: grid-level, tabular and vector data.
Finest resolution of data free of charge: Small-area data, for which are units with an average of 50-100 householdsin each. Grid-level data is free, and if it is not available, €80-200 are charged for customers from the private sector.
5.1.9. Italy
Main database openly available, containing data about rural development issues: IstatData is the latest aggregatedata dissemination platform of the Italian National Institute of Statistics (Istat) and available athttps://esploradati.istat.it/databrowser/#/en. It makes use of the open-source tools “Data Browser” and “Meta & DataManager” developed by Istat following the international SDMX (Statistical Data and Metadata eXchange) standardfor exchanging and sharing statistical data and metadata. Currently, six themes are covered: National Accounts,Population and Households, Household Economic Conditions, Agriculture, Enterprises, Welfare and Pension. TimeSeries (https://seriestoriche.istat.it/index.php?id=18&L=1) contains over 1,500 time series organized into 22thematic areas made available to inform about the environmental, social and economic changes in Italy
Data domains contained in open databases: see above.
Data types available: tabular and vector data.
Finest resolution of data free of charge: Agricultural plot level.
5.1.10. Moldova
Main database openly available, containing data about rural development issues: The statistical databank ofStatistics Moldova is available at https://statbank.statistica.md/. It contains data on environment, population anddemographic processes, social statistics, economic statistics, gender statistics, and regional statistics.
Data domains contained in open databases: Accessibility, agriculture, demography, economy, energy, environment,health, housing, infrastructure, mobility, tourism / recreation, transport.
Data types available: raster and tabular data.
Finest resolution of data free of charge: Currently the office disseminates at the level of communes (with a communebeing formed of one or several villages) – both in the Statistical databank and as static maps in its publications. In2024, the office plans to carry out a Population and Housing Census and will then have available spatial data bothat vector and grid-level. At the moment, the office does not provide spatial data against a fee.
5.1.11. Poland
Main database openly available, containing data about rural development issues, incl. data domains: TheKnowledge Database (https://dbw.stat.gov.pl/en) by Statistics Poland contains 31 domain areas includingDemography, Education, Energy, Social economy, Municipal and housing infrastructure, Agriculture, Labor Market,
16GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
7 According to Tim Berners-Lee, open data can be published at various levels of openness (see 5-star Open Data (5stardata.info).
Transport, Tourism, Living conditions, Health and healthcare. For some indicators data is available by voivodships,in the case of demographic data and of local government unit budgets also for lower levels of territorial division.The Knowledge Database is a publicly available and free of charge.Furthermore, there is also a Centre for Rural Statistics (https://olsztyn.stat.gov.pl/en/) and a Small Areas StatisticsCentre (https://poznan.stat.gov.pl/en/) in addition to regional statistics centers throughout the country.(https://stat.gov.pl/en/regional-statistics/). EUROSTAT Economic accounts for agriculture - values at current pricesand Economic accounts for agriculture - values at n-1 prices are also available.
Finest resolution of data free of charge: NUTS 2.
5.1.12. Portugal
Main database openly available, containing data about rural development issues of Statistics Portugal is availablehere https://www.ine.pt/xportal/xmain?xpid=INE&xpgid=ine_bdc_tree&contexto=bd&selTab=tab2. Themes alsoinclude Agriculture, forest and fisheries.
Data domains contained in open databases: agriculture, demography, economy, energy, environment, health,housing, mobility, tourism / recreation, transport in addition to culture, Prices, living conditions.
Data types available: grid-level and tabular data.
Finest resolution of data free of charge: For the Population and housing Census grid of 1km2 is free of charge. Forsome of the other domains, data is at parish-level.
5.1.13. Scotland
Main database openly available, containing data about rural development issues: The main database openlyavailable, containing data about rural areas are the 1) National Performance Framework(https://nationalperformance.gov.scot/measuring-progress/national-indicator-performance) with 26 out of 81indicators providing data for Rural Scotland and the 2) Rural Scotland Key Facts 2021(https://www.gov.scot/publications/rural-scotland-key-facts-2021/documents/) and a considerable number ofsources. There are several public sector open data portals, most allow for publication of data at the 3* level ofopenness (csv or equivalent).7 Many of Scottish Government statistics is available online, for free and withoutrestrictions. It contains around 300 open datasets and reference material, mainly at the 5* level – the highest levelof openness, with associated metadata.
Finest resolution of data free of charge: Grid-level data.
5.1.14. Serbia
Main database openly available, containing data about rural development issues of the Statistical Office of theRepublic of Serbia is available here: https://data.stat.gov.rs/?caller=SDDB&languageCode=en-US.
Data domains contained in open databases: accessibility, agriculture, demography, economy, energy, environment,health, housing, infrastructure, tourism / recreation, transport.Finest resolution of data free of charge: Spatial resolution depends on the coverage of statistical survey, from whichthe dataset is produced. In the annual plan of statistical surveys, the spatial resolution of available datasets whichare free of charge is defined.
5.1.15. Slovakia
Main database openly available, containing data about rural development issues of Slovak Statistics is calledDataCube and is available at https://datacube.statistics.sk/. It contains multidimensional tables for indicators ofeconomic and socio-economic development, for the following areas: demographic and social statistics,macroeconomic statistics, business statistics, sector statistics, environment, multi-domain statistics and selectedtables of the Eurostat database. 1 km x 1 km grid-level data is available from the 2011 and 2021 census athttps://slovak.statistics.sk/wps/portal/ext/themes/demography/census/indicators/.
17GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
8 The answers were provided by a regional authority related to the Living Lab Ourense.
Data domains contained in open databases: accessibility, agriculture, demography, economy, energy, environment,health, housing, infrastructure, tourism / recreation, transport.
Finest resolution of data free of charge: Municipalities - especially demographic data. All data in the database arefree of charge.
5.1.16. Slovenia
Main database openly available, containing data about rural development issues of Statistics Slovenia is availableat https://pxweb.stat.si/SiStat/en/Podrocja/Index/583/regionalni-pregled.
Data domains contained in open databases: agriculture, demography, economy, energy, environment, housing,infrastructure, mobility, tourism / recreation, transport.
Finest resolution of data free of charge: Grid-level data available at the STAGE pages at https://gis.stat.si/#lang=en.All data at Statistics Slovenia is free of charge.
5.1.17. Spain / Galicia8
Main database openly available, containing data about rural development issues of the Galician Institute of Statisticsis available at https://www.ige.gal/web/index.jsp?idioma=gl.
Data domains contained in open databases: demography, economy, tourism / recreation.
Finest resolution of data free of charge: Spatial distribution of the characteristics of the population of Galicia by gridof 1km2.
5.1.18. Sweden
Main database openly available, containing data about rural development issuesThe majority of Statistics Sweden's statistics are openly available at "Statistikdatabasen" (Statistical database)(https://www.statistikdatabasen.scb.se/pxweb/en/ssd/). In addition, other data openly available from StatisticsSweden includes geospatial data (https://www.scb.se/en/services/open-data-api/open-geodata/). Furthermore,there are 29 different public agencies, which publish official statistics, and partly cover other topics than StatisticsSweden: https://www.scb.se/en/About-us/official-statistics-of-sweden/government-agencies-responsible-for-official-statistics/.
Data domains contained in open databases: agriculture, demography, economy, energy, environment, housing,infrastructure, mobility, tourism / recreation, transport.
Data types available: grid-level, point, tabular and vector data.
Finest resolution of data free of charge: Grid-level data in Sweden is free of charge. In the database mentionedpreviously, some statistics are made available according to DeSO, "Demografiska statistikområden" (in English:"Demographic Statistical Areas"). There are also corresponding GIS layers available. For more information, pleasesee https://scb.se/hitta-statistik/regional-statistik-och-kartor/regionala-indelningar/deso---demografiska-statistikomraden/ (Swedish only) or https://www.scb.se/en/services/open-data-api/open-geodata/deso--demographic-statistical-areas/.
In addition to the database mentioned above, there are also data at the following spatial resolution, which fully orpartly go below municipal level: Grid statistics, preschools and agency and municipal offices, localities and smalllocalities, holiday-home areas, retail trade areas, activities zones, and RegSO (Regional Statistical Areas). Foraccessing the GIS layers, please see: https://www.scb.se/en/services/open-data-api/open-geodata/. Furthermore,in addition to Statistics Sweden, other agencies also publish similar data (including geospatial data/GIS layers).
18GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
9 The respondent is from an Italian authority.Yet, as shown above, the national statistical authority publishes various datasets.
5.2.Data types and domains available at no costs
All but one9 of the authorities and offices that took part in the survey make data openly available (Figure 3).
Figure 3. Is there any data openly available?
The majority of responding authorities and offices make more than 500 data files available free of charge (Figure 4).
Figure 4. Data files made available by each organization.
The main domains most frequently available and with relevance for rural development and governance aredemography, economy, agriculture, and tourism / recreation (Figure 5). More than half of the respondents alsoprovide data on energy environment and transport. Mobility data is provided by only one-fourth of the offices.Accessibility data, according to the survey, is provided by one only country (non-EU).
19GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
10 For a distinction of these data types, see for instance https://gisgeography.com/spatial-data-types-vector-raster/.
Figure 5. Data domains contained in open database(s).
There are differences between the countries. Statistics Finland, for instance, has open databases on all but healthdata, whilst the Greek national authority has an open database only for R&D and innovation (see also above andaccording to the survey).
More than 60% of the responding offices use published guidelines/frameworks. In case they do, this is Eurostat (allof them) and UN Statistics Division (two respondents) guidelines.
Most of the offices make tabular data available (87%) and more than half of them grid-level data. More than one-third provide vector data and slightly less also raster data. Only two offices said they make point data available(Figure 6).10 In addition, one office stressed that they publish time series and anonymized microdata of statisticalsurveys.
Figure 6. Data types available.
20GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
5.3.Customers, costs and revenues
In addition to whether and what kind of data is accessible for free (see above), we asked the authorities and officesabout the reasons for charging customers in case costs apply for users. Unfortunately, only less than half of therespondents provided information on this question. Of those who answered, 1 authority (Greece) charges forcompiling data, 2 for both compiling and structuring data (Finland and Italy) and 3 base their costs on other reasons(Croatia, Hungary and Sweden) (Figure 7).
Figure 7. Reasons for charging customers
Other reasons range from subscriptions to specific databases, retrievals from internal databases, individualcalculations based on the number of man-days required for compiling the data or any other individual tasks on behalfof the office.
We also asked whether public officials must pay for accessing data or whether specific discounts for students oracademia are offered.
Most of the offices, who answered this question, stated that public officials do not have to pay for accessing data(Figure 8).
Figure 8. Do public officials have to pay for accessing data?
21GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
Most of the offices do not offer specific discounts for students (Figure 9).
Figure 9. Specific discounts offered for students?
Discounts for academia are also rather an exception (Figure 10).
Figure 10. Specific discounts offered for academia (e.g. teachers, researchers)?
Most of the offices are neither supported by a government grant nor specific ministerial funding (Figure 11). This isin most of the cases coming from the state budget.
Figure 11. Our office is supported by…
Some offices, like Sweden, do commission projects upon request in addition to state funding. Finland also mentionedchargeable services and other central government authorities and financing from the EU.
22GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
We asked the respondents, who and which groups are mainly using their data and in a ranking exercise. Main usersare individuals, followed by research organizations and private sector (Figure 12).
Figure 12. Main user groups
One respondent added that public sector, research, and private sector are all using their data and are important, asare private individuals. Furthermore, according to one respondent, “actual usage between the groups might alsodiffer in relation to the size of the groups”.
Regarding the type of data customers mainly ask for and for what purpose, most organizations referred todemography and population census data. There is also demand for labor market and employment data, R&D, (rural)business development and funding data, as well as price development, tourism and immigration data. As onerespondent put it, whilst “the majority of users probably access data on their own, without asking questions” andneed mainly tabular data, there is a demand for more complex and granular data, as well as indicators to follow up.Respondents also referred to the interest to understand rural-urban interrelations, the need for good data fordecision-making purposes and to capture development and change.
23GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
6. Inspirational examples – Data, tools and approaches from Granularpartner countries.
Section 6 presents 9 inspirational examples, selected to inspire different types of actors to get familiar with interestingwork carried out throughout the EU and beyond, and possibly also to pursue a similar exercise. We provide casesfrom different scales – reaching from the local and Living Lab level to the EU and international levels. We alsohighlight diverse chains of development. Importantly, we invited developers to share their experience andconsiderations about motivation, enablers and potential challenges. The examples include reflections about variouscost dimensions, and contact information for follow-up conversation. The examples are presented in alphabeticalorder and following the country / region they are stemming from and going up to the Nordic Region, the EU and theglobal level. At the end of this section, a matrix will summarize key learnings, cost dimensions, policy implicationsand future considerations from the inspirational examples.
6.1.Finland: Indicator to follow Subjective Wellbeing (SWB) development of therural population during the CAP programming period – by Mikko Weckroth(Luke)
The Natural Resources Institute Finland (LUKE) has been actively involved in the planning of Finnish CAP plans asa policy support provided to the Ministry of Agriculture and Forestry (MMM). In earlier work, LUKE has utilizeddatasets on subjective wellbeing (SWB), which include exact location indicators, enabling detailed spatial analysis.Hence, LUKE was commissioned to develop a measure for evaluating one specific aim in Finnish CAP plan. Forthat end, LUKE researchers developed a novel indicator to follow development of SWB of the rural population duringthe CAP programming period 2023-27.
Technically, this indicator was created by merging a large (N=38 000)annual FinSote Survey (currently Healthy Finland Survey) collected by theFinnish Institute for Health and Welfare (THL), to 7 classes urban–ruralclassification system constructed by the Finnish Environment Institute,based on 250 X 250 m statistical squares. SWB of the rural population ismeasured by the standardized Mental Health Index MHI5 and a single itemquestion on perceived loneliness. More specifically, the index on SWB ofthe rural population is adjusted by age and gender (by estimated marginalmeans), in order to control for changes in socio-demographic compositionin rural areas. Additionally, the index is adjusted to national mean of SWBto control for national level changes in SWB and thus to reflect only therelative changes of SWB in the rural population. The indices generated fromthe data are processed and stored in LUKE's database and published inLUKE's indicator portal (https://www.luke.fi/en/statistics/indicators/cap-indicators).
Photo: Edit Kul
In summary, researchers at THL and LUKE, as well as civil servants from MMM involved in the project, see this asinspiring case and example of cooperation between sectoral research institutes and affiliated ministries. However,the institutional structure that allows data availability is rather fragile, since it is based on double affiliation of singleresearcher. The project represents an inspiring pilot on using a large SWB dataset in CAP policy evaluation.
24GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
Photo: Janne Poikolainen
Costs
Development Costs: The responsible investigator for this project at LUKE (Mikko Weckroth) has an affiliation withTHL. The survey data itself is collected by THL and budgeted on the basis of the agreement with the responsibleMinistry of Social Affairs and Health. However, in a case there would not be a researcher with shared affiliation, thewhole procedure would require involvement of the Finnish Social and Health Data Permit Authority (Findata) thatgrants permits for the secondary use of social and health care data (https://findata.fi/en/). The pricing for creatingthis dataset and giving permits would need to be estimated by Findata but would most likely be 8.000 – 12.000 €. Insum, the survey data is collected by THL - not for the purpose of developing this indicator but was utilized for itsdevelopment.
Data accessibility for users: Indexes are free at Agrigaattori portal for users.
Data infrastructure costs: None for the user.
Data governance & management costs: None for the user. However, for the development of the indicator, expertisein statistics and data management, particularly in the field of (subjective) wellbeing is needed. Data analysisexpertise is needed, too, e.g. calculating indexes and linking responses to urban-rural classification.
Further information: mikko.weckroth@luke.fi
6.2.Finland: The Rural Barometer – by Hilkka Vihinen & Michael Kull (LUKE)
The aim of the Rural Barometer is to shed light onhow Finnish citizens, public decision-makers,business decision-makers, the media and ruralexperts perceive the countryside. It includes themessuch as: elements of the good life, images of ruralareas, regional identity, multi-local living,entrepreneurship and livelihoods, rural development,opinions on policy measures and the future of ruralareas. The Rural Barometer provides a statisticallyrepresentative sample of the Finnish people's viewson the state and future of rural areas. The 4th RuralBarometer is currently conducted and continues theseries of Rural Barometers done in 2009, 2013(published 2014) and 2020. The Barometer is
commissioned by The Rural Policy Council (MANE)and implemented by LUKE and subcontractors, e.g.,the survey is designed and interpreted by LUKE.
Photo: Janne Poikolainen
25GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
11 More specifically, 294 young urban citizens, aged 15-29) took part. 579 respondents were from the Swedish-speaking population (aged15-79). Respondents were also business decision-makers, e.g. CEOs, development managers, CFOs and others in similar positions(industry, construction, trade and services (280 respondents) and public decision-makers, e.g. MPs, mayors and presidents ofcity/commune councils, provincial governors and councillors, MEPs (237 respondents). Sixty-eight media representatives, incl. editors-in-chief, news managers, regional editors, managing editors, political journalists, journalists (newspapers, TV, radio) took part. 121 “Ruralexperts”, i.e. experts, researchers, civil servants, third sector actors working on and following rural issues took part as well.
The Rural Barometer 2020 – Approach
Responses were collected through an online survey (Finnish-speaking population) and a telephone-informed survey(Swedish-speaking population). Overall, there were 1788 respondents, that is Finnish citizens aged 15-79.11
The Rural Barometer 2020 – Selected Findings
26GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
"A place for the good life" - The image of a good lifeis much or very much associated with the countrysideby 61% of Finns. More positive images are associatedwith rural than urban areas.
"I am both rural and urban" - 37% of Finns have adouble-identity: they consider themselves both ruraland urban.
"The countryside is the land of dreams" - 20% ofyoung urban dwellers consider the countryside to bethe place of their dreams.
"Rural development should be based primarily on theneeds of rural people." - 39% of Finns strongly agreewith this statement.
Photo: Michael Kull
Costs
Development costs: In the beginning, when the whole survey is drafted, about 6 PM (senior experts) are needed,depending on how wide-ranging the survey is supposed to be as to different dimensions, and how many respondentgroups there will be. After the first run about 3 PM should be enough for updating / improving questions and lists ofspecific respondents.
Data Governance & Management costs: Senior experts on rural studies for the substance, and if possible,professional survey companies to run the citizen sample with the help of their permanent panels. In Finland, thesurvey (covering two languages and the above-mentioned different respondent groups) costs about € 42-45,000.For one language only it would be €30-35,000.
Data analysis, processing & visualization: Planning work and interpretation done by a rural studies experts; in ourcase about 5 PM. Much depends on how many “mobile” parts the survey has, and on the quality of reporting. If PPTpresentations and newsletters suffice, it is possible to do it with 1-1,5 PM. A proper scientific report needs 3-4 PM.
Data Infrastructure costs: It would be possible to conduct the survey using, for instance, Webropol, which could be(almost) free of charge. However, to guarantee statistically representative samples and quality, professionalcompanies with their existing software and hardware might be used.
Data repositories / storage: We use the Finnish social science data archive (FSD)(https://www.tuni.fi/en/research/finnish-social-science-data-archive), a national infrastructure with open access anda part of CESSDA (PAN-EUROPEAN RESEARCH INFRASTRUCTURE). FSD is funded by the Ministry ofEducation and Culture, and it does not cost us to preserve the barometer data there.
Costs for data documentation and assistance in data use: Data is stored in FSD. In most cases, policy-makers orthe media use the interpretations and presentations we have made available in the Barometer report or on thewebsite of MANE. Depending on quality and quantity, costs vary between 1 to 6 PM.
Data security costs: Included in the costs of the survey company and in our own budget. Data security costs haveincreased a lot during the last 10-15 years, so it is wise to include them in the budget and PM.
The Barometer requires, by definition, repetition. 2–3-year intervals would be optimal. It is also important to designthe survey really well in the beginning, since the value of the Barometer decreases each time you change something.in Finland, we have decided to have approximately one “mobile” section among the 6 themes of the barometer – aset of questions that changes depending on what is regarded of current, urgent interest each time.
In addition, it is warmly recommended to use experts on qualitative attitude research at least in the beginning, whendesigning the survey. It would also be great if we could, someday, have several European Rural Barometers, whereat least some of the sections would be formulated exactly the same way, to render comparisons possible.
Further information: hilkka.vihinen@luke.fi & michael.kull@luke.fi
27GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
6.3.France: Monitoring mobility and road traffic at local scale – Case ConseilDépartemental des Pyrénées-Orientales (CD66) – by Louise Chasset, LenaïcDepontailler (Pays Pyrénées Méditerranée) & Jean-Claude Balagué(Département des Pyrénées-Orientales)
The Département des Pyrénées Orientales (CD66) has set up a system for observing and monitoring traffic levelson the roads it manages. As a local authority in the South of France, the CD66 is responsible for a network of2,154 km of roads, serving an area of over 4,000 km² with a population of 480,000 (source: INSEE 2020).
To help manage its road network, CD66 producesthis data with several objectives in mind:
- To understand traffic trends and help decision-making for long term general studies,
- Design road projects, in particular for sizingpavement structures,
- Adapt measures taken in terms of worksiteoperations, for example by choosing the timeslots with the least impact on traffic duringroadside mowing operations.
Photo: Pays Pyrénées Méditerranée
CD66 is responsible for all the stages involved in producing this data: from the installation and maintenance of themeters to the collection, consolidation, and analysis of the data, right through to making the information availableon open data platforms. Two operators and one data administrator are working on this system, which relies on anetwork of permanent, rotating and on-demand meters, as well as the ROUTE+ software and a virtual server fordata storage.
Photo: Pays Pyrénées Méditerranée
Costs
BUDGET OF THE WHOLE INITIATIVE: A total of around €165,000 per year in operating costs for the whole thedepartment. This include human resources (€100,000/year), costs of replacing and maintaining meters (€ 62,000 /year) including ROUTE+ software maintenance (€3,300 / year) and costs for acquisition and training (€ 30,000).
DATA GOVERNANCE & MANAGEMENT COSTS
Staff costs or PM: around €100,000 per year.
Costs for Data analysis: The processing and display of the data is carried out in house by the department's civilservants, whose salary costs are aggregated.
28GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
Consolidation of collected data: the software draws attention to aberrant results, on the basis of which theoperators and the data administrator check it. The director of the roads department finally validates the data. Datacleansing is carried out internally by the department's civil servants, whose salary costs are globalised.
DATA INFRASTRUCTURE COSTS:
SOFTWARE. The system is based on the ROUTE+ which costed around €30,000 to acquire (+ agent training).
Annual maintenance costs €3,300 excluding VAT/year.
HARDWARE. The system is based on meters, the hardware replacement and maintenance cost is approximately€62,000/year.
Data repositories / storage: The data is stored on one of the 700 virtual servers of the Conseil Départemental desPyrénées-Orientales.
COSTS FOR DATA PROCESSING & VISUALIZATION: The processing and display of the data is carried out inhouse by the department's civil servants, whose salary costs are aggregated. Processing the data and thenmaking it available on the open data platforms takes around 12 working days.
DATA SECURITY COSTS: The data is stored on one of the CD66's virtual servers, protected by a firewall. Dataaccess is allocated manually. Costs are therefore considered negligible today.
Further information: Jean-Claude Balagué, Département des Pyrénées-Orientales, jeanclaude.balague@cd66.fr.
6.4.Galicia – Spain: Telecare for the elderly at home in the rural areas of Ourense– by María Isabel Doval Ruiz & Breixo Martins (University of Vigo)
Photo: GRANULAR team University of Vigo
Home telecare is an uninterrupted telephone service with specific communications and computer equipment,especially designed for elderly people, who live alone, and in order to pay immediate attention in the event of anemergency. The system consists of an alarm unit carried by the person, a telephone terminal and a computerizedswitchboard that receives the calls and is located in the Care Centre. The service allows users to communicate withthe center, which is staffed by specialized personnel, in the event of any emergency situation by simply pressing thebutton on the device elderly people carry with them. This system is specific to rural local authorities (under 20,000inhabitants). It is noteworthy that only one local entity, out of a total of 92, exceeds this number of inhabitants.
The service aims to provide an immediate response that allows communication between the user and the carecenter 24 hours a day, 365 days a year. This offer is complemented by a diary-reminder service for certain tasks,such as the control of chronic medication or medical and social consultations. Other systems related to homeautomation and care for the elderly are also being implemented. An example of this is the system for detecting theopening of the doors of these people. Through a centralized system, it is possible to know the number of times aperson opens their door per day and if the opening range falls below a certain number, specific protocols areactivated.
The extracted data
The background that allows for a strong territorial analysis of this health care or welfare system is the continuousinternal system of data collection. The data collection has both a statistical and a territorial level. On the one hand,
29GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
a large amount of information is collected for each procedure performed by each user. Depending on the year, thenumber of users varies between 2000 and 3000 people, and the amount of information is large. Every time the userpresses the emergency button, the duration of the call and even, through a qualitative system, information on thereasons for each of the emergencies can be collected. This analysis has led to preliminary internal conclusions thatone of the main causes of emergencies is motivated by unwanted loneliness in the last stage of life.
On the other hand, perhaps the most innovative information collection is implemented from a territorial point of view.This is due to the geolocation of each of their users. In other words, there is the possibility of generating maps ofuser points which, together with the above statistics and the geolocation of the service centers, can generate veryuseful cartographic information systems. For example, by combining the number of emergency vehicles and users,we can create maps of the flow of emergency vehicles throughout the province with wide-ranging densities thatallow us to draw relevant conclusions. On the other hand, it would allow us to obtain information on the distortionbetween the location of services and users, areas of service reinforcement, areas of social exclusion or evendistortions of services in relation to the degree of urbanization or rural-urban areas.
Visualization and opening of data
As far as the visualization system is concerned, it is difficult to implement due to the high degree of protection of thedata processed. While it would be possible to display purely statistical data in anonymized form, this would be verycomplex in terms of the territorial representation of the data. This is due to the fact that all mobility and service-userdistortion flows are based on the exact location of each user and therefore, the very location of the householdseliminates the anonymity of the users. In any case, our Living Lab is starting to collect this data in order to analyzeit and then, if possible, develop appropriate reports or visualization systems.
Costs
The estimated budget of the whole initiative is complex as, in practice, it depends on different administrations.Potential travel is carried out by regional institutions and the telecare program is funded by the provincial authority.Similarly, data analysis is carried out by the university in collaboration with the provincial administration. In any case,on the basis of the call for tenders in 2021, the contract is estimated to be at around €1,300,000.
Further information: granular@uvigo.es & mdoval@uvigo.gal
6.5.Poland: Functional and spatial diagnosis for social revitalization at the local(municipal) level – by Agnieszka Kurdys-Kujawska (TU Koszalin)
Employees of the Faculty of Economics at KUT (Małgorzata Czerwińska-Jaśkiewicz, Ph.D.; Patrycjusz Zarębski,Ph.D.), collaborated with the Science for the Environment Foundation in their research on the creation of arevitalization program in the communes of the West Pomeranian Voivodeship located in the Special Exclusion Zone.The primary objective of this investigation was to identify a degraded area through an objective and comprehensiveassessment of social, economic, technical, environmental, and spatial issues. Based on the research, a model(including diagnostic instruments) for socio-economic diagnosis was created, and a model for implementing socialrevitalization was proposed.
To assess degraded areas in the commune, 22 indicators were used to describe the intensity of the phenomenonwithin a given commune. These indicators were grouped according to their nature and information content intosocial, economic, structural, spatial-functional, and environmental indicators. Data were obtained from publicstatistical portals, including the Local Data Bank of the Central Statistical Office, the Commune Office, the DistrictLabor Office, and the Commune Social Welfare Center. The indicators adopted for evaluation were also optional,which was helpful in considering the specific features of a given area, and the internal diversity of the analyzedanalytical units. The nature of these indicators was also determined. The indicators describing the phenomena wereassessed according to the principle that they indicate a crisis when their value exceeds the median for the entire setof analyzed units. On this basis, a critical indicator was built. It is the basis for defining a degraded area, i.e. in a stateof crisis due to the concentration of negative social phenomena and negative phenomena of a different nature, i.e.economic or environmental, spatial-functional or technical.
30GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
12 Project No. 1. "Revitalization in the communes of the West Pomeranian Voivodeship located in the Special Inclusion Zone", projectco-financed by the EU from the European Social Fund and the state budget under the Regional Operational Program of the WestPomeranian Voivodeship for 2014-2020, contract number: UDARPZP.07.01. 0032K601/1600 of September 21, 2016, implementationdate: October 1, 2016 - December 31, 2016; Project No. 2. "Social Revitalization", a project financed by the EU from the EuropeanSocial Fund and the state budget under the Regional Operational Program of the West Pomeranian Voivodeship for 2014-2020, Co-financing agreement No.: RPZP.07.01.0032K102/1800 of September 20, 2018. implementation date: 01/01/2019 -01/01/2021.
Problem phenomena and the causes of their occurrence were identified through an in-depth diagnosis based ondirect interviews, questionnaires, and focus group interviews. Revitalization areas were designated based on an in-depth diagnosis using four research methods: individual direct survey - with village heads in the commune; IDI –Individual In-Depth Interview, i.e. an individual in-depth interview – with people in power in the commune (mayor);FGI (focus group Interview) – with local leaders in the commune; interviews, animations, and research walks withresidents of villages. Field research was carried out by appropriately selected and instructed interviewers - mostlyrepresentatives of commune residents. These were people selected in terms of competencies, skills, and personalpredispositions, with good knowledge of the local environment.
The high usefulness of statistical data was ensured by the inclusion of local animators and independent mediatorsin the research process, who, on the one hand, were responsible for social activation in a specific region on anongoing basis (and knew it well), and, on the other hand, were independent experts looking at a given areaobjectively. The designation of revitalization areas took into account the participation of residents and local leaders.Statistical diagnosis was made based on a set of 14 independent indicators. The revitalization area was designatedin places where, as a result of statistical analysis, an accumulation of problem phenomena was observed. In a furtherstage of research, the development potential and the most urgent areas of intervention were considered. For thispurpose, various tools and methods of collecting information were used: interviews (based on a surveyquestionnaire); direct animation meetings with various stakeholders; preparation of maps of resources and needsdirectly by residents with the support of the Local Animator, as well as tutors and external specialists; focus meetings.
Source: https://www.facebook.com/ndsfund/?locale=pl_PL.
The model for diagnosing socio-economic structures and the original concept of implementing social revitalizationin rural areas were used to develop 18 Local Revitalization Programs (e.g.https://bip.dobragmina.pl/strony/menu/139.dhtml)
108 investment projects were implemented in municipalities based on Local Revitalization Programs with the totalamount of PLN 6,000,955 (appr. 1,390,000 €). 720 residents (direct participants of revitalization projects) from 18communes of the Special Inclusion Zone (including 490 people at risk of social exclusion) were covered bysubstantive and animation support in the field of revitalization. Three projects used the diagnostic researchmethodology to delimit degraded areas and implemented the social revitalization model.12
31GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
13 See for instance https://nenp.facebook.com/spolecznarewitalizacja/videos/gmina-brojce-rewitalizacja-spo%C5%82eczna/250991843084847/; https://ne-np.facebook.com/spolecznarewitalizacja/videos/gmina-radowo-ma%C5%82e-spo%C5%82eczna-rewitalizacja/4262501857190377.
Revitalization projects were implemented in 2016-2021 by the Koszalin Regional Development Agency S.A., theKoszalin Science for the Environment Foundation, Aktywa Plus, and 4C from Szczecin. These projects weresupported by funds from the European Social Fund and the state budget under the Regional Operational Programof the West Pomeranian Voivodeship for 2014-2020 and in communes located in the Special Exclusion Zone(based on the developed Local Revitalization Programs).13
Source: https://www.facebook.com/ndsfund/?locale=pl_PL.Costs:
Data management and management costs: None for the user.
Indicator development: specialist knowledge in statistics and data management was needed, especially in the fieldof social, economic, spatial-functional, technical, and environmental phenomena in rural areas. Knowledge of dataanalysis is also necessary.
Further information: agnieszka.kurdys-kujawska@tu.koszalin.pl.
6.6.Scotland: Scottish National Islands Plan Survey (2020): results explorer – byJonathan Hopkins and David Miller (Hutton)
In 2020, researchers at the James Hutton Institute were contacted by the Scottish Government to design, implementand analyze the National Islands Plan Survey, which collected data from residents of Scotland’s islands onperceptions and opinions on island life, which were aligned with strategic objectives of the National Islands Plan.The survey implemented a customized subregional geography to inform both survey sampling and the reporting ofresults, in order to identify diversity in lived experiences beneath the level of local authority regions; these regionshave subsequently been developed into an official geography for Scotland through further work by National Recordsof Scotland and the Scottish Government. The survey achieved 4,347 responses from 59 islands. However, in orderto expand the volume of results reported, a simple interactive tool was published, enabling end users to generategraphs and data summaries for 179 variables and tabulations with geographical, demographic and economiccharacteristics of respondents. The tool demonstrates holistic experiences of island life and illustrates variations inthese between people and regions, and highlights the extended evidence base for a type of geography for whichthere was limited data available.
The tool was published in 2021 and was developed as part of a short research project, using the Shiny packagewithin R. Cross-tables and weighted results summaries were pre-generated for the tool, with reproducible codeproduced for their calculation. Some results showing figures for island regions, and the islands overall, wereweighted by island region, age group and gender cohorts in order to account for differences in response rates. Dataprovided to the tool were screened, with variables and values redacted in places, to avoid the disclosure of data forlow numbers of people.
32GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
Screenshot of the tool, showing a comparison of perceptions by age group.
Costs
The project involved a team of six researchers with one leading on the tool development and publication. The toolutilised existing skills in R/Shiny programming. Data hosting costs for the tool are currently zero, through a freeaccount at shinyapps.io, although a subscription was taken out for that account previously. The value of the wholecontract for the Survey was approximately €70,000.
Further information: jonathan.hopkins@hutton.ac.uk
33GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
6.7.Nordic Countries: The Nordic Service Mapper - by Mats Stjernberg (Nordregio)
Nordic Service Mapper is an interactive online web-mapping tool that visualizes the proximity to different servicesacross the Nordic Region. It covers the territories of Denmark, Finland, Iceland, Norway, and Sweden as well as theFaroe Islands and Åland. The tool includes four different types of services, namely grocery stores, pharmacies,libraries, and schools. The tool was published in 2021 and it reflects the situation in December 2019.
Landing page of Nordic Service Mapper which can be access at http://nordicservicemapper.org/
The tool shows street-based proximity for the population to various service categories at different geographicallevels. Various data aggregation and normalisation options are provided in the tool, including access to services atregional and municipality level, according to the Eurostat Degree of Urbanisation at municipality level (cities,intermediate, rural), and at a more spatially detailed grid-level and based on a freehand selection of areas.
Nordic Service Mapper also includes an infographictool which allows for visualising service accessibilityaccording to selected territories and service types inthe form of charts.
Visualization of accessibility to different service typesin Finnish municipalities based on the EurostatDegree of Urbanization classification in NordicService Mapper’s infographic tool.
The calculations are based on spatial analysis carried out in ESRI´s Network Analyst (closest facility). Bridges, ferrylinks, road hierarchies and one-way restrictions were considered and included in the calculations. All networkcalculations have been done without considering national borders, which also enables for cross-border analysis.The data sources used include data on service locations which were purchased from HERE Technologies,population data on 1,000m × 1,000m grid-level and road network data from OpenStreetMap.
34GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
Zooming in on the accessibility to groceries across a cross-border area in northern Finland and Sweden
Costs
The Nordic Service Mapper tool was developed by Nordregio in collaboration with Ubigu. The tool was created bycommission of the Nordic Thematic Group on Sustainable Rural Development (2017–2020) as part of the projectRegional disparities and the geography of service within the Nordic countries. Mats Stjernberg and Oskar Penje atNordregio led the project and the accessibility calculations were carried out in-house at Nordregio. The work tocreate the web-mapping platform was led by Ubigu.
Data costs: The main costs were the costs for purchasing data on service points from Here technologies as well asfor working hours for carrying out the accessibility calculations and developing the web-mapping platform. The costsfor purchasing the service point data was around € 12.000.
Data analysis, processing & visualization: The number of PM for the carrying out the calculations and dataharmonisation including different methodological considerations was around four months. The development of theweb mapping platform also required around four months of work.
Further information: mats.stjernberg@nordregio.org
35GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
6.8.EU: Integrating text-to-image and image-to-text techniques to enhanceaccessibility and understanding of rural land-use data - Cross ModalityFramework – by Pallavi Jain (IAMM)
The tool integrates text-to-image and image-to-text techniques to enhance accessibility and understanding of ruralland-use data. It enables efficient retrieval, association, and analysis of specific land use categories or featureswithin satellite imagery using textual queries or visual analysis.
The tool currently utilizes sophisticated vision-language models to linkground-level images with satellite imagery, providing crucial contextfor understanding rural land use dynamics. It utilizes geolocations from LUCAS survey data to gather satelliteimagery from Bing and Sentinel-2 sources.
Further, the textual modelling aspect enables the implementation of Text-to-Image and Image-to-Text searchmethods, establishing essential connections between textual descriptions and visual representations. Thesemethodologies enable seamless interaction between textual and image data domains, enhancing the project'scomprehensive approach.
Through the comprehensive insights gained from the visual-language approach, the tool aims to empowerpolicymakers, researchers, and land managers. This support facilitates well-informed decisions in land use planning,conservation strategies, and resource management. The ultimate goal is to leverage these advanced models toeffectively understand, monitor, and manage rural land use.
Costs
Data Collection: The project initially uses LUCAS 2018 survey data to acquire ground-level visual context acrossEurope. Bing Aerial (15cm resolution) and Sentinel-2 (10m & 20m resolution) data are collected from Microsoft Bingand Planetary Computer API services, amounting to 230,000 location images obtained so far.
Hardware Infrastructure: For model training, four GPUs are utilised to support the computational requirements. Theestimated cost for the infrastructure is 8,000€
Data Costs: As of now, Bing and Planetary Computer services from Microsoft offer free APIs for data collection.However, downloading 230K images takes nearly a week.
Tool Cost: The development of the algorithm is estimated at 24PM. The foundational expenses for the tool wouldencompass creating a user interface, an API, and backend infrastructure, along with utilising online cloud serverslike AWS, Google Cloud, or Microsoft Azure for model deployment. Additionally, there might be additional API feesassociated with utilizing satellite image retrieval services.
Further information: jain@iamm.fr.
36GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
6.9. Global: Geo-Wiki Earth Observation & Citizen Science – by Ivelina Georgieva(IIASA)
The Geo-Wiki platform provides anyone with the means to engage in monitoring of the Earth's surface by classifyingsatellite, drone or ground-level imagery. Data can be input via desktop or mobile devices, with campaigns andgames used to incentivize input. These innovative techniques have been used to successfully integrate citizen-derived data sources with expert and authoritative data to address pressing policy-related questions (e.g. Europeanenvironmental policy, SDG indicators and more).
Geo-Wiki was established in 2010 in the Novel Data Ecosystems for Sustainability research group, part of theAdvancing Systems Analysis Program at the International Institute for Applied Systems Analysis (IIASA) inLaxenburg, Austria. Since its inception, Geo-Wiki has grown rapidly, with currently over 22,000 registered usershaving contributed more than 18 million image classifications from around the world. Furthermore, the Geo-Wikitoolbox has expanded to include numerous applications which help to address a variety of global challenges (e.g.,land use change, food security, pollution and more).
Since its creation, multiple citizenscience campaigns in the form ofcompetitions have been carried out,asking volunteers to perform visualinterpretation of VHR satellite imageryin the Geo-Wiki platform on topicsrelated to land use and land coverchanges. Some of the recentcampaigns include defining the driversof tropical forest loss, validating theglobal human settlement layer, anddefining the human impact on forests.Within these campaigns we haveinvolved hundreds of volunteers frommore than 20 countries worldwidewho had interest to contribute toscience and become part of thegrowing Geo-Wiki community. Thefigure above illustrates theirmotivations.
To facilitate the process of visualinterpretation, volunteers haveaccess to specifically defined (for thevalidation task at hand) Geo-Wiki
Motivations of contributors joining our citizen scientists campaigns.
37GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
functionalities. The principle set of tools which the platform includes are implemented features like Sentinel Hubtime series imagery, an NDVI tool for measuring the Normalized Difference Vegetation Index and Google Earthhistory imagery. The quality of contributions has been controlled from a group of experts during and after thecampaigns and scientific publications are used to share the data, which are later uploaded in public repositories toensure transperancy of the entire process.
Main development costs:
Initially, an early beta-version of Geo-Wiki was developed in partnership with the University of Freiburg, Germanyand the University of Wiener Neustadt, Austria, as part of the Geobene project. The development of the first, beta-version lasted 6 months and the further development of the Geo-Wiki v.1. required around 1,5 person years of work.There have been ongoing feature developments and improvements since 2011. In 2013 the first big refactoring ofthe Geo-Wiki platform happened, which lasted approximately 10 months. This included database refactoring, aswell as moving to newer technologies and frameworks. Furthermore, we moved to using a content managementsystem (CMS) for e.g., the user management.
Software costs:
No software costs (we are using only open source/free software)
Hosting costs: Costs forservers and services neededfor hosting the Geo-Wikisystem from 2011 until now:total: 35.000€ (Nov 2011 - Oct2023)Initial costs in Nov. 2011:70€/month.Current costs in Oct. 2023:500€/month (the monthlyhosting costs increasedbecause of more collectiontools & platforms & moreservers and services wereneeded).
A screenshot of the Geo-Wiki application branch for measuring human impact on tropical forests.
Further information: georgieva@iiasa.ac.at & mccallum@iiasa.ac.at
Overview of the Geo-Wiki branches and data collection tools
6.10. Key lessons learned, cost considerations, policy implications and ways forward
This section summarizes the inspirational examples presented above. In the matrix below (table 2), you can find each case and key lessons learned. We highlight also theexpertise needed to conduct such an exercise and what types of costs to consider. In addition to policy implications of each case we also try to consider what could be thenext steps, both in relation to the case and as outlined by its authors but also in terms of potential replication elsewhere.
Table 2. Inspirational examples: Lessons learned, cost considerations, policy implications and ways forward
Case Key lessons learned Expertise needed Cost considerations Policy Implications Future considerationsIndicator tomonitor thesubjectivewellbeing of therural populationduring the CAPprogrammingperiod - Finland
Effective collaboration,innovative indicatordevelopment, and acommitment to transparency.
Expertise in statistics, datamanagement, and dataanalysis, especially in thefield of SWB, for successfulindicator development.
Development costs linked to theshared affiliation of the keyresearcher, highlighting theimportance of such affiliations inavoiding additional expenses.Finnish Institute for Health andWelfare (THL) collects data for otherpurposes. Potential additional costsranging from 8,000 to 12,000 € if aresearcher with shared affiliationwas unavailable, requiring permitsfrom the Finnish Social and HealthData Permit Authority (Findata).
The case will contribute valuableinsights for public policy by usinglarge SWB datasets for policyevaluation in the context of the CAPprogramming period.
Work on SWB in Granular willcontinue. Exchange with othernational authorities in charge ofthe CAP welcome. (Fragility) ofthe institutional structure and theneed for specific expertise areidentified as importantconsiderations for futureinitiatives.
Rural BarometerFinland Careful planning, expertise,and consideration of variousdisciplines contribute to thesuccess and sustainability ofsuch surveys.
(Senior) experts in the fieldof rural studies providingsubject matter expertise toensure the survey contentaligns with the objectives.Experts in qualitative &quantitative researchneeded, too.
Development expenses ofapproximately 9 PM for initialdrafting and updates, datagovernance and management costsranging from €30,000 to €45,000 forsurvey execution, along withadditional expenses for dataanalysis, infrastructure,documentation, and security.
Barometers may guide policymakersin allocating resources to addressspecific challenges identified in ruralareas. The emphasis on datainfrastructure, storage, anddocumentation underscores theimportance of a data-drivenapproach to governance. TheBarometer's use of citizen samplesand the involvement of differentrespondent groups can encouragepublic participation in policymaking.
Continuation of the Barometer ina 2–3 year interval. Eventualdevelopment of a EuropeanRural Barometer / a Barometerelsewhere with standardized /harmonized sections forcomparability might be worth toexchange about.
39GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
Monitoringmobility and roadtraffic at localscale in France
Well-planned and integratedapproach to traffic monitoring,combining technology, humanresources, and governancestructures to effectivelymanage road networks andcontribute to informeddecision-making in planning.
Combination of variousexpertise incl. trafficengineering, datamanagement, geospatialanalysis and planning.
A total of around €165,000 per yearin operating costs. Key costdimensions are categorized intoseveral areas. The largest portion ofthe budget is for human resources(€100,000 p.a.), covering salaries foroperators, data administrators, andcivil servants involved in dataanalysis, consolidation, validation,processing, and visualization.Software costed appr. €30,000(+agent training). Annualmaintenance costs €3,300 excl.VAT.
Wide-ranging implications for publicpolicy, impacting areas such astransportation, regional planning,resource allocation, andenvironmental sustainability, throughbetter understanding traffic trendsand through flexible and adaptiveapproach to monitoring traffic levels.
Data generated by the systemcan guide future road design,adapting worksite operations andinfrastructure investments byidentifying areas with high trafficvolumes or congestion,influencing decisions on roadexpansions or alternativetransportation solutions.
Telecare for theelderly at home –Galicia / Spain
Implementing telecareprograms for elderlyindividuals living alone,especially in rural areas ismultifaceted. The importanceof a holistic and data-drivenapproach to improve the well-being of the target populationis emphasized.
A diverse set of expertiseincl. telecommunications,healthcare, data scienceand analytics able toaddress the technologicalinfrastructure, dataanalysis, user interfacedesign, communityacceptance. Collaborationwith academic institutionsfor research andcontinuous evaluation.
Costs are distributed across differentadministrations, and thecollaboration between regionalinstitutions, the provincial authority,and the university plays a role in theoverall initiative. The estimatedbudget is around €1,300,000.
Helps policymakers to addressissues of unwanted loneliness in theelderly population, suggestingpotential interventions to enhancesocial support. Geolocation dataanalysis underscores the importanceof aligning service distribution withdemographic realities, guidingpolicymakers in optimizing resourceallocation in rural areas
System is still in the process ofcollecting and analyzing data,and efforts are being made todevelop appropriate reports orvisualization systems whileconsidering data protectionconcerns. As society ages,policymakers may need toconsider scaling and adaptingsuch systems to meet theincreasing demand for elderlycare, potentially reshapinghealthcare infrastructure andservice delivery models.Functional &spatial diagnosisfor socialrevitalization -Poland
Research & subsequentprograms address complexdevelopment issues in ruralareas. Integration ofcommunity perspectives &utilization of various researchmethods underscore thecomprehensive nature of theproject
Multidisciplinary approachand collaboration betweenacademic, foundation, andregional developmententities. Indicatordevelopment requiredspecialist knowledge instatistics, qualitative &quantitative dada collectionand data management,particularly in the context ofsocial, economic, spatial-functional, technical, andenvironmental phenomenain rural areas.
Data costs were mentioned as nonefor the user. 18 Local RevitalizationPrograms and 108 investmentprojects were implemented with atotal cost of PLN 6,000,955 (appr.1,390,000 €), supported by fundsfrom the ESF and the state budget.
Integrated approach to ruraldevelopment;evidence-based decision-making;community engagement &participation;tailored interventions for localcontexts;funding mechanisms for ruralrevitalization;capacity building in rural areas;social inclusion;environmental sustainability.
Outcomes of the revitalizationprojects and the development ofLocal Revitalization Programsmay present a model that couldbe replicated in other regionsfacing similar challenges.Policymakers may consider thepotential for scaling upsuccessful models to benefit abroader range of communities.
Scottish NationalIslands PlanSurvey
Comprehensive approach todata collection, analysis, andvisualization provided
Expertise needed for areassuch as survey design andimplementation, geospatial
Value of the whole contract for thesurvey, including the tooldevelopment, was approximately
National Islands Plan Survey and theassociated tool provide a rich sourceof information for policymakers,
The survey, if repeated in thefuture, could facilitate longitudinalstudies, allowing for the tracking
40GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
valuable insights into theexperiences of residents onScotland's islands,contributing to thedevelopment andimplementation of theNational Islands Plan.
analysis, data analysis andstatistics, tool development,communication, and policyintegration.
€70,000.The cost-effective use of existingskills and free hosting options for thetool also adds to the efficiency of theproject.
enabling them to design andimplement targeted policies thataddress the specific needs andaspirations of residents on Scotland'sislands.
of changes in perceptions andopinions over time. Similarsurveys and tools could bedeveloped for other regions.
Web-mapping toolto visualiseproximity todifferent services- Nordic countries
Valuable tool for visualizingand analyzing the proximity ofvarious services in the NordicRegion, considering factorslike degree of rurality, roadinfrastructure, and cross-border dynamics.
Spatial analysis, webmapping platformdevelopment, knowledge ofdata harmonizationtechniques, Geographicinformation systems (GIS),accessibility calculations,OSM road network data.
Main costs included the purchase ofservice point data, amounting toaround €12,000. Development ofweb-mapping platform and theaccessibility calculations tookaround four months each.
Insights into service accessibility atdifferent geographical levels mayinform public policies aimed atreducing regional disparities byidentifying areas with limited accessto essential services – also acrossnational borders.
Ongoing updates andincorporation of more recent datacould enhance its adaptability.The tool's cross-border analysismay facilitate collaboration andcoordinated efforts in addressingcommon challenges acrossNordic territories.Enhancingaccessibility &understanding ofrural land usedata - EU
The tool utilizes sophisticatedvision-language models tolink ground-level images withsatellite imagery, providingcrucial context forunderstanding rural land usedynamics.
Integrating vision-languagemodels, utilizinggeolocation data, tooldevelopment, analysis ofrural land use data fromground-level images andsatellite imagery.
Hardware: for model training, fourGPUs are utilized to support thecomputational requirements =estimated cost for the infrastructureis 8,000€. Different services offerfree APIs for data collection, butdownloading images take nearly aweek.Tool cost / development of thealgorithm is estimated at 24PM.Potential additional fees for satelliteimage retrieval services and cloudserver deployment.
Successful integration of vision-language models in analyzing ruralland use data offers policymakers atool to make informed decisions inland use planning, conservation, andresource management.
The ultimate goal is to leveragethese advanced models toeffectively understand, monitor,and manage rural land use.
41GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
Earth Observation& Citizen Science– Geo-Wiki
Geo-Wiki is a remarkableexample of harnessing thecollective capacity ofindividuals worldwide tocontribute meaningfully toscientific research andaddress pressing globalchallenges. Monitoring theEarth's surface throughcitizen engagement and dataclassification, platformintegration of citizen-deriveddata with expert andauthoritative sources helps toaddress many policy-relevantquestions.
Combination of citizens andexperts including citizenscience platforms, earthobservation, remotesensing, environmentalpolicy, data management,technology development,geospatial tools.
Development costs in months /years: Early Beta-Version (6PM),version 1 (1.5 years), big refactoring(10 PM) + ongoing featuredevelopment & improvement.No software costs / open source/freesoftware used. Total Hosting Costs(Nov 2011 - Oct 2023): 35,000€Initial Hosting Costs 70€/month /current Hosting Costs (Oct 2023):500€/month. Increase in hostingcosts due to the need for morecollection tools & platforms,additional servers, and services.
Integration of citizen-derived datawith expert sources has substantialpublic policy implications byproviding robust and diversedatasets for evidence-baseddecision-making in areas likeenvironmental policy and SDGs. Thecase highlights the potential forcollaborative citizen scienceinitiatives to inform and shapepolicies addressing pressing globalchallenges.
As the platform continues toevolve, its impact onpolicymaking, environmentalmonitoring, and publicengagement holds the potentialto shift further towards moreinclusive and data-drivenapproaches to address complexglobal issues.
7. Discussion, conclusions and going forward
In this deliverable, we approached the question of data and tools availability, as well as how different cost dimensionsunfold, including staff costs, capacity to work with data and infrastructure costs. We highlighted both costs for usersas well as development and maintenance costs. Many of the tools and methods presented in this deliverable serveto get some initial information and knowledge for different types of user groups, ranging from the individual topolicymaker or planner at different governance levels. Our strategy was to approach this rather complex field byfocusing on three different areas that concern the GRANULAR project and paired with the hope that they serve toinform and inspire people in the Living Labs and beyond:
1) Datasets/ tools identified in previous WP3 work and considered relevant for the work of GRANULAR. WP3partners will continue to work with some of those. We presented them in this deliverable in the form of data
fiches.
2) A survey of national statistical offices and authorities – also to show what type of data is available from thesesources
3) Inspirational examples of data and tools that both Living Labs and GRANULAR partners work with, including asummary matrix of key learnings, cost dimensions, policy implications and future considerations.
The Data Fiches
While the European Union's (EU) commitment to open data has significantly enhanced transparency andaccessibility of both official and research data, the effective use of such open data in rural territories comes with itsown set of challenges and associated costs. This discussion will focus on two critical aspects that emerged from the27 data fiches of this deliverable: the necessity for capacity building in local territories in order to train personnel inGeographic Information System (GIS) and data management, and the necessity for quality assessment throughvalidation with available local databases, complementary data collection and/or ground-truthing.
As shown in this deliverable, while numerous datasets are open and freely accessible, the effective use of this datanecessitates basic GIS and data management skills. Hiring an entry-level GIS technician is essential to navigateand meaningfully analyse spatial data, ensuring its relevance to local contexts. In the EU, the estimated cost ofemploying such personnel ranges from €30,000 to €40,000 annually.
Another significant challenge associated with open data is the variability in the quality of datasets. To ensure thereliability of information for decision-making, data must undergo quality assessment before its use, through thefollowing processes: (i) leveraging locally available datasets or proxies; and/or (ii) engaging in complementary datacollection efforts and groundtruthing activities. While locally available datasets or proxies might serve as a cost-effective initial validation step, it is often necessary to acquire additional data to supplement and enhance existingdatasets. Hiring personnel for such activities, including surveying and data verification, might lead to annualexpenses ranging from €25,000 to €35,000. Such comprehensive quality assessments can improve the reliability ofopen data to ensure its usability for decision-making locally.
Survey
Survey results provide the reader with information – on a country-by-country level – about the main databasesopenly available, containing data about rural development issues, the data domains in the open databases andabout the data types and finest resolutions openly available. All but one of the offices that took part in the surveymake data openly available. The main domains most frequently available and with relevance for rural developmentand governance are demography, economy, agriculture and tourism / recreation. Since mobility data was providedby only one-fourth of the offices and accessibility data, according to the survey, by only one non-EU country,GRANULAR will make a very valuable contribution to fill this gap by providing new and novel datasets. Most of theoffices make tabular data available and more than half of those who responded to the survey, also grid-level data.
43GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
Statistical offices also provide insights into how data is accessed, the associated costs, funding sources, and theprimary user groups and their preferences. They stressed that their customers have a demand for complex andgranular data, as well as indicators to follow up. There is also a need to understand rural-urban interrelations anddifferences. Sound data is required for decision-making purposes and to understand and follow development andchange.
Future plans with repercussions for Living and Replication Labs, and beyond, include those bilateral efforts byStatistics Sweden, Statistics Finland and the Finnish Environment Institute, possibly constructing a new local areadefinition, inspired by the Swedish DeSO (Demographic Statistical Areas) and RegSO (Regional Statistical Areas)to form coherent areas based on population clusters/ neighborhoods.
Inspirational Examples
As a general conclusion, the identification and tracking of costs, especially for the inspirational examples, was byfar not a straightforward exercise and probably the most challenging dimension of case description. Whilst some ofthe examples are free for the user, development costs had different dimensions. Developers faced differentchallenges but also managed to solve them through different means.
The inspirational examples were mostly free for us as a potential user or provided data at no costs. Yet, much alsodepends on what the user wants to do with the data and tool. For a larger project, and more in-depth analyses, staffwith specific training, such as GIS, geography or statistics is needed. Likewise, for the replication of tools, naturallycosts occur, which we tried to describe as best as possible.
As a general observation, all data and tools providers faced different development costs, such as in relation tosoftware or hardware, data management systems or repositories, purchasing or generating data and last but notleast personnel costs. Tools development and calculations etc. require knowledge of data management, GIS,statistics, geography or computing / software development.
Data visualization is an important cost factor for many initiatives (GeoWiki, Rural Barometer, Service Mapper). Inthe case of telecare for the elderly in Ourense, and regarding visualization, difficulties are faced due to the highdegree of protection of the data processed.
Some of the examples also used external expertise, such as for web-mapping platforms (Nordic Service Mapper),survey design and implementation (Rural Barometer) or data collected for a different purpose (SWB indicator).
The Geo-Wiki is a citizen science initiative par excellence. Anyone can engage via desktop or mobile devices inmonitoring the Earth’s surface. Further, volunteers facilitate the process of visual interpretation and data integration.Costs occurred when the platform was developed and when refactoring / changing technologies and frameworks aswell as for hosting (services & servers). In addition, costs are incurred to establish and manage data collectioncampaigns, along with data curation.
In the case of developing the indicator to follow SWB in the CAP, data accessibility and keeping costs fairly low wasbased on key people’s affiliation with free access to data. Yet, this means dependency on individuals and fragility ifno more stable access to data at institutional level is build.
Geo-Wiki uses only open source/free software, thus, there are no software costs. Furthermore, the combination ofcitizens and experts is remarkable. Likewise, in the cross-modality framework example, different services offeredfree APIs for data collection but other costs, such as for tool and algorithm development, occurred.
Developers also stressed that whilst and since initiatives have started and investments have been made, it mademuch sense to continue with and invest in newer data sets, also to identify trends and changes (e.g. Nordic ServiceMapper, Rural Barometer, Geo-Wiki). In the case of monitoring mobility and road traffic at local scale in France,initial software investment in addition to annual maintenance and operational costs, enable the operators to betterunderstand traffic trends, help decision-making, design road projects, and adapt measures for worksite operationsand thus help to save costs elsewhere. The model for diagnosing socio-economic structures and the concept ofimplementing social revitalization in rural areas developed in the communes of the West Pomeranian Voivodeshipwere used to develop 18 Local Revitalization Programs, co-funded by national and EU ESF funds.
44GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
8. Conclusion
As a conclusion, this deliverable showed that local actors have to face a diverse array of costs in order to grasp thediversity within their territory. Traditional surveys demand investments in hiring and training surveyors, distributingmaterials, and managing data entry processes, while more technologically-driven approaches (e.g. remote sensing)incur significant expenses, such as the acquisition and interpretation of imagery coupled with the need for robusttechnology infrastructure. Moreover, deploying sensor/meters networks necessitates induces costs for installation,maintenance, and ensuring data quality. Even data collection methods based on citizen science have infrastructurecosts for running servers and personnel costs for data quality control.
On the analytical front, despite the free availability of many datasets that are relevant and useful for rural areas atthe appropriate granularity, their use is not without costs. Basic GIS and data management skills are essential foreffectively using and analyzing such data, and the necessity for complementary quality assessments for somedatasets further adds to the financial requirements. It thus appears necessary to improve local capacities in GIS,data management and data collection skills through capacity building programs to ensure that rural territories caneffectively leverage open data for informed policymaking.
The key take-away from this analysis is that research teams together with rural territories (including Living andReplication Labs) could leverage funding to build comprehensive capacity-building programs. Such programs couldbe designed to deliver tailored online training that equip rural communities with the skills necessary for the effectiveuse of geospatial open data and data collection methods, with thematic applications relevant to current transitionsthat rural areas face. Fostering collaboration and knowledge sharing about the use of open data for local policymaking should also be actively encouraged, promoting peer-to-peer learning and exchange of best practices amongrural territories. Moreover, supporting the development and deployment of innovative human resourcesmanagement by pooling personnel with specific skills between territories could ensure the sustainability of suchapproaches. Lastly, streamlining data access and management processes is recommended to simplify theprocedures for rural territories, thereby reducing the logistical burden associated with accessing, managing, andanalyzing open data. By embracing these recommendations, the EU can significantly enhance the capacity of ruralregions to leverage open data for informed decision-making.
The EU has been promoting the concept of open data as part of its broader digital agenda and that certain datashould be freely available for anyone to use, reuse, and redistribute. Public sector information should be availablefor reuse, promoting transparency and innovation. Implications of this study for the EU’s open data policy include:
 Capacity Building and Training: The need for capacity building in local territories was highlighted,adhering to the Open Data Directive, may involve training personnel in public sector bodies to effectivelymanage and release data in accordance with the directive.
 Quality Assessment and Validation: The release of high-quality public sector information is encouragedin the Directive. D3.2. emphasis on quality assessment and validation aligns with the Directive's objectiveof ensuring the reliability and usability of data for decision-making.
 Cost Considerations: D3.2 discusses the costs associated with utilizing open data, including the needfor hiring personnel, conducting quality assessments, and data management. This aligns with theDirective's goal of fostering fair competition and innovation by minimizing the costs associated withreusing public sector information.
 User Preferences and Complex Data: The Directive encourages public sector bodies to take intoaccount user needs and preferences. D3.2 findings on user demands for complex and granular dataresonate with the Directive's focus on providing valuable information to users.
 Collaboration and Knowledge Sharing: D3.2 suggests collaboration, knowledge sharing, and peer-to-peer learning, which aligns with the Directive's spirit of promoting cooperation among public sector bodiesto improve the availability and reuse of data.
45GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
 Sustainability and Accessibility: The recommendations in the document, such as comprehensivecapacity-building programs and streamlining data access processes, align with the Directive's goal ofensuring the sustainability and accessibility of public sector information.
 Innovation and Use of Open Data: D3.2 discussion of inspirational examples and the need for ongoinginvestments in newer datasets align with the Directive's goal of fostering innovation and maximizing theuse of open data for societal and economic benefits.
Concerning the next steps in the project it is suggested that:
 Work with data presented here continues, both as regards what was presented in the fiches, via differenttasks under WP3 as well as regards the inspirational examples.
 Living and Replication Labs may take a close look at the different data types and survey results presentedhere and discuss whether some of the examples are of further interest or might potentially even been
replicated in their areas.
 Some of data, such as the Strava dataset, as the largest collection of human-powered transportinformation in the world, would be worth exploring further by GRANULAR or the Living Labs, e.g. through
applying for free access.
 In the discussions – supported by WP 3 and other experts - Living and later Replication Labs should ofcourse reflect on their own needs, specific circumstances and eventual development challenges.
 Readers are warmly welcome to reach out to the authors and contacts of each inspirational example and datafiche to engage in discussing the options going forward.
 Continued Work with Data aligns with the Directive's encouragement for continuous efforts to improvethe availability and quality of public sector information.
 Encouraging Living and Replication Labs to examine survey results and inspirational examples alignswith the Directive's emphasis on collaborative efforts to enhance data reuse. Reflecting on their needs
and circumstances resonates with the Directive's encouragement for public sector bodies to tailor their
practices to local conditions.
 Further exploration of specific datasets, such as the Strava dataset, aligns with the Directive's goal ofencouraging the release of diverse and valuable datasets.
9. Acknowledgements
The editors of this deliverable would like to express – once again – their thanks and gratitude to all colleagues, andespecially to the members of the Living Labs, for all their contributions to this work and for their inspiration. The co-creation spirit, also during the final editing phase, and contributions of LP and WP lead was much appreciated aswere comments and feedback from all partners. We are very grateful to all colleagues in the national and regionalstatistical authorities for taking the time to fill in the survey and for follow-up discussions.
46GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
10. References
ARDECO - Annual Regional Database of the European Commission. Available at https://economy-finance.ec.europa.eu/system/files/2022-10/Reference%20Metadata%20AMECO_September%202022.pdf.
Barranco, R. (2022). UDP - Tourism capacity. European Commission, Joint Research Centre (JRC) [Dataset] PID:http://data.europa.eu/89h/659a45dd-5bc2-4aaf-8bcd-bb337ba03f92.
Batista e Silva, F., Herrera, M. M., Rosina, K., Barranco, R. R., Freire, S., & Schiavina, M. (2018). Analysingspatiotemporal patterns of tourism in Europe at high-resolution with conventional and big data sources. TourismManagement, 68, 101-115. https://doi.org/10.1016/j.tourman.2018.02.020.
Becker D. (2017). Predicting outcomes for big data projects: big data project dynamics (bdpd): research in progress,in: 2017 IEEE International Conference on Big Data (Big Data), 2017, pp.2320–2330.
Bondarenko M., Kerr D., Sorichetta A., and Tatem, A.J. 2020. Census/projection-disaggregated gridded populationdatasets for 51 countries across sub-Saharan Africa in 2020 using building footprints. WorldPop, University ofSouthampton, UK. doi:10.5258/SOTON/WP00682.
Byrne C., 2017). Development Workflows for Data Scientists, O’Reilly Media.
Castillo, C. P., e Silva, F. B., & Lavalle, C. (2016). An assessment of the regional potential for solar power generationin EU-28. Energy policy, 88, 86-99. https://doi.org/10.1016/j.enpol.2015.10.004.
Colas M, Finck I, Buvat J, Nambiar R, Singh R. (2014). Cracking the data conundrum: How successful companiesmake big data operational, Capgemini Consulting.
Edwards R., Bondarenko M., Tatem A. and Sorichetta A. Unconstrained subnational Population Weighted Densityin 2000, 2005, 2010, 2015 and 2020 (100m resolution ). WorldPop, University of Southampton, UK.doi:10.5258/SOTON/WP00703.
European Environment Agency (2016). Potential quiet areas in Europe, based upon Quietness Suitability Index(QSI). Retrieved from EEA’s website: http://data.europa.eu/88u/dataset/e9151c34-da65-48b9-a2ca-b9b835480812.
European Environment Agency (2016). Quiet areas in Europe. Technical report No 14/2016. Available athttps://www.eea.europa.eu/publications/quiet-areas-in-europe.
European Commission, Joint Research Centre (JRC) (2019). ENSPRESO - SOLAR - PV and CSP. EuropeanCommission, Joint Research Centre (JRC) [Dataset] PID: http://data.europa.eu/89h/18eb348b-1420-46b6-978a-fe0b79e30ad3.
European Commission – Eurostat/GISCO. NUTS geometries available athttps://ec.europa.eu/eurostat/fr/web/gisco/geodata/reference-data/administrative-units-statistical-units/nuts.
European Union (2019). Directive (EU) 2019/1024 of the European Parliament and of the Council of 20 June 2019on open data and the re-use of public sector information (recast). ELI: http://data.europa.eu/eli/dir/2019/1024/oj.
Eurostat. (2020). Healthcare services in Europe. Retrieved from Eurostat website: https://gisco-services.ec.europa.eu/pub/healthcare/metadata.pdf.
Eurostat. (2023). Healthcare services locations. Retrieved from Eurostat website:https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/healthcare-services.
Eurostat (2023). ICT usage in households and by individuals (isoc_i): reference metadata in Euro SDMX MetadataStructure (ESMS). EU Statistics Rural household internet access in [reference year(s)]. Available athttps://ec.europa.eu/eurostat/cache/metadata/en/isoc_i_esms.htm.
EU-SILC. Scientific use files available at https://cros-legacy.ec.europa.eu/EU-SILC-SUF/forum_en.
Fendrich, A. N., Matthews, F., Van Eynde, E., Carozzi, M., Li, Z., d'Andrimont, R., Lugato, E., Martin, P., Ciais, P.,Panagos, P., 2023. From regional to parcel scale: A high-resolution map of cover crops across Europe combiningsatellite data with statistical surveys. Science of the Total Environment, pp.162-300.https://doi.org/10.1016/j.scitotenv.2023.162300.
47GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
Fick, S.E. and R.J. Hijmans (2017). WorldClim 2: new 1km spatial resolution climate surfaces for global landareas. International Journal of Climatology 37 (12): 4302-4315.
Freire S., MacManus K., Pesaresi M., Doxsey-Whitfield E., Mills J. (2016). Development of new open and free multi-temporal global population grids at 250 m resolution. Geospatial Data in a Changing World; Association ofGeographic Information Laboratories in Europe (AGILE).
Gupta Uma & Cannon S (2020). A Practitioner's Guide to Data Governance: A Case-Based Approach, EmeraldPublishing Limited. ProQuest Ebook Central, https://ebookcentral.proquest.com/lib/nrifi-ebooks/detail.action?docID=6238638.
Hersbach, H., Bell, B., Berrisford, P., et al. (2017). Complete ERA5 from 1940: Fifth generation of ECMWFatmospheric reanalyses of the global climate. Copernicus Climate Change Service (C3S) Data Store (CDS). DOI:10.24381/cds.143582cf.
López-Antequera, M., Gargallo, P., Hofinger, M., Bulò, S.R., Kuang, Y., & Kontschieder, P. (2020). Mapillary Planet-Scale Depth Dataset. European Conference on Computer Vision.
Lugato, E., Smith, P., Borrelli, P., Panagos, P., Ballabio, C., Orgiazzi, A., Fernandez-Ugalde, O., Montanarella,L., Jones, A. (2018). Soil erosion is unlikely to drive a future carbon sink in Europe. Science Advances. 4, eaau3523.
Martinez I, Viles E, Olaizola I. (2021). Data Science Methodologies: Current Challenges and Future Approaches.Big Data Research, Volume 24. https://doi.org/10.1016/j.bdr.2020.100183.
OpenStreetMap. Available at https://www.openstreetmap.org/.
Pezzulo, C., Hornby, G., Sorichetta, A. et al. (2017). Sub-national mapping of population pyramids and dependencyratios in Africa and Asia. Sci Data 4, 170089. https://doi.org/10.1038/sdata.2017.89.
Saltz J & Shamshurin I (2016). Big data team process methodologies: a literature re-view and the identification ofkey factors for a project’s success, in: 2016 IEEE International Conference on Big Data (Big Data), 2016,pp.2872–2879.
Saltz J, Shamshurin I, Connors C (2017a). A framework for describing big data projects, in: W. Abramowicz, R. Alt,B. Franczyk (Eds.), Business Information Systems Workshops, Springer International Publishing, Cham, 2017,pp.183–195.
Saltz J, Shamshurin I, Connors C (2017b). Predicting data science sociotechnical execution challenges bycategorizing data science projects, J. Assoc. Inf. Sci. Technol. 68(12) (2017) 2720–2728, https://doi .org /10 .1002/asi .23873.
Sarsfield, S (2009). The Data Governance Imperative. IT Governance Ltd. ProQuest Ebook Central,https://ebookcentral.proquest.com/lib/nrifi-ebooks/detail.action?docID=480410.
Schiavina M., Freire S., Carioli A., MacManus K. (2023). GHS-POP R2023A - GHS population grid multitemporal(1975-2030). European Commission, Joint Research Centre (JRC). PID: http://data.europa.eu/89h/2ff68a52-5b5b-4a22-8f40-c41da8332cfe. DOI: https://doi.org/10.2905/2FF68A52-5B5B-4A22-8F40-C41DA8332CFE.
Schneider, M., Chan, A., & Körner, M. (2023). EuroCrops [Data set]. Zenodo.https://doi.org/10.5281/zenodo.10118572.
Sivarajah U, Kamal M, Irani, Weerakkody V. (2016). Critical analysis of big data challenges and analytical methods.J. Bus. Res. 70 (2017) 263–286, https://doi.org/10.1016/j.jbusres.2016.08.001.
Sorichetta, A., Hornby, G., Stevens, F. et al. (2015). High-resolution gridded population datasets for Latin Americaand the Caribbean in 2010, 2015, and 2020. Sci Data 2, 150045. https://doi.org/10.1038/sdata.2015.45.
Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping UsingRandom Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042.https://doi.org/10.1371/journal.pone.0107042.
Strava Metro. Human powered mobility. Available at https://metro.strava.com.
Tatem, A.J., Garcia, A.J., Snow, R.W. et al. (2013). Millennium development health metrics: where do Africa’schildren and women of childbearing age live?. Popul Health Metrics 11, 11. https://doi.org/10.1186/1478-7954-11-11.
48GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
Tiecke, T.G., Liu, X., Zhang, A., Gros, A., Li, N., Yetman, G., Kilic, T., Murray, S., Blankespoor, B., Prydz, E.B., &Dang, H.H. (2017). Mapping the world population one building at a time. https://arxiv.org/abs/1712.05839v1.
WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton;Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite deNamur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018).Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation(OPP1134076).
11. Appendix 1 – Overview of national databases openly available, containing data about ruraldevelopment issues
Country Main database with rural-development relevant data Data domains inopen databases Data typesavailable Finest resolution of data free of chargeBulgaria The IS Infostat platform (https://infostat.nsi.bg) publishes data relevant for rural development. Itincludes business, demographic social, macroeconomic, environment energy and multi-domainstatistics. Data on population and housing census is available free of charge. Paid databasesprovide users access to more detailed data at lower levels after disaggregation. Grid data forpopulation (total, age groups and sex) for 2011, 2021 is free of charge.
Demography,energy and health. N.a. Grid data for population: Total population,Age groups 0-14; 15-64 ;65+ and sex, for2011 and 2021.
Croatia Several databases are available but not strictly connected with rural development issues. The so-called PC-Axis databases are available at https://web.dzs.hr/PX-Web_e.asp?url=%22Eng/Archive/stat_databases.htm%22. Some data available is at municipalitylevel. Grid 1000 is accessible, free of charge, at the GeoSTAT - Web GIS portal of the CroatianBureau of Statistics (https://geostat.dzs.hr). The available grid-level data is on population – numberof populations, number of population by large age groups, population by educational attainment,population by activity, business register (active business entities). Tourism data on accommodationcapacities and tourist arrivals and nights. The PC-Axis databases https://web.dzs.hr) has somedata by municipalities. Some census data by settlements is available athttps://podaci.dzs.hr/en/statistics-in-line/. All data published online is free of charge. If special dataprocessing is needed for grid-level data, it is charged according to the subject’s hourly rate.
Agriculture,demography,economy, energy,environment,tourism / recreation,transport.
N.a. 1km grid. Available data free of charge atthe GeoSTAT - Web GIS portal for theCroatian Bureau of Statisticshttps://geostat.dzs.hr/?lang=en.
Cyprus The main database by Statistics Cyprus is CYSTAT-DB, available athttps://cystatdb.cystat.gov.cy/pxweb/en/8.CYSTAT-DB/. It contains data on agriculture, livestock,fishing, business register, construction, education, energy, environment, external trade, health,industry, information society, innovation, labor market, living conditions, social protection, nationalaccounts, population, price indices, public finance, research and development, services, tourismand trade.
Demography,Health, Education,ICT Usage.
N.a. 1000m for grid-level data.
Finland The main database published by Statistics Finland is called StatFin(https://www.stat.fi/tup/statfin/index_en.html). StatFin is freely accessible and includes data onpopulation, economy, housing, transport, tourism, consumption, prices, wages and salaries,energy, enterprises etc. The Paavo database (https://www.stat.fi/tup/paavo/index_en.html)contains data by postal code area on the population structure, education, income, housing,workplaces, households' life stage etc. There is also a grid-level database with grid sizes of 250m x 250 m, 1 km x 1 km and 5 km x 5 km (https://www.stat.fi/tup/ruututietokanta). The grids coverthe whole of Finland, but this is not for free and charged based on number of licenses and gridsize. Data is available on the areas' population structure, level of education, income of inhabitants
Agriculture,demography,economy, energy,environment,housing,infrastructure,mobility, tourism /recreation,transport.
N.a. Population structure grid data 1km x 1km.
50GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
and households, size and stage in life of households, buildings and dwellings, workplaces, andmain activities of inhabitants. Population structure grid data 1km x 1km is free of charge.France The geoservices.ign.fr site (https://geoservices.ign.fr/) and its Géoservices catalogue(https://geoservices.ign.fr/catalogue) by the Institut National de l’Information Géographique etForestière (IGN) are of relevance for anyone interested in geodata and web services related torural development. Data on the site is free of charge and available under an open license andaccessible without registration. It contains, vector databases, maps, ortho-images, cadastralparcels, 3D models as well as other applications and services. Information includes the CommonAgricultural Policy, forest geographical reference system, "Land-Sea Boundary" data, a RenewableEnergy Map Portal and Good Agricultural and Environmental Condition.A second data source is by the Centre for Studies on Risks, the Environment, Mobility and UrbanPlanning (CEREMA). Its catalogue with over 300 datasets, maps and series is open and availableat https://catalogue.cdata.cerema.fr/geonetwork/srv/eng/catalog.search#/home. Data covers suchfields like land cover, natural risks, energy use, habitats and biotopes and hydrography.The Office français de la biodiversité with its partners feeds an information system on biodiversity,with 14 datasets, services and maps, incl. characterization of Natura 2000 sites, protected naturalareas, inventory of Natural Areas of Ecological, Faunal and Floristic Interest etc.(https://www.ofb.gouv.fr/)Thematic data and datasets on agriculture, culture and heritage, sustainable development andenergy, economics and statistics, eductaion and reserach, international and EU issues, health andsocial issues, tourism and recreation, territories and transport can be found athttps://www.geoportail.gouv.fr/. This is the national portal for territorial knowledge providing openand interoperable data to facilitate the exchange and sharing of data in support of public policies.
N.a. N.a. Grid-level data.
Galicia /Spain Themain database openly available, containing data about rural development issuesof the GalicianInstitute of Statistics is available at https://www.ige.gal/web/index.jsp?idioma=gl. Demography,economy, tourism /recreation.
N.a. Spatial distribution of the characteristics ofthe population of Galicia by grid of 1km2.
Greece Currently, there is no dissemination database openly available to the public. However, data filesavailable for the public are uploaded at the website of the Hellenic Statistics Authority (ELSTAT)in the form of time series, tables and Public Use Files (PUFs). ELSTAT publishes statistical dataon its website at a level of analysis, where statistical confidentiality is not violated, and all userscan access them. Most of the data on ELSTAT's website refer to NUTS 2 level. Depending on thelimitations set due to statistical confidentiality, some data refer also to NUTS 3 level and few data(mainly population data) to LAU level. Statistical data that allow the indirect identification ofstatistical units are provided to users under certain conditions. Tailor-made data based on userrequirements are generally priced. The cost of providing these data depends on the number ofman-days required for their compilation by ELSTAT staff. The pricing policy of ELSTAT is availablehere: https://www.statistics.gr/en/microdata_pricing.
N.a. N.a. R&D, innovation, population data at NUTS-2 level.
Hungary There is no dedicated database for rural development data, but the main database contains dataon agriculture, environment, and many aspects of territorial data. The data can be downloaded incsv and xlsx format and are free of charge. The database can be accessed here:
Agriculture,demography,economy, energy,
N.a. Grid-level data. Charging for the data is notdetermined by the level of resolution or thetheme but by the capacity it requires from
51GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
https://statinfo.ksh.hu/Statinfo/themeSelector.jsp?&lang=en. Predefined data tables in theSTADAT system also contain data on the abovementioned topics, however, this is not a database,but ready-made tables are available, which can also be downloaded in csv and xlsx, also free ofcharge. The STADAT-system is available here: https://www.ksh.hu/stadat_eng.The third main resource where users can find territorial data is the Interactive Mapping Application,accessible here: https://map.ksh.hu/timea/?locale=en. Data can be downloaded from this interface,from the attribute table in csv. Unlike the aforementioned two sources, this application also containsgrid-level data.
environment,health, housing,infrastructure,tourism / recreation,transport.
the statisticians to prepare the data.
Ireland The Central Statistics Office (CSO) of Ireland produces a wide range of statistics. This includesbusiness sectors including agriculture and fisheries, census data, data on the economy,environment, labour market, people and society as well as other themed publications. CSO'sPxStat Open Data Platform is available at https://data.cso.ie/#. Data published on the platform isalso provided by other public sector databases, such as by the Department of Agriculture, Foodand the Marine, the Department of Housing, Local Government and Heritage, the SustainableEnergy authority etc.
See left. G r i d - l eve l ,tabular &vector data.
Small-area data, for which are units with anaverage of 50-100 households in each.Grid-level data is free, and if it is notavailable, €80-200 are charged forcustomers from the private sector.
Italy : IstatData is the latest aggregate data dissemination platform of the Italian National Institute ofStatistics (Istat) and available at https://esploradati.istat.it/databrowser/#/en. It makes use of theopen-source tools “Data Browser” and “Meta & Data Manager” developed by Istat following theinternational SDMX (Statistical Data and Metadata eXchange) standard for exchanging andsharing statistical data and metadata. Currently, six themes are covered: National Accounts,Population and Households, Household Economic Conditions, Agriculture, Enterprises, Welfareand Pension. Time Series (https://seriestoriche.istat.it/index.php?id=18&L=1) contains over 1,500time series organized into 22 thematic areas made available to inform abot the environmental,social and economic changes in Italy.
See left. Tabular andvector data. Agricultural plot level.
Moldova The statistical databank of Statistics Moldova is available at https://statbank.statistica.md/. Itcontains data on environment, population and demographic processes, social statistics, economicstatistics, gender statistics, and regional statistics.
Accessibility,agriculture,demography,economy, energy,environment,health, housing,infrastructure,mobility, tourism /recreation,transport.
Raster &tabular data. Currently the office disseminates at thelevel of communes (with a commune beingformed of one or several villages) – both inthe Statistical databank and as static mapsin its publications. In 2024, the office plansto carry out a Population and HousingCensus and will then have available spatialdata both at vector and grid-level. Currently,the office does not provide spatial dataagainst a fee.Poland The Knowledge Database (https://dbw.stat.gov.pl/en) by Statistics Poland contains 31 domainareas including Demography, Education, Energy, Social economy, Municipal and housinginfrastructure, Agriculture, Labor Market, Transport, Tourism, Living conditions, Health andhealthcare. For some indicators data is available by voivodships, in the case of demographic dataand of local government unit budgets also for lower levels of territorial division. The KnowledgeDatabase is a publicly available and free of charge. Furthermore, there is also a Centre for Rural
N.a. N.a. NUTS 2.
52GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
14 According to Tim Berners-Lee, open data can be published at various levels of openness (see 5-star Open Data (5stardata.info).
Statistics (https://olsztyn.stat.gov.pl/en/) and a Small Areas Statistics Centre(https://poznan.stat.gov.pl/en/) in addition to regional statistics centers throughout the country.(https://stat.gov.pl/en/regional-statistics/). EUROSTAT Economic accounts for agriculture - valuesat current prices and Economic accounts for agriculture - values at n-1 prices are available, too.Portugal The main database openly available, containing data about rural development issues of StatisticsPortugal is available herehttps://www.ine.pt/xportal/xmain?xpid=INE&xpgid=ine_bdc_tree&contexto=bd&selTab=tab2.Themes also include Agriculture, forest and fisheries.
Agriculture,demography,economy, energy,environment,health, housing,mobility, tourism /recreation,transport in additionto culture, prices,living conditions.
Grid-level &tabular data. For the Population and housing Censusgrid of 1km2 is free of charge. For some ofthe other domains, data is at parish-level.
Scotland The main database openly available, containing data about rural areas are the 1) NationalPerformance Framework (https://nationalperformance.gov.scot/measuring-progress/national-indicator-performance) with 26 out of 81 indicators providing data for Rural Scotland and the 2)Rural Scotland Key Facts 2021 (https://www.gov.scot/publications/rural-scotland-key-facts-2021/documents/) and a considerable number of sources. There are several public sector opendata portals, most allow for publication of data at the 3* level of openness (csv or equivalent).14Many of Scottish Government statistics is available online, for free and without restrictions. Itcontains around 300 open datasets and reference material, mainly at the 5* level – the highestlevel of openness, with associated metadata.
N.a. N.a. Grid-level data.
Serbia The main database openly available, containing data about rural development issues of theStatistical Office of the Republic of Serbia is available here:https://data.stat.gov.rs/?caller=SDDB&languageCode=en-US.
Accessibility,agriculture,demography,economy, energy,environment,health, housing,infrastructure,tourism / recreation,transport.
N.a. Spatial resolution depends on the coverageof statistical survey, from which the datasetis produced. In the annual plan of statisticalsurveys, the spatial resolution of availabledatasets which are free of charge is defined.
Slovakia The main database openly available, containing data about rural development issues of SlovakStatistics is called DataCube and is available at https://datacube.statistics.sk/. It containsmultidimensional tables for indicators of economic and socio-economic development, for thefollowing areas: demographic and social statistics, macroeconomic statistics, business statistics,sector statistics, environment, multi-domain statistics and selected tables of the Eurostat database.
Accessibility,agriculture,demography,economy, energy,environment,
N.a. Municipalities - especially demographicdata. All data in the database are free ofcharge.
53GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
1 km x 1 km grid-level data is available from the 2011 and 2021 census:https://slovak.statistics.sk/wps/portal/ext/themes/demography/census/indicators/. health, housing,infrastructure,tourism / recreation,transport.Slovenia The main database openly available, containing data about rural development issues of StatisticsSlovenia is available at https://pxweb.stat.si/SiStat/en/Podrocja/Index/583/regionalni-pregled. Agriculture,demography,economy, energy,environment,housing,infrastructure,mobility, tourism /recreation,transport.
N.a. Grid-level data available at the STAGEpages at https://gis.stat.si/#lang=en. Alldata at Statistics Slovenia is free of charge.
Sweden The majority of Statistics Sweden's statistics are openly available at "Statistikdatabasen"(Statistical database) (https://www.statistikdatabasen.scb.se/pxweb/en/ssd/). In addition, otherdata openly available from Statistics Sweden includes geospatial data(https://www.scb.se/en/services/open-data-api/open-geodata/). Furthermore, there are 29 differentpublic agencies, which publish official statistics, and partly cover other topics than StatisticsSweden: https://www.scb.se/en/About-us/official-statistics-of-sweden/government-agencies-responsible-for-official-statistics/. Furthermore, in addition to Statistics Sweden, other agenciesalso publish similar data (including geospatial data/GIS layers). In addition to the databasementioned above, there are also data at the following spatial resolution, which fully or partly gobelow municipal level: Grid statistics, preschools and agency and municipal offices, localities andsmall localities, holiday-home areas, retail trade areas, activities zones, and RegSO (RegionalStatistical Areas). For accessing the GIS layers, please see: https://www.scb.se/en/services/open-data-api/open-geodata/
Agriculture,demography,economy, energy,environment,housing,infrastructure,mobility, tourism /recreation,transport.
G r i d - l eve l ,point, tabular& vectordata.
Grid-level data in Sweden is free of charge.In the database mentioned previously,some statistics are made availableaccording to DeSO, "Demografiskastatistikområden" (in English: "DemographicStatistical Areas"). There are alsocorresponding GIS layers available. Formore information, please seehttps://scb.se/hitta-statist ik/regional-s t a t i s t i k - o c h - k a r t o r / r e g i o n a l a -i n de l n i nga r / deso - - - demog ra f i s ka -statistikomraden/or https://www.scb.se/en/services/open-d a t a - a p i / o p e n - g e o d a t a / d e s o - -demographic-statistical-areas/.
54GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
12. Appendix 2 - Data Fiches
12.1. Accessibility
55GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
12.2. Agriculture
56GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
57GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
58GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
12.3. Climate
59GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
60GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
12.4. Demography
61GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
62GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
63GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
64GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
65GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
66GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
67GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
12.5. Digitalisation
68GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
12.6. Economic Development
69GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
70GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
71GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
12.7. Energy
72GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
12.8. Health
73GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
12.9. Infrastructure
74GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
75GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
76GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
12.10. Mobility
77GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
12.11. Recreation
78GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
12.12. Transversal
79GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.
80GRANULAR has received funding from the European Union’s Horizon Europe Research and Innovation Programme under GrantAgreement No. 101061068. UK participants in the GRANULAR project are supported by UKRI- Grant numbers 10039965 (JamesHutton Institute) and 10041831 (University of Southampton). Views and opinions expressed are however those of the author(s) onlyand do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the EuropeanUnion nor the granting authority can be held responsible for them.