Why measure capacity?

So firstly, why should we measure capacity? Well, the United Nations Development Programme (UNDP) defines capacity as "the ability of individuals, institutions and societies to perform functions, solve problems, and set and achieve objectives in a sustainable manner" (UNDP, p. 2). They outline three overall stages to the capacity development process:

1. Determining the starting point, and identifying what capacities there are to begin with.

2. Identifying the barriers to capacity development, and designing tailored actions to address those barriers.

3. Measuring the change in an organisation's capacity to achieve its goals, and identifying where investments should be made to drive continuous improvement (ibid.).

Measuring the capacity to integrate geospatial and statistical data is a critical step in advancing data-driven decision-making. Self-assessment tools can play a pivotal role in this process by offering a structured and participatory approach to evaluate existing capabilities, identify gaps, and prioritise areas for improvement. These tools help organisations assess their current capabilities in a comprehensive and systematic way, considering critical aspects such as institutional frameworks, technical infrastructure, data governance, and human resources. By engaging the stakeholders themselves in the assessment process, a shared understanding of strengths and challenges can be fostered, building consensus around the steps needed to enhance integration, and creating a shared vision for integration goals.

The value of self-assessment tools lies not only in their diagnostic function, but also in their ability to drive actionable insights. By aligning the evaluation process with global standards and best practices, these tools can ensure that organisations are ready to meet emerging demands for integrated data. This is especially important as integrated data supports the measuring and monitoring of the Sustainable Development Goals (SDGs) and the response to complex global challenges. Self-assessment tools provide a scalable and repeatable method that allows the continuous monitoring of progress, and fosters a culture of accountability and adaptability that is essential for long-term success. As such, these tools can act as a bridge between aspiration and action, enabling organisations to harness the full potential of integrated data. You can read more about more UNECE's role in statistical capacity development as well as access a range of resources here.

So, tell me more about the new self-assessment tools . . .

As some context, UNECE recently led the completion of the EU-funded INGEST Project to develop greater capacity in the integration of statistical and geospatial information across the region (see here for more information). Since the project concluded in April, we have been carrying out follow-up activities in selected programme countries to help advance the integration of statistical and geospatial information at a national level. These activities have been funded by the fourteenth tranche of the UN Development Account project on Statistics and Data which is implemented by the UN Statistics Division, the five UN Regional Commissions and other UN bodies.

Through this work, UNECE has developed two self-assessment tools that allow national statistical and geospatial organisations to assess their ability to effectively integrate geospatial and statistical data:

1. INGEST Maturity Assessment Tool allows organisations to identify their current and target levels of maturity in integrating statistical and geospatial data across a set of dimensions and maturity levels

2. integRATE Assessment Tool allows organisations to assess their technical capabilities for data integration through the dual perspectives of data producers and data users, focusing on data, technical infrastructure and standards.

Both tools are available to view and download on the INGEST wiki space here. We'll now take a look at each of the self-assessment tools in some more detail:

How are these self-assessment tools being used?

As mentioned earlier, we have recently been working with a number of our programme countries to support them as they seek to advance their capabilities to integrate geospatial and statistical data. We have had the pleasure of working with the National Statistical Institutes (NSIs) and National Mapping and Cadastral Agencies (NMCAs) of the Republic of Albania, Republic of Armenia, Republic of Kazakhstan, and Republic of Moldova who have utilised the tools to help address their specific needs and requirements. We are publishing the main outcomes of this work as country-specific studies on our INGEST wiki space (see here) so watch this space!

As the tools are freely available to access on our wiki space, you are welcome to download and use the tools for your own requirements. If you do use the tools, we would love to hear about your experiences and any outcomes that come from their use so please get in touch!

Enhancing decision-making and policy impact through data integration

To unlock the full power of data in developing countries, the integration of geospatial data into official statistics is being increasingly recognised by incumbent parties as a vital component for improving decision-making processes, especially in the expectations of developing countries to achieve their sustainable development goals. As nations strive to address complex social, economic, and environmental challenges, the ability to analyse and utilise geospatial information alongside traditional statistical data becomes essential. The case studies of Kenya and Paraguay (discussed in more detail below) demonstrate that establishing formal and technical arrangements for data integration not only enhances the quality of data but also provides a comprehensive understanding of various phenomena, leading to more informed and less biased policy-making.

Furthermore, institutionalising the bonds of statistics and geospatial data is vital for creating inclusive policies that effectively address the complex challenges faced by increasingly diverse populations. Benefits of this kind of institutionalisation, when disaggregating and integrating statistics and other valuable data facilitate data-driven decision-making, enhance interoperability, improve resource allocation and advance monitoring and evaluation of policy impacts. In short, it can represent a leapfrog change in the realm of policy-making.

At this point, it is relevant to highlight the relationship between the UN Integrated Geospatial Information Framework (UN-IGIF) (also explored in a previous blog post) and the GSGF. Both frameworks complement each other and share a common goal: to enhance synergies between geospatial and statistical data, ultimately improving decision-making processes. Collaboration between these domains allows countries to create robust datasets reflecting real-world conditions. The primary idea behind these two recommended models, which do not consist of mandated rigid frameworks by international organisations, is to promote the enhancement of integrating statistical and geospatial information. No more, no less.

One developed country that has greatly benefited from the synergy between the UN-IGIF and the GSGF is Germany. The country has implemented these two frameworks to enhance its national geospatial information management, which supports various sectors including urban planning, environmental monitoring, and disaster management.

Core Functionalities

Complete R package integrating textual analysis with geospatial data

The genesis of this package lies in the increasing relevance of integrating textual analysis with geospatial data. As urban planning, environmental research, and even commercial targeting become more data-driven, the ability to correlate sentiments expressed on social media with geographic locations can offer invaluable insights. Our package is designed to bridge the gap between traditional text analysis and modern geospatial analytics, making it a pioneering tool in the field. This integration allows researchers to explore the spatial patterns of sentiment and opinions, enabling a deeper understanding of the relationships between textual content and geographical context. Furthermore, the package provides users with a comprehensive set of tools for processing, visualizing, and interpreting the combined textual and geospatial data, facilitating advanced analysis and interpretation of complex relationships within the data. Moreover, the package offers advanced functionalities for spatial clustering, sentiment mapping, and geovisualization, enhancing the exploration and interpretation of the intricate interplay between textual content and geographical features. Moreover, the integration of advanced functionalities such as spatial clustering, sentiment mapping, and geovisualization enhances the exploration and interpretation of the intricate interplay between textual content and geographical features, allowing for a more nuanced analysis of the data. Additionally, the incorporation of sophisticated analytical techniques like spatial clustering, sentiment mapping, and geovisualization enriches the exploration and interpretation of the intricate interplay between textual content and geographical features, thereby enabling a more nuanced and in-depth analysis of the data. Furthermore, the utilization of these advanced functionalities and analytical techniques enables researchers to delve deeper into the complexities of the data, providing a more comprehensive understanding of the relationships between textual content and geographical features.

Integration Aspects

A key strength of our weather sentiment package lies in its seamless integration capabilities, designed to fit smoothly into a wide array of data pipelines and analytics platforms. Whether you are working on a small-scale academic research project or handling large-scale commercial applications, this package offers flexibility and compatibility with various tools and libraries in the R ecosystem, as well as external systems.

The modular design of the package ensures that users can pick and choose individual functions that meet their specific needs without being tied to the entire framework. This allows for efficient integration into both existing and custom-built data workflows. Each function is built as an independent module that can be executed standalone or combined with other tools in the package, providing maximum versatility for developers, data scientists, and analysts.

Benefits to the Statistical and Geospatial Communities

The weather sentiment package offers powerful tools that go beyond simple text analysis, integrating sentiment analysis with geospatial data to provide deeper insights across various fields:

For Researchers:

• Socio-Economic Studies: Analyze regional variations in public sentiment on topics like inequality or public health, providing valuable insights for socio-economic research.
• Urban Planning: Assess local attitudes towards infrastructure and environment, aiding in the design of responsive urban spaces.
• Environmental Monitoring: Track public responses to environmental changes and disasters, enhancing research on climate change and crisis response.

For Businesses:

• Localized Marketing: Tailor strategies based on regional sentiment to optimize advertising and product launches.
• Brand Sentiment: Monitor brand perception across different regions to improve customer engagement and address concerns.
• Customer Experience: Understand how local events affect sentiment, enabling dynamic adjustments in customer service and operations.

For Policy Makers:

• Informed Decisions: Use spatial sentiment data to craft targeted policies that address regional public concerns on issues like climate change and healthcare.
• Public Opinion Monitoring: Track shifts in sentiment during elections or public debates for real-time insights.
• Crisis Management: Analyze community reactions to environmental events for better disaster planning and resource allocation.

UNECE and the modernisation of official statistics

Before we dive into the detail of the GeoGSBPM, it is useful to set the framework within its wider context and look at UNECE's role within the modernisation of official statistics. UNECE's Statistical Division plays a leading role in coordinating statistical activities across the UNECE region, particularly helping countries to strengthen, modernise and harmonise their statistical systems under the guidance of the Fundamental Principles of Official Statistics. The Division works with networks of experts to create statistical guidelines, recommendations and standards that are at the forefront of the discipline and have a global reach.

Of importance, the High-Level Group for the Modernisation of Official Statistics (HLG-MOS) was formed by the Conference of European Statisticians as a forum to drive the modernisation of official statistics in areas such as big data, machine learning and strategic communication. It consists of Chief Statisticians from national and international organisations who lead the implementation of new methods, technologies and other capabilities in statistical organisations. UNECE works with the HLG-MOS to create, implement and enhance standards for statistical production, with a particular focus on standards for metadata. In doing so, this ensures that “common definitions and processes are used within and between statistical organisations, helping to remove the barriers to collaboration on technical projects, fostering the sharing of knowledge and experiences, and serving as a basis for streamlined statistical production” (UNECE).

A geospatial view of the Generic Statistical Business Process Model (GeoGSBPM)

UNECE published the Geospatial View of the Generic Statistical Business Process Model (GeoGSBPM) in 2021 in recognition of the increasingly important role that geospatial information plays in the activities of statistical organisations and the production of geospatially-enabled statistics that are crucial to understanding (and addressing) some of the biggest challenges we face, such as the impacts of climate change, social inequality, and economic uncertainty. As such, they consider that "statistical organisations should be prepared to produce geospatially enabled statistical data in an efficient and timely manner . . . [and that] geospatially relevant activities and considerations should be integrated into the regular production processes of statistical organisations, so that the design and production of geospatially enabled statistics can be conducted in a systematic and consistent way" (UNECE). Hence, the GeoGSBPM was developed.

As well as phase-specific activities and considerations, the GeoGSBPM also presents three overarching and corporate level activities which should be applied at an organisational level:

• Strategic collaboration and cooperation, to ensure that coordination and cooperation is maximised across the data ecosystem and its various stakeholders (which can span multiple organisations).

• Metadata management, by growing statistical awareness of the variety of metadata associated with geospatial datasets, identifying the core metadata elements required across the production process, as well as progressing the alignment of statistical and geospatial metadata concepts.

• Quality management, through the identification of authoritative sources of reference data, developing feedback mechanisms across the statistical and geospatial domains, developing suitable quality metrics, as well as horizon-scanning and monitoring developments in the geospatial field to ensure that activities are current and in harmony with other communities.

In carrying out these activities, statistical organisations can produce high-quality geo-statistical data that follows the principles of the GSGF.

How will the GeoGSBPM benefit me and my organisation?

Adopting the GeoGSBPM will bring many benefits, such as:

By adopting the GeoGSBPM, statistical organisations can produce geospatially enabled statistics in both a consistent and systematic way which will drive the greater harmonisation and interoperability of data across organisations, from the national to the global scale. It also provides an important feedback mechanism to improve the quality and accessibility of geospatial data. Crucially, the GeoGSBPM acts as a pillar which sets out the practical steps for integrating geospatial information within the statistical process, ensuring that the resulting data meets the high standards required to support data-driven decision-making and policy development across multiple spatial scales.

Looking ahead . . .

The INGEST blog series was established as part of an EU-funded project to develop greater capacity in the integration of statistical and geospatial information. Following the completion of this project, we are still keen to continue providing regular blog posts for INGEST. If you have an interesting project, concept, activity, or perspective that falls within the realms of statistical and geospatial data integration, we would love to hear from you! Please get in touch with Sara Stewart (UNECE Consultant) or Taeke Gjaltema (UNECE Regional Advisor for Statistics) to discuss this further. Until next time!

Acknowledgements: We are very grateful to InKyung Choi (UNECE) for contributing to this post.

Guest Authors: Hossein Hassani (Webster University, Vienna, Austria), Leila Marvian (Big Data Lab, Imam Reza International University, Mashhad 178-436, Iran) and Steve MacFeely (World Health Organisation)

Each dataset comes with its unique set of metadata, accuracy levels, and update cycles, necessitating a nuanced approach to integration. The process of ensuring compatibility and coherence across datasets involves not just simple data cleaning procedures but also sophisticated techniques like coordinate transformation, conflation, and the reconciliation of semantic differences. Moreover, the rapid evolution of technology and data acquisition methods means that geospatial datasets are expanding not only in size but also in complexity. Historically, the lack of standardised tools tailored to tackle these multifaceted tasks has been a bottleneck, limiting the ability of professionals and researchers to carry out comprehensive geospatial analyses. Analysts often had to rely on a patchwork of software solutions or custom-built scripts, which could be time-consuming, prone to errors, and not easily reproducible. This piecemeal approach also hindered collaboration and sharing of geospatial analyses and insights.

The importance of overcoming these hurdles cannot be overstated, as geospatial data plays a crucial role in a wide array of critical applications. From urban planning and environmental monitoring to disaster response and global health initiatives, the insights derived from integrated geospatial data are indispensable. It guides decision-makers in policy formulation, business strategy, and scientific research, impacting lives and shaping the future of our societies. As such, the development of a comprehensive tool that can streamline the process of GIS data integration represents a significant leap forward for the field. By providing a standardised, efficient, and robust means to pre-process, clean, unify, and integrate diverse geospatial datasets, such a tool unlocks the full potential of geospatial analysis, facilitating more accurate, insightful, and actionable intelligence from the wealth of data available.

A solution at last: The GIS INTEGRATION R package

Recognizing the critical need for efficient and seamless GIS data integration, a ground-breaking R package, developed by a team of experts: Hossein Hassani (Adjunct Professor at Webster University), Leila Marvian (Lecturer at Imam Reza International University), Sara Stewart (UNECE Consultant), and Steve MacFeely (Director of Data and Analytics at WHO) was recently introduced. The package was rigorously tested using a range of data sources, including a statistical output geography introduced by the Northern Ireland Statistics and Research Agency (NISRA) after the 2021 Census, known as Super Data Zones, alongside other population data from the census. The authors are grateful to NISRA for the availability and quality of their data which proved invaluable to the testing process.

The newly released R package, called GIS INTEGRATION, emerges as a beacon of innovation, meticulously designed to address the multifaceted challenges of GIS data integration. The package is freely available through CRAN, the Comprehensive R Archive Network, which is R's central software warehouse containing an archive of the latest (and previous) versions of R distribution, supporting documents and associated packages for access and download.

A milestone for geospatial analysis

The unveiling of the GIS INTEGRATION R package stands as a transformative event in the world of geospatial analysis. This pivotal development represents far more than a mere incremental advancement; it is a paradigm shift that promises to redefine the landscape of spatial data exploration and utilisation.

Geospatial analysis has long been a cornerstone in disciplines ranging from environmental science to urban development, disaster management to public health. However, the potential for innovation and discovery within these fields has often been constrained by the cumbersome nature of integrating complex GIS datasets. The GIS INTEGRATION R package directly addresses this bottleneck, offering a suite of tools that effortlessly melds disparate data sources into a cohesive whole. This tool transcends traditional boundaries by drastically reducing the time and technical expertise required to prepare and harmonise spatial data. Its impact is twofold: it not only amplifies the existing capabilities of seasoned researchers and GIS professionals but also democratises access to sophisticated geospatial analysis for a broader audience. As a result, it facilitates a more inclusive environment where experts and novices alike can contribute to the collective understanding of spatial phenomena.

Next time . . .

We move our focus towards the use of standards to support the integration of statistical and geospatial information, first exploring UNECE's Geospatial View of the Generic Statistical Business Process Model (or GeoGSBPM for short) which describes a range of geospatial-related activities that are needed to produce geospatially-enabled statistics and, crucially, to integrate geospatial information within the statistical process. We hope to see you next time!

The UN Integrated Geospatial Information Framework

The UN Integrated Geospatial Information Framework (UN-IGIF) was developed by UN-GGIM in collaboration with the World Bank as a guide to support governments in the development and strengthening of integrated geospatial information management practices and their inclusion in national plans and strategies. It is also intended to be used as a tool for engagement that will lead to better “coordination, collaboration and coherence across government when working towards strengthening national geospatial information management” (UN-IGIF Part 1 p. 25). The framework was first developed as a means to support and guide lower to middle income countries in the development and strengthening of their own geospatial information management practices and related infrastructures. However, the broader benefits to many high income and developed countries also become apparent as the framework was developed due to its "integrative and inclusive strategic nature" (ibid. p. 1) and it has been applied much more widely than originally intended.

Through an overarching strategic framework, implementation guide, and templates and guides to create and implement country-level action plans, and its seven underpinning principles, eight goals and nine strategic pathways, the framework “creates an enabling environment where national governments can coordinate, develop, strengthen and promote efficient and effective use and sharing of geospatial information for policy formulation, decision-making and innovation” (ibid. p. 9). Both the GSGF and UN-IGIF, by design, allow flexibility to ensure that a range of statistical and geospatial capabilities can be accommodated, which is particularly useful for less-developed countries.

The UN-IGIF is presented as three separate, though interconnected, parts:

Part 1 takes the form of an Overarching Strategic Framework which presents a comprehensive overview of the UN-IGIF, sharing its key strategic messages and policy drivers. It presents the benefits of the framework, the drivers for change, and barriers to success which need to be overcome. It sets out why integrated geospatial information management is critical to sustainable development, both at national and global levels, particularly focusing on the framework's vision, mission, underlying principles, goals and strategic pathways. The Overarching Strategic Framework is designed to be read by a wide variety of stakeholders, especially decision-makers from institutions and organisations within and across government.

Part 2, the Implementation Guide, is a set of documents which provide specific guidance and detailed information on the actions which should be undertaken when implementing the framework as a means of solving the puzzle. The documents expand on each of the nine strategic pathways, presenting both strategic and operational needs, and sharing important guiding principles, actions, deliverables, outcomes and resources that enable governments to establish "nationally integrated geospatial information frameworks in countries in such a way that transformational change is enabled, visible and sustainable" (UN-IGIF Part 1 p. 8).

Part 3 consists of documentation to support the development of Country-Level Action Plans that operationalise the framework within unique national (and sub-national) contexts, through the provision of specific guides, recommended tasks and templates. Topics include stakeholder identification, a baseline survey, gap analysis, and needs assessment for example. The documents present the purpose of specific tasks and the methods to be undertaken which are clearly outlined in a series of detailed steps. These resources provide an important means to support countries in the preparation and implementation of their own action plans which take account of specific national circumstances and priorities.

Adopting the UN-IGIF has clear benefits

These benefits transcend across the three development pillars of society, the economy and the environment, and will help to address the many challenges we face such as climate change, global health issues, political conflict, and poverty. The UN-IGIF provides a mechanism for countries to take action and bridge the geospatial digital divide for the benefit of all.

Looking ahead

The UN Expert Group on the Integration of Statistical and Geospatial Information (EG-ISGI) has recognised the importance of the UN-IGIF as an enabler to the Global Statistical Geospatial Framework (which was presented in our previous post). The UN-GGIM Decision 12/108 highlighted the importance of leveraging the UN-IGIF for the statistical domain, to strengthen its linkages with the GSGF, and enhance the value that geospatial data can bring to statistical production. As part of their Work Plan 2022 - 2024, the EG-ISGI is currently working to identify a set of actions that expand upon the UN-IGIF for the statistical domain, including the development of a white paper on the subject.

There is also exciting work being undertaken across the European region as James Norris, International Policy Lead at Ordnance Survey (UK) and Chair of the UN-GGIM: Europe Working Group on Integrated Geospatial Information Framework, describes:

In Europe, the UN-GGIM: Europe Working Group on Integrated Geospatial Information Framework has been actively promoting the UN-IGIF within our Region. We were really pleased to be able to participate in the Joint UNECE, Eurostat, UN-GGIM Europe Workshop on Integration of Geospatial and Statistical Data and to see it focusing on the the themes of Effective Governance, Data and Technology, and People and Partnerships. It was also important to hear from Member States on the different ways that they are implementing the UN-IGIF. Looking forward into 2024 our focus is to continue delivering our Regional webinar series. This will build on the webinar An Introduction to the United Nations Integrated Geospatial Information Framework, and will be a more in-depth look at the different methodologies that Member States can use to implement the UN-IGIF, we hope that you will be able to join this discussion. It is also important for us to hear from you about how you are implementing the UN-IGIF and to develop case studies that can be shared. Please do get in touch if you'd like to share your experiences. 

With all these activities and support forthcoming, there is never a better time to adopt the UN-IGIF and realise its many benefits!

Next time . . .

We will present an exciting new R package which has recently been developed, allowing users to seamlessly integrate two GIS data sources by pre-processing, cleansing, and unifying the data based on common attributes. This package may solve all your technical problems so won't want to miss this! Until next time!

The Global Statistical Geospatial Framework

At a global level, the 2030 Agenda for Sustainable Development has been a driving force for the greater integration of statistical and geospatial data as it requires robust, harmonised data at granular levels of geography for the production and monitoring of SDG indicators. In support of the 2030 Agenda, the Population and Housing Censuses, and other important development initiatives ranging from local to global levels, UN-GGIM published the Global Statistical Geospatial Framework (GSGF) in 2019 as a key policy framework to act as a “bridge between statistical and geospatial professional domains, between NSOs [National Statistical Organisations] and NGIAs [National Geospatial Information Agencies], and between statistical and geospatial standards, methods, workflows and tools” (GSGF, p. 6). By applying the framework's five Principles and four Key Elements, the production of high-quality "harmonised, standardised and geospatially-enabled statistical data" (ibid., p. vii) can be realised. Such data plays a valuable role in informing, supporting and driving evidence-based decision-making and policy development across all geographic scales.

The GSGF is presented as two separate, but complementary, documents:

The Global Statistical Geospatial Framework itself as the main policy document which presents the context and key concepts of the framework. It is already available in six different languages (English, Arabic, French, Mandarin, Portuguese and Spanish) enabling its broad use. The information is presented in three very easy-to-digest parts:

• Part 1 presents the GSGF as a high-level overview of the framework, outlining its Inputs, Principles, Key Elements and Outputs. It is designed to draw the reader in by presenting key information on the framework, highlighting the importance of integrated data and the means to achieve integration.
• Part 2 goes into more detail on each of the five Principles, addressing the need for each Principle, what is covers, the main objective of its use, the requirements and benefits, the relationship to other principles, the data inputs required and the key stakeholders involved.
• Part 3 provides additional supporting information in the form of three annexes, including definitions of key terms, further information on the need to use internationally-adopted standards and the importance of assessing data quality across all stages of the framework, and further reading and resources on other national and international frameworks, standards, and privacy and data disclosure prevention.

The Global Statistical Geospatial Framework: Implementation Guide as an important supporting document which provides detailed guidance on how to implement the GSGF. It acts as a living document which will be periodically updated to include best practices, developments and innovations in the field. It consists of three main sections:

• Implementation guidance on four key areas that are seen as critical to successfully implementing the GSGF: geocoding, common geographies, fostering interoperability, and ensuring privacy and confidentiality. For each area, the relevant GSGF Principles are presented, the concept is defined, its importance highlighted, how it can be implemented is described, and further reading and relevant resources are shared.
• Terminology related to the integration of statistical and geospatial information is then presented as a means to improve understanding and the adoption of common definitions across the framework and its users.
• Experiences of implementing the GSGF are then presented from 30 different countries (including Australia, Cuba, Germany, Indonesia, and Senegal) and five regions (Africa, the Americas, Asia and the Pacific, Europe and Western Asia). The case studies discuss the level of overall implementation of the GSGF, share more detail on the implementation of each of its five Principles, and highlight the value of the GSGF to national- and regional-level responses to COVID-19 as a high-impact use case.

GSGF Europe: Adapting the GSGF to the European context

The GSGF has been described as "a framework for the world" and its concepts and requirements are broad enough to be applied to differing regional contexts. Within Europe, the European Cohesion Policy 2021-2027, which aims to correct imbalances between countries and regions in order to strengthen social, economic, and territorial cohesion across the EU, requires detailed and harmonised data across different spatial scales of analysis and geospatial data is already often used in conjunction with regional statistical data in various phases of the policy-making process (e.g. Territorial Impact Assessments). The INSPIRE Directive has also established an Infrastructure for Spatial Information in the European Community (INSPIRE) to “create a European Union spatial data infrastructure for the purposes of EU environmental policies and policies or activities which may have an impact on the environment . . . [to] enable the sharing of environmental spatial information across Europe and assist in policy-making across boundaries” (European Commission). Other important initiatives, such as the GEOSTAT projects led by Eurostat and the European Forum of Geography and Statistics (EFGS), have been working towards the greater integration of statistical and geospatial data across the EU through the development of common guidelines for grid-based and geospatial statistics for use by national statistical and geospatial organisations. Of significance here, the GEOSTAT 4 project has conceptualised and interpreted the GSGF within the European context to support its implementation at a regional level, resulting in the GSGF Europe guidance document, an associated User Guide, and a range of other supportive resources available on the GEOSTAT Information Service.

The GSGF Europe provides a "high-level summary of the conceptualisation and interpretation of the GSGF in the European context", supporting the global framework's regional implementation by adopting it to the European statistical and geospatial operating environment. The document consists of three main sections:

• Key aspects of the GSGF and its five Principles are first interpreted for the document and the relationship between the principles are highlighted.
• The GSGF is then examined within the context of the European operating environment, a GSGF Europe Reference Architecture is defined (as the basic structure and operation of the European statistical and geospatial community), the GSGF's four Key Elements are interpretated, and four topics for future enhancement are presented: quality, data collection, confidentiality and innovation.
• Finally, nine relevant statistical and geospatial frameworks which are actively applied in Europe are presented, with the authors recognising that the GSGF "is part of a family of frameworks that aim to integrate data and information" (GSGF Europe p. 5) that are complementary and should not be considered in isolation.

An important outcome of the GEOSTAT 4 project is the development of a set of Requirements and Recommendations which break down the framework into a series of small, concrete and management steps that enable the GSGF to be implemented more consistently and systematically across Europe. The diagram to the right below summarises these requirements and recommendations, but if you would like to look at them in more detail, you can access them here: Requirements and Recommendations | EFGS.

The GSGF in Finland project: Interpreting the GSGF within a national operating environment

Contributions by Mervi Haakana (Statistics Finland), Rina Tammisto (Statistics Finland) and Panu Muhli (National Land Survey of Finland).

We end this blog by sharing an exciting new EU-funded project, the GSGF in Finland—Integration of geospatial and statistical information in Finland project (or GSFI for short), which is currently being undertaken by Statistics Finland, the Finnish Environment Institute and the National Land Survey of Finland who are working in close cooperation with the National Network on Integration of Statistics and Geospatial Information in Finland. The project began in February 2023 and is taking place over a period of two years.

The project will interpret the GSGF and the GSGF Europe within the Finnish operating environment in order to gain a common understanding of the current and target state of integrating geospatial and statistical information in Finland as well as its development goals. To achieve this, an analysis of the current state and a target state architecture will be undertaken, the development needs defined, and the benefits of the GSGF model will be identified and described. 

There is a lot of value in adapting the GSGF to the Finnish context, as Mervi Haakana from Statistics Finland describes:

The focus of this project and the value of its results is to understand the theoretical models in practice and connected to the national policy and operating environment. Already at this stage of the work, examining different perspectives of the GSGF and GSGF Europe in our own environment has proven to be important. Even translating part of the text (not all) into Finnish has led to discussions and new understanding about the framework. One aim is also to bring the ability to share responsibilities and concrete tasks between organisations and to identify mutual processes. We also hope that the project results will serve as a valuable benchmark for other countries.

It is clear that the GSFI project is already achieving some great results and we look forward to hearing more as the project progresses! We also hope that the GSFI project will inspire you to embark on your own journeys towards successfully implementing the GSGF.

Adopting the GSGF will bring many benefits . . .

. . .  so why wait to get started?                    

Next time . . .

We will take a look at the UN Integrated Geospatial Information Framework, another key policy framework relating to data integration. Hopefully see you then!

Attendance

The workshop was attended by over 60 representatives from National Statistical Institutes (NSIs), National Mapping and Cadastral Agencies (NMCAs), intergovernmental organisations and private sector organisations, with 28 different countries represented from across the UNECE region, spanning from the United States of America in the west as far as Kazakhstan in the east.

The UNECE Member States in attendance were: Albania, Armenia, Austria, Belgium, Bosnia and Herzegovina, Finland, France, Georgia, Germany, Ireland, Italy, Kazakhstan, Kyrgyzstan, Malta, Moldova, the Netherlands, North Macedonia, Norway, Poland, Portugal, Serbia, Slovakia, Slovenia, Sweden, Switzerland, Ukraine, United Kingdom, and the United States of America. The workshop was also attended by representatives from EuroGeographics, Eurostat, the OECD, UN-GGIM: Europe, UNECE, UNHCR, the World Bank Group, and Esri. Such broad representation and expertise really made the event such a success and we are extremely grateful for the efforts that participants made to attend the workshop and the (sometimes considerable) distances that were travelled to get there!

So, where do we go from here?  

This blog post has focused on the top three strategic pathways which were rated as having the highest impact on data integration within the UNECE Survey, although there are also other wider issues at play. While much work has been undertaken to support the greater integration of geospatial and statistical information, more must be done to embed data integration activities within business-as-usual practices in a comparable and consistent fashion across the UNECE region. There are a range of multi-dimensional issues and obstacles still to be overcome, given the wide variance in national governance frameworks, laws and policies, the ability to access adequate and sustainable financial resources, the level of cooperation with other national and international bodies, the adherence to wider policy frameworks and common standards, the ability, skills and capacity to innovate, and effectively communicate the need for data integration activities and their associated benefits to both decision-makers and the wider user community.

In light of the issues and obstacles explored in this blog post, some recommendations can be made:

1. Identify and promote sustainable funding resources and models to support data integration activities at national levels.

2. Enhance communication and engagement strategies to grow awareness of the benefits of data integration and better support the sharing of best practice and new technologies.

3. Promote greater data standardisation and interoperability through the use of harmonised standards, operating models, production processes and services.

These recommendations could support and complement other key recommendations made by UNECE, Eurostat and others in the field of data integration and, in their adoption, may provide a driving force for change so that the value of data integration is fully realised and data of sufficient quality, accessibility, currency, reliability and granularity is produced consistently to protect people, the planet, prosperity, peace and partnerships so that “no one will be left behind” (United Nations).

Images in this blog were sourced from Flaticon.com.

The strategic partnerships and collaborative activities in place across the statistical and geospatial sectors (as outlined in our earlier post on Data Integration: Key players and recent developments) are strong, long-standing and of benefit to the data integration agenda. Eurostat has observed that statistical and geospatial data integration is growing rapidly in some European countries due to close cooperation between national statistical and geospatial organisations. The European Committee of the Regions further notes that “pan-European interoperability in most fields is still a future goal, however, good progress has been made in particular by several phases of the GEOSTAT projects also regarding the establishment of cooperation between institutions and the integration of spatial and statistical data”. PARIS21, as a global partnership of experts and policymakers in statistics, has also identified that governments in many low-income countries are already implementing multi-stakeholder approaches to progress statistical and geospatial data integration which is very promising. It is, however, important that the international and regional partnerships already in place, some of which are undertaking similar activities relating to data integration, work together to ensure that their workstreams are aligned, and not duplicated, so there is a clear overarching voice that transcends across the different policy frameworks and guidelines that national statistical and geospatial organisations are encouraged to adopt. At national levels, the traditional separation of statistical and geospatial organisations has historically hampered efforts to collaborate with each other, although this is now changing with many good examples of national collaboration in practice (more on that in a later post).

What did the UNECE Survey find out?

In light of the importance of effective collaboration and partnership arrangements to support activities to integrate statistical and geospatial data, the UNECE Survey asked respondents a series of questions to understand the level of involvement in wider activities relating to data integration at both national and international levels. Some key findings are presented below.

These snapshots from the survey indicate that while the overall level of participation in regional and international activities related to geospatial and statistical data is good overall, as is the breadth and variety of the working groups attended, more needs to be done to explore why levels of engagement from target countries are significantly lower and determine how this can best be remedied. Respondents highlighted the importance of established and agreed collaboration through multilateral partnerships as well as the need for build greater awareness about the strength of partnerships and cooperation amongst different data providers that ensure that reliable, objective, accurate and consistent data can be produced, shared and integrated.

Firstly, why look at the use of data and technology?

Well, on a global scale, society is becoming ever more data-driven with more than 2.5 quintillion bites of data being generated every day (which is quite something)! The availability of quality data that is accurate, comprehensive, at an appropriate level of detail and temporality, and from verifiable and authoritative sources, is critical for evidence-based decision-making and policy development across all levels. Data plays a central role in the 2030 Agenda and the ability to fully measure and monitor progress on the SDGs. Not only is the quality of data important, but also the strength of the technical infrastructure which underlies its creation, management, use, and dissemination within and across organisations. A strong data infrastructure will ensure better efficiency and productivity of its users, ease of collaboration between different groups, and securely managed access to organisational data for both internal and external users. By utilising appropriate digital technologies and methodological frameworks to collect, analyse and interpret data, real actionable insights can be obtained. Yet, these benefits have not yet been fully realised in a consistent way as the World Bank notes: “Even as new technology makes more data and wider uses of data possible, there are still many blank spaces on the global data map”.  While both the quality and availability of data has been growing over recent years, in general, it is considered that “statistical capacity still needs strengthening and data literacy must be enhanced at all levels of decision-making” which will “require coordinated efforts on the part of data producers and users from multiple data systems" (UN Statistics Division).

The quality and accessibility of statistical and geospatial data, and the strength of the technical infrastructure that supports it throughout its lifecycle, is central to the data integration agenda. At a global level, it has been recognised that advances in the integration of statistical and geospatial data have "benefitted from the availability of powerful geospatial tools that enhance the value and usability of official statistics by leveraging the application of the spatial context" (PARIS21). The GEOSTAT 4 / GISCO survey also revealed that around 50% of the countries surveyed reported that they had a strong and sustainable data infrastructure that could support the integration of statistical and geospatial information. The INSPIRE Directive also brought the importance of metadata, and its uniform structure, to the fore in the management of spatial datasets, providing definitions and lists of categories to describe the content, data type, and usage. As UNECE have highlighted, there is also the “prior existence of flexible frameworks for the modernisation of official statistics that can be adapted to include geospatial information with little impact on the existing organisational structure”. For example, UNECE’s Generic Statistical Business Process Model (GSBPM) has recently been enhanced to include a geospatial perspective, appropriately termed GeoGSBPM. We will be discussing the GeoGSBPM in more detail in a later post. From these few examples, it is clear that good progress has been made at global and regional scales to highlight and support the development and maintenance of high-quality data and robust technical infrastructures, but issues still remain which hinder the greater integration of statistical and geospatial information at national levels.

So, what did the UNECE Survey discover?

Recognising the central role that data and technology play in the integration of geospatial and statistical information, the UNECE Survey asked respondents a number of questions relating to their organisational use of data and technology. Some highlights are presented below:

These highlights from the survey suggest that organisations who responded to the survey are integrating geospatial/statistical data within their workflows and have a good level of access to relevant software to support such tasks. It is clear, however, that there are some disparities in the extent of data integration activities between NSIs and NMCAs and, similarly, between target and non-target countries. In a later post, we will share some of the issues and obstacles relating to data and technology which were highlighted by respondents to the survey.

In the first post of this mini-series, we will introduce you to the project and provide some background and context to the UNECE Survey. Are you ready to find out more? Let's go!!

So, tell me more about your EU-funded project to develop capacity in geospatial and statistical data integration . . .

As we outlined in our first post (Welcome to INGEST), the role of integrated data as a driver for evidence-based decision-making has never been more important and has been brought to the fore by the 2030 Agenda and its call for data that is accurate, current and of high-resolution to measure and monitor the achievement of its Sustainable Development Goals. In our previous post (Key players and recent developments) we also discussed how global efforts to drive the greater integration of statistical and geospatial data have been going on for a decade and many great outcomes have been achieved, however, the benefits have not yet been fully realised consistently across the UNECE region due a to range of complex but interconnected reasons (which we will discuss in a later post).

Recognising the potential for growth, the European Commission has funded a 21-month project, currently being led by UNECE, to develop greater capacity in statistical and geospatial data integration across the UNECE region to foster stronger links between the two communities, support greater collaboration and encourage greater data integration through the promotion of stronger institutional partnerships and the adoption of common standards. The project is supporting existing activities to strengthen the integration of statistical and geospatial information by Eurostat, UN-GGIM: Europe and others, and has a particular focus on sixteen selected target countries in Eastern Europe, the Caucasus and Central Asia.

 A number of key activities are being undertaken as part of the project including (amongst other things):

• Issuing an online survey to gain stakeholder insights on data integration (more on that below).
• Organising a Joint UNECE / Eurostat / UN-GGIM: Europe Workshop on Integrating Statistical and Geospatial Data which will take place in Belgrade, Serbia on the 4-5 October 2023.
• Establishing a UNECE-led task force on standards issues relating to data integration.
• Creating supportive material on data integration including a UNECE wiki space, this blog series, and other documentation.

By carrying out these activities, the project hopes to:

• Increase awareness of the need for more integration of geospatial and statistical data, especially within the project's target countries.
• Develop a better understanding of the limitations of current geospatial and statistical data standards in the context of data integration.
• Improve collaboration between the geospatial and statistical communities based on greater mutual awareness and understanding.

So, who are the key players in the quest for greater data integration?

Global efforts to drive the greater integration of geospatial and statistical data have been going on for more than a decade and have been centred on the work of the United Nations and its various divisions and bodies, particularly the UN Statistics Division and the UN Committee of Experts on Global Geospatial Information Management (UN-GGIM). From this global stage, data integration activities have been increasingly extended and adapted to different regional contexts. In Europe, for example, this work is led by four key players: UNECE, Eurostat, UN-GGIM: Europe, and the European Forum for Geography and Statistics. There are also great examples of data integration activities within the national statistical and geospatial organisations which are supporting other countries who are not so advanced to their data integration journeys.

The key players, at international, regional and national levels, are outlined in the diagram below:

A summary of each of the main organisations and bodies is outlined in the table below. If you would like to find out more, please click on the links provided.

What about recent developments in the field of data integration?

Activities to progress the greater integration of geospatial and statistical data have been going on for more than a decade and some of the highlights are provided below.

At a regional level:

• UN-GGIM: Europe's Working Group on Data Integration which focuses on the integration of geospatial data with other information including statistical data, and is currently running webinar series on data integration. See here for upcoming events.

• Eurostat's activities relating to GISCO (the Geographic Information System of the COmmission) which includes coordinating Commission-wide geographic information activities, promoting the use of geospatial data within the European Statistical System, chairing a working group on the integration of statistical and geospatial information, and overseeing annuals funding calls for project proposals relating to data integration.

• The European Forum for Geography and Statistics (EFGS) who have worked in collaboration with Eurostat on the GEOSTAT Projects which have focused on the development of common guidelines for grid-based and geospatial statistics for use by national statistical and geospatial organisations to promote the greater integration of statistical and geospatial data across the EU. The GEOSTAT 4 project has conceptualised and interpreted the Global Statistical Geospatial Framework within the European context, publishing GSGF Europe in 2021.

• UNECE's High-Level Group for the Modernisation of Official Statistics (HLG-MOS) whose role is to advance the modernisation of official statistics. They have published a Guide to Data Integration for Official Statistics as well as a Geospatial View of Generic Statistical Business Process Model (GeoGSBPM). UNECE has also recently embarked on a new project to produce internationally agreed guidance for the next round of censuses in 2030 under a number of key themes including technology, looking at GIS data and related approaches, and geospatial information and small area statistics for censuses which will further promote the data integration agenda within a global update of international guidance for censuses. See here for more information.