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Welcome back to INGEST, our blog series on integrating statistical and geospatial information. We are excited to be back again after a busy few months completing our EU-funded project which aimed to develop greater capacity in the integration of statistical and geospatial information across the UNECE region. You can read more about the background to the project here. We will also be publishing some key outcomes of the project as papers over the coming months so keep an eye out for those!
In our last blog post, our guest authors' presented a new R package (GIS INTEGRATION) which enables users to seamlessly integrate two GIS data sources by pre-processing, cleansing, and unifying the data based on common attributes. In this post, we now move our focus to the use of standardised processes to integrate statistical and geospatial information, exploring UNECE's Geospatial View of the Generic Statistical Business Process Model (or GeoGSBPM for short). This model sets out the broad activities and considerations needed to produce geospatially-enabled statistics and, crucially, acts a framework for integrating geospatial information within the statistical process. Are you ready to learn more? Let's get started!
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).
The cornerstones of the standards-based modernisation vision of HLG-MOS is a set of "ModernStats models" which includes the Generic Activity Model for Statistical Organisations (GAMSO), Generic Statistical Business Process Model (GSBPM) and Generic Statistical Information Model (GSIM).
Among these models, the GSBPM is the most widely implemented across the statistical organisations around the world, providing a standard framework as well as harmonised terminology that guides organisations as they modernise their production processes. The model consists of eight overarching processes (or phases) which are supported by a range of underlying sub-processes which are shown in the diagram to the right. As a model extensively used globally, the GSBPM is an optimal vehicle to incorporate the essential considerations and activities needed for geospatially-enabled statistics within the production process.
The HLG-MOS wiki site also contains a wealth of information on the activities, meetings and outcomes of the group, including detailed documentation, support and guidance on the standards-based models that they have helped to produce.
The Generic Statistical Business Process Model (Source: 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.
The GeoGSBPM emerged from a detailed review of the intersection of two important (and complementary) global frameworks: the Generic Statistical Business Process Model (GSBPM) (summarised in the previous section) and the Global Statistical Geospatial Framework (GSGF) (first published by UN-GGIM in 2019 as a key policy framework to support the production of high-quality harmonised and standardised geo-statistical data - you can find out more about this framework in one of our earlier blog posts).
The GSBPM has been described as "an enabling framework" (UNECE) for the overarching principles of the GSGF to be integrated within the statistical production process, providing a structure for all relevant geospatial activities to be undertaken across the correct stages of the process (see image to the right). The authors' also highlight the direct link between the GSBPM and Principle 4 of the GSGF, that of statistical and geospatial interoperability, however, its relevance is also evident across the other four GSGF principles.
Within the GeoGSBPM, the key activities to be undertaken to integrate geospatial and statistical data have been usefully summarised in a table which is structured according to GSGF Principle and GSBPM Phase. This provides a clear overview of the activities and considerations required across each stage of the statistical production process, each of which are then described in more detail in the following chapter (Geospatial related activities and considerations).
The intersection between the GSBPM and the GSGF (Source: UNECE)
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!