Welcome to INGEST, our new blog series on integrating geospatial and statistical data

So, tell me more about integrating statistical and geospatial data . . .

We live in an increasingly digital world and the way we live and work has been transformed by technology which is advancing at ever-faster rates. With all that digital transformation comes infinite amounts of data which can be used to better understand our world through both space and time, and provides an important evidence base to address some of the biggest challenges faced by society, such as climate change, global health issues, political conflict, and poverty (you can read more about these issues here). The adoption of the United Nations 2030 Agenda for Sustainable Development has driven the need for better data that is accurate, current, detailed, and comparable in order to measure and monitor the Sustainable Development Goals (SDGs). The integration of geospatial and statistical data (or data integration for short) has been recognised as one of the most promising ways to produce high-quality data that meets these requirements and can be defined as:

"the practice of incorporating and consolidating both kinds of sources [geospatial and statistical] into a single dataset, with the ultimate goal of providing users with consistent access to, and delivery of, information across the geographical, social, economic and environmental spectrums" (Source: PARIS21 Guide on Geospatial Data Integration in Official Statistics, pg. 11).

Global efforts to drive the greater integration of geospatial and statistical data have been going on for a decade and have centred on the work of the United Nations and other international and regional organisations (more on that later). As data providers, both national statistical and geospatial organisations also play a central role in this process.

Well, you may have heard the phrase, "Everything that happens, happens somewhere", and geography provides an ideal framework to bring together different layers of information across both space and time. Through the use of different spatial scales, ranging from tiny grid squares through to entire countries and regions, the possibilities of analysis and interpretation are endless. There are many benefits of integrating geospatial and statistical data - too many to include here - but in a nutshell, some of the main benefits include:

• Enhancing the value of the statistical and geospatial data itself by improving its quality - as data is integrated, quality issues (such as missing or inaccurate data) can be identified and corrected, leading to more accurate data in the long term.

• Improving the interoperability of datasets - connecting different systems and data sources through a common framework to easily share and combine information.

• Providing new possibilities for data analysis and presentation - moving from simple choropleth maps towards more complex geostatistical outputs and grid-based visualisations.

• Resulting in greater insights which can inform decision-making and policy development at all levels - from local, to regional, and global scales.