Page 256 - Special Topic Session (STS) - Volume 4

STS583 Mariana K. et al.
information. The work on achieving and monitoring the SDGs poses
substantial challenges for the statistical and geospatial communities. However,
it also offers a unique opportunity to demonstrate the power of statistical-
geospatial data integration across a wide range of themes. In response to the
growing need to add the “where” dimension in public information and
statistics, the statistical and geospatial communities have a common task to
build frameworks that can support the production of relevant, accurate and
timely information to allow evidence-based decision-making for all levels of
society.
One of the key areas of the European Statistical System (ESS) is to harness
new data sources comprising Big Data, administrative data and geospatial
data. Using data from a range of sources and for multiple purposes, not only
requires their integration into a common reference system of harmonised
concepts, but also a common location and temporal framework. Therefore,
users have not only increased their demand for location information but they
also require simpler integration of data across various data sources to use in
their analyses. Time and space are universal and well-defined concepts and,
hence, can be used to integrate data from a wide range of topics.
The international statistical and geospatial communities recognised this
challenge and responded by establishing the UN Expert Group on the
Integration of Statistical and Geospatial Information (UN EG‐ISGI) to develop
a Global Statistical Geospatial Framework (GSGF). At the Sixth Session of the
United Nations Committee of Experts on Global Geospatial Information
Management (UN-GGIM), held in August 2016, the five principles of the GSGF
were adopted. The GSGF should act as a bridge between statistics and
geospatial information, between statistical institutes and geospatial agencies,
and between statistical and geospatial standards, methods, workflows and
tools. Based on the ESS ambition to broaden the scope of statistical-geospatial
integration, Eurostat launched a series of four GEOSTAT projects supporting
financially National Statistical Institutes.
To enhance the ESS capability to integrate statistical and geospatial
information, three high-level strategic objectives were set:
1. To improve the geographical granularity of statistical products. The benefit
is to provide additional breakdowns for more local geographies and thus
reveal finer spatial patterns at more local scales. This action increases the
possibilities for spatial analysis.
2. To generalise the usage of statistical grids such as the 1km2 resolution
grid. Finer and coarser resolutions should also be considered depending
on the thematic domain. The main benefit of adopting statistical grids is
to remove the bias introduced by statistical units with irregular sizes and
shapes (e.g. Openshaw, S. & Taylor, J., 1979) and thus map statistics to the
users on more reliable and stable geographies.

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