The Role of Geospatial Information and Earth
Observations in the SDGs: A Policy
Perspective
Data, as the basis for evidence-based
decision-making, will be critical to the success
of the 2030 Agenda. The broad and transformative
nature of the 2030 Agenda has ushered in a new
era in thinking about sustainable development,
with renewed emphasis on countries being able to
measure and monitor progress with good policy,
science, technology and especially data. The
unprecedented data demands and expectations
relating to the SDGs necessitate new and
innovative data sources, acquisition and
integration approaches, and the need for “high
quality, timely, reliable and disaggregated
data, including Earth observations and
geospatial information” to address development
challenges and to “leave no one behind”.
Goal 17, in the area of data,
monitoring and accountability, requires us by
2020 to “support developing countries to
increase significantly the availability of
high-quality, timely and reliable data
disaggregated by income, gender, age, race,
ethnicity, migratory status, disability,
geographic location and other characteristics
relevant in national contexts”. But do we really
even understand the scale and dimensions of the
world’s development problems, where they are,
whom they impact, what the causes and
consequences are, and how they can be remedied,
let alone how to bring the data to bear to
measure and monitor these complex and
geographically interconnected problems?
1.1 Global Indicator Framework: measuring,
monitoring and reporting on the SDGs
The 17 SDGs and 169 Targets provide
the overall policy and results framework for the
2030 Agenda, but in terms of a robust and annual
follow-up and review mechanism for its
implementation, it is the Global Indicator
Framework where the data acquisition, integration
and disaggregation will be most needed. The task
of determining the Global Indicator Framework was
given to the UN Statistical Commission (UNSC). In
2015, the UNSC established the Inter-agency Expert
Group on Sustainable Development Goal Indicators
(IAEG-SDGs) to develop the Global Indicator
Framework as the quantitative means by which
national governments can consistently monitor
achievement on, and report progress towards, each
of the 169 Targets. In July 2017, the Global
Indicator Framework was adopted by the General
Assembly and comprises an initial 232 Indicators,
which will be reviewed from time to time.
The United Nations Committee of
Experts on Global Geospatial Information
Management (UN-GGIM) and the Group on Earth
Observations (GEO) worked closely with the
statistical community, at a national and global
level, to provide inputs into the processes to
develop the Global Indicator Framework with the
IAEG-SDGs. As depicted in Figure 1, key messages
were that, while global in nature, implementation
of the 2030 Agenda will still need to be ‘country
owned and country led’ and through national
development policies, strategies and frameworks.
Through this process, statisticians now better
understand that measuring and monitoring will
require not only statistics, but also geospatial
information, EO and other Big Data to provide new
and consistent data sources and methodologies to
integrate multiple ‘location-based’ variables to
support and inform official statistics and the
Indicators for the SDGs.
Figure 1: Measuring, monitoring and reporting
on the SDGs will require new sources of data
that are integrated, interoperable and
coordinated.
By bringing together information from
various sources, particularly those related to the
environment, these data and analytical methods can
fill data gaps and/or improve the temporal and
spatial resolutions of existing data. This
information integration is important, as the
Indicator Framework will be the primary conduit to
guide and inform Member States, based on
individual national circumstances, on how they
measure, monitor and report on the SDGs and
related targets in the years to come. However,
determining the Indicators is just the beginning,
as they need to then be appropriately interpreted
and implemented via national planning processes
and frameworks, and guided by robust metadata and
multidimensional data needs. Such data have the
real potential of forming a new and emerging ‘data
ecosystem’ for development, in which integrated
information systems that are comprehensive and
coordinated are able to monitor the state of the
Earth, people and planet, and to deliver timely
information necessary to citizens, organisations
and governments to build accountability and make
good, evidenced-based decisions from local to
global levels.
To address these issues
and specific areas relevant to SDG Indicator
implementation, the IAEG-SDGs established a
Working Group on Geospatial Information (WGGI) at
its third meeting in April 2016. The WGGI –
comprising statistical, EO and geospatial experts
from national governments, academia and
international organisations, including GEO –
provides expertise and advice to the IAEG-SDGs and
the broader statistical community as to how
geospatial information, EO and other new data
sources can reliably and consistently contribute
to the indicators. The Working Group has now met
four times and reports and provides updates to the
IAEG-SDGs, the UNSC and UN-GGIM. There is also a
close working relationship between the WGGI and
the GEO Earth Observations in Service of the 2030
Agenda for Sustainable Development (EO4SDG)
initiative.
1.2 Geographic location: the relationship
between people, their place and their
environment
While the initial development of the
Global Indicator Framework largely constituted a
statistical data approach, the need for
‘disaggregation by geographic location’ in a new
era of data needs is now well recognised by the
IAEG-SDGs to ensure that no one is left behind
(Figure 2). This is part of the mind-set change
reflected in the 2030 Agenda; development is no
longer only knowing about ‘people’ as national
aggregations, but also their ‘place’ and their
environment, their geographic location at a
sub-national level. This then cascades into more
detail. While having data that informs on the
‘how’ and the ‘what’ is valuable, such as how many
primary schools are needed or what vaccines are
being provided and in what volumes, it is
profoundly better if we are able to also know the
‘where’ in order to provide geographic context and
a richer understanding.
The effective
use and integration of EO and geospatial
technologies, combined with statistical and
demographic data, enable countries to analyse and
model where conditions are changing; create maps
and other visualizations; evaluate impacts across
sectors and regions; monitor change over time in a
consistent and standardized manner; and improve
decisions, policy and accountability. These
outcomes can have a transformational impact on
many of humanity’s most significant challenges in
the developing world, such as helping global
scientists, resource and planning managers and
politicians better monitor and protect fragile
ecosystems; ensure resilient infrastructure;
manage climate risks; enhance food security; build
more resilient cities; reduce poverty; and improve
governance. Data is essential for informed
policy-making, decisions and actions. Data allows
us to know the how, what, when and where for the
successful implementation and monitoring of the
SDGs.
However, we still need to democratize these
technologies and liberate the associated new and
alternative data sources and methodologies in
such a way that they are easily reachable and
useable by developing countries. To succeed in
our global development aspirations, we need to
go beyond the developing countries to reach the
poorest of the poor in the least developed
countries. Historically, relatively little
attention has been paid to the challenges these
countries face in effectively collecting and
producing data and in building and strengthening
their capacities within the national mapping
agencies and statistical offices. With the
enabling global mechanism of the 2030 Agenda,
the challenge is how to most effectively
transfer the available technology, data richness
and connectivity to the technology and data
poor.
Figure 2: Sustainable data for sustainable
development – the new data needs demand
disaggregation by geographic location in
order to ensure that no one is left
behind.
1.3 The problem: where is the data?
In the second annual report on
global sustainable development progress:
Sustainable Development Goals Report 2017,
released in July 2017, the United Nations
Secretary-General stressed that high-level
political leadership and new partnerships will
be essential for sustaining momentum,
underscoring the need for reliable, timely,
accessible and disaggregated data to measure
progress, inform decision-making and ensure that
everyone is counted. Two years into reporting
progress on the SDGs, the Global Indicator
Framework has been determined and the production
of Indicators for the review and follow-up on
the implementation has begun in earnest. Against
this backdrop, how are we progressing, what is
the role of geospatial information and EO, and
where is the sustainable data for sustainable
development?
Firstly, let us
consider the availability of EO and geospatial
‘data’ needed to measure progress, inform
decision-making and ensure that everyone is
counted. More observations and geospatial data
are being acquired and made available than ever
before, with petabytes being generated every day
and growing exponentially. But are we yet
bringing the science and data into the
development processes in a timely and reliable
manner – are we ‘industrializing’ our
capabilities? Perhaps not yet. Unfortunately,
phenomenal data volumes, computing gains and
processing speeds does not necessarily, or
easily, translate into useable information,
knowledge and decisions at the fingertips of
decision-makers. Further, while the most
developed countries are grappling with an
abundance of data, a data deluge, in many parts
of the world data scarcity is still the norm.
Additionally, the data deluge is not being
matched by our ability to apply the appropriate
analytics and modelling in a commensurate manner
in order to make informed and timely decisions.
This particularly applies to the Earth sciences
and environmental modelling, which are
incredibly complex systems in themselves. In
order to be robust and rigorous, there is either
a long time lag in analytical outcomes or a
compromise in quality. Then when the data is
available, there are still data access and
discovery challenges – it may be incomplete;
distributed across multiple agencies; not
accessible, interoperable or standards based;
and then there is the reality that, with such
big datasets, internet bandwidth remains a major
impediment for developing countries.
This brings us to the continuing
policy dilemma. The EO and geospatial
information environment is one that is dynamic,
innovative and growing at a rapid rate in data
quantity, quality and applicability.
Advancements in acquisition techniques and
technologies have led to the proliferation of
sensors that collect information on smaller
platforms, at higher resolutions and repeat
rates, faster and in larger volumes. Billions of
dollars in space infrastructure, with public and
private sector investments, are being realized,
giving the world the capability to provide
sophisticated, continuous and sustained
observations of the entire planet. Practitioners
and scientists are familiar with the application
of these observations to the task of
forecasting, tracking, and alerting society, but
this is still not well understood within
governments at the policy level when it comes to
providing practical development solutions and
outcomes.
An impediment continues to
be demonstrating how we translate and
communicate our data inputs and real-time
monitoring into intelligent information outputs
that are both understandable and actionable at
all levels – from local to global – and which
provide a means to make consistent evidence- and
science-based decisions. There is no denying
that EO and geospatial information are essential
operational tools, but still seen as a novelty
when it comes to policy and decision-making.
Therefore, how do we make these capabilities
necessary and invaluable – to the point that
countries cannot make decisions without their
inputs – forming a new and emerging data
ecosystem for development?
1.4 The solution: connecting policy with
‘data’ through the SDGs
Many countries still need
considerable guidance and support as to how they
can actively contribute to the implementation
of, and track progress on, the SDGs. While there
is an increasing realization of the relevance
and application of EO and geospatial information
for the production of specific Indicators, and
more so when we consider data disaggregation and
sub-national data, we need to be more
aspirational and proactive to strengthen
countries’ capabilities in integrative national
information systems that facilitate and enable a
growing data ecosystem that leverages an
accessible, integrative and interoperable
local-to-global system-of-systems.
While the global Indicators
presently provide the national to global
reporting outcomes, the future success of the
global development agenda will be dependent on
data – not whether it is statistical, EO,
geospatial, environmental, economic, health,
demographic, education or other data – just
data! As we are seeing with consumers and users
in civil society, it may no longer be a
necessity for governments to know exactly where
the data they are using and consuming has come
from or who has generated it. They will just
want assurances that it is authoritative,
reliable, repeatable, the best available and
fit-for-purpose to make the right decisions and
policy. Such outcomes will require data to be
more open, platforms to be more usable,
analytics to be more accessible and systems to
be more integrated. If this is achieved, we will
see fundamental EO, geospatial information,
positioning infrastructure, policy frameworks,
institutional capacity and economic development
moving up the value chain in all countries,
including at the policy level.
The
future reporting needs of the Global Indicator
Framework will have to consider disaggregated
data, from the sub-national to national level,
while also allowing for aggregated global
reporting that builds directly on the national
data developed by countries, as well as that
from custodian agencies. Additionally, national
level indicators will be developed by countries,
and likely not be produced by each country in
the same way. The good news is that the
statistical community is familiar with data
aggregations and national data, while the EO and
geospatial communities are familiar with data
disaggregation and sub-national data. With a
unique understanding of context and
circumstances, and allowing for incremental
improvement, our combined professional expertise
is well positioned to contribute to measuring
and monitoring the SDGs and tracking annual
progress with EO and geospatial information.
