The ‘Urban’ SDG and the Role for Satellite Earth
Observations
UN-Habitat is leading the coordination of
several methodological developments for
monitoring of urban-related SDG indicators as a
Custodian Agency and is supporting the
cross-sectoral coordination of the human
settlements indicators to allow for synergies
and consistency in the monitoring and reporting
amongst institutions on all urban-related
indicators. This role also involves developing
new ways of data collection and guiding partners
on use of new technologies in monitoring locally
and globally the urban-related SDGs. This
article highlights some of our experience using
Earth observation (EO) data and the associated
challenges and opportunities for measuring and
monitoring the performance of cities through
such data.
6.1 Sustainable cities
SDG 11, “Make cities and human
settlements inclusive, safe, resilient, and
sustainable”, stands out as a goal that has placed
explicit focus on the measurement of indicators at
a sub-national level (cities or human
settlements), with several indicators requiring
geospatial data for monitoring. This geospatial
data dependency offers a unique opportunity to
integrate geospatial information into the national
and global statistical data infrastructure demands
in a more holistic and policy-driven manner. But
it is also symptomatic of the need for capacity
development at multiple levels across the entire
national statistical systems that will support SDG
data collection.
The focus on cities
and urbanisation underscores their roles as
predominant sites of economic, social,
environmental and health issues at the centre of
global development policy discussions today.
Whilst the linkages between cities and
urbanisation to development outcomes may be clear
conceptually, measuring SDG indicators at the
level of cities and human settlements raises a
number of challenges, such as:
−
definition - of what constitutes cities and
settlements;
− scope – and which
cities or urban areas to include for monitoring;
− what capacities exist at national
statistical levels to support new ways of data
collection;
− the potential for EO data - to inform SDG
monitoring efforts; and
− its integration with existing
datasets for spatially explicit definitions of
cities in a globally consistent manner.
Significant work has been invested
in establishing an understanding of the
distribution and size of human settlements that
can help with the issues of definition. As with
all of the SDGs, it is imperative to have
consistent definitions applied across national
reporting mechanisms if meaningful and
consistent comparisons and global statistics are
to be derived. National Statistical Offices
(NSOs) employ differing criteria to classify
settlements along the urban-rural divide, such
as population or population density thresholds,
and the threshold values may vary greatly across
countries. Since an urban settlement in one
country may be rural according to another
country’s standard, national definitions cannot
be used to derive and apply a consistent global
assessment of urban versus rural. The recent
availability of global built-up area datasets
(see the further information links at the end of
the article) makes it possible to pursue
spatially explicit and globally consistent
approaches to defining settlements that provide
a more accurate assessment of the number of
settlements, their boundaries and their
associated areas.
Work on the Atlas
of Urban Expansion that was led by UN-Habitat
and New York University provides an estimate of
the total population living in ‘large’ cities
with populations of at least 100,000 (in 2010).
This work identified 4,231 self-standing cities
and metropolitan areas, representing a total
population of approx. 2.5 billion people. The
names, locations and populations of these cities
were identified after a year and a half of
research, comparison and consultation with
multiple data sources and organisations,
including www.citypopulation.de, the UN
Population Division, the Chinese Academy of
Sciences and the European Commission.
The contribution to global
population of these cities of different sizes is
illustrated in Figure 1. This shows, for
example, that cities with a population of more
than 12.8 million people were home to a total of
around 328 million people in 2010. It also
illustrates that definition and scope of
monitoring urban environments will have a
significant impact on how much of the global
population and their settlements is covered by
the SDG indicator framework – with over 300
million more people (and another 4000+ cities)
included should the scope extend to cities as
small as 50,000 in population.
Figure 1: The 4,231 cities in the 2010
universe of cities arranged in population
bins
6.2 Our use of EO data
Urban extent and boundaries are an
obviously important part of getting scope and
definitions consistent and EO data can certainly
help here. However, translating EO datasets into
settlement boundaries requires analytical
approaches that group remotely-sensed built-up
areas and open space pixels in ways that match our
preconceived notions of how cities and
metropolitan areas manifest spatially. Not all
cities meet the simplistic notion of a compact
cluster of built-up area completely surrounded by
wide open countryside. Individual clusters may be
completely surrounded by open space but they are
not necessarily individual settlements;
non-contiguous built-up areas and the open spaces
surrounding them and captured by them may
represent a singular connected area, such as an
integrated labour market we associate with a
metropolitan area.
One criterion that
we know is available globally, and that we have
applied in our analysis, exploits the spatial
relationships of built-up and open pixels
contained within the remotely-sensed datasets. We
analyse raster datasets and employ a variation of
a gravity model whereby non-contiguous clusters of
built-up area are joined together if their sizes
and the distance between them meet some threshold,
suggesting that the clusters ‘interact’ across
space as part of an integral unit. The spatial
clustering rules we employed are visually
intuitive and easy to apply with existing data
sources.
The above rules were applied
globally to 200 study areas to delineate
settlement boundaries or urban extent boundaries
across three time periods, 1990, 2000 and 2015.
The results were positive in that they delineated
settlement boundaries both for large metropolitan
areas and small cities of 100,000 with high
accuracy, to the extent that the boundaries
matched expert opinion of what acceptable
settlement boundaries would be. Certain settlement
types, such as very large conurbated regions or
areas separated by large bodies of water required
manual editing; additional work is needed to
refine the automated procedure for these cases.
Rules incorporating commuting or
mobility data, which indicate actual spatial
interaction and the level of connectedness between
non-contiguous areas, or rules that use population
or employment densities measured over small areas,
representing the level of human activity across
space, can be applied to devise more sophisticated
and externally objective grouping rules. These
criteria are in fact applied by statistical
agencies where this data is available, but today
this is typically only in a small number of OECD
countries.
At the global level,
‘urban’-related SDGs require an operational human
settlement or city definition that brings these
objects of study into focus. The definition should
be intuitive and measurable and it must be
applicable globally with existing or easily
obtainable data sources. More importantly, the
definition should ensure that it is easy to count
and account for all the spaces and settlements in
the statistics in line with the SDG’s principle of
“leaving no one behind”.
EO data
provides researchers an increasingly better
understanding of the location, number and size of
human settlements on the planet, since they are
typically associated with impervious surfaces used
for roads or building materials. The technological
capability to identify these surfaces from space
has existed at least since the early 1970s and
with improved spatial resolution and revisit
frequency from multiple satellite series today.
But EO data used in this way will always need to
be supplemented with in-situ observations and
interpretation since not all built-up areas
represent human settlements and not every human
settlement may be of interest for monitoring urban
indicators associated with SDGs.
Many
of the Goal 11 targets address social, economic,
environmental and health concerns that require
some level of in-situ data collection within
settlement boundaries. The data collection
strategy must be comprehensive in the sense that
the outcome should be an accurate measure over the
settlement area of its population. A few Goal 11
targets may be observable from space or largely
observable from space, such as those related to
air quality, transport and urban sprawl, but
measuring Goal 11 indicators will almost certainly
require on-the-ground data collection efforts,
either by the municipal authorities within the
settlement boundary or by outside parties. Given
the extremely low likelihood that this data can be
collected for all settlements (however the
universe of settlements is defined), a sampling
approach seems more feasible, the results of which
can be generalised to understand the distribution
of values for the regions and countries of
interest. UN-Habitat has developed a guide for
member states to apply this model, commonly
referred to as the “national sample of cities
(NSC)” approach.
6.3 Going forward on SDG 11
Enablers such as the internet, cloud
computing, Big Data, mobile devices, unmanned aerial
systems, social media and the explosion of
location-based services have ensured that people all
over the globe are beginning to study and
characterise their settlements more thoroughly and
frequently.
EO data is no doubt going to
play a significant and central role in the global
reporting processes for the next 15 years. Its use
will not be in isolation and must be guided by
issues around definition and scope and supported by
complementary in-situ information. Concrete guidance
on definitions, measurements and unified standards
is necessary to make sure that we work with
harmonized and mutually agreed concepts.
For Goal 11, the following indicators
will have a heavy dependence on EO data for their
feasibility:
11.1.1 Proportion of urban
population living in slums, informal settlements or
inadequate housing.
11.2.1 Proportion of
population that has convenient access to public
transport, by sex, age and persons with
disabilities.
11.3.1 Ratio of land
consumption rate to population growth rate.
11.6.2 Annual mean levels of fine
particulate matter (e.g. PM2.5 and PM10) in cities.
11.7.1 Average share of the built-up
area of cities that is open space for public use for
all, by sex, age and persons with disabilities.
6.4 Examining progress on SDG 11.3.1
Efforts on monitoring SDG indicator
11.3.1 on ‘land consumption rates’ (from our global
sample of cities work) have demonstrated that the
opportunities and challenges for global monitoring
come in equal measure. At the global level, more EO
data is now available today with higher revisit
frequency and at higher resolution to facilitate the
monitoring of several urban SDG indicators including
11.3.1. But data itself is not sufficient and
capacity building of national data systems, as well
as removal of data complexity, must be addressed.
So too must the need for standard
methodologies and definitions to allow consistent
and comparable national reporting. UN-Habitat
proposes the use of ‘urban extent’ for the
delimitation and measurement of cities and urban
agglomerations in monitoring and reporting on
indicator 11.3.1. The adoption of this concept will
enable national governments and development partners
to standardise the definition and the unit of
measurement for global urban reporting. This
standard definition will not necessarily usurp local
definitions but it will prevent inconsistencies
arising from the use of different urban definitions
when collecting and analysing information at city
and sub-city levels.
Figure 2: UN-Habitat - a guide for member
states on the application of the concept of
National Sample of Cities is now available:
http://unhabitat.org/national-sample-of-cities
The application of EO data at local or
sub-national levels will no doubt create steep
learning curves for even the most data advanced
countries. At the national level, we anticipate
several challenges given the variations in levels of
understanding and ability to apply or deploy the use
of EO data in many national statistical
organisations. At the local level, skill shortages
will be an issue. North-South and South-South
cooperation around capacity development will be
needed and should be coordinated through existing
regional bodies and networks as an initial starting
point.
Article Contributors
Robert P Ndugwa (Global-Urban Observatory Unit,
Research and Capacity Development Branch,
UN-Habitat)
Global Urban Observatory, UN-Habitat
2016. National Sample of Cities: A model approach
to monitoring and reporting performance of cities
at national levels:
unhabitat.org/national-sample-of-cities
