1
Bright Lights, Big Cities?
Review of research and findings on global urban expansion1
David Mason, World Bank
February 2017
1 This paper has been prepared as a background input to the World Resource Institute’s upcoming World Resource
Report “Towards a More Equitable City.” The author is grateful for comments and guidance received from Mark
Roberts, Chandan Deuskar, Anjali Mahendra and Sumila Gulyani.
2
Abstract
A common refrain among urban scholars and policy makers is that a majority of the world’s population is
lives in urban areas. However, the global extent of what is or is not “urban” remains unclear, often because
this definition varies by country. This makes it difficult to accurately compare urban expansion and
population growth trends between countries and globally. Recent empirical work has focused on
population-based and map-based techniques of estimating urban areas and urban expansion. This paper
provides an overview of these techniques as well as the main advantages and drawbacks in application as
well as gaps in knowledge and areas for future research and technical refinement. It finds substantial
divergences from previous estimates of global urban population as well as evidence that in many cases,
existing urban areas are expanding and becoming less dense. It also provides a brief review of recent
research utilizing remote sensing data to document the patterns and forms of urban expansion across the
world by region.
3
AI Agglomeration Index
CIESIN
Center for International Earth Science Information
Network
DMSP-OLS
Defense Meteorological Satellite Program -
Operational Linescan System
GHSL Global Human Settlements Layer
GHSpop Global Human Settlements Population Grid
GRUMP Global Rural-Urban Mapping Project
GPW Gridded Population of the World
IMPSA Impervious Surface Area Map
MODIS Moderate Resolution Imaging Spectroradiometer
NASA National Aeronautic and Space Administration
SPOT Satellite Pour l'Observation de la Terre
TanDEM-X
TerraSAR-X add-on for Digital Elevation
Measurement
VIIRS Visible Infrared Imaging Radiometer Suite
WUP World Urbanization Prospects
4
1) Introduction
Cities are home to a greater share of the world’s population every year, and according to the United
Nations, most people now live in urban areas (UN 2012). According to some, we have embarked on a new
“urban century” (Kourtit et al. 2014) where in the next two decades, the most populous continents, Asia
and Africa, are expected to become places with a majority of urban dwellers (Montgomery 2008). In
contrast to earlier views of urbanization as a disorderly, chaotic or maladaptive process, more recent
research has begun to improve our understanding of the processes and patterns behind how urban population
growth and expansion unfolds (Batty 2008). It is now clear that cities can hold certain kinds of persistent
social and economic advantages that rural areas do not have (Bloom et al 2008). Available case study
research shows that while most cities across the world have added population and built up area, they have
done so through different processes and different scales both between and within countries (Seto et al 2010).
There are also various institutional, environmental, economic and topographical features that shape the type
and form of urban growth (or decline). The confluence of a broad set of remote sensing data and improved
computing capacity have enabled researchers to develop multiple data sets, each utilizing different data
types, appropriate for different scales (city, regional, global), and with different purposes and intended
users.
Urbanization and economic growth appear to be strongly correlated and tend to occur
simultaneously (Henderson 2010; Glaeser and Maré 2011).2 Urban forms, particularly those that enable the
concentration and mix of people and firms, provide the basis for agglomeration economies (World Bank
2009; Glaeser 2011). In agglomerations, a diverse set of firms and labor are able to more efficiently sort
and match according to market needs (Venables 2010; Scott 2001). They also enable the rapid facilitation
of knowledge, ideas and learning, which further enhances innovation and growth (Jacobs 1969; Porter 2001;
Storper and Venables 2008). Agglomerations also enable economies of scale which make it easier and less
2 Africa appears to be a notable exception to this trend, where urbanization has occurred alongside lower levels of
per-capita GDP growth (cf Glaeser 2014).
5
expensive for governments to provide networked goods such as roads and trunk infrastructure (Dobbs et al
2013; Libertun de Duren and Compeán 2015). Proximity is also particularly important for securing
economic security and social mobility. New migrants or informal workers often rely on social networks to
secure employment and access to housing and services (Mortensen and Vishwanth 1994; Massey 1990;
Reingold 1999; Helsely and Zenou 2014). Such networks are key gateways for economic mobility for
migrants and enclaves of ethnic or racial minorities (Portes and Landolt 2001; Edin et al 2001).
Despite the advantages that cities and urbanization can provide to social and economic
development, there are also costs. As incomes rise, urban dwellers tend to consume more and have a larger
natural resource footprint than people in rural areas, especially in developed economies (Hammer et al.
2007; Wackernagel 2006).3 Urban population growth also generates congestion costs or agglomeration
diseconomies, such as a greater incidence of air and water pollution, consumption of agricultural land,
traffic congestion, crime and the communicability of diseases (Ellis and Roberts 2015). In large cities, the
opportunities for employment may be constrained by the cost and time required to locate and commute to
such jobs.
New migrants and the poor often find difficulty in accessing public services and well-located
affordable housing given the comparatively higher costs of urban living (Molina et al 2002). A growing
body of work4 also examines how urbanizing areas may deepen or sustain social exclusion along different
lines including gender (Hagan 1998; Chant 2013), migrant status (Li 2006) and caste (Vithayathil and Singh
2012), among others. These present costs that can inhibit or attenuate the benefits that cities can provide
because they weaken the easy circulation of people and goods, limiting economic productivity, lowering
educational and health outcomes and reducing incentives for investment and innovation (World Bank
3 As McNeil (2000) wryly observes “Fast growing cities, like teenagers, have higher metabolisms than those that
have stopped growing” pg. 285. 4 See McGrahanan et al. (2016) for a timely review of these issues in relation to the Sustainable Development Goals
and World Bank (2013) for a broader treatment of the topic.
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2009). While all growing cities face these and other challenges, planning and strategic management of
urban population growth and expansion can reduce these negative impacts (World Bank 2013b).
While the many implications of future urban population growth are increasingly clear, the
definition and measurement of “urban areas” across the world is not (Cohen 2004; Satterthwaite 2010;
Roberts, et al. 2016; Deuskar and Stewart 2016). This is important for several reasons. First, without a
consistent definition of what is “urban,” it is difficult to accurately compare changes in both urban built up
areas and urban populations across time and space. The expansion of built up areas has direct impacts
quality of life, including increased commute times, diminished access to urban services, proximity to
agricultural land and green spaces. Second, the type and form of urban expansion matters because such
information can inform decisions by both policymakers and planners about how to ensure that space is used
both efficiently for land markets and the circulation of people and goods, but also improving social inclusion
and economic mobility. Third, these data can also be used in other areas to improve the assessment of
exposure to cities in terms of disaster exposure (McGranahan et al 2007), improving public health and
epidemiological applications for reducing disease incidence (Hay et al. 2004, Linard et al 2010), and
assessing hydrological and soil condition changes in food systems (Atzberger 2013).
As detailed in the UN’s World Urbanization Prospects (2014)5, definitions of urban areas differ
markedly across countries, covering a range of specifications and justifications. Out of 232 countries, 99
do not have an official definition of urban and 103 countries use a minimum settlement population standard
(Deuskar and Stewart 2016).6 In other countries, population density thresholds, settlements with economic
specializations, or the presence of services and infrastructure are used as definitions.7 As each country
5 Available at: https://esa.un.org/unpd/wup/ 6 For example, in Argentina this includes areas of 2,000 people or more whereas in Senegal settlements of 10,000
people or more are officially classified as urban. In Mali, urban areas were those with at least 5,000 people up
through 1987, then at settlements of least 30,000 people from 1998 onward. 7 The Philippines for example uses several requirements: density (at least 1,000 people/km2), designated
“administrative centers,” designated barrios with at least 2,000 inhabitants, barrios with at least 1,000 inhabitants
contiguous to an administrative center, and all designated municipalities with a population density of at least 500
people/km2. In Malaysia, urban areas require a population of at least 10,000, at least 60 percent of those over 10
years old engaged in non-agricultural activities and, finally, housing units with modern toilets (UN 2014).
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measures urban population differently, cumulative totals of urban population across regions - or the world
- lack reliability due to this inconsistency (Cohen 2004). Nonetheless, these figures are often cited as
evidence that more than half of the world’s population lives in cities, (UN 2008; Sudjic and Burdett 2007)
and as the basis for the UN’s estimation of future urban population growth in the coming decades (UN
2014). Without a consistent and comparable measure of urban areas and the populations they contain, it is
difficult to perform cross-country analysis of the relationship between urban population growth and
expansion and other areas of policy concern, such as land use planning, economic growth, climate change,
and disaster risk management, among others.
In recent years, advances in the processing of remote sensing technologies combined with
improvements in the quality and availability of population data has greatly improved the accuracy of
estimating the location and population of urban areas across the world (Potere and Schneider 2007; Seto et
al 2010). Over the last fifteen years, the utilization and refinement of these tools is improving our
understanding of the spatial and temporal dimensions of urban change, in terms of land use coverage,
population densities, urban expansion and the distribution of economic activity. Yet, there are limits and
tradeoffs to the current approaches and there is no universally accepted dataset or platform. Despite the
progress, there remains a lack of consensus on some basic questions, due, in part, to definitions and also
limitations and tradeoffs within the data sets constructed. Users have different needs; some may concentrate
more on land use and built environment measures, while others are more concerned with the distribution of
population, which leads to different definitions of urban areas. Finally, while the availability of data has
expanded rapidly, changes and improvements to the data sets are very frequent, which makes older sources
outdated and hinders comparison of observations across time.
While this work remains in a relatively nascent stage it is important because it will allow scholars
and policy makers a more consistent approach to understand the characteristics and qualities of urbanization
and land use changes, especially in places where it has previously been overlooked or poorly measured with
conventional approaches, such as is the case in South Asia and Latin America (World Bank 2015; Roberts
8
et al. 2016). Additionally, the Sustainable Development Goal 11.3 aims to “by 2030 enhance inclusive and
sustainable urbanization and capacities for participatory, integrated and sustainable human settlement
planning and management in all countries” (UN 2016). A proposed measure for this goal is tracking the
land consumption rate to new population growth. For this purpose, a globally consistent measure and
methodology is necessary to effectively monitor the progress and outcomes of this indicator.
This paper aims to provide an introduction to research on urban expansion, in particular the datasets
and methodologies utilized and current trends observed at a global level, with examples from different
regions. This is important because while there is general agreement that the world is becoming more
“urban,” and that cities are expanding to accommodate this growth, the ways in which such change is
observed and measured depend on the map and population data used. A review datasets and approaches is
useful for informing policymakers and practitioners managing urban growth and expansion about the tools
available, their limitations and how they may be applied in policy settings at city, regional and national
levels. This paper will review different datasets and approaches to documenting and measuring urban
expansion, and offer caution on how these data may be applied for different purposes and suggest areas for
future inquiry.
Urban expansion refers to the growth in occupied built up area as part of urban settlements. “Urban”
has two related definitions. Under the first, approaches using satellite imagery or other remote sensing data
aim to classify urban areas based on a defined set of criteria for observed artificial built-up areas in relation
to other types of land coverage (forests, deserts, agricultural areas and so forth). The second draws from a
demographic approach; using built up area data as an input for estimating the distribution of populations in
and around known settlements in order to define urban populations by using criteria such as minimum
population, minimum population density or catchment area of travel to defined urban point. In each case
however, general, universal definitions of “urban” remain elusive. This is because what is “urban” exists
along a continuum; there may be a basic agreement that the central cores of very large cities have similar
9
characteristics (a high concentration of buildings and residents), but there is a large variation in land use
types and intensities and patterns in population concentrations moving outward toward more rural areas.
This paper is organized into three sections. The first will review the remote sensing tools and population
sources that form the basis for current standardized measures of urbanization, distinguishing between
approaches that define “urban” through observable built up areas and those using population and density
characteristics of urban settlements. It will assess the assumptions, benefits and drawbacks to these
approaches. The second will survey findings from recent empirical work utilizing these tools to track
changes in urban form and populations from different regions, documenting trends and patterns observed.
A final section will summarize the conclusions and identify future avenues of research. Detailing the
explanations and causes for differences in growth patterns is critical for scholars and policymakers, though
it is beyond the scope of this paper.
2) Estimating Urban Expansion: Definitions and Methodologies
In recent years, the observation that most of the world’s population lives in cities has become an
increasingly common refrain among practitioners and urban scholars (UN 2014). However, this assertion
overlooks disagreements and differences of a precise definition of “urban” along with the methods for
identifying and comparing urban areas and urban populations across the world. In recent years there have
been rapid improvements in the quality and availability of remote sensing data and advances in its
application to estimate urban land coverage and urban populations. Much of this work has attempted to
draw estimates of built-up areas and combine them with spatially disaggregated gridded population data
sets. This section reviews the main sources of satellite imagery and the population databases used for
developing global urban population layer products.
Using Satellite Imagery to Map Urban Areas
There are several satellite-based maps available for examining global urban expansion by
estimating the area and changes in built environment based on analysis of multi-spectrum satellite
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observations (cf. Schneider et al. 2009). “Built environment” refers to land surface covered with human
constructed materials, such as roads, buildings and other artificial surfaces. The resolution of available
satellite maps influences how built environment surfaces are measured. For visible spectrum photographs
this ranges from coarse resolution (>500m; MODIS), moderate (30m; Landsat), high (~ 5m; Rapid Eye and
very high resolutions (<1m; Quickbird). There are several ways to identify and classify built environments.
This includes visual review, classification and tracing, semi-automated detection using both algorithms and
manual review and fully automated detection relying on computer-assisted review and classification of the
coverage types for each pixel.
Built environments may be classified according to manual tracing or analysis of multispectral
images8, or with algorithms that estimate probable built up coverage using specified parameters based on a
spectral analysis of the color, patterns and reflectivity of certain areas compared to others. Other sources,
such as nighttime lights (which dates back to 1992), capture the extent of artificial lights at a very coarse
(~0.5 km) resolution by specifying a threshold cut off measure for brightness separating urban and rural
areas.9 Different automation systems also use different definitions for urban features. For example, IMPSA
(Global Impervious Surface Area) categorizes urban areas as impervious surfaces, MODIS500 defines them
as areas of 1km2 with at least 50 percent coverage of artificial surfaces, GRUMP uses a nighttime lights
along with a secondary estimate of urban built up area (Potere et al. 2009; SEDAC 2016) This is an
imperfect process as distinguishing artificial from natural ground cover is subject to both errors due to
specification and also issues with image quality and clarity (as well as obstructions such as clouds or smoke)
(Small 2005). These different approaches to land use classification account for the substantial variation in
the estimates of the total area of built up urban land (See Figure 1 for comparison).
8 This includes images captured within the visible portion of the electromagnetic spectrum, but also higher
frequency infrared and radio waves 9 For example, observed brightness is assigned a scale numeric value, with those areas exceeding a certain number
deemed to be “urban” while areas with lower scores are rural.
11
Figure 1: Urban Built-up Areas of Washington, D.C. – Baltimore Metropolitan Region, Select
Maps,10
Landsat 30m
Modis 500m
GRUMP 1km
IMPSA, 1km
There is no single ideal mapping platform for assessing global urban expansion. Satellite imagery
varies by the date of first observation, the frequency of regular updates or passbys, the resolution and spectra
of light frequencies captured, and cost. Other satellites, such as the German TanDEM-X also capture high
resolution, non-optical frequencies based on radar, infrared and laser reflections. These data can be used to
10 Drawn from Schneider et al. 2010, pg. 1741. The frame size for each map is identical. Yellow areas are classified
as “urban,” while darker green areas are rural or natural environments. Dark blue/black areas represent water bodies.
12
refine the definitions of urban built up areas because they allow for distinguishing variations in the height
and shape of ground cover, such as buildings towers and poles (Taubenbock et al. 2011a). For global urban
mapping, cost and data coverage availability often requires the use of coarse resolution (250-1000m) or
moderate resolution (20-30m) mapping data (Mertes et al 2015). For automatic and semi-automatic
classification systems, resolution influences how different land coverage types are classified because a
single pixel unit may contain ambiguous or mixed coverage. At both coarse (>300m or more) and higher
resolutions (5m or less) it becomes difficult to identify and distinguish the surface variation within a given
pixel, so it can be difficult to classify both differences between built-up and non-built up areas, or to classify
different types of built-up areas at a broader, comparative scale.11
Recent research has also focused on identifying techniques to better estimate the extent of built up
area in order to enhance the calibration of population grids for regional and global comparison (Mertes et
al 2015; Tatem et al. 2004). This has involved comparing the estimates of urban land coverage generated
by automated classification systems from different low resolution maps with samples of higher resolution
reference locations and testing for agreement in terms of urban land cover estimates (Potere et al. 2009)12
Indeed, even among mapping data systems, there is considerable variation in the total amount of built up
area estimated due in part to image resolutions, conditions for distinguishing built-up areas from natural
environments and differing years of image collection. In general, the greater the resolution of maps the
higher the estimated total built-up area. For example, as Schneider (2007) observes, one early map, Vector
Map, drawn from a set of digitized and harmonized maps and navigational charts at a low resolution,
provides an estimated total urban surface area of just under 0.3 million km2. By contrast, GRUMP, which
11 Consider the great variety of colors, reflectivity, patterns and materials of building roofs, appurtenances and street
surfaces within a single city. In order to use these as markers or inputs for identifying built up areas in other cities
within the region or across the world, this “language” of colors and patterns for urban classification would have to
be refined considerably. It would have to both include a much wider variety of different land surface coverage types,
but also remain internally consistent enough so that these classifications are not applied to non-urban areas in error. 12 In the Potere and Schneider paper, reference points were drawn from Google Earth as well as other studies where
urban boundaries and land coverage were clearly delineated.
13
still utilizes a coarse resolution of about 1km and a different algorithm, estimates this figure to be 3.5 million
km2 or 11 times larger (Potere and Schneider 2007).
In a comparison study of four early global map products,13 Potere and Schneider (2007) find that
there is some degree of inter-map correlation by region, with North America having the highest level of
convergence (r=0.90) and Asia the lowest (r= 0.63) with Europe, Latin America and Sub Saharan Africa
midway between these two. Such variation may likely be due not only to differences in the resolution of
the underlying satellite imagery, but also differences in automation algorithms and years of observation;
where rapid urban expansion may be recorded in different maps across two different time periods. They
also suggest that the strongest factor is likely due to differences in discerning urban land; “in the absence
of a clear set of definitions, each group constructs an implicit model of urban land that can be inferred from
their methodologies” (pg. 23). A follow up analysis (Potere et al. 2009) compared eight map products by
assessing their consistency in matching a sample of sites and defined cities as well as predicting the built
up area of these cities. As with previous studies by the same authors (Schneider et al 2009), the MODIS500
map was found to have the highest level of agreement with high resolution reference images, comparatively
superior detail and resolution and a lower level of omitted city errors. The authors use this map to estimate
Earth’s total urban surface area (around 2001) at 657,000km2 or about 0.44% of total land area, suggesting
that the total space occupied by urban land is rather small.14
Other work has focused on how to triangulate estimates of urbanized areas using multiple map
products (including those at different resolutions) which can be used as a baseline for monitoring future
urban expansion in a consistent manner. For example, Mertes et al. (2015) developed such a technique by
using a sample of East Asian cities which are otherwise subject to greater measurement error due to
13 The analysis includes Vector Map Level zero (VMAP0), Global Land Cover 2000 (GLC00), the History Database
of the Global Environment v3 (HYDE3), Global Impervious Surface Area (IMPSA), MODIS Urban Land Cover
and GRUMP 14 Angel et al. (2010) use MODIS500 to estimate current and future urban land coverage. They estimate this figure
to be 605,875 in 2010, with projected growth between 853,355km2 and 1.2 million km2 by 2020. This map classifies
“urban” as areas of 1km2 with at least 50 percent built up area.
14
persistent cloud cover and a pattern of rapid urban expansion which has been difficult to consistently
identify as built up area.
An emerging strand of work utilizing multiple map sources has been completed as inputs for the
Global Human Settlement Layer (GHSL) (Pesaresi et al. 2013). The impetus for this work emerged
following the Indonesia tsunami of 2004, in an effort by aid agencies to obtain a large scale mapping
platform that could be regularly updated for disaster risk management planning and to monitor urban
expansion and refugee settlement issues (Pesaresi et al 2013). This layer is calibrated from seven high and
very high resolution satellite maps sources with lower resolution base layers as references for the period
circa 2010. The data also include four periods (1975, 1990, 2000 and 2014) which permit comparative
temporal analysis at the global level. This platform provides among the most comprehensive global level
estimates of urban coverage, with a recent update in 2015.
Other remote sensing techniques have utilized imagery of ambient night time lights from human
settlements. Nighttime lights data from the Defense Meteorological Satellite Program Optical Line Scanner
(DMSP-OLS) (archiving of images began in 1992) measure the extent of ambient light from human
settlements. The data allow for tracking both urban expansion by the location and extension of new lights
around cities, as well as changes of intensity and brightness of lights within cities, which suggest the
presence or absence of population concentrations (Zhang and Seto 2011). The resolution of these images is
coarser than other data sets (approx. 1 km at the equator), but they do allow for estimation of built-up areas
that can be cross-checked with other sources as is used by GRUMP (Balk 2009).
A key advantage of nighttime lights data is the broad scale and extent of coverage, which is useful
for mapping urban settlement expansion patterns at regional or global levels. Another advantage compared
to daytime, visible spectrum maps is that they also capture variations in brightness, especially within known
built up areas, which can help determine the relative intensity of economic activity and concentration of
population. There are some limits in the application of nighttime lights maps. For example, the resolution
is too coarse to reliably record very small settlements, and the data also suffers from a “blooming” or
15
“overglow” effect where areas of intense brightness may obscure both the variation of brightness within a
settlement as well as its overall contour and bounds.15 Recent work has developed techniques to improve
the estimates of urban boundaries (Abrahams et al. 2016) and has begun to test newer, alternative nighttime
lights sets, such as NASA’s Visible Infrared Imaging Radiometer Suite (VIIRS). This instrument, originally
designed for recording cloud changes, surface temperature and fires (among other earth science
applications), can map nighttime lights at a higher resolution (742 m2 versus 5km2) which allows for
improved automated correction of blurring and glow effects (Shi et al 2014; Baugh et al. 2014).
Calibrated nighttime lights data also have other applications when combined with complementary
data. For example, there is evidence that the form and brightness of nighttime lights is correlated with GDP
and many economists have used this approach it to measure differences in the location and intensity
economic activity the urban level. (Henderson et al 2012; Chen and Nordhaus 2011; Mellander et al. 2013;
Ellis and Roberts 2015; Bundervoet et al. 2015).16 Figure 2 below shows sample nighttime lights images.
15 Corrections to this effect can diminish the intensity of lights from smaller settlements (Bloom et al. 2007). There
are several products with different applications; see Doll (2008) for a review. 16 Changes in the intensity and density of urban light patterns may also be proxy measures for population density
(Bagan and Yamagata 2015) and access to electricity (Doll and Pachuari 2010). Related applications include the use
of these data for estimating losses in the events of natural disasters (Gunasekera et al. 2015) estimating the extent of
global poverty (Elvidge et al 2009), assessing emissions of greenhouse gases (Elvidge, 1997), and the efficiency of
urban energy grids (Fragkias et al. 2016). There are a growing number of promising applications of nighttime lights
data as a complement to other geographic and socioeconomic data sources, especially as calibration methods and
corrections continue to improve.
16
Figure 2: Select nighttime lights images of urban areas from South Asia17
Hyderabad, DMSP-OLS (2010)
Red indicates the brightest areas, green and blue
less bright. Black lines represent the city’s
administrative boundaries.
Central Indo-Gangetic Plain, VIIRS (2014)
The image shows a chain of cities (white areas) from Lahore at the upper
left hand corner stretching to the Delhi in the lower right hand corner
Linking Urban Areas with Population Data
As previously discussed, the UN World Urbanization Prospects data have the advantage of
simplicity because they draw on available census or survey population estimates. For each country,
estimates are based on available population counts and projections for urban and rural areas from observed
trends dating back to 1950. Across countries, the granularity of census data units or tracts varies widely and
some countries have not had regular decennial censuses, leaving large demographic data gaps.18 Changes
in urban populations are interpolated from available data, which may classify population boundaries for the
17 From Roberts et al. 2015, pages 62 and 65, respectively. 18 For example, Pakistan, one of the most populous countries on earth, completed its last census in 1998.
17
same urban area in different ways in different years, making population trends inconsistent (Montgomery
and Balk 2011).19
The use of satellite imagery to distinguish land use types may allow for refinement of population
estimates according to built-up areas. Satellite images of built-up areas are overlaid with sets of gridded
population data based on recent or estimated population counts within administrative boundaries (see
Deichmann et al. 2001 for an overview of early approaches). These databases then estimate the likely
population for a given grid cell (e.g. 1 ha or 1km2) based on the built-up area and the known or estimated
population of the corresponding administrative area.
An early database, the Gridded Population of the World (GPW) developed by the National Center
for Geographic Information and Analysis at the University of California, Santa Barbara, with assistance
from the Center for International Earth Science Information Network (CIESIN) at Columbia University.
The most recent edition (version 4) covers 2000-2020 in five year increments20) uses population data from
the smallest administrative units available. Projections are estimated using census administrative units and
projecting a uniform population distribution across grids of 1km2, a technique called areal weighting
(Mennis 2003). Figure 3, drawing from Deichmann et al 2001, shows this using the example of Haiti. The
drawback to this approach is that populations are often not distributed evenly within cells which leads to
biases in country level urban population estimates where there are fewer census input units. However, the
advantage is that new, more granular census data can easily be integrated and population estimates quickly
refined (Doxsey-Whitfeld et al. 2015). For example, the first version, released in 1995, used 19,000
administrative units, where the third version, released in 2005, contained nearly 400,000 and the most recent
version, updated in 2015, has 12.5 million (SEDAC 2015).
19 For example, depending on available data, the population of a city may be arbitrarily reported to the UN in terms
of city proper, agglomeration or metropolitan region. 20 The figures for each of the years are extrapolated from 2010 census administrative unit inputs.
18
Figure 3: Gridded Population Maps, Haiti, 2001
This map shows administrative boundaries and with a
1km2 grid matrix for an area of northern Haiti. Cells with
darker red have higher population densities.
This map shows the gridded population data for the
entire country, again where darker red areas
represent higher population densities.
Other products have attempted to correct for this with more sophisticated estimates of population
distribution. These include LandScan and GRUMP which use a dasymetric estimation approach through
integrating additional information to construct population distribution estimate within a given grid.21 For
example, LandScan, developed by the Oak Ridge Laboratories in Tennessee, estimates vegetation cover,
topographic variation and transportation corridors to calibrate population distributions within each cell
(LandScan 2016).22 However, in contrast to GPW, it uses a proprietary method for assigning population
which is regularly updated, making reliable population comparisons across time periods difficult. Similarly,
GRUMP uses a set of 55,000 settlement points with populations of at least 1,000 people and draws on an
urban extent layer drawn from multiple sources including nighttime lights data from DMSP-OLS to
calibrate the population weighting (Bloom et al 2007).
21 This approach has also been developed and utilized for the most recent GPW set. 22 For example, in a given cell, a greater share of population may be distributed closer to roads and intersections,
rather than in forested or undeveloped areas.
19
Worldpop and GHSPop provide gridded population data based on census inputs and permit global
level comparative analysis of urban populations. WorldPop uses available census and population data,
nighttime lights data, and several other layers of data to map urban extents, along with a machine learning
algorithm to model population to 100m2 cells (Stevens et al. 2015). The data currently exist for several
dozen countries with more being added regularly in Asia, Africa and Latin America.23 The Global Human
Settlements Population Layer (GHSPop) was developed by the European Commission’s Joint Research
Center and (JRC). Recent versions draw population data from CIESIN’s GPW and now utilize the GHSL
layer for the extent of urban coverage to distribute population in urban areas four periods beginning in 1975
at a resolution of 250m (Freire et al. 2015).24 The global coverage, depth of data, ease of access and
refinement have increased the attention and interest in their utilization for estimating urban expansion
(Deuskar and Stewart 2016).
Demographic Approaches to Defining Urban Areas
The previous section examined how urban areas are measured in terms of the surface coverage of
built up areas and the distribution of populations within them. Another approach defines urban areas in
terms of the population characteristics of settlements, such as minimum total population, minimum
population density and or distance from a known central point within a settlement. One of these methods,
the Agglomeration Index (AI), (Chomitz, et al, 2005 Uchida and Nelson 2010, World Bank 2009) uses each
of these three conditions to construct a measure of urban agglomeration. Agglomerations represent
concentrations of workers and firms that derive mutual benefit and increasing returns to scale from close
proximity in the same geographic space (Marshall 1961; Storper and Venables 2004). Agglomerations
provide a functional concept of urban areas by defining them in terms of a concentration of population
23 Older data exist for additional countries, though the estimation technique used is different compared to the current
edition. 24 CIESIN is also developing a gridded population product with Facebook that utilizes the company’s image
recognition algorithm to process images with built up layers at very high resolution (50cm) for 21 countries.
http://blogs.ei.columbia.edu/2016/02/22/working-with-facebook-to-create-better-population-maps/
20
around economic activities.25 Rural areas, by contrast, are characterized by more dispersed populations and
points of exchange. The AI applies a population condition (settlements of at least 50,000 people) a density
threshold (at least 150 people/km2) and the catchment of a 60 minute travel time to city center points
identified on GRUMP or LandScan maps. Travel time provides an important qualifier because it is a proxy
measure for some level of labor market participation within the agglomeration. However, part of the
weakness for using the AI to track changes in urban populations over time is the lack of broad availability
and timeliness of travel time data.26
The “Cluster” method, introduced by the European Commission, represents a similar, population-
based approach but without travel catchment times. It was initially applied to European cities but has since
been updated and expanded (Dijkstra and Poelman 2014; Deuskar and Stewart, 2016; Roberts et al., 2016).
The Cluster method takes population layers from a gridded population distribution map (such as WorldPop
and GHSPop) and categorizes them as clusters depending on population size and density. Urban clusters
composed of contiguous gridded population cells with a minimum population of at least 5,000 and a density
of 300 people/km2. Within this set, ‘high density’ clusters are defined as clusters of built-up area with at
least 1,500 people/km2 and a settlement size of at least 50,000 (Dijkstra and Poelman 2014). All other cells
are classified as “rural.” A benefit to this method is that thresholds can be adjusted according to need for
different types of comparative analyses as it arranges the values of gridded population cells according to
specification. However, the selection and use of different population condition thresholds requires an
appropriate analytical justification.
Revisiting Urban Population Estimates Work
Recent analytical work has compared the estimates of global urban population using the AI and
Cluster methods by drawing from WorldPop, GHSPop, and LandScan gridded population datasets with
25 Or as Bertaud (2003) more succinctly observes “the raison d’etre of large cities is the increasing return to scale
inherent in large labor markets” (pg. 1). 26 The layer recording city center points, from which the travel time catchment is calculated, had been based on the
original points from 2000 and was updated in Roberts et al. (2016).
21
figures proposed in the WUP. Roberts et al. (2016) compare these datasets for a global sample of countries
using the most recent data (circa 2014) utilizing both the Cluster and AI methods to derive estimates of
national urban population shares by region. They find that each approach estimates a higher proportion of
urban population in most regions than does the WUP, most notably in South Asia, East Asia and Sub
Saharan Africa. GHSPop estimates for South and East Asia are even higher; suggesting that about 80
percent of the population of these regions is urban (versus approximately 57 percent in WUP and 64 percent
in WorldPop, respectively).27 Figure 4, drawing from Deuskar and Stewart (2016) demonstrates the
difference in urban area estimates between WorldPop and GHSPop when the same density and population
thresholds under Cluster method are applied to Jakarta, Indonesia. It shows that Worldpop estimates a much
larger urban area overall than does GHSPop, while GHSPop identifies a greater extent of high density
clusters.
Figure 4: Comparison between WorldPop and GHSPop using the Cluster Method, Jakarta28
WorldPop, 2015
27 Among the two population distribution inputs (WorldPop and GHS Pop), this difference is due to how population
is assigned within each cell; Worldpop uses a more conservative specification which tends toward a more diffuse
population distribution with cells. It also draws from lower level census data with more granularity and specificity,
whereas GHSpop assigns the total population to larger (1km2) cells and often draws from less granular population
data. 28 From Deuskar and Stewart, 2016
22
GHSPop, 2015
The Cluster Method classifies cells within a gridded population distribution map according to density and then clusters
them by areas of similar density. High density clusters (shown in red) are contiguous cells of at least 1,500 people per
km2 with a total population of at least 50,000. Urban clusters (shown in yellow) are defined by contiguous cells with
a density of at least 300 people per km2 and a population of at least 5,000.
WUP data has also suggested that Latin America is much more urban than would otherwise be
assumed based on GDP-per capita and agricultural contributions to GDP. Roberts et al. (2016) applied the
AI and Cluster methods utilizing these three gridded population datasets for the region and found the share
of urban population is estimated to be much lower and more in line with global averages and the expected
relationship. The authors find that the AI method generates similar estimates with either the WorldPop or
GHSPop datasets and is slightly more robust than the cluster method (likely due to the inclusion of the
central city travel metric). This suggests that the choice of gridded population data (LandScan, WorldPop
and GHSPop) is a key determinant in estimated urban population outcomes and additional comparative
work utilizing these data is required to better understand these differences.
This work highlights both the advantages and limits of current approaches to measuring global
urbanization. The availability and variety of mapping data from remote sensing sources has allowed
scholars and planners to better identify the extent of built up areas. However, there remain several limits to
23
tracking urbanization in terms of built up area due to differences in availability and granularity of data and
also differences in how to reliably classify different types of built up areas observed, particularly along the
urban periphery (rather than central city areas) where expansion is occurring. Compared to standard census
or gazetteer sources, gridded population layers have improved estimates of how populations are likely to
be distributed in and around known urban settlements and rural areas. The cluster and AI methods have
also provided a consistent and globally comparable way of classifying of urban areas based on a functional
definition of urban population concentrations (though the population conditions they use are somewhat
arbitrary). However, there lacks an explicit spatial dimension to this approach. We do not know for example,
how clusters and agglomerations overlap with built-up areas, administrative boundaries or artificial land
cover changes at global level.
Remote Sensing to Track Land Uses
As previously mentioned, medium and coarse resolution mapping data such as MODIS and
nighttime lights have been used to distinguish “urban” and “rural” areas based on identifying and
distinguishing artificial built up area from agricultural or natural land coverage. These maps, however, do
not show differences particular types of built up areas within urban spaces. For example, central city areas
with high rise buildings are clearly distinct from low rise residential suburbs, expansive industrial parks or
informal settlements, each of which may be located within the same functional urban area (or adjacent to
each other) but do not share the same distribution of population, economic activity, let alone land cover
pattern. These distinctions are important for better understanding city-level issues related to access to
services and employment, economic activity, transport and mobility and urban land markets and
administration, among other issues.
The quality and increasing availability of high and very high resolution (< 2m) satellite imagery
from multiple sources has also provided data for case specific analyses of urban fabric and land uses
(Graesser et al. 2012). These approaches utilize algorithms that classify map data either by shapes or by
vectors in order to distinguish colors, textures and patterns that are associated with certain types of land
24
uses or neighborhoods, such as informal settlements (Hoffman et al. 2008). This method is further
strengthened by the inclusion of “ground truthing” photos taken by observers to improve the reliability of
the algorithm by triangulating the satellite maps with on-the-ground observations of building types, colors
and materials (Kim and Liu 2004, Lozano-Gracia et al. 2016). As additional data are included and the
algorithm conducts recursive tests, predictive accuracy is improved through a process of machine learning.
This process improves the identification of specific features, such as roads, vegetation but also building
types, heights and even distinguishing between informal and informal settlements (Kuffer et al 2016
Henderson et al 2016). There is ready value for this approach in mapping the location and extent of different
land use types within the larger urban fabric that would otherwise be missed by medium or coarser
resolution maps, such as hidden or “pocket” slums and informal settlements built with non-standard
materials (Kim and Liu 2004) or the location and accessibility of parks, green spaces and urban services.
3) Urban expansion: Trends and patterns across the world.
While researchers are improving techniques for consistently measuring the global urban footprint
and its evolution of time, a number of studies have examined urban expansion in cities and regions
throughout the world using satellite imagery. Due to the limited amount of comparable data across time
periods, much of this work utilizes purposive sample of city-level case studies and attempts to discern
patterns or typologies of urban change (Angel 2005; 2010). Since 2009, the World Bank has developed
several regional studies utilizing medium- (MODIS) and coarse (DMSP-OLS nighttime lights) resolution
satellite imagery to map urban expansion changes, especially in Asia (see, in particular, World Bank, 2015,
and Ellis and Roberts, 2015). This section provides an overview of findings from these and other recent
studies.
Despite disagreement on the precise amount, there is a consensus that urban areas, or at least built-
up areas, across the world are expanding (Seto et al. 2011; Angel 2005). In a recent meta-analysis, Seto et
al. (2011) reviewed 326 studies using remote sensing imagery and found all regions over the period of
25
1970-2000, urban land expansion rates are equal to or greater than population growth, with the greatest
gains in India, China and Africa. In these areas, they estimated a global increase 58,000km2 of urban for
the period 1970-2000, with India, China and Africa having the fastest rates of urban expansion. In coastal
China, for example, annual urban expansion rates exceeded 13 percent over this period, while in areas
where expansion has not been as rapid, such as North America, growth rates were around 3 percent per
annum. Extrapolating from these patterns they use MODIS land cover maps, population and economic
growth projections to estimate that by 2030 the world will add approximately 1.5 million km2 or roughly
twice the amount of urban area estimated for 2001 (Schneider et al. 2009).29
Angel et al. (2005) find evidence for declining average population densities in cities across the
world. They utilized a global sample of 120 cities with populations of at least 100,000 and used Landsat
(30m) maps from 1990 and 2000.30 While they also find that cities in developing countries are on average
more dense than those in developed countries, in all sampled cities, population density declined on average
by about 2 percent per year or from an average of around 144 persons/hectare to about 112 persons/hectare
(pg. 57). They also find significant positive relationships between increasing density decline and cities with
rapid income growth, high initial densities and few physical or topographic constraints.
An emergent area of research attempts to classify differences in growth patterns within individual
cities to develop typologies of urban form and density. Schneider and Woodcock (2008) observe similar
patterns with a sample of 25 cities using a similar methodology drawing from census figures and Landsat
data from between 1990 and 2000. While all cities are marked by an overall decline in average density,
they identify four types of growth: about half of the cities are classified as “low growth” where more than
29 In each case, “urban” is defined in terms of artificial built environment categorized using medium/coarse
resolution satellite maps. 30 Locations were validated using Google Earth central reference points, but utilized available census data rather
than gridded population data. The study also draws is drawn from a previous sample of 3,945 cities with a
population of at least 100,000. A parallel analysis of 30 cities from across the world over the period 1800-2000
utilized historical maps and gazetteer sources also found a tendency for density to decline by about 1 percent per
year.
26
half of converted land is infill development, suggesting a more compact growth pattern. 31 “High growth”
cities are characterized by a majority of land converted into scattered, non-contiguous development.32
American cities are classified as “expansive;” low density, with a large footprint and dispersed or
fragmented built up areas. By contrast, Chinese cities have followed a “frantic” growth pattern which
includes fragmented development, but at a consistently higher density.
East Asia: Urbanizing with density
The World Bank (2015) drew from MODIS500 maps and WorldPop data from 2000 and 2010 to
analyze urban expansion changes in East Asia.33 The report identified and assessed 869 urban areas which
consist of the built up area of cities with populations of at least 100,000.34 In contrast to the findings by
Angel et al. (2005), the report identified a pattern of both spatial expansion and densification in urban areas
of 100,000 people or more (Schneider et al 2015). The report finds that about 36 percent of the region’s
population lives in urban areas of this size. Over this time, urban expansion averaged 2.4 percent per year.
The highest rates of built up area expansion occurred in upper income countries, while urban population
growth averaged 3 percent but with higher rates in lower income countries. Mean urban population density
also increased 0.5 percent to 5,776 people per km2 in 2010.35
The study found different patterns across countries. Some two thirds of new urban land growth in
the entire region occurred in China alone (23,600 km2). Urban Indonesia became denser, with a density
increasing 27 percent to 9,400 people per km2 or adding only about 40m2 of urban area per person
(compared to 260m2 in China). The region’s eight “megacities” (built up urban areas with greater than 10
million people) had lower rates of population growth and land consumption than did small (<1 million) and
31 These include Guadalajara, Curitiba, Cairo, Nairobi and Ahmedabad. 32 Examples include Brasilia, Ankara and Bangalore. 33 This includes China, Indonesia, Vietnam, the Philippines, Japan, Korea, Korea DPR, Mongolia, Thailand,
Cambodia, Lao PDR, Myanmar, Malaysia, Timor-Leste, Papua New Guinea, Singapore, Taiwan and Brunei
Darussalam. 34 Urban areas are defined as the contiguous urban land in and around known settlements with a population
threshold of at least 100,000 people. Commuting or density conditions are not used to define this sample. 35 If China is excluded, mean urban population density rises to 6,600/km2
27
medium (1-5 million) urban areas. Among these, megacities in lower middle income countries (such as
Manila and Jakarta) had the largest urban populations, but most new population growth occurred in smaller
urban areas. However, upper-middle income countries, with a more even distribution across different sized
urban areas saw the greatest growth in the largest cities (e.g. Shanghai).
These findings are in line with study of 142 Chinese cities over a longer time period (1978 to 2010)
which finds differences in the expansion and population growth of Chinese cities by size class (Schneider
and Mertes 2014).36 For example, the authors find that existing large (> 1 million population) cities,
especially in the coastal region were the main consumers of new land, most of the population gains over
this period occurred in neighboring smaller cities, often within the same agglomerations surrounding large
cities. This difference may be due in part to restrictions which deter rural migrants from large cities, but as
others have found, rural-urban migration has accounted for as much as 56 percent of urban population
growth in China from 2000-2010 (World Bank and DRC 2014).37
The World Bank report also highlighted a regional trend toward metropolitan fragmentation,
defined as urban areas where no administrative district contains more than 50 percent of the built up area.
Excluding in China, 41 percent of the region’s 869 identified urban areas are “fragmented,” (examples
include Metro Manila and Tokyo, Japan). The second most common expansion pattern consisted of
“spillover” urban areas - those where one administrative district has more than half of the built up area, but
less than 100 percent (examples include Hangzhou in China and Bandung in Indonesia). Large urban areas
– “megacities” (>10 million) _are entirely fragmented, with greater shares of spillover observed among
those between 0.5-5 million, while most of the smallest urban areas in the sample are still contained within
one administrative boundary. The expansion of urban built up areas across administrative boundaries has
36 However, where the World Bank study finds that urban land and urban population growth in China from 2000-
2010 are roughly equivalent (~3.2 percent annually), Schneider and Mertes (2014) find that urban land from the
sample of cities tripled, while urban populations only doubled, suggesting a trend toward less density in the cases
examined. 37 For example, the World Bank (2015) estimates that between 2000 and 2010 China added some 200 million urban
residents.
28
important implications for intergovernmental coordination for service delivery as well as ensuring access
to housing and employment across diverse and often competing jurisdictions.
South Asia: Natural Growth and Urban Fragmentation
The World Bank’s South Asia Urban Review (Ellis and Roberts 2015) tracked changes in urban
expansion patterns using both nighttime lights data and the AI rather than WorldPop and optical satellite
imagery. In contrast to the patterns of urban expansion in East Asia being driven by rural-urban migration
(Schneider and Mertes 2015; Zhang and Song 2003), urban expansion in South Asia appears to be driven
more by natural population growth and the reclassification of administrative boundaries (Ellis and Roberts
2015). 38 In contrast to East Asia, particularly, China, the study finds that changes in the national level urban
population share in South Asia is often exceeded by the urban population growth rate.39 This means that
while urban areas are gaining population, the population growth in rural areas is not being offset by rural-
urban migration. For example, in Pakistan, in the period 1981-1998, just 26 percent of urban population
growth was from rural to urban migration (Karim and Nasar 2003).
This study uses both the AI to measure urban areas in terms of population conditions and also
applies nighttime lights data to track the extension of urban areas in the region. DMSP-OLS nighttime lights
data used by the report to define urban footprints show that between 1999-2010 urban areas in the region
as a whole, driven mostly by India (11 percent), grew at about 5 percent a year, more than double the urban
population growth rate over this period.40 In Pakistan and Sri Lanka, urban expansion rates were modest;
4.7 and 1 percent, respectively. Major cities in the region such as Delhi, Mumbai, Hyderabad and Colombo
(Sri Lanka) each experienced faster population growth in surrounding districts than in the city
administrative areas themselves. A consequence of this is a pronounced pattern of built up areas
38 This region includes Afghanistan, Pakistan, India, Bangladesh, Nepal, Sri Lanka, Bhutan and Maldives. 39 These estimates are derived from UN population data. 40 As discussed earlier, nighttime lights are used to estimate urban land coverage here by assigning a brightness
threshold and classifying urban and rural areas based on brightness measurements
29
overflowing administrative boundaries. Using an analysis of Landsat imagery, the Indian Institute for
Human Settlements (2011) found that in Chennai and Kolkata for example, a greater share of the built up
footprint is outside of the principal administrative area than is within it. The result of this pattern is an
increase in multicity urban agglomerations (defined as continuously lit urban areas which contain at least
two cities with populations of at least 100,000), which have expanded at an annual rate of 8.6 percent.41
The result is a blossoming of new, large agglomerations around major cities (such as in Coimbatore, India)
and the linking of multiple cities in a continuous agglomeration of lighted urban area stretching hundreds
of miles from Delhi to Lahore and containing roughly 73 million people (Ellis and Roberts 2015).
Urban Density: City Level Findings
Urban density is an important indicator of population distribution within a city and its proximity to
services and jobs. However, simple density measures (population per unit of built up area) do not capture
variations in internal population distribution according to land use types, nor proximity to or concentrations
of economic activity.42,43 Certain types of urban density provide economic and mobility advantages by
reducing the cost of movement of people and goods. Density gradients have long been utilized for assessing
the concentration of people within cities (Clark 1951). In a classic monocentric city model, density is very
high in the central business district and declines at an exponential rate for each distance unit further outward
due to the increased transportation costs and the declining premium of land rents from these areas (Muth
1965; Alonso 1969).44
Available evidence suggests that this basic framework of a smooth monotonic decline in density
from a central point still describes the density form of most cities. Malpezzi and Bertaud (2003; 2014)
41 Multicity agglomerations here consist of a two cities with at least 100,000 people living it its administrative
boundaries and which share a continuous built up space. In 1999 there were 37 such agglomerations with an average
4 cities within the boundaries. In 2010 there were 45, with an average city count per agglomeration of just under 5. 42 For example, the density of the borough of Manhattan, in New York City is roughly twice that of the city as a
whole (ACS 2008). 43 For discussion of alternative measures of urban form, see the work of MIT’s City Lab
http://cityform.mit.edu/projects/metropolitan-form-analysis-toolbox-for-arcgis 44 Scholars have also used interpretation of satellite imagery to better define and classify polycentric “mega-regions”
in and around the world’s largest cities, see Taubenbock et al 2014 and Taubenbock and Wiesner 2015.
30
utilizing a global sample of 40 and 57 cities respectively, estimate population density gradient, from across
the world, using data from 1990-2009. They find that most cities generally fit the negative density gradient
predicted, but there are a number of cities (such as Moscow, Johannesburg, Brasilia and Seoul) which do
not fit the expected gradient (density is either relatively stable or actually increases the further one travels
from the city center). They suggest this may be due to differences in regulatory environments, but current
measures of these factors to date are imperfect and not uniform.
In a sample of 84 cities, Richardson et al. (2000) find that density levels in differences in population
density of central city areas with surrounding urban areas, finding that average density levels in cities in
developing countries tend to be higher and density gradients less steep than in developed countries. This
finding is supported by Huang et al.’s (2007) assessment of the manually traced urban areas drawn from 77
global cities. They observe “the compactness, density and regularity of urban areas in developing regions
generally exceed levels throughout developed countries” (pg. 11).45 This finding is intuitive; in dense cities
where incomes are low we would expect the amount of space per person to be lower than in less dense,
higher income cities. However, in part due to data limitations, these studies combined only include two
cities (Abdijan and Dakar) from sub-Saharan Africa. This suggests the trend toward compactness is not
uniform and that other factors, such as changes income levels, may be linked to the forms of urban
expansion observed in different regions.
Latin America: Losing Compactness
The trend toward declining urban density also appears to be occurring in Latin American cities.
Using Landsat images of 10 major Latin American cities and then manually tracing and cross checking the
urban boundaries, Inostroza et al. (2013) assessed changes in urban form and density between 1985 and
2010.46 While existing city cores still constitute 92 percent of the urbanized area in the sample, little infill
45 This analysis utilized a technique of manually tracing of built-up boundaries and transferring them to GIS
shapefiles. 46 Census data at the lowest available administrative level were used as population inputs.
31
development has accommodated population growth over this period. They find only about 30 percent of
new development took place within infill areas; only two Colombian cities, Bogotá and Cordoba grew with
infill development, the former increasing population density by 27 percent. Approximately 21 percent of
total expansion could be characterized as “leap frog” development, driven by Montevideo and Buenos
Aires. The remaining 49 percent of built up area growth occurred along existing primary radial roads (most
common in La Paz, Asunción, Brasilia and Santa Cruz). Fringe areas were found to be growing at a rate of
11 percent annually, whereas core areas expanded at just 2.9 percent.
A recent World Bank report on urbanization in Central America (Maria et al. 2015) uses Worldpop
to identify 167 agglomerations with populations greater than 15,000. The report identifies two trends in the
region; a greater urban primacy than official statistics suggest, and a pattern of low density expansion. For
example, the Managua agglomeration holds 55 percent of the Nicaraguan population; twice the figure of
27 percent reported by official sources. In Costa Rica, the San Jose agglomeration contains an estimated 85
percent of the country’s urban population. Between 2000 and 2010, most population growth concentrated
in secondary cities of populations between 15,000-100,000. As with other regions, the annual rate of urban
expansion has exceeded annual population growth.47 This is compounded by the finding that 43 percent of
the agglomerations now span three or more municipalities, suggesting highly fragmented urbanization.
As with other studies - with East Asia being a clear regional exception - recent data again show a
pattern moving toward lower urban densities.48 Large urban centers hold the majority of urban dwellers,
but there is a trend toward growth in secondary cities and along the urban periphery. As urban
agglomerations expand and consolidate smaller settlements, these areas also become more fragmented, as
is the case in Asia. Bogotá stands in contrast as an increasingly dense city, marching against the general
trend in the region toward greater urban space per-capita through low density or leapfrog expansion.
Africa: Dense cities, but Few and Far Between
47 The total built up area in the region since 1975 has tripled, growing at an average annual rate of 7.5 percent. 48 There are also important exceptions at the country level, such as patterns observed in Pakistan
32
Urbanization in Africa has attracted growing attention in recent years as the region is undergoing
urbanization without the same per-capita economic gains historically observed in other regions (Fay and
Opal 2000). Linard et al. (2010) utilized Afripop data (part of the Worldpop platform) to assess the
distribution and accessibility of settlement patterns throughout the continent. In Northern and Southern
Africa, settlements tend to be spatially clustered more closely, suggesting closer proximity to major urban
centers, especially in higher GDP countries such as Libya, Egypt and South Africa.49 In these areas, 90
percent of the population live on 11.5 percent and 7.6 percent of the land versus the Central, East and West,
where the same share of the population occupies between 23 and 36 percent of land, providing a very
general contrast of settlement concentration versus dispersion.50 Settlements in the Central, East and West
regions are marked by greater dispersion across the land area where the average distances from large cities
tend to be greater, again illustrative of lower overall concentration of population in and around major cities
in these regions.
New research using remote sensing data is also beginning to describe the form of major African
cities. Large cities tend to share the same negative density gradients observed in other cities across the
world, but cities in the region standout for the comparatively scarce concentration of economic activities in
city centers (World Bank 2016). Nighttime lights data reveal that the concentration of economic activity in
African cities is low; at 5km from the city center it is around half the intensity observed in cities of other
regions with similar population densities. However, Henderson and Nigamaulina (2016) find that while
African cities have very high peak population densities compared to other regions, they also have much
steeper density gradients, with density declining 14 percent per kilometer from the city center (versus 9
percent in other regions). This suggests that city centers are dense, but there are few pockets of contiguous
density in urban cores.
49 However, this correlation is not statistically significant. 50 This difference is also reflected in average travel times to cities of at least 50,000 people. In the North and South,
this averages around 2 hours versus nearly 5 hours in the Central and Eastern parts of Africa.
33
Urban expansion trends, however, do not show high levels of infill development. In Maputo and
Harare for example, 30 percent of land within 5km of the city center is vacant (World Bank 2016). In an
analysis of built up area changes in 21 cities from 2000-2010 based on Landsat (30m) and SPOT (2.5m and
10m) satellite imagery, the World Bank (2016) finds between 44 and 77 percent of new urban growth
occurred at or beyond the edge of existing urban areas.51 In Bamako and Maputo for example, half of new
growth occurred in a leapfrog pattern of patches of non-contiguous built up areas. Additionally, the rate of
leapfrog growth has increased in all but two cities (Windhoek and Addis Abbaba) between the periods
1990-2000 and 2000-2010. This demonstrates that cities in the region show a trend toward low density and
fragmented development.
Urban Decline and Population Dispersion
While there is general consensus that the majority of developing country cities have experienced
expansions in both area and population, this is not universally the case. An emergent literature on
“shrinking cities” has highlighted some of the trends and implications for this type of urban change and its
relationship to economic competitiveness (Pallagst et al. 2009). Over the last 50 years, 450 cities with
populations of over 100,000 have seen population decline of at least 10 percent (Oswalt and Reinitz 2006).
Examples of this trend occur in developed countries such as the United States, Canada and Australia as well
as in former command economies in Eastern Europe and the former Soviet Union, where, in each case,
overall urbanization levels are historically high.52
Ukraine provides a unique case study. Since the 1990s, the average annual population growth rate
in the country has been negative. Despite this, the country has had the fifth greatest increase globally in
built up 1975-2014 according to GHSL (Ionkova and Sulukhia 2015).53 Furthermore, night time lights
51 This is based on a dataset assembled by Baruah (2015) 52 China provides an interesting example where a large construction boom and investment in “new towns” has
provided a large tracts of unoccupied apartments and vacant commercial buildings. See Shepard (2015) for a
detailed case study. 53 The area totaled 21,222 km2, behind only China, the United States, India and Russia and ahead of Indonesia,
Brazil, Turkey and South Africa.
34
analysis of urban expansion and brightness reveal declining urban footprints and nighttime lights brightness
in the formerly urban east, while western cities became larger and brighter (World Bank 2015). The report
also finds that economic growth is closely correlated with cities that have both expanded their footprints
and have central cores that have become brighter. By contrast, cities with declining economic
competitiveness have dimmer cores, and those with the lowest economic competitiveness actually have
experienced dimming of both cores and peripheries, but an overall increase in the total built up area (pg.
184). For scholars, the study raises additional questions about the relationship between urban expansion
and economic competitiveness as these patterns contrast with the findings in India using a similar analysis
of nighttime lights and urban GDP data (Tiwari et al 2016). For policymakers, these findings call attention
to questions about how to manage infrastructure provision in cities experiencing net outmigration or
negative population growth. The case also demonstrates how nighttime lights data can be an important
complement in establishing contrasts between the extents of built up areas and the concentration of
economic activity within them – which may not otherwise be apparent.
Summary
Large cities in poorer countries also appear to have greater population density than those in
wealthier countries, but nighttime lights data demonstrate that they are not necessarily witnessing increases
in the intensity of economic activity in central city areas. There are some exceptions; Bangalore for
example, maintained brightness levels in central city areas (Ellis and Roberts 2015) and in Central America
the Guatemala City agglomeration increased its share of the total brightness observed for the country
between 1996 and 2010 (Maria et al. 2016). In parts of Eastern Europe, population movements and
structural changes in the economy appear to be reflected in a general dimming of declining urban cores and
in the case of Ukraine, increased, but dim, growth of the periphery. The comparatively rapid expansion of
medium sized or secondary cities also underscores the need to understand how these cities can plan for
future growth, along with establishing what tradeoffs new migrants are making by locating to these places,
rather than larger primary cities. While this paper does not offer explanations for these patterns, attention
35
should be given to differences in land administration regimes, structural economic shifts and urban and
metropolitan governance institutions and arrangements as key factors.
4) Conclusion and Future Directions
This paper has reviewed recent changes in tools and approaches to measuring urban area and urban
populations. While there have been rapid improvements in the availability and quality of global maps of
urban areas and population data derived from remote sensing sources, there remains no single or universal
map or population tool for documenting urban population growth and physical expansion. Current tools
available combining satellite maps with gridded population estimates are most advanced in this regard, but
still suffer from drawbacks in the availability and comparability of data inputs at the global level. There
are tradeoffs in terms of image resolutions, and the availability of comparable images over time. Methods
for determining the types of land use using daytime satellite imagery covers require careful calibration from
multiple images to both correct for surface interference and to accurately distinguish the colors and patterns
associated with various urban built environments across the world.
Nighttime lights data as well as the higher spectrum, non-optical satellite data products (such as the
Global Urban Footprint’s radar imagery) are an alternative option that does not require the same type of
corrections. These factors make broad estimates difficult as different types of urban land uses may or may
not be included in the urban land areas of different cities. Users also have different needs in terms of scale
and resolution; very high resolution data for example could be useful in examining urban fabrics of specific
cities, but other uses such as assessing economic activity or potential damages from natural disasters
utilizing corresponding GDP data could take advantage of coarser maps with other resolutions and
alternative frequencies (such as nighttime lights) more readily.
There has also been improvement in the reliability of population estimates for urban areas,
particularly the improved availability and granularity of census data for administrative units within
countries. This has been a key improvement for improving the accuracy of gridded population data sets.
Given the wide and arbitrary differences for defining urban places utilized by countries around the world,
36
gridded population layers and standardized urban definitions have provided a way to comparably estimate
the number of people in urban areas across the world. They show that, contrary to WUP and administrative
area-bounded data, Africa, the Middle East and Asia have among the greatest shares of urban populations,
while Latin America comparatively less so. However, when utilizing the same population-based thresholds
for urban areas (such as minimum settlement population or density level) the estimates of urban populations
provided by different gridded population layers diverge. This is in part due to the different methods of
distributing population inputs within each grid, but further research is needed to better account for these
differences. These differences have important consequences for how governments can and should prioritize
investments to better accommodate new urbanization or leverage the advantages of existing cities.
Population-based urban estimation methods such as AI and Cluster also depend on defining the parameters
for measuring urban areas in addition to the gridded population layers used as inputs. If minimum
population and population density thresholds are low, more settlements will be classified as urban, though
there may be important qualitative differences between urban areas at the upper and lower bounds of this
distribution. This remains an area for continued exploration (Deuskar and Stewart 2016). Finally, there
currently exists no globally comparable panel set of built-up area maps linked to these two population-
based approaches. Such data would help to understand worldwide changes in the shape and location of
population clusters or agglomerations over time.
Studies in the remote sensing tradition - which equates urban areas with built-up areas or areas
which are brightly lit at night - show that urban expansion is occurring in different patterns across the world.
There also appears to be substantial evidence from available case studies that many cities (apart from urban
agglomerations in East Asia and selected countries such as Pakistan in South Asia) are becoming less dense
as land consumption rates eclipse population growth rates. In Sub Saharan Africa, Latin America and parts
of East Asia, discontinuous and leapfrog urban expansion appears to have become increasingly common in
recent decades, suggesting avenues for future research in identifying what factors may be driving this type
of expansion. Another commonality is the expansion of urban agglomerations across administrative
boundaries, both in regions where rural-urban migration or natural population increases are coinciding with
37
urban expansion. These large and contiguous agglomerations, such as those in northern India and the Pearl
River Delta in China raise questions about how polycentric urban forms emerge and how current
governance arrangements can support and sustain them.
There also remain a number of topics for continued exploration and refinement utilizing satellite
mapping and gridded population data. For example, recent work by the World Bank has linked GDP data
with population and built up layers to estimate the value of potential damages under different natural
disaster scenarios. Additional refinement for global land cover mapping is needed, despite advancements
in algorithms and machine learning for classifying land use classes and types, there remain considerable
differences between the accuracy of these tools across spatial scales. This is due in part to differences in
how image analysts construct algorithms; the parameters they use and the inputs they are based on (Ban et
al. 2015). Location-based social media and geo-coded data could also be integrated into mapping exercises
(an example is GPW’s use of Facebook technology) and monitoring, especially as a means to confirm or
ground truth interpretations of objects or layers of built up areas, or to report and track public health issues
or disaster events “using humans as sensors” (Mertes et al. 2015, pg. 345).
Other avenues of research interest center on improving our understanding of urbanization and how
it relates to climate change impacts. This includes tracking the amount and location of arable land around
cities, the location and intensity of carbon emissions and assessing the risks of potential flooding from rising
sea levels. For example, some 50 percent of global urban land cover is within 116km of water bodies (Angel
2011; McGranahan et al. 2007). City level mapping exercises distinguishing urban land use types and
densities as well as vacant land for urban regeneration and infill development potential. This can also
improve options for restorative ecologies that can improve water collection and drainage by mapping
impermeable surfaces and the potential for urban agriculture.
Finally, apart from Huang et al. (2007) and Sevstuk and Amindarbari (2012) there has been little
comparative work classifying differences in the specific morphological dimensions of different urban
forms, such as shapes, levels of centrality, density and differences in shares of open space within built up
38
areas. This would provide deeper insight on correlates between different types of urban morphologies and
other variables at the city or metropolitan level, such as, the location and extent of informal settlements,
access to services, economic activity and mobility, among others (Chakraborty et al. 2015). Such research
could begin to develop a typology of dimensions of urbanization which can in turn be used for calibrating
comparative land cover and urban population analyses. This would also have particular relevance to case-
specific analyses of shrinking cities, where there is a paucity of research to date utilizing remote sensing
tools to document or compare changes in declining urban populations.
39
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