Atlas of the Human Planet 2018 : a world of cities.
Date: 2018-12-13
Institutions: Joint Research Centre
Subjects: atlas, concentration of the population, database, geographical distribution, migration for settlement purposes, population density, remote sensing, report, town, urbanisation

Atlas of the Human Planet 
2018 

A World of Cities 

2018  

EUR 29497 EN 

 
 
 
This publication is a Science for Policy report by the Joint Research Centre (JRC), the European Commission’s 
science and knowledge service. It aims to provide evidence-based scientific support to the European 
policymaking process. The scientific output expressed does not imply a policy position of the European 
Commission. Neither the European Commission nor any person acting on behalf of the Commission is 
responsible for the use that might be made of this publication. 

Contact information  
Name: Thomas Kemper 
Address: Via Fermi, 2749 21027 ISPRA (VA) - Italy - TP 267 
European Commission - DG Joint Research Centre 
Space, Security and Migration Directorate 
Disaster Risk Management Unit E.1 
Email: thomas.kemper@jrc.ec.europa.eu 
Tel.: +39 0332 78 55 76 

EU Science Hub 
https://ec.europa.eu/jrc 

JRC114316 

EUR 29497 EN 

PDF 

ISBN 978-92-79-98185-2 

ISSN 1831-9424 

doi:10.2760/124503 

Luxembourg: Publications Office of the European Union, 2018  

© European Union 2018  

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Cover Image: The map classifies the Urban Centres included in the GHS-UCDB 2018 by the built-up space per inhabitant in the 
year  2015.  The  values  are  mapped  in  a  colour  range  from  red  (low)  to  blue  (high).  The  map  illustrates  the  strong  contrast 
between the densely populated cities of India, SE-Asia and Africa on the one hand, and the sprawling North American cities on 
the other hand.  

How to cite this report: European Commission, Joint Research Centre, Atlas of the Human Planet 2018 – A 
World of Cities, EUR 29497 EN, European Commission, Luxembourg, 2018, ISBN 978-92-79-98185-2, 
doi:10.2760/124503, JRC114316. 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Atlas of the Human Planet 
2018 

A World of Cities 

2018  

 
 
 
 
 
 
 
 
 
Contents 

Abstract ............................................................................................................... 3 

Acknowledgements ................................................................................................ 4 

Executive summary ............................................................................................... 6 

1  Introduction ...................................................................................................... 9 

2  The GHSL conceptual framework ....................................................................... 12 

2.1  Background............................................................................................... 12 

2.2  Principles .................................................................................................. 13 

2.2.1  Real-word (Big) Data Scenarios ........................................................... 13 

2.2.2  Evidence-based output analytics .......................................................... 14 

2.2.3  Full Repeatability ............................................................................... 14 

2.3  The GHSL data releases .............................................................................. 15 

2.3.1  Past data releases .............................................................................. 15 

2.3.2  Improvements in the GHSL 2018 release .............................................. 15 

2.4  Fundamentals ........................................................................................... 16 

2.4.1  From Earth’s surface to built-up area ................................................... 19 

2.4.2  From Built-up area to population grid ................................................... 20 

2.4.3  An example from the city of Madrid, Spain ............................................ 21 

2.5  The GHSL institutional context .................................................................... 22 

3  Global Definition of cities and settlements ........................................................... 24 

3.1  Policy Context ........................................................................................... 24 

3.2  Model Description ...................................................................................... 27 

3.3  Examples of urban centres across the globe .................................................. 28 

4  Data .............................................................................................................. 34 

4.1  Multi-temporal built-up grids (GHS-BU) ........................................................ 34 

4.2  Multi-temporal Population Grids (GHS-POP) .................................................. 36 

4.3  Urban Centres Delineation .......................................................................... 37 

4.4  Urban Centres Database Attributes .............................................................. 39 

5  Urban Centres Database ................................................................................... 43 

5.1  Geography ................................................................................................ 46 

5.1.1  Biome .............................................................................................. 47 

5.1.2  Soil .................................................................................................. 49 

5.1.3  Elevation .......................................................................................... 52 

5.1.4  Climate............................................................................................. 55 

5.1.5  Temperature ..................................................................................... 56 

5.1.6  Precipitation ...................................................................................... 59 

5.1.7  River Basin ....................................................................................... 62 

i 

5.2  Socio-Economic ......................................................................................... 65 

5.2.1  Built-up Areas ................................................................................... 66 

5.2.2  Population ......................................................................................... 68 

5.2.3  Night Time Lights ............................................................................... 70 

5.2.4  Income ............................................................................................. 72 

5.2.5  GDP ................................................................................................. 74 

5.2.6  Travel Time to Capital ........................................................................ 76 

5.3  Environment ............................................................................................. 79 

5.3.1  Greenness ........................................................................................ 80 

5.3.2  Particulate Matter (PM2.5) .................................................................. 84 

5.3.3  Carbon Dioxide (CO2) ......................................................................... 85 

5.4  Disaster Risk Reduction .............................................................................. 89 

5.4.1  Flood ................................................................................................ 90 

5.4.2  Earthquake ....................................................................................... 93 

5.4.3  Storm Surge ..................................................................................... 96 

5.4.4  Heatwave ......................................................................................... 99 

5.5  Sustainable Development Goals ................................................................. 102 

5.5.1  Land Use Efficiency –SDG 11.3.1 ....................................................... 104 

5.5.2  Access to Green – SDG 11.7.1 ........................................................... 106 

5.5.2.1  Access to green spaces ............................................................... 106 

5.5.2.2  Access to open spaces ................................................................ 109 

6  GHSL Applications.......................................................................................... 114 

6.1  Accessibility ............................................................................................ 114 

6.2  Estimating Carbon Footprints for 13,000 Cities ............................................ 114 

6.3  Economic Survey of India ......................................................................... 114 

6.4  Functional Urban Area .............................................................................. 115 

6.5  Sentinel 1 RGB Mosaic .............................................................................. 116 

7  Conclusion .................................................................................................... 127 

8  References .................................................................................................... 130 

List of abbreviations ........................................................................................... 137 

List of definitions ............................................................................................... 140 

List of figures .................................................................................................... 142 

List of tables ..................................................................................................... 146 

ii 

Abstract 

The  Atlas  of  the  Human  Planet  2018  describes  the  Urban  Centre  Database,  produced  in 
the  framework  of  the  Global  Human  Settlement  Layer  (GHSL)  project,  by  applying  a 
global definition of cities and settlements to the GHSL data. 

The  Atlas  presents  the  key  findings  of  the  analysis  of  geographic,  environmental  and 
socio-economic variables that were gathered from free and open sources for each  urban 
centre in the world. 

3 

 
Acknowledgements 

This  Atlas  is  the  product  of  collaboration  between  the  whole  Global  Human  Settlement 
Layer  (GHSL)  team  of  the  European  Commission’s  Joint  Research  Centre,  Directorate  E 
“Space,  Security  and  Migration”,  Disaster  Risk  Management  Unit  (JRC.E.1)  and  experts 
from other groups in the E1 unit, in other JRC directorates, in the European Commission 
as well as from other institutions.  

This is the third edition of the Atlas and we would like to acknowledge not only the work 
of  the  main  contributing  authors,  but  also  of  the  supporting  team  that  with  their 
extraordinary dedication and motivation made this Atlas happen, and in particular:   

—  Michele Melchiorri, for the general coordination support and editorship of the present 
Atlas, the generation of high quality figures and the  contribution to the  GHSL Urban 
Sustainable Development Goals (SDG) section; 

—  Aneta  J.  Florczyk,  for  the  GHSL  External  Data  Integration,  the  GHSL  Data  Quality 

Assessment and the GHSL Data Publication Coordination 

—  Christina  Corbane,  for  the  GHSL  Earth  Observation  Data  Processing  Methodological 

Development and Coordination  

—  Sergio  Freire,  for  the  GHSL  Census  Spatial  Data  Analytics  and  Population  Modelling 

Methodological Development and Coordination  

—  Daniele Ehrlich, for the GHSL Disaster Risk Reduction (DRR) Thematic Coordination 

—  Luigi Zanchetta, for the GHSL IT Infrastructure Development and Coordination 

—  Marcello Schiavina, for the GHSL Population Modelling Development 

—  Panagiotis Politis, Luca Maffenini, and Filip Sabo, for the GHSL Data Processing Tools 

Development 

—  Donato Airaghi, for the GHSL IT Infrastructure Support 

—  Pierpaolo Tommasi, for the GHSL Web Platform Development 

—  Martino Pesaresi, for the GHSL Scientific Catalysis, and  

—  Thomas  Kemper, 
Management; 

for  the  GHSL  Scientific  Leadership  and  the  GHSL  Team 

Moreover,  we  are  grateful  to  our  colleagues  that  were  essential  for  the  successful 
completion of the Atlas, and in particular: 

—  Lewis  Dijkstra,  Head  of  the  Economic  Analysis  Sector  at  Directorate  General  for 
Regional  and  Urban  Policy,  European  Commission,  for  his  unexhausted  capacity  to 
provide  stimulating  challenges  to  the  GHSL  methodological  development  and  to  the 
GHSL data processing system  

—  Pierre Soille, Vasileios Syrris, and Pieter Kempeneers of the EC JRC Earth Observation 
Data  and  Processing  Platform  (JEODPP)  team  in  the  EC  JRC  Directorate  I 
“Competences”,  Text  and  Data  Mining  Unit  (JRC.I.3),  for  their  extreme  availability 
and  patience  in  addressing  the  everlasting,  always  urging  and  unbelievable 
processing requests from the GHSL team 

—  Elisabetta  Vignati,  Monica  Crippa,  and  Diego  Guizzardi,  of  the  EC  JRC  Directorate  C 
“Energy,  Transport and Climate”, Air and Climate Unit (JRC.C.5), for  their extremely 
valuable  help  in  integrating  and  understanding  the  data  from  the  JRC  Emissions 
Database for Global Atmospheric Research (EDGAR)  

—  Luca  Montanarella  and  Panos  Panagos  of  the  EC  JRC  Directorate  D  “Sustainable 
Resources”, Land Resource Unit (JRC.D.3), for their timely and prodigious support in 
interpreting the data collected from the Harmonized World Soil Database  

4 

—  Peter Salomon and the JRC Global Flood Awareness System team (GloFAS) of the EC 
JRC  Directorate  E  “Space,  Security  and  Migration”,  Disaster  Risk  Management  Unit 
(JRC.E.1),  for  their  precious  help  provided  in  integrating  the  hydrological  data 
included in this Atlas 

—  Alessandro  Dosio,  of  the  EC  JRC  Directorate  E  “Space,  Security  and  Migration”, 
Disaster  Risk  Management  Unit  (JRC.E.1)  for  the  valuable  help  provided  for 
apprehending  the  secrets  of  the  Heatwave  Magnitude  Index  data  integrated  in  this 
Atlas 

—  Gustavo  Naumann  of  the  EC  JRC  Directorate  E  “Space,  Security  and  Migration”, 
Disaster  Risk  Management  Unit  (JRC.E.1)  for  the  great  help  provided  in  integrating 
the  data  on  annual  precipitation  and  temperature  from  the  CRU  TS  v.  4.02  gridded 
time-series dataset 

—  Luca  Vernaccini,  supporting  the  Disaster  Risk  Management  Knowledge  Centre  of  the  
EC JRC, for the help provided in integrating the data from the UN Global Assessment 
report (GAR). 

—  Alexander  Mackie  and  Ivan  Hascic,  of  the  Environmental  Performance  Information 
Division,  in  the  Environmental  Directorate  of  the  Organisation  for  Economic  Co-
operation  and  Development  (OECD),  for  their  help  in  integrating  the  PM2.5 
concentration data used in this Atlas 

—  John Schneider and Marc Pagani of the Global Earthquake Model (GEM) Initiative for 

the support in the accessing the new Global Seismic map integrated in this Atlas 

—  The Center for International Earth Science Information Network (CIESIN) in the Earth 
Institute  of  the  Columbia  University  for  their  extremely  valuable  and  continuous 
partnership in the production of the GHSL population grids, in the frame of the inter-
ministerial Group on Earth Observation (GEO), Human Planet Initiative 

Finally, we would like to thank the reviewers for their fast and constructive response: 

—  Alice Siragusa, 

—  Harm Greidanus,  

—  Tom De Groeve 

Authors (in alphabetical order) 

Christina  Corbane,  Daniele  Ehrlich,  Aneta  Jadwiga  Florczyck,  Sergio  Freire,  Thomas 
Kemper, Michele Melchiorri, Martino Pesaresi, Marcello Schiavina 

5 

 
 
Executive summary 

The 2018 edition of the Atlas of the Human Planet is dedicated to the World of Cities. The 
urban  areas  are  today  home  to  more  than  half  of  the  world’s  seven  billion  people  and 
their share will increase rapidly for years to come. However, there is a gap in the global 
monitoring  of  the  urbanization  process  and  all  its  dimensions.  Until  today,  there  is  no 
globally  harmonised  definition  of  cities  and  settlements,  which  would  be  important  for 
international comparability of cities. This Atlas uses a globally harmonised definition and 
presents  the  first  globally  consistent  Urban  Centre  Database  (UCDB)1.  The  database 
combines  the  city  location  (name)  with  the  city  extent  (surface,  shape),  and  describes 
each city with a set of geographical, socio-economic and  environmental attributes, many 
of them going back 25 or even 40 years in time. 

Policy context 

The Atlas of the Human Planet 2018 contributes directly to the voluntary commitment to 
develop  a  global,  people-based  definition  of  cities  and  settlements2.  The  definition  is 
essential  for  monitoring  of  progress  in  achieving  the  goals  of  the  2030  Agenda  for 
Sustainable  Development3;  several  of  the  indicators  linked  to  this  goal  are  highly 
sensitive to where the boundary is drawn around a city. This Atlas is based on the UCDB, 
which  provides  harmonised  city-level  information  for  city  networks  like  the  Covenant  of 
Mayors4, and as such supports also the implementation of the New Urban Agenda5. 

For  the  Sendai  Framework  for  Disaster  Risk  Reduction  2015-2030  (DRR)6,  the  findings 
related to the exposure of cities to disasters will be included in the UN Global Assessment 
Report  on  Disaster  Risk  Reduction  (GAR)7,  which  is  the  flagship  report  of  the  United 
Nations on worldwide efforts to reduce disaster risk.  

The  Atlas  of  the  Human  Planet  2018  is  also  a  deliverable  to  the  GEO  (Group  on  Earth 
Observations)  Human  Planet  Initiative8.  The  initiative  maximises  the  use  of  (big)  open 
data through artificial intelligence (AI) to bring EO data in the socio-economic and other 
domains.  By  developing  a  new  generation  of  measurements  and  information  products, 
the initiative provides new scientific evidence and a comprehensive understanding of the 
human  presence  on  the  planet  that  can  support  global  policy  processes  with  agreed, 
actionable and goal-driven metrics.  

Key conclusions 

The  focus  on  the  urban  centres  used  in  this  edition  of  the  Atlas  was  possible  thanks  to 
the  voluntary  commitment  of  the  EU,  OECD,  World  Bank,  FAO,  and  UN-HABITAT  to 
develop a global harmonised definition of cities and settlements. Applying this definition 
globally  changes  our  perception  on  urbanisation,  and  therefore  the  partner  of  the 
commitment believe this definition is essential for a number  of policy areas beyond this 
Atlas.  In  particular,  the  Sustainable  Development  Goals  will  profit  from  the  better 
comparability of indicators. 

The  Urban  Centre  Database  (UCDB)  is  a  prime  example  of  open,  coordinated  and 
sustained  data  sharing,  as  proposed  by  the  Group  on  Earth  Observations  (GEO).  The 
Atlas  and  the  UCDB  demonstrate  that  the  combination  of  open  data  sets  can  generate 
new information. The new technology used for generating the Global Human Settlement 
Layer (GHSL) products allows producing global, yet locally consistent data for local action 
at the city level. However, the uptake  of the data by decision makers for local, national 
or global reporting requires a regular update of the information, which could be achieved 

1 https://ghsl.jrc.ec.europa.eu/ucdb2018Overview.php 
2 https://ec.europa.eu/eurostat/cros/content/about_en 
3 https://sustainabledevelopment.un.org/post2015/transformingourworld 
4 https://www.covenantofmayors.eu/en/ 
5 http://habitat3.org/the-new-urban-agenda/ 
6 https://www.unisdr.org/we/coordinate/sendai-framework 
7 https://www.unisdr.org/we/inform/gar 
8 https://www.earthobservations.org/activity.php?id=119 

6 

                                           
for example through integration of products in the portfolio of the EU’s Copernicus space 
programme. 

Main findings 

Although the discussions on the final definition of cities and settlements are ongoing, it is 
clear  that  the  degree  of  urbanisation  will  change  our  view  on  urbanisation.  With  the 
current implementation of the Definition, the global degree of urbanisation reaches 81%, 
of  which  48%  of  the  population  live  in  urban  centres.  The  analysis  derived  from  the 
UCDB  highlights  very  diverse  spatial  development  patterns  across  cities,  regions  of  the 
world and income groups. Large population growth produces moderate increases in built-
up surfaces for urban centres located in low-income countries, while moderate population 
growth  produces  large  increases  in  built-up  surfaces  for  UC  located  in  high-income 
countries. Most of the urban centres expand over soils with a high agricultural suitability, 
posing  important  challenges  and  responsibilities  to  careful  use  of  soil resources.    Urban 
centres in Asia, Africa  and Oceania have more than half of their urban population living 
below  the  global  average  night-time 
illumination  value,  threatening  access  to 
opportunities,  decent  housing  and  adequate  standards  of  living.  Urban  centres 
concentrate more than 40% of the global population, in many of them people and assets 
are  exposed  to  natural  hazards.  Especially  in  Asia  and  Africa,  the  increase  in  people 
exposure is due to natural population increase. Some continents are more exposed than 
others  to  certain  hazards.  For  example,  the  number  of  urban  centres  exposed  to  storm 
surge in Asia is higher than the total number of urban centres in all the other continents 
combined.  The  GHS-UCDB  can  be  of  direct  relevance  to  support  policy  as  it  offers 
baseline  information  for  Sustainable  Development  Goals  indicators  to  quantify  the 
efficiency in the use of land and the generalised access to green areas. 

Related and future JRC work 

At the centre of the GHSL framework is the understanding of the planet aligned with the 
JRC  space  strategy.  The  project  supports  several  Knowledge  Centres9  (Disaster  Risk, 
Territorial Policies,  Migration and Demography). The  GHSL project is one key test  cases 
of the Joint Research Centre Earth Observation Data and Processing Platform (JEODPP). 
The  processing  power  and  storage  of  JEODPP  are  essential  for  the  success  of  GHSL, 
which relies on artificial intelligence approaches applied to global fine scale data sets.  

The UN Statistical Commission is expected to vote on the global definition in 2020. In the 
run-up  to  the  vote,  the  GHSL  project  supports  the  partners  of  the  commitment  (OECD, 
World  Bank,  FAO,  and  UN-HABITAT)  with  the  promotion  of  the  definition  in  the  UN 
member states through workshops and pilot applications. 

Following  the  successful  test  of  the  fitness  for  purpose  the  JRC,  together  with  the 
Directorate-General  for  Regional  and  Urban  Policy  (DG  REGIO)  and  Directorate-General 
(DG) for Internal Market, Industry, Entrepreneurship and SMEs (DG GROW), are working 
towards an integration of GHSL products based on Sentinel-1 and Sentinel-2 in the Land 
Service of the Copernicus programme. 

Quick guide 

The  Atlas  of  the  Human  Planet  2018  is  based  on  the  UCDB,  which  relies  on  the  GHSL 
data. GHSL combines satellite and socio-economic data to produce high resolution, global 
open  information  on  built-up  area  and  population.  In  the  current  release,  it  covers  the 
epochs  1975,  1990,  2000  and  2015.  The  data  sets  are  used  in  combination  with  socio-
economic data sets to understand, where and in which built environment people live, and 
how  the  settlements  and  the  population  change  over  time.  This  knowledge  is  used  in 
policy  areas  including  environmental  impact  assessment,  disaster  risk  assessment, 
transport,  health  care  services,  education,  natural  disasters  and  hazards  and  urban 
planning.  

9 https://ec.europa.eu/knowledge4policy/ 

7 

                                           
GHS-UCDB 

1.0 

8 

 
1  Introduction 

The Atlas of the Human Planet 2018 is dedicated to the cities. Cities and urban areas are 
today home to more than half of the world’s seven billion people. The latest urbanization 
trends indicate that an additional three billion people will be living in urban areas by 2050 
(United Nations, Department of Economic and Social Affairs, Population Division, 2018), 
increasing  the  urban  share  of  the  world’s  population  even  more.  However,  the  current 
era  of  rapid  urbanization  has  been  marred  with  inadequacy  of  capacity  and  sometimes 
resources  to  match  urban  development  needs.  Moreover,  there  is  a  gap  in  the  global 
monitoring  of  the  urbanization  process  in  all  its  dimensions.  Until  today,  there  is  no 
globally  harmonised  definition  of  cities  and  settlements,  defining  their  size  and 
boundaries. 

This  report  uses  the  Degree  of  Urbanisation  (Dijkstra  and  Poelman,  2014),  a  definition 
used  to  outlinr  the  spatial  extent  of  cities  and  settlements,    to  create  the  first  global, 
harmonized,  consistent  database  of  urban  centres,  the  Urban  Centre  Database  (UCDB). 
The  database  combines  the  city  location  (name)  with  the  city  extent  (surface,  shape), 
and describes each city with a number of geographic, socio-economic and  environmental 
attributes, many of them going back 25 or even 40 years in time.     

The  (UCDB)  is  based  on  the  Global  Human  Settlement  Layer  (GHSL)  data.  The  GHSL 
project    produces  new,  global,  spatial  information,  evidence-based  analytics  and 
knowledge  describing  the  human  presence  on  the  planet  based  mainly  on  two 
quantitative factors: i) the spatial distribution (density) of built-up structures, and ii) the 
spatial  distribution  (density)  of  resident  population.  Both  factors  are  observed  in  the 
long-term temporal domain and per uniform surface units in order to support trends and 
indicators for monitoring the implementation of international framework agreements. The 
GHSL  uses  various  input  data  including  global,  multi-temporal  archives  of  fine-scale 
satellite  imagery,  census  data,  and  volunteered  geographic  information.  The  satellite 
archives  and  available  census  data  allow  generating  information  layers  for  four  epochs: 
1975, 1990, 2000, and 2015. 

The GHSL addresses the urbanization globally in a consistent fine-scale way.  The pace at 
which  urbanization  occurs  in  different  parts  of  the  world,  poses  a  number  of  challenges 
such  as  providing  a  safe,  liveable  urban  environment  with  green  spaces,  efficient 
transport, protection against natural hazard risks and economic opportunities. In fact, the 
growth of urban area changes also the economic conditions and the demands for energy 
and  resources  (Zhou  et  al.  2015).  The  GHSL  addresses  the  lack  of  essential  global 
information on settlements focussing on the spatial extent  of the built-environment and 
the population that lives within.  

The  GHSL  uses  satellite  remote  sensing  as  a  primary  source  of  information  to  delineate 
and size the physical extent of human settlements from large megacities to villages and 
towns. In the GHSL framework, the physical extent of the human settlement as collected 
by  the  satellite  sensor  is  called  “built-up  area”.  This  GHSL  built-up  area  variable  – 
referred to as GHS-BU – is produced by application of automatic supervised classification 
data  processing  methods  to  global  streams  of  open  decametric-resolution  satellite 
imagery collected by the Landsat and Sentinel missions.  

The  GHSL  project  produces  also  global  population  density  grids,  called  GHS-POP,  by 
combining the GHS-BU with census data through spatial modelling techniques. The data 
integration process foresees the downscaling of the information from the national census 
district  level  to  a  regular,  finer-scaled,  gridded  built  up  density  information  layer.  The 
result is a population density information layer available at a 250x250 m² and 1x1  km2 
grid scale.  

This  document  takes  the  analysis  further  by  using  the  GHS-BU  and  GHS-POP  layers  as 
input  for  an  urban-rural  settlement  classification  model,  producing  a  third  GHSL 
information  layer  called  GHS-SMOD.    The  GHS-SMOD  builds  on  the  “Degree  of 
Urbanization” concept (Dijkstra and Poelman, 2014) applied to the GHSL data. The GHS-

9 

SMOD  is  implemented  at  the  1x1  km2  grid  scale  and,  in  the  version  used  here, 
distinguishes between three main typologies of human settlements, based on population 
density cut-off values: “Urban Centres”, “Urban Clusters” and “Rural Settlements”.  

The  urban  centres,  which  represent  the  most  densely  inhabited  part  of  human 
settlements, are analysed in more detail in this atlas. They are extracted from the GHS-
SMOD  of  the  epoch  2015  and  include  more  than  10,000  individual  cities.  Each  urban 
centre  is  characterised  by  a  number  of  variables  describing  the  geography  and  the 
environment  of  the  place  as  well  as  socio-economic  parameters  and  the  potential 
exposure of an urban centre to natural disasters.  The high-level process  of information 
extraction and aggregation of the information in the Urban Centre Database (GHS-UCDB) 
is schematised in Figure 1. 

Figure 1 - conceptual schema of the GHSL input data, processing and products 

This Atlas provides thus a global overview of over 10,000 cities and their characteristics 
collected in a systematic way using state-of-the-art global information sources available 
in the open (scientific) domain. The information is used to generate indicators for use by 
policymaking  and/or  for  prioritizing  funding  allocation.  The  information  are  also  used  to 
generate indicators to monitor targets and goals set by the 2030 Agenda for Sustainable 
Development and the linked international framework including the Sendai Framework for 
DRR, the Paris Climate Agreements and the New Urban Agenda. 

10 

 
 
Madrid, Spain 

Multi-temporal 
evolution 

2.0 

11 

 
 
2  The GHSL conceptual framework 

2.1  Background 

The  GHSL  concept  was  introduced  by  the  European  Commission  (EC),  Joint  Research 
Centre  (JRC)  during  the  years  2008-2011,  in  the  frame  of  the  former  program  named 
“Information Support for Effective and Rapid External Action” (ISFEREA), developing new 
image information mining technologies in support to geo-spatial information analysis for 
global security and stability (Halkia et al., 2007). The notion of the basic categories and 
information abstractions used by the GHSL were introduced after a critical revision of the 
available  satellite-derived  information  products  (land-use/land-cover)  when  applied  to 
the  quantification  of  the  presence  of  built-up  structures  in  support  to  spatial  modelling 
and  assessment  activities  (Pesaresi  and  Ehrlich,  2009),  (Pesaresi  et  al.,  2013).  In 
particular,  the  first  application  areas  in  post-disaster/post-conflict  damage,  needs  and 
reconstruction  assessment  (including  refugee  camps  and  temporary  or  rapidly  changing 
settlement  monitoring  (Jasani  et  al.,  2009),  (Pesaresi  et  al.,  2013),  (Pesaresi  et  al., 
2015a)) set the requirements for the GHSL’s automatic image-data mining methods:  

 

robustness  against  real-world,  big  earth  data  scenarios,  involving  large-volume, 
largely heterogeneous/unstructured data  sources (incl. remote sensing and other 
data) and rapidly changing data specifications, 

 

robustness against multi-stakeholder scenarios with diverging analytical requests,  

  effectiveness  in  providing  output  analytics  under  conditions  of  time  constraints 
limited  computational  resources  such  as  during  crisis  management 

and 
operations. 

On  21  February  2011,  the  JRC  announced  the  successful  completion  of  the  first  large-
scale  experiment  of  the  global  human  settlement  layer  (GHSL)  using  European  medium 
resolution  ENVISAT  ASAR  satellite  data  and  automatic  image  pattern  recognition 
algorithms10 for delineation of built-up surfaces. Later in 2012, an initial prototype of the 
GHSL  system  was  successfully  tested  in  a  large-scale  exercise.  The  test  included  the 
automatic recognition of built-up areas over more than 50 millions  km² of land surface, 
using a heterogeneous set of data collected from optical sensors ranging from 0.5 to 10 
meters spatial resolution, with a large variety of sensor spectral characteristics. The test 
demonstrated the technical feasibility of the GHSL concept, including the satellite-sensor-
agnostic  nature  of  the  GHSL  remote  sensing  data  processing  paradigm  (Pesaresi  et  al., 
2013).  

In  2013,  the  GHSL  data  processing  system  was  successfully  tasked  to  produce  the  first 
seamless  pan-European  settlement  layer  at  2.5  m  spatial  resolution  in  support  to 
European cohesion policies (Ferri et al., 2014), (Florczyk et al., 2015). 

In support to the discussions of the post-2015 international frameworks, the initial set of 
requirements  was  extended  to  the  monitoring  of  the  implementation  of  these  the 
frameworks:  the  Sendai  Framework  for  Disaster  Risk  Reduction  (DRR)  2015-2030,  the 
2030  Agenda  for  Sustainable  Development,  the  Paris  Climate  Agreement,  and  the  New 
Urban Agenda (Martino Pesaresi et al., 2016b), (Pesaresi et al., 2017).  

These frameworks are accompanied by targets and further elaborated through indicators 
that focus on measurable outcomes. Indicators are action oriented, global in nature and 
universally applicable well-fitting with the synoptic and globally systematic characteristics 
of remote sensing data.  

10  EUScience  Hub:  Mapping  Human  Settlements  Globally  (https://ec.europa.eu/jrc/en/news/mapping-human-

settlements-globally-8276) 

12 

                                           
2.2  Principles 

One  of  the  aims  of  the  GHSL  project  was  to  provide  a  public  platform  prototype  to 
support  alternative  implementations  of  indicators  for  the  monitoring  of  international 
frameworks.  Consequently,  the  GHSL  data  sets  were  designed  with  a  modular, 
hierarchical  abstraction  schema.  This  schema  facilitates  the  knowledge  sharing  and  the 
conceptual convergence even in complex, international, multi-stakeholder discussions. In 
addition,  the  GHSL  information  production  system  is  fully  open  and  free,  is  in  line  with 
the strategy of the Group of Earth Observation (GEO) and the  Global Earth Observation 
System  of  Systems  (GEOSS)  (Doldirina,  2015),  (Anderson  et  al.,  2017),  and  the 
European Commission’s Open Data Policy11.  

The GHSL concept it is currently framed around three general goals or requirements:   

i)  operating in an open and free data and methods access policy (open input, open 

method, open output),  

ii)  enabling  reproducible,  scientifically  defendable,  fine-scale,  synoptic,  complete, 

planetary-size, and cost-effective information production, and  

iii)  facilitating information sharing and multilateral democratization of the information 

production, and collective knowledge building.  

The  first  and  second  requirements  call  for  public,  scientific  control  of  the  data  and  the 
information  production  methods  generating  the  GHSL  information  and  derived  findings. 
The  second  and  third  general  requirements  call  for  automatic  information  production 
methods  being  able  to  process  systematically  the  large  mass  of  baseline  data  lowering 
down the cost of the information production. Consequently, various independent entities 
may  produce  the  information.  This  moves  the  human  efforts  from  the  information 
extraction to the discussion of the observed facts and ultimately to derive decisions.  In 
the frame of the above, there are three main principles applied in the design of the GHSL 
automatic  information  production  system.  They  are  shortly  recalled  here:  i)  test  and 
apply real-word (big) data scenarios, ii) produce evidence-based output analytics, and iii) 
facilitate repeatability of the results. 

2.2.1  Real-word (Big) Data Scenarios 

The  real  world  data  scenario  is  calling  for  a  pragmatic,  adaptive  approach  in  complex 
data-information-stakeholder  scenarios.  On  the  one  hand,  the  planetary-size,  multi-
temporal,  fine-scale  data  generate  large  volumes  of  information.  On  the  other  hand, 
international,  multi-stakeholder  scenarios  coupled  with  fast  technological  development 
generate  rapidly  changing  data  specifications  and  even  contradictory  semantics  and 
assumptions. An old-fashioned layering of not centrally harmonized data generates gaps 
and  inconsistencies  in  both  spatial  and  thematic  components  of  the  data.  In  such 
scenarios,  the  adoption  of  a  strict  normative  approach  would  easily  lead  to  the  system 
irrelevance and failure with respect to the reality check. 

The  GHSL  takes  instead  a  pragmatic  adaptive  perspective.  Artificial  intelligence  (AI)  is 
used  to  find  relevant  association  between  different  data  streams  at  different  levels  of 
abstraction/semantics and different scales with a minimal set of possible assumptions. In 
this  perspective,  the  whole  universe  of  data  shall  be  used  for  testing  of  hypothesis. 
Efficient computational machine learning methods allow discovering association between 
any given data and information at any collected sensor/data parameters conditions, with 
no  necessity  of  model  transfer  (Pesaresi  et  al.,  2013),  (Martino  Pesaresi  et  al.,  2016a), 
(Pesaresi, 2014), (M. Pesaresi et al., 2016c). 

The  GHSL  information  production  workflow  is  supported  by  the  definition  of  a  new  AI 
approach for the satellite data classification process named “Symbolic Machine Learning” 

11 Communication on open data (COM (2011) 882) 

13 

 
                                           
learning  process.  The  noise  may 

(SML), and working with the similar principles  used in genomics  for the DNA  sequences 
characterization (M. Pesaresi et al., 2016b).  The SML approach allows machines to learn 
efficiently  new  association  rules  between  satellite  data  instances  and  target  abstraction 
classes in the case of GHSL for instance the “built-up areas”. The SML is robust against 
the  presence  of  high  levels  of  noise  in  the  training  set  or  “examples”  used  by  the 
machine 
large  temporal,  scale,  and 
thematic/semantic differences among the input “examples” used by the machine learning 
process, and the satellite data records to be classified or the target class abstractions to 
be  recognized  (Pesaresi  et  al.,  2015b).  The  SML  algorithm  allows  designing  a  robust 
supervised  data  classification  process  using  available  global  data  as  training  examples, 
just  approximating  the  scale  and  thematic  requirements  of  the  information  retrieval 
tasks. This fact drastically reduces the human labour traditionally needed for the training 
set collection. 

include 

2.2.2  Evidence-based output analytics  

International framework agreements such as the New Urban Agenda, the SDGs, and the 
Sendai Framework for DRR are linked to national and multi-national (as in the case of the 
European  Union)  policy  processes  driven  by  decision  makers  operating  at  various  levels 
of  governance.  Data  and  evidences  may  (shall)  contribute  to  policy  processes  by 
providing indicators that focus on measurable outcomes and allow evaluating, comparing 
and  benchmarking  alternatives  of  action.  In  order  to  be  suitable  for  supporting  policy 
decisions,  the  data  and  the  AI-driven  analytics  must  be  defendable  from  the  accusation 
to  be  affected  by  prejudice  supporting  specific  political  and/or  economical  interest.  In 
order to mitigate that risk, the GHSL implements two explicit design solutions:  

i) 

ii) 

The output categories or abstractions delivered by the AI processes applied to the 
data  generating  the  GHSL  information  are  designed  with  a  modular  abstraction 
schema  allowing  multi-stakeholder  and  multi-disciplinary  communication  flows, 
and improving semantic interoperability of the GHSL information. This approach it 
is innovative compared to common practices in remote sensing derived products, 
e.g. the classical land cover products  (Pesaresi and Ehrlich, 2009). 

The  data  processing  rules  generated  by  the  AI  machine  learning  processes  are 
accessible  by  human  inspection  and  understanding  allowing  the  collective 
discussion  of  the  whole  information  production  process,  without  introducing 
processing segments acting as black boxes.  

A corollary of the “evidence-based output analytics” principle, also linked to the next “full 
repeatability”  principle,  is  that  some  common  practices  applied  to  remote  sensing  data 
products  are  not  allowed  in  the  GHSL  information  production.  For  instance,  no  expert-
driven  post-processing  and  masking  operations  are  applied  to  the  output  of  the  GHSL 
that is considered as integrally automatic (then formally explicit and controllable), data-
driven  information  production  system.  If  main  anomalies  are  reported  in  a  given  GHSL 
output  release,  they  are  summarized  in  the  data  disclaimer  and  in  the  data  quality 
description layers, and they are used to benchmark the improvements of next GHSL data 
and method releases. 

2.2.3  Full Repeatability 

Repeatability of measurements refers to the ability to repeat measurements made on the 
same subject under identical conditions (National Institute for Standards and Technology, 
2007). Variability in measurements made on the same subject under identical conditions 
can  be  attributed  to  errors  originated  by  the  measurement  process  itself.  The 
measurements generated by the GHSL system are fully repeatable. The same input data 
and  the  same  information  extraction  method  produces  identical  numerical  result  or 
measure  until  the  last  significant  digit.  This  is  a  requirement  needed  to  maintain  full 
control of data, methods and results lineages in the “Real-word (Big) Data Scenarios” as 
described  in  the  first  principle  above.  It  is  also  a  requirement  for  measurements 

14 

implicating  legal  consequences  as  in  the  case  of  international  frameworks  monitoring. 
Moreover,  full  repeatability  increases  trust  in  the  new  data  and  analytics  generated  by 
new technologies, especially if non-experts are involved in the evaluation of the results. 
A corollary of this principle is that GHSL avoids the use of AI methods based on random 
iterative  optimization  processes  not  allowing  full  repeatability  of  the  same  results  under 
the same initial conditions.   

2.3  The GHSL data releases  

2.3.1  Past data releases 

The  first  public  release  of  the  GHSL  data  including  built-up  surface  grids,  resident 
population grids, and urban/rural classification schema for the epochs 1975, 1990, 2000, 
and  2015  was  announced  at  the  third  United  Nations  Conference  on  Housing  and 
Sustainable  Urban  Development  (Habitat  III)  in  October  2016.  The  data  is  freely 
accessible  through  the  JRC  Open  Data  Portal  (https://data.jrc.ec.europa.eu/)  and  the 
GEOSS portal (http://www.geoportal.org/).  

Since  2017,  the  GHSL  data  and  tools  contribute  to  the  GEO  Human  Planet  Initiative, 
which is  one  of  the  initiatives  in  the  GEO  work  program12.  The  free  and  open  access  to 
data is one of the  core  principles of GHSL and  GEO  Human Planet Initiative, in order to 
favour  an  open 
international  stakeholders, 
governmental  bodies  and  private  firms.  To  support  such  discussion,  a  prelease  of  GHSL 
data  is  shared  first  within  the  GEO  Human  Planet  community.  In  2018,  such  a  data 
package was shared with the community (Florczyk et al., 2018). 

for  discussion 

for  academy, 

forum 

The public release of the new data sets is accompanied with a new edition of the Atlas of 
the  Human  Planet  that  describes  the  core  findings  linked  to  the  new  data  release 
(Martino  Pesaresi  et  al.,  2016b;  Pesaresi  et  al.,  2017).  Accordingly,  this  Atlas  of  the 
Human  Planet  2018  supports  the  latest  GHSL  data  package,  which  is  described  in  the 
following. 

2.3.2  Improvements in the GHSL 2018 release 

Since its public release in October 2016, the GHSL data were used by a large number of 
users  from  different  thematic  domains  as  well  as  from  all  regions  of  the  world.  Many 
provided  valuable  feedback  on  the  quality  of  the  data.  In  addition,  new  technical 
developments in terms  of data availability and refinement of methodology allowed for a 
significant  improvement  of  the  data.  With  respect  to  the  previous  release  in  2016,  the 
current GHSL release 2018 implements two main improvements: 

i) 

ii) 

Improved GHS-BU and GHS-POP baseline information production 

Introduction of the new Urban Centres Database (UCDB)  

The new GHS-BU data were produced during 2017 and 2018 by reprocessing the global 
Landsat image data collections with an improved classification method mainly due to the 
introduction  of  better  global,  fine-scale  training  sets  that  were  made  available  recently. 
The  improved  GHSL  workflow  integrated,  amongst  others,  new  Sentinel-1  (radar)  data 
collected during 2016, and implemented a new global information optimization processes 
aiming  to  improve  the  reliability  and  stability  of  the  automatic  information  extraction 
from  satellite  images  through  automated  selection  of  the  best  from  a  number  of 
alternative processing options (Corbane et al., 2017a).  

Consequently,  the  new  GHS-POP  grids  were  also  reprocessed  using  the  improved  GHS-
BU  input  grids,  and  including  improvements  in  the  spatial  harmonization  and  gap-filling 
processes applied to the input census data (Freire et al., 2018). 

Both  new  data  sets  were  subsequently  used  to  derive  an  updated  settlement  model 
based on the degree of urbanisation. From the 2015 grid of GHS-SMOD, the new Urban 

12 http://earthobservations.org/documents/work_programme/geo_2017_19_Work_Programme.pdf 

15 

                                           
Centres  Database  (GHS-UCDB)  was  derived.  The  GHS-UCDB  was  generated  by  spatial 
integration  of  the  urban  centres  with  the  GHSL  grid  data  (built-up  areas,  resident 
population) and with other sources  supporting  the characterization of  the urban centres 
(see chapters 5 for details). 

Figure  2  Evolution  of  the  GHSL  framework  and  key  science  for  policy 
deliverables 

The  urban  centres  database  proof  of  concept  (developed  in  the  first  trimester  of  2018) 
supported  the  JRC  contribution  to  the  World  Urban  Forum  9  convened  by  UN-Habitat in 
February  2018  with  the  goal  to  improve  the  collective  knowledge  of  sustainable  urban 
development.  The  preliminary  version  of  the  urban  centres  database  characterised  the 
circa  10,000  urban  centres  with  attributes  including  built-up  areas  and  population  (and 
built-up areas per capita) in the four GHSL epochs (1975, 1990, 2000, and 2015), night-
time  lights  (also  multi-temporal).  In  addition  to  these  variables,  the  database  included 
environmental metrics (greenness index, PM2.5 concentration, imperviousness index and 
proximity  to  natural  protected  areas),  and  other  proxy  information  useful  to  exposure 
analysis  (i.e.  people  and  built-up  areas  in  the  urban  centre  located  at  the  sea  level  or 
below  or  located  in  slopes  greater  than  15°).  This  information  was  made  available 
through the GHSL website and supported training activities and policy talks (especially by 
DG-REGIO). 

2.4  Fundamentals 

The  GHSL  consists  of  three  main  information  components  hierarchically  placed  at  three 
different  levels  of  abstraction:  Global  Human  Settlement  built-up  areas  (GHS-BU),  the 
GHS  population  grids  (GHS-POP)  and  the  GHS  urban/rural  classification  model  (GHS-
SMOD).  

At the base of the hierarchy - including the most spatially accurate and the least abstract 
information  level  -  we  have  a  layer  collecting  concrete  evidences  about  the  human 
presence on the planetary surface as seen from global Earth Observation systems. In the 
GHSL  paradigm,  the  fundamental  link  between  Earth  Observation  sensor  data  and  the 
human presence is the observable presence of built-up structures or buildings. From the 
GHSL perspective, the “building” makes the physical part of the human settlement fabric 
or spatial extension that is observable and measurable using the available global sensors. 
At  this  basic  level  the  GHSL  reports  about  built-up  areas  (GHS-BU),  as  areas  (spatial 
units)  where  buildings  can  be  found  (Pesaresi,  2016).  The  concept  of  “buildings” 
formalized by the GHSL are  enclosed  constructions above ground which are intended  or 
used for the shelter of humans, animals, things or for the production of economic goods 

16 

 
and that refer to any structure constructed or erected on its site (Pesaresi et al., 2013). 
This  abstraction  is  very  similar  to  the  standard  topographic  definition  of  the  “building” 
class as compiled in the INSPIRE directive13, except that the condition of the permanency 
of  the  structure  it  is  not  in  the  GHSL  definition.  The  GHSL  definition  of  built-up  also  to 
includes  refugee  camps,  informal  settlements,  slums  and  other  temporary  settlements 
and shelters.  

Figure 3 GHSL input data and main output information 

The intermediate abstraction information layer of the GHSL is the population grid or GHS-
POP that is produced in an in-between spatial resolution. This information layer is derived 
from the combination of global collections of national population census data and global 
built-up  areas  as  extracted  from  Earth  Observation  data  analytics  (GHS-BU).  In  the 
approach  taken  by  the  GHSL,  the  population  data  collected  by  national  censuses  with 
heterogeneous criteria and heterogeneous update time are harmonized in the space and 
time  domains  in  to  the  GHS-POP  grids,  by  systematic  and  consistent  application  of  the 
same  set  of  data  interpolation  and  spatial  disaggregation  methods  to  the  best  available 
global spatial baseline data {Freire_al_2016}.   

The  top  abstraction  information  layer  of  the  GHSL  it  is  the  urban/rural  classification 
model (GHS-SMOD). It is provided with the least spatial detail (1 km) by combining the 
two less-abstract and more-spatially-detailed built-up and population grids, GHS-BU and 
GHS-POP, respectively. The GHS-SMOD model implemented by the GHSL it is consistent 
with  the  “Degree  of  urbanisation”  (DEGURBA)  model  adopted  by  EUROSTAT14.  It 
discriminates  3  settlement  class  abstractions:  1)  Cities,  2)  Towns  and  suburbs  and  3) 
Rural  areas.  The  discrimination  is  based  on  the  population  density  in  the  square 
kilometre grid15, total settlement population and other spatial generalization parameters.  

In  the  GHSL  paradigm,  the  base  layer  GHS-BU  it  is  designed  to  be  the  most  stable 
against  different  visions  and  approaches,  while  GHS-SMOD  is  the  most  abstract  and  as 
such  exposed  to  conceptual  changes  and  alternative  problem  settings  proposed  by  the 
different stakeholders involved in the post-2015 international framework processes.  The 
modular  hierarchical  abstraction  schema  used  in  the  GHSL  design  allows  to  protect  the 
investment  made  in  the  global,  fine-scale  information  gathering  from  perturbations  on 

13  INSPIRE  Infrastructure  for  Spatial  Information  in  Europe  D2.8.III.2  Data  Specification  on  Buildings  –  Draft 
Guidelines 

Technical 
http://inspire.ec.europa.eu/documents/Data_Specifications/INSPIRE_DataSpecification_BU_v3.0rc3.pdf  

14 http://ec.europa.eu/eurostat/web/degree-of-urbanisation/overview  
15 densely, intermediate density and thinly populated areas 

17 

 
                                           
the  abstract  classification  schema  that  may  be  introduced  by  different  decision-makers 
involved  in  the  process  and  potentially  producing  different  problem  setting  and 
abstractions.  On  the  other  side,  the  modular  hierarchical  abstraction  schema  facilitates 
the  test  of  alternative  abstract  models  on  the  same  agreed  information  baseline, 
facilitating  the  discussion  and  the  comparison  of  the  results  also  between  international 
stakeholders not necessary sharing the same high abstraction definitions.  

The following sections help the reader to understand fundamental concepts of GHSL and 
its  data.  The  first  subparagraph  deals  with  extraction  of  information  from  satellite 
imagery (2.4.1) and built-up definition.  

The  second  paragraph  explore  the  process  allows  to  combine  built-up  grids  with  census 
data to produce the population grids (2.4.2).  

The  third  paragraph  (2.4.3)  illustrates  the  key  elements  and  rules  of  the  settlement 
model, derived from the New Degree of Urbanization  (Lewis Dijkstra and Hugo Poelman 
2014):  specifically,  the  rules  for  defining  Urban  Centres,  Urban  Clusters  and  rural 
settlements are illustrated.  

The  forth  paragraphs  show  with  simple  images,  and  example  of  three  GHSL  datasets 
(GHS Built-up, GHS POP and S-MOD) for the city of Madrid, Spain (2.4.4).  

Figure  4  Transition  from  imagery  to  built-up  areas  extraction  (GHS-BU), 
population  modelling  (GHS-POP),  and  settlements  classification  (GHS-SMOD), 
examples in the area of Bangkok (Thailand). 

18 

 
 
2.4.1  From Earth’s surface to built-up area 

Figure  5  The  figure  describes  the  information  extraction  process  from  the 
satellite  images  of  the  earth  surface  (bottom)  to  the  built-up  area  extraction 
(middle) to the aggregated built-up area density (top). 

19 

 
 
2.4.2  From Built-up area to population grid 

Figure 6 The figure illustrates the combination of GHS-BU with the census data 
to produce a regular fine scale grid of population density. 

20 

 
2.4.3  An example from the city of Madrid, Spain 

Figure  7  The  GHS-BU  and  GHS-POP  are  combined  to  classify  the  gird  cells  into 
rural and urban areas. See chapter 3 for details on the model. 

21 

 
2.5  The GHSL institutional context 

In 2018, the GHSL activities are supported by the JRC scientific working plan 2018-2019 
in the frame of the JRC Directorate E “Space, Security & Migration”. At the centre of the 
GHSL  framework  is  the  understanding  of  the  planet,  fully  aligned  with  the  JRC  space 
strategy. At the JRC, the GHSL framework of data and tools supports several Knowledge 
Centres  (KC):  the  KC  for  Disaster  Risk  Management,  the  KC  for  Sustainable 
Development,  the  KC  for  Territorial  Modelling,  and  the  KC  for  Security  &  Migration. 
Moreover, the GHSL is one key use cases for processing and analysis of big earth data of 
the  JRC  Earth  Observation  Data  and  Processing  Platform  (JEODPP)  (Soille  et  al.,  2017). 
The  GHSL  project  is  testing  in  the  JEODPP  the  use  of  AI  for  automated  information 
extraction from large data cubes such as those provided by the Sentinel satellites of the 
European Copernicus program. In fact, the next GHSL releases will integrate Copernicus 
Sentinel  data  (Corbane  et  al.,  2017a).  Following  the  successful  test  of  the  fitness  for 
purpose of GHSL, the JRC is working, together with the Directorate-General for Regional 
and  Urban  Policy  (DG  REGIO)  and  DG  for  Internal  Market,  Industry,  Entrepreneurship 
and  SMEs  (DG  GROW)  towards  a  regular  and  operational  monitoring  of  global  built-up 
area and population densities based on the processing of Sentinel data. The experiences 
made  with  the  JEODPP  will  facilitate  the  implementation  of  the  approaches  on  the  new 
Data and Information Access Services (DIAS)16 of the Copernicus Programme.  

GHSL  information  are  particularly  salient  in  the  disciplinary  contexts  of  disaster  risk 
reduction,  urbanization  and  human  settlements  dynamics,  where  fine  scale  information 
on  the  presence  of  people  and  built-up  areas  are  of  high  importance  (Chmutina  and 
Bosher,  2017).  GHSL  data  served  to  quantify  the  process  of  urbanization  (Melchiorri  et 
al.,  2018),  observe  population  density  (Smith,  2017),  and  inform  policy  making  on  40 
years of human settlements development  (Martino Pesaresi et al., 2016b) and exposure 
to natural hazards (Pesaresi et al., 2017). 

Figure 8 GHSL products supporting specific sectors of DGs 

16 https://www.copernicus.eu/en/access-data/dias 

22 

 
 
                                           
Southern 
and Sri Lanka  

India 

Application  of  the  Degree 
of  Urbanisation  in  India 
and Sri Lanka 

3.0 

23 

 
  
3  Global Definition of cities and settlements 

The Department of Economic and Social Affairs of the United Nations reports in the 2018 
Revision  of  the  World  Urbanisation  Prospect:  “globally,  more  people  live  in  urban  areas 
than in rural areas, with 55 % of the world’s population residing in urban areas in 2018.”  
However, the UN acknowledges that the analysis relies on the data produced by national 
sources,  which  reflect  the  definitions  and  criteria  established  by  national  authorities. 
Therefore, the criteria used to identify urban areas vary from country to country and may 
not be consistent even between different data sources within a given country. About half 
of  the  definitions  described  in  the  report’s  methodological  annex,  include  a  minimum 
population  size,  either  exclusively  or  in  combination  with  other  indicators  or  criteria.  A 
specific size threshold is mentioned for 100 countries. Of these, the vast majority (85%) 
use  a  threshold  of  5,000  or  less.  The  most  popular  thresholds  are  5,000  with  27 
countries and 2,000 with 24 countries. Japan and China are outliers with thresholds that 
are ten to twenty times higher, respectively 50,000 and 100,000. 

To  address  this  issue,  the  European  Union,  the  OECD  and  the  World  Bank  launched  a 
voluntary  commitment  to  develop  a  global,  people-based  definition  of  cities  and 
settlements  during  the  third  United  Nations  Conference  on  Housing  and  Sustainable 
Urban  Development  (Habitat  III)  in  October  2016.  Since  then  Food  and  Agriculture 
Organisation  (FAO)  and  UN-Habitat  have  also  joined  this  commitment.  FAO  is  the 
‘custodian’  UN  agency  for  21  SDG  indicators  for  which  a  harmonised  definition  of  rural 
areas is needed. UN-Habitat is the ‘custodian’ UN agency for 8 SDG indicators, for which 
a harmonised of city definition is needed.  

These definitions are designed to support the New Urban Agenda17, the global strategy to 
improve  agricultural  and  rural  statistics  (GSARS)18  and  the  monitoring  of  Sustainable 
Development  Goals  (SDG).  Agreeing  the  first  global  consistent  definition  of  cities  and 
rural areas can also help many other policies and research areas.     

Figure 9 List of SDG indicators requiring urban/rural data disaggregation 

3.1  Policy Context 

With  the  adoption  of  the  Sustainable  Development  Goals  and  its  monitoring  framework, 
the  need  to  harmonise  data  collection  and  indicators  monitoring is  a  cornerstone  of  the 

17 http://nua.unhabitat.org/ 
18 http://gsars.org/en/ 

24 

 
 
                                           
implementation  process  of  the  2030  Development  Agenda.  Several  of  the  indicators 
linked to goals require disaggregation in urban and rural classes, and are highly sensitive 
to where the boundary is drawn around a city (Figure 10).  

Figure 10 List of urban SDG indicators sensitive to city boundaries 

Figure 11 presents the examples of the fictive cities of Broadville and Narrowtown. In the 
former  an  expansive  boundary  is  used  to  define  a  city,  in  the  latter  instead,  a  more 
restrictive  one.  It  sheds  light  on  indicators  that  are  substantially  sensitive  to  density  of 
people and to the settings of the urban realm that can be drastically affected by the city 
definition  being  used.  While  the  city  centre  tends  to  have  convenient  access  to  public 
transport, the outskirts of a city are often less well endowed. Accordingly, if a restrictive 
boundary is drawn the SDG performance could be better off. Similar considerations apply 
to air pollution, and share of open spaces (Table 1). An additional remark is proposed for 
demographic accounting. In Figure 11 the total population of the area between 1990 and 
2015 is constant. While it declines in the central portion of the  city. When a narrow city 
definition  is  adopted  this  area  would  report  about  demographic  decline  while  from  a 
functional standpoint the area of interest maintains its population and need for resources. 

The  goal  of  the  voluntary  commitment  is  to  propose  a  definition  to  the  UN  Statistical 
Commission in 2020 that will complement national definitions  and facilitate international 
comparisons.  A  harmonised  definition  of  cities  can  support  SDG  monitoring  by  making 
data  collection  and  reporting  homogeneous  across  the  globe.  National  definitions,  and 
consequently  the  reporting  of  the  United  Nations  Department  for  Economic  and  Social 
Affairs  (UNDESA)  through  the  World  Urbanization  Prospects,  are  considerably 
heterogeneous across territories (Buettner 2015; Forstall and Chan 2015). In particular, 
half the national definitions are based on an administrative designation which cannot be 
replicated  in  other  countries.  Even  when  national  definitions  rely  on  population  size 
and/or density, the thresholds vary substantially. The variation in national definitions will 
have a direct effect on the collection of data, statistics, and SDG reporting. 

25 

 
Figure 11 Examples of Broadville and Narrowtown to display an expansive and 
restrictive definition of a city. While between 1990 and 2015 population 
increases in Broadville, it declines in Narrowtown. Not just SDG indicators 
reporting depends on the boundaries, but also demographic information such as 
city size and population change. 

Table 1 Effects of narrow or broad drawing of city boundaries 

Population  Density  Air 

pollution 

Access 
to 
transport 

Share 
of 
open 
space 

Land 
use 
growth 

Broadville 

600,000 

Low 

Low 

Poor 

High 

Rapid 

Narrowtown  150,000 

High 

High 

Excellent 

Low 

No 
growth 

26 

 
 
  
 
3.2  Model Description 

The “Urban Centres” (UC) using the Degree of Urbanisation are defined as: “high-density 
clusters of contiguous grid cells of 1 km2 with a density of at least 1500 inhabitants per 
km2 and a minimum population of 50000”19. The UC as implemented in the current GHSL 
settlement  model  (SMOD)  formulation  is  defined  as:  “the  spatially-generalized  high-
density  clusters  of  contiguous  grid  cells  of  1  km2  with  a  density  of  at  least  1,500 
inhabitants  per  km2  of  land  surface  or  at  least  50%  built-up  surface  share  per  km2  of 
land surface, and a minimum population of 50,000.” 

Figure 12  Schema representing  an  abstract urban centre,  grid cell criteria,  and 
population size threshold 

The  original  degree  of  urbanisation  was  based  on  the  population  size,  density  and 
contiguity of local administrative units level 2 (LAU2). However, this method is based on 
LAU2s, which vary considerably in area  size; hence the results are distorted and reduce 
the comparability between countries with large LAU2s and small LAU2s. This well-known 
problem,  known as  Modifiable Area unit problem  (Gehlke and Biehl, 1934), is solved by 
using the GHSL population grid (or any other high-resolution population grid). 

This is illustrated for the city of Cork, Ireland (Figure 13). Cork lies at the mouth of river 
lee into Lough Mahon. The land use is characterised by a typical mix of residential areas 
and some patches of industrial/commercial units. According to the land use map the city 
is well confined and is surrounded by agricultural areas and semi-natural vegetation (top 
left). The highest population densities are in the urban centre and the residential areas, 
dropping  significantly  in  the  agricultural  areas  (top  right).  The  Degree  of  Urbanisation 
grid (bottom right) clearly reflects this. It maps very well the dense urban centre of Cork 
and classifies the less dense suburbs as urban clusters connected with the urban centre 
or  as  individual  town.  When  comparing  this  with  the  Degree  of  Urbanisation  applied  to 
the LAU2 (bottom-left), the urban  centres  match well. However, the  surrounding LAU2s 
are all classified as towns and suburbs, despite the fact that only a small part of the LAU2 
is  occupied  by  settlements.  This  problem  aggravates,  when  working  at  a  global  scale, 
where administrative layers are often available only at district level or worse. 

19 European Commission, Regional Working Paper 2014, A harmonised definition of cities and rural areas: the 

new degree of urbanisation,  Lewis Dijkstra and Hugo Poelman 
http://ec.europa.eu/regional_policy/sources/docgener/work/2014_01_new_urban.pdf  

27 

 
                                           
Figure  13  City  of  Cork  (Ireland).  Land  use  map  based  on  Urban  Atlas20  (top 
left);  GEOSTAT  population  grid21  (top  right);  Degree  of  Urbanisation  applied  to 
the  GEOSTAT  grid  (bottom  right);  Degree  of  Urbanisation  applied  to  the  LAU2 
(bottom left). 

3.3  Examples of urban centres across the globe 

As  the  Global  Definition  of  Cities  and  Settlements  is  applied  to  the  Global  Human 
Settlement  Layer  grids  (built-up  areas  and  population  grids)  that  possess  global 
coverage,  it  is  possible  to  identify  more  than  10,000  urban  centres  across  the  entire 
planet Earth (Figure 14). 

In order to illustrate morphological diversity, the series of figures below present selected 
examples  of  1  km2  urban  centre  cells  with  similar  population  density  of  different 
geographical  areas.  Examples  are  sourced  in  the  urban  centres  of:  Dakar  (Senegal, 
Africa),  Chandigarh  (India,  Asia),  Busto  Arsizio  (Italy,  Europe),  and  Havana  (Cuba, 
Caribbean).  While  the  population  density  estimate  in  GHS-POP  within  the  1  km2  in  the 
area within the red box is constant (5,000 people ± 5%), the samples below display the 
degree of heterogeneity of morphological characteristics (i.e. the physical morphology of 
the  urban  setting  and  landscape  –built-up  areas  density,  or  vegetation  patterns  –
greenness).  

20 https://land.copernicus.eu/local/urban-atlas 
21 https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/population-distribution-demography/ 

28 

 
 
 
                                           
Figure 14 Global representation of urban centres location 

Many  of  the  characteristics  that  can  be  observed  in  the  list  of  urban  centres  samples 
below, are also computed and provided as attributes of the 10,000 urban centres in the 
Urban  Centred  Database.  Examples  include:  population  density,  share  of  open  spaces, 
and  greenness.  The  examples  below  provide  physical  and  morphological  evidences  to 
reinforce the perception of diversity of cities (World Cities Report 2016 Urbanization and 
Development: Emerging Futures 2016). 

The  first  example  (Figure  15)  shows  the  urban  centre  of  Dakar  and  it  displays  a  VHR 
satellite  imagery,  through  which  the  human  eye  can  perceive  the  building  footprints. 
Figure  16  displays  the  very  same  location  and  extent  using  Landsat  (30m  resolution) 
imagery, which is used in the GHSL workflow to map built-up areas (GHS-BU). While this 
examples  show  the  substantial  difference  between  VHR  and  decametric  resolution 
imagery,  some  consideration  should  be  devoted  to  the  availability  of  the  two  different 
information  types.  Although  for  some  parts  of  the  planet  Earth  VHR  image  has  been 
collected,  this  information  is  oftentimes  produced  for  commercial  purposes,  subject 
access  costs  (i.e.  licensing,  prices),  moreover  it  rarely  results  in  global  coverage,  and 
frequently  information  over  long  periods  is  unavailable.  In  contrast,  Landsat  resources 
were  made  open  and  free  by  USGS  in  2009,  contain  records  since  1975  yet,  their 
resolution is not as detailed as the former typology of information.  

29 

 
 
Figure 15 Example of Urban Centre in Africa, in the area of Dakar (Senegal) 

Figure  16  Landsat  satellite  imagery  in  the  area  of  Dakar  (same  location  as  in 
Figure  15)  showing  the  30m  resolution  of  the  sensor  from  which  GHS-BU  is 
produced. 

30 

 
 
 
Figure 17 Example of Urban Centre in Asia, in the area of Chandigarh (India) 

Figure  18  Example  of  Urban  Centre  in  Europe,  in  the  area  of  Busto  Arsizio 
(Italy) 

31 

 
 
Figure  19  Example  of  Urban  Centre  in  the  Caribbean,  in  the  area  of  Havana 
(Cuba) 

Figure 20 © Adobe Stock, 2018 

32 

 
 
 
Johannesburg  & 
Pretoria, 

South Africa 

Built-up  Area  Map  for 
the  Gauteng  Province, 
South Africa 

4.0 

33 

 
  
4  Data 

4.1  Multi-temporal built-up grids (GHS-BU) 

The built-up grids used in this Atlas are based on the global, multi-temporal evolution of 
built-up  surfaces  derived  from  Landsat  data  collections  organized  in  four  epochs  1975, 
1990,  2000  and  2014  (referred  to  as  GHS_LDSMT_2017).  It  is  the  result  of  the  re-
processing  33,202  Landsat  images  organized  in  four  data  collections,  centred  at  1975, 
1990, 2000 and 2014, as follows:  

•  7,597 scenes acquired by the Multispectral Scanner (MS) (collection 1975); 

•  7,375  scenes  acquired  by  the  Landsat  4-5  Thematic  Mapper  (TM)  (collection 

1990); 

•  8,788 scenes acquired by the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) 

(collection 2000); and 

•  9,442 scenes acquired by Landsat 8 (collection 2014). 

The  first  version  of  this  data  set  was  released  in  2016  (GHS_LDSMT_2015)  and  is  now 
significantly  improved  as  described  in  the  following.  The  GHSL  method  for  built-up 
surface recognition from satellite data is fully automatic and reproducible. The procedure 
is  based  on  the  Symbolic  Machine  Learning  (SML,  Pesaresi  et  al.  2016b)  and  uses 
existing  global  land  cover  products  as  training  sets.  For  each  pixel,  it  provides  a 
probability (confidence) that it is built-up or not. 

The resulting multi-temporal product encodes the presence of built-up surfaces according 
to  the  earliest  epoch  for  which  the  built-up  surface  presence  was  detected  (i.e.  1975, 
1990, 2000, or 2014 epoch) at approximately 38 m resolution.   

The  characteristics  of  the  SML  classifier  allow  incremental  learning  and  improvement  of 
the classification outputs as soon as new data depicting built-up areas become available. 
In  other  words,  the  outputs  of  the  SML  classifier  in  one  experiment  can  be  used  as  a 
training set in the subsequent experiments. 

The approach based on incremental learning in the frame of the SML allows incorporating 
new,  updated  learning  sets  and  adjusting  what  has  been  learned  according  to  previous 
examples.  This  learning  method  has  the  advantage  of  achieving  superior  efficiency  for 
training  minimizing  the  impact  on  accuracy.  Another  beneficial  point  is  that  the 
performance  can  slightly  improve.  The  results  can  only  get  better  after  each  iteration 
since the level of noise is progressively decreasing with each run of the GHSL workflow. 
Having  this  in  mind,  the  four  Landsat  data  collections  were  reprocessed  on  the  JEODPP 
with a revised workflow for built-up area extraction and for the fusion of the four epochs 
into  one  multi-temporal  product.  The  global  built-up  layer  derived  from  Sentinel-1  data 
(GHS_S1)  provides  a  more  detailed  learning  set  of  built-up  areas.  The  Sentinel-1 
(GHS_S1)  data  set  was  in  turn  derived  from  the  GHS_LDSMT_2015,  leading  to  a 
successive refinement and improvement of the results (see Corbane et al., under review 
for details). According to the results of a global validation exercise using detailed building 
footprints (Corbane et al., under review ) for 277 areas of interest spread across different 
continents,  the  new  GHS-LDMST_2017  provides  the  best  balanced  accuracy  (BA), 
yielding BA=0.86. 

34 

  Figure 21. Examples from selected cities showing the reduction of over-detection and 
under-detection errors in the new multitemporal built-up layer. The results of 
GHS_LDSMT_2015 are compared against those of GHS_LDSMT_2017 respectively for the 
cities of New Delhi (India -A), Shanghai (China -B) and Riyadh (Saudi Arabia -C) 

35 

 
Most importantly, we observe a gradual improvement in the accuracy measures starting 
from the first layer GHS-LDMST_2015 (BA=0.83), the GHS_S1 layer (BA=0.84) and the 
last  GHS-LDMST_2017.  The  observed  trend  of  increasing  BA  rates  in  subsequent 
generations of GHSL data including the precedent releases as learning set, is an empirical 
confirmation  of  the  positive  impact  of  the  incremental  learning  approach  of  the  GHSL 
framework. 

4.2  Multi-temporal Population Grids (GHS-POP) 

This  spatial  raster  dataset  depicts  the  distribution  and  density  of  population,  expressed 
as the number of people per cell. Residential population estimates for target years 1975, 
1990, 2000 and 2015 provided by CIESIN Gridded Population of the World, version 4.10 
(GPWv4.10)  were  disaggregated  from  census  or  administrative  units  to  grid  cells, 
informed  by  the  distribution  and  density  of  built-up  as  mapped  in  the  Global  Human 
Settlement  Layer  (GHSL)  global  layer  per  corresponding  epoch  (for  disaggregation 
method see Freire et al., 2016).  

The  new  version  of  the  GHSL  population  distribution  grids  aimed  at  incorporating 
improvements originating from input datasets, namely population estimates and built-up 
presence.  While  the  disaggregation  relied  essentially  on  the  same  clear  and  simple 
approach,  there  were  significant  differences  to  the  input  data  that  had  a  positive  effect 
on the final quality and accuracy of population grids. For the new GHS-POP, the GHS-BU 
was used as target for disaggregation of population estimates. Cells declared as “NoData” 
in built-up layers were treated as zero for population disaggregation. The base source of 
population estimates (both counts and geometries) for the four epochs was the Gridded 
Population  of  the  World,  version  4.10  (GPWv4.10),  from  CIESIN/SEDAC.  Respect  to  the 
previous  release  of  GHSL  grids  (R2015A),  this  pre-Release  used  GPW  source  data  that 
incorporated boundary or population updates for 67 countries. Due to the previous GHSL 
population grids being produced in last quarter of 2015, before the final GPWv4 data set 
was  fully  assembled,  more  changes  were  included  in  population  sources  in  the  current 
pre-Release  than  those  incorporated  in  the  GPW  data  between  GPWv4  and  the  current 
GPWv4.10. For detailed information on what has changed in GPWv4.10, refer to:  

http://beta.sedac.ciesin.columbia.edu/data/collection/gpw-v4/whatsnewrev10 

Figure 22 © Adobe Stock, 2018 

36 

 
4.3  Urban Centres Delineation 

Be X a squared grid of cells x with uniform surface of 1 km2 representing the global Earth 
surface  with  a  Mollweide  pseudo  cylindrical  geographical  projection.  Be  POPx  the 
estimated  amount  of  resident  population  in  the  cell  x  by  the  GHSL,  and  be  BUx  the 
estimated  share  of  built-up  surface  in  the  cell  x  by  the  GHSL  by  observing  30-m-res 
satellite measurements. Be LANDx the estimated share of permanent land surface in the 
cell x by a GHSL processing of the JRC Global Surface Water (GSW) input data22. LANDx 

 the share of surface water occurring for more 
was estimated as 
than 80%  of the 30-m-resolution satellite measurements of the past  20 years (15 days 
interval) in the cell x. 

, with 

Given  the  above,  the  densities  of  population  and  built-up  areas  per  land  surface  are 
calculated as follows: 

Consequently, the support set of the HDC spatial domain is determined as follows: 

 spatial clusters are determined by the application of the  “contiguous grid cells 
The 
of  1km2”  criteria  of  the  root  definition,  by  assuming  4-connectivity  rule  on  the  grid  X 

representing the 
 set.  4-connected samples are neighbours to every sample that 
touches one of their edges. These samples are connected horizontally and vertically. The 
4-conn rule it is showed in the schema below. Respect to the sample X, the cells in the 
horizontal and vertical directions and one step of displacement are considered adjacent.  
The cells in the diagonal along the grid are not considered adjacent. 

The population size of each cluster c of the 

 is calculate as 

Finally, the 

 clusters are selected so that 

individual clusters that passed the above test are processed by 
Subsequently, the 
a spatial generalization procedure G including an iterative local union-majority filter (also 
called  “smoothing”)  until  idempotence  it  is  reached  followed  by  a  gap  filling  step.  That 
gap filling step is filling all the holes remaining after the smoothing, and having an area 

22  Jean-Francois  Pekel,  Andrew  Cottam,  Noel  Gorelick,  Alan  S.  Belward,  High-resolution  mapping  of  global 
surface  water  and  its  long-term  changes.  Nature  540,  418-422  (2016).  (doi:10.1038/nature20584) 
https://global-surface-water.appspot.com/download  

37 

not yesnotyesXyesnot yesnot 
 
 
 
 
    
 
 
                                           
less  than  15  km2.  The  local  union-majority  filter  applied  in  the  generalization  G  has  a 
kernel K of 3x3 spatial units in the grid X, corresponding to a surface of 9 km2.  

At each iteration, the union-majority filter 

 it is defined as follows: 

With   being the half of the number of samples included in the kernel K considered in the 

spatial filtering. In the specific case,  

The iterative local union-majority filter 
 it is applied individually to each HDC, testing at 
each iteration that the total number of HDCs determined before the filtering process it is 
maintained  constant.  Consequently,  avoiding  the  merging  of  two  distinct  HDCs  because 
of the spatial filtering process.  

Figure 23 example at 1km resolution of GHSL baseline data (GHS-BU, GHS-POP) 
for the delineation of urban centres in the Northern Region of China.  

38 

 
   
 
4.4  Urban Centres Database Attributes 

The  new  Urban  Centres  Data  Base  (GHS-UCDB)  builds  on  the  improved  GHS-BU  and 
GHS-POP  grids.  The  Urban  Centres  were  spatially  delimited  by  applying  the  “degree  of 
urbanization” model. The GHS-UCDB (Figure 24) was generated by spatial integration of 
the  urban  centres  with  the  GHSL  data  and  with  other  sources  related  to  five  main 
thematic  areas:  geography,  socio-economic,  environment,  Disaster  Risk  Reduction,  and 
Sustainable  Development  Goals  (Table  1).  The  principal  methods  used  to  derive  the 
attributes  to  characterise  urban  centres  through  geospatial  processing  is  displayed  in 
Figure  25.  An  additional  list  of  attribute  complements  the  one  by  thematic  area.  It 
includes mainly classification and reference attributes (such as name, latitude-longitude, 
etc.).  

Figure 24 Anatomy of the key information components and data 
processing/integration used to create the GHS-UCDB 

Figure 25 Elements of geospatial operations tasks to derive attributes of urban 
centres 

39 

 
 
 
Figure 26 GHS-UCDB regional coverage, share of centres by major region of the 
world 

Figure 27 Urban centres in the northern Gulf of Guinea 

40 

 
 
 
 
Table  2  List  of  UCDB  attributes  by  thematic  area  and  temporality.  Some 
attributes  are  classification  of  the  urban  centres  and  their  temporal  coverage 
(here,  2015)  refers  to  the  epoch  for  which  the  urban  centres  were  delineated. 
Land Use Efficiency attribute refers to a temporal span. 

Thematic 
area 

Attribute 

Geography 

Travel time to capital centre 
Elevation 
Biome type 
Climate class 
Income group 
Soil group 
River basin 
Temperature 
Precipitation 

Socio-
economic 

Environment 

Built-up areas 

Population 
Night-time light 
GDP 

Greenness 

CO2  
PM2.5 

DRR 

SDG 

Exposure to floods 

Exposure to earthquake 
Exposure to storm surge 
Exposure to heatwave 
Land Use Efficiency (11.3.1) 
Open spaces (11.7.1 –proxy) 

Temporal coverage 

1975  1990  2000  2015 

2015 

1990–2015 

Table 3 List of additional UCDB attributes. The temporal coverage (here, 2015) 
refers to the epoch for which the urban centres were delineated  

Variable 

Attribute 

Position 

Latitude 

Name 

Longitude 

Main name 

Temporal 
coverage 

List  of  names  of  populated  places  in  the  urban 
centre area 

2015 

UN Geographic region level 2 

41 

  
 
 
 
 
 
 
 
 
  
  
  
  
  
  
  
  
  
 
 
 
  
  
  
  
  
  
  
  
  
  
  
  
 
 
 
 
 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
  
  
  
  
 
Urban  Centres  in 
Japan 

5.0 

42 

 
5  Urban Centres Database 

The  Urban  Centres  Database  derived  from  the  Global  Human  Settlement  data  (GHS-
UCDB) is the first global, harmonised, consistent 4-D city database worldwide.  
It is a 4-dimensional database, offering for each city: 

  Location (geographic position and names); 

  Extent (surface and shape); 

  N attributes which can be considered and represented as volumes (i.e. population, 

greenness, night-time light etc.); 

 

Information over time (for several attributes) 

Some  established  city  databases  contain  some  of  the  above  features.  For  example,  the 
UN  World  Urbanization  Prospects  (UNDESA,  2018)  contains  names  of  urban 
agglomerations, their location, their population, and its variation over time. However, the 
spatial  extent  is  not  available  to  date.  Moreover,  as  compared  to  the  UN  World 
Urbanization Prospects accounting cities greater than 300,000-population size, the GHS-
UCDB  extends  the  universe  of  cities  until  the  50,000-population  size  cut-off  value, 
expanding significantly the universe of cities described by the data (Figure 28). 

Figure  28  Population  (year  2015)  rank-size  plot  of  the  cities  included  in  the 
WUP as compared to the GHS-UCDB records 

The GHS-UCDB can support diversified needs across several thematic domains, including 
baseline  for  SDG  indicators,  Disaster  Risk  Reduction  exposure  estimates,  environmental 
analyses and geographic analyses (Figure 29). 

The GHS-UCDB is a key information source when the user is interested in analysing and 
comparing  cities  across  the  world  ensuring  the  consistency  of  the  comparison.  In 
particular, the user can query the database to answer questions such as: 

  Which are the cities with value above X in the attribute Y? 

Be  Y  the  population  attribute  and  X  the  threshold  of  10  million  people  (the  established 
megacity  threshold).  The  result  of  the  query  will  be  a  list  of  urban  centres  that  are 
megacity in the selected epoch (i.e. 2015). The user can consequently analyse the GHS-

43 

 
 
UCDB  attributes  for  this  group  of  centres  (i.e.  built-up  areas  and  population  in  the 
previous epochs. 

  How many cities are there below the value X in the attribute Y? 

Be Y the Modified Mercalli Intensity class attribute (intensity scale of earthquakes), and X 
the class V of the scale (moderate shaking). The result of the query will be a list of cities 
exposed  to  earthquakes  below  class  V  intensity.  The  user  can  consequently  analyse 
where these urban centres are and i.e. population and built-up areas over time.  

  How much is the total/average of the attribute Y in cities? 

Be  Y  the  greenness  attribute  of  an  urban  centre,  it  is  possible  to  calculate  the  average 
NDVI value of the city. 

  How much more is the attribute Y in the city A vs B? 

Be Y the night-time light emission attribute. The result of this query is useful to compare 
how  much  light  two  or  more  cities  emit  at  night.  This  comparison  is  particularly  useful 
when  aggregated  with  other  attributes  i.e.  income  groups,  region  of  the  world,  or 
population size. 

  How much the attribute Y has changed in city A? 

Be Yt the temperature attribute for the epoch 1990 and Yt+1 the temperature attribute for 
the  epoch  2015.  The  user  can  calculate  the  change  in  the  temperature  for  any  of  the 
urban centres. 

The  GHS-UCDB  is  organised  in  five  main  thematic  areas:  geographic,  socio-economic, 
environment,  Disaster  Risk  Reduction,  and  Sustainable  Development  Goals  –  as 
presented  in  the  data  section.  This  section  presents  the  features  of  the  GHS-UCDB, 
proposes ways, in which the GHS-UCDB can be used and in dedicated thematic sections 
presents results and key messages derived from the analysis of the GHS-UCDB. 

Figure 29 Examples of GHS-UCDB uses 

44 

 
 
  The Urban Centres  

in BIOMES 

45 

 
 
5.1  Geography 

Geographic  conditions  impact  cites  and  urban  centres.  They  have  long  enabled  the 
emergence and expansion of cites and urban centres, but also affect and constraint their 
growth  and  development.  The  global  set  of  urban  centres  as  mapped  by  GHSL  was 
intersected  with  a  set  of  relevant  geographic  variables  for  which  global  geospatial 
datasets were available, but novel attributes were also computed for the purpose of this 
Atlas  (e.g.  travel  time  to  country  capital).  Enriching  the  urban  centres  database  with 
these  physiographic  attributes  enables  analysis  of  the  interplay  of  cities  with  their 
geographic  setting.  The  geographic  variables  discussed  in  the  UCDB  report  are:  Biome 
type, Soil type, Elevation, Climate, Temperature, Precipitation, and River Basins. 

Figure 30 © Adobe Stock, 2018 

5.1 
Geography 

46 

 
 
 
 
5.1.1  Biome 

Urban centres according to biomes.  

Figure 31 Urban centres according to their location in the biomes of the world.  

Note: Tropical and Subtropical Dry Broadleaf Forests (T&SubT Dry BF), Mediterranean Forests, Woodlands, and 
Scrub (Med. F-W-S), Temperate Grasslands, Savannas, and Shrublands (Temperate G-S-S), Deserts and Xeric 
Shrublands (Desert & XS), Temperate Coniferous Forests (Temperate CF), Tropical and Subtropical Coniferous 
Forests  (T&SubT  CF),  Temperate  Broadleaf  and  Mixed  Forests  (Temperate  B/MixF),  Boreal  Forests/Taiga 
(BF/Taiga),  Tropical  and  Subtropical  Moist  Broadleaf  Forests  (T&SubT  Moist  BF),  Tropical  and  subtropical 
grasslands,  savannas,  and  shrublands  (T&SubT  G-S-S),  Flooded  Grasslands  and  Savannas  (Flooded  G-S), 
Montane Grasslands and Shrublands (Montane G-S) 

Biomes  are  the  most  basic  units  that  ecologists  use  to  describe  global  patterns  of 
ecosystem  form,  process,  and  biodiversity.  Interestingly,  existing  descriptions  of  biome 
systems  mostly  ignore  human  influence  or  use  a  limited  number  of  anthropogenic 
ecosystem  classes  (Ellis,  2018;  Ellis  and  Ramankutty,  2008).  Without  entering  into  the 
on-going discussions in ecology, this section uses the Terrestrial Ecosystems of the World 
(TEOW)  map,  which  contains  a  distinct  assemblage  of  natural  communities  sharing  a 
large  majority  of  species,  dynamics,  and  environmental  conditions,  to  map  each  urban 
centre  into  one  of  its  biomes  (Figure  31).  Urbanization  is  reported  as  one  of  the  most 
important  threats  to  biodiversity  worldwide  as  urban  areas  may  threaten  ecosystems 
through  direct  habitat  conversion  (Clergeau  et  al.  1998,  Blair  1999,  McKinney  2002). 
Also, high concentrations of human population has various indirect effects, which include 
freshwater  contamination,  waste  generation,  resource  use  or  habitat  fragmentation 
(Mikusinski and Angelstam 1998). 

The  urban  centres  are  classified  according  to  the  biome  within  which  the  given  urban 
centre  is  located  (considering its  extent).  Almost  2/3  of  all urban  centres  are  located  in 
three  biomes:  (1)  Tropical  and  Subtropical  Moist  Broadleaf  Forests,  (2)  Temperate 
Broadleaf and Mixed Forests, and (3) Deserts and Xeric Shrublands with a share of global 
urban centre population in 2015 of 32 %, 26% and 11%, respectively in 2015. Asia has 
the highest share of urban centre population living in these three biomes. Most of urban 
centre  population  of  Africa  lives  in  Tropical  and  subtropical  grasslands,  savannas,  and 
shrublands,  while  the  urban  centre  population  of  Latin  America  and  Caribbean  region  is 
concentrated  in  Tropical  and  Subtropical  Moist  Broadleaf  Forests  and  Deserts  and  Xeric 
Shrublands. 

Between 1990 and 2015 the population of urban centres increased by almost one billion. 
A large part of the population (about 1/3 of the global increase) live in urban centres of 
the  Tropical  and  Subtropical  Moist  Broadleaf  Forests,  resulting  in  a  relative  increase  of 
47% in this class. The urban centres population living in Boreal Forests/Taiga and Tundra 
has  declined  between  1990  and  2015,  while  the  urban  centre  population  settled  in  (1) 
Montane Grasslands and Shrublands, (2) Tropical and subtropical grasslands,  savannas, 

47 

 
and shrublands, and (3) Lakes have doubled during this period. Figure 32 shows for each 
bioe  the  urban  centre  population  change  in  absolute  numbers  and  the  ratio  of  change 
between 1990 and 2015. 

Figure 32 Change of urban centre population between 1990 and 2015 by biome. 
The total change of population (in millions) is provided per biome, and the ratio 
of population change is given in brackets. 

Key Messages 

● 

● 

 In  2015,  the  biome  with  the  highest  share  (32%)  of  city  dwellers  is  the  Tropical 
and  Subtropical  Moist  Broadleaf  Forests  biome  (also  known  as  rainforest). 
Population  in  this  biome  increased  by  47%  between  1990  and  2015  and  puts 
significant pressure on remaining natural rain forests. 

In  the  observation  period  1990-2015  the  urban  centre  population  in  the  African 
savannahs  (e.g.  the  Sahel),  montane  grasslands  and  shrublands  (e.g.  Ethiopia) 
almost  doubled.  Both  biomes  are  prone  to  degradation,  risking  the  livelihoods  of 
their population. 

●  Population in arctic regions (Tundra and Taiga) shrunk by 20%. 

48 

 
 
 
5.1.2  Soil 

The rapid urbanisation and population growth described by the GHSL data have a strong 
impact  on  soils.  In  the  process  of  urbanisation  many  soils  are  permanently  sealed  or 
considerably  altered  due  to  excavation,  mixing  and  compacting.  This  changes  the 
capacity  of  food  production  and  other  diverse  ecological  services.  It  consequently 
increases  the  risk  of  potential  floods  and  water  scarcity,  endangers  biodiversity,  and 
leads to environmental change on a larger scale. This section analyses, which are the soil 
types that are mostly affected by urbanisation. 
In 2015, 69.7% of the built-up surface in urban centres is concentrated in soil types with 
a  high  agricultural  suitability,  which  are  accounting  for  29.4%  of  the  Earth  landmass. 
Collectively,  we  are  consuming  0.46%  of  these  soils  for  built-up  surface  use  in  urban 
centres,  making  home  for  nearly  2  billion  urban  dwellers  globally  (62.4%  of  the  total 
dwellers in urban centres). 

Figure 33 Share of urban centres built-up areas by soil type 

Table 4 Aggregation of soil types according to agricultural suitability 

Medium 
Agricultural 
Suitability 

Lixisols 
Vertisols 
Ferralsols 
Arenosols 
Podzoluvisols 
Alisols 
Greyzems 

High Agricultural 
Suitability 

Fluvisols 
Anthrosols 
Cambisols 
Luvisols 
Acrisols 
Phaeozems 
Nitisols 
Andosols 
Chernozems 
Planosols 
Kastanozems 

Low Agricultural 
Suitability 

Black Soils 

Chernozems 
Kastanozems 
Phaeozems 

Calcisols 
Gleysols 
Leptosols 
Regosols 
Solonchaks 
Podzols 
Plinthosols 
Histosols 
Solonetz 
Gypsisols 

The  most  frequent  location  of  urban  centres  in  the  world  is  on  Fluvisols.  In  2015,  the 
13.6%  of  the  population  living in  urban  centres  (more  than  423  million)  are  settling  on 
Fluvisols,  which  are  accounting  for  2.7%  of  the  Earth  Landmass.  This  is  not  surprising, 

49 

 
 
 
since  Fluvisols  typically  occur  along  rivers  and  lakes,  in  deltaic  areas  and  in  areas  of 
recent  marine  deposits  like  coastal  lowlands.  These  are  typical  areas  of  large  urban 
agglomerations  mostly  related  to  the  availability  of  water  and  transport  opportunities. 
The  good  natural  fertility  of  most  Fluvisols  and  attractive  dwelling  sites  on  river  levees 
and  on  higher  parts  in  marine  landscapes  were  recognized  in  prehistoric  times.  Later, 
great  civilizations  developed  in  river  landscapes  and  on  marine  plains.  Hence  the  high 
percentage of urbanization on these soils. 

The  second most important class are Anthrosols; 11.5% (more than  358 million) of the 
urban population in the year 2015 are living in urban centres constructed on Anthrosols, 
which  are  accounting  for  0.4%  of  the  Earth  landmass.  Anthrosols  comprise  soils  that 
have been modified profoundly through human activities, such as addition of  organic or 
mineral  material,  charcoal  or  household  wastes,  or  irrigation  and  cultivation.  Not 
surprisingly, they are very frequent in urban agglomerations, where the anthropic impact 
has been profoundly modifying the local soil properties, essentially making artificial man-
made soils as a result of human activities. 

About 10.4% (more than 325 million) of the urban  population in the year 2015 living in 
urban centres are settling on Cambisols, and 1,100 (12.3%) urban centres are located on 
this  soil  type.  Cambisol  is  one  of  the  most  common  soil  types,  particularly  well 
represented in temperate and boreal regions that were under the influence of glaciations 
during  the  Pleistocene,  partly  because  the  parent  material  of  the  soil  is  still  young,  but 
also  because  soil  formation  is  slow  in  cool  regions.  Cambisols  generally  make  good 
agricultural  land  and  are  used  intensively.  Cambisols  on  irrigated  alluvial  plains  in  dry 
zones are used intensively for production of food and  oil crops.  Cambisols in undulating 
or  hilly  terrain  are  planted  with  a  variety  of  annual  and  perennial  crops  or  are  used  as 
grazing land. 

Figure 34 © Adobe Stock, 2018 

Chernozems,  Kastanozems  and  Phaeozems  are  among  the  best  soils  in  the  world,  and 
they  form  the  so-called  "Black  Soils"  group,  that  accounts  for  6.5%  of  the  Earth 
landmass. Protecting those soils from urbanization is strategic for maintaining the global 

50 

 
food  production  and  for  global  food  security.  510  (5.8%)  Urban  centres  are  located  on 
Black Soils, providing home for more than 164 million (5.3%) of urban dwellers in 2015. 
In the 25 years from 1990 to 2015, the built-up surface included in the urban centres on 
Black  Soils  was  growing  from  21,178  to  26,336  km2,  causing  an  estimated  Black  Soil 
consumption for urban use of 5,158 km2. 

Table  5  Absolute  and  relative  (in  2015)  built-up  areas  surface,  resident 
population,  number  of  urban  centres,  and  share  of  global  land  mass  per  soil 
typologies in the High Agricultural Suitability class. 

SOILTYPE 

Built-up surface (106 Km2) 

Resident Population (106 people) 

Urban Centres 

Landmass 

Fluvisols 

Anthrosols 

Cambisols 

Luvisols 

Acrisols 

Phaeozems 

Nitisols 

Andosols 

Chernozems 

Planosols 

Kastanozems 

1990 

2000 

2015 

BU15% 

1990 

2000 

2015 

P15% 

Number 

% 

Surface 
 (106 Km2) 

% 

      29          34          37       13% 

      308           355          424       14% 

   1,118   12.5% 

           4      2.7% 

      27          33          37       13% 

      268           305          359       12% 

      759  

8.5% 

           1      0.4% 

      21          24          26      

9% 

      248           281          325       10% 

   1,100   12.3% 

          12      7.8% 

      27          32          35       12% 

      175           202          242       8% 

      740  

8.3% 

           8      5.4% 

      19          22          24      

8% 

      162           188          221       7% 

      698  

7.8% 

           7      4.5% 

      12          14          15      

5% 

       89             99          109       3% 

      286  

3.2% 

           3      2.0% 

        4            5            5      

2% 

       68             81          106       3% 

      233  

2.6% 

           1      0.8% 

        6            7            7      

2% 

       60             68            78       2% 

      131  

1.5% 

           1      0.7% 

        6            6            7      

2% 

       35             35            34       1% 

      133  

1.5% 

           3      1.8% 

        3            4            4      

1% 

       17             20            23       1% 

       71  

0.8% 

           1      0.6% 

        3            4            4      

1% 

       16             19            22       1% 

       91  

1.0% 

           4      2.7% 

Key Messages 

●  Urban centres are mostly (69%) built on soils with a high agricultural suitability. 
Rapid,  unplanned  urbanisation  processes  may  reduce  the  local  capacity  for  food 
production. Moreover, the reduced ecological services of soils due to urbanisation 
may  increase  the  risk  of  potential  floods  and  water  scarcity,  and  endangers 
biodiversity, leading to environmental change on a larger scale.   

●  Fluvizols,  Anthrosols  and  Cambisols  are  mostly  affected  by  urbanisation  (35%  of 

built-up area in urban centres). 

●  The  most  valuable  soils  for  food  production  (Chernozems,  Kastanozems  and 
Phaeozems) are strategic for maintaining the global food production and for global 
food  security.  Urbanisation  processes  should  be  limited  and  strictly  controlled  on 
these soils. 

51 

 
 
 
 
5.1.3  Elevation 

Most of the urban centres population is located in elevation from sea level to 500 m but a 
number  of  urban  centres  in  Asia,  Latin  America,  and  Africa  area  to  be  found  in  all 
elevations including that of above 2500m.  

Figure 35 Urban centres located in elevation classes below 3m and above 500m.  

Elevation affects humans in direct and indirect way. The direct effect is on human health. 
In  fact,  over  the  2500  m  altitude  that  the  lower  oxygen  content  may  be  not  suited  to 
everybody,  in  addition,  altitude  brings  harsher  climatic  conditions.  The  indirect  effect 
relate to the ruggedness that comes with living in elevated areas that makes , it makes it 
more prone to natural hazards, especially landslides, flash floods, but also earthquakes.  
Elevation  and  ruggedness  also  hampers  accessibility  of  urban  centres  that  are  more 
difficult to get to and for which goods and transports are more costly to deliver. A direct 
effect  of  elevation  in  low  elevated  coastal  areas  is  the  increase  risk  to  coastal  flooding 
due to sea level surge and also climate induced sea level rise. This analysis only looks at 
the elevation of urban centres.  

The  urban  centre  elevation  attribute  is  computed  as  the  average  altitude  within  the 
spatial  extent  of  each  urban  centres  expressed  in  metres  above  sea  level  (MASL).  We 
used  the  ALOS  DSM  dataset  and  processed  it  using  uses  Google  Earth  Engine.  The 
analysis groups population in urban centres based on seven elevation classes (Figure 35) 
with elevation of the urban centres; 1) below 3 m and less, 2) between 3 and 500m, 3) 
between 500 and 1000m, 4) between 1000 and 1500, 5) between 1500 and 2000m, 6) 
between  2000  and  2500m,  and  7)  above  2500m.  The  first  class  includes  the  low  lying 
cities  in  the  centre  of  continents  and  in  those  in  low  lying  coastal  areas  that  may  be 
susceptible  to  coastal  flooding.  The  7th  class  aims  to  locate  the  cities  at  high  elevation 
that may not be suitable for habitation for all people.   

Figure 35 shows the population of urban centres from below 3m to those above 2500 m. 
Of the low elevated urban centres a number cluster at the edge of the Caspian sea, a few 
in  major  rivers  delta  including  that  in  the  Netherlands,  in  the  Nile  river  Delta  and 
Guangzhou  in  the  Pearl  River  delta.  More  urban  centre  are  to  be  found  in  low  lying 
coastal areas that are not selected is this analysis as we are using an average elevation 
measured  within  the  urban  centres  boundaries  -  not  the  lowest  elevations  of  the  urban 
centres. Figure 35 also shows the urban centres in all elevation above 500. For example, 
there  are  45  urban  centres  above  2500  meters  elevation  a  most  are  clustered  in  along 
the Andes in South America, in Central Asia and in the Horn of Africa.  

Most  urban  centres  of  the  world  are  located  at  an  elevation  suitable  for  human 
habitation;  few  are  at  very  high  elevation  (Figure  36).  That,  however,  varies  with 
continents.  In  Asia,  the  mean  and  the  median  of  the  urban  centres  in  the  elevation 
classes overlap. Most of the population (83%) is in the lower class. In Europe and North 

52 

 
America  population  in  the  class  between  sea  level  and  500  m  is  above  90%.  For  Africa 
and Latin America this is different. Africa shows population also in classes above 500, up 
to 1500 with three urban centres above 2500m. Latin America shows population between 
6 and 8 % in all elevation classes above 500m including that above 2500m.  

Figure  36.  Total  urban  centre  population  by  elevation  class  per  region  (Gm  – 
global mean, Rm – regional mean).  

The  statistics  on  the  number  of  urban  centres  reveals  their  distribution  in  all  elevation 
classes (Table 6). Urban centres can be found at elevation above 2500 m in all continents 
except  of  Oceania  and  Europe  and  Northern  America.    Europe  and  Oceania  list  urban 
centres in classes bellow 1000 m only while North America also above 2000 meters. Latin 
America shows 37 urban centres in elevation zone above 2500 m.  The urban centres in 
elevated  areas  are  smaller  and  account  for  less  population  on  average  than  those  in 
lower elevation classes. 

53 

 
 
 
 
Table 6 Number of urban centre by elevation classes 

Elevation classes in metres above sea level 

UN Major 
Regions 

 <3 

3-500 

500-
1000 

1000-
1500 

1500  – 
2000 

2000-
2500 

>2500 

Asia 

49 

3957 

528 

253 

131 

20 

5 

America 
Latina & 
Caribbean 

3 

590 

223 

54 

58 

42 

37 

Africa 

14 

972 

222 

192 

89 

13 

North 
America 

Europe 

Oceania 

1 

328 

19 

16 

22 

0 

983 

38 

45 

3 

0 

0 

8 

0 

0 

0 

0 

0 

3 

0 

0 

0 

TOTAL 

89 

6868 

1040 

515 

286 

75 

45 

Key Messages 

●  Most of the urban centres in the world are located at elevation below 500m 

●  A  number  of  urban  centres  are  located  on  low  elevated  areas.  Those  in  the 

proximity of low elevated coastal zones are at risk of sea level rise  

●  South  America  and  Asia  host  urban  centres  at  higher  elevation  classes  including 

that of above 2500m. 

54 

 
 
 
 
 
 
5.1.4  Climate 

Urban centres across climate zones 

Figure 37 Urban centres classification according to the Köppen-Geiger climate 
zones map 

Human  settlements  interact  with  the  climate  conditions  in  which  they  lay  (Landsberg 
1976). Climate characteristics are principally important for weather related disasters (i.e. 
floods,  cyclones),  energy  use,  for  the  adoption  of  specific  building  construction 
techniques  and  materials,  and  for  health  (i.e.  air  pollution).  The  GHS-UCDB  contains  a 
climate typology attribute, derived from (Rubel et al. 2017) on the basis of the Köppen-
Geiger climate zones map. Mild temperate zones concentrate the largest share of urban 
centres  (40%)  hosting  1.3  billion  people  in  2015.  Urban  centres  in  tropical  zones  are 
more than 3,000 and accommodate a population of almost 1 billion people. 16% of the 
urban  centres  are  located  in  zones  of  steppe;  their  population  reaches  almost  500 
million.  About  8%  and  7%  of  the  centres  lay  in  desert  and  snow  zones,  respectively. 
Population increased the most between 1990 and 2015 in  urban centres in desert areas 
(+70%), while increasing by half in tropical and steppe zones, by 1/3 in tundra and mild 
temperate zones, and by about 8% in snow zones (Figure 38). 

Figure  38  Share  of  urban  centres  by  climate  zone  and  population  changes 
between 1990 and 2015 

55 

 
 
 
5.1.5  Temperature 

Changes in temperatures in the period 1990-2015 

Figure 39 Changes in the temperature (C) per urban centre in the period 1990-
2015 

Global temperature is a popular metric for summarizing the state of global climate.   The 
Intergovernmental  Panel  on  Climate  Change  in  its  most  recent  report  (AR5)  in  2013 
stated:    'Warming  of  the  climate  system  is  unequivocal,  and  since  the  1950s,  many  of 
the observed changes are unprecedented over decades to millennia. The atmosphere and 
ocean have warmed, the amounts of snow and ice have diminished, sea level has risen, 
and the concentrations of greenhouse gases have increased’. According to Morice et al., 
2012,  the  period  2001-2010  (0.49°C  above  the  1961-90  average)  was  0.21°C  warmer 
than the 1991-2000 decade (0.28°C above the 1961-90 average).  

33 Million 

115 Million 

66 Million 

202 Million 

130 Million 

Figure 40 population living in cities where temperature increase was more than 
0.8 C in the period 1990-2015 

Data  on  annual  average 
the  CRU  database 
temperatures  obtained 
(http://www.cru.uea.ac.uk/data)  were  used  in  the  analysis(Harris  et  al.,  2014).  These 
data  are  based  on  observations  from  ground  stations  combined  with  interpolation  at  a 
coarse  resolution  aiming  for  global  coverage.  Therefore,  they  do  not  (yet)  consider 

from 

56 

 
 
 
 
localized city effects  such as urban heat island that may modify intra-city observed and 
perceived temperatures.  

Average temperatures  per urban centre were  calculated  for three time intervals  centred 
on the years 1990, 2000 and 2015 as follows: for 1990, the interval spans from 1988 to 
1991; for 2000, the interval spans from 1999 to 2002; for 2015, the interval spans from 
2012  to 2015. The intervals were chosen in order to match the dates of the Landsat data 
collections  used  to  derive  the  multi-temporal  built-up  areas  (GHS-BUILT)  and  to  reduce 
inter-annual and seasonal variability that may affect the change analysis. 

The map in Figure 39 above shows changes in average yearly temperatures. They depict 
how much areas, in which the cities of the world are located, have warmed or cooled in 
the period 1990-2015: 

  The most significant warming occurred in the cities located in Egypt,  followed by 

the cities in the Balkans and those on the western coast of the Caspian Sea. 

  Temperature  has  also  increased  on  the  western  coast  of  United  States,  in 

Venezuela and Brazil. 

  Despite a general increase in average urban temperatures, in some areas the city 

temperatures decreased mainly in Bangladesh and North-Eastern China.  

  Considering  a  cut-off  value  of  +0.8°C  for  the  increase  in  temperatures,  we  can 
observe that in Asia around 202 Million people living in cities are affected by the 
increase  in  temperatures,  130  Million  in  Africa  and  115  Million  in  America.  At 
global  level,  546  Million  people  living  in  cities  are  affected  by  an  increase  in 
temperatures of more than 0.8°C. 

Figure 41 Number of urban centres affected by changes in the temperatures 
(C) in the period 1990-2015 

57 

 
 
 
Figure 41 classifies cities according the changes in temperatures in the period 1990-
2015. It shows the following: 

  A  total  of  486  cities  have  experienced  a  decrease  of  average  temperatures 

between 1990-2015.  

  The majority of these cities affected by a decrease in temperatures are located 

in Asia (414 cities). 

  The majority of the cities experienced increase in average temperatures : 

o  Asia: 4262 cities out of 4946 (86%),  
o  Africa: 1487 cities out of 1506 (98%), 
o  Europe: 1014 out of 1051 (96%), 
o  Latin America and the Caribbean: 974 out of 1007 (97%),  
o  Northern America: 332 out of 372 (0.89%), 
o  Oceania: 30 out of 41 (73%). 

Key Messages 

Though  warming  has  not  been  uniform  across  the  planet,  the  upward  trend  in  the 
globally  averaged  temperature  shows  that  more  cities  are  warming  than  cooling.  Since 
1901,  the  planet’s  surface  has  warmed  by  0.7–0.9°  Celsius  (1.3–1.6°  Fahrenheit),  but 
the  rate  of  warming  has  nearly  doubled  since  1975  to  1.5–1.8°  Celsius  (2.7–3.2° 
Fahrenheit),  according  to  the  international  State  of  the  Climate  in  2017  report  (NOAA, 
2018). The 10 warmest years on record have all occurred since 1998. 

  The most significant warming occurred in cities in Egypt, followed by cities in the 
Balkans and those on the western coast of the Caspian Sea. Temperature has also 
increased on the US West Coast, in Venezuela and Brazil. 

  Despite a general increase in average urban temperatures, in some areas the city 

temperatures decreased mainly in Bangladesh and North-Eastern China.  

  Considering  a  cut-off  value  of  +0.8°C  for  the  increase  in  temperatures,  we  can 
observe that in Asia around 202 Million people living in cities are affected by the 
increase  in  temperatures,  130  Million  in  Africa  and  115  Million  in  America.  At 
global  level,  546  Million  people  living  in  cities  are  affected  by  an  increase  in 
temperatures of more than 0.8°C. 

58 

 
 
 
5.1.6  Precipitation 

Changes in precipitation in the period 1990-2015 

Figure 42 Relative changes in the average precipitation (%) calculated for the 
urban centres in the period 1990-2015 and long term mean precipitations (mm) 
in the period 1966 – 2015. 

Among the effects of global warming is the increase in atmospheric evaporative demand, 
which  intensifies  the  hydrological  cycle,  resulting  in  more  intense  and  frequent  storms, 
but also contributing to drying over some land areas. Increasing global temperatures are 
very  likely  to  lead  to  changes  in  precipitation  pattern,  due  to  changes  in  atmospheric 
circulation  (see  section  5.1.5).  Overall,  global  land  precipitation  has  increased  by  about 
2%  since  the  beginning  of  the  20th  century(Jones  and  Hulme,  1996).  The  increase  is 
statistically  significant,  though  neither  spatially  nor  temporally  uniform(Doherty  et  al., 
1999).  
the  CRU  database 
Using  global  annual  precipitation  data,  derived 
(http://www.cru.uea.ac.uk/data,  (Harris  et  al.,  2014)),  changes  in  the  average 
precipitations  were  calculated  for  the  urban  centres  in  the  period  1990-2015  and 
compared to the long term mean precipitations in the period 1966 – 2015. 

from 

59 

 
 
 
 
The results presented in Figure 42 show the following: 

  Precipitation increased relatively in European cities, in the Sahel and some of the 

cities of Western Latin America (e.g. in Ecuador). 
In Asian-Pacific cities, there is a general decrease in precipitations.  

 
  Decreasing  precipitations  are  generally  observed  around  the  Mediterranean  Sea, 
in India and Bangladesh, Southern Africa and Latin America despite some regional 
differences.  

  Overall,  the  majority  of  the  cities  have  experienced  no  significant  change  in 
precipitations,  but  24%  have  experienced  an  increase  in  precipitations  in  the 
period 1990-2015 and 19% a decrease in precipitations during the same period. 

The  results  observed  in  precipitation  changes  in  the  period  1990-2015  need  to  be 
analysed in parallel with the long term mean precipitations in the last 50 years. It can be 
seen that: 

  Despite  a  general  impression  of  decreasing  precipitations  in  urban  centres,  it  is 
important  to  note  that  long-trend  analysis  needs  to  complement  these  results. 
Besides,  the  exact  rates  of  precipitations  changes  depend  on  the  method  of 
calculating these changes. Difficulties in the measurement of precipitation remain 
an  area  of  concern  in  quantifying  the  extent  to  which  global  and  regional  scale 
precipitation has changed. 

Figure 43 classifies cities according the relative changes in precipitations in the period 
1990-2015. It shows the following: 

  A  total  of  1672  cities  have  experienced  a  decrease  of  average  precipitations 

between 1990-2015. Most of these cities are located in Asia and Africa. 

  A  total  2091  cities  have  experienced  an  increase  in  precipitations.  Most  of 

these cities are located in Asia, Europe and Latin America. 

60 

 
 
Figure 43. Number of city centres per classes of relative changes in 
precipitations (in %) 

Key Messages 

Overall,  global  land  precipitation  has  increased  by  about  2%  since  the  beginning  of  the 
20th century. The results of our analysis at city level confirm these observations. 

  24%  of  the  cities  in  the  World  experienced  an  increase  in  precipitations  in  the 

period 1990-2015 

  19% of the cities experienced a decrease in precipitations during the same period. 

  While African cities are the most affected by the decrease in precipitations, these 
same  cities  have  the  highest  share  of  population  increase  with  an  average 
increase of 375% between 1990 and 2015. 

61 

 
 
 
 
 
5.1.7  River Basin 

Urban centres located in Major River Basins of the world 

Figure 44 Urban Centres located in the selected Major River Basins of the World 
(MRBW). 

Water  is  an  essential  element  in  the  Earth’s  system.  It  is  critical  for  socio-economic 
development,  healthy  ecosystems  and  for  human  survival  itself.  At  the  same  time,  the 
excess of water (in the form of inundation and flooding, see section 5.4.1 –p. 90) poses a 
threat  to  socio-economic  development.  Water  and  water  management  in  all its  forms  is 
at  the  core  of  any  sustainable  development.  However,  population  growth,  agricultural 
intensification, urbanization and industrial production are putting pressure on fresh water 
resources.  The  Sustainable  Development  Goals  have  recognized  this  issue  and  dedicate 
Goal  6  to  ‘Ensure  availability  and  sustainable  management  of  water  and  sanitation  for 
all’.  

This  section  of  the  Atlas  looks  at  the  urban  centres  that  are  located  in  the  major  river 
basins of the world. Not by chance, the first civilisations developed along river systems: 
the Mesopotamian civilization along Euphrates and Tigris, the Egyptian Empire along the 
Nile, the Harappa culture along the Indus, and the Chinese Empires along the Huang He 
(Yellow  River).  All,  except  the  Euphrates  and  Tigris,  still  feature  in  the  top  ten  list  as 
shown  in  Table  7.  The  global  dataset  of  Major  River  Basins  (MRB)23  used  in  this  study 
consists  in  405  basins  across  the  globe.  Out  of  these  basins  158  (approx.  40%)  are  in 
scarcely  populated  areas  (Siberia,  Alaska,  Amazonia,  Australia).  The  remaining  247 
MRB’s  include  5731  urban  centres,  which  are  two  thirds  of  all  cities  around  the  world. 
They  are  home  to  63%  of  the  global  city  population  in  2015,  and  64%  of  total  urban 
centre built-up area is falling within these MRB’s. 

Figure  44  shows  the  location  of  the  top-ranking  MRB’s  in  terms  of  number  of  urban 
centres and amount of population and built area in the urban centres. According to these 
criteria,  the  Ganges  and  Yangtze  in  India/Bangladesh  and  China,  respectively,  are  the 
most important MRB’s hosting together 980 cities with more than 360 million inhabitants. 
Several megacities are located along these rivers (e.g. Kolkata, Dhaka, Shanghai). These 
long rivers with large populations living along the shores tend to be highly polluted, since 
the current waste management is not adequate. The Yangtze, for example, deposits 1.5 
million  metric  tonnes  (55%)  of  the  total  2.75  million  metric  tonnes  of  plastic  waste 
deposited into the ocean by rivers each year (Schmidt et al., 2017). 

23  Global  Runoff  Data  Centre  (2007):  Major  River  Basins  of  the  World  /  Global  Runoff  Data  Centre.  Koblenz, 
(BfG).  

Germany: 
https://www.europeandataportal.eu/data/en/dataset/1b19599d-a17e-49f3-9829-d59d1eab43b7 

Hydrology 

Institute 

Federal 

of 

62 

 
                                           
 
The  Ganges  and  Yangtze  are  followed  by  two  other  Asian  river  systems,  namely  the 
Indus  and  the  Huang  He  (also  known  as  Yellow  River).  Overall,  seven  out  of  the  ten 
highest-ranking river catchments in terms of population are in Asia. The Asian rivers are 
exceeded only in the category of amount of built-up area in urban centres by the North 
American  river  systems  of  the  Mississippi  and  the  St.  Lawrence.  It  is  only  in  this 
category,  where  also  the  European  rivers  Rhine  and  Volga  appear.  The  most  important 
MRB in South America is the Parana catchment that includes the megacity of Sao Paulo. 
In Africa, the most important MRB is the Nile with almost 60 million urban dwellers along 
the river, most of them living in Cairo and the Nile Delta. 

Table  7  Mayor  river  basins  ranked  by  the  number  of  urban  centres,  city 
population  (2015),  and  built-up  area  (2015).  RB  –  river  basin;  MR  –  Major 
Region (UN) 

Urban Centres 

Population (2015) 

Built-up area (2015) 

RB 

MR 

  RB 

Yangtze 

Asia  

519 

  Ganges 

Ganges 

Asia  

461 

  Yangtze  

Indus 

Asia   

283 

Indus 

Huang  

Asia   

259 

  Huang 

MR 

Asia  

Asia 

Asia 

Asia 

millions 

  RB 

MR 

km2 

225.2 

  Mississippi 

N.Am. 

15226 

137.1 

  St.Lawrence  N.Am. 

89.3 

  Yangtze 

65.6 

  Huang  

Asia 

Asia  

9223 

9087 

6599 

Niger 

Africa   

202 

  Nile 

Africa  

59.5 

  Ganges 

Asia  

6147 

Parana 

Lat.Am.  177 

  Parana 

S.Am. 

54.8 

  Parana 

Lat.Am. 

5465 

Nile 

Africa 

170 

  Yongding 

Asia 

52.1 

  Yongding 

Asia 

5254 

Yongding  Asia 

138 

  Bei Jiang 

Asia 

45.0 

  Rhine 

Europe  

5050 

Krishna 

Asia   

132 

  Brahmaputra  Asia  

41.6 

  Tone 

Asia  

4301 

Congo 

Africa   

118 

  Niger 

Africa 

41.5 

  Volga 

Europe 

4135 

Key Messages 

●  Rivers  have  been  the  lifelines  of  human  civilisations  and  socio-economic 
development.  However,  population  growth,  and  rapid  urbanization,  agricultural 
intensification,  and  industrial  production  are  putting  pressure  on  the  rivers 
systems and threaten their functioning.  

●  The majority of urban centres (63%  of the global city dwellers) is located in 257 

Major River Catchments. 

●  Ganges  (India,  Bangladesh)  and  Yangtze  (China)  are  hosting  together  980  cities 

with more than 360 million inhabitants.  

●  These  findings  underline  the  importance  of  policies  to  protect  the  rivers,  and 
water  in  general,  as  an  essential  resource  for  a  sustainable  and  resilient  urban 
future. 

63 

 
 
 
 
 
 
 
 
 
GDP of Urban Centres 

64 

 
 
5.2  Socio-Economic 

The UCDB  variables directly linked to the socio-economic development discussed are: i) 
the resident population 1975-1990-2000-2015, ii) the built-up surface 1975-1990-2000-
2015,  iii)  the  nightlight  emissions  2015,  iv)  the  income  class  2015,  v)  gross  domestic 
product, and VI) travel time to the capital 2015.  

The resident population and the built-up surface data are derived  from the GHS-POP and 
GHS-BU  products  at  the  different  epochs.  The  GHS-BU  is  generated  by  spatial 
aggregation  (upscaling)  of  the  information  collected  at  various  decametric  spatial 
resolution  satellite  image  data  records  (10-15-30-80  metres)  available  in  the  different 
GHSL epochs and different satellite platform (M. Pesaresi et al., 2016a),  (Corbane et al., 
2017b) , (Corbane et al., 2018a), to the 250x250 metres resolution grid. The GHS-POP is 
generated  by  spatial  disaggregation  (downscaling)  of  census  spatial  data  to  250x250 
metres resolution grid, using GHS-POP as principal spatial covariate (Freire et al., 2016).  

The Night Light Emission (NLE) data recorded by satellite platforms have been introduced 
in  a  number  of  application  areas  (Elvidge  et  al.,  2007)  (Elvidge  et  al.,  2017).  In 
particular, they have been prosed for global urban delineation  (Elvidge et al., 2010), for 
the production of spatially explicit measure of human development (Elvidge et al., 2012), 
as  proxy  measure  of  human  well-being  (Ghosh  et  al.,  2013),  and  for  post-conflict 
humanitarian  needs  assessment  (Corbane  et  al.,  2016).  The  NLE  data  integrated  in  the 
current UCDB are  the Version 1 VIIRS (Visible  Infrared Imaging Radiometer Suite) DNB 
(Day/Night  Band)  Night-time  Lights  Composites  suite24  produced  by  the  Earth 
Observations Group (EOG) at NOAA/NCEI. These grids span the globe from 75N latitude 
to 65S and have a resolution  of 15 arc-second in WGS84 geographic coordinates (EPSG 
4326), which corresponds to  roughly 500  m at the equator.  The following analysis uses 
the  "vcm-orm-ntl"  (VIIRS  Cloud  Mask  -  Outlier  Removed  -  Night-time  Lights)  layer 
showing the cloud-free average radiance emitted, expressed as nano-watt per steradian 
per square centimetre (nW cm-2  sr-1) with outlier  removal process to filter out  fires and 
other ephemeral lights. The year of reference for these data is 2015. 

The  income  class  (IC)  reported  in  the  UCDB  it is  derived  from  the  country-level income 
class  structured  in  four  income  groups:    High  Income  Countries  (HIC),  Upper-middle 
Income  Countries  (UMIC),  Lower-middle  Income  Countries  (LMIC)  and  Low  Income 
Countries  (LIC).  The  classification  schema  is  according  to  the  World  Urbanization 
Prospects  2018,  (UNDESA  2018).  The  UC  entities  have  been  associated  to  the  single 
Country  by  a  spatial  join.  In  case  of  cross-Country  UCs,  the  Country  showing  the 
majority of resident people in the UC was selected for the join in the UCDB. 

The  Gross  Domestic  Product  (GDP)  is  a  key  indicator  of  the  development  status  of  an 
area as it is a measure of the monetary value of goods and services produced in a given 
period  of  time.  The  urban  centres  GDP  is  calculated  using  the  gridded  global  annual 
Gross  Domestic  Product  (Purchasing  Power  Parity)  (Kummu  et  al.,  2018),  which  is 
summed within the extent of each urban centre for the epochs 1990, 2000 and 2015. 

The  travel  time  to  country  capital  represents  the  travel  distance  to  reach  the  country 
capital urban centre from each urban centre considered, flight connections not taken into 
account. Travel time to capital city is important measure of accessibility and remoteness 
of  an  urban  centre  and  it  represents  the  distance  from  each  person  to  the  central 
administration of the country. 

24 https://www.ngdc.noaa.gov/eog/viirs/download_dnb_composites.html#NTL_2015 

65 

 
 
 
 
 
                                           
5.2.1  Built-up Areas 

Status and evolution of built-up areas in Urban Centres between 1975 and 2014 

Figure 45. Percentage changes in built-up areas in urban centres between 1975 
and 2014 

Knowledge of the global spatial distribution and evolution of human settlements is one of 
the  key  requirements  for  monitoring  progress  toward  sustainable  development  of  urban 
and  rural  areas.  Human  settlements  and  built-up  areas  feature  in  several  goals  and 
targets  of  the  major  international  frameworks  agreed  in  2015  and  2016,  namely  the 
United  Nations  Sustainable  Development,  Goals  (SDGs)25,  the  Sendai  Framework  for 
Disaster  Risk  Reduction26,  the  Paris  Agreement  on  Climate  Change,  and  New  Urban 
agenda 27.  

These agreements articulate a set of indicators that require homogeneous and consistent 
information on the spatial distribution and the dynamics of human settlements. E.g. the 
indicators  on  pressure  on  natural  habitats  and  biodiversity  (SDG  Goal  15),  resilience  to 
natural  hazards  (Priority  3  of  the  Sendai  Framework  for  Disaster  Risk  Reduction),  land 
use  efficiency  and  access  to  basic  services  (SDG  Goal  11  Monitoring  Framework  –  UN-
Habitat, 2017). 

Using  Landsat  data  archives  organized  in  four  collections  (1975,  1990,  2000  and  2014) 
and advanced methods in automatic information extraction, global grids  reporting about 
the presence of built-up areas in the past 40 years  were produced in the context of the 
GHSL  project(Corbane  et  al.,  2017b).  The  results  reported  here  are  derived  from  the 
multi-temporal built-up area grids (GHS-BU) aggregated at 1 km. The built-up areas are 
assessed within the spatially delineated urban centres. 

Figure 45 shows the percentage changes in built-up areas between 1975 and 2014 for all 
urban centres: 

  Built-up areas have overall significantly increased in most of the urban centres 
  This trend is less marked in European cities and the cities located on the Eastern 
coast  of  United  States,  North  of  Latin  America,  Japan  and  North  of  China  and 
Northern Korea. 

The  percentage  change  in  built-up  areas  is  further  disaggregated  by  each  period  of  the 
GHS-BU dataset: between 1975 and 1900, between 1990 and 2000,  and between 2000 
and 2014. Figure 46 shows the probability density function for each of the three periods: 

25 (United Nations, 2015) 
26 Sendai Framework for Disaster Risk Reduction 2015-2030, March 18, 2015, 

http://www.wcdrr.org/uploads/Sendai_Framework_for_Disaster_Risk_Reduction_2015-2030.pdf. 

27 United Nations Conference on Housing and Sustainable Urban Development. 

66 

 
                                           
We  can  notice  that  the  most  significant  increase  in  built-up  areas  occurred  in  the  first 
period  1975-1990,  followed  by  the  period  1990-2000.  In  the  last  period  2000-2014, 
through  built-up  areas  continued  to  expand,  the  rate  of  increase  has  significantly 
dropped in comparison to the first period. 

Figure 46. Percentage change in built-up areas per each period of the GHS-BU 
multi-temporal dataset 

In Figure 47, we can see a further breakdown of the percentage changes per continent. 
The results show that: 

  The most significant increase in built-up areas occurred in Asia followed by Africa 

over the three time periods.   

  The  cities  located  in  Latin  America  and  the  Caribbean  witnessed  a  significant 

expansion in built-up areas between 1990-2000 

  Urban  centres  in  Europe  have  the  lowest  rate  of  increase  for  all  three  epochs  at 

just 4.6% in the period 2000-2014. 

Figure 47. Percentage change in built-up areas per each period of the GHS-BU 
multi-temporal dataset 

67 

 
 
 
 
 
5.2.2  Population 

Status and evolution of population of urban centres between 1975 and 2015 

Figure 48. Percentage changes in population in urban centres between 1975 
and 2015 

Population  is  one  key  variable  for  characterizing  cities  and  urban  centres,  respect  to  its 
size,  density,  and  temporal  changes.  Population  distribution  grids  for  each  epoch,  as 
mapped by GHSL (GHS-POP), were aggregated to the current urban centre extent. 

Figure  48  shows  the  percentage  changes  in  population  between  1975  and  2015  for  all 
urban centres considered: 

  While overall population in urban centres has grown by about  92% in the period, 

it decreased in about 9% of cities. 

  Cities  that  have  decrease  in  population  are  mostly  concentrated  in  Europe 

(including Russia), China, and Japan. 

  Population has more than doubled in 46% of cities. 

The  rate  of  growth  of  population  in  cities  is  a  strong  indicator  of  urban  vitality.  Growth 
rates  averaged  by  year  help  to  standardize  measure  accounting  for  different  length  of 
epochs  analysed.  Figure  49  shows  for  each  continent  the  differing  average  change  of 
population (in percent) per year in the epochs considered:  

  Population  change  rates  are  positive  in  all  continents  and  have  been  generally 

decreasing from 1975 to 2015.  

  Despite a general decrease in growth rates, these have increased slightly between 

2000-2015 in Africa, Europe, and Oceania.  

Cities in Africa have been displaying the highest growth rates in all epochs (at or above 
3%), while Europe displays the lowest, and followed by Northern America.  

The  concentration  of  people  within  cities,  measured  through  their  population  density, 
reveals  different  underlying  policies,  settlement  patterns,  and  living  conditions.  Urban 
centres classified by their overall population density are shown in Figure 50: 

  The classification of cities according to their population density reveals some clear 

regional patterns. 

  Cities of Northern America and parts of  Oceania have lower population densities, 
while  the  highest  population  densities  occur  in  Africa  and  in  the  India  sub-
continent.    

  141 cities display very high population densities, above 20,000 people/km2. 

68 

 
 
Figure 49. Annual average percent change of population in urban centres, by 
continent 

Figure 50. Population density of urban centres in 2015 

Figure 51 © Adobe Stock, 2018 

69 

 
 
 
 
 
5.2.3  Night Time Lights 

More  than  half  of  people  leaving  in  urban  centres  have  less  night-time  light  than  the 
global average 

Figure 52 Total 2015 Night-time light emitted in urban centres 

Night-time light (NL) represents the average illumination of an area as the emitted cloud-
free  average  radiance  (expressed  as  nW  cm-2  sr-1)  produced  by  the  Earth  Observation 
Group  (EOG)  at  NOAA/NCEI  as  a  global  composite  from  the  VIIRS  (Visible  Infrared 
Imaging Radiometer Suite) satellite sensor. At present, the global NL is produced for the 
reference  year  2015.  In  the  UCDB,  we  collected  NL  data  for  each  urban  centre  as  the 
total  radiation  emitted,  the  average  radiation  and  its  standard  deviation  within  each 
urban centre boundary. 
Presence  and  intensity  of  NL  has  been  observed  and  utilized  in  several  studies  as  an 
indicator of human economic activity (Sutton et al., 2007; Sutton and Costanza, 2002), 
associated  with  human  well-being  or  deprivation(Ghosh  et  al.,  2013),  with  insight  of 
informal economy (Ghosh et al., 2009) and inequality in human development (Elvidge et 
al., 2009). 
Figure  52  shows  the  radiation  emitted  by  each  urban  centre  considered.  Almost  3000 
urban  centres  are  in  the  very  low  illuminated  class:  52%  of  those  are  located  in  Africa 
and 46% are located in Asia. In the top 100 most illuminated urban centres 36% are in 
North  America;  34%  are  in  Asia;  14%  in  Europe  and  9%  in  Latin  America  and  the 
Caribbean. The top  five most illuminated urban centres are located in the United States 
(Los Angeles; Chicago; New York; Houston; Dallas).  
Figure  53  (right)  shows  for  each  major  region  of  the  globe  the  local  share  of  urban 
centres  with  an  average  illumination  below  the  global  average  (grey)  and  below  half  of 
the  global  average  (black).  Asia,  Africa  and  Oceania  have  more  than  75%  of  urban 
centres with an average illumination below the global average. In Africa and Asia most of 
them,  69%  and  70%  of  their  urban  centres  respectively,  are  even  below  half  of  the 
global average. Figure 53 (left) shows how Asia, Africa and Oceania have also more than 
75% of their urban population living in low illuminated areas: in Africa 77% (about 330 
million  people);  in  Asia  76%  (about  1.4  billion  people)  and  in  Oceania  96%  (about  15 
million people). When the threshold is set to half the global average the urban population 
shares  of  Europe,  Latina  America  and  Caribbean,  North  America  and  Oceania  fall  below 
10% with North America even below 1%; Africa has the highest share (58%) with almost 
250 million people in such conditions and Asia 47% (about 860 million people). 

70 

 
Figure  53  Regional  shares  of  urban  centres  and  population  in  low  illuminated 
areas 

Key Messages 

  52%  of  urban  centres  in  the  very  low  illuminated  class  are  located  in  Africa  and 

 

46% in Asia; 
In  the  top  100  most  illuminated  urban  centres  36%  are  in  North  America;  34% 
are in Asia; 14% in Europe and 9% in Latin America and the Caribbean; 

  Asia,  Africa  and  Oceania  have  more  than  75%  of  their  urban  population  living 

 

below the global average illumination value; 
In Africa 58% of urban population lives below half the global average illumination 
value; in Asia this value is 47%. 

Figure 54 © Adobe Stock, 2018 

71 

 
 
 
 
 
5.2.4  Income 

Disparities between cities across income groups 

This  section  groups  the  urban  centres  according  to  the  income  group  of  the  country 
where the centre is located. Territories are classified in four income groups, High Income 
Countries  (HIC),  Upper-middle  Income  Countries  (UMIC),  Lower-middle  Income 
Countries  (LMIC)  and  Low  Income  Countries  (LIC),  according  to  the  World  Urbanization 
Prospects 2018 (UNDESA 2018). 

More than 75% of the global population in urban centres in 2015 lives in LMIC and UMIC 
(1.5  and  1.1  billion  people  respectively).  The  Urban  centres  in  HIC  are  responsible  for 
40% of the global built-up areas of urban centres (circa 130 thousand km2) despite the 
fact  they  host  only  16%  of  the  global  urban  centres  population  (about  580  million 
people). Urban centres in HIC have in 2015 an average areal extent and population size 
greater than the one of all other income groups (average size of 150 km2 and population 
of  400  thousand  people).  Urban  centres  in  LIC  are  on  average  the  smallest  in  area  (20 
km2) and population size (200 thousand inhabitants). While urban centres in HIC tend to 
be  bigger  on  average  in  areal  extent  and  population  size,  urban  centres  in  UMIC  and 
LMIC are more numerous (more than 6300 and 3800 respectively), and their size is quite 
large  (more  than  50  km2  and  60  km2  respectively).  Variable  is  also  the  average 
population density in urban centres. It ranges in 2015 between 2.5 thousand inhabitants 
per km2 in HIC, to 10 thousand inhabitants per km2 in LIC. 

Figure 55 © Adobe Stock, 2018 

The  changes  in  population  and  in  the  extent  of  built-up  areas  between  1990  and  2015 
are diversified across income groups.  Figure 56 highlights this differentiation and shows 
the  built-up  areas  and  population  of  urban  centres  in  1975,  1990,  2000  and  2015.  It 
shows  that  the  ratio  between  new  inhabitants  and  new  built-up  areas  in  urban  centres 
increases  with  the  income  group  tier.  While  in  HIC  this  ratio  corresponds  to  195  m2  of 
built-up areas per inhabitant, this value is less than half in UMIC (about 90 m2), it is 1/3 
of  this  latter  one  in  LMC  (circa  30  m2),  and  it  is  half  of  it  in  LIC  (15  m2).  Accordingly, 
while  population  of  urban  centres  in  LIC  more  than  doubles  between  1990  and  2015 

72 

 
(+140  million  people),  and  built-up  areas  expand  by  40%  (+2,000  km2)  the  chart 
displays  a  rather  vertical  trajectory.  Lines  tend  to  be  more  flat  towards  higher  income 
group tiers. In HIC, population in 2015 is 21% higher than the one in 1990 (+100 million 
people) and built-up areas expanded by 18% (+20 thousand km2). The highest absolute 
population  change  and  built-up  areas  expansion  of  urban  centres  takes  place  in  LMIC  
where centres settle 500 million more people in 2015 (+50%), and account 17 thousand 
km2 (40%) more built-up area compared to 1990. 

Figure  56  multi-temporal  trajectories  of  built-up  areas  and  population 
development by income group 

Key Messages 

  Urban  centres  in  Low  Income  Countries  have  doubled  in  population  in  25  years, 
are  on  average  the  smallest  (20  km2),  the  most  densely  populated  (with  10,000 
inhabitants  per  km2)  and  have  the  lowest  built-up  areas  per  person  –
approximately 15 m2 per person for each new inhabitant between 1990 and 2015. 
  Urban  centres  in  Lower  Middle  Income  Countries  are  the  ones  that  concentrated 
the  largest  share  of  population  growth  (+500  million  people),  while  concerning 
built-up areas this has occurred in Upper Middle Income Countries (+17 thousand 
km2). 

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5.2.5  GDP 

Almost  half  of  African  urban  centres  have  a  very  low  GDP  while  most  of  Northern 
American urban centres have very high GDP 

Figure 57 Total Gross Domestic Product of urban centres in 2015 

Gross Domestic Product (GDP) is a key indicator of the development status of an area as 
it is a measure of the monetary value of goods and services produced in a given period of 
time. 
The  urban  centres  GDP  is  calculated  using  the  gridded  global  annual  Gross  Domestic 
Product  (Purchasing  Power  Parity)  (Kummu  et  al.,  2018)  summed  within  the  extent  of 
each  urban  centre  for  the  epochs  1990,  2000  and  2015.  Given  the  input  dataset,  it 
represents the constant 2011 international US dollars of the total values produced in an 
urban centre in each epoch. 
Figure 57 shows the 2015 GDP of each urban centre considered, grouped in five classes 
(quantiles). Most  of the urban centres in the very low  GDP class  are in Asia and Africa: 
45%  are  in  Asia  (16%  of  Asian  urban  centres);  41%  are  in  Africa  (48%  of  all  African 
cities);  10%  are  in  Latin  America  and  the  Caribbean  (18%  of  Latin  America  and  the 
Caribbean)  and  the  rest  (less  than  5%)  is  in  Europe  and  Oceania.  Half  of  the  urban 
centres in the very high GDP class are in Asia; 18% in Europe (30% of European urban 
centres);  14%  in  Northern  America  (66%  of  Northern  America);  12%  in  Latin  America 
and  the  Caribbean  (21%  of  Latin  America  and  the  Caribbean)  and  the  rest  in  Oceania 
(almost half of Oceanian urban centres). 
The top 10 urban centres with the highest 2015 GDP (ranging between 372 billion dollars 
and  1008  billion  dollars)  are,  in  decreasing  order:  Tokyo;  New  York;  Los  Angeles; 
Shanghai;  Bangkok;  Seoul;  Jakarta;  Singapore;  Osaka;  Sao  Paulo.  Most  of  them  are  in 
Asia  (70%);  two  of  them  in  Northern  America  and  one  in  Latin  America  and  the 
Caribbean. Figure 58 (left) shows the built-up area, population and 2015 GDP of these 10 
urban centres: Los Angeles is the biggest urban centre in terms of built-up area followed 
by New York, while Jakarta has the largest population. Tokyo, the urban centre with the 
highest  GDP,  ranks  third  in  built-up  surface  and  second  in  population  size.  Figure  58 
(right) shows the relative evolution of population and GDP in the period 1990-2015. The 
most  dynamic  urban  centres  were:  Shanghai,  with  an  almost  9-times  increase  in  GDP 
and  135%  increase  in  population;  Bangkok,  with  the  highest  increase  in  population 
(141%) and similar increase in GDP (176%); Jakarta, with a 3-times increase in GDP and 
77%  increase  in  population;  and  Singapore,  241%  increase  in  GDP  and  almost  doubled 
population.  As  predicted  in  some  studies  (Bettencourt  and  West,  2010;  Bettencourt, 
2013),  GDP  grows  faster  than  population  in  these  urban  centres.  In  some  cases  (New 
York, Osaka, Seoul, and Shanghai), the ratio of GDP increase and population variation is 
around seven.  

74 

 
Figure 58 Top 10 urban centres by 2015 GDP. Population, Built-up area and GDP 
(circle size) of 2015; grey lines represents the average urban centres 
population and built-up area; percentage population variation and GDP variation 
between 1990 and 2015 (right). 

Key Messages 

In 2015, 45% of African urban centres are in the very low GDP class; 

 
  66% of Northern American urban centres are in the very high GDP class in 2015; 
  The  top  10  urban  centres  with  the  highest  2015  GDP  (372  -  1008  billion  USD) 
are:  Tokyo;  New  York;  Los  Angeles;  Shanghai;  Bangkok;  Seoul;  Jakarta; 
Singapore; Osaka; Sao Paulo; 
In  New  York,  Osaka,  Seoul,  and  Shanghai  GDP  has  increased  seven  times  more 
than population in the period 1990-2015.  

 

75 

 
 
 
 
 
 
5.2.6  Travel Time to Capital 

Measuring the by-land travel time to reach the country capital urban centre 

Figure 59 Travel time to country capital for all urban centres 

The  travel  time  to  country  capital  represents  the  travel  distance  to  reach  the  country 
capital urban centre from each urban centre considered. It is calculated by looking for the 
shortest path between each urban centre boundary and the country capital urban centre 
boundary  according  to  the  accessibility  of  the  area  between  them.  The  accessibility  is 
evaluated using the impedance layer(Weiss et al., 2018) that allows the calculation of the 
time  needed  to  cross  each  areas.  Therefore,  flight  connections  are  not  taken  into 
account. Travel time to capital city is important measure of accessibility and remoteness 
of  a  urban  centre  and  it  represents  the  distance  from  each  person  to  the  central 
administration of the country. 

Figure 59 shows the global view of urban centres associated with their travel time to the 
capital. Many national patterns are easily recognizable as for Japan, United States, China 
and  India.  The  possible  patterns  are  due  to  morphological  reasons  such  as  in  Japan, 
where the northern area is closer to Tokyo than the more mountainous southern part; or 
in US and China where the travel time gradually increases from the capital urban centres 
(Washington  and  Beijing,  respectively)  and  the  surrounding  plain  area.  In  India  the 
visible  pattern  is  due  to  the  impedance  that  is  very  high  all  over  the  country  as  a 
consequence of the level of connections among urban centres, with the only exception of 
the main road between New Delhi and Kolkata. Urban centres close to this road are the 
only below 2 days of travelling to reach their country capital. 

At global level, only 1335 urban centres (15%) are  closer than 2 hours to their country 
capital;  and  4492  (52%)  are  below  half  day  of  travel.  More  than  2000  urban  centres 
(25%) are more than a day of travel away from the country capital. Figure 60 shows the 
population  living  in  urban  centre  at  given  travel  time  from  their  country  capital.  More 
than 300 million people (12%) are closer than 2 hours to the country capital while 665 
million  are  farer  than  one  day  of  travel  from  the  country  capital.  Europe  is  the  region 
with the highest share of population (68%, about 130 million people) living in less than 
12  hours  from  the  country  capital  followed  by  Latin  America  and  the  Caribbean  (64%; 
170  million  people)  and  Africa  (63%,  190  million  people).  North  America,  is  the  region 
with the highest share of population (44%, 80 million people) living more than a day far 
from the country capital, followed by Oceania (40%, 3.4 million people) and Asia (30%, 
about 450 million people).  

76 

 
 
Figure  60  Total  population  distribution  per  class  of  travel  time  to  country 
capital. Shares represent the abundance of urban centres for each class. 

Key Messages 

  1335 urban centres (15%) are closer than 2 hours to their country capital; 
  more  than  2000  urban  centres  (25%)  are  more  than  a  day  of  travel  away  from 

the country capital; 

  more  than  300  million  people  (12%)  are  closer  than  2  hours  to  the  country 

capital; 

  665 million are farer than one day of travel from the country capital. 

Figure 61 © Adobe Stock, 2018 

77 

 
 
 
 
 
 
 
PM2.5 CONCENTRATION 

of Urban Centres 

78 

 
5.3  Environment 

Urban centres comprise less than 1% of the Earth's surface, but there is an extraordinary 
concentration  of  population,  industry  and  energy  use,  leading  often  to  massive  local 
pollution  and  environmental  degradation.  Urban  environmental  problems  are  mostly 
related to pollution of soil, water and air through traffic, industrial production, inadequate 
wastewater/solid  waste  management.  It  leads  to  loss  of  green  and  natural  spaces,  and 
urban  sprawl.  All  these  problems  are  particularly  serious  in  developing  countries  and 
countries  with  economic  transition,  where  there  is  often  a  conflict  between  the  short-
term economic plan and the protection of the environment. 
Cities  consume  much  of  the  world’s  energy  and  account  significantly  to  global  CO2 
emissions. In particular, in developing countries, cities are faced with the worst urban air 
pollution  in  the  world,  which  occurs  as  a  result  of  rapid  industrialization  and  increased 
motorized traffic.  

This section provides information about the environmental status of the urban centres in 
the  world.  Many  of  the  above-mentioned  environmental  issues  are  only  collected  at  the 
local  (city)  level  and  are  not  available in  a  globally  harmonized  manner.  Therefore,  this 
section focusses on two parameters that can be derived from global data sets, the urban 
green  and  the  PM2.5  concentration.  Both  variables  are  linked  to  the  SDG’s  (indicator 
11.7.1 for the urban green and 11.6.2 for the air pollution). 

Figure 62 © Adobe Stock, 2018 

79 

 
5.3.1  Greenness 

Multi-temporal analysis of greenness 

Figure 63 Spatial distribution of changes in greenness values in the built-up 
areas of the urban centres in the period 1990–2015 (no data is shown in grey) 

The  presence  of  green  spaces  within  urban  centres  has  been  recognized  as  an 
essential component of the urban environment (Lee et al., 2015). Green spaces in cities 
are  mostly  composed  of  semi-natural  vegetation  cover,  e.g.  street  trees,  lawns,  parks, 
gardens, forests, green roofs (Gan et al., 2014). Improving availability of green spaces in 
cities  is  considered  in  the  United  Nations  Sustainable  Development  Goals  (SDG), 
specifically  in  target  11.7,  which  aims  to  achieve  the  following:  ‘By  2030,  provide 
universal access to safe, inclusive and accessible, green and public spaces, in particular 
for  women  and  children,  older  persons  and  persons  with  disabilities’  (United  Nations, 
2015).  

The  data  on  green  spaces  (also  called  greenness)  within  urban  centres  has  been 
produced by analysing Landsat annual Top-of-Atmosphere (TOA) reflectance composites 
available  as  collections  in  the  Google  Earth  Engine  (GEE)  platform  for  the  period  1990-
2015. These composites are created by considering the highest value of the Normalized 
Difference Vegetation Index (NDVI) as the composite value (i.e. greenest pixel).  
Two types of analysis are presented here: 

1)  Changes in the amount of greenness within cities are investigated in the periods 
centred  on  1990,  2000  and  2015  by  estimating  it  within  the  respective  built-up 
areas.  Given  our  interest  in  analysing  changes  in  greenness  in  urban  centres  in 
relation  to  urbanization  in  the  period  1990–2015,  for  each  of  the  10,323  urban 
centres, the average of all greenest pixels  located within the built-up area of the 
urban  centre  was  calculated  for  three  time  intervals  centred  on  the  years  1990, 
2000 and 2015 as follows: for 1990, the interval spans from 1988-01-01 to 1991-
12-30;  for  2000,  the  interval  spans  from  1999-01-01  to  2002-12-30;  for  2015, 
the interval spans from 2012-01-01 to 2015-12-30. The intervals were chosen in 
order to match the dates of the Landsat data collections used to derive the multi-
temporal  built-up  areas  (GHS-BU)  and  to  mitigate  inter-annual  variability  and 
seasonal  anomalies  that  may  affect  the  greenness  change  analysis.  The  detailed 
methodology on multitemporal assessment of greenness with the built-up areas is 
described in Corbane et al., 2018b.  

2)  The  continuous  greenness  values  per  each  epoch  calculated  following  the 
methodology described in point 1, are classified into three classes as follows: 

80 

 
 
 
  Low  green  for  greenness  <  0.1  :  corresponding  to    barren  rock,  sand  or 

snow or impervious surfaces (e.g. built-up areas) 

  Medium  green  for  0.2  <Greenness  <0.5:  corresponding  to  shrubs  or 

agriculture 

  High  green  for  0.6  <Greenness  <  0.9:  corresponding  to  dense  vegetation 

(e.g. forest, private gardens, etc.). 

The areas of  each  of the three  classes were estimated for each  epoch within the 
boundaries delimited by the urban centres. 

Figure 64 Classified greenness values for the urban centre of Milwaukee (United 
States) for the period 199 and 2015. 

Figure  63  provides  an  overview  of  the  spatial  distribution  of  greening  and  ‘browning’ 
world  urban  centres  for  the  period  1990–2014  according  to  the  results  of  the  analysis 
presented in point 1: 

  The map depicts a concentration of decreasing greenness in the urban centres 

 

of Europe, India and Nigeria. 
In  China,  we  observe  contrasting  situations  with  increasing  and  decreasing 
greenness  in  urban  centres  with  evidence  of  decreasing  green  in  the  coastal 
cities. 

When  observed  at  a  global  scale,  changes  in  greenness  can  be  eventually  explained  by 
changes  in  CO2  concentrations  and  climate  factors.  Human  influences  are  more  difficult 
to  assess  at  this  scale.  As  suggested  by  Schut  et  al.,  2015  on  a  global  scale,  land  use 
change is not expected to affect greenness as strongly as the changes in climate or CO2. 
Figure 65 shows changes in the average share of green surfaces relative to the areas of 
the  urban  centres  in  the  periods  1990,  2000  and  2015.  The  three  classes  of  green 
surfaces are shown: 

  Between  1990  and  2015,  the  share  of  high  green  surfaces  (corresponding  to 
dense  vegetation,  such  as  forests)  has  increased.  In  2015,  the  average  share  of 
high green surfaces in urban centres is around 47%.  

  The  increase  in  high  green  surfaces  is  evidently  accompanied  with  a  decrease  in 

the areas of low green and medium green surfaces. 

To complement the results presented in Figure 65, we calculated for the period 2015 
the number of urban centres where the share of high green surfaces is greater than 
the share of low green  surfaces.  The counts per  continent are shown in  Figure 66 . 
They  confirm  that  in  the  majority  of  the  cities,  the  share  of  high  green  surfaces  is 
larger than that of low green surfaces, in particular in Asia and Europe. 

81 

 
 
 
 
Figure 65. Changes in the average share of green surfaces relative to the areas 
of the urban centres in the periods 1990, 2000 and 2015 

Figure 66. Number of urban centres per continent where the share of high green 
surfaces is greater than the share of low green surfaces in the period 2015. 

82 

 
 
 
Key Messages 

  Linked  to  the  increasing  global  temperatures  (as  in  5.1.5  –p.  56)  and  the  rapid 
urbanization,  the  analysis  of  greenness  in  urban  centres  shows  an  increase  in 
green urban areas and in the share of green surfaces.  

  These  results  should  be  analysed  in  conjunction  with  land  cover  or  land  use 

transitions to better understand the main drivers for this trend.  

  Future works should be directed towards a better understanding of the combined 
effects of urbanization and climate factors on the dynamics of greenness in urban 
centres  (e.g.  relationship  between  greenness  and  Gross  Domestic  Product, 
influence  of  physical/topographic  factors  on  greenness,  relationship  between 
greenness  and  air  temperature  and  precipitation,  and  changes  in  population  and 
built-up  areas).  This  information  may  provide  a  valuable  reference  for  urban 
planners and decision-makers to mitigate extreme urban heat island phenomena. 

Figure 67 © Adobe Stock, 2018 

83 

 
 
 
 
5.3.2  Particulate Matter (PM2.5) 

Changes in PM2.5 emissions and concentration across urban centres 

“Fine particulate matter (PM2.5) is responsible for significant negative impacts on human 
health28” (Directive 2008/50/EC of The European Parliament and Of The Council). PM2.5 
is of natural (i.e. sand and dust) or of anthropogenic source (i.e. combustion residuals), 
and  its  concentration  is  of  high  concern  especially  in  urban  agglomerations  that 
concentrate many people and develop fast (Chan and Yao 2008). 
The GHS-UCDB contains information on PM2.5 emissions (by sector, and over time 1975-
2012), and on PM2.5 concentration (2000-2005-2010-2014). 
Half of the PM2.5 emission in cities in 2015 is due to industrial sources,  such emissions 
exceed  3  million  ton/year.  Emissions  from  industrial  sources  have  increased  by  40% 
between 2000 and 2012.  

Figure 68 Urban centres multi-temporal PM2.5 emissions per income group, and 
PM2.5 concentration in 2000 and 2014 per region of the world 

These  emissions  have  mostly  increased  in  Asia  (+74%),  and  Africa  (+45%)  while  they 
have  declined  substantially  in  Europe  (-40%),  and  Northern  America  (-26%).  The 
residential sectors is responsible for 1/3 of the PM2.5 emissions but total emissions have 
increased  by  9%  since  2000.  Considerable  reductions  took  place  in  Europe  (-60%)  and 
Northern America (-35%). Similar patterns of change are observed about emissions from 
the  transport  sector.  Considering  a  classification  of  urban  centres  by  income  groups,  it 
emerges that only HIC have reduced the PM2.5 emissions (by about 40%) in the period 
1975-2012. LIC increased total emissions by 50%, led by almost doubling the ones of the 
transport sector, increasing by ¾ the ones of the residential sector and by 1/3 the ones 
from  industrial  sources.  LMIC  and  UMIC  more  than  doubled  the  emissions  of  the 
transport  sector,  while  UMIC  have  by  far  increased  the  most  the  emissions  of  their 
industrial sectors in cities (+80%). 
Regarding  PM2.5  concentration,  globally  it  has  constantly  increased  across  the  epochs 
2000-2005-2010-2014. Regional changes return a pattern of increase in Africa and Asia, 
and  of  decrease  in  the  other  major  regions  of  the  world.  Across  income  groups,  PM2.5 
concentration in 2014 has moderately increased in UMIC (+6%), LMIC (+11%) and LIC 
(+16%) and decreased by 6% in HIC compared to concentrations in 2000. 

28 PM2,5’ shall mean particulate matter which passes through a size-selective inlet as defined in the reference 
method for the sampling and measurement of PM2,5, EN 14907, with a 50 % efficiency cut-off at 2,5 μm 
aerodynamic diameter; 

84 

 
 
                                           
5.3.3  Carbon Dioxide (CO2) 

CO2 emissions across urban centres 

Figure  69  Urban  centres  classified  by  annual  CO2  emission  per  inhabitant 
(ton/year/person) in 2012. 

Carbon dioxide (CO2) is the primary greenhouse gas contributing to global warming. It is 
naturally present in the atmosphere as part of the Earth's carbon cycle. However, human 
activities are adding more CO2 to the atmosphere. In addition, human activities influence 
the  ability  of  natural  sinks,  like  forests  and  soils,  to  remove  CO2 from  the  atmosphere. 
While  CO2 emissions  come  from  a  variety  of  natural  sources,  human-related  emissions 
are responsible for the increase that has occurred in the atmosphere since the industrial 
revolution.  The  industrial  activities  have  raised  atmospheric  carbon  dioxide  levels  from 
280 parts per million to 400 parts per million in the last 150 years (IPCC, 2014). 
The data on CO2 emission for the urban centres have been extracted from the Emissions 
Database  for  Global  Atmospheric  Research  (EDGAR  v4.3.2)  that  compiles  gaseous  and 
particulate air pollutant emissions for the years 1970 to 2012 (Crippa et al., 2018). The 
calculation  of  the  emissions  includes  all  human  activities,  except  large  scale  biomass 
burning  and  land  use,  land-use  change,  and  forestry.  The  results  are  comparable 
between  countries  thanks  to  the  bottom-up  compilation  methodology  of  sector-specific 
emissions  applied  consistently  for  all  world  countries.  The  sectors  definition  uses  the 
following IPCC 1996 codes: energy - Power Industry (IPCC 1A1a), residential: Energy for 
buildings  (IPCC  1A4),  waste  (IPCC  6),  industry:  Oil  refineries  and  Transformation 
industry (IPCC 1A1b, 1A1c), Combustion for manufacturing (IPCC 1A2), Fuel exploitation 
(IPCC 1B), Industrial Processes (IPCC 2), Solvents and products use (IPCC 3), transport - 
Transport (IPCC 1A3), and agriculture: Agriculture (IPCC 4). 

The  global  annual  emissions  of  urban  centres  have  increased  steadily  since  the  epoch 
1975  with  the  strongest  increase  of  33%  between  2000  and  2012  (Table  8).  Although 
industrial  production  is  still  the  biggest  global  source  of  CO2  emissions  from  urban 
centres, the energy sector is increasing its share. 

The  regional  contribution  of  cities  and  their  inhabitants  to  the  global  CO2  emissions  is 
very  diverse.  Figure  69  Urban  centres  classified  by  annual  CO2  emission  per  inhabitant 
(ton/year/person)  in  2012.  Figure  69  maps  CO2  emission  per  inhabitant  of  the  urban 
centres (calculated as total emission of urban centre divided by total population of urban 
centre).  In  most  urban  centres  of  the  Global  South,  the  respective  population  produces 
less  than  one  ton  CO2  per  year  in  2012,  while  the  urban  centres  of  more  developed 
countries  including  China  are  emitting  between  one  and  ten  (and  often  more)  tons  per 
inhabitant in a year. In addition, there are large differences in the temporal evolution of 
the CO2 emissions in the various regions (Figure 70). For the epoch 1975, the industrial 
sectors of Europe, North America, and Asia contributed more or less equally to the global 
emissions of CO2. However, with the development of the Asian, and in particular Chinese, 

85 

 
industrial  production  in  the  following  epochs,  the  emissions  from  the  Asian  industrial 
sector  more  than  doubled  between  1975  and  2012.  In  addition,  emissions  from  the 
energy sector in Asian urban centres increased even faster matching in 2012 that of the 
industrial sector. The period 1975 to 2012 is also characterised by the strongest rates of 
urbanisation  in  Asia.  Therefore,  also  the  emissions  from  the  residential  and  transport 
sector in Asian cities increase. In 2012, Asian urban centres  are the largest contributors 
to global CO2  emissions compared with the urban centres of  other  regions in the world. 
The trend in Europe and Northern America is in the opposite direction; CO2 emissions in 
urban centres are stable or decreasing with the exception of the transport sector, which 
slowly but continuously increase, and the energy sector in Europe, which peaked in 1990 
and  started  to  increase  the  emissions  again  between  2000  and  2012.  Since  the  1990s 
also the emissions from the industrial sector in urban centres of Africa, Latin America and 
the Caribbean increased steadily. 

Table 8 Global annual CO2 emission of urban centres in billion tons per year and 
relative contribution of human activity sectors for nominal epochs (1975-1990-
2000-2012). 

CO2 source 

Fossil 

Bio fuels 

Total 

Sector 

Agriculture 

1975 

4,376 

0,684 

1990 

5,368 

0,675 

2000 

5,932 

1,049 

2012 

8,020 

1,300 

5,061 

6,043 

6,981 

9,319 

1975 

0.2% 

1990 

0.3% 

2000 

0.3% 

2012 

0.3% 

Residential 

24.8% 

23.0% 

21.4% 

16.7% 

Transport 

Industry 

Energy 

5.6% 

53.4% 

16.0% 

7.4% 

45.9% 

23.9% 

8.5% 

44.5% 

25.3% 

8.4% 

42.9% 

31.8% 

86 

 
Figure 70 Emission  of C02 of urban centres  split by sector for  Major Regions in 
the period 1975-2012. 

Key Messages 

  The annual emission of CO2 per person in urban centres in 2012 is much higher in 
North America, Europe, and NE Asia compared to other regions of the world.  
  The  overall  emission  of  CO2  in  cities  of  Europe  and  North  America  is  decreasing, 

while in other regions it is growing, with Asia in the first place. 

  The CO2 emission from the energy and industry sectors in Asian urban centres is 
growing  much  faster  compared  to  the  increase  in  CO2  emission  by  the  other 
sectors. 

87 

 
 
 
 
 
 
The Urban Centre’s 
The urban centres 

EXPOSURE TO EARTHQUAKES 

EXPOSURE TO EARTHQUAKES 

88 

 
 
 
5.4  Disaster Risk Reduction 

Natural  hazardous  events  –  those  that  release  high  energy  or  that  impact  human 
nutrition  and  health  –  are  part  of  the  Earth  system  processes  of  Planet  Earth. 
Atmospheric circulation may generate strong winds and hurricanes and associated storm 
surges  in  coastal  area.    High  intensity  precipitation  may  cause  flash  floods  and 
inundation.  Plate  tectonics  that  continuously  shape  the  topography  of  Planet  Earth 
generate  volcanic  eruptions  and  earthquakes;  and  tsunamis  originate  when  seismic 
shaking  occurs  in  the  proximity  of  low  lying  coastal  areas.  Droughts  the  impact  life 
supporting system of humanity and in heat waves impacts health condition directly. 

When  high-energy  events  are  released  over  or  in  the  proximity  of  populated  areas, 
disaster unfolds. Over time, these natural events may increase or decrease in magnitude 
and  frequency  due  to  astronomical  forces,  Planet  Earth  plate  tectonics  or  as  a  result  of 
human  activities  that  are  altering  the  biogeochemical  cycles  and  that  in  turn  affect  the 
climate.  These  events  are  here  to  stay  and  humanity  will  have  to  co-exist  as  it  always 
has.   The only option we have is to make sure that we know the frequency and intensity 
with  which  these  events  may  affect  a  given  place  so  that  we  may  have  the  option  to 
mitigate,  avoid  –  when  possible  by  re-locating  –  or  prepare  for.  Climate  change  may 
have also changed temperature in many cities generating hazardous living conditions. 

Cities  are  most  at  risk  because  they  concentrate  high  population  and  infrastructural 
assets  as  built-up  areas.  In  addition,  some  cities  have  developed  in  hazardous  prone 
areas  and  re-locating  them  elsewhere  is  not  a  viable  option.  In  fact,  due  to  natural 
population growth and urbanization, many cities are increasing in size and density. Risk 
of damage to hazardous events  can be reduced, but not eliminated. Risk of heat  waves 
may  be  reduced  through  adopting  cooling  technology,  a  local  solution  that  may  not  be 
affordable to everybody. Technology can be used to mitigate mostly through avoiding the 
hazards, alerting, or preparing.  

The pre-condition to reduce risk and create resilient city to natural hazards is to quantify 
the  exposure  to  hazards.    The  four  sections  below  succinctly  describe  the  results  of  the 
crossing  of  the  spatial  information  of  urban  centres  with  the  hazardous  information 
contained in four global hazards maps. The resulting  maps of exposure of urban centres 
to hazard are the best global knowledge for that given hazard at the time of writing that 
is  derived  from  open  source  data.  It  is  by  far  not  the  ultimate  hazard  information    as 
hazard  information  are  incrementally  improved  with  the  availability  of  new  knowledge 
and  also  to  take  into  account  the  changing  nature  of  the  hazard  (Ehrlich  et  al.,  2018). 
The  analysis  of  this  Atlas  associates  city  geographical  location  and  extent  with 
information on the exposure to the different hazards. 

89 

 
5.4.1  Flood 

Floods occur in urban centres across the globe 

Figure 71 Population of urban centres potentially exposed to floods in 2015 
considering a 100-year return period flood hazard map.  

Riverine Floods – hereafter referred as floods - affect urban centres across the globe. In 
fact, human settlements are often located in the proximity of rivers and in the flat fertile 
low-lying terrain that is the preferred geographical areas for humans to live in. Flooding 
is the most recurrent and damaging disaster type and most of the countries of the world 
have  experienced  damaging  floods  and  will have  to  respond  to  floods  in  the  future  also 
due to the increasing population and infrastructure in flood prone areas. 

Figure 71 shows the urban centres of the globe exposed to potential flooding when using 
a hazard flood map with return period of 100 years. That map was produced by selecting 
urban centres having at least 20000 people within that urban centre potentially exposed. 
It shows over 4000 urban centres that account to 32% of all the urban centres selected 
from the GHS-UCDB city database. The size of the circles indicates the size of population 
exposure  in  the  urban  centres  grouped  in  four  classes:  less  than  half  million,  between 
half  million  and  1  million,  between  1  and  5  million,  and  over  5  million  inhabitants.  The 
map  also  shows  that  11  of  the  12  urban  centres  with  more  than  5  million  inhabitants 
exposed  to  floods  are  located  in  Asia,  and  that  urban  centres  in  all  continents  may 
experience floods.  

The  analysis  conducted  using  the  2015  GHSL  population  datasets  shows  that  globally 
over  590  million  people  living  in  urban  centres  are  potentially  exposed  to  floods.  That 
accounts  for  19%  of  the  overall  population  living  in  urban  centres.  The  share  of  global 
population in urban centres exposed to floods has not changed over the 1990-2015 time 
period.    

Figure 72 shows the potential exposure  of population per major  region in percentage  of 
affected population. It  shows two bar charts:  the  total share  of urban centre population 
per  continent  for  1990  (a)  and  for  2015  (b).  The  population  share  refers  to  the  total 
urban centre population potentially affected by flood to the total urban centre population 
for that region. Nearly three quarters (73.4%) of the total world urban centre population 
exposed to floods in 2015, lived in Asia.  That  city population in Asia accounted for 300 
million  in  1990  and  450  million  in  2015.  In  percentage  of  the  total  urban  centre 
population  Asia  has  changed  by  few  decimal  points  from  22.9%  in  1990  to  23.2%.  In 
global terms, the change of the share per major region over time, has slightly increased 
in Africa and Asia, and decreased in the remaining continents.  The total built-up area of 
all urban centres exposed to floods accounts for 16% of the total area of urban centres. 
That percentage has remained unchanged when compared to the epochs 1990 and 2000. 

90 

 
Figure  72  Regional  share  of  the  total  population  living  in  Urban  Centres  and 
exposed to flood hazard in 1990 (a) and in 2015 (b). 

Figure  74  shows  the  number  of  urban  centres  (y-axis)  grouped  based  on  the  rate  of 
change of population in urban centres (x-axis) exposed to flood hazard in the 1990-2015 
period per continent. In Latin America, North America and in Europe the  rate of changes 
of  population  in  urban  centres  exposed  to  flood  has  mostly  decreased.  Asia  shows  that 
while  population  change  rates  exposed  to  floods  has  decreased  in  a  large  number  of 
urban  centres,  still  the  number,  where  it  has  increased,  is  significant  and  greater  than 
that  of  all  other  continents  combined.  Africa  shows  an  increase  in  the  number  of  urban 
centres exposed to floods where population change rate has significantly increased. 

Figure 73 © Adobe Stock, 2018 

91 

 
 
Figure 74 Number of urban centres by ratio of population change (1990-2015) 
potentially exposed to floods per region (Gm – global mean, Rm – regional 
mean). 

Key Messages 

  Most  of  the  people  potentially  exposed  to  floods  in  cities  live  in  Asia,  as  Asia  is 

also the most populated continent. 

  Asia  and  Africa  are  the  continents,  where  city  population  exposed  to  floods  has 

increased most over the 1990-2015 time period.  

  Eleven  out  of  twelve  of  the  urban  centres  with  over  5  million  people  potentially 

exposed to floods are located in Asia. 

  The increase in exposure to floods in Asia is largely due to the overall increase in 

population. 

92 

 
 
 
 
 
 
5.4.2  Earthquake 

One out of 10 urban centres dwellers globally is exposed to damaging earthquakes   

Figure 75 Exposure of urban centres to seismic hazard, considering MMI 
intensity equal or greater than five (GEM2019, beta version).  

Earthquake is a major devastating hazard that strikes unexpectedly and damages urban 
centres  in  seismic  prone  areas  of  the  world  (Figure  75).  The  estimation  of  exposure  in 
urban  centres  is  related  to  seismic  shaking  maps  and  information  layers.  Those  seismic 
maps – based on probabilistic assessment of the frequency and magnitude of the shaking 
intensity  -  continue  to  be  improved  with  the  accumulation  of  data  and  knowledge.  The 
seismic hazard map used to produce the urban centre exposed to hazard maps shown in 
Figure  75  was  made  available  by  the  Global  Earthquake  Model  (GEM)  initiative.  The 
seismic  map  is  the  most  up  to  date  global  seismic  hazard  layer  available  to  date  and 
assembled from regional seismic models by GEM. The version used in this Atlas does not 
contain yet the updates for South East Asia that will be available at the end of 2018.  

Figure  75  includes  the  3077  the  urban  centres  exposed  to  intensity  of  the  Mercalli 
Modified  Intensity  scale  (MMI)  of  value  5  or  greater  in  2015.  Figure  76  shows  the  10 
urban  centres  with  highest  population  exposed.  Eight  of  the  most  exposed  cities  are 
located  in  Asia,  (one,  Istanbul,  across  Europe  and  Asia  and  one,  Cairo,  in  Africa).  The 
map  shows  also  the  increase  in  population  exposed  from  1990  to  2015.  The  fastest 
population  growth  in  urban  centres  occurs  in  rapidly  growing  cities  of  low  and  medium 
income  countries  where  fertility  is  highest.  In  fact,  increase  in  population  exposure  is 
largely due to general population increase.  

Figure 78 shows the population in urban centres in the six continents, in the class 5-to 8 
of  the  MMI  scale.  Earthquake  cause  increased  damages  with  increased  class  intensity 
class  on  the  MMI  scale.    The  figures  of  exposed  urban  centres  to  earthquake  are 
subdivided per continent. Figure 78 reveals the relatively low population in urban centres 
exposed to earthquakes in Oceania, and the relatively low population in urban centres in 
Africa.  In  addition,  there  are  no  urban  centres  in  Africa  that  are  likely  to  experience 
shakes  with  seismic  intensity  7  or  8.  Europe’s  urban  centres  are  exposed  to  shaking 
intensity  corresponding  to  classes  5  and  6  and  account  for  less  than  60  million.  Asia 
remains the continent with the highest population exposed.  

Figure  78  does  not  take  into  account  Southeast  Asia,  for  which  we  have  not  updated 
seismic hazards for this Atlas. Despite that, in Asia, the two highest intensity MMI classes 
that  of  7  and  8  –  those  that  always  cause  devastation  –  account  for  over  180  million 
people that is more than the MMI class 7-8 of Oceania, North America, Africa and Europe 
combined.  Latin  America  is  the  continent  with highest  proportion  of  population in  urban 
centres  exposed  to  seismic  hazard.  That  population  is  approximately  180  million,  of 
which 65 million in the most devastating classes 7 and 8. 

93 

 
Figure  76  Top  ten  urban  centres  population  exposed  to  seismic  hazard  with 
magnitude greater than class 5 in MMI scale 

Figure 77 © Adobe Stock, 2018 

94 

 
 
Figure 78  Absolute  urban centre  population  exposed to  seismic hazard  in 2015 
by MMI seismic class and per UN Major Regions. 

Key Messages 

  Earthquake  occurrence  is  not  predicable  and  over  700  million  people  living  in 

urban centres are exposed. That is one in every 10 people globally. 

  Asia  remains  the  continent  with  the  highest  exposed  population  in  cities  for  its 
high hazard probability and the large amount of population and built –up exposed. 
  Latin  America  and  the  Caribbean  show  also  a  very  high  percentage  of  urban 

centre population exposed 

  The  combined  urban  centre  population  of  ten  of  the  largest  cities  exposed  to 

earthquakes account for more than 200 million people. 

  The  large  cities  of  the  developing  world  exposed  to  earthquakes  are  growing 

faster than those in high income countries. 

95 

 
 
 
 
 
5.4.3  Storm Surge 

Most of the people potentially exposed to storm surges live in Asia. 

Figure 79 Population of urban centres potentially exposed to storm surges in 
2015 calculated with a hazard return period of 250 years. 

Storm  surges  generated  by  the  air  pressure  of  tropical  storms  and  hurricanes  on  the 
ocean  surfaces  can  affect  a  number  of  cities  located  in  low-lying  coastlines.  Figure  79 
show the exposure of urban centres to storm surge calculated based on a hazard return 
period  of  250  years.  The  figure  is  the  result  of  the  intersection  of  the  hazard  layer  and 
the spatial extent of the urban centres. The figure shows the urban centres that include 
at least 80% share of built up area in the urban centre and at least 20 thousand people 
exposed to the hazard in 2015.  

In  2015,  the  total  urban  centre  population  of  the  world  potentially  affected  by  storm 
surges  accounted  to  330  million;  that  is  10%  of  the  overall  global  urban  centre 
population. That percentage has not changed over the decades analysed in this research 
(1990-2015).  In  2015,  the  cumulated  urban  built-up  area  exposed  to  sea  level  surge 
accounted  to  16%  of  the  total  built  up  area  in  urban  centres.  Also,  that  percentage  did 
not change over the decades analysed in this research (1990-2015). 

The  regional  analysis  by  UN  Major  Region  (Figure  80)  shows  that  Asia  is  by  far  the 
continent with the most population in urban centres potentiallyexposed to sea level surge 
in urban centres. That population accounts to less than 200 million in 1990 and grew to 
250  million in  2015.  Asia  hosts  more  population  in  urban  centres  exposed  to  sea  surge 
than all the other continents combined.  In addition to Asia, also North America and Latin 
America and Caribbean are significantly exposed. North America has less than 30 million 
people exposed and Latin America and the Caribbean has over 250 million in 2015.   

North  America,  shows  the  highest  percentage  of  population  in  urban  centres  potentially 
exposed to sea level surge. Nearly one out of five people in North America living in urban 
centres is exposed to storm surge and that percentage has slightly decreased from 20% 
in 1990 to 19.3% in 2015.  Also, Oceania shows over 20% of city’s population exposed to 
storm surge – 1 out of 5 – and that percentage slightly increases over the period under 
scrutiny. 

96 

 
 
 
Figure  80.  Population  of  urban  centres  potentially  exposed  to  storm  surge  per 
UN  Major  Region  in  1990  and  2015,  as  totals  and  as  percentage  of  overall 
population in the Region’s cities. 

Figure  81  shows  for  each  Major  Region  the  number  of  urban  centres  (y-axis)  grouped 
based  on  the  rate  of  change  of  population  in  urban  centres  (x-axis)  in  the  1990-2015 
period. The change classes range between -1 and + 2 changes. The chart shows the total 
number  of  urban  centres  exposed.  Asia  accounts  for  the  majority  of  urban  centres 
exposed.  That  number  is  larger  than  the  sum  of  all  the  urban  centre  of  the  other 
continents  combined.  The  mean  change  rate  of  exposure  in  Asia  is  slightly  below  the 
mean change  rate  of the global population exposure. Figure 81 also  shows that in Latin 
America  and  the  Caribbean  the  number  of  urban  centres,  in  which  the  exposed 
population change rate has decreased, is similar to those where exposure has increased 
over  the  1990-2015  period.  However,  a  number  of  urban  centres  show  rapidly  growing 
population exposed to storm surge. In North America, the average growth rate is below 
the  global  average,  but  the  region  has  a  wide  spread  including  some  cities  with 
population decrease in cities potentially exposed to storm surge. In Oceania, a number of 
urban centres show slight decrease in change of exposed population.  

97 

 
 
 
Figure  81  Number  of  urban  centres  exposed  to  storm  surge  grouped  by 
population  change  rate  between  1990  and  2015  (Gm  –  global  mean,  Rm  – 
regional mean). 

Key Messages 

  Storm surge may especially affect the low lying coastal cities of the tropical belt. 
 

In  Asia,  the  total  urban  centre  population  potentially  exposed  to  storm  surge 
exceeds 250 million. 

  There are more urban centres exposed to storm surge in Asia than in all the other 

continents combined. 

  One out of five urban centre dwellers in North America is exposed to storm surge. 
  Latin  America  and  the  Caribbean  show  the  second  highest  number  of  urban 

centres exposed to storm surge. 

98 

 
 
  
 
 
 
 
5.4.4  Heatwave 

The heatwave magnitude index (HWMId) observed during 1982-2010 shows an incidence 
of heat wave occurrences in urban centres 

Figure 82 Maximum heatwave magnitude (HWMId) observed during 1980-2010 

One  of  the  most  severe  effects  of  global  warming  is  the  increase  in  the  frequency  and 
intensity  of  extreme  events  such  as  heatwaves29.  The  severe,  extreme  and  exceptional 
heatwaves that occurred over the Balkans in 2007, France in 2003 or Russia in 2010 are 
associated with increased mortality and reduced labour productivity30.  

Figure  82  shows  a  global  map  of  heat  waves  in  urban  centres  based  on  the  Heatwave 
Magnitude  Index  (HWMId).    Russo  et  al.  (2015)31  designed  the  HWMId  to  take  into 
account  both  heatwave  duration  and  intensity.  The  Heatwave  Magnitude  Index  was 
successfully  applied  to  classify  observed  heatwaves  that  occurred  globally  in  the  period 
1980-201032.  

HWMId  is  defined  as  the  maximum  magnitude  of  the  heatwaves  occurring  in  a  year, 
where  a  heatwave  is  defined  as  the  periods  of  at  least  three  consecutive  days  with 
maximum temperature above the calendar 90th percentile centreed on a 31 day window 
reference period.  Yearly gridded data Heatwave Magnitude Index on  a 0.5 x 0.5 degree 
grid were used in this analysis and classified as follows: HWMId levels of 20, 40 and 80 
are  considered  as  reference  levels  for  severe,  extreme  and  exceptional  heatwaves, 
respectively.  

The  analysis  at  city  level,  allows  assessing  the  number  of  cities  in  the  world  located  in 
areas  that  suffered  from  severe  (20<HWMId<40),  extreme  (40<HWMId<80)  and 
exceptional (HWMId >80) heatwaves in the 30 years period and estimate the potentially 
affected population (Figure  83).   A total of 385 cities located in Asia and 206 in Europe 
have suffered from either severe, extreme or exceptional heatwaves.  

29 Sonia I. Seneviratne et al., “Allowable CO2 Emissions Based on Regional and Impact-Related Climate Targets,” Nature 529 (January 20, 

2016): 477, https://doi.org/10.1038/nature16542. 

30 Alessandro Dosio et al., “Extreme Heat Waves under 1.5 °C and 2 °C Global Warming,” Environmental Research Letters 13, no. 5 (May 1, 

2018): 054006, https://doi.org/10.1088/1748-9326/aab827. 

31  Simone  Russo,  Jana  Sillmann,  and  Erich  M  Fischer,  “Top  Ten  European  Heatwaves  since  1950  and  Their  Occurrence  in  the  Coming 
(December  1,  2015):  124003,  https://doi.org/10.1088/1748-

Decades,”  Environmental  Research  Letters  10,  no.  12 
9326/10/12/124003. 

32 Matteo Zampieri et al., “Global Assessment of Heat Wave Magnitudes from 1901 to 2010 and Implications for the River Discharge of the 

Alps,” Science of The Total Environment 571 (November 2016): 1330–39, https://doi.org/10.1016/j.scitotenv.2016.07.008. 

99 

 
 
                                           
  The  population  in  the  cities  affected  by  severe,  extreme  or  exceptional 

heatwaves is estimated from the 2015 population data.  

  Around 23% of the population in Europe and 7.43% of the population in Asian 

cities live in cities that suffered from heatwaves in the last 30 years.  

  Lambayeque  in  Peru  is  the  city  that  has  experienced  the  highest  number  of 
heatwaves  in  the  30  years  period  (1980-2010)  with  a  total  of  7  heatwaves 
(HWMId > 20). 

  Kota  Bharu  in  Malaysia  registered  the  second  highest  number  of  heatwaves 

with a total of 6 heatwaves. 

  Trujillo  also  located  in  Peru  has  experienced  a  total  of  5  heatwaves  in  the 

period 1980-2010 

Figure  83  Urban  centres 
in  areas  that  suffered  from  severe 
(20<HWMId<40),  extreme  (40<HWMId<80)  and  exceptional  (HWMId  >80) 
heatwaves. 

located 

Key Messages 

At the 21st Conference of the Parties in Paris (2015) governments committed to keep 
global warming to below 2C above the pre-industrial levels, with the aim of limiting it 
to  1.5C.  Some  of  the  most  severe  effects  of  global  warming  will  be  related  to  an 
increase  in  the  frequency  and  intensity  of  heatwaves.  The  analysis  of  observed 
heatwaves during the period 1980-2010 in the cities across the globe shows that: 

  Most of the cities that experiences severe, extreme or exceptional heatwaves are 

located in Asia and Europe. 

  Europe  (including  Russia  and  Balkans)  has  the  highest  share  of  population  living 
in cities that were affected by one or more heatwaves in the period 1980-2010.  
  Peru is the country in that records the highest number of occurrence of heatwaves 

during the period 1980-2010. 

100 

 
 
 
 
 
 
The Urban Centre’s 

POPULATION DENSITY IN EXPANSION AREAS 

101 

 
 
5.5  Sustainable Development Goals 

With  the  unanimous  adoption  of  the  United  Nations  (UN)  General  Assembly  resolution 
70/1 “Transforming our World: the 2030 Agenda for Sustainable Development” Member 
States  agreed  upon  a  framework  of  17  Sustainable  Development  Goals  (SDG)  to  guide 
societal  development.  The  action  plan,  building  on  the  experience  of  the  Millennium 
Development  Goals  intertwines  aspirational  goals  with  an  ambitious  monitoring 
framework  composed  of  169  targets  to  monitor  progress  made  in  meeting  the  SDGs. 
However,  the  capacity  to  monitor  such  progress  is  entangled  by  the  lack  of  data  and 
statistical  capacity  to  support  the  monitoring  framework  (UN  Statistical  Commission, 
2017).  Alternative  and  innovative  sources  of  data,  especially  derived  Earth  Observation 
(EO)  offer  significant  information,  and  especially  data  to  support  the  SDG  reporting 
(Anderson et al., 2017; Paganini and Petiteville, 2018; G. A. United Nations, 2015). 

According  to  the  United  Nations  Department  of  Economic  and  Social  Affairs,  the  human 
society  is  predominantly  urban  as  more  than  half  of  global  population  lives  in  cities 
(UNDESA,  2008).  The  relevance  of  urban  areas  is  also  recognized  in  the  2030 
Development Agenda, which devoted to urban areas a specific Goal, SDG 11 that aspires 
to  “Make  cities  and  human  settlements  inclusive,  safe,  resilient  and  sustainable”.  Many 
SDG  11  indicators  require  fine  scale  local  data  that  are  to  be  sourced  locally,  making it 
more  difficult  to  reach  adequate  data  availability  –  especially  in  countries  in  transitions 
and  data-poor  territories.  Against  this  condition,  remote  sensing  and  EO  are  capable  to 
collect information, at a large scale, at high degree of spatial resolution, repeatedly over 
time,  and  over  wide  geographical  areas  serving  multiple  applications  (Donaldson  and 
Storeygard,  2016;  Zell  et  al.,  2012),  especially  in  the  SDG  framework  (“Earth 
Observations  in  supports  of  the  2030  Agenda  for  Sustainable  Development,”  2017; 
Noort, 2017; Paganini and Petiteville, 2018), or for generic urban development indicators 
(Chrysoulakis et al., 2014). 

The  232  individual  indicators  of  the  2030  Development  Agenda  monitoring  framework 
require  local  yet  globally  consistent,  multi-temporal  data.  The  GHSL  maps  human 
settlements and produces fine scale built-up areas and population density grids.  

The  GHS-UCDB integrates core GHSL information  on built-up areas and population over 
time  with  additional  information  to  characterise  urban  centres.  The  database  can  be 
applied in support the SDG framework. In the GHS-UCDB there is a specific attribute on 
the  Land  Use  Efficiency  Indicator  SDG  11.3.1  of  the  urban  centre  between  1990  and 
2015, and proxy indicators for SDG 11.7.1 on the share of open spaces available in the 
urban centre and the share of urban centre population living in areas with high presence 
of green. SDG 11.3.1 is classified by the Inter-Agency Expert Group on SDG Indicators as 
a Tier II indicator (meaning an indicator is conceptually clear and with a methodology for 
its monitoring, but for which data are not regularly produced or available). SDG 11.7.1 is 
instead  classified  as  Tier  III  indicator,  meaning  “No  internationally  established 
methodology or standards are yet available for the indicator, but methodology/standards 
are being (or will be) developed or tested”33.  
For the estimation of the Land Use Efficiency indicator, we adopted the extent of built-up 
areas  as  the  input  data  for  land  consumption,  and  population  as  input  for  demographic 
change,  and  applied  the  internationally  agreed  methodology34.  To  complement  UN-
Habitat  reporting  on  SDG  11  at  the  High  Level  Political  Forum  in  2018  (United  Nations 
2018),  the  GHS-UCDB  offers  an  analysis  of  the  indicator  for  the  circa  10,000  urban 
centres in section 5.5.1 and the baseline data in the GHS-UCDB open data. SDG 11.7.1 
does  not  have  an  established  monitoring  framework,  yet  with  GHS-UCDB  data,  it  is 
possible  to  propose  a  characterisation  of  urban  centres  based  on  the  presence  of 
greenness  (NDVI)  and  of  open  spaces.  Section  5.5.2  presents  two  proxy  indicators  to 
estimate “Average share of the built-up area of cities that is open space”35. With remote 

33 https://unstats.un.org/sdgs/iaeg-sdgs/tier-classification/ 
34 https://unstats.un.org/sdgs/metadata/files/Metadata-11-03-01.pdf 
35 https://unstats.un.org/sdgs/metadata/files/Metadata-11-07-01.pdf 

102 

                                           
sensing  it  is  however  not  possible  to  fully  align  with  the  aspirations  of  the  indicator 
formulation,  that  adds  a  disaggregation  “for  public  use  for  all,  by  sex,  age  and  persons 
with  disabilities”.  Therefore,  the  proposed  estimates  may  be  regarded  as  proxies  to 
support the ongoing discussion for a viable method to monitor this indicator. 

Figure 84 © Adobe Stock, 2018 

103 

 
 
5.5.1  Land Use Efficiency –SDG 11.3.1 

Less than half the urban centres in the globe use land more efficiently than in the past 

Figure 85 LUE value corresponding to the dynamics of population change and 
built-up areas expansion in the period 1990-2015 for 10,000 urban centres in 
the GHSL UCDB 

Land Use  Efficiency (LUE) is the indicator internationally agreed to monitor the  “ratio  of 
land consumption growth rate to population growth rate” in the framework of SDG 1136. 
The indicator estimates the interdependence between spatial expansion of urban centres 
and demographic change that takes place in them. In the GHS-UCDB the LUE indicator is 
computed  measuring  the  expansion  of  the  built-up  areas  in  each  urban  centre  between 
1990  and  2015,  and  the  changes  in  population  counts  in  the  same  period.  GHS-UCDB 
data  offers  an  opportunity  to  support  the  SDG  11.3.1  with  a  baseline  information.  At 
present,  the  indicator  is  a  Tier  II  one  (as  a  globally  agreed  methodology  to  estimate  it 
exists, but data are not regularly produced).   

Over the last 25 years, in almost 40% of the centres the rate of demographic growth has 
been greater than the one of spatial expansion. In these centres, more than half the ones 
in Africa, Latin and Northern America, the land consumed by each new settled inhabitant 
has  been  lower  than  the  built-up  areas  per  capita  available  to  residents  in  1990.  These 
urban  centres  have  increased  their  population  density  and  the  areas  of  expansion  and 
achieved an abstract population density that is higher compared to the city that existed 
in 1990. The LUE value in these  centres is between 0 and 1 (in green in Figure 85).  In 
more than 40% of urban centres the rate of spatial expansion has been greater than that 
of population, in 20% LUE value has been between 1 and 2 (displayed in orange), and in 
¼  of  the  centres  the  spatial  expansion  occurred  at  rates  more  than  double  the  one  of 
population  growth  (in  red).  In  the  remaining  18%  of  the  sample,  the  LUE  value  is 
negative corresponding to spatial expansion and demographic decline. 

Geographical clusters of LUE can be observed: substantial concentration of negative LUE 
values  (where  population  declines  and  built-up  areas  expand)  emerges  in  central  China 
and central Europe (LUE<-1), Eastern Europe, Russia and Japan (LUE between 0 and 1). 
In  Macedonia  and  Poland  more  than  half  of  the  urban  centres  developed  with  a 
corresponding  LUE<-2,  the  same  LUE  value  is  computed  in  up  to  ¼  the  centres  in 
countries  including:  Belgium,  France,  Bosnia  and  Herzegovina,  Armenia,  Puerto  Rico, 
Slovakia, Taiwan, China, Thailand, Italy, Denmark, and Jamaica. In more than 1/3 of the 
centres  in  China,  India,  Sri  Lanka,  Ethiopia  urban  centres  expanded  in  space  at  least 
twice as fast as they have grown in population (LUE>2).  

36 https://unstats.un.org/sdgs/metadata/files/Metadata-11-03-01.pdf 

104 

 
                                           
The land consumed by each new inhabitant in urban centres is very diverse across urban 
centres.  Grouping them by income class, in more than 90% of the centres in LIC and in 
70%  in  LMIC  each  new  inhabitants  consumes  less  than  50  m2  of  land.  For  each  new 
inhabitant in 1/3 of the urban centres in HIC between 50 and 150 m2 are consumed. As 
global  average  15,800  people  are  settled  in  each  km2  of  urban  centres  expansion.  This 
global value is very diverse across  regions.  It is almost three times higher in centres in 
Africa,  where  with  each  km2  of  urban  expansion  more  than  40,000  people  are 
accommodated. In centres in Northern America and Europe each km2 of urban expansion 
would  host  less  than  approx.  3,000  people.  Making  the  example  of  the  two  America 
regions, we report about a development dynamic that the LUE indicator is not capable to 
capture.  The  LUE  is  0.9  and  0.8  respectively  in  Latin  America  and  the  Caribbean  and 
Northern  America.  By  LUE  definition,  the  value  results  into  higher  efficiency  in  urban 
centres  expansion  in  Northern  America  (LUE  close  above  0  and  below  1).  However,  the 
abstract  population  density  in  areas  of  expansion,  in  Northern  America  is  equivalent  to 
2,870 people settled per each km2 of land expansion of an urban centre. In Latin America 
and  the  Caribbean,  for  each  km2  of  land  expansion  of  an  urban  centre,  15,000  people 
were potentially settled. Indeed, the values generated through this metrics is reflected in 
the  total  land  consumption  in  the  regions,  where  for  the  almost  38  million  new  urban 
centres inhabitants in Northern America, an overall 13,200 km2 of land was consumed. In 
Latin America, urban centres have  expanded by 7,000 km2 to accommodate 105 million 
people in total. 

Figure 86 comparison of population density in areas of urban centres expansion 
between 1990 and 2015 per region of the world 

Key Messages 

  With GHS-UCDB data it is possible to estimate the Land Use indicator, potentially 

lifting it from its Tier II classification; 

  More  than  half  of  the  urban  centres  in  the  globe  use  land  less  efficiently  than  in 

the past; 

  The present internationally agreed LUE formulation is substantially subject to path 
dependency in quantifying the efficiency of new development anchoring it to past 
trajectories; 

  The  LUE  value  (dimensionless)  can  be  assisted  by  proxy  measures  like  the 
population  density  per  unit  of  spatial  expansion  that  more  closely  capture  the 
characteristics of the socio-spatial development. 

105 

 
 
5.5.2  Access to Green – SDG 11.7.1 

Proxies for Tier III indicators 

Figure 87 classification of urban centres according to the share of 2015 
population with generalized access to green areas 

5.5.2.1  Access to green spaces  

Green  spaces  have  many  functions  that  can  moderate  the  climate  change  impact  and 
help  prevent  disease  and  thus  alleviate  public  health  expenses  in  a  context  of  aging 
societies (Ngom et al., 2016). The World Health Organization (WHO) suggests that green 
spaces  with  a  minimum  size  of  one  ha  and  a  maximum  distance  of  300  m  to  people’s 
residence should be used as threshold values for accessibility (Annerstedt van den Bosch 
et  al.,  2016).  The  European  Environment    Agency  (Stanners  et  al.,  1995)  recommends 
that people should have access to green spaces within 15 min. walking distance (that is 
1.61  km  considering  an  average  walking  speed).  Despite  this  average,  access  to  urban 
green spaces is not sufficient, because the spatial distribution may result in a significant 
bias towards certain locations and hence social groups (Le Texier et al., 2018). Besides, 
many researchers now argue on the appropriate walking distance to consider for a global 
scale analysis. Until today, there is still no methodological consensus about how access to 
conceptualize and measure the provision of urban green spaces and its access.  

In  this  chapter,  access  to  green  spaces  is  measured  using  a  proxy  metric,  “generalised 
potential  access  to  green  areas”,  which  is  based  on  the  calculation  of  the  amount  of 
people  living  in  high  green  surfaces  at  the  generalisation  scale  of  the  spatial  data  used 
for  the  assessment.  If  this  space  is  actually  accessible  (i.e.  not  fenced)  for  the  public 
cannot  be  answered.  The  metric  builds  on  the  greenness  metric  derived  from  remote 
sensing Landsat imagery and described in details in section 5.3.1 (p.80) and in (Corbane 
et al., 2018b). The methodology for calculating access to green spaces in 2015 consists 
in two main steps: 

1)  The  continuous  greenness  values  extracted  form  satellite  data  for  period  2015 
(with  0  corresponding  to  low  green  spaces  and  1  corresponding  to  high  green 
spaces) are further classified into three classes as follows: 

  Low  green  for  greenness  <  0.1  :  corresponding  to    barren  rock,  sand  or 

snow or impervious surfaces (e.g. built-up areas) 

  Medium  green  for  0.2  <Greenness  <0.5:  corresponding  to  shrubs  or 

agriculture 

  High  green  for  0.6  <Greenness  <  0.9:  corresponding  to  dense  vegetation 

(e.g. forest, private gardens, etc.). 

106 

 
 
 
2)  The share of people living in High Green areas is calculated using the GHS-POP for 

2015 per each urban centre of the urban centres database. 

Figure 87 shows the estimated share of people in 2015 living in High Green areas (areas 
where  the  greenness  value  is  greater  than  0.6).  The  percentages  refer  to  the  share  of 
people  with  generalised  access  to  high  greenness  value  areas  with  respect  to  the  total 
number of people living in the urban centre. We can observe the following: 

  High concentration of cities with large share of people with access to urban green 
spaces  on  the  Eastern  coast  of  the  United  States,  Northern  Europe,  India, 
Bangladesh as well as Indonesia. 

  Cities with very low share of people with access to urban green spaces are mainly 
located  around  the  Mediterranean  sea,  in  desert  areas  (e.g.  in  Africa),  western 
coast of North America, in Latin America and China. 

  The  patterns  of  population  access  to  urban  green  spaces  may  be  related  to 
climate  zones,  patterns  in  precipitations  and  temperatures  but  also  to  the 
characteristics  of  the  urban  centres  in  terms  of  density  of  population  and  total 
area of the urban centre.  

Globally, the presence of green areas drops as the density of built-up surface exceed 
one fifth of their area. Half of the cities where less than one third of their area is built-
up have the majority of their surface characterized by high presence of green. 

Figure 88 shows some examples of the continuous greenness values in the built-up areas 
derived  from  Landsat  NDVI  composites.  The  greenness  values  are  shown  here  for  the 
time interval centred on 2014 and the six selected cities are displayed here at the same 
scale and for comparable map extents. The results reflect the variability across cities with 
each city having varying magnitudes and spatial distribution of greenness. In particular, 
the  assessment  of  greenness  within  the  built-up  areas  of  Lagos  (Nigeria)  with  a 
population  of  1.7  million  people  and  Minneapolis  (United  States  of  America)  with  about 
0.5  million  people,  highlights  strong  differences  in  the  structure  and  patterns  of  green 
vegetation  in  the  two  cities.  In  Minneapolis,  pixels  with  high  greenness  values  are 
abundant and located inside the built-up area of the urban centre. Whereas in Lagos, the 
high greenness values are observed in the fringes of the built-up area and the centre of 
the city is mostly dominated by very low greenness values. Not only there are differences 
in  the  values  of  greenness  between  the  two  cities,  but  also  the  spatial  arrangement  of 
the green spaces is quite different. While green spaces are equally spread in the built-up 
area of Minneapolis city, the pattern is more compact in Lagos.  

107 

 
 
Figure 88. Greeness values in the built-up (period 2015) derived from Landsat 
NDVI composites in the urban centres of Minneapolis, Lagos, Manila, Mumbai, 
Paris and Buenos Aires. The maps are shown at the same scale and cover the 
same extent (from Corbane et al., 2018) 

108 

 
In  Figure  89,  the  average  share  of  population  with  generalised  access  to  high  green 
surfaces  in  2015  is  disaggregated  per  continent.  It  reflects  the  differences  observed  in 
Figure 88 at the city level: 

  With  a  global  generalised  access  to  green  spaces  of  25.8%,  Northern  American 
cities  have  the  highest  share  of  population  living  in  high  green  areas  (50.6%), 
followed by Europe (36.1%) and Oceania (30.3%). 

  Latin America has the lowest average population generalised access to high green 

areas (17.8%) but also the lowest variability in the values. 

  Africa and Asia have close values of average population generalised access to high 

green areas  with 22.2% and 25% respectively. 

Figure 89. Breakdown of the average share of people exposed to high green 
spaces by continent 

5.5.2.2  Access to open spaces  

Open  spaces  can  be  of  different  nature  and  may  include  beaches,  parks,  playing  fields 
and also green spaces. The landscape of urban open spaces can range from playing fields 
to highly maintained environments to relatively  natural landscapes.  One definition holds 
that,  "As  the  counterpart  of  development,  urban  open  space  is  a  natural  and  cultural 
resource,  synonymous  with  neither  'unused  land'  nor  'park  and  recreation  areas." 
Another  is  "Open  space  is  land  and/or  water  area  with  its  surface  open  to  the  sky, 
consciously  acquired  or  publicly  regulated  to  serve  conservation  and  urban  shaping 
function in addition to providing recreational opportunities. (Myers, 1975) " In almost all 
instances, the space referred to by the term is, in fact, green space. However, there are 
examples of open space which, though not green (e.g. beaches) that are still considered 
as open spaces.  

Owing  to  the  variety  of  types  of  open  spaces,  we  measured  the  surface  of  open  spaces 
within  urban  centres  as  the  union  of  the  areas  of  non-built-up  surfaces  and  high  green 
surfaces. The principle is illustrated in Figure 90 for the city centre of Paris with the area 
of  non-built-up  surfaces  shown  in  grey  in  Figure  90-a,  the  area  of  High  Green  surfaces 
shown in green in Figure 90-b and the proxy area of open spaces shown in red in Figure 
90-c.  

109 

 
 
Figure 90. Illustration of the method used to estimate the area of open spaces 
based on areas of non-built-up surfaces and high green surfaces within the 
urban centre of Paris.   

110 

 
 
 
Figure 91. Average share of open spaces in urban centres by continent 
(reference year 2015). 

Figure  91  presents  the  average  share  of  open  spaces  in  urban  centres  estimated 
according  to  the  method  illustrated  in  Figure  90.  The  highest  shares  of  open  spaces 
(greater than 70%) are observed in African and Asian cities.  Latin America, Europe, and 
Northern  America  have  similar  shares  of  open  spaces  (around  60%).  Cities  located  in 
Oceania show the lowest share of open spaces (48%). 

Table 9.Summary table on average share of population living in areas of high 
green (%) and average share of open spaces (%) together with the average 
land area of the urban centres (km2) 

Average share of 
population living in 
areas of high green 
(%) 

Average share of 
open spaces (%) 

Average 
urban centre 
area (km2) 

Africa 

Asia 

Europe 

Latin  America  and  the 
Caribbean 

Northern America 

Oceania 

Global Average 

22.2 

25.1 

36.1 

17.8 

50.6 

30.3 

25.8 

74.4 

70.6 

60.1 

63.9 

56.8 

48.5 

68.5 

31.5 

52.8 

75.1 

55.8 

272.7 

151.7 

59.1 

111 

 
 
The summary Table 9 includes the results on the access to green and to open spaces per 
continent together with the mean areas of urban centres. It highlights the following main 
findings: 

  North American urban centres are the largest in the world in terms of land area; 
they have the highest shares of population generalised access to green spaces but 
a low average share of open spaces compared to the global average of 68.5%. 
  While African cities are the smallest in terms of land area, they have the highest 
share of open spaces that does not necessarily correspond to green spaces since 
the share of people with access to green spaces is among the lowest in the world 
(below the global average of 25.8%). 

Key Messages 

Green  spaces  and  in  general  public  open  spaces  are  vital  to  urban  communities  and 
should  be  prioritized  in  urban  policy.    Provision  and  access  to  safe  and  quality  green 
spaces and open spaces are key strategies for achieving sustainable development. While 
there  is  no  consensus  on  how  to  measure  and  report  on  the  availability  and  access  to 
open and green spaces, we propose here two metrics derived from remote sensing data 
namely,  the  greenness  and  the  built-up  layer  from  GHS-BU.  The  main  findings  can  be 
summarized in the following: 

  At  the  measured  scale  of  1km2,  Northern  American  cities  have  the  highest 
share of population living in high green areas (50.6% of urban population) but 
have paradoxically a low share of open spaces (58.8%) compared to the cities 
located in other regions of the world. 

  African cities have the lowest share of people living in high green areas (22% 
of urban population) but the highest average share of open spaces (74%) that 
may not be necessarily maintained or public spaces. 

  Different  factors  play  a  role  in  the  uneven  accessibility  to  open  and  green 
spaces: regional climate conditions, topographic factors and vegetation types. 
Urban planning policies are likely to have significant influence on urban green 
spaces as well as morphology of the cities, their population, and their built-up 
areas.  To  get  a  better  understanding  of  the  regional  disparities  in  access  to 
green  and  open  spaces,  not  only  the  aforementioned  factors  should  be 
analyzed  but  also  the  quality  of  green  spaces  and  categories  of  open  spaces 
must  be  also  taken  into  consideration  for  more  informed  decisions  and 
inclusive policies. 

112 

 
 
  
 
 
 
The Urban Centres 

ACROSS BORDERS 

113 

 
 
 
 
 
6  GHSL Applications 

This  section  aims  at  illustrating  some  applications  of  GHSL  data  for  research  and/or 
policy,  and  its  usefulness  in  expanding  knowledge  in  different  domains  and  at  different 
scales. 

6.1  Accessibility 

A  team  led  by  the  University  of  Oxford  (Weiss  et  al.,  2018)  have  used  the  universe  of 
13,840  urban  centres  (i.e.  high  density  clusters,  HDC)  as  mapped  by  GHS-SMOD 
released in 2016 to develop a map that quantifies travel time to these cities for 2015 at a 
spatial  resolution  of  approximately  one  kilometre.  This  was  done  by  integrating  ten 
global-scale surfaces that characterize factors affecting human movement rates. The aim 
was to assess inequalities in accessibility to cities in 2015. 

The  results  show  differences  in  accessibility  relative  to  wealth,  as  only  51%  of  the 
population  living  in  low-income  countries  (concentrated  in  sub-Saharan  Africa)  reside 
within an hour of a city compared to 91% of people in high-income settings. The Global 
map of travel time to cities and the friction surface can be used for any application that 
requires consideration of travel time and is available at: 

https://map.ox.ac.uk/research-project/accessibility_to_cities/ 

Reference: Weiss, D.J., Nelson, A., Gibson, H.S., Temperley, W., Peedell, S., Lieber, A., 
Hancher, M.,Poyart, E., Belchior, S., Fullman, N. and Mappin, B., 2018. A global map of 
travel  time  to  cities  to  assess  inequalities  in  accessibility  in  2015.  Nature,  553(7688), 
333. 

6.2  Estimating Carbon Footprints for 13,000 Cities 

Moral et al. (2018) have built on GHSL data (settlements and population grid, R2016 and 
R2015)  to  estimate  carbon  footprint  for  cities  and  urban  areas  by  creating  a  Gridded 
Global Model of City Footprints (GGMCF).  

Urban and rural areas from GHS-SMOD were used to distribute the total urban and rural 
footprint  on  the  basis  of  the  share  of  aggregate  purchasing  power  in  each  cell.  High 
density  clusters  (HDC)  were  used  to  estimate  the  carbon  footprints  for  each  of  the 
13,844 cities mapped by GHSL. Per-city GDP (gross domestic product) was calculated by 
applying the GHS-SMOD city boundaries to the G-Econ 4.0 global gridded model of GDP. 
The  GHS  population  grid  for  2015  at  250m  was  used  to  determine  the  aggregate 
purchasing  power  per  grid  cell,  by  multiplying  the  population  in  the  cell  by  the  mean 
purchasing power at that location. 

Results reveal that a relatively few number of urban areas account for a disproportionate 
share of the world’s carbon footprint. The emissions are concentrated in a small number 
of  cities,  with  the  highest  emitting  100  urban  areas  accounting  for  18%  of  the  global 
carbon footprint (while containing 11% of population).  

Reference:  Moran,  Daniel,  Keiichiro  Kanemoto,  Magnus  Jiborn,  Richard  Wood,  Johannes 
Többen,  and  Karen  C  Seto.  2018.  “Carbon  Footprints  of  13  000  Cities.”  Environmental 
Research Letters 13 (6): 064041. https://doi.org/10.1088/1748-9326/aac72a. 

6.3  Economic Survey of India 

The  Economic  Division  of  the  Ministry  of  Finance  of  India  has  used  data  from  GHSL, 
within the Economic Survey of India (2017). GHSL is used to look at how built-up areas 
show the evolution of human settlements across India since 1975.  

Both data on High Density Clusters from GHS-SMOD and built-up areas from GHS-BU are 
considered to map built-up areas in India and to characterize its degree of urbanization. 
The report acknowledges that GHSL data allows “to go into much greater level of spatial 

114 

detail  with  this  data  to  uncover  important  insights  for  promulgating  expeditious  public 
policy at centre, state and urban local body level”. 

Reference:  Government  of  India,  Department  of  Economic  Affairs  of  the  Ministry  of 
Finance, 2017. Economic Survey 2016-2017, vol.2, 458pp. 

6.4  Functional Urban Area 

OECD  and  JRC  have  used  GHSL  data  (settlements  and  population  grid,  R2016  and 
CR2018)  to  map  and  analyse  the  extent  of  urban  economic  agglomerations  –  or 
functional urban areas (FUAs) – globally.  

A  new  model  was  developed  and  trained  with  the  previously  existing  set  of  FUAs  in  31 
countries  based  on  commuting/official  statistics.  Building  on  the  EC-OECD  definition  of 
FUAs,  which  are  composed  of  urban  cores  with  at  least  50,000  people  surrounded  by 
interconnected  commuting  zones,  FUAs  were  mapped  for  all  13,000  urban  centres  from 
GHS-SMOD  and  the  GHS  population  grid  for  2015  was  used  both  in  the  model  and  to 
quantify results.  

This work allows assessing worldwide basic patterns of suburbanisation, for the first time. 
Results  indicate  that  around  54%  of  the  world  population  live  in  urban  agglomerations 
(FUA),  out  of  which  12%  live  in  their  commuting  zones,  and  that  both  these  shares 
increase with level of development.  

Figure 92 Population in FUAs and Commuting zones by world region, 1990-
2015. Source: Moreno-Monroy et al., 2018 

The global map of FUAs will soon be made available open and free, and can be used for 
any  application  that  requires  consideration  of  the  extent  of  functional  or  economic 
agglomeration of a city, or of a city and its commuting zone.  

Reference:  A.  Moreno-Monroy,  Schiavina  M.,  and  Veneri,  P.,  2018.  Urban  economic 
agglomerations  and  their  suburbanization  patterns.  A  global  analysis.  Journal  of  Urban 
Economics (submitted). 

115 

 
 
6.5  Sentinel 1 RGB Mosaic 

Copernicus is the European Union's Earth Observation Programme, looking at our planet 
and  its  environment  for  the  ultimate  benefit  of  all  European  citizens37.  It  offers 
information  services  based  on  satellite  Earth  Observation  and  in  situ  (non-space)  data. 
The  Programme  is  coordinated  and  managed  by  the  European  Commission.  It  is 
implemented in partnership with the Member States, the European Space Agency (ESA), 
the  European  Organisation  for  the  Exploitation  of  Meteorological  Satellites  (EUMETSAT), 
the  European  Centre  for  Medium-Range  Weather  Forecasts  (ECMWF),  EU  Agencies  and 
Mercator  Océan.  Vast  amounts  of  global  data  from  satellites  and  from  ground-based, 
airborne  and  seaborne  measurement  systems  are  being  used  to  provide  information  to 
help  service  providers,  public  authorities  and  other  international  organisations  improve 
the quality of life for the citizens of Europe. The information services provided are freely 
and openly accessible to its users.  

Sentinel-1  is  the  first  of  the  Copernicus  Programme  satellite  constellation  conducted  by 
the European Space Agency38. This space mission is composed of two satellites, Sentinel-
1A  and  Sentinel-1B,  that  carry  a  C-band  synthetic-aperture  radar  instrument  which 
provides a collection of data in all-weather, day or night. The first satellite, Sentinel-1A, 
was  launched  on  3  April  2014,  and  Sentinel-1B  was  launched  on  25  April  2016.  Both 
satellites lifted off from the same location in Kourou, French Guiana, and each on a Soyuz 
rocket. 

The  Sentinel-1  imagery  data  supports  the  automatic  recognition  of  built-up  surfaces  in 
the GHSL baseline data as accounted in the present Atlas (Corbane et al., 2017b).  

A terapixel global mosaic of Copernicus Sentinel-1 data39 at about 20m spatial resolution 
and  covering  most  the  land  mass  has  been  created  on  the  JRC  Earth  Observation  Data 
and Processing Platform (JEODPP40) in collaboration with the JRC GHSL team. The mosaic 
it  is  made  by  level-1  ground  range  detected  Sentinel-1  (A  and  B)  image  products  with 
dual  polarisation  (VV+VH  or  HH+HV),  covering  the  entire  globe  and  represented  as  a 
false colour RGB composition. The scope of this mosaic is to provide a global base layer 
for visual assessment of natural and man-made features such as built-up areas (Syrris et 
al., 2018).  

The  examples  included  in  this  section  have  been  selected  from  the  Sentinel-1  global 
mosaic produced by the JRC. The purpose of this collection is to display  – as seen from 
the  Sentinel-1  sensors  -  the  complex  human  interactions  on  the  Planet  Earth  including 
physical,  biological,  cultural,  economic  and  socio-political  factors  contributing  to  shape 
the anthroposphere. 

37 https://www.copernicus.eu/  
38 https://sentinel.esa.int/web/sentinel/missions/sentinel-1  
39 http://data.jrc.ec.europa.eu/document/jrc-bigdataeoss-s1-mosaic_2018_11_30_15_33.pdf  
40 https://cidportal.jrc.ec.europa.eu/services/webview/jeodpp/databrowser/?default=jeodppS1Mosaic2016  

116 

 
 
                                           
  Singapore 
The city state 

1°12'54.45"N 

103°52'24.18"E 

117 

 
 
Guangzhou 
China 

22°53'26.83"N 

113°33'19.41"E 

118 

 
Khulna 
Bangladesh 

22°13'33.60"N 

89°42'5.49"E 

119 

 
Algeciras, Gibraltar, 

Tangier, Ceuta 
Strait of Gibraltar  

35°54'59.16"N 

 5°33'21.57"W 

120 

 
 
North Sea 
Offshore Windfarms 

54°31'26.94"N 

8° 7'48.17"E 

121 

 
 
Mississipi 
United States 

29°17'40.97"N 

89°22'0.89"W 

122 

 
Panama 
The channel 

9° 6'46.74"N 

79°46'3.60"W 

123 

 
 
Tierra del Fuego 
Ushuaia, Argentina 

54°49'44.00"S 
68°17'28.30"W 

124 

 
Niger delta 
Nigeria 

5°30'3.94"N 
5°22'7.57"E 

125 

 
Longyarbyen 
Svalbard, Norway 

78°13'6.03"N 

15°39'4.14"E 

126 

7.0 

 
 
 
7  Conclusion 

We  live  in  a  world  of  cities.  The  urban  areas  are  today  home  to  more  than  half  of  the 
world’s  seven  billion  people  and  their  share  will increase  for  years  to  come.  Yet,  before 
this  Atlas,  it  was  impossible  to  assess  the  whole  number  of  cities  existing  in  the  planet 
Earth  in  a  harmonised,  comparable  way.  Countries  use  their  own  definition.  These 
definitions  vary  from  few  hundred  people  in  smaller,  less  urbanised  countries,  up  to 
100,000 inhabitants, as in China. The amalgamation of the spatio-temporal GHSL data on 
population  and  built-up  areas  allowed  testing  and  demonstrating  the  concept  of  the 
Degree  of  Urbanisation  developed  in  the  European  and  the  OECD  context  to  the  whole 
Planet.  This  led  to  the  voluntary  commitment  at  the  Habitat  III  conference  in  Quito  in 
October  2016  to  the  develop  a  global,  harmonised  definition  of  cities  and  settlements 
that  is  now  supported  by  mayor  international  stakeholders,  including  the  European 
Commission  (REGIO,  EUROSTAT,  JRC),  the  OECD,  the  World  Bank,  the  FAO,  and  UN-
Habitat. 

The GHSL settlement model (GHS-SMOD), porting the Degree of Urbanization concept in 
the  GHSL  data  ecosystem,  is  the  baseline  for  the  delineation  of  the  urban  centres  that 
are assessed in this Atlas. The Urban Centre Database is the first global dataset on cities 
derived  from  an  explicit  universal  definition  of  “what  is  a  city”  that  is  based  on 
measurable facts, and that is expanding considerably the universe of cities described by 
open  data  before  today.  The  database  combines  basic  information  as  city  location  and 
name  with  the  city  extent  (surface,  shape),  and  describes  each  city  with  a  set  of 
geographical,  socio-economic  and    environmental  attributes,  many  of  them  going  back 
25 or even 40 years in time. It is important to note that the urban centre extent is data-
driven and does not take administrative  borders into account  – neither at the municipal 
level nor at  country borders.  In  fact, there are a number  of cross-border cities that are 
described  as  one  unit  for  the  purpose  of  this  Atlas  (the  database  denotes  clearly  the 
countries sharing these urban centres).  

The  Atlas  of  the  Human  Planet  2018  contributes  directly  to  the  joint  voluntary 
commitment (UN  Habitat III 2016) to develop a global, people-based definition of cities 
and settlements. Such a definition is essential for the monitoring of progress in achieving 
the  goals  of  the  2030  Agenda  for  sustainable  Development;  several  of  the  indicators 
linked to this goal are highly sensitive to where the boundary is drawn around a city. This 
commitment  supports  also  the  implementation  the  New  Urban  Agenda.  For  the  Sendai 
Framework  for  Disaster  Risk  Reduction  2015-2030  (DRR),  the  findings  related  to  the 
exposure  of  cities  to  disasters  will  be  included  in  the  UN  Global  Assessment  Report  on 
Disaster  Risk  Reduction  (GAR),  which  is  the  flagship  report  of  the  United  Nations  on 
worldwide efforts to reduce disaster risk.  

The GEO (Group on Earth Observations) Human Planet Initiative supports the Atlas of the 
Human  Planet  2018.  The  initiative  is  part  of  GEO  work  programme  2017-2019  and  is 
committed  to  developing  a  new  generation  of  measurements  and  information  products 
that  provide  new  scientific  evidence  and  a  comprehensive  understanding  of  the  human 
presence  on  the  planet  and  that  can  support  global  policy  processes  with  agreed, 
actionable  and  goal-driven  metrics.  The  Urban  Centre  Database  is  a  prime  example  of 
open,  coordinated  and  sustained  data  sharing,  as  proposed  by  the  Group  on  Earth 
Observations (GEO). The Atlas and the UCDB demonstrate that the combination of open 
data  sets  can  generate  new  information  and  new  collective  understanding  of  the 
processes  shaping  our  planet  Earth.  The  new  technology  used  for  generating  the  GHSL 
products allows producing globally-consistent, yet locally-precise data for local action at 
the  city  level  now  available  for  benchmarking  and  comparison  of  best  practices  at  all 
decisional levels from local to global. The uptake of this data for multi-level reporting and 
decision-making call for a regular update of the information in an operational frame that 
could integrate the already available products in the portfolio of the European Copernicus 
Space Programme. 

127 

Nowadays,  a  considerable  amount  of  geo-spatial  data  is  accessible  in  the  public  open 
domain, and can be used to describe the urban centres of the world if they are precisely 
delineated  as  in  the  GHS-UCDB.  This  includes  on  the  one  hand  the  stable  geographic 
description  of  biome,  soil  and  climate,  river  basin,  elevation,  and  travel  time  to  the 
capital.  On  the  other  hand,  it  includes  more  dynamic  variables  like  temperature  and 
precipitation, built-up area  expansion, population dynamics, night-time lights emissions, 
income,  vegetation,  air  quality  parameters  including  particulate  matter,  carbon  dioxide 
emissions  as  well  as  disaster  risk  related  variable  like  exposure  to  flood,  earthquake, 
storm surge and heatwaves. 

Human  settlements  developed  often  along  rivers,  because  they  provide  direct  access  to 
water  for  consumption,  but  also  as  a  means  of  transport  exchanges.  In  addition,  the 
fluvial  soils  provide  fertile  ground  for  food  production.  Today,  69%  of  all  urban  centres 
are  built  on  soils  with  a  high  agricultural  suitability,  and  fluvisols  are  the  biggest  group 
therein.  

In  the  recent  past  the  population  increase  of  urban  centres  significantly  shifted  to  the 
developing world. Urban centres in Low Income Countries have doubled in population in 
25 years. Although these cities are on average the smallest in area (20 km2), the most 
densely  populated  (with  10,000  inhabitants  per  km2)  and  with  the  lowest  amount  of 
built-up  areas  per  person  –  approximately  15  m2  per  person  for  each  new  inhabitant 
between  1990  and  2015.  Many  of  them  are  in  the  tropical  areas;  one  third  of  the  city 
dwellers (32%) lives in areas that are still or were covered by rainforest. In these areas, 
the  population increased  by  47%  between  1990  and  2015  and  puts  significant  pressure 
on remaining natural rain forests. 

At  the  21st  Conference  of  the  Parties  in  Paris  (2015)  governments  committed  to  keep 
global  warming  below  2°C  above  the  pre-industrial  levels,  with  the  aim  of  limiting  it  to 
1.5°C.  However,  the  climate  measurements  for  cities  reveal  already  significant  changes 
in  the  precipitation  and  temperature  regime.  Precipitation  increased  relatively  in  North 
American and European cities; 73% and 67% of the urban centres in North America and 
in  Europe,  respectively,  are  located  in  areas  with  increasing  precipitations.  In  Asia, 
around  60%  of  the  urban  centres  are  located  in  areas  with  decreasing  precipitations; 
most of those urban centres fall in Eastern India and Bangladesh.  

Figure 93 Kigali, Rwanda. © Andreas Brink, European Commission 2018 

128 

 
Some of the most  severe  effects  of global warming will be related to  an increase in the 
frequency  and  intensity  of  heatwaves.  The  analysis  of  observed  heatwaves  during  the 
period  1980-2010  in  the  cities  across  the  globe  shows  that  most  of  the  cities  that 
experience  severe,  extreme  or  exceptional  heatwaves  are  located  in  Asia  and  Europe. 
European  continent  (including  Russia  and  Balkans)  has  the  highest  share  of  population 
living  in  cities  that  were  affected  by  one  or  more  heatwaves  in  the  period  1980-2010. 
However,  Peru  is  the  country  in  that  records  the  highest  number  of  occurrence  of 
heatwaves  during  the  period  1980-2010.  Linked  to  the  increasing  global  temperatures 
and the rapid urbanization the analysis of greenness in urban centres shows an increase 
in green urban areas and in the share of green surfaces.  

The  changing  climate  increases  also  the  risk  of  urban  centres  for  be  affected  by  floods 
and storm surges. Most of the people potentially exposed to floods live in Asian cities, as 
Asia  is  also  the  most  populated  continent.  Asia  and  Africa  are  the  continents,  where 
urban population exposed to floods has increased most over the 1990-2015 time period. 
Eleven out of twelve of the biggest urban centres with over 5 million people exposed to 
floods are located in Asia. However, the increase in exposure to floods in Asia is largely 
due  to  the  natural  increase  of  population.  Sea  level  surge  affects  the  low-lying  coastal 
cities of the tropical belt. In Asia the total urban centre population exposed to sea level 
surge  exceeds  250  million;  that  is  more  than  in  all  the  other  continents  combined. 
However,  in  North  America  one  out  of  5  urban  centre  dwellers  in  is  exposed  to  storm 
surges. 

Generated  inside  the  general  GHSL  paradigm,  the  aim  of  the  new  Urban  Centres 
Database  featured  by  this  Atlas  is  to  provide  a  first  but  significant  step  toward  new 
comprehension  of  the  complex,  multi-faceted  linkages  between  Humans  and  the  planet 
Earth. This Atlas puts the finger in the exact places where those linkages are prominently 
evident, called “cities”.  

Coherently  with  the  precedent  editions  of  the  Atlas,  the  perspective  of  this  work  is  that 
understanding  the  spatial  dimensions  of  the  human  development  will  be  the  key  to 
understand the limits and the possibilities of the forthcoming societal advances, in a finite 
planetary space development paradigm. 

129 

8  References 

Anderson, K., Ryan, B., Sonntag, W., Kavvada, A., Friedl, L., 2017. Earth observation in 
service of the 2030 Agenda for Sustainable Development. Geo-spatial Information 
Science 20, 77–96. https://doi.org/10.1080/10095020.2017.1333230 

Annerstedt van den Bosch, M., Mudu, P., Uscila, V., Barrdahl, M., Kulinkina, A., Staatsen, 
B.,  Swart,  W.,  Kruize,  H.,  Zurlyte,  I.,  Egorov,  A.I.,  2016.  Development  of  an 
urban green space indicator and the public health rationale. Scandinavian Journal 
of Public Health 44, 159–167. https://doi.org/10.1177/1403494815615444 

Bettencourt, L., West, G., 2010. A unified theory of urban living. Nature 467, 912. 

Bettencourt,  L.M.A.,  2013.  The  Origins  of  Scaling  in  Cities.  Science  340,  1438–1441. 

https://doi.org/10.1126/science.1235823 

Buettner,  T.,  2015.  Urban  Estimates  and  Projections  at  the  United  Nations:  The 
Strengths,  Weaknesses,  and  Underpinnings  of  the  World  Urbanization  Prospects. 
Spatial Demography 3, 91–108. https://doi.org/10.1007/s40980-015-0004-2 

Chan, C.K., Yao, X., 2008. Air pollution in mega cities in China. Atmospheric Environment 

42, 1–42. https://doi.org/10.1016/j.atmosenv.2007.09.003 

Chmutina,  K.,  Bosher,  L.,  2017.  Rapid  Urbanisation  and  Security:  Holistic  Approach  to 
Enhancing  Security  of  Urban  Spaces,  in:  Dover,  R.,  Dylan,  H.,  Goodman,  M.S. 
(Eds.),  The  Palgrave  Handbook  of  Security,  Risk  and  Intelligence.  Palgrave 
Macmillan UK, London, pp. 27–45. https://doi.org/10.1057/978-1-137-53675-4_2 

Chrysoulakis, N., Feigenwinter, C., Triantakonstantis, D., Penyevskiy, I., Tal, A., Parlow, 
E., Fleishman, G., Düzgün, S., Esch, T., Marconcini, M., 2014. A Conceptual List of 
Indicators  for  Urban  Planning  and  Management  Based  on  Earth  Observation. 
ISPRS 
980–1002. 
https://doi.org/10.3390/ijgi3030980 

Geo-Information 

International 

Journal 

3, 

of 

Corbane,  C.,  Kemper,  T.,  Freire,  S.,  Louvrier,  C.,  Pesaresi,  M.,  2016.  Monitoring  the 
Syrian  Humanitarian  Crisis  with  the  JRC’s  Global  Human  Settlement  Layer  and 
Night-Time Satellite Data. https://doi.org/10.2788/48956 (print),10.2788/297909 
(online) 

Corbane, C., Lemoine, G., Pesaresi, M., Kemper, T., Sabo, F., Ferri, S., Syrris, V., 2018a. 
Enhanced  automatic  detection  of  human  settlements  using  Sentinel-1 
interferometric coherence. International Journal of Remote Sensing 39, 842–853. 
https://doi.org/10.1080/01431161.2017.1392642 

Corbane, C., Martino, P., Panagiotis, P., Aneta, F.J., Michele, M., Sergio, F., Marcello, S., 
Daniele,  E.,  Gustavo,  N.,  Thomas,  K.,  2018b.  The  grey-green  divide:  multi-
temporal  analysis  of  greenness  across  10,000  urban  centres  derived  from  the 
Global Human Settlement Layer (GHSL).  International Journal of Digital Earth 1–
18. https://doi.org/10.1080/17538947.2018.1530311 

Corbane,  C.,  Pesaresi,  M.,  Politis,  P.,  Syrris,  V.,  Florczyk,  A.J.,  Soille,  P.,  Maffenini,  L., 
Burger,  A.,  Vasilev,  V.,  Rodriguez,  D.,  Sabo,  F.,  Dijkstra,  L.,  Kemper,  T.,  2017a. 
Big  earth  data  analytics  on  Sentinel-1  and  Landsat  imagery  in  support  to  global 
human 
118–144. 
https://doi.org/10.1080/20964471.2017.1397899 

settlements  mapping. 

Earth 

Data 

Big 

1, 

Corbane,  C.,  Pesaresi,  M.,  Politis,  P.,  Syrris,  V.,  Florczyk,  A.J.,  Soille,  P.,  Maffenini,  L., 
Burger,  A.,  Vasilev,  V.,  Rodriguez,  D.,  Sabo,  F.,  Dijkstra,  L.,  Kemper,  T.,  2017b. 
Big  earth  data  analytics  on  Sentinel-1  and  Landsat  imagery  in  support  to  global 
human 
118–144. 
https://doi.org/10.1080/20964471.2017.1397899 

settlements  mapping. 

Earth 

Data 

Big 

1, 

Crippa,  M.,  Guizzardi,  D.,  Muntean,  M.,  Schaaf,  E.,  Dentener,  F.,  van  Aardenne,  J.A., 
Monni, S., Doering, U., Olivier, J.G.J., Pagliari, V., Janssens-Maenhout, G., 2018. 

130 

Gridded  emissions  of  air  pollutants  for  the  period  1970–2012  within  EDGAR 
v4.3.2. Earth System Science Data 10, 1987–2013. https://doi.org/10.5194/essd-
10-1987-2018 

Dijkstra,  L.,  Poelman,  H.,  2014.  A  harmonised  definition  of  cities  and  rural  areas:  the 
new degree of urbanisation (Working Papers), Regional Working Paper 2014. 

Doherty, R.M., Hulme, M., Jones, C.G., 1999. A gridded reconstruction of land and ocean 
precipitation for the extended tropics from 1974 to 1994. International Journal of 
https://doi.org/10.1002/(SICI)1097-
Climatology 
0088(199902)19:2<119::AID-JOC358>3.0.CO;2-X 

119–142. 

19, 

Doldirina, C., 2015. Open Data and Earth Observations: The Case of Opening Up Access 
to  and  Use  of  Earth  Observation  Data  Through  the  Global  Earth  Observation 
System of Systems. J. of Intellectual Property, Inf. Tech. and E-Commerce Law 6, 
73–85. 

Donaldson, D., Storeygard, A., 2016. The View from Above: Applications of Satellite Data 
171–198. 

Perspectives 

Economic 

Journal 

30, 

of 

Economics. 

in 
https://doi.org/10.1257/jep.30.4.171 

Dosio, A., Mentaschi, L., Fischer,  E.M., Wyser,  K., 2018. Extreme heat waves under 1.5 
°C  and  2  °C  global  warming.  Environmental  Research  Letters  13,  054006. 
https://doi.org/10.1088/1748-9326/aab827 

Earth Observations in supports of the 2030 Agenda for Sustainable Development, 2017. 

Ehrlich, D., Melchiorri, M., Florczyk, A., Pesaresi, M., Kemper, T., Corbane, C., Freire, S., 
Schiavina,  M.,  Siragusa,  A.,  2018.  Remote  Sensing  Derived  Built-Up  Area  and 
Population Density to Quantify Global Exposure to Five Natural Hazards over Time. 
Remote Sensing 10, 1378. https://doi.org/10.3390/rs10091378 

Ellis,  E.C.,  2018.  Anthropocene:  A  Very  Short  Introduction.  Oxford  University  Press. 

https://doi.org/10.1093/actrade/9780198792987.001.0001 

Ellis,  E.C.,  Ramankutty,  N.,  2008.  Putting  people  in  the  map:  anthropogenic  biomes  of 
the  Environment  6,  439–447. 

in  Ecology  and 

the  world.  Frontiers 
https://doi.org/10.1890/070062 

Elvidge, C.D., Baugh, K., Zhizhin, M., Hsu, F.C., Ghosh, T., 2017. VIIRS night-time lights. 
5860–5879. 

Sensing 

Journal 

38, 

International 
https://doi.org/10.1080/01431161.2017.1342050 

Remote 

of 

Elvidge, C.D., Baugh, K.E., Anderson, S.J., Sutton, P.C., Ghosh, T., 2012. The Night Light 
Development  Index  (NLDI):  a  spatially  explicit  measure  of  human  development 
from satellite data. Social Geography 7, 23–35. 

Elvidge, C.D., Safran, J., Tuttle, B., Sutton, P., Cinzano, P., Pettit, D., Arvesen, J., Small, 
C.,  2007.  Potential  for  global  mapping  of  development  via  a  nightsat  mission. 
GeoJournal 69, 45–53. https://doi.org/10.1007/s10708-007-9104-x 

Elvidge, C.D., Sutton, P.C.,  Ghosh, T.,  Tuttle, B.T., Baugh, K.E., Bhaduri, B., Bright, E., 
2009. A global poverty map derived from satellite data. Computers & Geosciences 
35, 1652–1660. https://doi.org/10.1016/j.cageo.2009.01.009 

Elvidge,  C.D.,  Sutton,  P.C.,  Tuttle,  B.T.,  Ghosh,  T.,  Baugh,  K.E.,  2010.  Global  urban 
mapping based on nighttime lights. Global Mapping of Human Settlements, Taylor 
and Francis, London 129–144. 

Ferri,  S.,  Syrris,  V.,  Florczyk,  A.,  Scavazzon,  M.,  Halkia,  M.,  Pesaresi,  M.,  2014.  A  new 
map  of  the  European  settlements  by  automatic  classification  of  2.5m  resolution 
SPOT  data.  Presented  at  the  Geoscience  and  Remote  Sensing  Symposium 
(IGARSS), 2014 IEEE International, pp. 1160–1163. 

131 

Florczyk, A., Ehrlich, D., Corbane, C., Freire, S., Kemper, T., Melchiorri, M., Pesaresi, M., 
Politis,  P.,  Schiavina,  M.,  Zanchetta,  L.,  2018.  Community  pre-Release  of  GHS 
Data  Package  (GHS  CR2018)  in  support  to  the  GEO  Human  Planet  Initiative 
Version 1.0. 

Florczyk, A.J., Ferri, S., Syrris, V., Kemper, T., Halkia, M., Soille, P., Pesaresi, M., 2015. 
A  New  European  Settlement  Map  From  Optical  Remotely  Sensed  Data.  IEEE 
Journal  of  Selected  Topics  in  Applied  Earth  Observations  and  Remote  Sensing. 
https://doi.org/10.1109/JSTARS.2015.2485662 

Forstall,  R.L.,  Chan,  K.W.,  2015.  Urban  Places:  Statistical  Definitions,  in:  International 
Encyclopedia  of  the  Social  &  Behavioral  Sciences.  Elsevier,  pp.  854–861. 
https://doi.org/10.1016/B978-0-08-097086-8.74042-6 

Freire, S., Doxsey-Whitfield, E., MacManus, K., Mills, J., Pesaresi, M., 2016. Development 
of new open and free multi-temporal global population grids at 250 m resolution, 
in:  Proc.  of  the  19th  AGILE  Conference  on  Geographic  Information  Science. 
Helsinki, Finland. 

Freire,  S.,  Schiavina,  M.,  Florczyk,  A.,  MacManus,  K.,  Pesaresi,  M.,  Corbane,  C., 
Bokovska, O., Mills, J., Pistolesi, L., Squires, J., Sliuzas, R., 2018. Enhanced data 
and methods for improving open and free global population grids: putting ‘leaving 
no  one  behind’  into  practice.  International  Journal  of  Digital  Earth  11. 
https://doi.org/10.1080/17538947.2018.1548656 

Gan,  M.,  Deng,  J.,  Zheng,  X.,  Hong,  Y.,  Wang,  K.,  2014.  Monitoring  Urban  Greenness 
Dynamics  Using  Multiple  Endmember  Spectral  Mixture  Analysis.  PLoS  ONE  9, 
e112202. https://doi.org/10.1371/journal.pone.0112202 

Gehlke, C.E., Biehl, K., 1934. Certain Effects of Grouping Upon the Size of the Correlation 
Coefficient in Census Tract Material. Journal of the American Statistical Association 
29, 169. https://doi.org/10.2307/2277827 

Ghosh, T., Anderson, S., Elvidge, C., Sutton, P., 2013. Using Nighttime Satellite Imagery 
as  a  Proxy  Measure  of  Human  Well-Being.  Sustainability  5,  4988–5019. 
https://doi.org/10.3390/su5124988 

Ghosh, T., Anderson, S., Powell, R., Sutton, P., Elvidge, C., 2009. Estimation of Mexico’s 
Informal Economy and Remittances Using Nighttime Imagery. Remote Sensing 1, 
418–444. https://doi.org/10.3390/rs1030418 

Halkia,  S.,  Buda,  D.,  European  Commission,  Joint  Research  Centre,  Institute  for  the 
Protection and the Security of the Citizen, 2007. Information support for effective 
and rapid external action (ISFEREA) support to external security unit. Publications 
Office, Luxembourg. 

Harris, I., Jones, P.D., Osborn, T.J., Lister, D.H., 2014. Updated high-resolution grids of 
monthly  climatic  observations  -  the  CRU  TS3.10  Dataset:  UPDATED  HIGH-
RESOLUTION  GRIDS  OF  MONTHLY  CLIMATIC  OBSERVATIONS.  International 
Journal of Climatology 34, 623–642. https://doi.org/10.1002/joc.3711 

IPCC,  2014.  Climate  change  2014:  Synthesis  Report.  Contribution  of  Working  Papers  I, 
II,  and  III  to  the  Fifth  Assessment  Report  of  the  Intergovernmantal  Panel  on 
Climate Change. IPCC, Geneva. 

Jasani, B., Pesaresi, M., Schneiderbauer, S., Zeug, G. (Eds.), 2009. Remote Sensing from 

Space - Supporting International Peace and Security. 

Jones,  P.D.,  Hulme,  M.,  1996.  CALCULATING  REGIONAL  CLIMATIC  TIME  SERIES  FOR 
ILLUSTRATIONS. 
361–377. 

TEMPERATURE  AND 
International 
https://doi.org/10.1002/(SICI)1097-0088(199604)16:4<361::AID-
JOC53>3.0.CO;2-F 

PRECIPITATION:  METHODS  AND 

Climatology 

Journal 

16, 

of 

132 

Kummu,  M.,  Taka,  M.,  Guillaume,  J.H.A.,  2018.  Data  from:  Gridded  global  datasets  for 
Gross  Domestic  Product  and  Human  Development  Index  over  1990-2015. 
https://doi.org/10.5061/dryad.dk1j0 

Landsberg, H.E., 1976. WEATHER, CLIMATE AND HUMAN SETTLEMENTS (No. 448), World 

Meteorological Organization - SPECIAL ENVIRONMENTAL REPORT 7. 

Le  Texier,  M.,  Schiel,  K.,  Caruso,  G.,  2018.  The  provision  of  urban  green  space  and  its 
accessibility:  Spatial  data  effects  in  Brussels.  PLOS  ONE  13,  e0204684. 
https://doi.org/10.1371/journal.pone.0204684 

Lee, A., Jordan, H., Horsley, J., 2015. Value of urban green spaces in promoting healthy 
living  and  wellbeing:  prospects  for  planning.  Risk  Management  and  Healthcare 
Policy 131. https://doi.org/10.2147/RMHP.S61654 

Lewis  Dijkstra,  Hugo  Poelman,  2014.  A  harmonised  definition  of  cities  and  rural  areas: 
the new degree of urbanisation (Regional Working Paper 2014 No. WP 01/2014). 
European Commission, Regional and Urban Policy, Bruxelles. 

Melchiorri,  M.,  Florczyk,  A.,  Freire,  S.,  Schiavina,  M.,  Pesaresi,  M.,  Kemper,  T.,  2018. 
Unveiling  25  Years  of  Planetary  Urbanization  with  Remote  Sensing:  Perspectives 
from 
the  Global  Human  Settlement  Layer.  Remote  Sensing  10,  768. 
https://doi.org/10.3390/rs10050768 

Morice, C.P., Kennedy, J.J., Rayner, N.A., Jones, P.D., 2012. Quantifying uncertainties in 
global  and  regional  temperature  change  using  an  ensemble  of  observational 
estimates:  The  HadCRUT4  data  set:  THE  HADCRUT4  DATASET.  Journal  of 
Geophysical 
n/a-n/a. 
https://doi.org/10.1029/2011JD017187 

Atmospheres 

Research: 

117, 

Myers,  M.,  1975.  Decision  Making  in  Allocating  Metropolitan  Open  Space:  State  of  the 
Art.  Transactions  of  the  Kansas  Academy  of  Science  (1903-)  78,  149. 
https://doi.org/10.2307/3627339 

National  Institute  for  Standards  and  Technology,  2007.  Guidelines  for  Evaluating  and 

Expressing the Uncertainty of NIST Measurement Results. 

Ngom,  R.,  Gosselin,  P.,  Blais,  C.,  2016.  Reduction  of  disparities  in  access  to  green 
spaces:  Their  geographic  insertion  and  recreational  functions  matter.  Applied 
Geography 66, 35–51. https://doi.org/10.1016/j.apgeog.2015.11.008 

NOAA National Centers for Environmental Information, 2018. State of the Climate: Global 

Climate Report for Annual 2017,. 

Noort,  M.,  2017.  Earth  Observation  and  Sustainable  Development  Goals  in  the 
Netherlands  Towards  more  synergetic  use  of  Earth  Observation:  An  exploratory 
study. 

Paganini,  M.,  Petiteville,  I.,  2018.  Satellite  Earth  Observations  in  Support  of  the 
Sustainable  Development  Goals,  The  CEOS  Earth  Observation  Handbook.  CEOS  - 
ESA. 

Pesaresi, M., 2014. Global Fine-Scale Information Layers: the Need of a Paradigm Shift, 
in:  Soille,  P.,  Marchetti,  P.G.  (Eds.),  .  Presented  at  the  Conference  on  Big  Data 
from Space (BiDS’14), JRC. 

Pesaresi,  M.,  Ehrlich,  D.,  2009.  A  methodology  to  quantify  built-up  structures  from 
optical VHR imagery, in: Gamba, P., Herold, M. (Eds.), Global Mapping of Human 
Settlement Experiences, Datasets, and Prospects. CRC Press, pp. 27–58. 

Pesaresi,  M.,  Ehrlich,  D.,  Ferri,  S.,  Florczyk,  A.,  Carneiro  Freire  Sergio,  M.,  Halkia,  S., 
Julea,  A.,  Kemper,  T.,  Soille,  P.,  Syrris,  V.,  2016a.  Operating  procedure  for  the 
production  of  the  Global  Human  Settlement  Layer  from  Landsat  data  of  the 
epochs 1975, 1990, 2000, and 2014. Publications Office of the European Union. 

133 

Pesaresi,  M.,  Ehrlich,  D.,  Ferri,  S.,  Florczyk,  A.,  Freire,  S.,  Haag,  F.,  Halkia,  M.,  Julea, 
A.M.,  Kemper,  T.,  Soille,  P.,  2015a.  Global  Human  Settlement  Analysis  for 
Disaster  Risk  Reduction.  ISPRS  -  International  Archives  of  the  Photogrammetry, 
Remote  Sensing  and  Spatial 
Information  Sciences  XL-7/W3,  837–843. 
https://doi.org/10.5194/isprsarchives-XL-7-W3-837-2015 

Pesaresi,  Martino,  Ehrlich,  D.,  Ferri,  S.,  Florczyk,  A.,  Freire,  S.,  Halkia,  M.,  Julea,  A., 
Kemper,  T.,  Soille,  P.,  Syrris,  V.,  2016a.  Operating  procedure  for  the  production 
of  the  Global  Human  Settlement  Layer  from  Landsat  data  of  the  epochs  1975, 
1990,  2000,  and  2014  (JRC  Technical  Report  No.  EUR  27741  EN).  Publications 
Office of the European Union, Ispra, Italy. 

Pesaresi, M., Ehrlich, D., Kemper,  T., Siragusa, A., Florczyk, A., Freire, S., Corbane, C., 
2017.  Atlas  of  the  Human  Planet  2017,  Global  Exposure  to  Natural  Hazards. 
Publications Office of the European Union. 

Pesaresi,  M.,  Huadong,  G.,  Blaes,  X.,  Ehrlich,  D.,  Ferri,  S.,  Gueguen,  L.,  Halkia,  M., 
Kauffmann,  M.,  Kemper,  T.,  Lu,  L.,  Marin-Herrera,  M.A.,  Ouzounis,  G.K., 
Scavazzon,  M.,  Soille,  P.,  Syrris,  V.,  Zanchetta,  L.,  2013.  A  Global  Human 
Settlement Layer From Optical HR/VHR RS Data: Concept and First Results. IEEE 
Journal  of  Selected  Topics  in  Applied  Earth  Observations  and  Remote  Sensing  6, 
2102–2131. https://doi.org/10.1109/JSTARS.2013.2271445 

Pesaresi,  Martino,  Melchiorri,  M.,  Siragusa,  A.,  Kemper,  T.,  2016b.  Atlas  of  the  Human 
Planet  2016.  Mapping  Human  Presence  on  Earth  with  the  Global  Human 
Settlement Layer (No. EUR 28116 EN). Publications Office of the European Union, 
Luxembourg. 

Pesaresi,  M.,  Syrris,  V.,  Julea,  A.,  2016b.  A  New  Method  for  Earth  Observation  Data 
Analytics  Based  on  Symbolic  Machine  Learning.  Remote  Sensing  8,  399. 
https://doi.org/10.3390/rs8050399 

Pesaresi,  M.,  Syrris,  V.,  Julea,  A.,  2015b.  Benchmarking  of  the  Symbolic  Machine 
Learning classifier with state of the art image classification methods  - application 
to  remote  sensing  imagery,  EUR  27518  (JRC  Technical  Report  No.  EUR  27518), 
JRC Technical Report. Publications Office of the European Union. 

Pesaresi,  M.,  Vasileios,  S.,  Julea,  A.,  2016c.  Analyzing  big  remote  sensing  data  via 
symbolic  machine  learning., in:  Proceedings  of  the  2016  Conference  on  Big  Data 
from Space (BiDS’16). Presented at the Big Data from Space (BiDS’16), pp. 156–
159. https://doi.org/10.2788/854791 

Pesaresi,  M.E.,  2016.  Operating  procedure  for  the  production  of  the  Global  Human 

Settlement Layer from Landsat data of the epochs 1975, 1990, 2000, and 2014. 

Rubel,  F.,  Brugger,  K.,  Haslinger,  K.,  Auer,  I.,  2017.  The  climate  of  the  European  Alps: 
Shift  of  very  high  resolution  Köppen-Geiger  climate  zones  1800–2100. 
Meteorologische 
115–125. 
Zeitschrift 
https://doi.org/10.1127/metz/2016/0816 

26, 

Russo, S., Sillmann, J., Fischer, E.M., 2015. Top ten European heatwaves since 1950 and 
their  occurrence  in  the  coming  decades.  Environmental  Research  Letters  10, 
124003. https://doi.org/10.1088/1748-9326/10/12/124003 

Schmidt,  C.,  Krauth,  T.,  Wagner,  S.,  2017.  Export  of  Plastic  Debris  by  Rivers  into  the 
12246–12253. 

Technology 

51, 

& 
Sea. 
https://doi.org/10.1021/acs.est.7b02368 

Environmental 

Science 

Schut,  A.G.T.,  Ivits,  E.,  Conijn,  J.G.,  ten  Brink,  B.,  Fensholt,  R.,  2015.  Trends  in  Global 
Vegetation Activity and Climatic Drivers Indicate a Decoupled Response to Climate 
Change. PLOS ONE 10, e0138013. https://doi.org/10.1371/journal.pone.0138013 

Sendai Framework for Disaster Risk Reduction 2015-2030, 2015. 

134 

Seneviratne, S.I., Donat, M.G., Pitman, A.J., Knutti, R., Wilby, R.L., 2016. Allowable CO2 
emissions based on regional and impact-related climate targets. Nature 529, 477. 

Smith, D.A., 2017. Visualising world population density as an interactive multi-scale map 
using  the  global  human  settlement  population  layer.  Journal  of  Maps  13,  117–
123. https://doi.org/10.1080/17445647.2017.1400476 

Soille, P., Burger, A.,  De Marchi, D., Kempeneers,  P., Rodriguez, D.,  Syrris, V., Vasilev, 
V.,  2017.  A  versatile  data-intensive  computing  platform  for  information  retrieval 
Syst. 
geospatial 
from 
https://doi.org/10.1016/j.future.2017.11.007 

Comput. 

Futur. 

data. 

Gen. 

big 

Stanners, D.A., Bourdeau, P., European Environment Agency Task Force, United Nations 
the  Dobříš  assessment.  European 

(Eds.),  1995.  Europe’s  environment: 
Environment Agency, Copenhagen. 

Sutton,  P.C.,  Costanza,  R.,  2002.  Global  estimates  of  market  and  non-market  values 
derived  from  nighttime  satellite  imagery,  land  cover,  and  ecosystem  service 
valuation.  Ecological  Economics  41,  509–527.  https://doi.org/10.1016/S0921-
8009(02)00097-6 

Sutton,  P.C.,  Elvidge,  C.D.,  Ghosh,  T.,  2007.  Estimation  of  gross  domestic  product  at 
sub-national  scales  using  nighttime  satellite  imagery.  International  Journal  of 
Ecological Economics &amp; Statistics 8, 5–21. 

Syrris,  V.,  Corbane,  C.,  Pesaresi,  M.,  Soille,  P.,  2018.  Mosaicking  Copernicus  Sentinel-1 
1–1. 

Transactions 

Big  Data 

Scale. 

on 

Data 
https://doi.org/10.1109/TBDATA.2018.2846265 

at  Global 

IEEE 

UN  Statistical  Commission,  2017.  Work  of  the  Statistical  Commission  pertaining  to  the 

2030 Agenda for Sustainable Development. 

UNDESA, 2018. World Population Prospects The 2018 Revision. 

UNDESA, 2008. World Urbanization Prospects The 2007 Revision. 

United  Nations,  2015.  Sustainable  development  goals  -  United  Nations  [WWW 
URL 

Document]. 
http://www.un.org/sustainabledevelopment/sustainable-development-goals/ 
(accessed 10.17.17). 

Development. 

Sustainable 

Nations 

United 

United  Nations  Conference  on  Housing  and  Sustainable  Urban  Development,  2016.  The 
New  Urban  Agenda  [WWW  Document].  Habitat  III.  URL  http://habitat3.org/the-
new-urban-agenda/ (accessed 10.17.17). 

United  Nations,  Department  of  Economic  and  Social  Affairs,  Population  Division,  2018. 

World Urbanization Prospects: The 2018 Revision. 

United  Nations,  G.A.,  2015.  Transforming  Our  World:  the  2030  Agenda  for  Sustainable 

Development. 

United  Nations,  H.S.P.,  2018.  Tracking  Progress  Towards  Inclusive,  Safe,  Resilient  and 
Sustainable  Cities  and  Human  Settlements  SDG  11  SYNTHESIS  REPORT  HIGH 
LEVEL POLITICAL FORUM 2018. 

Weiss, D.J., Nelson, A., Gibson, H.S., Temperley, W., Peedell, S., Lieber, A., Hancher, M., 
Poyart, E., Belchior, S., Fullman, N., Mappin, B., Dalrymple, U., Rozier, J., Lucas, 
T.C.D.,  Howes, R.E., Tusting, L.S., Kang, S.Y.,  Cameron,  E., Bisanzio, D., Battle, 
K.E.,  Bhatt,  S.,  Gething,  P.W.,  2018.  A  global  map  of  travel  time  to  cities  to 
assess 
in  2015.  Nature  553,  333–336. 
https://doi.org/10.1038/nature25181 

in  accessibility 

inequalities 

World  Cities  Report  2016  Urbanization  and  Development:  Emerging  Futures,  2016.  . 

United Nations Human Settlements Programme (UN-Habitat). 

135 

Zampieri, M., Russo, S., di Sabatino, S., Michetti, M., Scoccimarro, E., Gualdi, S., 2016. 
Global assessment of heat wave magnitudes from 1901 to 2010 and implications 
for the river discharge of the Alps. Science of The Total Environment 571, 1330–
1339. https://doi.org/10.1016/j.scitotenv.2016.07.008 

Zell,  E.,  Huff,  A.K.,  Carpenter,  A.T.,  Friedl,  L.A.,  2012.  A  User-Driven  Approach  to 
Determining Critical Earth Observation Priorities for Societal Benefit. IEEE Journal 
of  Selected  Topics  in  Applied  Earth  Observations  and  Remote  Sensing  5,  1594–
1602. https://doi.org/10.1109/JSTARS.2012.2199467 

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List of abbreviations  

AI – Artificial Intelligence 

ALOS – Advanced Land Observation Satellite 

ASAR – Advanced Synthetic Aperture Radar 

BA – Balanced Accuracy 

BU – Built-up 

CIESIN – Center for International Earth Science Information Network  

CO2 – Carbon dioxide 

DESA – Department of Economic and Social Affairs 

DG  GROW  –  Directorate  General  for  Internal  Market,  Industry,  Entrepreneurship  and 
SMEs 

DG REGIO Directorate General for Regional and Urban Policy 

DIAS – Data and Information Access Services  

DNA – Deoxyribonucleic acid 

DNB – Day/Night Band 

DRR – Disaster Risk Reduction 

DSM – Digital Surface Model 

EC – European Commission 

ECMWF – European Centre for Medium-Range Weather Forecasts 

EDGAR – Emissions Database for Global Atmospheric Research 

EEA – European Environment Agency 

ENVISAT – Environmental Satellite 

EO – Earth Observation 

EOG – Earth Observations Group  

EPSG – European Petroleum Survey Group 

ESA – European Space Agency 

ETM – Enhanced Thematic Mapper 

EU – European Union 

EUMETSAT – European Organisation for the Exploitation of Meteorological Satellites  

EUROSTAT – Statistical Office of the European Union 

FAO –   Food and Agriculture Organization of the United Nations 

FUA – Functional Urban Area  

GAR – Global Assessment Report 

GDP – Gross Domestic Product 

GEE – Google Earth Engine 

GEM – Global Earthquake Model 

GEO – Group on Earth Observation 

GEOSS – Global Earth Observation System of Systems 

137 

 
GHS-S1 – Global Human Settlement data from Sentinel 1 sensor 

GHS-BU – Global Human Settlement data on built-up surfaces 

GHS-BUILT – Global Human Settlement data on built-up surfaces 

GHSL – Global Human Settlement Layer 

GHS-POP – Global Human Settlement data on resident population  

GHS-SMOD – Global Human Settlement data on rural vs. urban classification  

GHS-UCDB – Global Human Settlement data on urban centres  

GloFAS – Global Flood Awareness System 

GPW – Gridded Population of the World 

GSARS – Global Strategy to improve Agricultural and Rural Statistics  

HABITAT – United Nations Human Settlements Programme 

HDC – High Density Clusters 

HIC – High Income Countries  

HPI – Human Planet Initiative  

HR – High Resolution  

HWMId – HeatWave Magnitude Index 

HWSD – Harmonized World Soil Database 

ISFEREA – Information Support for Effective and Rapid External Action 

JEODPP – Earth Observation Data and Processing Platform 

JRC – Joint Research Centre 

KC – Knowledge Centres 

LIC – Low Income Countries 

LMIC – Lower-middle Income Countries  

LUE – Land Use Efficiency 

MASL – Metres Above Sea Level 

MMI – Mercalli Modified Intensity scale 

MR – Major Region (UN) 

MRB – Major River Basins 

MRBW – Major River Basins of the World 

NCEI – National Centers for Environmental Information 

NDVI – Normalized differential vegetation index 

NLE – Night Light Emission  

NOAA – National Oceanic and Atmospheric Administration 

OECD – Organisation for Economic Co-operation and Development 

PM2.5 – Fine Particulate Matter  

RB – River Basin 

SDG – Sustainable Development Goals  

SME – Small and Medium Enterprises  

138 

SML –Symbolic Machine Learning 

SMOD – Settlement MODel 

TM – Thematic Mapper 

TOA – Top-of-Atmosphere 

UC – Urban Centre 

UCDB – Urban Centre Database 

UMIC – Upper-middle Income Countries  

USGS – United States Geological Survey 

VHR – Very High Resolution  

VIIRS – Visible Infrared Imaging Radiometer Suite 

WB – World Bank 

WGS84 – World Geodetic System 1984 

WHO – World Health Organization 

WUP – World Urbanization Prospect 

139 

 
List of definitions 

Building  –  in  the  GHSL  framework,  buildings  are  defined  as:  enclosed  constructions 
above ground, which are intended or used for the shelter of humans, animals, things or 
for  the  production  of  economic  goods  and  that  refer  to  any  structure  constructed  or 
erected on its site (Pesaresi et al., 2013). This abstraction is contiguous to the standard 
topographic definition of the “building” class as compiled in the INSPIRE directive, except 
for  the  fact  that  the  condition  of  the  permanency  of  the  structure  it  is  not  in  the  GHSL 
definition. This fact allows including also refugee camps, informal settlements, slums and 
other  temporary  settlements  and  shelters  in  the  notion  of  built-up  area  in  the  GHSL 
concept. 

Built-up  area  –  is  the  union  of  all  the  remote  sensing  fine-scale  spatial  units,  where 
buildings  can  be  found  (Pesaresi,  2016).    Assuming  an  equal-area  geographical 
projection of the spatial units, the sum of the spatial units classified as built-up area are 
estimating  the  total  built-up  surface  quantity.  The  current  Atlas  is  based  on  remote 
sensing data spatially harmonized at the scale of 30x30 metres. 

Built-up  density  –  the  amount  of  built-up  surface  over  the  total  surface  of  the  spatial 
unit considered. In the  case of the aggregated GHS-BUILT grids from fine-scale satellite 
records, the spatial units considered are 250x250metres and 1x1km.   

Built-up grids – see GHS-BU 

Built-up share – see built-up density 

Built-up structure – see Building 

Built-up surface – see built-up area  

City  –  see  Urban  Centre.  For  the  purpose  of  the  present  Atlas,  the  notion  of  “city”  it is 
reduced to the one of Urban Centre, as ported in the GHSL universe of data 

Degree  of  Urbanization – A harmonised definition of cities and rural areas introduced 
by  the  European  Commission  in  2014  and  generated  from  resident  population  density 
criteria,  estimated  on  uniform  grids  of  1x1km2  scale,  and  distinguishing  between  three 
classes: “urban centres”, “towns or suburbs”, and “rural areas” 

Earth – A planet in the Solar System, and the third planet from the Sun to the external 
space. 

Earth  Observation  –  the  collection  of  information  about  the  physical,  chemical,  and 
biological systems of the planet via remote-sensing technologies 

GEO  –  inter-ministerial  Group  on  Earth  Observation.  A  partnership  of  more  than  100 
national  governments  and  in  excess  of  100  Participating  Organizations  that  envisions  a 
future  where  decisions  and  actions  for  the  benefit  of  humankind  are  informed  by 
coordinated, comprehensive and sustained Earth observations. 

GHS-BU – sometimes referred to as GHS-BUILT – Built-up areas grids produced by the 
GHSL. They reports about the presence of buildings in every spatial sample of the Earth 
land mass, with a resolution of 30x30 metres, also aggregated (reporting about built-up 
surface share) at 250x250metres , and 1x1km2 , in the four GHSL Epochs.  

GHSL  –  Global  Human  Settlement  Layer.  A  new  open  and  free  tool  for  assessing  the 
human presence on the planet. Produces new global spatial information, evidence-based 
analytics  and  knowledge  describing  the  human  presence  on  the  planet.  Operates  in  an 
open and free data and methods access policy (open input, open method, open output). 
Supported by the DG JRC and the DG REGIO of the European Commission, together with 
the international partnership GEO Human Planet Initiative.  

140 

 
GHSL Epochs – the nominal years used as temporal reference for the harmonization of 
the  GHSL  grids.  They  are  four  GHSL  epochs,  corresponding  to  1975,  1990,  2000,  and 
2015. 

GHS-POP  –  Population  grids  data  produced  by  the  GHSL.  They  reports  about  resident 
population  in  every  spatial  sample  of  the  Earth  land  mass,  with  a  resolution  of 
250x250metres also aggregated at 1x1km2 , in the four GHSL Epochs  

GHS-SMOD  –  Settlement  Model  Grids  produced  by  the  GHSL.    It  is  the  porting  of  the 
Degree of Urbanization model in the GHSL universe of data aggregated at 1x1km2, in the 
four GHSL Epochs 

GHS-UCDB  –  Urban  Centres  Data  Base  produced  by  the  GHSL.  It  contains  attributes 
characterising urban centres 

Green – The vegetation as observed from  remote  sensing platforms, by analysis of the 
reflected energy in wavelengths related to the photosynthesis activity of the plants 

NDVI –  Normalized differential vegetation index.  Index accounting the energy  reflected 
in the red and near-infrared wavelengths as collected from remote sensing sensors, and 
highly correlated to the presence of living vegetation on the ground. 

OECD – Organisation for Economic Co-operation and Development 

Planet – see Earth  

PM – Particulate matter, defined as the sum of all solid and liquid particles suspended in 
air,  which  are  hazardous.  This  complex  mixture  includes  both  organic  and  inorganic 
particles, such as dust, pollen, soot, smoke, and liquid droplets.  

Population Grids – see GHS-POP 

Remote  Sensing  –  acquisition  of  information  about  an  object  or  phenomenon  without 
making  physical  contact  with  the  object  and  thus  in  contrast  to  on-site  observation.  In 
this Atlas, it is assumed using space borne (artificial orbiting satellites) sensor platforms 

Settlement Model Grids – see GHS-SMOD 

Symbolic  Machine  Learning  –  it  is  a  fully  repeatable  statistical  learning  procedure 
based on data sequencing, data reduction (quantization) and associative analysis. In the 
GHSL,  the  automatic  recognition  of  built-up  areas  from  remote  sensing  data  it  is  based 
on Symbolic Machine Learning procedures (Pesaresi et al. 2016b).  

Urban  Centre  –  In  the  DEGURBA  the  Urban  Centers  are  defined  as:  “high-density 
clusters  of  contiguous  grid  cells  of  1km2  with  a  density  of  at  least  1500  inhabitants  per 
km2  and  a  minimum  population  of  50000”  .    The  Urban  Centre  abstraction  as 
implemented  in  the  current  GHSL  settlement  model  (SMOD)  formulation  is  defined  as: 
“the  spatially-generalized  high-density  clusters  of  contiguous  grid  cells  of  1  km2  with  a 
density  of  at  least  1,500  inhabitants  per  km2  of  land  surface  or  at  least  50%  built-up 
surface share per km2 of land surface, and a minimum population of 50,000.” 

141 

 
List of figures 

Figure 1 - conceptual schema of the GHSL input data, processing and products ...........10 

Figure 2 Evolution of the GHSL framework and key science for policy deliverables ........16 

Figure 3 GHSL input data and main output information .............................................17 

Figure 4 Transition from imagery to built-up areas extraction (GHS-BU), population 
modelling (GHS-POP), and settlements classification (GHS-SMOD), examples in the area 
of Bangkok (Thailand). ..........................................................................................18 

Figure 5 The figure describes the information extraction process from the satellite images 
of the earth surface (bottom) to the built-up area extraction (middle) to the aggregated 
built-up area density (top). ....................................................................................19 

Figure 6 The figure illustrates the combination of GHS-BU with the census data to 
produce a regular fine scale grid of population density. .............................................20 

Figure 7 The GHS-BU and GHS-POP are combined to classify the gird cells into rural and 
urban areas. See chapter 3 for details on the model. ................................................21 

Figure 8 GHSL products supporting specific sectors of DGs ........................................22 

Figure 9 List of SDG indicators requiring urban/rural data disaggregation ....................24 

Figure 10 List of urban SDG indicators sensitive to city boundaries .............................25 

Figure 11 Examples of Broadville and Narrowtown to display an expansive and restrictive 
definition of a city. While between 1990 and 2015 population increases in Broadville, it 
declines in Narrowtown. Not just SDG indicators reporting depends on the boundaries, 
but also demographic information such as city size and population change. .................26 

Figure 12 Schema representing an abstract urban centre, grid cell criteria, and population 
size threshold ......................................................................................................27 

Figure 13 City of Cork (Ireland). Land use map based on Urban Atlas (top left); GEOSTAT 
population grid (top right); Degree of Urbanisation applied to the GEOSTAT grid (bottom 
right); Degree of Urbanisation applied to the LAU2 (bottom left). ...............................28 

Figure 14 Global representation of urban centres location ..........................................29 

Figure 15 Example of Urban Centre in Africa, in the area of Dakar (Senegal) ...............30 

Figure 16 Landsat satellite imagery in the area of Dakar (same location as in Figure 15) 
showing the 30m resolution of the sensor from which GHS-BU is produced. .................30 

Figure 17 Example of Urban Centre in Asia, in the area of Chandigarh (India) ..............31 

Figure 18 Example of Urban Centre in Europe, in the area of Busto Arsizio (Italy) .........31 

Figure 19 Example of Urban Centre in the Caribbean, in the area of Havana (Cuba) ......32 

Figure 20 © Adobe Stock, 2018 .............................................................................32 

Figure 21. Examples from selected cities showing the reduction of over-detection and 
under-detection errors in the new multitemporal built-up layer. The results of 
GHS_LDSMT_2015 are compared against those of GHS_LDSMT_2017 respectively for the 
cities of New Delhi (India -A), Shanghai (China -B) and Riyadh (Saudi Arabia -C) .........35 

Figure 22 © Adobe Stock, 2018 .............................................................................36 

Figure 23 example at 1km resolution of GHSL baseline data (GHS-BU, GHS-POP) for the 
delineation of urban centres in the Northern Region of China. ....................................38 

Figure 24 Anatomy of the key information components and data processing/integration 
used to create the GHS-UCDB ................................................................................39 

Figure 25 Elements of geospatial operations tasks to derive attributes of urban centres 39 

142 

Figure 26 GHS-UCDB regional coverage, share of centres by major region of the world .40 

Figure 27 Urban centres in the northern Gulf of Guinea .............................................40 

Figure 28 Population (year 2015) rank-size plot of the cities included in the WUP as 
compared to the GHS-UCDB records .......................................................................43 

Figure 29 Examples of GHS-UCDB uses ...................................................................44 

Figure 30 © Adobe Stock, 2018 .............................................................................46 

Figure 31 Urban centres according to their location in the biomes of the world. ............47 

Figure 32 Change of urban centre population between 1990 and 2015 by biome. The 
total change of population (in millions) is provided per biome, and the ratio of population 
change is given in brackets. ...................................................................................48 

Figure 33 Share of urban centres built-up areas by soil type ......................................49 

Figure 34 © Adobe Stock, 2018 .............................................................................50 

Figure 35 Urban centres located in elevation classes below 3m and above 500m. .........52 

Figure 36. Total urban centre population by elevation class per region (Gm – global 
mean, Rm – regional mean). .................................................................................53 

Figure 37 Urban centres classification according to the Köppen-Geiger climate zones map
 ..........................................................................................................................55 

Figure 38 Share of urban centres by climate zone and population changes between 1990 
and 2015 .............................................................................................................55 

Figure 39 Changes in the temperature (C) per urban centre in the period 1990-2015 ..56 

Figure 40 population living in cities where temperature increase was more than 0.8 C in 
the period 1990-2015 ...........................................................................................56 

Figure 41 Number of urban centres affected by changes in the temperatures (C) in the 
period 1990-2015 .................................................................................................57 

Figure 42 Relative changes in the average precipitation (%) calculated for the urban 
centres in the period 1990-2015 and long term mean precipitations (mm) in the period 
1966 – 2015. .......................................................................................................59 

Figure 43. Number of city centres per classes of relative changes in precipitations (in %)
 ..........................................................................................................................61 

Figure 44 Urban Centres located in the selected Major River Basins of the World (MRBW).
 ..........................................................................................................................62 

Figure 45. Percentage changes in built-up areas in urban centres between 1975 and 2014
 ..........................................................................................................................66 

Figure 46. Percentage change in built-up areas per each period of the GHS-BU multi-
temporal dataset ..................................................................................................67 

Figure 47. Percentage change in built-up areas per each period of the GHS-BU multi-
temporal dataset ..................................................................................................67 

Figure 48. Percentage changes in population in urban centres between 1975 and 2015 .68 

Figure 49. Annual average percent change of population in urban centres, by continent 69 

Figure 50. Population density of urban centres in 2015 ..............................................69 

Figure 51 © Adobe Stock, 2018 .............................................................................69 

Figure 52 Total 2015 Night-time light emitted in urban centres ..................................70 

Figure 53 Regional shares of urban centres and population in low illuminated areas ......71 

143 

Figure 54 © Adobe Stock, 2018 .............................................................................71 

Figure 55 © Adobe Stock, 2018 .............................................................................72 

Figure 56 multi-temporal trajectories of built-up areas and population development by 
income group .......................................................................................................73 

Figure 57 Total Gross Domestic Product of urban centres in 2015 ...............................74 

Figure 58 Top 10 urban centres by 2015 GDP. Population, Built-up area and GDP (circle 
size) of 2015; grey lines represents the average urban centres population and built-up 
area; percentage population variation and GDP variation between 1990 and 2015 (right).
 ..........................................................................................................................75 

Figure 59 Travel time to country capital for all urban centres .....................................76 

Figure 60 Total population distribution per class of travel time to country capital. Shares 
represent the abundance of urban centres for each class. ..........................................77 

Figure 61 © Adobe Stock, 2018 .............................................................................77 

Figure 62 © Adobe Stock, 2018 .............................................................................79 

Figure 63 Spatial distribution of changes in greenness values in the built-up areas of the 
urban centres in the period 1990–2015 (no data is shown in grey) .............................80 

Figure 64 Classified greenness values for the urban centre of Milwaukee (United States) 
for the period 199 and 2015. .................................................................................81 

Figure 65. Changes in the average share of green surfaces relative to the areas of the 
urban centres in the periods 1990, 2000 and 2015 ...................................................82 

Figure 66. Number of urban centres per continent where the share of high green surfaces 
is greater than the share of low green surfaces in the period 2015. ............................82 

Figure 67 © Adobe Stock, 2018 .............................................................................83 

Figure 68 Urban centres multi-temporal PM2.5 emissions per income group, and PM2.5 
concentration in 2000 and 2014 per region of the world ............................................84 

Figure 69 Urban centres classified by annual CO2 emission per inhabitant 
(ton/year/person) in 2012. ....................................................................................85 

Figure 70 Emission of C02 of urban centres split by sector for Major Regions in the period 
1975-2012. .........................................................................................................87 

Figure 71 Population of urban centres potentially exposed to floods in 2015 considering a 
100-year return period flood hazard map. ...............................................................90 

Figure 72 Regional share of the total population living in Urban Centres and exposed to 
flood hazard in 1990 (a) and in 2015 (b). ................................................................91 

Figure 73 © Adobe Stock, 2018 .............................................................................91 

Figure 74 Number of urban centres by ratio of population change (1990-2015) potentially 
exposed to floods per region (Gm – global mean, Rm – regional mean). .....................92 

Figure 75 Exposure of urban centres to seismic hazard, considering MMI intensity equal 
or greater than five (GEM2019, beta version). .........................................................93 

Figure 76 Top ten urban centres population exposed to seismic hazard with magnitude 
greater than class 5 in MMI scale ............................................................................94 

Figure 77 © Adobe Stock, 2018 .............................................................................94 

Figure 78 Absolute urban centre population exposed to seismic hazard in 2015 by MMI 
seismic class and per UN Major Regions. .................................................................95 

Figure 79 Population of urban centres potentially exposed to storm surges in 2015 
calculated with a hazard return period of 250 years. .................................................96 

144 

Figure 80. Population of urban centres potentially exposed to storm surge per UN Major 
Region in 1990 and 2015, as totals and as percentage of overall population in the 
Region’s cities. .....................................................................................................97 

Figure 81 Number of urban centres exposed to storm surge grouped by population 
change rate between 1990 and 2015 (Gm – global mean, Rm – regional mean). ..........98 

Figure 82 Maximum heatwave magnitude (HWMId) observed during 1980-2010 ..........99 

Figure 83 Urban centres located in areas that suffered from severe (20<HWMId<40), 
extreme (40<HWMId<80) and exceptional (HWMId >80) heatwaves. ....................... 100 

Figure 84 © Adobe Stock, 2018 ........................................................................... 103 

Figure 85 LUE value corresponding to the dynamics of population change and built-up 
areas expansion in the period 1990-2015 for 10,000 urban centres in the GHSL UCDB 104 

Figure 86 comparison of population density in areas of urban centres expansion between 
1990 and 2015 per region of the world .................................................................. 105 

Figure 87 classification of urban centres according to the share of 2015 population with 
generalized access to green areas ........................................................................ 106 

Figure 88. Greeness values in the built-up (period 2015) derived from Landsat NDVI 
composites in the urban centres of Minneapolis, Lagos, Manila, Mumbai, Paris and Buenos 
Aires. The maps are shown at the same scale and cover the same extent (from Corbane 
et al., 2018) ...................................................................................................... 108 

Figure 89. Breakdown of the average share of people exposed to high green spaces by 
continent ........................................................................................................... 109 

Figure 90. Illustration of the method used to estimate the area of open spaces based on 
areas of non-built-up surfaces and high green surfaces within the urban centre of Paris.
 ........................................................................................................................ 110 

Figure 91. Average share of open spaces in urban centres by continent (reference year 
2015)................................................................................................................ 111 

Figure 92 Population in FUAs and Commuting zones by world region, 1990-2015. Source: 
Moreno-Monroy et al., 2018 ................................................................................. 115 

Figure 93 Kigali, Rwanda. © Andreas Brink, European Commission 2018 ................... 128 

145 

 
List of tables 

Table 1 Effects of narrow or broad drawing of city boundaries ....................................26 

Table 2 List of UCDB attributes by thematic area and temporality. Some attributes are 
classification of the urban centres and their temporal coverage (here, 2015) refers to the 
epoch for which the urban centres were delineated. Land Use Efficiency attribute refers to 
a temporal span. ..................................................................................................41 

Table 3 List of additional UCDB attributes. The temporal coverage (here, 2015) refers to 
the epoch for which the urban centres were delineated .............................................41 

Table 4 Aggregation of soil types according to agricultural suitability...........................49 

Table 5 Absolute and relative (in 2015) built-up areas surface, resident population, 
number of urban centres, and share of global land mass per soil typologies in the High 
Agricultural Suitability class. ..................................................................................51 

Table 6 Number of urban centre by elevation classes ................................................54 

Table 7 Mayor river basins ranked by the number of urban centres, city population 
(2015), and built-up area (2015). RB – river basin; MR – Major Region (UN) ...............63 

Table 8 Global annual CO2 emission of urban centres in billion tons per year and relative 
contribution of human activity sectors for nominal epochs (1975-1990-2000-2012). .....86 

Table 9.Summary table on average share of population living in areas of high green (%) 
and average share of open spaces (%) together with the average land area of the urban 
centres (km2) ..................................................................................................... 111 

146 

 
 
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doi:10.2760/124503 

ISBN 978-92-79-98185-2