Applying the degree of urbanisation method to national population and housing census with GHSL tools.
Date: 2021-05-06
Institutions: Joint Research Centre
Subjects: Colombia, disaster resilience, habitat, housing, indigenous population, population census, population statistics, remote sensing, research report, rural region, sustainable development, urban area, urbanisation

Applying the Degree of Urbanisation 
Method to National Population and 
Housing Census with GHSL Tools 

The example of Colombia 

Melchiorri Michele, Schiavina Marcello,  
Freire Sergio, Maffenini Luca, Kemper Thomas, 
Dijkstra Lewis 

2021 

EUR 30692 EN 

 
 
 
 
 
 
 
 
This publication is a Technical 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 
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of the European Union concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation 
of its frontiers or boundaries. 

Contact information  
Name: Thomas Kemper 
Address: Via Fermi, 2749 21027 ISPRA (VA) - Italy - TP 130 
European Commission - DG Joint Research Centre 
Space, Security and Migration Directorate 
Disaster Risk Management Unit E.1 
Email: thomas.kemper@ec.europa.eu 
Tel.: +39 0332 78 55 76 

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

JRC124765 

EUR 30692 EN 

PDF 

ISBN 978-92-76-37556-2 

ISSN 1831-9424 

doi:10.2760/939289 

Luxembourg: Publications Office of the European Union, 2021  

© European Union, 2021  

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How to cite this report: Melchiorri Michele, Schiavina Marcello, Freire Sergio, Maffenini Luca, Kemper Thomas, and Dijkstra Lewis, Applying 
the Degree of Urbanisation Method to National Population and Housing Census with GHSL Tools, EUR 30692 EN, Publications Office of 
the European Union, Luxembourg, 2021, ISBN 978-92-76-37556-2, doi:10.2760/939289, JRC124765. 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Contents 

Abstract ................................................................................................................................................................................................................................... 2 

Executive Summary ......................................................................................................................................................................................................... 3 

1  Introduction ................................................................................................................................................................................................................... 4 

2  Data and Methods .................................................................................................................................................................................................... 5 

2.1  Colombian Censo Nacional de Población y Vivienda 2018 ................................................................................................. 5 

2.2  Global Human Settlement Built-up Layer from Sentinel-2 Convolutional Neural Networks ......................... 5 

2.3  The Degree of Urbanisation .................................................................................................................................................................... 5 

2.3.1 

Grid Cell classification ................................................................................................................................................................ 5 

2.3.2 

Classification of local units ..................................................................................................................................................... 6 

2.4  GHSL Tools to implement the Degree of Urbanisation .......................................................................................................... 8 

2.4.1 

Creating a population grid with the GHS-POP2G Tool ............................................................................................ 8 

2.4.2 

Producing a settlement classification grid with the GHS-DUG Tool .............................................................. 9 

2.4.3 

Classifying local units by Degree of Urbanisation with the GHS-DU-TUC ................................................. 9 

2.5  Methodology .................................................................................................................................................................................................. 10 

3  Results ........................................................................................................................................................................................................................... 14 

3.1  Population grid ............................................................................................................................................................................................. 14 

3.2  Degree of Urbanisation grid ................................................................................................................................................................ 16 

3.2.1 

Settlement entities .................................................................................................................................................................... 17 

3.3  Classification of territorial units by Degree of Urbanisation .......................................................................................... 20 

3.3.1 

Comparing grid based statistics to territorial units statistics ......................................................................... 21 

3.3.2 

Comparing the national definition with the Degree of Urbanisation ......................................................... 21 

3.4  Classification of demographic and housing variables by Degree of Urbanisation ........................................... 23 

3.4.1 

Population age and sex by Degree of Urbanisation ............................................................................................. 23 

3.4.1.1 

Population age .................................................................................................................................................................. 23 

3.4.1.2 

Population sex ................................................................................................................................................................... 24 

3.4.2 

Access to electricity ................................................................................................................................................................... 26 

3.4.3 

Access to internet ....................................................................................................................................................................... 27 

3.4.4 

Access to water ............................................................................................................................................................................ 28 

3.4.5 

Access to the sewage system ............................................................................................................................................. 29 

3.4.6 

Access to waste management ........................................................................................................................................... 30 

4  Conclusion ................................................................................................................................................................................................................... 31 

5  References .................................................................................................................................................................................................................. 32 

List of abbreviations and definitions ................................................................................................................................................................. 33 

List of GHSL Resources ............................................................................................................................................................................... 33 

List of figures ................................................................................................................................................................................................................... 34 

List of tables ..................................................................................................................................................................................................................... 35 

i 

 
 
Authors 

Michele Melchiorri2, Marcello Schiavina1, Sergio Freire1, Luca Maffenini3, Thomas Kemper1 and Lewis Dijkstra4 

1 European Commission-Joint Research Centre, Ispra, Italy, 2 Engineering S.p.A, 3 UniSystems Luxembourg SàRL 

4 European Commission Directorate General for Urban and Regional Policy, Bruxelles, Belgium. 

1 

 
 
 
 
Abstract 

The  Degree  of  Urbanisation  is  a  method  to  delineate  urban  and  rural  areas  for  international  statistical 
comparison. It relies on geospatial data that are generally available in National Statistical Offices and can be 
implemented  with  open  and  free  data  and  tools.  This  report  explains  the  application  of  the  Degree  of 
Urbanisation  method  to  the  2018  edition  of  the  National  Census  of  Colombia,  using  tools  produced  in  the 
framework of the  Global  Human Settlement  Layer  project of the European Commission Directorate General 
Joint  Research  Centre.  The  Degree  of  Urbanisation  is  applied  in  a  two-stage  process  to  a  population  grid 
(obtained  by  combining  the  2018  Colombian  census  data  with  a  built-up  surface  layer  extracted  from 
Copernicus Sentinel-2 imagery): first grid cells are classified based on population size and density criteria, then 
small  local  administrative  units  are  classified  into  cities,  towns  and  semi-dense  areas,  and  rural  areas.  The 
completeness and open data policy of the 2018 census of Colombia allows to classify small territorial units, 
and to disaggregate six demographic and housing variables by Degree of Urbanisation. These include sex ratio 
and access to infrastructure, like connectivity to internet and access to electricity among others. This technical 
report  contains  practical  guidance  for  the  application  of  the  Degree  of  Urbanisation  method  to  national 
demographic and housing census/surveys and complements the EUROSTAT manual on how to apply the Degree 
of Urbanisation, produced at the request of the 51st Session of the United Nations Statistical Commission. 

2 

 
Executive Summary 

In the past few years the European Commission led a voluntary commitment of six International Organisations 
and  agencies  –  the  Food  and  Agriculture  Organization  of  the  United  Nations,  the  United  Nations  Human 
Settlements Programme, the International Labour Organization, the Organisation for  Economic Co-operation 
and  Development  and  The  World  Bank  –  to  develop  a  harmonised  definition  of  urban  and  rural  areas  for 
statistical comparison, the Degree of Urbanisation.  The commitment was launched at the Habitat III conference 
in 2016 and after few years of testing at global level and consultations with over 80 countries, the 51st Session 
of the United Nations Statistical Commission adopted the Degree of Urbanisation as method to delineate cities, 
urban and rural areas for international statistical comparisons. A common approach to define urban and rural 
areas  is  key  to  monitor  progress  made  on  the  2030  Development  Agenda,  development  and  cooperation 
policies, and disaster risk reduction among other sectors. 

The  Joint  Research  Centre  (JRC)  facilitated  the  development  of  the  Degree  of  Urbanisation  and  its  testing 
outside Europe with the Global Human Settlement Layer (GHSL) providing global and multi-temporal built-up 
area and population grids, and developing a model to apply the Degree of Urbanisation to these grids. With the 
adoption of the Degree of Urbanisation the JRC developed a suite of open and free tools to apply this method 
to nationally produced data, such as national census or survey data. The tools support the capacity enhancement 
activities carried out by the European Commission and its partners.  

This report applies the  Degree of Urbanisation method to the  2018 edition of the Colombia Population and 
Housing Census by creating a population and a settlement classification grid, plus the classification of local 
units by Degree of Urbanisation and finally the disaggregation of selected population and housing variables 
important in the Sustainable Development Goals (SDG) perspective by Degree of Urbanisation. Results show, 
among other things, that share of urban population based on classified territorial units is 77.9%, with share of 
women being lower than men only in rural areas; that access to the Internet is three times higher in cities than 
towns, with Semi-dense towns being under-served respect to peri-urban areas; and that rate of connection to 
the  sewage  system  is  3.8  times  higher  in  cities  than  in  rural  areas.These  statistics  can  be  internationally 
compared  with  other  ones  collected  from  population  and  housing  census  and  aggregated  according  to  the 
standard methodology defined by the Degree of Urbanisation. 

The principal purpose of this technical report is to explain the typologies of input data, chains of operations, and 
tools that  can  be  used  to  apply  the  Degree  of  Urbanisation  to  nationally  produced  data.  The  work  is  made 
possible by the open data policy of the National Statistical Office of Colombia (DANE) and is a technical follow-
up to the EUROSTAT manual “Applying the Degree of Urbanisation” produced at the request of the 51st United 
Nations Statistical Commission. 

3 

 
1 

Introduction 

Demographic, housing and health surveys are key sources of information for policymakers that supply essential 
data  on  population  and  their  living  conditions.  These  data,  usually  collected  with  national  censuses,  are 
indispensable for several policy domains and they are also required to monitor the implementation of the 2030 
Agenda for Sustainable Development, which requires the reporting of several indicators disaggregated by urban 
and rural areas. Accordingly, the capacity to define urban and rural areas is needed both to collect data, and to 
analyse the different conditions in these areas. The definition of urban and rural areas was not  unique and 
commonly  agreed  worldwide  before  the  51st  United  Nations  Statistical  Commission  that  adopted  such  a 
harmonised  definition for international comparison: the Degree of  Urbanisation. The Degree of  Urbanisation 
method  helps  to  standardise  information  collection  and  reporting  across  countries  by  offering  a  common 
method to classify urban and rural areas for statistical comparison based on population size and density criteria 
and by a geospatial method relying on contiguity. 

Geospatial data are powerful assets linking information to their specific geographical location (e.g. a building 
or a larger designation like an administrative area). Geospatial data application to official statistics is widely 
recognised, and increasingly used by National Statistical Offices (UN-GGIM 2020). Oftentimes official geospatial 
data  on  demographic  and  housing  census  is restricted  information  available  only  within  National  Statistical 
Offices due to security and privacy concerns associated with fine scale and sensitive information. In other cases, 
no recent census or survey data are available due to adverse situations (e.g. conflicts, ethnic tensions or swift 
economic changes). In these cases population modelling is used to estimate populations to support essential 
planning and delivery of services, infrastructure and aid (Wardrop et al. 2018).The Degree of Urbanisation is a 
method that classifies administrative or statistical areas into three classes starting from population information 
generally produced for census rounds. EUROSTAT and other international Organisations produced a the request 
of  the  United  Nations  Statistical  Commission  a  methodological  manual  about  the  Degree  of  Urbanisation 
(European Commission and Statistical Office of the European Union 2021). 

The basic requirement to apply the Degree of Urbanisation method is a population raster on a regular grid in 
equal area  projection that can be  derived  from population  data in  geospatial format  (population counts per 
polygon or per census point i.e. household locations). The first stage is the application of a set of criteria to the 
population grid (settlement classification definitions –see 2.3.1) to obtain a settlement classification grid. The 
settlement classification grid, combined with the population grid, is then used in the second stage to classify 
local administrative units based on population majority per class (at the grid level). Local units classified by 
Degree  of  Urbanisation  allow  the  analysis  of  statistics  by  Degree  of  Urbanisation.  This  process  is  named 
application of the Degree of Urbanisation, and the latter step is the disaggregation of variables by Degree of 
Urbanisation. The Degree of Urbanisation has been tested at global level by Dijkstra et al. (Dijkstra et al. 2020), 
and  the  Organisation  for  Economic  Cooperation  and  Development  (OECD)  and  the  European  Commission 
analysed  the  relationships  between  urbanisation  and  development  at  global  level  (OECD  and  European 
Commission 2020).  

The  Departamento  Administrativo  Nacional  de  Estadística  of  Colombia  (DANE)  conducted  a  new  round  of 
population  and  housing  census  in  a  multi-year  process  and  released  the  results  as  detailed,  open  and  free 
geospatial data1 during 2020. The availability of such recent and rich data source has presented an opportunity 
to apply the Degree of Urbanisation method to Colombia, and analyse some demographic and housing variables 
by  Degree  of  Urbanisation.  This  implementation  builds  upon  the  European  Commission  guide  “Applying  the 
Degree  of  Urbanisation  -  a  methodological  manual  to  define  cities,  towns  and  rural  areas  for  international 
comparisons” requested by the 51st Session of the United Nations Statistical Commission upon adoption of the 
Degree of Urbanisation method. This report serves as practical implementation of the method to post 2015 
census information and is a showcase exercise of the reach of the Degree of Urbanisation to make the most 
impact for international comparison out of national demographic and housing census. 

The implementation presented in this report relies on the Global Human Settlement Layer (GHSL) framework of 
data and tools that support the implementation of the Degree of Urbanisation. The process is performed using 
the GHSL suite of free tools2 that operationalise the Degree of Urbanisation method. These GHSL tools enable 
the production of population grids (GHS-POP2G Tool), settlement grids (GHS-DUG Tool), and the classification 
of local units by Degree of Urbanisation (GHS-DU-TUC). 

1 Geospatial census data access portal: https://geoportal.dane.gov.co/servicios/descarga-y-metadatos/visor-descarga-geovisores/  
2 https://ghsl.jrc.ec.europa.eu/tools.php 

4 

 
                                           
2  Data and Methods 

This section describes the two main data sources used in this study: the 2018 National Population and Housing 
Census of Colombia, and the GHSL framework of data and methods. 

2.1  Colombian Censo Nacional de Población y Vivienda 2018  

The national population and housing census for Colombia was driven by three main objectives: to know how 
many people live in Colombia, where these people are located, and how they live. The production of periodic 
census is required by law and the census constitutes the main source of the national statistical system. The 
whole process unfolded between 2013 and 2020, and included legal, logistical, practical and analytical phases. 
Data  collection  included  several  variables  grouped  in  thematic  areas  of  location  (i.e.  hierarchical  from 
department  to  municipality,  etc.),  vivienda  (typology,  access  to  infrastructure  and  utilities,  etc.),  households 
(structure of the household etc.) and people (i.e. sex, age, education etc.). These data are publically available 
and were obtained through the DANE Geoportal3. 

The 2018 edition of the census has been the first one in Colombia also facilitated by digitalisation (12.5% of 
the total population was surveyed digitally). 

2.2  Global  Human  Settlement  Built-up  Layer  from  Sentinel-2  Convolutional 

Neural Networks 

Global  mapping  of  human  settlements  has  improved  considerably  with  the  open  availability  of  Earth 
Observation (EO) data provided by programmes like Landsat and Copernicus. In parallel, also the capacity to 
process these data and extract valuable information has leaped thanks to advances in machine learning and 
artificial  intelligence.  The  GHS-BUILT-S2  R2020A  is  the  first  global  product  mapping  built-up  areas  for  the 
reference year 2018 using Convolutional Neural Networks (GHS-S2Net, Corbane et al. 2020). The layer builds 
on a new deep learning framework for pixel-wise large-scale classification of built-up areas named GHS-S2Net 
to produce a global map of built-up areas expressed in terms of a probability grid at 10 m spatial resolution 
derived from a Sentinel-2 global image composite. 

The main improvement of the GHS-BUILT-S2 R2020A layer compared to other remote sensing derived human 
settlement maps are the high balanced accuracy (exceeding 0.7) and the 2018 reference year, which makes it 
very suitable as covariate for disaggregating recent demographic data. 

2.3  The Degree of Urbanisation 

The Degree of Urbanisation is a method to classify the entire territory of a country  or any smaller area into 
three classes: 1) cities, 2) towns and semi-dense areas and 3) rural areas based on a combination of population 
density  and  size,  and  a  geospatial  concept  of  grid  cells  contiguity.  The  51st  United  Nations  Statistical 
Commission  adopted  the  Degree  of  Urbanisation  as  method  to  delineate  cities,  urban  and  rural  areas  for 
international statistical comparisons.  

The  method is applied to a  regular kilometric  population  grid  in equal area  projection (e.g. World  Mollweide 
EPSG: 54009) in two stages: first grid cells are classified into one of the three classes producing a settlement 
grid,  then  small  local  spatial  units  are  classified  based  on  the  grid  typology  where  the  majority  of  units’ 
population is located. 

2.3.1  Grid Cell classification 

Groups of 1 km² population grid cells are plotted in relation to their neighbouring cells to identify: 

in  other  words,  excluding  diagonals)  with  a  population  density  of  at 

  An urban centre (high-density cluster), a cluster of contiguous grid cells of 1 km² (using four-point 
contiguity, 
least 
1,500 inhabitants per km² and collectively a minimum population of 50,000 inhabitants. Gaps in this 
cluster are filled (if smaller than 15 km2) and edges are smoothed (3-by-3 majority filter). If needed, 
contiguous  cells  (using  four-point  contiguity)  that  are  at  least  50%  built-up  may  be  added.  In  the 
geospatial raster grid, urban centre cells are coded with value “3”. 

3 https://geoportal.dane.gov.co/servicios/descarga-y-metadatos/visor-descarga-geovisores/ 

5 

 
                                           
  An  urban  cluster  (moderate-density  cluster), a cluster of contiguous grid cells of 1 km² (using 
eight-point  contiguity,  in  other  words,  including  diagonals)  with  a  population  density  of  at  least 
300 inhabitants per km²  and  a  minimum  population  of  5,000 inhabitants.  In  a  last  step,  after  the 
verification of the minimum population, the urban centre cells are removed from the urban cluster. In 
the geospatial raster grid, urban cluster cells are coded with value “2”. 

  Rural grid cells (or mostly low-density cells), grid cells that are not identified as urban centres or 

as urban clusters. In the geospatial raster grid, rural grid cells are coded with value “1”. 

The Degree of Urbanisation has also an extension, named Level 2 (whereas the above description is the Level 1 
–  3  classes)  that  has  7  classes  as  hierarchical  sub-classification  of  the  Level 1.  The  second  level  has  been 
introduced to capture the full settlement hierarchy of large, medium and small settlements or, in simpler terms, 
cities, towns and villages. The definition of the first class remains unchanged: 

  An urban centre consists of contiguous (using four-point contiguity) grid cells with a density of at 
least  1,500 inhabitants per km2.  An  urban  centre  has  a  collective  population  of  at 
least 
50,000 inhabitants. Gaps in this cluster are filled (if smaller than 15 km2) and edges are smoothed (3-
by-3  majority  filter).  If  needed,  contiguous  cells  (using  four-point  contiguity)  that  are  at  least  50% 
built-up may be added. In the geospatial raster grid, urban centre cells are coded with value “30”. 

The urban cluster cells that are not part of an urban centre can be subdivided into three types. 

  A dense urban cluster consists of contiguous (using four-point contiguity) grid cells with a density of 
at least 1,500 inhabitants per km2, with a collective population of at least 5,000 inhabitants and less 
than 50,000 inhabitants in the cluster. In the geospatial raster grid, dense urban cluster cells are coded 
with value “23”. 

  A semi-dense urban cluster consists of contiguous (using eight-point contiguity) grid cells with a 
density of at least 300 inhabitants per km2 and has a collective population of at least 5,000 inhabitants 
(in other words, an urban cluster) and this cluster is neither contiguous with nor within 2 km of a dense 
urban cluster or an urban centre(4). In the geospatial raster grid, semi-dense urban cluster cells are 
coded with value “22”. 

  Suburban or peri-urban cells are the remaining urban cluster cells, in other words those not part of 
a dense or semi-dense urban cluster. These grid cells are part of an urban cluster that is contiguous 
(using  eight-point  contiguity)  or  within  2 km  of  a  dense  urban  cluster  or  an  urban  centre.  In  the 
geospatial raster grid, suburban or peri-urban cells are coded with value “21”. 

Rural grid cells can be categorised into three types.  

  A rural cluster consists of contiguous (using eight-point contiguity) grid cells with a density of at least 
300 inhabitants per km2 and a collective population between 500 and 5,000 inhabitants in the cluster. 
In the geospatial raster grid, rural cluster cells are coded with value “13”. 

  Low density rural grid cells are rural grid cells with a density of at least 50 inhabitants per km2 and 
are not part of a rural cluster. In the geospatial raster grid, low density rural grid cells are coded with 
value “12”. 

  Very  low  density  rural  grid  cells  are  rural  grid  cells  with  a  density  of  less  than 
50 inhabitants per km2. In the geospatial raster grid, very low density rural grid cells are coded with 
value “11”. 

2.3.2  Classification of local units 

The  Degree  of  Urbanisation  classifies  local  units  based  on  population  majority 5  applied  to  the  grid  level 
classification. Each local unit belongs exclusively to one class at Level 1 and one at Level 2 (hierarchy based). 
Local  units  can  be  administrative  units  -  such  as  municipalities  -  or  statistical  units  -  such  as  census 
enumeration areas. 

4 Measured as outside a buffer of three grid cells of 1 km2 around dense urban clusters and urban centres. This ensures that adjacent, but 

not contiguous suburbs are taken into account. 

5 To classify the spatial units without any population, the same rules explaned below should be applied to their area instead of to their 

population 

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Once  all  grid  cells  have  been  classified  as  urban  centres,  urban  clusters  and  rural  grid  cells  the  next  step 
concerns overlaying these results onto local units: 

—  Cities (or densely populated areas): local units that have at least 50% of their population in urban centres, 

are coded in their attribute table under degree of urbanisation Level 1 with the code “3”. 

—  Towns and semi-dense areas (or intermediate density areas): local units that have less than 50% of 

their population in urban centres and less than 50% of their population in rural grid cells, code “2”. 

—  Rural areas (or thinly populated areas): local units that have at least 50% of their population in rural grid 

cells, code “1”. 

Urban areas consist of cities plus towns and semi-dense areas. 

At  the  Level 2  the  same  logic  is  implemented  as  hierarchical  sub-classification  of  the  Level 1.  Local  units 
classified as “towns and semi-dense areas” can be divided into three classes: 

—  Dense Towns have a larger share (>= 50%) in dense plus semi-dense urban clusters than in suburban or 
peri-urban cells (i.e. it is a town) and a larger share (>= 50%) of their population in dense urban clusters 
than in semi-dense urban clusters (i.e. it is dense), code “23”. 

—  Semi-dense  Towns  have  a  larger  share  (>=  50%)  in  dense  plus  semi-dense  urban  clusters  than  in 
suburban or peri-urban cells (i.e. it is a town) and a larger population share (> 50%) in semi-dense urban 
clusters than in dense urban clusters (i.e. it is semi-dense) and, code “22”. 

—  Suburban or peri-urban areas have a larger population share (> 50%) in suburban or peri-urban cells 

than in dense plus semi-dense urban clusters, code “21”. 

Dense and semi-dense towns can be combined into a single class of towns. This reduces the number of classes 
and is especially useful if the population share in semi-dense towns is low. 

Local units classified as “rural areas” can be divided into three classes: 

—  Villages have the largest share of their rural grid cell population living in a rural cluster6, code “13”. 

—  Dispersed rural areas have the largest share of their rural grid cell population living in low density rural 

grid cells7, code “12”.  

—  Mostly  uninhabited  areas  have  the  largest  share  of  their rural  grid  cell  population  living  in  very  low 

density rural grid cells, code “11”. 

Once local units are classified by Degree of Urbanisation it is possible to  summarize statistics by classes at 
Level 1  or  Level 2.  This  is  particularly  useful  to  harmonise  reporting  for  Sustainable  Development  Goals 
indicators, or cross compare statistics across countries. 

6 Special cases: share equal to the proportion of rural grid cell population living in low density rural grid cells and to the proportion of rural 

grid cell population living in very low density rural grid cells; or share equal to one and larger than the other. 

7 Special case: share larger then the proportion of rural grid cell population living in a rural cluster and equal to the proportion of rural grid 

cell population living in very low density rural grid cells 

7 

 
 
 
                                           
2.4  GHSL Tools to implement the Degree of Urbanisation 

The Degree of Urbanisation is a simple geospatial concept implemented through geospatial and geostatistical 
processing. Such processing requires a set of notions and a specific workflow to handle geospatial data in a 
rigorous way. In order to streamline this processing, and to expand as much as possible the users of the Degree 
of  Urbanisation,  the  Joint  Research  Centre  of  the  European  Commission  developed  a  set  of  tools  for  the 
implementation of the Degree of Urbanisation method, along its various steps. GHSL Tools were developed as 
standalone executables relying on MATLAB Runtime, and as ArcGIS Toolboxes. 

The  tools  described  below  address  three  production  steps  also  applied  in  this  study.  The  first  step  is  the 
construction  of  a  regular-spaced  population  grid  from  available  geospatial  population  data  in  the  form  of 
polygons or points with the GHS-POP2G Tool. The second step is the application of the Degree of Urbanisation 
methodology with the GHS-DUG Tool to the population grid resulting from the previous step. Next, the derived 
grid cell classification is used to classify small spatial units into cities, towns and semi dense areas, or rural 
areas with the GHS-DU-TUC (Figure 1). 

Figure 1 Applying the Degree of Urbanisation with GHSL Tools 

2.4.1  Creating a population grid with the GHS-POP2G Tool 

The Population-To-Grid tool (GHS-POP2G)  produces  geospatial raster grids of  population counts at different 
spatial resolutions. The tool allows the creation of population grids at 50 m, 100 m, 250 m, and 1 km spatial 
resolutions, starting from census data stored as point or polygon vector data (in latter case requires additional 
covariate as input for dasymetric disaggregation). 

The  GHS-POP2G  allows  to  produce  a  population  grid  both  by  the  method  of  dasymetric  disaggregation  of 
population data stored as polygons (using a built-up area layer as covariate), and by the method of aggregation 
of population data stored as points (like micro-census data). 

8 

 
 
 
The GHS-POP2G user guide (Maffenini, Schiavina, Freire, et al. 2020) explains in detail the operations to produce 
a population grid. 

2.4.2  Producing a settlement classification grid with the GHS-DUG Tool 

The  Degree  of  Urbanisation  Grid  (GHS-DUG)  Tool  produces  geospatial  grids at  1 km  by  mapping  settlement 
classes and extracts related statistics. GHS-DUG requires a population grid as input (i.e. produced with the GHS-
POP2G)  and  optionally  other  ancillary  data  like  a  built-up  area  layer  or  a  custom  land/water  layer  used  to 
compute densities. 

The  GHS-DUG  user  guide  (Maffenini, Schiavina, Melchiorri, et  al.  2020a)  explains  in  detail  the  operations  to 
produce a settlement classification grid. 

2.4.3  Classifying local units by Degree of Urbanisation with the GHS-DU-TUC 

The Degree of Urbanisation Territorial Units Classifier (GHS-DU-TUC) Tool classifies the territorial units based 
on the Degree of Urbanisation and extracts related statistics. The tool classifies territorial units by Degree of 
Urbanisation at level 1 (3 classes) and level 2 (7 classes) based on population majority by gridded settlement 
classes produced with GHS-DUG Tool. 

The GHS-DU-TUC is designed as an operational tool to perform the classification of local administrative units 
based  on  the  previous  described  settlement  classification  grid.  The  tool  requires  as  input  the  settlement 
classification grid (e.g. produced with the GHS-DUG Tool), the population grid used to produce the settlement 
classification grid (e.g. produced with the GHS-POP2G Tool) at its highest resolution (between 50 m and 1 km)8 
and a geometry of territorial units to be classified by Degree of Urbanisation. 

The GHS-DU-TUC user guide (Maffenini, Schiavina, Melchiorri, et al. 2020b) explains in detail the operations to 
classify local units by Degree of Urbanisation. 

8 The GHS-DU-TUC solves the small spatial unit issue (see chapter 6.3 of  European Commission and Statistical Office of the European 
Union 2021) by using a raster with a 50 m spatial resolution. Therefore, all population grids with resolution that are multiple of 50 m 
are suitable. 

9 

 
 
 
                                           
2.5  Methodology 

The application of the Degree of Urbanisation to the 2018 Censo Nacional de Población y Vivienda of Colombia 
has been implemented in four distinct phases. First, the demographic and built-up area data were pre-processed 
to obtain data compatible with the GHSL Tools workflow  (1 and 2 in Table 1). Next, the tools were  used to 
produce the Degree of Urbanisation layers (3). Last, the demographic and housing information was obtained by 
Degree of Urbanisation and analysed (4). 

In the first phase (1), the built-up layer has been pre-processed transform the GHS-BUILT Sentinel 2 built-up 
probability tiles hosted on the GHSL website to a continuous layer of built-up surface (i.e. suitable for population 
disaggreagation) at 50 m resolution. This was achieved through four actions with which the single tiles were 
first binarised (i.e. transform the probability into built-up presence/absence) by thresholding with a value of 0.2 
- as recommended in Corbane et al. 2020 (1.1), the thresholded tiles where then warped and oversampled to 
5 m resolution in World Mollweide projection (1.2). Next, the binary warped tiles at 5 m were mosaicked into a 
single continuous raster covering the full extent of Colombia census (1.3), and last the continuous thresholded 
layer was warped to 50 m (1.4). 

The second phase has been the preparation of Colombia census by merging the finest nationally defined rural 
and urban available geometries in the census (the Manzana and Seccion Rural) into a single shapefile (2.1). 
Once this continuos shapefile containing population counts per unit was available, a new field containing a new 
unique identifier (UID) for each of the unit was added (2.2). 

The third phase consisted in running the GHS-Tools to produce the population grid with the GHS-POP2G tool 
(3.1), to produce the settlement classification grid with the GHS-DUG (3.2), and to classify local units by Degree 
of Urbanisation with the GHS-DU-TUC (3.3). The GHS-POP2G standalone tool was configured to output the grid 
at 50 m, to source population counts from the TP_27 field, to identify polygons by the UID field, and to use as 
built-up layer the output of step 1.4 (Figure 2). The processing has run with the multicore option for about 4 
hours on a server with 256 GB of RAM and Intel® Xeron ® CPU E-5 2650. 

Figure 2 GHS-POP2G interface with the parameters set for the production of the population grid 

10 

 
 
The GHS-DUG (Figure 3) was configured to obtain the settlement classification grid using the population grid 
(1 km) obtained in 3.1 and the built-up surface from 1.4 to apply the 50% built-up rule in urban centres. In the 
same computing infrastructure mentioned above the process took about 2 minutes to complete. 

The  GHS-DU-TUC  (Figure  4)  was  configured  to  obtain  the  classification  of  local  units  (2.2)  by  Degree  of 
Urbanisation based on the settlement classification grid (3.2), and the population grid at the finest available 
resolution (3.1). Using the same infrastructure, the process completed in about 4 hours. 

Figure 3 GHS-DUG interface with the parameters set for the production of the settlement classification grid 

Figure 4 GHS-DU-TUC interface with the parameters set for the classification of the local units by Degree of Urbanisation 

11 

 
 
 
In the last phase, the spreadsheet resulting from the territorial units classification were analysed with a pivot 
table to disaggregate demographic and housing statistics by Degree of Urbanisation. 

Some selected variables available for viviendas relevant in the Sustainable Development Goals context were 
analysed by Degree of Urbanisation: 

-  Number of housing units with and without electric energy infrastructure (TP19_EE_1, and TP19_EE_2); 

-  Number of housing units with and without water infrastructure (TP19_ACU_1, and TP19_ACU_2); 

-  Number  of  housing  units  with  and  without  a  connection  to  the  sewage  system  (TP19_ALC_1,  and 

TP19_ALC_2); 

-  Number of housing units with and without solid waste collection (TP19_RECB1, and TP19_RECB2); 

-  Number  of  housing  units  with,  without,  and  unknown  internet  service  (TP19_INTE1,  TP19_INTE2, 

TP19_INTE9); 

To obtain the number of viviendas with the characteristics above, we performed a statistical analysis counting 
all the viviendas within each Degree of Urbanisation class (at Level 1 and Level 2). This operation was performed 
with pivot tables. This calculation has been the basis for obtaining the shares of units with and without access 
per class and at national level. 

Figure 5 Pivot table used to compute the counts and shares of population age classes by degree of urbanisation: (1 = 
Rural Area; 2 = Towns and Semi-dense areas; 3 = City) 

12 

 
 
17N_PROB_T02.tif, 17P_PROB_T02.tif, 
18M_PROB_T02.tif, 18N_PROB_T02.tif, 
18P_PROB_T02.tif, 19M_PROB_T02.tif, 
19N_PROB_T02.tif, 19P_PROB_T02.tif 
17N_54009_5m.tif, 17P_54009_5m.tif, 
18M_54009_5m.tif, 18N_54009_5m.tif, 
18P_54009_5m.tif, 19M_54009_5m.tif, 
19N_54009_5m.tif, 19P_54009_5m.tif 

GHS_BUILT_TS2_54009_5m_Colombia.tif 

GHS_BUILT_TS2_54009_50m_Colombia.tif 

Nearest neighbour 
resampling 

Mosaic with 
average algorithm 

Average 
resampling 

Table 1 Workflow to apply the Degree of Urbanisation with GHS-Tools with pre-processing and analysis steps 

Step 

Action 

Input files 

Method 

Output files 

1. Binarization of built-up 

probability layer in original UTM 
zone  

17N_PROB.tif, 17P_PROB.tif, 18M_PROB.tif, 18N_PROB.tif, 
18P_PROB.tif, 19M_PROB.tif, 19N_PROB.tif, 19P_PROB.tif 

Thresholding 

1. Pre–processing  
Built-up layer 

2. Warp with oversampling to 5 m 

World Mollweide 

17N_PROB_T02.tif, 17P_PROB_T02.tif, 18M_PROB_T02.tif, 
18N_PROB_T02.tif, 18P_PROB_T02.tif, 19M_PROB_T02.tif, 
19N_PROB_T02.tif, 19P_PROB_T02.tif 

3. Mosaic the 5 m tiles into a one 

continuous layer  

4. Aggregate the 5 m layer into a 

50 m layer 

1. Merge urban and rural 

shapefiles at the most detailed 
geometry level 

2. Pre-processing 

Population census 
data 

17N_54009_5m.tif, 17P_54009_5m.tif, 18M_54009_5m.tif, 
18N_54009_5m.tif, 18P_54009_5m.tif, 19M_54009_5m.tif, 
19N_54009_5m.tif, 19P_54009_5m.tif 

GHS_BUILT_TS2_54009_5m_Colombia.tif 

MGN_ANM_SECCION_RURAL.shp 
MGN_ANM_MANZANA.shp 

Merge 

MGN_ANM_merge_54009.shp 

2. Add new unique ID field 

MGN_ANM_merge_54009.shp 

Add field 

MGN_ANM_merge_54009.shp 

1. Produce population grid 

MGN_ANM_merge_54009.shp, 
GHS_BUILT_TS2_54009_50m_Colombia.tif 

GHS-POP2G 

3. Run GHS-Tools 

2. Produce settlement 
classification grid 

GHS-POP2G_1000m.tif, 
GHS_BUILT_TS2_54009_50m_Colombia.tif 

GHS-DUG 

GHS-POP2G_1000m.tif, 
GHS-POP2G_50m.tif 
GHS-DUG_GRID_L1.tif, 
GHS-DUG_GRID_L2.tif, 
GHS-DUG_L1_stats.xls, 
GHS-DUG_L2_stats.xls, 
GHS-DUG_URBAN_CENTRE.shp, 
GHS-DUG_SEMI_DENSE_URBAN_CLUSTER.shp, 
GHS-DUG_RURAL_CLUSTER.shp, 
GHS-DUG_DENSE_URBAN_CLUSTER.shp 

4. Disaggregate 
statistics 

3. Classify local units by Degree of 

Urbanisation 

1. Disaggregate demographic and 
housing information by Degree 
of Urbanisation 

GHS-POP2G_50m.tif, 
GHS-DUG_GRID_L2.tif, 
MGN_ANM_merge_54009.shp 

GHS-DU-TUC 

MGN_ANM_merge_54009_GHS-DU-TUC.shp, 
MGN_ANM_merge_54009_GHS-DU-TUC.xlsx 

MGN_ANM_merge_54009_GHS-DU-TUC.xlsx 

Pivot table 

MGN_ANM_merge_54009_DEGURBA_stats.xlsx 

13 

 
3  Results 

This section  presents the  results  and  statistics of  the  different  outputs  generated  by  the  application  of  the 
Degree of Urbanisation method to census 2018 of Colombia. 

3.1  Population grid 

The total population contained in the population grid is 46,925,773 people. Overall 4.6% of this figure is counted 
in polygons, where GHS-BUILT from Sentinel 2 detected no built-up areas (in such cases population is spread 
evenly in the cells forming the respective rasterised polygon – areal weighting). 

Figure 6 Cumulative (blue) and reverse cumulative (red) population distributions per levels of density in the population grid 
at 50 m 

Table 2 Population distribution by density classes in both 50 m and 1 km population grids 

Population density 
(people/km2) 
0 - 50 
50 - 100 
100 - 200 
200 - 400 
400 - 800 
800 - 1,600 
1,600 - 3,200 
3,200 - 6,400 
6,400 - 12,800 
12,800 - 25,600 
25,600 - 51,200 
51,200 - 102,400 
102,400 - 204,800 
204,800 - 409,600 

50 m Population grid 

1 km Population grid 

814,794 
204,113 
531,285 
1,359,687 
3,255,748 
7,159,852 
11,607,296 
12,153,439 
7,248,326 
1,876,363 
480,359 
159,392 
61,713 
13,405 

1.7% 
0.4% 
1.1% 
2.9% 
6.9% 
15.3% 
24.7% 
25.9% 
15.4% 
4.0% 
1.0% 
0.3% 
0.1% 
0.0% 

1,589,006 
1,094,135 
1,665,624 
2,229,843 
2,735,425 
3,178,089 
3,867,562 
5,709,058 
8,644,510 
10,360,733 
5,746,725 
105,063 
- 
- 

3.4% 
2.3% 
3.5% 
4.8% 
5.8% 
6.8% 
8.2% 
12.2% 
18.4% 
22.1% 
12.2% 
0.2% 
0.0% 
0.0% 

Table 2 and Figure 6 show the population distributions in the population grids produced. Half of population lives 
in areas of 50 m by 50 m (pixels) accommodating more than 3,200 people per km2, while less than 20% of 
people in  both  grids lives in  areas with a density lower than  1,600 people per km2. The punctual  density (at 
50 m) shows a peak (25.9% of total population) of more than 12 million people between 1,600 people per km2 
and 3,200 people per km2, while the density on a broader scale (at 1 km) has a peak (22.1% of total population) 
of more than 10 million people between 12,800 and 25,600 people per km2. Figure 7 shows a detail of 50-m 
population grid in the centre of Bogotá. 

14 

 
Figure 7 Centre of Bogotá, population grid at 50 m 

15 

 
 
 
 
3.2  Degree of Urbanisation grid 

The Degree of Urbanisation has been produced at Level 1 and Level 2 (summary in Table 3). At Level 1, 22.4% 
of the population is accounted in rural grid cells (about 10.5 million people), 20.9% in urban cluster grid cells 
(about 9.8 million people), and 56.6% in urban centres grid cells (about 26.5 million people). At Level 2, urban 
cluster breaks down into 13% of the total population in dense urban clusters, 2.8% in semi-dense urban cluster 
and 5% in suburban or peri-urban grid cells. The rural domain at Level 2 breaks down into 9.1% of the total 
population in rural clusters, 10% in low density rural grid cells and the remaining 3.4% in very low density rural 
grid cells. The share of urban population based on Degree of Urbanisation grid is 77.5%. 

60%

40%

20%

0%

60%

40%

20%

0%

Rural area Urban cluster Urban centre

Very low density rural grid cells
Rural cluster
Semi-dense urban cluster
Urban centre

Low density rural grid cells
Suburban or peri-urban grid cells
Dense urban cluster

Figure 8 Population share by Degree of Urbanisation Level 1 (left) and Level 2 (right) 

Concerning the number of cells in each class (i.e. surface), the vast majority of grid cells are classified as rural 
grid cells (99.7% of the total), 0.2% as urban cluster and 0.1% as urban centres. The class with the largest 
extent  is  low  density  rural  grid  cells.  In  the  urban  domain,  the  largest  is  the  urban  centre  class  (exceeding 
2,800 km2). 

Population density (people/km2) varies considerably across settlement types, from less than one person per km2 
in very low density grid cells to almost 10,000 people in urban centre grid cells (Table 3). 

Table 3 Degree of Urbanisation Level 1 and Level 2 population and area statistics at grid level 

Level 1 
Class 

Population 
count 

Area 
(km2) 

Population 
share 

Level 2 Class 

Population 
count 

Area  
(km2) 

Populatio
n share 

Population 
density 
people/km2 

Rural 
area 

10,529,239 

2,976,980 

22.4% 

9,832,695 

5,886 

21.0% 

Urban 
cluster 

Urban 
centre 

Water 
Very low 
density rural 
grid cells 
Low Density 
Rural grid 
cells 
Rural cluster 
suburban or 
peri-urban 
grid cells  
Semi-dense 
urban cluster 
Dense urban 
cluster 

- 

796,250 

0.0% 

- 

1,578,418 

2,141,415 

3.4% 

0.7 

4,669,827 

34,039 

10.0% 

137.1 

4,280,994 

5,276 

9.1% 

811.4 

2,351,571 

3,067 

5.0% 

766.7 

1,307,343 

1,150 

2.8% 

1136.8 

6,173,781 

1,669 

13.2% 

3699.1 

26,563,840 

2,836 

56.6% 

Urban Centre 

26,563,840 

2,836 

56.6% 

9366.6 

16 

 
 
 
3.2.1  Settlement entities 

Regarding  the  delineation  of  individual  settlements  (entities),  the  application  of  the  Degree  of  Urbanisation 
method to the 2018 census identifies 74 urban centres in Colombia. The largest (both in surface and population) 
corresponds to the capital city of Bogotá, having a surface of 449 km2, more than 7.73 million inhabitants and 
a built-up area surpassing 212 km2. The second largest urban centre in area overlays with Medellin, it has a 
its  built-up  area  exceeds  46 km2.
surface  of  261 km2, 

it  hosts  about  3.59 million people  and 

Figure 9 Delineation of urban centres in the area of Medellin (left) and settlement grid over the same area (right) 

Forty-eight  urban  centres  (65%  of  the  total)  are  in  a  population  size  class  between  50  and 
150 thousand inhabitants; other nine urban centres are in the class 150 – 250 thousand inhabitants, four in the 
class 350 – 450, and three in the class 750 – 850 thousand. The remaining classes (to185 thousand people 
upper bound) count only one urban centre. The great majority (90%) of urban centres are smaller than 60 km2 
while six are larger than 100 km2 (Table 4). 

Table 4 Ten largest urban centres (population rank) in Colombia 

Place 

Area 
(km2) 

Population 
count 

Bogota 
Medellin 
Cali 
Barranquilla 
Cartagena 
Cúcuta 
Bucaramanga 
Pereira 
Ibagué 
Valledupar 

449 
261 
164 
127 
154 
118 
62 
66 
49 
49 

7,733,295 
3,594,872 
1,837,624 
1,597,096 
831,565 
760,038 
758,334 
524,457 
422,133 
398,845 

The  Degree of  Urbanisation method identifies 440 dense  urban clusters with a population ranging between 
5,034 and 48,605 people (ten largest in population in Table 5). The largest one is in the area of Santa Cruz de 

17 

 
  
 
Lorica. Almost half the dense urban clusters (213 of them) are in the population class below 10,000 inhabitants, 
20% between 10 and 15 thousand, 12% between 20 and 25 thousand. 

Figure 10 Delineation of dense urban cluster in the area of Santa Cruz de Lorica (left) and settlement grid over the same 
area (right) 

Seventy-five dense urban clusters are in the size classes between 25 and 40 thousand people, 14 between 40 
and 50 thousand. Among the largest dense urban clusters are the coastal settlements in the area of Santiago 
de Tolú (40 km2 and hosting about 25,000 people) and Puerto Colombia (22 km2 and about 33,000 people). The 
extent of dense urban cluster varies between 2 and 48 km2, of which 85% are less than 5 km2. 

Table 5 Ten largest dense urban clusters (population rank) in Colombia 

Place 

Area 
(km2) 

Population 
count 

Santa Cruz de Lorica 
San Andres 
Chigorodó 
Espinal 
San Gil 
Turbo 
El Carmen de Bolívar 
Sahagún 
Santander de Quilichao 
Chiquinquirá 

10 
48 
6 
10 
11 
8 
17 
7 
9 
9 

48,605 
48,119 
47,679 
47,579 
47,173 
46,621 
45,960 
44,885 
44,378 
44,024 

The  Degree  of  Urbanisation  method  identifies  192  semi-dense  urban  clusters  with  a  population  ranging 
between 5,002 and 27,849 people. Most of these clusters (60%) are in a population size class between 5,000 
and  6,500 inhabitants,  112  clusters  (25%)  between  6,500  and  8,000,  the  remaining  16  clusters  have  a 
population between 8,000 and 30,000 inhabitants, whereas only two exceeds 15,000. The size of semi-dense 
urban cluster varies between 2 and 35 km2. The majority of these settlements (60%)  is smaller than 5 km2. 
About 30% are between 5 and 10 km2. Semi-dense urban clusters between 10 and 20 km2 are about 12% of 
the total, and the remaining two clusters are greater than 20 km2. Other 55 clusters are between 6 and 10 km2. 
Seven are between 11 and 20 km2, while three are larger than 20 km2. 

18 

 
 
In the rural domain, the degree of urbanisation maps 2,905 rural clusters. Almost half the clusters (1,311) have 
a population  between  500 and 1,000 inhabitants. About  20% of the  rural  clusters have a  population below 
1,500 people and 4% host a population between 4,000 and 5,000 people. Half of these clusters are very small 
(1 km2), or small (30%, 2 km2), about  20% are between  3 and 5 km2, 26 are between 6 and 8 km2 and the 
remaining three rural clusters are 9 km2. 

19 

 
 
3.3  Classification of territorial units by Degree of Urbanisation 

Based on the Degree of Urbanisation grid and the population grid derived from the Censo Nacional de Población 
y Vivienda Colombia 2018 geometries it was possible to code the Manzana and Seccion Rural administrative 
units by Degree of Urbanisation. The Degree of Urbanisation classifies local units based on population majority 
applied to the grid level classification.  

The 551,028 units comprising the territorial units layer (merging Seccion Rural and Manzana) are classified at 
Degree of Urbanisation Level 1 in 151,855 rural area units, 152,294 towns and semi-dense areas and 246,849 
city units. The corresponding population split is 23% in rural area units, 21% in towns and semi-dense areas 
and 56% in city units (Figure 11 and Table 6). The rural areas and towns and semi-dense areas classes account 
for a higher share of units (28% in both classes), but host lower shares of the national population compared to 
cities. 

The share of urban population based on classified territorial units is 77.9%. 

At level 2 of the Degree of Urbanisation, 7% of the units  are classified as mostly uninhabited areas hosting 
about 1 million people. Dispersed rural areas are 8% of the units and host 4.7 million people, villages are 12% 
of  the  units  (almost  66,000)  and  host  4.4 million people.  In  the  urban  domain,  6%  of  census  polygons  are 
classified as suburban or peri-urban areas (and host 2.1 million people), semi-dense towns are 4% of the census 
units and host 1.3 million people, dense towns are 18% of the census units and host 6.1 million people (the city 
class remains the same as in level 1). 

Figure 11 Classification of Manzanas and Seccion Rural by Degree of Urbanisation at level 1in the area of Cali 

20 

 
 
  
 
 
Table 6 Degree of Urbanisation level 1 and level 2 population and area statistics at territorial unit level 

Level 1 
type 

number 
of units 

population  % 

units 

% 
pop 

Level 2 type 

number 
of units 

population  % 

units 

% 
pop 

Rural 
area 

Towns 
and 
suburbs 

151,885  10,278,891 

28% 

23% 

152,294 

9,612,470 

28% 

21% 

Mostly 
uninhabited 
areas 
Dispersed rural 
areas 

40,386 

1,055,683 

7% 

2% 

45,541 

4,771,144 

8%  11% 

Villages 

65,958 

4,452,064 

12%  10% 

Suburban or 
peri-urban 
areas 
Semi-dense 
towns  

32,527 

2,156,241 

6% 

5% 

21,015 

1,332,600 

4% 

3% 

Dense towns 

98,752 

6,123,629 

18%  14% 

City 

246,849  24,977,425 

45% 

56% 

City 

246,849  24,977,425 

45%  56% 

3.3.1  Comparing grid based statistics to territorial units statistics 

The Degree of Urbanisation is applied in two stages, first at the grid level and second at territorial units level. 
Population  statistics  derived  from  these  two  processes  can  vary.  This  is  due  to  scale  (a  factor  that  affect 
geospatial analysis), but also to the fact that at the grid level, population per class is calculated based only on 
the cells classified as rural, urban cluster or urban centre. For local units, once the unit is classified based on 
the population majority at the grid level, all the population in that unit is accounted in that class. 

In the case of Colombia, population in grid cells in the urban domain (grid cell codes 2 and 3) is 76.7% compared 
to 77.9% when the calculation is based on local units. A similar  difference is appreciable when we consider 
classes Urban Centre (55.6%) and Cities (56.7%). 

3.3.2  Comparing the national definition with the Degree of Urbanisation 

When information available in the census contain the national classification of units in urban and rural domains, 
it  is  possible  to  compare  the  national  classification  with  the  one  obtained  by  applying  the  Degree  of 
Urbanisation, by doing a cross tabulation. This tabulation intersects the units and their population as classified 
by the national definition and the Degree of Urbanisation (by population or by number of units). The result is 
indicative on the level of agreement or divergence between what is classified as urban or rural by the national 
definition and what it is by the Degree of Urbanisation. This helps understanding how the intermediate category 
of towns and semi-dense area is generally classified by the national definition. This intermediate category in 
some contexts may be more likely to be classified as rural, while in others as urban. 

Overall, the share of population counted in units classified urban in the census of Colombia is 78.8% compared 
to 77.1% in the Degree of Urbanisation. 

In the case of Colombia (Table 7), 97% of the territorial units classified as rural in the census, are also classified 
as rural areas by the Degree of Urbanisation, and 78% of the units classified as urban in the census are also 
in the urban domain according to the Degree of Urbanisation method. In particular, 47.1% of the urban units in 
the census are cities, and 30.6% are towns and semi-dense areas. Some 22.3% of the units classified as urban 
in the census are rural in the Degree of Urbanisation, and 2.6% of the units classified as rural in the census are 
towns and semi-dense area in the Degree of Urbanisation. Only 0.4% of the units are classified rural in the 
census and are cities in the Degree of Urbanisation. 

21 

 
 
 
Table 7 Cross tabulation between the national definition classification and the Degree of Urbanisation classification (Level 1) of territorial 
units per share of population and share of territorial units: 
“how many census defined Urban/Rural (U/R) are classified Rural area/Towns and semi-dense areas/City by Degree of Urbanisation?” 

Population 
U 
R 

Polygons 
U 
R 

Rural area  75.1%  7.9% 
Towns and 
semi-dense areas 

20.9%  21.6% 

Rural area  97.0%  22.3% 
Towns and 
semi-dense areas 

2.6%  30.6% 

City  4.0%  70.6% 

City  0.4%  47.1% 

The match is also high when classifications are compared by population: 92.1% of the population considered 
urban by the census designation is also classified in the urban domain by the Degree of Urbanisation. About 
71% of the population counted as urban by the national definition for the Degree of Urbanisation is in cities, 
and an additional 21.6% is in towns and semi-dense areas. More than 75% of the population defined rural in 
the census is counted in rural areas by the Degree of Urbanisation. About 21% of the population classified rural 
in the census is in the intermediate category towns and semi-dense areas in the Degree of Urbanisation. 

The  Degree  of  Urbanisation  level 2  offers  some  insights  about  the  cases  in  which  there  are  the  slight 
discrepancies (Table 8) mentioned above. About 45% of the population counted in  villages in the Degree of 
Urbanisation is classified urban population in the census; in this case, more than 95% of the territorial units 
that are villages are classified urban in the census.  

In total, the difference between the national definition and the one by the Degree of Urbanisation of the urban 
population  is  0.9%  (based  on  territorial  units).  The  match  by  population  remains  high  when  comparing  the 
results at grid level (difference of 2.1%). 

Table 8 Cross tabulation between the Degree of Urbanisation classification (Level 1 and Level 2) and the national definition per share of 
population and share of territorial units: 
“How many classified Rural area/Towns and semi-dense areas/City by Degree of Urbanisation are census defined Urban/Rural (U/R)?” 

Population 
U 
R 

Polygons 
U 
R 

Rural area  73.3%  26.7% 
Towns and semi-dense areas  21.8%  78.2% 
City  1.6%  98.4% 

Rural area  33.8%  66.2% 
  Towns and semi-dense areas  1.0%  99.0% 
City  0.1%  99.9% 

R 

U 

R 

U 

Mostly uninhabited areas  97.9%  2.1% 
Dispersed rural areas  85.1%  14.9% 
Villages  54.8%  45.2% 
Suburban or peri-urban areas  38.3%  61.7% 
Semi-dense towns  37.1%  62.9% 
Dense towns  12.6%  87.4% 
City  1.6%  98.4% 

Mostly uninhabited areas  84.4%  15.6% 
Dispersed rural areas  23.9%  76.1% 
Villages  4.5%  95.5% 
  Suburban or peri-urban areas  2.0 %  98.0% 
Semi-dense towns  1.5%  98.5% 
Dense towns  0.5%  99.5% 
City  0.1%  99.9% 

22 

 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3.4  Classification  of  demographic  and  housing  variables  by  Degree  of 

Urbanisation 

The  classification  of  territorial  units  allows  to  relate  specific  indicators  contained  in  the  demographic  and 
housing census to the Degree of Urbanisation classification of the survey/reporting units (this process is named 
disaggregation by Degree of Urbanisation). 

The source dataset contains a number of variables related to demographic (sex and age) and housing attributes 
(infrastructural  endowment).  These  variables  can  be  disaggregated  by  Degree  of  Urbanisation  to  identify 
variations  across  the  urban-rural  gradient.  The  disaggregation  of  indicators  by  Degree  of  Urbanisation  is 
particularly relevant to monitor Sustainable Development Goals indicators, and to compare the results across 
countries. The results presented in the next sub-sections are based on the number of viviendas (i.e. dwelling or 
residence) with access to a specific infrastructure or service. Several SDG targets refer to the proportion of the 
population with access to a specific infrastructure or service. This information cannot be extracted directly from 
the information publicly available in Colombia census. Other approaches could  attempt to model an average 
person  per  viviendas,  but  we  do  not  perform  this  estimate  to  reduce  assumptions.  With  the  Degree  of 
Urbanisation it is possible to determine which viviendas are located in cities, towns and semi-dense areas and 
rural areas to disaggregate data and eventually compute the indicator in combination with other data available 
at local level. 

3.4.1  Population age and sex by Degree of Urbanisation 

Population counts by age and sex are typical census variables. This information is vital to derive population age 
pyramids, and sex ratio. Both these indicators are widely used across policy sectors (including education, labour, 
health, etc.). 

3.4.1.1  Population age 

The population age structure in Colombia varies across the urban-rural gradient (Figure 12). Younger ages (0-9 
years and 10-19 years) are dominant in rural areas, and more than half the population in these areas is 29 
years or younger. Urban areas have a higher share of the age ranges between 20 and 69 years. Cities host the 
majority of population groups in the ranges 20-29 years, 30-39 years, 40-49 years, 50-59 years and 60-69 
years. Up to 18% of the individuals between 20 and 29 years are in cities. Cities also host the highest share in 
the range 30-39 years (15.7% compared to 14.1% in towns and semi-dense areas and 13% in rural areas). 
These characteristics may relate to higher access to opportunities offered by cities (e.g. jobs and education) as 
reported in literature. Elderly people also account for a considerable share of city population: about 25% of city 
population  is older than  50.  Towns and  semi-dense  areas have  a  blended  pattern  between  rural  areas and 
Cities. These areas have an higher share of younger generations (<19 years) of about 3% per class compared 
to cities, but account for a consistently lower share of middle age population (between 20 and 50 years) of 
about 1% across classes. 

Rural areas

Towns and suburbs

80+ years

2.0%

70-79 years

4.1%

60-69 years

6.9%

2.0%

3.8%

6.7%

2.0%

3.9%

City

7.6%

50-59 years

40-49 years

30-39 years

20-29 years

10-19 years

0-9 years

9.9%

11.4%

13.0%

15.2%

19.8%

17.6%

10.3%

11.9%

14.1%

16.7%

18.5%

16.0%

11.7%

12.6%

15.7%

18.1%

15.4%

13.1%

0%

10%

20%

0%

10%

20%

0%

10%

20%

 Figure 12 Population age classes by Degree of Urbanisation Level 1 

23 

 
 
At the second level of the Degree of Urbanisation, the pattern above is more evident in some  types of areas. 
The dominance of the youngest age groups (0-9 and 10-19) emerges in mostly uninhabited areas (class 11), 
where each of these two classes are above 20% of the population (Table 9). The other classes of the Degree 
of Urbanisation Level 2 account a higher share of youngest groups in semi-dense towns (22) compared to dense 
towns (23) –age groups unitl 19 years of age, after the pattern reverses with a higher share of the age groups 
between 30 and 59. Again, a prevalence of the oldest population groups (60 to 80+) is observed in semi-dense 
towns compared to dense towns. 

Table 9 Population age share by Degree of Urbanisation Level 2 and national average (NAT) 

Mostly 
uninhabited areas 
Dispersed rural 
areas 
Villages 
Suburban or peri-
urban areas 
Semi-dense 
towns  
Dense towns 
City 
NAT 

0-9 
years 

10-19 
years 

20-29 
years 

30-39 
years 

40-49 
years 

50-59 
years 

60-69 
years 

70-79 
years 

80+ 
years 

20.9% 

20.7% 

15.3% 

12.8% 

11.1% 

9.1% 

5.9% 

3.0% 

1.3% 

16.9% 

19.5% 

14.9% 

13.0% 

11.6% 

10.3% 

7.3% 

4.3% 

2.1% 

17.5% 

19.8% 

15.5% 

13.2% 

11.3% 

9.7% 

6.7% 

4.1% 

2.2% 

15.7% 

18.2% 

16.7% 

14.3% 

12.2% 

10.4% 

6.7% 

3.7% 

1.9% 

16.4% 

19.0% 

16.1% 

13.6% 

11.7% 

10.1% 

6.8% 

4.1% 

2.2% 

16.0% 
13.1% 
14.7% 

18.4% 
15.4% 
17.0% 

16.8% 
18.1% 
17.1% 

14.2% 
15.7% 
14.8% 

11.9% 
12.6% 
12.2% 

10.3% 
11.7% 
11.0% 

6.7% 
7.6% 
7.2% 

3.7% 
3.9% 
3.9% 

2.0% 
2.0% 
2.0% 

3.4.1.2  Population sex 

The gender distribution between urban and rural areas in Colombia has a  clear split. Male population is the 
majority in Rural areas (51.7%), and female is the majority in the urban domain (52.1%), and highest in cities 
(52.5%) compared to a national proportion of 48.7% of males and 51.3% of females.  

51.7%

48.9%

47.5%

51.1%

52.5%

48.3%

60%

45%

30%

15%

0%

M

F

Rural area

Towns and semi-dense area

City

Figure 13 Sex ratio (M males; F females) at Degree of Urbanisation Level 1 

At  Level 2  of  the  Degree  of  Urbanisation,  the  share  of  female  population  is  lowest  in  class  11-mostly 
uninhabited areas (45.8%), and  dominant in classes 21-suburb or  peri-urban,  23-dense and  22-semi-dense 
towns (Table 10). 

24 

 
 
  
 
 
Table 10 population gender share by Degree of Urbanisation Level 2 and national average (NAT) 

Class 
Male  Female 
45.8% 
54.2% 
Mostly uninhabited areas 
47.8% 
52.2% 
Dispersed rural areas 
49.4% 
50.6% 
Villages 
50.5% 
Suburban or peri-urban areas  49.5% 
50.4% 
49.6% 
Semi-dense towns  
51.4% 
48.6% 
Dense towns 
52.5% 
47.5% 
City 
51.3% 
48.7% 
NAT 

25 

 
 
 
3.4.2  Access to electricity 

The Target 7.1 of Sustainable Development Goal 7 on “Affordable and Clean Energy” aims to ensure universal 
access to affordable, reliable and modern energy services. One of the indicators used to monitor this Target 
assesses the proportion of population with access  to electricity (7.1.1). In Colombia, knowing the amount of 
viviendas having access to electricity is important information in that perspective, and understanding how the 
proportion  varies  by  Degree  of  Urbanisation  (cities,  towns  and  semi-dense  areas  and  rural  areas)  can  help 
meeting the SDG targets and inform policy to speed transition. 

In Colombia 96.3% of the viviendas are reported to have access to electricity. The Degree of Urbanisation helps 
disaggregating this national figure to identify variations. Urban areas have a higher access: for example, 99.5% 
of viviendas in cities are reported having access to electricity, compared to 97.6% in towns and semi-dense 
areas, and to 86.3% in rural areas (10% less compared to the national average) (Figure 14 left).  

At the second level of the Degree of Urbanisation, the situation is more nuanced, but access to electricity still 
increases with urbanization. Mostly uninhabited areas report the lowest national share of viviendas with access 
to electricity (58.7%), while villages have the highest share in the rural domain (87.7%). In the urban domain, 
Dense towns have a higher share of viviendas with access to electricity (98%) compared to suburban or peri-
urban areas (97.2%) and semi-dense towns (96.4%), but lower than cities (99.5%) (Figure 14 right). 

100%

80%

60%

40%

20%

0%

%
7
3
1

.

%
5
0

.

%
4
2

.

N

Y

Rural areas

Towns and semi-dense area

Cities

100%

80%

60%

40%

20%

0%

%
3
1
4

.

%
3
2
1

.

%
7
8

.

%
8
2

.

%
6
3

.

%
0
2

.

%
5
0

.

Y

N

Mostly uninhabited areas

Dispersed rural areas

Villages

Suburban or peri-urban areas

Semi-dense Towns

Dense Towns

Cities

Figure 14 Share of viviendas with access (Y) and no access (N) to electricity by Degree of Urbanisation Level 1(left) and 
Level 2 (right) 

26 

 
 
  
 
 
3.4.3  Access to internet 

Access to internet is a growing need to exploit the opportunities brought by digitalisation. Also in the SDG context 
specific  goals aim at  promoting peoples’ access to internet connectivity with broadband and  mobile phones 
(SDG 9 and SDG 17). The census of Colombia counts the number of viviendas with access to internet, and at 
national level 41.5% of the viviendas are reported with access and 57.6% with no access (0.9% of the viviendas 
are  reported  with  an  undetermined  access).  However,  analysis  by  Degree  of  Urbanisation  shows  striking 
variations in this access. More than 60% of the viviendas in cities have access to the internet, while this share 
drops to 21.6% in towns and semi-dense areas and to 5.3% in rural areas. 

The second level of the Degree of Urbanisation shows the lowest access to internet in mostly uninhabited areas 
(1.8% of the viviendas), and a significant increase in villages (6.9%). In the urban domain, access to internet in 
suburban or peri-urban areas is almost double the one in semi-dense towns (21% and 11% respectively) and 
increases to almost ¼ of the viviendas in dense towns. Access to internet in cities is above 60%, so more than 
20% higher compared to national average. 

%
8
3
9

.

%
6
7
7

.

%
2
7
3

.

100%

80%

60%

40%

20%

0%

Y

N

N/A

Rural areas

Towns and semi-dense area

Cities

%
5
6
9

.

%
7
4
9

.

%
3
2
9

.

.

%
8
7
%8
6
7
7

.

%
4
5
7

.

%
2
7
3

.

100%

80%

60%

40%

20%

0%

Y

N

N/A

Mostly uninhabited areas
Villages
Semi-dense Towns
Cities

Dispersed rural areas
Suburban or peri-urban areas
Dense Towns

Figure 15 Share of viviendas with access (Y) no access (N) and not available (N/A) to Internet by Degree of Urbanisation Level 1(left) and 
Level 2 (right) 

27 

 
 
 
 
 
3.4.4  Access to water 

The Target 6.1 of the Sustainable Development Goal 6. “Ensure availability and sustainable management of 
water and sanitation for all” aims at by 2030, achieve universal and equitable access to safe and affordable 
drinking water for all. 

The 2018 census of Colombia reports the number of viviendas with access to water. Combining this data with 
the Degree of Urbanisation shows a split in access to water at Level 1, with access in 54.3% of the viviendas 
in rural areas, 85.6% of the viviendas in towns and semi-dense areas, and 97.1% of the viviendas in cities. The 
national average is 85.6%. Also in the case of access to water, cities are much more endowed compared to 
other areas. In rural areas, more than 45% of viviendas have no access to water. 

The  Degree  of  Urbanisation  Level 2  further  disaggregates  this  pattern,  showing  that  less  than  20%  of  the 
viviendas  in  mostly  uninhabited  areas  have  access  to  water.  The  share  of  serviced  viviendas  increases 
substantially  in  dispersed  rural  areas (50.2%) and  in  villages,  where  more  than  65%  of  the  viviendas  have 
access to water (Figure 16). In towns and semi-dense area and suburban and peri-urban areas the access to 
water is about 81% compared to 88% in Dense towns. 

100%

80%

60%

40%

20%

0%

%
7
5
4

.

%
4
4
1

.

N

%
9
2

.

Y

Rural areas

Towns and semi-dense area

Cities

100%

80%

60%

40%

20%

0%

%
7
0
8

.

%
8
9
4

.

%
1
3
3

.

%
5
8
1

.

%
4
8
1

.

%
1
2
1

.

%
9
2

.

Y

N

Mostly uninhabited areas

Dispersed rural areas

Villages

Suburban or peri-urban areas

Semi-dense Towns

Dense Towns

Cities

Figure 16 Share of viviendas with access (Y) and no access (N) to water by Degree of Urbanisation Level 1(left) and Level 
2 (right) 

28 

 
 
  
 
 
3.4.5  Access to the sewage system 

In  the  same  policy  framework  in  which  access  to  water  is  a  key  target,  also  access  to  sanitation  plays  an 
important role (SDG 6). SDG Target 6.2 strives to ensure, by 2030, access to adequate and equitable sanitation 
and hygiene for all and end open defecation, paying special attention to the needs of women and girls and those 
in vulnerable situations. The 2018 Colombian census counts the number of viviendas with and without access 
to a sewage system. At the national level, 74.6% of the viviendas are connected to such systems. Analysing 
this by Degree of Urbanisation shows that the share of viviendas that are connected is much higher in cities 
(95.1%) compared to towns and semi-dense areas (68.3%) and rural areas (24.6%) (Figure 17). 

At the second level of the Degree of Urbanisation, in the rural domain villages are the most connected (39.9%), 
while in dispersed rural areas only 15% of the viviendas are connected, and in mostly uninhabited areas this 
value drops to 5%. In the urban domain, dense and semi-dense towns have more viviendas connected to (75.5% 
and 56.4% respectively) compared to the ones in suburban or peri-urban areas (54.9%). Accordingly, the most 
significant differences are observed between dispersed rural areas and villages, and between suburban or peri-
urban areas and cities. 

100.0%

80.0%

60.0%

40.0%

20.0%

0.0%

Y

Rural areas

%
4
5
7

.

%
7
1
3

.

N

%
9
4

.

Towns and semi-dense area

Cities

100%

80%

60%

40%

20%

0%

%
9
4
9

.

%
4
5
8

.

%
1
0
6

.

%
1
5
4

.

%
6
3
4

.

%
5
4
2

.

%
9
4

.

Y

N

Mostly uninhabited areas

Dispersed rural areas

Villages

Suburban or peri-urban areas

Semi-dense Towns

Dense Towns

Cities

Figure 17 Share of viviendas connected (Y) or not (N) to a sewage system by Degree of Urbanisation Level 1(left) and 
Level 2 (right) 

29 

 
 
  
 
 
3.4.6  Access to waste management 

Sustainable Development Goal 11 aiming to “Make cities and human settlements inclusive, safe, resilient and 
sustainable”  has  an  Indicator  (11.6.1)  focusing  on  the  share  of  city  solid  waste  collected  and  managed  in 
controlled facilities. Beside the need to produce local data for this indicator, it is important to harmonise the 
ways in which urban and rural areas are defined, to make sure this indicator is consistently reported across 
countries.  

The 2018 census of Colombia reports the number of viviendas with access to waste management. Overall 80% 
of the viviendas have access, but this accessibility varies significantly by Degree of Urbanisation. The share is 
highest in cities (98%), higher than national average in towns and semi-dense areas (81.5%) and about 30% 
in rural areas. 

At the second level of the Degree of Urbanisation, in the rural domain, villages stand out with 46.1% of the 
viviendas with access to waste management compared to less than half this figure in  dispersed rural areas 
(20.1%) and to the 6.1% of viviendas in mostly unpopulated areas. In the urban domain, the share of viviendas 
with access to waste management is higher in suburban or peri-urban areas (73.2%) compared to semi-dense 
towns (66.7%), but lower compared to dense towns where more than 87.5% of the viviendas are served. 

100%

80%

60%

40%

20%

0%

Y

Rural areas

%
1
0
7

.

%
5
8
1

.

N

%
0
2

.

Towns and semi-dense area

Cities

100%

80%

60%

40%

20%

0%

%
9
3
9

.

%
9
9
7

.

%
9
3
5

.

%
3
3
3

.

%
8
6
2

.

%
5
2
1

.

%
0
2

.

Y

N

Mostly uninhabited areas
Villages
Semi-dense Towns
Cities

Dispersed rural areas
Suburban or peri-urban areas
Dense Towns

Figure 18 Share of viviendas with access (Y) and no access (N) to waste management by Degree of Urbanisation Level 1(left) and Level 2 
(right) 

30 

 
 
4  Conclusion 

The Degree of Urbanisation method to delineate urban and rural areas for international statistical comparison 
was endorsed and recommended at the 51st session of the United Nations Statistical Commission to support 
the monitoring of the Sustainable Development Goals. This report contains the first application of the Degree 
of Urbanisation method to a post 2015 national demographic and housing survey (census) with tools developed 
in the framework of the  Global Human Settlement Layer. The showcase is made  possible by the open data 
policy  of  the  Departamento  Administrativo  Nacional  de  Estadística  of  Colombia  (DANE)  that  makes  fully 
available on the internet the 2018 census information in geospatial and statistical format.  

The report  presents the whole process to apply the  Degree of Urbanisation method to census data:  first  by 
generating a  population grid  with the GHS-POP2G Tool  using census geospatial data and the most updated 
GHSL  built-up  layer  to  date,  obtained  by  processing  Copernicus  Sentinel  2  imagery;  second,  by  producing  a 
settlement classification grid using the GHS-DUG Tool based on the population grid obtained in the previous 
step; third, by classifying local units (in this report the finest census units available, Manzana and Seccion Rural) 
by Degree of Urbanisation; and fourth, by disaggregating other demographic and housing indicators contained 
in the census by Degree of Urbanisation (population sex and age structure, and viviendas access to electricity, 
internet, water, a sewage system and waste management). 

The results show a very close match between the classification of local units based on the national definition 
or urban/ural areas and the  Degree of  Urbanisation method, and a  gradient between  urban and rural areas 
across all the variables analysed by Degree of Urbanisation. 

With  the  methods  and  application  framework  presented  in  this  report,  the  aim  is  to  explain  the  practical 
implementation of the Degree of Urbanisation method to census rounds and demographic and housing surveys. 
The  report  serves  as  implementation  extension  to  the  report  “Applying  the  Degree  of  Urbanisation  -  a 
methodological manual to define cities, towns and rural areas for international comparisons” jointly developed 
by  the  European  Commission,  FAO  and  OECD  (European  Commission  and  Statistical  Office  of  the  European 
Union 2021). 

31 

 
5  References 

Corbane, Christina,  Vasileios  Syrris,  Filip  Sabo,  Panagiotis  Politis,  Michele  Melchiorri,  Martino  Pesaresi, Pierre 
Soille, and Thomas Kemper. 2020. “Convolutional Neural Networks for Global Human Settlements Mapping from 
Sentinel-2  Satellite  Imagery.”  Neural  Computing  and  Applications,  October.  https://doi.org/10.1007/s00521-
020-05449-7. 

Dijkstra,  Lewis,  Aneta  J.  Florczyk,  Sergio  Freire,  Thomas  Kemper,  Michele  Melchiorri,  Martino  Pesaresi,  and 
Marcello  Schiavina.  2020.  “Applying  the  Degree  of  Urbanisation  to  the  Globe:  A  New  Harmonised  Definition 
Reveals  a  Different  Picture  of  Global  Urbanisation.”  Journal  of  Urban  Economics,  November,  103312. 
https://doi.org/10.1016/j.jue.2020.103312. 

European Commission, and Statistical Office of the European Union. 2021. Applying the Degree of Urbanisation 
- a Methodological Manual to Define Cities, Towns and Rural Areas for International Comparisons. 

Maffenini, L., M. Schiavina, S. Freire, M. Melchiorri, M. Pesaresi, and T. Kemper. 2020.  GHS-POP2G User Guide. 
Luxembourg: Publications Office of the European Union. https://doi.org/10.2760/500887. 

Maffenini, L., M. Schiavina, M. Melchiorri, M. Pesaresi, and T. Kemper. 2020a. GHS-DUG User Guide. Luxembourg: 
Publications Office of the European Union. https://doi.org/10.2760/916217. 

———.  2020b.  GHS-DU-TUC  User  Guide.  Luxembourg:  Publications  Office  of  the  European  Union. 
https://data.europa.eu/doi/10.2760/633624. 

OECD,  and  European  European  Commission.  2020.  Cities 
ilibrary.org/content/publication/d0efcbda-en. 

in 

the  World.  https://www.oecd-

UN-GGIM.  2020.  Future  Trends  in  Geospatial  Information  Management:  The  Five  to  Ten  Year  Vision.  United 
Nations. 

Wardrop, N. A., W. C. Jochem, T. J. Bird, H. R. Chamberlain, D. Clarke, D. Kerr, L. Bengtsson, S. Juran, V. Seaman, 
and A. J. Tatem. 2018. “Spatially Disaggregated Population Estimates in the Absence of National Population and 
Housing  Census  Data.”  Proceedings  of  the  National  Academy  of  Sciences  115 
(14):  3529–37. 
https://doi.org/10.1073/pnas.1715305115. 

32 

 
 
 
 
List of abbreviations and definitions 

DANE 
DEGURBA 
EUROSTAT 
F 
GHS-BUILT 

GHS-DUG 
GHS-DU-TUC 
GHSL 
GHS-POP 

GHS-POP2G 
GHS-SMOD 

M 
N 
N/A 
NAT 
R 
RAM 
SDG 
U 
UID 
UN-GGIM 
Y 

Departamento Administrativo Nacional de Estadística of Colombia  
Degree of Urbanisation 
The statistical office of the European Union 
Females 
Multitemporal information layer on built-up area presence as derived from satellite data, from 
GHSL 
GHSL Degree of Urbanisation Grid Tool 
GHSL Degree of Urbanisation – Territorial Units Classifier Tool 
Global Human Settlement Layer 
Spatial raster dataset that depicts the distribution of population, expressed as the number of 
people per cell, from GHSL 
GHSL Population to Grid Tool 
Settlement classification raster dataset applying the Degree of Urbanisation method to GHS-POP 
and GHS-BUILT data 
Males 
Viviendas with no access to the infrastructure 
Accessibility to infrastructure data not available in the census 
National average 
Rural 
Random-access memory 
Sustainable Development Goals 
Urban 
Unique Identifiers 
United Nations Committee of Experts on Global Geospatial Information Management 
viviendas with access to the infrastructure 

List of GHSL Resources 

GHS-BUILT 
GHS-DUG 

GHS-DU-TUC 

GHS-POP 
GHS-POP2G 

GHS-SMOD 

Dataset information: https://ghsl.jrc.ec.europa.eu/ghs_bu_s2_2018.php  
Tool: https://ghsl.jrc.ec.europa.eu/tools.php  
User Guide: https://ghsl.jrc.ec.europa.eu/documents/GHS-DUG_4_User_Guide.pdf?0.6375801745442322  
Tool: https://ghsl.jrc.ec.europa.eu/tools.php  
User Guide: https://ghsl.jrc.ec.europa.eu/documents/GHS-DUTUC_User_Guide.pdf?0.6827332891117774  
Dataset information: https://ghsl.jrc.ec.europa.eu/ghs_pop2019.php  
Tool: https://ghsl.jrc.ec.europa.eu/tools.php  
User Guide: https://ghsl.jrc.ec.europa.eu/documents/GHS-POP2G_2_User_Guide.pdf?0.9026048752029705  
Dataset information: https://ghsl.jrc.ec.europa.eu/ghs_smod2019.php  

33 

 
 
 
 
List of figures 

Figure 1 Applying the Degree of Urbanisation with GHSL Tools ...........................................................................................................8 

Figure 2 GHS-POP2G interface with the parameters set for the production of the population grid ......................... 10 

Figure 3 GHS-DUG interface with the parameters set for the production of the settlement classification grid 11 

Figure 4 GHS-DU-TUC interface with the parameters set for the classification of the local units by Degree of 
Urbanisation ....................................................................................................................................................................................................................... 11 

Figure 5 Pivot table used to compute the counts and shares of population age classes by degree of 
urbanisation: (1 = Rural Area; 2 = Towns and Semi-dense areas; 3 = City) .............................................................................. 12 

Figure 6 Cumulative (blue) and reverse cumulative (red) population distributions per levels of density in the 
population grid at 50 m .............................................................................................................................................................................................. 14 

Figure 7 Centre of Bogotá, population grid at 50 m ................................................................................................................................. 15 

Figure 8 Population share by Degree of Urbanisation Level 1 (left) and Level 2 (right) ................................................... 16 

Figure 9 Delineation of urban centres in the area of Medellin (left) and settlement grid over the same area 
(right) ...................................................................................................................................................................................................................................... 17 

Figure 10 Delineation of dense urban cluster in the area of Santa Cruz de Lorica (left) and settlement grid 
over the same area (right) ........................................................................................................................................................................................ 18 

Figure 11 Classification of Manzanas and Seccion Rural by Degree of Urbanisation at level 1in the area of Cali
 .................................................................................................................................................................................................................................................... 20 

Figure 12 Population age classes by Degree of Urbanisation Level 1 .......................................................................................... 23 

Figure 13 Sex ratio (M males; F females) at Degree of Urbanisation Level 1 ......................................................................... 24 

Figure 14 Share of viviendas with access (Y) and no access (N) to electricity by Degree of Urbanisation Level 
1(left) and Level 2 (right) ........................................................................................................................................................................................... 26 

Figure 15 Share of viviendas with access (Y) no access (N) and not available (N/A) to Internet by Degree of 
Urbanisation Level 1(left) and Level 2 (right) ............................................................................................................................................... 27 

Figure 16 Share of viviendas with access (Y) and no access (N) to water by Degree of Urbanisation Level 
1(left) and Level 2 (right) ........................................................................................................................................................................................... 28 

Figure 17 Share of viviendas connected (Y) or not (N) to a sewage system by Degree of Urbanisation Level 
1(left) and Level 2 (right) ........................................................................................................................................................................................... 29 

 Figure 18 Share of viviendas with access (Y) and no access (N) to waste management by Degree of 
Urbanisation Level 1(left) and Level 2 (right) ............................................................................................................................................... 30 

34 

 
List of tables 

Table 1 Workflow to apply the Degree of Urbanisation with GHS-Tools with pre-processing and analysis steps
 .................................................................................................................................................................................................................................................... 13 

Table 2 Population distribution by density classes in both 50 m and 1 km population grids ........................................ 14 

Table 3 Degree of Urbanisation Level 1 and Level 2 population and area statistics at grid level.............................. 16 

Table 4 Ten largest urban centres (population rank) in Colombia ................................................................................................... 17 

Table 5 Ten largest dense urban clusters (population rank) in Colombia ................................................................................... 18 

Table 6 Degree of Urbanisation level 1 and level 2 population and area statistics at territorial unit level .......... 21 

Table 7 Cross tabulation between the national definition classification and the Degree of Urbanisation 
classification (Level 1) of territorial units per share of population and share of territorial units: “how many 
census defined Urban/Rural (U/R) are classified Rural area/Towns and semi-dense areas/City by Degree of 
Urbanisation?” .................................................................................................................................................................................................................... 22 

Table 8 Cross tabulation between the Degree of Urbanisation classification (Level 1 and Level 2) and the 
national definition per share of population and share of territorial units: “How many classified Rural 
area/Towns and semi-dense areas/City by Degree of Urbanisation are census defined Urban/Rural (U/R)?” ..... 22 

Table 9 Population age share by Degree of Urbanisation Level 2 and national average (NAT) ................................... 24 

Table 10 population gender share by Degree of Urbanisation Level 2 and national average (NAT) ........................ 25 

35 

 
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doi:10.2760/939289 

ISBN 978-92-76-37556-2