Cultural and creative cities monitor : 2023 update.
Date: 2023-09-06
Institutions: Joint Research Centre
Subjects: EU Member State, artistic creation, comparative analysis, cultural policy, cultural promotion, culture, performance measurement, research report, statistical method, town

ISSN 1831-9424 

JRC TECHNICAL REPORT 

Cultural and Creative Cities Monitor 

2023 Update 

Alberti, V., Caperna, G., Mauri, C., Panella, F., Tacao 
Moura, C., Banys, K., Symeonidis, K., Dominguez 
Torreiro, M., Saisana, M. 

2023 

 EUR 31641 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  contents  of  this  publication  do  not 
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authorities, or concerning the delimitation of its frontiers or boundaries. 

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JRC134516 

EUR 31641 EN 

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ISBN 978-92-68-06841-0 

ISSN 1831-9424 

doi:10.2760/975348  KJ-NA-31-641-EN-N 

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How to cite this report: Alberti, V., Caperna, G., Mauri, C., Panella, F., Tacao Moura, C.J., Banys, K., Symeonidis, K., Dominguez Torreiro, M. and 
Saisana,  M.,  Cultural  and  Creative  Cities  Monitor,  Publications  Office  of  the  European  Union,  Luxembourg,  2023,  doi:10.2760/975348, 
JRC134516.  

 
 
 
 
 
 
 
 
 
Contents 

Abstract ..............................................................................................................................................................................................................................................................1 

Acknowledgements ..................................................................................................................................................................................................................................2 

1.

Introduction ............................................................................................................................................................................................................................................3 

2. Methodology .........................................................................................................................................................................................................................................4 

2.1.  Conceptual framework ....................................................................................................................................................................................................4 

2.2.  Updated indicators .............................................................................................................................................................................................................4 

2.3.  Data sources ............................................................................................................................................................................................................................6 

2.4.  City selection ...........................................................................................................................................................................................................................6 

2.5.  Grouping ................................................................................................................................................................................................................................... 10 

2.6.  Data treatment ................................................................................................................................................................................................................... 11 

2.7.  Missing data estimation ............................................................................................................................................................................................. 11 

2.7.1. 

Transformation for imputation ........................................................................................................................................................... 11 

2.7.2. 

Trend estimation .............................................................................................................................................................................................. 12 

2.7.3. 

Imputation ............................................................................................................................................................................................................. 13 

2.8.  Normalisation, weighting and aggregation ................................................................................................................................................ 13 

2.9.  Comparability ....................................................................................................................................................................................................................... 14 

2.9.1.  Comparability over time ............................................................................................................................................................................ 14 

2.9.2.  Comparability between cities ............................................................................................................................................................... 14 

3. Statistical assessment .............................................................................................................................................................................................................. 15 

3.1.  Data availability ................................................................................................................................................................................................................. 15 

3.2.  Statistical coherence ..................................................................................................................................................................................................... 16 

3.2.1.  Correlation analysis ...................................................................................................................................................................................... 16 

3.2.1.1.  Correlation analysis between indicators of the same group ....................................................................... 16 

3.2.1.2.  Correlation analysis between aggregates and with the index ................................................................... 18 

3.2.2. 

Principal component analysis of the C3 Index ...................................................................................................................... 21 

3.2.3. 

Impact of the modelling assumptions on the C3 Index results .............................................................................. 22 

3.2.3.1. 

Extra extra large cities .................................................................................................................................................................. 22 

3.2.3.2. 

Extra large cities ................................................................................................................................................................................. 23 

3.2.3.3. 

Large cities .............................................................................................................................................................................................. 23 

3.2.3.4.  Small and medium cities ............................................................................................................................................................. 24 

4. Online platform ............................................................................................................................................................................................................................... 25 

4.1.  Visually explore European cultural and creative cities .................................................................................................................... 25 

4.2.  Compare and contrast similar cities ................................................................................................................................................................ 26 

4.3.  Follow the project and use the data ................................................................................................................................................................ 27 

5. Conclusions ......................................................................................................................................................................................................................................... 28 

References .................................................................................................................................................................................................................................................... 29 

List of figures ............................................................................................................................................................................................................................................ 30 

i 

List of tables ............................................................................................................................................................................................................................................... 31 

Annexes ........................................................................................................................................................................................................................................................... 32 

Annex 1. Full list of cities included in the 2023 update of the monitor ........................................................................................ 32 

Annex 2. Cities not included in the 2023 update of the monitor ......................................................................................................... 33 

Annex 3. Indicators framework .......................................................................................................................................................................................... 34 

ii 

Abstract 

The Cultural and Creative Cities Monitor (hereafter ‘the monitor’) is a monitoring and benchmarking tool that 
was first developed in 2017 to allow European cities to compare and contrast their areas of excellence and 
improvement in terms of culture and creativity. Since it was first launched, the monitor has served as a guidance 
tool for policymakers at the local, national and European levels. 

The 2023 update of the monitor involved a methodological improvement and a revision of selected indicators, 
to ensure that the tool remains reliable and coherent, enabling meaningful comparisons of cities’ performance 
over time. The monitor’s latest results are available for three different reference years, with a focus on the 
most recent year, 2019. These results provide a snapshot of the situation just before the COVID-19 pandemic. 

The updated statistical assessment of the monitor described in this report gives the user an understanding of 
how to use the monitor, enabling the informed use of the data and monitoring tools provided. 

The monitor’s online platform has also been updated. In addition to the pre-existing functionalities, the online 
tool now also allows users to compare and contrast the performance of 196 European cities at three different 
points in time. 

1 

Acknowledgements 

The 2023 Cultural and Creative Cities Monitor report was prepared by the Competence Centre on Composite 
Indicators and Scoreboards (COIN) within the unit for Science for Modelling, Monitoring and Evaluation (S.3) of 
the Joint Research Centre (JRC) of the European Commission in Ispra, Italy. The primary authors are Valentina 
Alberti, Giulio Caperna, Caterina Mauri, Francesco Panella Marcos, Dominguez-Torreiro and Michaela Saisana. 
The monitor project relies on its web platform, which has been designed by Valentina Alberti (User Interface) 
and developed and maintained by colleagues Carlos Jorge Tacao Moura, Kostas Banys and Klimis Symeonidis. 

The authors would like to thank colleague Eleni Papadimitriou for providing very helpful insights that enhanced 
the overall quality of this data revision. They also pay special thanks to former colleague Valentina Montalto 
for invaluable work on the monitor throughout the years. 

The authors would also like to thank Hugo Poelman (European Commission, Directorate-General for Regional 
and Urban Policy) for the provision of tailored data on transport connections and for the always kind and timely 
support,  Teodora  Brandmueller  and  Hannes  Reuter  (European  Commission,  Eurostat)  for  the  tailored  and 
dedicated support on data on European cities and on administrative boundaries, and colleagues and former 
colleagues in the Eurostat Working Group on Cultural Statistics (Marta Beck-Domzalska, Andrea Gallelli, Roberto 
Lombardini, Jarko Pasanen and Tomasz Budek) for the continued open interactions on the assessment of the 
cultural and creative sectors and industries. 

In addition, the authors would like to thank colleagues and former colleagues from the European Commission’s 
Directorate-General  for  Education,  Youth,  Sport  and  Culture  (Inna  Garkova,  Barbara  Stacher,  Marta  Jimenez 
Pumares and Catherine Magnant), who took part in open and constructive exchanges throughout the years to 
both improve the monitor and steer its future direction. 

Lastly,  the  authors  would  like  to  thank  all  national,  regional  and  local  authorities  that  have  been  using  the 
monitor over the last few years and have provided valuable feedback and input, driving the evolution of the 
tool. 

Authors of the technical update report 

Valentina  Alberti,  Giulio  Caperna,  Caterina  Mauri,  Francesco  Panella,  Marcos  Dominguez-Torreiro,  Michaela 
Saisana 

Authors of the web platform 

Carlos Jorge Tacao Moura, Valentina Alberti, Kostas Banys, Klimis Symeonidis 

2 

1.  Introduction 

The  first  edition  of  the  Cultural  and  Creative  Cities  Monitor  (hereafter  ‘the  monitor’)  was  released  in  2017, 
covering a sample of 168 European cities of different sizes. Subsequently, in 2019, the sample of cities was 
increased  to  190.  The  development  of  the  monitor  was  requested  and  supported  by  Commissioner  Tibor 
Navracsics (Juncker Commission), to provide a benchmarking tool for culture and creativity in Europe at the city 
level. A thorough review of 40 indices related to cities’ culture and creativity helped to identify gaps that a new 
measurement and benchmarking tool could aim to fill. The result is a toolkit that provides comparable data at 
the city level for a wide sample of cities across Europe. The monitor aggregates metrics related to the cultural 
and creative sectors of each city and allows users to compare the relative strengths of similarly sized cities in 
these domains. 

The monitor is made up of three components: this technical report, an online tool (1) and an annex containing 
the data used to construct the monitor itself (2). The 2023 update of the monitor provides a synthetic measure 
of culture and creativity in 196 cities in Europe for three reference years: 2015, 2017 and 2019. 

Since  its  first  edition,  the  monitor  has  been  showcased  in  more  than  30  cities  and  has  helped  local 
administrations to identify areas of unexplored potential in their cities. It has been used as a benchmarking and 
peer-learning tool by a number of European cities, and has fed into EU policy documents, including the new 
European agenda for culture (3). 

Since the 2017 edition, the monitor has looked at how cities are able to provide local residents with cultural 
amenities, explored the ability of cities to attract visitors and attract and retain talent, and produced innovative 
outputs. Moreover, the monitor has provided an overview of the performance of European cities in areas that 
support the cultural and creative sectors, such as the openness of their populations and the quality of their 
transport connections and regional governance. 

While the 2023 update of the monitor aims to continue the mapping and benchmarking exercises initiated in 
2017, the situation has profoundly changed since then. At the time of writing, data to perform a meaningful 
analysis of the impacts of and ability to recover from the COVID-19 pandemic’s effects on culture and creativity 
in European cities were not available for a sufficient number of cities in the sample. Nevertheless, the present 
update of the monitor provides a pre-pandemic reference point (as it is based on data from 2019) that could 
be used to analyse the effects of the COVID-19 pandemic on European cultural and creative cities once data 
for the post-pandemic period are available. 

The  2023  update  of  the  monitor  brings  some  important  methodological  improvements.  First,  it  allows  for 
comparisons of cities’ performance over time. In this update, benchmarking is possible not only vis-à-vis peer 
cities with similar populations, gross domestic product (GDP) per capita and employment rates, but also for the 
same  city  over  time.  The  aim  is  to  help  cities  to  identify  opportunities  and  challenges, and  to  monitor  their 
progress over time. Second, some amendments have been made in terms of data gathering and imputation 
methodology, thus allowing for more meaningful comparisons over time. While both previous editions of the 
monitor (2017 and 2019) used the latest data available for each city and indicator, bringing together observed 
data for multiple years, the revised approach uses data from only the reference years (i.e. 2015, 2017 and 
2019) and imputes missing data, thus allowing for more meaningful comparisons over time. 

This  technical  report  has  three  main  chapters:  Chapter 2  ‘Methodology’  provides  an  overview  of  the 
methodology used for the development of the monitor and the main changes implemented since the 2019 
edition; Chapter 3 ‘Statistical assessment’ provides an assessment of the 2023 update; and Chapter 4 ‘Online 
platform’ provides a quick guide to the online tool. The methodology chapter is intended to aid the accurate 
interpretation of the monitor’s results, the statistical assessment chapter is designed to allow a more specialised 
audience to explore the details and limitations of the monitor, and Chapter 4 offers support to users of the 
monitor’s accompanying online tool. 

(1) 
(2)  
(3) 

https://composite-indicators.jrc.ec.europa.eu/cultural-creative-cities-monitor.  
  https://composite-indicators.jrc.ec.europa.eu/cultural-creative-cities-monitor/docs-and-data. 
European  Parliament  resolution  of  11 December  2018  on  the  new  European  agenda  for  culture  (2018/2091(INI))  (https://eur-
lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A52018IP0499). 

3 

                                                 
2.  Methodology 

2.1.  Conceptual framework 

The monitor is a composite indicator and was developed based on the methodology outlined in the Handbook 
on Constructing Composite Indicators – Methodology and user guide (OECD and JRC, 2008). Since its first edition 
(2017),  the  monitor  has  revolved  around  three  main  concepts –  or  subindices –  considered  relevant  when 
attempting to measure a city’s cultural and creative liveliness: Cultural Vibrancy, Creative Economy and Enabling 
Environment  (Figure 1).  The  Cultural  Vibrancy  subindex  explores,  on  the  one  side,  the  provision  of  cultural 
infrastructure (Cultural Venues & Facilities) and, on the other, the ability of a city to attract visitors and local 
cultural participation (Cultural Participation & Attractiveness), with the aim of providing a synthetic measure 
describing how liveable and active the cultural scene is. The second subindex explores the Creative Economy 
domain, with two dimensions assessing, respectively, the current number and creation of new jobs in the cultural 
and  creative  sectors  (Creative  &  Knowledge-based  Jobs,  and  New  Jobs  in  Creative  Sectors)  and  a  third 
dimension synthesising the ability of the city to produce innovative outputs (Intellectual Property & Innovation). 
The picture provided by the first and second subindices, Cultural Vibrancy and Creative Economy, is completed 
by the third subindex, Enabling Environment, which provides insights on Human Capital & Education, Openness, 
Tolerance & Trust, Local & International Connections (transport) and Quality of Governance. The three subindices 
are aggregated into the ‘C3 Index’, which provides a synthetic measure that brings together the three underlying 
concepts. 

Figure 1. The Cultural and Creative Cities Monitor conceptual framework 

2.2.  Updated indicators 

As  anticipated,  this  year’s  release  of  the  monitor  is  to  be  regarded  as  an  update  in  terms  of  data  and 
methodology, as no substantial changes have been made from a conceptual perspective. The main aims were 
to maintain consistency, while making sure that the data provided are as reliable as possible. 

To this end, as shown in Table 1, the conceptual framework remains mostly unchanged, with an exception made 
for two indicators: community design application and average appearance in university rankings. The first has 
been  dropped  in  favour  of  an  indicator  on  gross  expenditure  in  research  and  development,  as  the  original 
indicator has been discontinued by Eurostat. The indicator on average appearance in university rankings has 
been dropped because it is based on four different rankings (the CWTS Leiden Ranking, QS World University 
Rankings, Shanghai Ranking and Times Higher Education), adopting different methodologies and rationales to 
define which universities are included. 

In addition, although indicators on satisfaction with cultural facilities (Dimension 1.2, Cultural Participation & 
Attractiveness), Tolerance of foreigners, Integration of foreigners and People trust (Dimension 3.2, Openness, 

4 

 
Tolerance & Trust) (4) have been retained in the framework as an indication of their conceptual relevance, all 
three indicators have been temporarily excluded from this edition because of low coverage and lack of data 
from 2017 and 2019 (< 30 %, last data available from 2015). 

Table 1. Updated conceptual framework 

Sub-index 

Dimension 

 Indicator 

1. Cultural Vibrancy  

1.1 Cultural Venues 
& Facilities  

1.2 Cultural Participation & 
Attractiveness  

2. Creative Economy  

2.1 Creative & 
Knowledge-based Jobs 

2.2 Intellectual Property & 
Innovation  

2.3 New Jobs in Creative 
Sectors  

3. Enabling Environment  

3.1 Human Capital & 
Education  

3.2 Openness, Tolerance & 
Trust  

3.3 Local & International 
Connections  

Sights & landmarks 

Museums & art galleries 

Cinemas 

Concert & music halls 

Theatres 

Tourist overnight stays 

Museum visitors  

Cinema attendance  

Satisfaction with cultural facilities 

Jobs in arts, culture & entertainment 

Jobs in media & communication 

Jobs in other creative sectors 

ICT patent applications  

Gross expenditure in research & development 

Jobs in new arts, culture & entertainment enterprises 

Jobs in new media & communication enterprises  

Jobs in new enterprises in other creative sectors 

Graduates in arts & humanities 

Graduates in ICT  

Foreign graduates 

Foreign-born population 

Tolerance of foreigners 

Integration of foreigners  

People trust 

Passenger flights  

Potential road accessibility 

Direct trains to other cities  

NB: Data for indicators shaded dark grey were not available for this update and therefore these indicators have been omitted. 

3.4 Quality of Governance  

Quality of governance  

(4) 

These  indicators  are  from  the  same  source:  Flash  Eurobarometer  366  survey –  ‘Quality  of  Life  in  European  Cities’.  
https://data.europa.eu/data/datasets/s1035_366?locale=en  

5 

                                                 
2.3.  Data sources 

As  shown  in  Figure 2,  most  data  (approximately  66 %)  come  from  official  sources,  and  data  on  essential 
indicators  also  come  from  other  sources,  including  the  European  Tertiary  Education  Registry  (ETER) and  the 
University of Gothenburg. In addition, all indicators in Dimension 1.1 ‘Cultural venues and facilities’ come from 
web  sources:  information  from  both  OpenStreetMap  and  TripAdvisor  contribute  to  the  construction  of  this 
dimension, as detailed in Annex 3. 

While the approach to data gathering and analysis for the current update of the monitor has remained largely 
unchanged since the 2019 edition, the shock imposed by the COVID-19 pandemic has had a relevant impact 
on the availability and timeliness of data. At the time of writing, official data covering both 2020 and 2021 
were available for only a small sample of the 196 cities included in this analysis and for a reduced number of 
indicators. While the two previous editions of the monitor faced the same issue (i.e. lack of data for the exact 
reference year), this was addressed by adopting a ‘last observation carried forward’ approach: for instance, if 
data for 2019 were not available, data for 2018 or 2017 were used to replace the missing values. This was 
considered unfeasible for the 2023 update: data from 2021 would need to be interpreted very differently from 
data from 2020, which in turn would portray a wildly different picture from 2019 data. Moreover, data from 
non-official sources might not yet reflect the changes that have happened since the onset of the COVID-19 
pandemic.  For  these  reasons,  it  was  decided  that  the  2023  update  of  the  monitor  should  be  used  as  an 
opportunity  to  revise  part  of  the  methodology,  the  data  imputation  approach  in  particular,  with  the  aim  of 
improving time-wise comparability in the process. 

Figure 2. Data sources for the indicators included in the 2023 update of the monitor 

NB: European Commission data include data from Eurostat. OECD, Organisation for Economic Co-operation and Development. 

2.4.  City selection 

The 2023 update of the monitor includes 196 European cities with more than 50 000 inhabitants. This threshold 
was  set  because  of  the  availability  of  data  in  Urban  Audit  datasets.  As  in  the  previous  releases,  cities  are 
included according to three main selection criteria, namely: 

1.  cities that have been or will be European Capitals of Culture up to 2028, or that have been shortlisted 

to become a European Capital of Culture up to 2028; 

2.  UNESCO Creative Cities; 

3.  cities hosting at least two regular international cultural festivals. 

6 

 
 
 
 
Since the 2019 edition, 22 new cities have made their way into the monitor (Table 2 shows the full list of new 
cities included in the ranking), although the overall number of cities analysed has increased by only six. This is 
because a number of cities are no longer analysed. 

• 

• 

Fourteen cities from the United Kingdom are no longer included in the monitor following the departure 
of the United Kingdom from the European Union and the resulting discontinuation of its official data 
provision (5). 

Following the redefinition of the Eurostat Urban Audit administrative boundaries in 2021, the city of 
Terrassa (Spain) is within the boundaries attributed to the ‘city’ area of Barcelona. The same is true for 
San Cristóbal de La Laguna, which is among the newly selected cities for the 2023 update but is within 
the Urban Audit administrative boundaries of Santa Cruz de Tenerife (6). 

• 

The city of Limassol (Cyprus) is no longer part of the Eurostat Urban Audit database (7). 

Table 2. New cities included in the 2023 update of the monitor listed by country 

Country 

AT  

BG  

CZ  

DE  

ES  

ES 

FI  

FI 

FR 

HU 

HU 

IT 

IT 

IT 

IT 

IT 

NL 

PL 

PT 

PT 

PT 

SK 

City 

Salzburg 

Stara Zagora 

České Budějovice 

Chemnitz 

Valladolid 

Santa Cruz de Tenerife 

Oulu 

Kuopio 

Rouen 

Miskolc 

Kecskemét 

Bergamo 

Catania 

Como 

Modena 

Pordenone 

Tilburg 

Gdynia 

Ponta Delgada 

Aveiro 

Viseu 

Trenčín 

(5) 

(6) 

Glasgow and Liverpool (European Capitals of Culture); Bradford, Bristol, Dundee, Edinburgh, Manchester, Norwich, Nottingham and 
York (UNESCO Creative Cities); Birmingham, Brighton and Hove, Leeds and London (hosted festivals). 
Geographical 
statistical-units/urban-audit.  

reference  available  at:  https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-

(7)  More details and definitions are provided by Eurostat at: https://ec.europa.eu/eurostat/web/cities/data/database.  

7 

 
                                                 
Table 3 provides an overview by category of the number of cities included in the two previous editions and in 
the 2023 update of the monitor. 

Table 3. Cities included in the 2023 update of the monitor by selection criterion 

Criterion 

European Capital of Culture 

UNESCO Creative City 

Festivals hosted 

Total 

2017 count 

2019 count 

2023 count 

92 

22 

54 

168 

98 

33 

59 

190 

102 

29 

65 

196 

The  decrease  in  the  number  of  UNESCO  Creative  Cities  can  be  attributed  to  the  absence  of  cities  from  the 
United Kingdom in the last edition. Figure 3 provides an overview of all of the cities in the 2023 update of the 
monitor, and shows which of the three criteria resulted in their inclusion. 

8 

 
 
Figure 3. Cities included in the 2023 update of the monitor  
NB: © EuroGeographics for administrative boundaries 

9 

 
 
 
 
2.5.  Grouping 

To  provide  meaningful  interpretation,  the  monitor  discourages  the  comparison  of  widely  different  cities.  In 
particular,  it  differentiates  cities  based  on  their  population  size,  GDP  per  capita  and  employment  rate,  thus 
allowing the creation of ‘grouped’ rankings. 

Table 4 and Table 5, show the grouping thresholds and group compositions used in the 2023 update of the 
monitor, respectively. The grouping approach has remained largely unchanged since the last edition, with only 
the employment rate group ranges being updated, to better reflect the characteristics of the sample of cities 
analysed. While the employment rate ranges have changed, the methodology used to define the ranges has 
not: the employment rate groups have again been calculated based on the natural breaks algorithm (8). 

Table 4. Grouping thresholds in the 2023 update of the monitor 

Population 

  Group 

GDP per capita 

Group 

Employment rate 

Group 

> 1 million 

500 000–1 million 

250 000–500 000 

100 000–250 000 

50 000–100 000 

XXL 

XL 

L 

M 

S 

> 45 000 

35 000–45 000 

27 000–35 000 

19 000–27 000 

≤ 19 000 

1 

2 

3 

4 

5 

> 78.5 % 

73.4–78.5 % 

66.2–73.4 % 

54.8–66.2 % 

≤ 54.8 % 

1 

2 

3 

4 

5 

NB: L, large; M, medium; S, small; XL, extra large; XXL, extra extra large. 

Table 5. Groups in the 2023 update of the monitor 

Population 

GDP per-capita 

Employment rate 

Population 
group 

Number of 
cities per 
group  

Cities per 
group (%) 

GDP per 
capita 
group 

Number of 
cities per 
group  

Cities per 
group (%) 

Employme
nt rate 
group 

Number of 
cities per 
group  

Cities per 
group (%) 

XXL 

XL 

L 

S-M 

24 

33 

44 

95 

12 % 

17 % 

22 % 

48 % 

1 

2 

3 

4 

5 

50 

37 

37 

23 

49 

26 % 

19 % 

19 % 

12 % 

25 % 

1 

2 

3 

4 

5 

53 

78 

39 

21 

5 

27 % 

40 % 

20 % 

11 % 

3 % 

NB: L, large; M, medium; S, small; XL, extra large; XXL, extra extra large. 

(8) 

As described in the 2019 edition of the monitor, based on Jenks’ (1967) definition, ‘natural breaks … is a data clustering method 
designed to determine the best arrangement of values into different classes. This is done by seeking to minimise each class’s average 
deviation from the class mean, while maximising each class’s deviation from the means of the other groups. In other words, the 
method seeks to reduce the variance within classes and maximise the variance between classes’ (Montalto et al., 2019, Annex B, 
p. 3). 

10 

 
 
 
 
 
                                                 
2.6.  Data treatment 

Most of the indicators (listed in Table 1) represent total numbers of things such as museums, jobs or graduates. 
These are hard to compare across cities of different sizes, even within the same size group. For this reason, 
these indicators are expressed per 1 000 city population for the analysis. 

Only  four  of  the  indicators  in  our  analysis  are  not  divided  by  the  city  population:  the  number  of  ICT  patent 
applications  is  divided  by  the  population  of  the  corresponding  NUTS  (nomenclature  of  territorial  units  for 
statistics) region because these data are not collected at the city level; the gross expenditure in research and 
development is given as a share of total spending; the foreign-born population is already expressed as a share 
of the total population; and the quality of governance index is used as is. 

2.7.  Missing data estimation 

Except for the variables in the local and international connections dimension, the city-level datasets used in the 
monitor, and described in Section 2.1, are incomplete, with missing values for years and/or cities within our 
sample. Incomplete datasets, in which missing values are either ignored or excluded from analysis, could lead 
to  biased  results  (e.g.  lack  of  comparability)  or  to  extremely  limited  final  coverage.  For  this  reason,  for  the 
datasets used in the monitor, missing values have been filled in using statistical techniques, as described below, 
in a process known as imputation. 

The aim of this process was to fill missing values with values that are reasonable in a broad sense, relying on 
information from other cities, years or variables to define such reasonable values. Whenever possible, a dataset 
larger than the one ultimately used for the final indicator was employed for the purposes of imputation. This 
has  increased  the  amount  of  information  that  can  be  drawn  on.  For  example,  the  raw  dataset  for  tourist 
overnight stays included over 800 cities with observations starting from 1990. 

The imputation method used involves estimating a time trend for a given indicator using data on many cities 
and  applying  this  time  trend  to  cities  for  which  some  data  points  are  missing.  This  step  assumes  that  any 
differences between cities are constant, an assumption that is often not warranted for the indicators used. For 
this  reason,  indicators  were  transformed  before  imputation  in  such  a  way  that  the  assumption  could  more 
reasonably be made. These transformations were reversed after imputation was complete. 

Our process can be described in three steps: (1) the indicators were transformed to fit the imputation method, 
(2) time trends were estimated for each indicator and (3) the time trends were applied to missing data points 
and the transformations reversed. 

2.7.1.  Transformation for imputation 

The second column of Table 6 describes the transformations undertaken for each indicator before imputation. 
Each involved a judgement call along the lines outlined above. 

The  most  common  transformation  applied  is  the  natural  logarithm,  which  is  helpful  where  distributions  are 
heavily skewed. A logit transformation is applied for variables that are expressed as shares or percentages. This 
ensures that small differences between very small values (close to 0 %) or between very large values (close to 
100 %) are properly weighted in the imputation. 

For the quality of governance index, the mean of the regional group was subtracted from the variable. This 
variable  contains  many  missing  values  and  varies  significantly  across  regions.  The  regional  de-meaning  is 
meant to make values comparable, so that a common time trend can be estimated for all cities for which data 
are available. 

11 

 
 
2.7.2.  Trend estimation 

For each (transformed) indicator, time trends were then estimated that describe how the indicator changed over 
time  on  average.  When  enough  non-missing  data  points  were  available  for  an  indicator,  time  trends  were 
estimated separately by groups of cities. These groups were based on either city population or GDP per capita, 
depending  on  which  of  these  measures  was  judged  to  be  more  likely  to  correlate  with  differences  in  the 
development of an indicator over time. The third column of Table 6 shows the number of these groups and the 
variable by which the grouping was performed. 

Table 6. Details on imputation method for each variable imputed 

Indicator 

Sights & landmarks 

Museums & art galleries 

Cinemas 

Concert & music halls 

Theatres 

Tourist overnight stays 

Museum visitors  

Cinema attendance  

Jobs in arts, culture & entertainment 

Jobs in media & communication 

Jobs in other creative sectors 

ICT patent applications  

Gross expenditure in research & development 

Jobs in new arts, culture & entertainment enterprises 

Jobs in new media & communication enterprises  

Jobs in new enterprises in other creative sectors 

Graduates in arts & humanities 

Graduates in ICT  

Foreign graduates 

Transformation 
for imputation  Groups 

Log 

Log 

Log 

Log 

Log 

None 

None 

Log 

None 

None 

None 

Log 

Logit 

Log 

Log 

Log 

Log 

Log 

Log 

Three, by mean city population 

Three, by mean city population 

Three,, by mean city population 

Three, by mean city population 

Three, by mean city population 

Three, by mean city population 

Three, by mean city population 

Three, by mean city population 

Three, by mean city population 

Three, by mean city population 

Three, by mean city population 

Three, by mean city population 

Three, by GDP per capita 

Three, by GDP per capita 

Three, by GDP per capita 

Three, by GDP per capita 

None 

None 

None 

Foreign-born population 

Logit 

Three, by mean city population 

Quality of governance  

De-mean by 
region 

None 

12 

Each time trend was estimated using a regression analysis that seeks to explain the available data points (i.e. 
the values of an indicator for a set of cities), as a function of time. To allow for smooth but non-linear time 
trends, a set of cubic splines was used in the regressions (9). The estimated time trends were used for imputation 
only within the period for which data were available, so as not to rely on so-called out-of-sample predictions (10). 

2.7.3.  Imputation 

Imputation for missing values was carried out at four different levels, depending on which related data were 
available. 

1. 

Interpolation. If data points were available for the same city both before and after a missing value, 
the value was filled by linear interpolation (i.e. a linear increase or decrease is assumed between the 
nearest previous value of the transformed indicator and the nearest following value). 

2.  Extrapolation. If data points were available for the same city only either before or after a missing 
value,  then  the  estimated  trend  was  used  along  with  the  nearest  existing  data  point  to  impute.  To 
extrapolate values for a given city, the trend estimated for the group to which the city belongs was 
shifted up or down to fit the nearest existing data point for the city and then followed to the missing 
year to determine the imputed value. 

3.  Trend only. If no data points were available for a given city, then all values for that city were set equal 

to the estimated trend for the grouping to which the city belongs (11). 

4.  Closest imputed. For the three indicators that were obtained from TripAdvisor, Sights & landmarks, 
Museums & art galleries, Concert & music halls), no information was available from before 2017. The 
missing values for 2015 and 2017 were filled using the closest observed or imputed values (i.e. the 
values for 2017). 

Finally,  the  transformations  used  for  imputation  were  reversed.  For  instance,  the  exponential  function  was 
applied to the log number of cinemas per capita and regional means were added back to the imputed values 
of the quality of governance index. 

2.8.  Normalisation, weighting and aggregation 

The approaches to normalisation, weighting and aggregation have remained substantially unchanged since the 
2019 edition of the monitor (12). Namely, the data have been normalised on a scale of 0–100, where 0 is the 
lowest value and 100 is the highest value for each indicator, thus ensuring consistency among indicators in the 
framework. 

The weighting values used, defined with input from experts during the development of the first edition of the 
monitor,  are  intended  to  highlight  the  particular  relevance  of  the  cultural  vibrancy  and  creative  economy 
subindices (both 40 %) and the complementarity of the third subindex, Enabling Environment (20 %). 

At the dimension level, equal weighting has been attributed to the dimensions of the cultural vibrancy subindex. 
In the Creative Economy subindex, the dimensions related to jobs are attributed a 40 % weighting, while the 
dimension on Intellectual Property & Innovation has a lower weighting (20 %). Different weightings have also 
been  defined for  the  third  subindex,  Enabling  Environment,  with Human  Capital  &  Education  and Openness, 
tolerance & Trust being attributed a weighting of 40 % each, and Local & International Connections and Quality 
of Governance being attributed weightings, respectively, of 15 % and 5 %. 

(9) 

(10) 

Cubic  splines  are  a  mathematical  tool  commonly  used  in  data  analysis  to  estimate  smooth  and  continuous  curves.  They  are 
particularly useful when dealing with complex data patterns that may contain non-linear relationships. 
For example, if no data were available for a given indicator before 2016, then a trend estimated based on these data is used to 
impute missing values for 2016, but never for 2015. In three cases, the subsequent analysis required data points outside the range 
given by the data. In these cases, another approach was used that did not rely on the trend (see point 4 in section 2.7.3). 
This process is very similar to taking the average level of the cities in the same group. 

(11) 
(12)  More details are available in The Cultural and Creative Cities Monitor – 2019 edition (Montalto et al., 2019, Annex A).  

13 

                                                 
2.9.  Comparability 

2.9.1.  Comparability over time 

To ensure comparability over time, data in the 2019 edition were recalculated to cover observations pertaining 
to the 2019 edition’s reference period (13). 

In the 2023 update, while aiming to maintain consistency with the 2017 and 2019 editions, a different approach 
was adopted to improve the comparability over time, with a clear reference to a 1-year-only observation (or 
imputation). While the 2017 and 2019 editions of the monitor used data for a range of years to populate each 
indicator,  the  2023  update  takes  into  account  only  data  in  the  reference  year,  observed  or  imputed.  This 
approach means that, on the one hand, more missing data will be present in the initial dataset, as the criteria 
used to define which observations to use has changed (as outlined in section 2.3), while, on the other hand, it 
allows more meaningful comparisons to be made over time. 

2.9.2.  Comparability between cities 

From a spatial perspective, the definition of cities remains unchanged from the 2019 edition, relying on the 
boundaries provided for cities in the Urban Audit initiative by Eurostat. In particular, the current update of the 
monitor relies on the 2021 Urban Audit definition of cities (14), which aims to ensure comparability between 
urban  areas.  Compared  with  the  Urban  Audit  areas  used  in  the  2019  edition  of  the  monitor,  some  notable 
changes have taken place, including the redefinition of borders for the cities of Paris (France) and Milan and 
Naples (Italy) to include areas surrounding the main local administrative units identifying the administrative 
‘communes’ (15). In addition, some cities have been incorporated within the boundaries of neighbouring cities. 
This is the case for the Spanish cities of Terrassa, incorporated within the Urban Audit boundaries of Barcelona, 
and San Cristóbal de La Laguna, incorporated within the Urban Audit boundaries of Santa Cruz de Tenerife. 

These changes do not affect comparability among the sample of cities analysed in the 2023 update. However, 
data from the 2023 update of the monitor are not comparable with data from the two previous editions. 

(13)  More details are available in The Cultural and Creative Cities Monitor – 2019 edition (Montalto et al., 2019, Annex A). 
(14) 
https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units/urban-audit.  
(15)  More details on the definition of ‘cities’ are available at Eurostat (https://ec.europa.eu/eurostat/statistics-

explained/index.php?title=Territorial_typologies_manual_-
_cities,_commuting_zones_and_functional_urban_areas#Links_to_other_spatial_concepts.2Ftypologies) and in the Urban Europe 
report (Eurostat, 2016). 

14 

 
 
                                                 
3.  Statistical assessment 

3.1.  Data availability 

Table 7 provides an overview of the imputation method for dealing with missing data adopted at indicator level. 

Table 7. Observed and imputed indicator data for 2019 data points 

Sub-index 

Dimension 

Indicator 

Observed 

Interpolated  Extrapolated 

Trend 

Cultural 
Vibrancy  

Cultural Venues 
& Facilities  

Cultural 
Participation & 
Attractiveness  

Creative & 
Knowledge-
based Jobs 

Creative 
Economy  

Intellectual 
Property & 
Innovation  

New Jobs in 
Creative 
Sectors  

Human Capital 
& Education  

Openness, 
Tolerance & 
Trust  

Enabling 
Environment  

Local & 
International 
Connections  

Quality of 
Governance  

Sights & landmarks 

Museums & art galleries 

Cinemas 

Concert & music halls 

Theatres 

Tourist overnight stays 

Museum visitors  

Cinema attendance  

Satisfaction with cultural 
facilities 
Jobs in arts, culture & 
entertainment 

Jobs in media & communication 

Jobs in other creative sectors 

ICT patent applications  

Gross expenditure in research & 
development 
Jobs in new arts, culture & 
entertainment enterprises 
Jobs in new media & 
communication enterprises  
Jobs in new enterprises in other 
creative sectors 

Graduates in arts & humanities 

Graduates in ICT  

Foreign graduates 

Foreign-born population 

Tolerance of foreigners 

Integration of foreigners  

People trust 

Passenger flights  

Potential road accessibility 

Direct trains to other cities  

89% 

89% 

97% 

88% 

98% 

64% 

39% 

44% 

67% 

67% 

67% 

68% 

87% 

77% 

77% 

77% 

67% 

64% 

78% 

10% 

100% 

100% 

100% 

0% 

0% 

0% 

0% 

0% 

1% 

9% 

3% 

0% 

0% 

0% 

0% 

1% 

1% 

1% 

0% 

0% 

0% 

0% 

6% 

0% 

0% 

0% 

11% 

11% 

1% 

10% 

0% 

29% 

44% 

0% 

0% 

2% 

2% 

2% 

7% 

8% 

42% 

11% 

22% 

11% 

22% 

11% 

22% 

11% 

17% 

14% 

1% 

12% 

0% 

23% 

0% 

23% 

0% 

23% 

19% 

14% 

19% 

16% 

12% 

10% 

33% 

51% 

0% 

0% 

0% 

0% 

0% 

0% 

Quality of governance  

0% 

77% 

0% 

23% 

NB: The quality of governance indicator has observed values for 2010, 2013, 2017 and 2021. Therefore, no observed values exist for 
2019. However, observations for 2019 are imputed. Data for indicators shaded dark grey have not been included in this update of the 
monitor (latest observed values are too old). 

15 

  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
 
 
3.2.  Statistical coherence 

The assessment of statistical coherence consists of a multilevel analysis of the correlations of variables, and a 
comparison of rankings at the C3 Index level with their constituent subindices and dimensions. 

3.2.1.  Correlation analysis 

As suggested by the Joint Research Centre’s Competence Centre on Composite Indicators and Scoreboards (JRC-
COIN), for the statistical assessment of composite indicators, the statistical coherence of an index should be 
considered a useful tool for evaluating the soundness of an index, despite it not being sufficient alone (Caperna, 
2022). The main tool used for this assessment is correlation analysis, because it helps to assess the extent to 
which the observed data are in line with the conceptual framework. Within each level of the index, there should 
ideally be positive significant correlations (JRC-COIN suggests ≥ 0.30). This makes it more likely that the overall 
index  scores  will  adequately  reflect  the  values  of  the  underlying  indicators.  The  framework  should  avoid 
redundancy, which can be identified by very high correlations (≥ 0.92). This is because, if two indicators are 
collinear, then this may result in the double counting (and thus overweighting) of the same phenomenon. 

In what follows, we report the correlations between indicators in the same subindex, between indicators and 
their relative aggregates, and finally between dimensions, subindices and the C3 Index. The analysis results and 
figures presented in this section (Section 3.2) were derived from the COINr package developed by JRC-COIN 
(Becker et al., 2022). 

3.2.1.1.  Correlation analysis between indicators of the same group 

The first step in the analysis of internal coherence is based on the correlation between indicators included in 
the same aggregation groups. Table 8 shows the correlation coefficients between indicators within the same 
subindex. Boxes within each subindex identify indicators grouped into respective dimensions. The correlation 
between indicators of the same dimension is the most important aspect to observe here. We can see that the 
vast  majority  of  the  indicators  are  well  correlated  with  the  others  within  their  dimensions.  There  are  two 
exceptions. First, the indicator of cinema attendance in the Cultural Participation and Attractiveness dimension 
shows a correlation with the other indicators of this dimension that is close to zero or slightly negative; this 
may suggest that this indicator carries an underlying message that does not have much in common with the 
others. The main risk in this kind of situation is under-representing the uncorrelated indicator in the aggregates 
(dimension, subindex, index). Fortunately, this indicator shows a satisfactory correlation with all the higher-level 
aggregates (see Table 9). The second case of low correlation is between the two indicators of the Openness, 
Tolerance and Trust dimension (a correlation of 0.16 between foreign graduates and foreign-born population). 
This implies that these two indicators will conflict a little when aggregated, but not in a strong way, considering 
that the correlation is not negative. In observing the relation of the two indicators with the openness, tolerance 
and  trust  dimension,  the  indicator  foreign-born  population  shows  more  relevance  and  similarity  with  the 
aggregate (correlation of 0.93 v 0.52; see Table 9), suggesting that it may be more represented than the foreign 
graduates indicator in the following steps of aggregation. Nevertheless, the correlations of the two indicators 
with the subindex and the index prove to be similar and sufficiently strong, confirming a balanced representation 
of the two indicators in the index. 

16 

 
 
Table 8. Correlations between indicators in the same subindex 

NB: Numbers represent Pearson correlation coefficients. Good correlations (i.e. Pearson correlation coefficients of > 0.30 and < 0.92) are 
shaded light green. Correlations with low values (here < 0.30) are shaded grey. 

Further  observation  of  Table 9  shows  that  almost  all  indicators  have  a  good  correlation  with  their  relative 
aggregates. In a few cases, the correlations with the C3 Index seem a bit small but not in a drastic way. Specific 
mention is needed for Jobs in new arts, culture, and entertainment, which has a correlation close to zero with 
the  index.  This  indicator  is  not  well  represented  in  the  final  aggregate,  which  does  not  mean that it  has  no 
impact. This effect is mainly due to the relation of the New jobs dimension with the other dimensions of the 
subindex, which is commented on below. 

17 

 
 
 
Table 9. Correlations between indicators and their aggregates 

NB:  Numbers  represent  Pearson  correlation  coefficients.  Good  correlations  (i.e.  Pearson  correlation  coefficients  > 0.30  and  < 0.92)  are 
shaded light green. Correlations with low values (here < 0.30) are shaded grey. Correlations suggesting a risk of redundancy (here > 0.91) 
are shaded darker green.  

3.2.1.2.  Correlation analysis between aggregates and with the index 

Correlation analysis at the dimension level 

The analysis of correlations between the aggregates, especially in the top levels, is important because it reflects 
the consistency of the defined concepts. Among the nine dimensions, only two show low correlations with all 
the  other  dimensions  of  their  subindex:  New  jobs  in  the  Creative  Sector  and  Human  Capital  &  Education 
(Table 10). In both cases, the dimensions, despite the clear internal correlation, are not aligned with the other 
elements of their subindices. This is because the indicators of these two dimensions are correlated within the 
dimensions, but have mostly negative correlations with the other elements of the subindex (see Table 8). In this 
phase,  there  is  no  reason  to  consider  any  modification  of  these  elements  of  the  index.  This  is  because  the 
structure of the index comes from previous editions, where the coherence with the rest of the framework was 
stronger, and the consistency of the tool requires it to remain as similar as possible in structure to the previous 
editions. Moreover, Table 11 shows how these two dimensions are penalised because they are the least related 
of their groups with the higher aggregates (subindex and index), but the penalisation observed is not sufficient 
to identify the two dimensions as irrelevant or under-represented (correlation with the index equal to 0.28). 
Future editions of the index may need to pay specific attention to these elements, at both the dimension and 
indicator levels. In general, the correlation of dimensions with the C3 Index is balanced (0.45 – 0.72, excluding 
those already discussed). 

18 

 
 
 
Table 10. Correlation analysis between dimensions 

NB:  Numbers  represent  Pearson  correlation  coefficients.  Good  correlations  (i.e.  Pearson  correlation  coefficients  > 0.30  and  < 0.92)  are 
shaded light green. Correlations with low values (here < 0.30) are shaded grey.  

19 

 
 
 
 
 
Table 11. Correlation of dimensions with subindices and index 

NB: Numbers represent the Pearson correlation coefficients. Good correlations (i.e. Pearson correlation coefficients > 0.30 and < 0.92) are 
shaded light green. Correlations with low values (here < 0.30) are shaded grey. Correlations suggesting a risk of redundancy (here > 0.91) 
are shaded darker green. 

Correlation analysis of subindices 

The highest aggregation steps, between subindices and from subindex to index, display a statistical relation 
that requires attention in the interpretation. High statistical reliability among the main components can be the 
result of redundancy of information. As shown in Table 12, overall, the subindices of Creative Economy and 
Enabling Environment are positively related, whereas Cultural Vibrancy seems to share a very limited proportion 
of its variance with them. This is not reflected in the correlation of the three subindices with the C3 Index, as 
they are all correlated in a strong and balanced way. Nevertheless, this lack of correlation may represent a limit 
to the robustness of the index. 

Table 12. Correlations between the subindices and with the index 

NB:  Numbers  represent  Pearson  correlation  coefficients.  Good  correlations  (i.e.  Pearson  correlation  coefficients  > 0.30  and  < 0.92)  are 
shaded light green. Correlations with low values (here < 0.30) are shaded grey. CE, Creative Economy; CV, Cultural Vibrancy; EE, Enabling 
Environment. 

20 

 
 
 
 
 
3.2.2.  Principal component analysis of the C3 Index 

In a further step in the analysis of statistical coherence, we performed a principal component analysis (PCA). 
The aim of PCA is to assess to what extent the conceptual framework is confirmed by statistical approaches. 
The objective is to observe only one principal component that explains the large majority of the total variance 
(approximately 70%), which would suggest the presence of a common, unidimensional phenomenon underlying 
the subindices. 

Figure 4. Factor map of the three subindices and comparison with the overall C3 Index 

As shown in Figure 4, the three subindices cannot be largely explained by a single statistical dimension because 
the first principal component summarises only 52.1 % of the total variance. The second principal component is 
influential because it accounts for 32.9 % of the total variance and describes mainly one subindex. This result 
depends on the correlation structure: the PCA shows a higher level of agreement between the subindices of 
Enabling Environment and Creative Economy, while Cultural Vibrancy seems to describe a different concept, not 
a conflicting concept, just an unrelated one. The distance between the behaviour of Cultural Vibrancy and the 
other subindices was partly observed in the previous edition of the C3 Index, but in this update it is stronger. 
Future  editions  of  the  index  may  benefit  from  a  general  revision  and  update  of  this  PCA,  to  gain  a  better 
understanding of the effects of future cultural trends on the structure of the index. 

21 

 
 
 
3.2.3.  Impact of the modelling assumptions on the C3 Index results 

An  informative  step  in  the  analysis  of  a  composite  indicator  is  the  assessment  of  the  effect  of  different 
modelling assumptions among reasonable alternatives. Some of these choices may be based on experts’ opinion 
or technical considerations, driven by statistical analysis or by criteria related to the communication of the index. 
This analysis examines to what extent some of the decisions involved in creating the C3 Index have an impact 
on the ranking of cities. It is desirable for any composite index to remain relatively unchanged if some of the 
steps taken to create it are changed. If small changes in methodology have a large impact on the final ranking 
of an observed unit, then it can be said that the index creates a ranking with a high level of uncertainty for that 
unit. 

The  uncertainty  analysis  here  includes  changing  the  procedure  used  for  the  treatment  of  outliers  and  the 
perturbation  of  weighting  at  the  dimension  and  subindex  levels,  as  outlined  in  Table 13.  These  steps  were 
chosen, as there are plausible alternative pathways for each of them in the construction of the index that can 
be relatively easily investigated. Other assumptions were tested in the previous edition of the index. 

Table 13. Alternative assumptions considered in the uncertainty analysis 

Assumption 

Maximum number of winsorised values before log 
transformation 

Weighting system for subindices and dimensions 

Reference 

Alternative 

5 

1–5 

Nominal weighting 

Perturbed 
weighting (± 25 %) 

In the following sections, the results of this analysis are briefly shown for every population group. The results 
suggests  that  the  main  source  of  uncertainty  is  the  perturbation  of  weighting.  This  is  mainly  due  to  the 
correlation structure of the index. In particular, the low correlation between the Cultural Vibrancy subindex and 
the other subindices causes the high influence the weights show. Further details are presented below. 

3.2.3.1.  Extra extra large cities 

As shown in Figure 5, among the extra extra large (XXL) cities, only Barcelona and Budapest have an interval of 
ranks  of  larger  than  5.  This  suggests  that  they  are  more  influenced  by  the  variation  of  the  criterion  on 
winsorisation than others. Without this criterion, the interval of ranks for these cities is 5 or less, but most of 
the variability still remains because it is related to the perturbation of weighting. 

It should be noted that a more restrictive approach (e.g. an interval of > 3 instead of > 5) would highlight some 
instability at the top ranks. The medians are close to the nominal ranking, and this can be considered proof that 
the nominal rankings are representative of a plurality of scenarios. 

Figure 5. Impact of the modelling assumption for XXL cities 

22 

 
3.2.3.2.  Extra large cities 

Figure 6 shows that, among the extra large (XL) cities, seven have an interval of ranks of larger than 5, and 
these  cities  are  close  to  each  other  in  the  ranking:  this  indicates  that  their  scores  are  probably  similar  or 
unbalanced and so they could easily switch their positions and are influenced by weighting. 

Excluding  Zagreb,  the  variability  depends  on  the  weighting  and  is  particularly  visible  among mid-  and  high-
performing cities. The remaining ranks are robust. 

Figure 6. Impact of the modelling assumption for XL cities 

3.2.3.3.  Large cities 

Figure 7 shows that, as for the previous group, the large (L) cities group is very robust, although seven cities 
show more variability. Also in this case, most variability can be attributed to weighting, although few cities with 
larger degrees of uncertainty are isolated and their large interval is due to their specific characteristics. 

Figure 7. Impact of the modelling assumption for L cities 

23 

 
 
3.2.3.4.  Small and medium cities 

S and M cities are usually more specialised and are cultural and creative in different ways. This is also true for 
the impact of the assumption on winsorisation, which is large for a few cities, as shown in Figure 8. 

When  accounting  for  the  effects  of  winsorisation,  there  is  high  variability  across  the  sample,  with  some 
extremes  for  some  cities.  Nevertheless,  the  main  source  of  variability  are  the  weighting  values,  thus 
substantiating the hypothesis that the cities analysed might be cultural and creative in their own specific ways. 

The  ranking  of  the  middle  and  low  part  of  the  group  should  not  be  used  for  direct  inference  because  it  is 
dependent on the assumptions. 

Figure 8. Impact of the modelling assumption for S and M cities 

24 

4.  Online platform 
Since its first edition in 2017, the monitor has relied on an online tool (16) that allows users to explore data for 
each city, thus allowing meaningful comparisons based on the population, GDP per capita and employment rate 
groups. 

Figure 9. Home page of the monitor’s online tool  
NB: the figure above is only meant to provide an example of the visualisations. Refer to the online platform for the most updated version. 

The dashboard of this update of the monitor (Figure 9) retains most of the functionalities of the previous edition, 
while including an additional layer of information on the evolution of the performance of the cities at both an 
aggregate  level  and  an  indicator  level.  The  2023  update  of  the  platform  also  allows  users  to  explore  the 
evolution of the performance of the cities over time. 

The following sections provide a quick tour of the main features of the tool. 

4.1.  Visually explore European cultural and creative cities 

The monitor does not offer one single ranking to assess culture and creativity in European cities. Instead, its 
online  tool  provides  different  rankings  depending  on  the  point  of  observation  (Figure 10).  For  example,  it  is 
possible to use the performance map to visualise the top five cities for the subindex Cultural Vibrancy, Creative 
Economy or Enabling Environment. Moreover, rankings are provided for each of the three groupings: population, 
GDP per capita and employment rate. Each ranking can be explored for the three years observed: 2015, 2017 
and 2019. 

Figure 10. Performance map of the monitor  
NB: the figure above is only meant to provide an example of the visualisations. Refer to the online platform for the correct rankings. 

(16) 

The Cultural and Creative Cities Monitor – online tool (https://composite-indicators.jrc.ec.europa.eu/cultural-creative-cities-monitor).  

25 

 
 
                                                 
The  25  cities  highlighted  on  the  map  are  the  top  five  cities  appearing  in  the  ranking,  capturing  cultural 
infrastructure and participation in each group (Figure 10 shows cities with a similar population size). Each of 
the group rankings can be explored in full by selecting the ‘+’ icon at the top of each ranking. 

4.2.  Compare and contrast similar cities 

The monitor’s online tool provides 196 city profiles summarising the main aspects captured by the composite 
indicator using three main interactive visualisations. 

The overall performance visualisation (Figure 11) displays the full ranking and the cities’ performances in the 
C3 Index, in the three subindices or in the nine dimensions. 

Figure 11. Overall performance: the monitor – 2023 update 
NB: the figure above is only meant to provide an example of the visualisations. Refer to the online platform for the correct rankings. 

The second visualisation of the city profile (Figure 12) offers a more detailed picture of the city performance, 
allowing the user to explore how cities perform in each dimension and the underlying indicators. 

Figure 12. Performance by dimension 
NB: the figure above is only meant to provide an example of the visualisations. Refer to the online platform for the correct data. 

Figure 13 shows a comparison of the performances of Florence with the average values for other similar cities. 
As shown in Figure 12, Florence is the best performer among cities, having between 250 000 and 500 000 
inhabitants. It performs better than average in cultural vibrancy and its dimensions (Cultural Venues & Facilities; 
Cultural  Participation  &  Attractiveness).  Despite  this,  Florence  is  aligned  with  the  average  performance  for 
Creative Economy and is below average for Enabling Environment. This suggests that even top performers like 
Florence can improve to reinforce or empower cultural and creative factors in the city. 

26 

 
 
Figure 13. Compare my city with...  
NB: the figure above is only meant to provide an example of the visualisations. Refer to the online platform for the correct data. 

A third visualisation (Figure 13) allows the user to select peer cities and compare each aspect of the framework 
(overall index, subindices, dimensions and indicators). The performance of several cities can be displayed side 
by side and differences can be observed, allowing the user to distinguish between cities performing well (black) 
and less well (grey). 

4.3.  Follow the project and use the data 

A  section  entitled  ‘Docs  &  Data’  (17)  provides  an  overview  of  the  past  editions  of  the  monitor,  along  with 
publications related to the project. The full dataset used to produce the monitor has also been made available 
for the 2017 and 2019 editions and the 2023 update. 

(17)   https://composite-indicators.jrc.ec.europa.eu/cultural-creative-cities-monitor/docs-and-data  

27 

 
                                                 
5.  Conclusions 

The 2023 update of the monitor was developed with the aim of building on the monitor’s strong framework, 
while making methodological adjustments to ensure that results are comparable over time. It should be noted 
that the data and analyses provided in the online tool of the 2023 update are not comparable with those of 
thee 2017 and 2019 editions of the monitor. An effort to maintain comparability across cities is made by relying 
on Urban Audit boundaries, which are based on the European Commission’s definition (18), which aims to provide 
a harmonised – and thus comparable – definition of cities. However, Urban Audit boundaries have limitations 
in defining city areas: in addition to features characterising contiguous dense urban areas, they also take into 
account administrative boundaries, which are in general not designed with comparability with other cities in 
mind. 

The updated results are available for three reference years, 2015, 2017 and 2019, for a total of 196 European 
cities, and allow policymakers to compare cities’ relative strengths both across cities of similar size and over 
time. 

The  revision  of  the  methodology  will  also  simplify  data  gathering  and  analysis  once  updated  official  data 
become  available.  This  will allow  changes  in  the  cultural and  creative  environment  of  European  cities to  be 
monitored in the future. Meaning unclear. Suggest ‘Although the current update does not delve into the effects 
of  the  COVID-19  pandemic  and  consequent  restrictive  measures,  owing  to  the  lack  of  data  available, 
developments during the different pandemic phases are likely to be of particular interest in future evolutions 
of the project. 

This report has described the updated methodology in detail and has provided a statistical assessment of the 
updated results. It also contains a guide for users on the accompanying online platform. 

18   Urban Audit 2021 boundaries have been used. The boundaries are defined based on Dijkstra and Poelman (2012) and are made 
available  by  Eurostat  at  this  address:  https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-
statistical-units/urban-audit. 

28 

 
                                                 
References 

Becker, W., Caperna, G., Del Sorbo, M., Norlen, H., Papadimitriou, E. and Saisana, M. (2022), ‘COINr: an R package 
for  developing  composite 
indicators’,  Journal  of  Open  Source  Software,  Vol.  7,  No 78,  4567, 
doi:10.21105/joss.04567. 

Caperna, G. (2022), JRC Statistical Audit of Africa Regional Integration Index 2019, Publications Office of the 
European Union, Luxembourg, doi:10.2760/291502, JRC131063. 

Dijkstra,  L.  and  Poelman,  H.  (2012),  Cities  in  Europe:  The  new  OECD-EC  definition,  European  Commission, 
Brussels. 

Eurostat (2016), Urban Europe – Statistics on cities, towns and suburbs: 2016 edition, Publications Office of the 
European Union, Luxembourg, doi:10.2785/91120. 

Jenks,  G. F.  (1967),  ‘The  data  model  concept  in  statistical  mapping’,  International  Yearbook  of  Cartography, 
Vol. 7, pp. 186–190. 

Montalto, V., Tacao Moura, C., Panella, F., Alberti, V., Becker, W. and Saisana, M. (2019), The Cultural and Creative 
Cities Monitor – 2019 edition, Publications Office of the European Union, Luxembourg, doi:10.2760/257371, 
JRC117336. 

OECD  and  JRC  (Organisation  for  Economic  Co-operation  and  Development  and  European  Commission,  Joint 
Research Centre) (2008), Handbook on Constructing Composite Indicators– Methodology and user guide, OECD 
Publishing, Paris. 

29 

 
List of figures 

Figure 1. The Cultural and Creative Cities Monitor conceptual framework ................................................................................. 4 

Figure 2. Data sources for the indicators included in the 2023 update of the monitor ...................................................... 6 

Figure 3. Cities included in the 2023 update of the monitor .............................................................................................................. 9 

Figure 4. Factor map of the three subindices and comparison with the overall C3 Index .............................................. 21 

Figure 5. Impact of the modelling assumption for XXL cities .......................................................................................................... 22 

Figure 6. Impact of the modelling assumption for XL cities............................................................................................................. 23 

Figure 7. Impact of the modelling assumption for L cities ............................................................................................................... 23 

Figure 8. Impact of the modelling assumption for S and M cities ................................................................................................ 24 

Figure 9. Home page of the monitor’s online tool .................................................................................................................................. 25 

Figure 10. Performance map of the monitor ............................................................................................................................................ 25 

Figure 11. Overall performance: the monitor – 2023 update ......................................................................................................... 26 

Figure 12. Performance by dimension .......................................................................................................................................................... 26 

Figure 13. Compare my city with... .................................................................................................................................................................. 27 

30 

 
List of tables 

Table 1. Updated conceptual framework ....................................................................................................................................................... 5 

Table 2. New cities included in the 2023 update of the monitor listed by country ............................................................... 7 

Table 3. Cities included in the 2023 update of the monitor by selection criterion................................................................. 8 

Table 4. Grouping thresholds in the 2023 update of the monitor ................................................................................................ 10 

Table 5. Groups in the 2023 update of the monitor ............................................................................................................................. 10 

Table 6. Details on imputation method for each variable imputed .............................................................................................. 12 

Table 7. Observed and imputed indicator data for 2019 data points ........................................................................................ 15 

Table 8. Correlations between indicators in the same subindex .................................................................................................... 17 

Table 9. Correlations between indicators and their aggregates ..................................................................................................... 18 

Table 10. Correlation analysis between dimensions ............................................................................................................................. 19 

Table 11. Correlation of dimensions with subindices and index .................................................................................................... 20 

Table 12. Correlations between the subindices and with the index ............................................................................................. 20 

Table 13. Alternative assumptions considered in the uncertainty analysis ............................................................................. 22 

31 

 
Annexes 

Annex 1. Full list of cities included in the 2023 update of the monitor 

City 
Graz 
Linz 
Antwerp 
Bruges 
Brussels 
Mons 
Plovdiv 
Sofia 
Varna 
Veliko Tarnovo 
Nicosia 
Ostrava 
Pilsen 
Prague 
České Budějovice 
Berlin 
Chemnitz 
Essen 
Weimar 
Aarhus 
Copenhagen 
Tallinn 
Athens 
Kalamata 
Patras 
Thessaloniki 
Burgos 
Cordova 
Las Palmas 
Madrid 
Salamanca 
San Sebastián-
Donostia 
Santiago 
Zaragoza 
Helsinki 
Oulu 
Turku 
Avignon 
Bordeaux 
Lille 
Lyon 
Marseille 
Paris 
Toulouse 
Osijek 
Pula 
Rijeka 
Debrecen 
Győr 
Pécs 

Country 
AT 
AT 
BE 
BE 
BE 
BE 
BG 
BG 
BG 
BG 
CY 
CZ 
CZ 
CZ 
CZ 
DE 
DE 
DE 
DE 
DK 
DK 
EE 
EL 
EL 
EL 
EL 
ES 
ES 
ES 
ES 
ES 
ES 

ES 
ES 
FI 
FI 
FI 
FR 
FR 
FR 
FR 
FR 
FR 
FR 
HR 
HR 
HR 
HU 
HU 
HU 

City 
Veszprém 
Cork 
Dublin 
Galway 
Limerick 
Waterford 
Bologna 
Cagliari 
Florence 
Genoa 
Lecce 
Matera 
Perugia 
Ravenna 
Kaunas 
Klaipeda 
Vilnius 
Luxembourg 
Liepāja 
Riga 
Valletta 
Amsterdam 
Eindhoven 
Leeuwarden 
Maastricht 
Rotterdam 
Bergen 
Stavanger 
Gdańsk 
Katowice 
Kraków 
Lublin 

Country 
HU 
IE 
IE 
IE 
IE 
IE 
IT 
IT 
IT 
IT 
IT 
IT 
IT 
IT 
LT 
LT 
LT 
LU 
LV 
LV 
MT 
NL 
NL 
NL 
NL 
NL 
NO 
NO 
PL 
PL 
PL 
PL 

Warsaw 
Wrocław 
Aveiro 
Guimarães 
Lisbon 
Ponta Delgada 
Porto 
Baia Mare 
Bucharest 
Cluj-Napoca 
Sibiu 
Timișoara 
Lund 
Stockholm 
Umeå 
Maribor 
Košice 
Nitra 

PL 
PL 
PT 
PT 
PT 
PT 
PT 
RO 
RO 
RO 
RO 
RO 
SE 
SE 
SE 
SI 
SK 
SK 

City 
Prešov 
Trenčín 
Ghent 
Kortrijk 
Brno 
Hannover 
Heidelberg 
Mannheim 
Tartu 
Barcelona 
Bilbao 
Granada 
Seville 
Terrassa (*) 
Valladolid 
Limoges 
Rouen 
Saint-Etienne 
Budapest 
Bergamo 
Milan 
Modena 
Parma 
Pesaro 
Rome 
Turin 
Utrecht 
Gdynia 
Łódź 
Braga 
Norrköping 
Ljubljana 

Salzburg 
Vienna 
Leuven 
Liège 
Namur 
Ostend 
Stara Zagora 
Basel 
Bern 
Geneva 
Zurich 
Karlovy Vary 
Olomouc 
Bochum 
Bremen 
Cologne 
Dresden 
Frankfurt 

Country 
SK 
SK 
BE 
BE 
CZ 
DE 
DE 
DE 
EE 
ES 
ES 
ES 
ES 
ES 
ES 
FR 
FR 
FR 
HU 
IT 
IT 
IT 
IT 
IT 
IT 
IT 
NL 
PL 
PL 
PT 
SE 
SI 

AT 
AT 
BE 
BE 
BE 
BE 
BG 
CH 
CH 
CH 
CH 
CZ 
CZ 
DE 
DE 
DE 
DE 
DE 

City 
Hamburg 
Karlsruhe 
Leipzig 
Mainz 
Munich 
Nuremberg 
Stuttgart 
Odense 
Lleida 
San Cristóbal de La Laguna (*) 
Santa Cruz de Tenerife 
Valencia 
Espoo 
Kuopio 
Tampere 
Montpellier 
Nantes 
Split 
Zagreb 
Kecskemét 
Miskolc 
Szeged 
Brescia 
Catania 
Como 
Naples 
Pordenone 
Trento 
Trieste 
Venice 
‘s-Hertogenbosch 
Amersfoort 

Groningen 
Leiden 
The Hague 
Tilburg 
Oslo 
Poznań 
Toruń  
Coimbra 
Faro 
Sintra 
Viseu 
Iași 
Gothenburg 
Malmö 
Uppsala 
Bratislava 

Country 
DE 
DE 
DE 
DE 
DE 
DE 
DE 
DK 
ES 
ES 
ES 
ES 
FI 
FI 
FI 
FR 
FR 
HR 
HR 
HU 
HU 
HU 
IT 
IT 
IT 
IT 
IT 
IT 
IT 
IT 
NL 
NL 

NL 
NL 
NL 
NL 
NO 
PL 
PL 
PT 
PT 
PT 
PT 
RO 
SE 
SE 
SE 
SK 

1.
European 
C apital of 
C ulture

2.
UNESC O 
C reative 
C ity

3.
Festivals

(*) San Cristóbal de La Laguna is within the boundaries and analysis for Santa Cruz de Tenerife, and Terrassa is within the boundaries 
and analysis for Barcelona. 

32 

 
 
 
 
 
 
 
Annex 2. Cities not included in the 2023 update of the monitor 

The following cities are not included in the 2023 update of the monitor, despite fulfilling the inclusion criteria. 

Inclusion criterion 

City 

Festivals 

Limassol 

UNESCO Creative City 

Terrassa 

Reason for exclusion 

Not in Urban Audit any more 

Part of Barcelona 

San Cristóbal de La Laguna 

Part of Santa Cruz de Tenerife 

Festivals 

Festivals 

Festivals 

European Capital of 
Culture 

London 

Birmingham 

Glasgow 

UNESCO Creative City 

Bradford 

European Capital of 
Culture 

Liverpool 

UNESCO Creative City 

Edinburgh 

UNESCO Creative City  Manchester 

UNESCO Creative City 

Nottingham 

UNESCO Creative City 

York 

UNESCO Creative City 

Dundee 

UNESCO Creative City 

Norwich 

Festivals 

Leeds 

UNESCO Creative City 

Bristol 

Festivals 

Brighton and Hove 

UNESCO Creative City 

Belfast 

Portsmouth 

Worcester 

Festivals 

Festivals 

Festivals 

Festivals 

United Kingdom 

United Kingdom 

United Kingdom 

United Kingdom 

United Kingdom 

United Kingdom 

United Kingdom 

United Kingdom 

United Kingdom 

United Kingdom 

United Kingdom 

United Kingdom 

United Kingdom 

United Kingdom 

United Kingdom 

United Kingdom 

United Kingdom 

Bath and North East Somerset 

United Kingdom 

Cheltenham 

United Kingdom 

33 

 
Annex 3. Indicators framework 

Dimension 

Indicator 

Short description 

Geographical 
level 

Source 

Cultural Venues 
& Facilities 

Sights & landmarks 

Number of points of historical, cultural and or artistic interest, such 
as architectural buildings, religious sites, monuments and statues, 
churches and cathedrals, bridges, towers and fountains, divided by 
the total population and then multiplied by 100 000. 

City 

TripAdvisor 

Museums & art 
galleries 

Number of museums and art galleries that are open to the public 
divided by the total population and then multiplied by 100 000. 

Cinemas 

Number of cinemas in the city divided by the total population and 
then multiplied by 100 000. 

Concert & music 
halls 

Number of theatres and other music venues (concert halls, clubs, 
etc.) divided by the total population and then multiplied by 
100 000. 

Theatres 

Number of theatres in the city divided by the total population and 
then multiplied by 100 000. 

Cultural 
Participation & 
Attractiveness 

Tourist overnight 
stays 

Total annual number of nights that tourists/guests have spent in 
tourist accommodation establishments (hotel or similar) divided by 
the total population. 

City 

City 

City 

City 

City 

TripAdvisor 

OpenStreetMap 

TripAdvisor 

OpenStreetMap 

Eurostat (Urban Audit) 

Museum visitors 

Total number of museum tickets sold during the reference year 
divided by the total population and then multiplied by 1 000. 

City 

Eurostat (Urban Audit) 

Cinema attendance 

Total number of tickets sold, referring to all films screened during 
the year, divided by the total population and then multiplied by 
1 000. 

City 

Eurostat (Urban Audit) 

Satisfaction with 
cultural facilities 

Percentage of population that is very satisfied with cultural 
facilities in the city. 

City 

Flash Eurobarometer 366 
survey: ‘Quality of Life in 
European Cities’ 

Creative & 
Knowledge-based 
Jobs 

Jobs in arts, culture 
& entertainment 

Number of jobs in arts-, culture- and entertainment-related 
activities, such as performing arts, museums and libraries, divided 
by the total population and then multiplied by 1 000 (NACE Rev. 2, 
R-U).

City 

Eurostat (Urban Audit) 

34 

Intellectual 
Property & 
Innovation 

Jobs in media & 
communication 

Jobs in other 
creative sectors 

ICT patent 
applications 

Number of jobs in media- and communication-related activities, 
such as book and music publishing, film and television production , 
divided by the total population and then multiplied by 1 000 (NACE 
Rev. 2, J). 

Number of jobs in professional, scientific and technical, 
administrative and support service activities, such as architecture, 
advertising, design and photographic activities, divided by the total 
population and then multiplied by 1 000 (NACE Rev. 2, M-N). 

City 

Eurostat (Urban Audit) 

City 

Eurostat (Urban Audit) 

Three-year average number of ICT patent applications (including 
consumer electronics, computers and office machinery, and 
telecommunications) filed to the European Patent Office by priority 
year, divided by the total population and then multiplied by 1 
million. 

NUTS 3 

Organisation for Economic 
Co-operation and 
Development Regpat 
database 

Gross expenditure 
in research & 
development 

Research and development expenditure in business enterprise, 
government, higher education, private non-profit (aggregated) as 
a proportion of GDP. 

NUTS 2 

Eurostat (regional statistics) 

New Jobs in 
Creative Sectors 

Jobs in new arts, 
culture & 
entertainment 
enterprises 

Number of persons employed in the enterprises established in the 
reference year in arts, culture and entertainment activities, such as 
performing arts, museums and libraries, divided by the total 
population and then multiplied by 100 000. 

NUTS 3 

Eurostat (regional statistics) 

Jobs in new media 
& communication 
enterprises 

Number of persons employed in the enterprises established in the 
reference year in media and communication activities, such as 
book and music publishing, film and television production, divided 
by the total population and then multiplied by 100 000. 

NUTS 3 

Eurostat (regional statistics) 

Jobs in new 
enterprises in other 
creative sectors 

Number of persons employed in the enterprises established in the 
reference year in professional, scientific and technical activities, 
such as architecture, advertising, design and photographic 
activities, divided by the total population and then multiplied by 
100 000. 

NUTS 3 

Eurostat (regional statistics) 

Human Capital & 
Education 

Graduates in arts & 
humanities 

Number of tertiary education graduates (ISCED 2011 levels 5–8) in 
arts and humanities courses in the reference year, divided by the 
total population and then multiplied by 100 000. 

City 

ETER 

Graduates in ICT 

Number of tertiary education graduates (ISCED 2011 levels 5–8) in 
ICT courses in the reference year, divided by the total population 
and then multiplied by 100 000. 

City 

ETER 

35 

Openness, 
Tolerance & Trust 

Foreign graduates 

Number of foreign graduates enrolled in tertiary education courses 
in the reference year, divided by the total number of tertiary 
education graduates (ISCED 2011 levels 5–8) in the same 
academic year. 

City 

ETER 

Foreign-born 
population 

Percentage of the total population who is foreign-born. 

City 

Eurostat (Urban Audit) 

Tolerance of 
foreigners 

Percentage of the population who very strongly agrees with the 
statement ‘The presence of foreigners is good for this city’. 

City 

Integration of 
foreigners 

Percentage of the population who very strongly agrees with the 
statement ‘Foreigners who live in this city are well integrated’. 

People trust 

Percentage of the population who very strongly agrees with the 
statemen: ‘Generally speaking, most people in this city can be 
trusted’. 

Passenger flights 

Population-weighted average number of accessible passenger 
flights per day, within 1 hour 30 minutes of travel by road. 

Potential road 
accessibility 

Population accessible within 1 hour 30 minutes by road, as share 
of the population in a neighbourhood of 120 km radius. 

Direct trains to 
other cities 

Population accessible within 1 hour 30 minutes by rail (average 
total travel time), as share of the population in a neighbourhood of 
120 km radius. 

City 

City 

City 

City 

City 

Flash Eurobarometer 366 
survey: ‘Quality of Life in 
European Cities’ 

Flash Eurobarometer 366 
survey: ‘Quality of Life in 
European Cities’ 

Flash Eurobarometer 366 
survey: ‘Quality of Life in 
European Cities’ 

Directorate-General for 
Regional and Urban Policy 

Directorate-General for 
Regional and Urban Policy 

Directorate-General for 
Regional and Urban Policy 

Quality of 
governance 

Computed indicator measuring the quality of government in three 
areas of public services: education, healthcare and law 
enforcement. 

NUTS 2, NUTS 1 
and NUTS 0 

Directorate-General for 
Regional and Urban Policy 

Local & 
International 
Connections 

Quality of 
Governance 

NB: Indicators shaded in grey have not been updated or included in the 2023 update of the monitor due to low data coverage.

36 

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