Perception Survey on the Quality of
Life in European Cities 2019 Evaluation Report
Contract n° 2017CE160AT133 Prepared by: Ipsos Date: 9 March 2020
Regional and Urban Policy
EUROPEAN COMMISSION Produced by on behalf of the Directorate-General for Regional and Urban Policy Unit B1 - Policy Development and Economic Analysis E-mail: [email protected] European Commission B-1000 Brussels
EUROPEAN COMMISSION
Perception Survey on the Quality of
Life in European Cities 2019
Evaluation Report
Directorate-General for Regional and Urban Policy 2020 3
EUROPEAN COMMISSION
Directorate-General for Regional and Urban Policy 2020 5
TABLE OF CONTENTS 1 INTRODUCTION ...... 5 2 PROJECT OVERVIEW ...... 6 2.1 Timing ...... Error! Bookmark not defined. 2.2 Sample design...... 6 2.2.1 Sample size and associated margin of error ...... 6 2.2.2 Sample methodology ...... 7 2.3 Questionnaire design ...... 14 2.3.1 Screening questions ...... 14 2.3.2 Socio-demographic background questions ...... 16 2.3.3 Weighting questions ...... 16 2.3.4 Changes related to GDPR compliance ...... 17 2.4 Pilot testing...... 18 2.5 Main fieldwork ...... 18 2.5.1 Timing ...... 18 2.5.2 Fieldwork follow-up ...... 18 2.6 Reporting and data delivery ...... 19 2.6.1 Pre-fieldwork reporting ...... Error! Bookmark not defined. 2.6.2 Data delivery ...... 19 3 WEIGHTING ...... 20 3.1 Weighting procedure ...... 20 3.1.1 Post-stratification & design weighting ...... 20 3.1.2 Weight trimming ...... 22 3.2 Weighting benchmarks ...... 23 3.2.1 Age and gender ...... 23 3.2.2 Phone ownership ...... 24 3.3 Design effects and weighting efficiency per city ...... 26 4 SAMPLE PERFORMANCE ANALYSIS ...... 28 4.1 Target population versus achieved distribution ...... 28 4.1.1 Age ...... 28 4.1.2 Gender ...... 33 4.1.3 Phone ownership ...... 35 4.1.4 Eligibility ...... 37 5 FIELDWORK PERFORMANCE ANALYSIS ...... 40 5.1 Interview validation ...... 40 5.2 Interview breakoffs ...... 41 5.3 Item non-response ...... 41 5.4 Response rates ...... 43 6 DATA COMPARISON 2019-2015 ...... 46 7 RECOMMENDATIONS FOR FUTURE WAVES ...... 47 ANNEX 1. FINAL QUESTIONNAIRE ...... 49
1 Introduction
This evaluation report is one of two parts of the final report for the 2019 Perception Survey on the Quality of Life in European Cities. It presents an overview of the design, preparation and execution of the Perception Survey. It also discusses the survey’s performance in terms of sampling, fieldwork quality and accuracy of the collected data. Finally, this report also lays out some recommendations for possible changes to the survey design that could improve the performance of future waves of the Perception Survey.
This evaluation report is accompanied by a technical report, which forms the second part of the final report. The technical report lists, per city, the most important sample performance data (amount of sample used, eligibility rate, refusals, response rate, average interview length, etc.)
2 Project overview
This chapter gives a concise overview of the different steps of the 2019 Perception Survey, from the questionnaire design until the final data deliveries.
2.1 Sample design
The Perception Survey targets citizens of all (greater) cities within the scope of the survey – covering a total of 83 cities. The target population includes all people aged 15 and above, who satisfy the requirements outlined below:
1. Being a resident of the city surveyed;
2. Having sufficient command of (one of) the respective national/regional language(s) or English, which allows them to comfortably answer the questionnaire;
3. Living in a private household, which means that the target population will exclude prisoners, residents of retirement homes, etc. who are difficult to reach via a telephone survey.
Regarding the first requirement, the scope is technically defined for each city in terms of a set of Local Area Units (LAUs) that together comprise the area of the city under scope. The residence of a given respondent in one of these LAUs determines their eligibility for the survey. The list of LAUs in scope per city is added to this Evaluation Report as Annex 2.
Regarding the second requirement, the language command was assessed by the interviewer at the start of the survey. In case it was clear that a respondent is not able to answer questions in one of the official languages, they were offered to conduct the interview in English.
Regarding the third requirement, the survey in practice targeted all residents aged 15+ with private access to a telephone, which is de facto confirmed by a given respondent being reachable by phone during the fieldwork.
2.1.1 Sample size and associated margin of error The target sample size was 700 complete interviews in each city surveyed. This means that interviews were gathered from 58 100 respondents in total, all of which are citizens who are resident in one of the (greater) cities under scope The following chart depicts how the margin of error associated with survey estimates can vary as a function of sample size, assuming a confidence level of 95%.
Quality of Life in European Cities Survey 2019
±9.8%
±6.9%
±5.7% ±4.9% ±4.4% ±4.0%
Marginof Error ±3.7% ±3.5% ±3.3% ±3.1% ±3.0% ±2.8% ±2.7% ±2.6% ±2.5%
100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 Required Sample Size
2.1.2 Sample methodology Telephone samples require a specific design in order to cover the entire target population and to reduce a) potential coverage bias and b) non-response bias. Some aspects are country-specific, such as prefixes, operators, overall telephone penetration, penetration of mobile phones and penetration of mobile only. As a growing share of the population is becoming “mobile only” (i.e. persons who only have access to a mobile phone), the optimal composition of telephone samples should take into account the incidence of households that are reachable only via mobile numbers. Each telephone mode (fixed line or mobile) also covers a specific profile with parameters such as age and urbanization degree. According to the 2017 Eurobarometer on E-communications, omitting “mobile only” persons implies 37% of the EU households are not included in the sample frame.1
In order to ensure maximum population coverage resulting in a representative sample, a mixed (or “dual frame”) approach was taken for this Perception Survey, which takes into account the respective distributions of persons who only have access to a mobile phone (i.e. “mobile only”), persons who only have access to a fixed line phone (i.e. “fixed only”) and persons who have access to both mobile and fixed line phones (i.e. “mixed”). Based on these data, the necessary distribution of mobile phone and landline sample units needed in the sample frame is calculated. By utilizing two separate, overlapping sample frames to interview a population of interest, this approach currently guarantees the maximum and most representative coverage of the population of interest.
Below we provide an overview of the methodology used for sampling, including the procedures for random selection of telephone numbers from the sampling frames as well as for respondent selection within a given household. Both are identical across the surveyed cities.
The sampling frames based on the dual-frame methodology proposed were developed via the following steps:
Step 1: Sampling frames
1 http://ec.europa.eu/commfrontoffice/publicopinion/index.cfm/ResultDoc/download/DocumentKy/83478
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For every city, a random gross sample was drawn from a larger sample frame. This ensures that each person belonging to the target universe will have a chance to participate in the survey.
The landline sample was generated through Random Digit Dialling (RDD). With RDD, dedicated software is used to generate telephone numbers, starting from an initial list of prefixes (which can be linked to a great extend to cities) and primary numbers. This initial list is taken from available public records like registers and phone books. From the numbers in this list, new telephone numbers are created and used by adding and subtracting digits from the existing telephone number. For example if in the register/phone book +322/xxx.xx.xx links to the city of Brussels, then new telephone numbers for Brussels can be generated keeping the prefix and replacing e.g. the last two digits (e.g. +322/xxx.xx.yy). In this way the number has been randomly generated.
The mobile sample is compiled based on national phone number registers (if available for mobile numbers in a given country) and through publicly available online data (such as from social media). It should be noted that such data is collected anonymously and only with reference to the available geographic information linked to the number (such as the country or region in which the phone number resides). The latter is necessary to identify the mobile phone number as belonging to a certain country or city.
Where detailed geographic information is available it was also used to verify in this first stage that the numbers in the sample are spread proportionally over the different subregions of the city. Concretely, we wanted to avoid that most or all of the sample is located in one specific subregion of a city, which could greatly bias the results of the Survey.
The composition of the prefixes and primary numbers is thus a key element to guarantee that sufficient geographical spread can be obtained. Once the primary numbers and their geographic information are determined, the landline samples for specific cities are generated via Random Digit Dialling. Next the sample is pulsed to filter out non-connected and invalid numbers (=numbers that don’t exist/don’t result in a connection) as well as business/non-residential numbers.
Step 2: Gross sample composition
When the sample frames per city are determined, the gross samples can be drawn from it. As already indicated above, the 2019 Perception Survey uses a dual frame sampling approach, using both mobile and fixed line numbers. The mobile sample is drawn at random from the available mobile numbers for each city. To determine the size of the gross sample needed for a target complete sample of 700 per city, we estimate the necessary oversampling rate based on an assumed response rate, and set the oversampling rate in the gross samples for the main field is defined at a ratio of 1:24. This amounts to a gross sample of 16 800 numbers per city.
For each city, separate landline and mobile frames are built and separate samples are drawn from that for each city. The size of the sample drawn per phone type depends on the phone type ownership data for each city. However, as reliable statistics on phone ownership on city level are not available, the proportion of mobile and landline numbers in each city sample is based on available data on the country level (i.e., for all cities in a given country we use the same phone type distribution). For the EU countries, United Kingdom, Norway and Iceland the distribution data are calculated based on phone
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ownership data collected in the 2017 wave of the Consumer Market Monitoring Survey.2 The rationale behind using these alternative targets is further elaborated in the weighting section. For the other countries, phone ownership data is based on the latest available Eurobarometer data or on data made available by local statistics institutes. The phone ownership targets are used for defining the sampling frames, but are also used for monitoring the sample performance during the fieldwork and used for weighting.
Population statistics on phone ownership distinguish between landline ownership, mobile phone ownership and mixed ownership (i.e., those who have both types of phone). However, in order to determine the proportion of landline and mobile numbers in our gross samples, the “mixed” population needs to be recalculated to come to a binary sample distribution. For that reason, the mobile and landline samples are defined and calculated as follows:
Mobile sample: potential respondents within a given country that can be reached via a mobile line (regardless of whether they can also be reached via a fixed line). As such, this sample includes respondents from both the mobile only and mixed population.
푷풓풐풑풐풓풕풊풐풏 풐풇 풎풐풃풊풍풆 풍풊풏풆풔 푴 + 푴푭 % 푴풐풃풊풍풆 풔풂풎풑풍풆 = = 푻풐풕풂풍 풑풐풑풖풍풂풕풊풐풏 풐풇 풑풉풐풏풆 풏풖풎풃풆풓풔 (푴 + 푴푭) + (푭 + 푴푭)
F = fixed only; M = mobile only; and MF = mobile and fixed (mixed)
Fixed sample: potential respondents within a given country that can be reached via a fixed line (regardless of whether they can also be reached via mobile line). As such, this sample includes respondents from both the fixed line only and mixed population.
푷풓풐풑풐풓풕풊풐풏 풐풇 풇풊풙풆풅 풍풊풏풆풔 푭 + 푴푭 %푭풊풙풆풅 풍풊풏풆 풔풂풎풑풍풆 = = 푻풐풕풂풍 풑풐풑풖풍풂풕풊풐풏 풐풇 풑풉풐풏풆 풏풖풎풃풆풓풔 (푴 + 푴푭) + (푭 + 푴푭)
F = fixed only; M = mobile only; and MF = mobile and fixed (mixed)
For example, if Germany would be set to have the following proportions in the study: 83% mixed, 9% fixed only, 8% mobile only, the local teams would compose a gross sample of 50.3% fixed numbers, calculated as ((83%+9%)/((83%+9%)+(83%+8%))), and 49.7% mobile numbers ((83%+8%)/((83%+9%)+(83%+8%))).
It should be noted that these distributions are not fixed targets but are rather used as an instrument to determine the composition of the gross samples so that they are maximally representative of the population in terms of phone ownership. As such, they represent the calling proportion of mobile versus fixed lines within each country. The table below presents an example of how the % Mobile sample and % Fixed sample are linked to these distributions of phone ownership for each country, based on available phone ownership data.
2 https://ec.europa.eu/info/policies/consumers/consumer-protection/evidence-based-consumer-policy/market- monitoring_en
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Target sample Population distribution distribution COUNTRY Mixed Fixed telephone only Mobile telephone only Fixed Mobile BE 85% 4% 11% 48% 52% BG 48% 2% 50% 34% 66% CZ 25% 0% 75% 20% 80% DK 41% 2% 58% 30% 70% DE 92% 4% 4% 50% 50% EE 44% 0% 55% 31% 69% IE 58% 1% 41% 37% 63% EL 81% 7% 12% 49% 51% ES 84% 4% 12% 48% 52% FR 89% 5% 6% 50% 50% IT 66% 2% 31% 41% 59% CY 68% 4% 28% 43% 57% LV 27% 0% 73% 21% 79% LT 38% 0% 62% 28% 72% LU 86% 2% 12% 47% 53% HU 35% 7% 58% 31% 69% MT 93% 6% 1% 51% 49% NL 82% 2% 15% 46% 54% AT 62% 1% 36% 39% 61% PL 56% 3% 41% 38% 62% PT 79% 2% 19% 45% 55% RO 60% 1% 39% 38% 62% SI 78% 3% 19% 46% 54% SK 44% 1% 55% 31% 69% FI 16% 1% 83% 15% 85% SE 77% 1% 22% 44% 56% UK 78% 5% 17% 47% 53% HR 63% 11% 25% 46% 54% AL 23% 0% 75% 19% 81% TR 14% 3% 81% 15% 85% MK 32% 4% 61% 28% 72% RS 75% 7% 17% 47% 53% ME 25% 2% 72% 22% 78% NO 40% 1% 59% 29% 71% IS 88% 1% 11% 47% 53%
Step 3: Sample building and effective phone type distribution
Based on the above distributions and the established oversampling factor of 24:1, the required amount of mobile and fixed numbers is drawn from the sample frame to create the gross sample for each city. At this point, in some cities, a necessary adjustment to the Directorate-General for Regional and Urban Policy 2020 10
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target phone type distribution was made. This is done when there were not enough primary phone numbers with geolocation information to reach the target amount of mobile phone numbers for the gross sample. While sources to collect primary landline numbers from are abundantly available, for some cities such information is much less easy to collect, either because public listings are incomplete or because it depends on what information is publicly obtainable via social media and other open sources. This explains why there can be clear differences even within a country. Extra data collection efforts were made for cities where the originally available primary number list was insufficient. A number of possible ways to tackle this issue were considered. A first possibility was to include in the gross sample also mobile phone numbers for which no geographic information is available. This, however, would in most cities considerably raise the risk of lowering the incidence rate of the sample (i.e., the amount of respondents on mobile numbers that live in the target cities), because for any random mobile phone number in a given country the chances are low that the associated respondent lives in one of the target cities. Including such numbers would then decrease the calling efficiency and therefore increasing the time and resources needed for the fieldwork. Therefore, this option was only applied in countries where the population of the target city/cities is a considerable part of the total country population – and where there is consequently a high enough chance that a randome mobile phone respondent is a resident of the target city. Specifically, this was done in Valletta (Malta) and Luxembourg City (Luxembourg).
In countries where this was not a practically feasible option, the lower proportion of mobile phone numbers was compensated by adding more landline numbers to the gross sample. This led to the following effective stratification of the gross sample for phone type, in each city:
TARGET (%) SAMPLE (%) Mobile Fixed Mobile Fixed Graz 61% 39% 61% 39% Wien 61% 39% 61% 39% Antwerpen 52% 48% 52% 48% Bruxelles / Brussel 52% 48% 52% 48% Liège 52% 48% 52% 48% Burgas 66% 34% 56% 44% Sofia 66% 34% 66% 34% Zagreb 54% 46% 54% 46% Lefkosia 57% 43% 23% 77% Ostrava 80% 20% 73% 27% Praha 80% 20% 80% 20% Aalborg 70% 30% 70% 30% København 70% 30% 70% 30% Tallinn 69% 31% 29% 71% Helsinki / Helsingfors 85% 15% 85% 15% Oulu / Uleåborg 85% 15% 85% 15% Bordeaux 50% 50% 50% 50% Lille 50% 50% 50% 50% Marseille 50% 50% 50% 50% Rennes 50% 50% 50% 50% Strasbourg 50% 50% 50% 50% Paris 50% 50% 50% 50% Directorate-General for Regional and Urban Policy 2020 11
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Berlin 50% 50% 50% 50% Dortmund 50% 50% 50% 50% Essen 50% 50% 50% 50% Hamburg 50% 50% 50% 50% Leipzig 50% 50% 50% 50% Munich 50% 50% 50% 50% Rostock 50% 50% 40% 60% Athina 51% 49% 51% 49% Irakleio 51% 49% 32% 68% Budapest 69% 31% 64% 36% Miskolc 69% 31% 19% 81% Dublin 63% 37% 63% 37% Bologna 59% 41% 59% 41% Naples 59% 41% 59% 41% Palermo 59% 41% 59% 41% Rome 59% 41% 59% 41% Turin 59% 41% 59% 41% Verona 59% 41% 52% 48% Vilnius 72% 28% 30% 70% Luxembourg 53% 47% 53% 47% Riga 79% 21% 11% 89% Valletta 49% 51% 49% 51% Amsterdam 54% 46% 48% 52% Groningen 54% 46% 28% 72% Rotterdam 54% 46% 52% 48% Białystok 62% 38% 62% 38% Gdańsk 62% 38% 62% 38% Kraków 62% 38% 62% 38% Warszawa 62% 38% 62% 38% Braga 55% 45% 55% 45% Lisboa 55% 45% 55% 45% Bucharest 62% 38% 62% 38% Cluj-Napoca 62% 38% 62% 38% Piatra Neamt 62% 38% 32% 68% Bratislava 69% 31% 69% 31% Košice 69% 31% 52% 48% Ljubljana 54% 46% 54% 46% Barcelona 52% 48% 52% 48% Madrid 52% 48% 52% 48% Málaga 52% 48% 52% 48% Oviedo 52% 48% 52% 48% Malmö 56% 44% 29% 71% Stockholm 56% 44% 56% 44% Belfast 53% 47% 53% 47%
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Cardiff 53% 47% 53% 47% Glasgow 53% 47% 53% 47% London 53% 47% 53% 47% Manchester 53% 47% 53% 47% Tyneside conurbation 53% 47% 53% 47% Reykjavík 53% 47% 48% 52% Oslo 71% 29% 71% 29% Genève 25% 75% 25% 75% Zurich 25% 75% 25% 75% Tirana 81% 19% 81% 19% Skopje 72% 28% 72% 28% Podgorica 78% 22% 6% 94% Beograd 54% 46% 54% 46% Ankara 85% 15% 49% 51% Istanbul 85% 15% 60% 40% Antalya 85% 15% 24% 76% Diyarbakir 85% 15% 3% 97%
In cities where the proportion of mobile phone numbers in the gross sample was lower than the assumed country proportion, mobile phone numbers were prioritized in the fieldwork. This means that in the first weeks of the fieldwork a higher number of mobile phone numbers were called, with the aim of maximizing the proportion of mobile numbers in the final sample.
Identification of eligible postcodes.
Postcodes are central to the sample design of the Perception Survey and were also used during the fieldwork to determine in the majority of the cities the eligibility of respondents. It was thus very important that all (and only) the postcodes belonging to the target city regions were identified. To achieve this, a multi-step process was followed.
First, GIS-data from postcode areas in all countries (obtained from national postal administrations) were overlayed on GIS-data from the target LAUs per city. This way it could be determined which postcodes were used within the cities’ boundaries.
In most countries, the boundaries of postcode areas and LAUs coincide. If that is the case, it can be exactly determined which postcodes belong to which LAUs. However, in some countries, both types of areas crosscut each other. This means that if we know for a given sample unit or respondent the postcode, it cannot be determined in which LAU they live.
If the postcode area falls fully within the target city, this doesn’t pose large problems. However, when a postcode area falls partly within and partly outside of the target city, it is impossible to determine with 100% certainty whether a sample unit with this postcode is eligible or not. To determine how likely it is that any sample unit with such a postcode is eligible for participation in the survey, we calculated the proportion of the population in these postcode areas that lives within the target city. We propose that if this proportion is 25% or higher, a sample unit with this postcode is considered always eligible. If the proportion is below that threshold, we consider the sample unit always ineligible.
Concretely, this would mean the following:
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In the postcode areas that we would not include in the sample, where less than 25% of the population lives within the target city, the average population proportion living within the target city is just 4% (i.e., a random sample unit with this postcode has 96% chance of being ineligible)
In the postcode areas that we would keep in the sample, where 25% or more of the population lives within the target city, the average population proportion living within the target city is 79%) (i.e., a random sample unit with this postcode has 21% chance of being ineligible)
These figures show that a cut-off of 25% guarantees a high chance that ineligible units are kept out of the sample, while at the same time only removing a very small number of eligible respondents.
2.2 Questionnaire design
The primary objective when preparing the questionnaire for use in the 2019 Perception Survey was to keep the substantive questions as much as possible identical to the 2015 questionnaire, so that comparisons could be drawn. Some changes were made, however, to the screening questions, the socio-demographic background questions, and the questions needed for weighting. This was done either at the inception of the project or after the pilot test. All of these changes were made with the objective of increasing data accuracy. Also, a few changes were made to the questionnaire to comply fully with the GDPR, in force since 2018.
2.2.1 Screening questions Multiple screening questions where added to questionnaire, to be asked at the start of the questionnaire. The goal of these screening questions is to make sure that all respondents to the survey are within the target scope of the survey – aged 15 or over and residing in one of the target cities.
For the screening of age, we use the following questions. When calling a landline number, the age screening is combined with the “last birthday” question. This is used to randomly select a member of the household, thus avoiding the bias of self-selection by the person that has picked up the phone. In comparison to the previous wave, the age question has been made more general, no longer asking for a specific date of birth, but only for the age of the respondent. This is easier to respond and less intrusive, thus likely increasing the response rate, and it also only collects the necessary information as we don’t need the exact date of birth.
Screening questions for age:
What is your age? (Mobile numbers)
Please can I speak to the person aged 15 or older within your household whose birthday it was most recently? (Landline numbers)
For the screening on regional eligibility, in most cities we asked the target respondent the postcode of their residence, and subsequently matched that with a list of all postcodes used in the city (specifically, the postcodes used in the LAUs that together form the target city). If the postcode is not used in the city, this implies that the respondent lives outside of the city, and the respondent was screened out. An exception is the situation where the Directorate-General for Regional and Urban Policy 2020 14
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postcode given by the respondent belongs to another city included in the survey. In that case, the interview was still conducted, and the respondent was moved to the sample from the city in which he resides.
Screening question for postcode:
What is your postcode?
In some countries asking for the respondent’s postcode was not possible. This is either because the postcode could allow to identify an individual household, in which case recording the postcode would have required additional consent from the respondent. Or, in some countries it could be guaranteed that all respondents would know their postcode, typically because of recent changes to the postcode system causing people to know their old, but possibly not yet their new postcode (or vice versa). The countries where this is the case are the Netherlands, the United Kingdom, Portugal, Bulgaria, Romania and Ireland. In these countries, instead of asking for postcode to determine eligibility, we ask directly for the respondent’s region of residence. In order to determine how to best ask this, the main principle is to keep the question as simple as possible. For example, for London, the target region is Greater London. Because Greater London is a commonly known region, we can ask a respondent directly whether they live in Greater London. However, in a city like Glasgow, such a single denomination for the whole target city region does not exist. For that reason, in such cities we include in the question all subregions (e.g., municipalities, counties) needed to identify the residence of the respondent.
Screening question for region (example of London and Glasgow)
London
Do you live in Greater London?
Glasgow
Do you live in …
1. The city of Glasgow
2. The council area of East Dunbartonhsire
3. The council area of East Renfrewshire
4. The council area of Renfrewshire
Finally, in Lisbon, both screening via postcode as well as via region (Friguesia in Lisbon) proved challenging. The pilot test showed that part of the respondents could not with certainty confirm either, and therefore tend to answer “Lisbon” when asked their region of residence – referring to the city at large. Because their responses still indicated that they live in the target region, we added to the screening question an extra response option “Lisboa”. This avoided that respondents who don’t know their Freguesia, although there is nevertheless a high chance that they live in the target city, would need to be screened out.
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2.2.2 Socio-demographic background questions
After the pilot test, some changes were made to the socio-demographic background questions. The reasons for this was in all cases that the background questions were sometimes long and difficult to answer, putting large burden on the respondents and creating a risk of inaccuracies in their responses. These changes concerned the following questions in the original questionnaire:
D8. Which of the following best describes your household composition? With household, we mean all people that typically live with you in the same residence. Please include anyone who is temporarily away for work, study or vacation
D9. How many people usually live in your household? Please include yourself.
D8 caused confusion among several respondents, as well as some resistance because of the length of the response options – especially because it comes at the end of the interview. Additionally, the follow-up question in D9 also showed to confuse respondents because they have the feeling that they already gave that information in D8.
However, since D8 is a standardized background question, too large changes to D8 were difficult, because this can jeopardize comparability with previous waves and other cities that have organized the survey independently. Nevertheless, we proposed to make one adjustment, by replacing the question order and asking D9 before D8. This allowed to ask only D9 to people living alone, because they could automatically be coded as a one-person household in D8.3 Second, in doing so, the response option list in D9 becomes slightly shorter, because the option “one-person household” does not need to be asked anymore.
D11. Which of the following best describes your current working status?
For D11 as well, the large number of response options - that are in themselves also rather long and similar to each other - regularly caused confusion and impatience among respondents. We made a similar adjustment as for D8, by first asking a simpler question that covers most of the respondents: “Do you currently have a job? (Interviewer instruction: include employees, employers, self-employed and people working as a relative assisting on family business)”
This question was applied to 60% of the pilot survey sample. Asking this simpler question first thus avoids presenting the full list of response options to the majority of respondents, and it shortened the list of response options to those that fall in another category and still needed to be asked question D11.
2.2.3 Weighting questions Specific weighting questions were added to allow collection of the data needed to calculate weights. This applies to 2 aspects of the weighting:
3 This concerns about 20% of the sample according to the pilot test.
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. Phone ownership. The sample design assumes that landline phones are accessible by all household members, and that mobile phones are personally owned and thus accessible only to the person that answers the phone. In the survey, we measure the access to mobile and landline phones, so that we can weight for the higher selection probability of people that have access to both a landline and a mobile phone (as opposed to only a landline or a mobile phone).
D14. Do you personally own a mobile phone?
D15. Do you have a landline phone in the household?
. Household size. The target population of the Perception Survey are city residents aged 15 or over. In order to accurately calculate the design weight for the landline sample (to take into account the selection probability of people reached within their household via a landline), we need to measure the number of eligible people within each household – i.e., all household members aged 15 or over. A question to gather this was added to the final questionnaire, as a follow-up to question D9:
D9. How many people usually live in your household? Please include yourself.
D9b. How many of these are aged 15 and older? Please include yourself.
2.2.4 Changes related to GDPR compliance The European Union’s General Data Protection Regulation (GDPR, Regulation (EU) 2016/679), entered into force on 24 May 2016 and applicable since 25 May 2018, puts strong responsibility on survey organizers to assure the protection of people’s privacy and the correct handling of their personal data. Informing respondents of their rights and how any personal data are treated, and acquiring consent to collect, process and store their data are two key elements of the GDPR. To that end, the questionnaire was adjusted in several places:
A privacy notice (to which respondents are referred for a full overview of what kinds of personal data are gathered from them, how these are stored, what respondents’ rights are with regard to these data and who they can contact with questions, concerns or the request to delete their data) was added.
. The design of the introduction was designed in such a way that it was ensured that informed consent was gathered from the selected respondent. In practice, this means that for respondents on the landline sample, consent is only confirmed by the final respondent. Because of within-household selection, this might not necessarily be the first person to answer the phone. The consent confirmation was therefore moved from the very start of the interview (with the first respondent on the phone) to the moment that the finally selected target respondent comes to the phone.
. Q15 asks respondents about their current health situation. Under GDPR, health information is considered a special category of personal data. Consequently, stricter rules apply for gathering these data. Respondents need to be asked consent to ask such information before the question is asked and need to be told explicitly that answering the question is voluntary. To comply with this, a question is added right before Q15:
Q15a. The next question is about your health status. Please remember that all your responses will be treated confidentially. You do not have to answer this question if you do not want to. Are you happy to proceed? Directorate-General for Regional and Urban Policy 2020 17
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1. Yes
2. No
In order to avoid that this emphasis on the personal nature of the question scares off respondents and leads to preliminary break-offs, question Q15 was moved to the very end of the questionnaire. If respondents refused to be asked a question about their health, this was coded as a refusal for question Q15, and the interview was considered a complete.
2.3 Pilot testing
The pilot test methodology was identical to the setup that is envisaged for the main fieldwork. The exact same sample, script, translations and technical infrastructure were used for the questionnaire. Fieldwork monitoring procedures and quality checks were also the same as used for the main fieldwork. All interviewers and supervisors participating in the survey were briefed beforehand, and those participating in the pilot test received specific instructions about what to focus on in the test. This allowed to evaluate the full survey design in all countries.
The pilot test took place between 6 and 15 May. Calling took place predominantly in the late afternoon and evening during weekdays. Other times of the day were available for appointments. In each city, 30 complete interviews were conducted. No quota were set.
2.4 Main fieldwork
2.4.1 Timing Because the survey fieldwork was estimated to take about 9 weeks and the fieldwork could not begin earler than June, it was decided to split the fieldwork in two parts, with a pause between 15 July and 1 September. This way it could be avoided to conduct fieldwork in the summer period of July and August, 2 months that generally see a steep decline in response rates because of the summer holidays.
The fieldwork start was originally set for 6 June, but was eventually moved to 12 June – the reason for this being a number of adjustments to the questionnaire that had to be implemented and replaced.
Despite this short delay in the start of the fieldwork, the fieldwork ended on 27 September 2019, as originally scheduled.
2.4.2 Fieldwork follow-up The fieldwork was followed up closely on a weekly basis along the following parameters:
. Total completes, per city (in absolute numbers and percentage of the total target)
. Distribution of the sample for each city according to:
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o Age and gender (to monitor deviations from the population)
o Phone type (to monitor sample performance in the mobile and landline frames)
o LAU (where available4, to monitor the spread of the sample over the whole city)
. Percentage of ineligible screened-out respondents because of their sub-city region of residence, per city (to monitor the quality of the sample in terms of incidence rate)
. Number of bad quality cases, per city
. Average interview duration, per country
. For the interrupted interviews: how often a particular question was the last question answered (to help evaluate whethere there are any questions that have a higher chance of leading to interview break-offs)
. Non-response percentage per question
In the first week of the fieldwork, members from the research team also listend in to live interviews in several countries to evaluate the interview quality (in Belgium, the Netherlands, France, Germany and Poland).
2.5 Reporting and data delivery
2.5.1 Data delivery The following data files were prepared:
. A first datafile containing ‘conventional’ question/response labels and codes, corresponding to the questions and responses as given in the English master questionnaire. Besides the response data this file also contains paradata such as interview time and date, weighting factors and sample background data.
. A second datafile which uses Eurobase labels and codes. This coding system is developed by Eurostat and has been revised and expanded so that it can be applied to the 2019 version of the Perception Survey.
. In addition to these microdata files, a table of indicators (i.e., question responses) in aggregated form, as weighted totals computed for each city. This is delivered as an Excel file.
2.5.2 Recoding of question Q13_5 for Tirana Question 13_5 asked to what extent respondents agreed to the following statement:
4 In Ireland and the United Kingdom it was not possible to follow up on the distribution of the sample at LAU level, because respondents are only screened for their residence in the city as a whole (e.g., “Greater London”).
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‘There is corruption in my local public administration’
In the Albanian translation used in Tirana (AL), this was erroneously translated as ‘there is no corruption in my local public administration’ – that is, the inverse of the master item. For this reason to allow for consistent reporting and comparability between all cities of the survey, the responses for Tirana to this question were recoded to their inverse as well. Specifically, the following recoding was applied:
‘Strongly agree’ to ‘strongly disagree’
‘Somewhat agree’ to ‘somewhat disagree’
‘Somewhat disagree’ to ‘somewhat agree’
‘Strongly disagree’ to ‘strongly agree’
Don’t know responses were not recoded.
3 Weighting
3.1 Weighting procedure
The following calibration weighting factors were taken into account in the weighting approach:
• Age (four subgroups: 15-24, 25-39, 40-54 and 55+)
• Gender (male and female)
Initially, it was also considered to include sub-city level (commune) residence into account for weighting, to ensure that the results would be representative for the whole city by avoiding biases stemming from the possibility that some parts of a city would be overrepresented in the data. This, however, ran against the practical difficulty that in many cities the number of lower-level communes is very high, making outright weighting at that level impossible. Grouping of communes into larger groups is in theory a measure to resolve that issue. However, in order to do that, it would be necessary to first determine which communes would for coherent wholes, and on what basis such merging would be done besides mere geographical adjacency. This also means that, in absence of clear parameters to determine the properties of such larger city regions (e.g., does one region clearly differ from another in terms of population age, income, social status), there is no clear way to verify whether a weighting according to regional distribution of the sample is even warranted. For these reasons, it was decided to not weight the data according to sub- city level region. However, because of the fact that the base principle of striving for a good spread across city regions is still useful and will likely improve the quality of the sample, as an alternative measure this spread was aimed for as much as possible in the sampling stage. That is, in the gross sample for each city it was checked whether the distribution of phone numbers over the city LAUs was proportionate to the population distribution.
In addition to a post-stratification weight on age and gender, a design weight was also applied to control for unequal selection probabilities of sample units (see the following section for more information on the rationale behind this approach), based on phone type ownership (% mobile, % fixed, % mobile and fixed).
3.1.1 Post-stratification & design weighting The sample was weighted in each country using a post-stratification weight, including age and gender, and a design weight. Directorate-General for Regional and Urban Policy 2020 20
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The use of a design weight has become common in telephone surveys when calling on both mobile and fixed lines (dual frame) as there is an overlap between frames with respondents who could be sampled from both. This means that the probability to be selected equals the probability of being called on one’s fixed line plus the probability of being called on one’s mobile line minus the probability of being called both on one’s fixed and mobile line.
휋푖 = 휋푖 (퐹푁) + 휋푖(푀푁) − 휋푖(퐹푁 ⋂ 푀푁)
(Where FN is the population of people with a fixed line and MN the population of people with a mobile line.)
The latter term, however, is generally very small and can be excluded from the analysis:
휋푖 = 휋푖 (퐹푁) + 휋푖(푀푁) − 휋푖(퐹푁 ⋂ 푀퐹)
Another aspect to take into account is that a mobile line is typically used by an individual, while a fixed line is typically a household device, and is thus shared by several (eligible) persons; however, only one person in the household will answer the phone, which means that his/her selection probability will be lower. A full calculation of the selection probability should therefore rely on data on the number of phone lines per respondent as well as the number of people per line. This is taken into account in the following formula:
푛퐹 퐹푖 푛푀 푀푖 휋푖 ≈ ∗ + ∗ 푁퐹 푍푖 푁푀 푍푖
nF = sample size fixed numbers; NF = population size fixed numbers ; nM=sample size mobile numbers; NM=population size mobile numbers
Fi = number of fixed lines the respondent can be reached on, Zi = number of persons that can be reach via these fixed lines
Mi = number of mobile lines the respondent can be reached on, Zm = number of persons that can be reach via these mobile lines
However, this theory has come under pressure over the past years due to several flaws: Having several people using the same fixed line in a household lowers their probability to be selected, but chances are also higher that at least one person is at home, which increases the selection probability.
If someone uses several mobile lines, their selection probability increases, although it is unlikely that this person will have both mobile phones with them and switched on at all times.
Based on these comments and the need to include several additional questions for the full approach, a different approach was applied. The expected number of people available per line was set to 1 for both fixed and mobile lines, resulting in the following formula:
푛퐹 푛푀 휋푖 ≈ ∗ 퐹푖 + ∗ 푀푖 푁퐹 푁푀
In this formula, the terms Fi and Mi are equal to 1 if the respondent owns respectively a fixed/mobile line, regardless of the number of fixed/mobile lines they can be reached on.
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3.1.2 Weight trimming Weight trimming was also applied so that any computed non-response weights outside the following limits are recoded to the boundary of these limits:
퐸(푤퐻퐷) 1 퐻퐷 ≤ 푤 ≤ 3 3 퐸(푤퐻푁) 푤퐻푁
wHD = household design weight wHN = the weight determined after adjustment (calibration) E(wHD) and E(wHN) = their respective mean values
This approach does not rely on an absolute threshold, but offers a relative threshold based on the data.
3.1.3 Population extrapolation Finally, in addition to the two weights used, the Perception Survey microdata also contain a population weighting factor. This allows to extrapolate the results from each city to their actual population size, instead of the sample sizes of the survey. This is useful in case one wants to group the data from multiple cities (e.g., all cities from one country) – in that case it can be preferred to have each city only contribute to the grouped results proportionate to their population size (e.g., Groningen less than Amsterdam and Rotterdam in the Netherlands).
3.1.4 Using the weights The microdata contains 3 weighting variables:
1. The design weight factor
2. A factor combining the design weight and the post-stratification weight (named ‘reslinweight’)
3. The population weight factor
The aggregated results by city have been calculated using the second factor, ‘reslinweight’. When replicating the results for single cities, this factor should always be used. In case one wants to balance the samples using other socio-demographic samples than age and gender, this is possible, and then the design weight factor can be used as a starting point. In case one wants to recalculate the design weight, the variable ‘mobfix’ contains the phone type information (which respondents had access to a mobile phone, landline or both) needed to do that.
Finally, as said, the population weight factor should only be used if one wants to combine the results from multiple cities and wants to take into account the differences in population sizes between those cities. This factor has not been used to produce the aggregate results by city under this contract, as those tables only consider cities individually. However, for the calculation of significant differences between cities the sample size does come into play, since the significant differences as shown in the tables are calculated by comparing a city’s result to the average of all cities. For this average, the population differences were Directorate-General for Regional and Urban Policy 2020 22
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not taken into account, since it was considered most appropriate in this context to compare cities as equal entities, rather than groups of people that differ in size.
3.2 Weighting benchmarks
3.2.1 Age and gender
Weighting benchmarks for age and gender (which were also used during the fieldwork for monitoring of the sample performance) were based on Eurostat data for all cities within the EU and the United Kingdom.5 For all these countries, age and gender targets were determined for the population aged 15 or over. For other countries, local sources were used. For cities in these countries it was not always possible to determine the gender distribution for the 15+ population. In that case, the distribution of the full population was used. Given that gender distributions differ only marginally over age, this can safely be assumed to have no significant impact. An overvies of the sources used for non-EU cities is presented in the table below. Also, for these cities the population data are not always fully corresponding to the city as defined for the Perception Survey. Wherever this was not possible, the closest equivalent region was used.
City Source Remark
Zürich Age: Data for gender are for full https://www.bfs.admin.ch/bfs/en/ho population, not 15+ me/statistics/catalogues- databases/tables.assetdetail.5886149 .html
Gender: https://www.bfs.admin.ch/bfs/en/ho me/statistics/catalogues- databases/tables.assetdetail.5866903 .html
Genève Age: Data for gender are for full https://www.bfs.admin.ch/bfs/en/ho population, not 15+ me/statistics/catalogues- databases/tables.assetdetail.5886149 .html
Gender: https://www.bfs.admin.ch/bfs/en/ho me/statistics/catalogues- databases/tables.assetdetail.5866903 .html
Tirana http://databaza.instat.gov.al/pxweb/e Data for the Tirana prefecture n/DST/START__Census2011/Census2
5 https://ec.europa.eu/eurostat/web/cities/data/database
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103/?rxid=7fdc84f5-4567-4c7b-96ae- 0ac767c7a2eb
Reykjavik https://px.hagstofa.is/pxen/pxweb/en /Ibuar/Ibuar__mannfjoldi__2_byggdir __sveitarfelog/MAN02005.px/table/ta bleViewLayout1/?rxid=ae1b6f70- 8f91-4f12-8056-4b048c1f64fd
Podgorica http://monstat.org/eng/pxweb.php
Skopje http://makstat.stat.gov.mk/PXWeb/p Data for Skjopje region xweb/en/MakStat/MakStat__Naseleni e__ProcenkiNaselenie/225_Popis_reg _3112_PolVoz_ang.px/?rxid=46ee0f6 4-2992-4b45-a2d9-cb4e5f7ec5ef
Oslo https://www.ssb.no/en/statbank/tabl e/07459/tableViewLayout1
Beograd http://publikacije.stat.gov.rs/G2018/P dfE/G201813045.pdf
Ankara https://biruni.tuik.gov.tr/bolgeselistat istik/tabloOlustur.do#
Antalya https://biruni.tuik.gov.tr/bolgeselistat istik/tabloOlustur.do#
Diyarbakir https://biruni.tuik.gov.tr/bolgeselistat istik/tabloOlustur.do#
Istanbul https://biruni.tuik.gov.tr/bolgeselistat istik/tabloOlustur.do#
3.2.2 Phone ownership Phone ownership targets were determined estimated based on the achieved sample for the Market Monitoring Survey 2017 (MMS 2017).6 The MMS 2017 data is based on a bigger sample size and the more robust data. First, with a sample of 137,608 respondents, the achieved sample of the MMS 2017 is much larger than the sample of the Eurobarometer study (n = 27,739 respondents). Second, the phone ownership information in the MMS data is based on a sample of all contacted respondents, while the Eurobarometer study only includes respondents that were willing to participate in the study. In this way, the MMS data is more robust, as it does not include a participation bias.
6 https://ec.europa.eu/info/policies/consumers/consumer-protection/evidence-based-consumer-policy/market- monitoring_en
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Household telephone access per country based on the Market Monitoring Survey 2017
In the countries where MMS data are not available, we resort in the first place to the most recent Eurobarometer/Eurostat data. If no recent data can be found there, national statistic institute sources were used instead.
COUNTRY Population distribution Sample distribution Mixed Fixed telephone only Mobile telephone only Fixed Mobile BE 85% 4% 11% 48% 52% BG 48% 2% 50% 34% 66% CZ 25% 0% 75% 20% 80% DK 41% 2% 58% 30% 70% DE 92% 4% 4% 50% 50% EE 44% 0% 55% 31% 69% IE 58% 1% 41% 37% 63% EL 81% 7% 12% 49% 51% ES 84% 4% 12% 48% 52% FR 89% 5% 6% 50% 50% IT 66% 2% 31% 41% 59% CY 68% 4% 28% 43% 57% LV 27% 0% 73% 21% 79% LT 38% 0% 62% 28% 72% LU 86% 2% 12% 47% 53% HU 35% 7% 58% 31% 69% MT 93% 6% 1% 51% 49% NL 82% 2% 15% 46% 54% AT 62% 1% 36% 39% 61% PL 56% 3% 41% 38% 62% PT 79% 2% 19% 45% 55% RO 60% 1% 39% 38% 62% SI 78% 3% 19% 46% 54% SK 44% 1% 55% 31% 69% FI 16% 1% 83% 15% 85% SE 77% 1% 22% 44% 56% UK 78% 5% 17% 47% 53% HR 63% 11% 25% 46% 54% AL 23% 0% 75% 19% 81% TR 14% 3% 81% 15% 85% MK 32% 4% 61% 28% 72% RS 75% 7% 17% 47% 53% ME 25% 2% 72% 22% 78% NO 40% 1% 59% 29% 71% IS 88% 1% 11% 47% 53%
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3.3 Design effects and weighting efficiency per city
The below table gives an overview of the design effects for each city (combining the design weight and the post-stratification weight), as well as the sample balance, used here as a measure for weighting efficiency. The weighting efficiency is good in almost all cities, but can be considered low in Antalya, Diyarbakir and Ankara, and to a lesser extent Istanbul and Riga (taking into account a desired weighting efficiency of 70% or higher). Given that in all these cities the deviations in age and gender distribution are not especially high, the reason for this lower efficiency needs to be sought in the phone type access deviations that have impacted the design weight.
City Design Effects7 Sample balance8
Graz 1.18 84.4 Wien 1.19 84.4 Antwerpen 1.10 90.5 Bruxelles / Brussel 1.12 89.4 Liège 1.13 88.6 Burgas 1.22 81.8 Sofia 1.22 81.9 Zagreb 1.13 88.9 Lefkosia 1.13 88.1 Ostrava 1.36 73.8 Praha 1.42 70.4 Aalborg 1.26 79.3 København 1.26 79.1 Tallinn 1.27 79.0 Helsinki / Helsingfors 1.19 84.1
Oulu / Uleåborg 1.20 83.6 Bordeaux 1.09 91.8 Lille 1.11 90.2 Marseille 1.12 89.1 Rennes 1.16 86.2 Strasbourg 1.13 88.7
7 The design effect (deff) for each city is calculated using Kish’s formula (1965). The deff indicates how much the expected sampling error in a survey deviates from the sampling error that can be expected under simple random sampling which is the gold standard in sample surveys. To calculate deff, the number of sample observations is multiplied by the sum of the squared weights over the square of the sum of the weights for each city.
8 The sample balance is the inverse of the weight factor – i.e., 1 divided by the weight factor. It shows the size of the weighted sample as a percentage of the unweighted sample.
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Paris 1.12 89.7 Berlin 1.11 90.4 Dortmund 1.13 88.5 Essen 1.11 89.7 Hamburg 1.10 90.9 Leipzig 1.13 88.6 München 1.12 89.5 Rostock 1.15 86.7 Athina 1.10 91.1 Irakleio 1.07 93.6 Budapest 1.29 77.7 Miskolc 1.33 75.1 Dublin 1.21 82.5 Bologna 1.14 87.7 Napoli 1.15 86.8 Palermo 1.16 86.2 Roma 1.17 85.6 Torino 1.17 85.4 Verona 1.16 86.5 Vilnius 1.23 81.2 Luxembourg 1.10 90.7 Rīga 1.46 68.6 Valletta 1.09 91.4 Amsterdam 1.16 86.6 Groningen 1.09 91.4 Rotterdam 1.10 90.6 Białystok 1.15 87.0 Gdańsk 1.14 87.7 Kraków 1.14 87.6 Warszawa 1.13 88.5 Braga 1.12 89.5 Lisboa 1.16 86.3 Bucureşti 1.18 84.4 Cluj-Napoca 1.19 84.2 Piatra Neamţ 1.18 84.4 Bratislava 1.20 83.1 Košice 1.29 77.4 Ljubljana 1.17 85.5 Barcelona 1.12 89.5 Madrid 1.12 89.3 Málaga 1.11 89.9 Oviedo 1.11 89.9 Malmö 1.20 83.6 Directorate-General for Regional and Urban Policy 2020 27
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Stockholm 1.23 81.1 Belfast 1.19 84.4 Cardiff 1.18 85.0 Glasgow 1.13 88.3 London 1.16 86.4 Manchester 1.10 90.8 Tyneside conurbation 1.11 90.3
Reykjavík 1.17 85.4 Oslo 1.18 84.6 Genève 1.10 90.6 Zürich 1.11 89.9 Tirana 1.18 84.6 Skopje 1.36 73.4 Podgorica 1.25 79.8 Beograd 1.16 85.8 Ankara 1.60 62.6 Istanbul 1.45 68.7 Antalya 1.80 55.7 Diyarbakir 1.70 58.9
4 Sample performance analysis
4.1 Target population versus achieved distribution
4.1.1 Age
The table on the following page shows the unweighted distribution of the sample over broader age groups. Throughout the fieldwork, potential skews, particularly towards older age groups, were an important focus point.
Wit the full sample collected, a slight underrepresentation of younger people can indeed be observed. Looking at the individual age categories, the deviation is no source of concern, taking as a rule of thumb that a deviation of 5% in any direction is acceptable. This is confirmed by the weighting efficiency figures (see 3.3 above), which see no strong impact from this skew. Even when counting together the deviations from the 2 youngest age groups, the average deviation remains at 4%. There are, however, differences between cities. It can be noted, for instances, that several cities in the UK (London, Cardiff, Belfast, Glasgow) have an underrepresentation of the 15-34 age group of between 9 and 11% - though the skew is far less in Newcastle and the Tyneside Conurbation. Other cities that show a larger skew when combining the two younges age groups are Rennes (-12%), Stockholm (-10%) and Podgorica (-10%).
On ther other hand, in all Turkish cities – but in no other city in any country – there is an overrepresentation of younger people. An exceptionally high overrepresentation of younger people is seen in Diyarbakir (+18% in the age group 15-34).
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The reasons for these differences between cities are not clear and cannot immediately be found in the available data. The general skew towards older people, however, is not uncommon in CATI surveys. Keeping this tendency in mind, the 2019 Perception Survey performed rather well, keeping the skew limited to acceptable levels.
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city 15-24 years 25 – 34 years 35 –44 years 45 – 54 years 55 – 64 years 65 and older Average sample target sample target sample target sample target sample target sample target deviation per city Graz 15% 17% 17% 20% 14% 15% 18% 16% 16% 12% 20% 20% 2% Wien 12% 14% 15% 19% 15% 17% 20% 18% 16% 13% 22% 20% 3% Antwerpen 11% 14% 17% 20% 18% 17% 17% 15% 14% 13% 23% 21% 2% Bruxelles / Brussel 13% 15% 19% 22% 20% 19% 19% 16% 14% 12% 15% 16% 2% Liège 10% 14% 20% 18% 15% 16% 17% 16% 17% 15% 21% 22% 2% Burgas 9% 11% 18% 16% 21% 20% 17% 16% 15% 16% 20% 21% 1% Sofia 10% 12% 18% 21% 20% 20% 15% 14% 14% 13% 23% 20% 2% Zagreb 12% 13% 16% 18% 15% 17% 18% 16% 17% 16% 22% 20% 2% Lefkosia 13% 16% 20% 22% 17% 18% 18% 16% 16% 13% 17% 15% 2% Ostrava 8% 11% 16% 16% 20% 18% 15% 17% 17% 15% 24% 23% 2% Praha 7% 9% 16% 18% 23% 22% 17% 16% 15% 13% 22% 22% 1% Aalborg 15% 19% 14% 17% 14% 15% 19% 16% 15% 14% 23% 20% 3% København 16% 18% 25% 28% 19% 19% 14% 13% 12% 10% 14% 12% 2% Tallinn 8% 11% 17% 20% 18% 18% 16% 14% 17% 15% 24% 22% 2% Helsinki / Helsingfors 12% 14% 19% 21% 17% 18% 16% 15% 15% 13% 21% 19% 1% Oulu / Uleåborg 14% 18% 16% 19% 15% 17% 16% 14% 18% 14% 21% 19% 3% Bordeaux 18% 21% 17% 18% 15% 15% 14% 14% 15% 12% 21% 19% 2% Lille 19% 21% 16% 19% 14% 16% 17% 15% 12% 13% 22% 17% 3% Marseille 14% 16% 12% 16% 13% 16% 17% 16% 18% 14% 26% 23% 3% Rennes 22% 28% 14% 19% 13% 13% 14% 12% 16% 11% 21% 17% 4% Strasbourg 15% 21% 18% 19% 13% 15% 18% 14% 15% 13% 21% 18% 3% Paris 13% 16% 15% 20% 17% 18% 19% 16% 16% 13% 20% 17% 3% Berlin 9% 11% 18% 20% 15% 16% 18% 17% 18% 14% 22% 22% 2% Dortmund 10% 13% 13% 16% 15% 14% 19% 18% 19% 15% 24% 23% 2% Essen 9% 12% 15% 16% 11% 14% 19% 17% 18% 15% 28% 25% 3% Hamburg 9% 12% 17% 19% 16% 16% 20% 18% 15% 13% 23% 21% 2% Leipzig 8% 11% 19% 22% 17% 15% 15% 15% 14% 13% 27% 24% 2%
Quality of Life in European Cities Survey 2019
München 9% 12% 20% 22% 17% 17% 19% 17% 16% 12% 19% 20% 2% Rostock 7% 11% 17% 20% 11% 13% 18% 15% 18% 15% 29% 26% 3% Athina 10% 12% 14% 18% 17% 19% 17% 17% 19% 14% 23% 21% 3% Irakleio 13% 16% 15% 20% 22% 20% 18% 16% 16% 13% 16% 16% 3% Budapest 8% 11% 13% 17% 18% 20% 19% 14% 18% 15% 25% 23% 3% Miskolc 10% 13% 10% 14% 16% 18% 17% 15% 20% 17% 27% 23% 3% Dublin 12% 16% 22% 25% 21% 19% 16% 15% 11% 12% 19% 14% 3% Bologna 8% 9% 14% 14% 15% 17% 16% 18% 16% 14% 31% 29% 1% Napoli 13% 15% 13% 15% 16% 17% 21% 18% 17% 15% 20% 20% 2% Palermo 9% 13% 16% 14% 17% 16% 18% 18% 17% 15% 23% 23% 2% Roma 8% 10% 9% 12% 20% 17% 21% 20% 17% 15% 25% 25% 2% Torino 8% 10% 14% 13% 11% 16% 20% 18% 18% 15% 29% 29% 2% Verona 9% 11% 10% 12% 14% 15% 17% 18% 19% 15% 31% 29% 2% Vilnius 9% 11% 23% 23% 20% 18% 17% 15% 11% 14% 20% 19% 2% Luxembourg 9% 11% 17% 22% 19% 19% 16% 15% 13% 11% 26% 21% 3% Riga 7% 10% 17% 19% 19% 17% 17% 15% 16% 16% 24% 24% 2% Valletta 11% 13% 13% 18% 17% 16% 15% 13% 17% 16% 27% 23% 3% Amsterdam 11% 15% 19% 23% 16% 18% 18% 16% 17% 13% 19% 14% 4% Groningen 24% 28% 19% 21% 16% 14% 15% 13% 12% 11% 14% 13% 2% Rotterdam 12% 16% 18% 20% 19% 17% 19% 16% 13% 13% 19% 18% 2% Bialystok 9% 13% 22% 21% 20% 18% 17% 15% 15% 16% 17% 18% 2% Gdansk 10% 11% 21% 20% 20% 18% 15% 13% 16% 17% 18% 21% 2% Kraków 11% 12% 23% 21% 18% 18% 11% 13% 15% 16% 22% 20% 1% Warszawa 9% 9% 21% 20% 17% 19% 14% 12% 19% 17% 20% 22% 2% Braga 11% 14% 16% 15% 22% 19% 20% 18% 15% 16% 16% 19% 2% Lisboa 8% 11% 14% 13% 21% 18% 19% 16% 16% 15% 22% 28% 3% Bucuresti 6% 8% 15% 18% 20% 22% 16% 17% 19% 16% 24% 20% 3% Cluj-Napoca 6% 8% 17% 19% 20% 21% 16% 17% 18% 16% 23% 19% 2% Piatra Neamt 7% 10% 15% 16% 19% 20% 19% 17% 19% 18% 21% 19% 2% Bratislava 5% 8% 19% 18% 23% 22% 17% 14% 18% 16% 18% 21% 2%
Directorate-General for Regional and Urban Policy 2020 31 Quality of Life in European Cities Survey 2019
Košice 8% 12% 16% 18% 18% 20% 18% 16% 19% 15% 21% 19% 2% Ljubljana 9% 14% 13% 16% 16% 18% 18% 16% 19% 15% 25% 21% 3% Barcelona 10% 11% 13% 15% 17% 20% 20% 17% 15% 14% 25% 23% 2% Madrid 11% 11% 12% 15% 19% 20% 22% 18% 16% 14% 20% 22% 2% Málaga 12% 12% 14% 15% 18% 19% 19% 18% 17% 15% 20% 20% 1% Oviedo 8% 9% 11% 12% 20% 19% 20% 18% 19% 17% 22% 25% 2% Malmö 12% 14% 17% 23% 14% 18% 18% 14% 18% 12% 21% 19% 4% Stockholm 8% 13% 16% 20% 14% 18% 21% 16% 18% 13% 23% 18% 5% Belfast 11% 17% 13% 18% 12% 16% 21% 17% 18% 13% 25% 19% 5% Cardiff 17% 22% 14% 20% 11% 15% 18% 14% 18% 12% 22% 17% 5% Glasgow 12% 17% 18% 23% 18% 15% 20% 16% 17% 13% 15% 16% 4% London 8% 14% 21% 24% 17% 20% 19% 16% 15% 12% 20% 15% 4% Manchester 14% 16% 16% 19% 12% 16% 19% 17% 15% 13% 24% 19% 3% Tyneside conurbation 12% 17% 16% 17% 17% 14% 18% 16% 18% 14% 19% 21% 3% Reykjavík 10% 16% 18% 21% 14% 17% 20% 15% 17% 14% 21% 17% 4% Oslo 12% 14% 22% 26% 14% 19% 18% 15% 15% 12% 19% 15% 3% Genève 12% 11% 14% 11% 15% 13% 21% 15% 17% 15% 21% 33% 5% Zürich 9% 10% 8% 15% 19% 16% 17% 15% 18% 12% 29% 17% 5% Tirana 22% 24% 23% 18% 17% 16% 14% 16% 11% 13% 13% 13% 2% Skopje 9% 15% 16% 18% 22% 19% 22% 17% 18% 14% 13% 18% 4% Podgorica 13% 18% 15% 20% 20% 17% 16% 17% 19% 15% 17% 13% 4% Beograd 8% 13% 15% 18% 16% 16% 19% 16% 19% 18% 23% 19% 3% Ankara 14% 15% 18% 16% 23% 16% 22% 13% 10% 10% 13% 8% 4% Istanbul 17% 15% 24% 17% 25% 18% 15% 13% 12% 8% 7% 7% 4% Antalya 15% 14% 18% 15% 21% 17% 20% 14% 14% 10% 12% 8% 4% Diyarbakir 25% 19% 28% 17% 22% 13% 10% 8% 6% 5% 9% 5% 6% Average deviation per age group 3% 3% 2% 2% 3% 3%
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4.1.2 Gender The table below shows the unweighted gender distribution in the final sample for each city. Only one skew stands out from other cities: For Skopje the sample contains 7% more males than expected based on population figures (56% vs. 49). While this might be in part due to the fact that the Skopje population data used for benchmarking do not cover the exact city as defined for the Perception Survey, the discrepancy between males and females is still higher than what one would normally expect in a general population survey. Here too, however, the reason for this skew cannot be identified from the available survey data.
city male female Deviation sample target sample target Per city Graz 47% 48% 53% 52% 1% Wien 49% 48% 51% 52% 1% Antwerpen 47% 49% 53% 51% 2% Bruxelles / Brussel 46% 49% 54% 51% 2% Liège 48% 48% 52% 52% 1% Burgas 46% 47% 54% 53% 1% Sofia 48% 47% 52% 53% 0% Zagreb 46% 46% 54% 54% 0% Lefkosia 49% 47% 51% 53% 2% Ostrava 44% 48% 56% 52% 4% Praha 49% 48% 51% 52% 1% Aalborg 51% 50% 49% 50% 1% København 50% 49% 50% 51% 1% Tallinn 47% 44% 53% 56% 3% Helsinki / Helsingfors 48% 48% 52% 52% 0% Oulu / Uleåborg 49% 50% 51% 50% 0% Bordeaux 46% 46% 54% 54% 0% Lille 47% 47% 53% 53% 0% Marseille 46% 47% 54% 53% 1% Rennes 47% 47% 53% 53% 0% Strasbourg 47% 47% 53% 53% 0% Paris 49% 47% 51% 53% 2% Berlin 45% 49% 55% 51% 3% Dortmund 47% 49% 53% 51% 2% Essen 48% 48% 52% 52% 0% Hamburg 48% 49% 52% 51% 1% Leipzig 47% 49% 53% 51% 2% München 51% 48% 49% 52% 3% Rostock 48% 49% 52% 51% 1% Athina 50% 47% 50% 53% 3% Irakleio 46% 48% 54% 52% 2% Budapest 47% 46% 53% 54% 2% Miskolc 44% 46% 56% 54% 1%
Quality of Life in European Cities Survey 2019
Dublin 51% 48% 49% 52% 2% Bologna 46% 47% 54% 53% 1% Napoli 51% 48% 49% 52% 3% Palermo 49% 47% 51% 53% 2% Roma 46% 47% 54% 53% 1% Torino 47% 47% 53% 53% 0% Verona 47% 47% 53% 53% 0% Vilnius 45% 44% 55% 56% 2% Luxembourg 49% 50% 51% 50% 1% Riga 44% 43% 56% 57% 1% Valletta 50% 50% 50% 50% 0% Amsterdam 50% 49% 50% 51% 2% Groningen 51% 49% 49% 51% 1% Rotterdam 49% 49% 51% 51% 0% Bialystok 49% 46% 51% 54% 2% Gdansk 48% 47% 52% 53% 1% Kraków 48% 46% 52% 54% 2% Warszawa 47% 45% 53% 55% 2% Braga 48% 47% 52% 53% 1% Lisboa 47% 46% 53% 54% 2% Bucuresti 46% 46% 54% 54% 0% Cluj-Napoca 48% 46% 52% 54% 1% Piatra Neamt 46% 46% 54% 54% 0% Bratislava 48% 46% 52% 54% 1% Košice 43% 47% 57% 53% 4% Ljubljana 44% 48% 56% 52% 4% Barcelona 48% 48% 52% 52% 0% Madrid 48% 47% 52% 53% 2% Málaga 49% 47% 51% 53% 2% Oviedo 50% 46% 50% 54% 4% Malmö 50% 49% 50% 51% 1% Stockholm 52% 49% 48% 51% 2% Belfast 48% 48% 52% 52% 0% Cardiff 52% 49% 48% 51% 3% Glasgow 49% 48% 51% 52% 1% London 49% 50% 51% 50% 1% Manchester 47% 49% 53% 51% 2% Tyneside conurbation 50% 49% 50% 51% 2% Reykjavík 47% 51% 53% 49% 3% Oslo 49% 50% 51% 50% 0% Genève 49% 48% 51% 52% 0% Zürich 49% 50% 51% 50% 1%
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Quality of Life in European Cities Survey 2019
Tirana 52% 49% 48% 51% 3% Skopje 56% 49% 44% 51% 7% Podgorica 50% 48% 50% 52% 2% Beograd 49% 47% 51% 53% 2% Ankara 51% 50% 49% 50% 2% Istanbul 52% 50% 48% 50% 2% Antalya 51% 50% 49% 50% 1% Diyarbakir 52% 51% 48% 49% 1%
Average deviation per 2% 2% gender category
4.1.3 Phone ownership Since the 2019 Perception Survey worked with dual-frame approach for the sample design, it was important to monitor during the fieldwork the distribution of numbers from the 2 sample frames (landline and mobiles) in the completed sample. This distribution is not only relevant for the calculation of the design weights (see chapter 3), but it also indirectly impacts the socio-demographic profile of the sample. For instance, mobile phone users are typically somewhat younger than landline users.
As already noted (see chapter 2), for the mobile number frame it was not in each city possible to build a gross sample that contained the envisaged amount of phone numbers (24 times the target sample). As a countermeasure more landline numbers were included in the gross sample in these cities. For that reason mobile phone numbers were prioritized in these cities an called more often if needed, with the goal to increase the proportion of mobile numbers in the final sample and bring it as close as possible to the target distribution. This was a success in most cities. Only in 12 cities, there was a deviation from the mobile sample targets of more than 10%, with notable spikes in Riga and Podgorica.
It should be noted, however, that such deviations are only problematic to the extent that they result in biases in the socio-demographic profile of the samples as well. As shown above, such biases are only limited for the parameters taken into consideration (age and gender).
City Mobile sample Mobile target deviation Fixed sample Fixed target mobile sample9 Graz 61% 61% 0% 39% 39% Wien 61% 61% 0% 39% 39% Antwerpen 52% 52% 0% 48% 48% Bruxelles / 52% 52% 0% 48% 48% Brussel Liège 52% 52% 0% 48% 48% Burgas 56% 66% -10% 44% 34% Sofia 66% 66% 0% 34% 34% Zagreb 53% 54% -1% 47% 46%
9 The deviation for the fixed sample is the inverse – e.g., +10% for Burgas
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Quality of Life in European Cities Survey 2019
Lefkosia 38% 57% -19% 62% 43% Ostrava 73% 80% -7% 27% 20% Praha 80% 80% 0% 20% 20% Aalborg 70% 70% 0% 30% 30% København 69% 70% -1% 31% 30% Tallinn 50% 69% -19% 50% 31% Helsinki / 85% 85% 0% 15% 15% Helsingfors Oulu / 85% 85% 0% 15% 15% Uleåborg Bordeaux 49% 50% -1% 51% 50% Lille 50% 50% 0% 50% 50% Marseille 50% 50% 0% 50% 50% Rennes 51% 50% 1% 49% 50% Strasbourg 49% 50% -1% 51% 50% Paris 50% 50% 0% 50% 50% Berlin 50% 50% 0% 50% 50% Dortmund 50% 50% 0% 50% 50% Essen 50% 50% 0% 50% 50% Hamburg 50% 50% 0% 50% 50% Leipzig 50% 50% 0% 50% 50% München 56% 50% 6% 44% 50% Rostock 50% 50% 0% 50% 50% Athina 51% 51% 0% 49% 49% Irakleio 30% 51% -21% 70% 49% Budapest 64% 69% -5% 36% 31% Miskolc 51% 69% -18% 49% 31% Dublin 63% 63% 0% 37% 37% Bologna 59% 59% 0% 41% 41% Napoli 59% 59% 0% 41% 41% Palermo 59% 59% 0% 41% 41% Roma 59% 59% 0% 41% 41% Torino 59% 59% 0% 41% 41% Verona 52% 59% -7% 48% 41% Vilnius 54% 72% -18% 46% 28% Luxembourg 56% 53% 3% 44% 47% Rīga 32% 79% -47% 68% 21% Valletta 49% 49% 0% 51% 51% Amsterdam 47% 54% -7% 53% 46% Groningen 26% 54% -28% 74% 46% Rotterdam 54% 54% 0% 46% 46% Białystok 62% 62% 0% 38% 38% Gdańsk 62% 62% 0% 38% 38% Kraków 62% 62% 0% 38% 38%
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Quality of Life in European Cities Survey 2019
Warszawa 62% 62% 0% 38% 38% Braga 55% 55% 0% 45% 45% Lisboa 55% 55% 0% 45% 45% Bucureşti 62% 62% 0% 38% 38% Cluj-Napoca 62% 62% 0% 38% 38% Piatra Neamţ 62% 62% 0% 38% 38% Bratislava 69% 69% 0% 31% 31% Košice 50% 69% -19% 50% 31% Ljubljana 54% 54% 0% 46% 46% Barcelona 53% 52% 1% 47% 48% Madrid 51% 52% -1% 49% 48% Málaga 52% 52% 0% 48% 48% Oviedo 51% 52% -1% 49% 48% Malmö 52% 56% -4% 48% 44% Stockholm 57% 56% 1% 43% 44% Belfast 54% 53% 1% 46% 47% Cardiff 53% 53% 0% 47% 47% Glasgow 54% 53% 1% 46% 47% London 54% 53% 1% 46% 47% Manchester 53% 53% 0% 47% 47% Tyneside 53% 53% 0% 47% 47% conurbation Reykjavík 52% 53% -1% 48% 47% Oslo 71% 71% 0% 29% 29% Genève 25% 25% 0% 75% 75% Zürich 23% 25% -2% 77% 75% Tirana 81% 81% 0% 19% 19% Skopje 72% 72% 0% 28% 28% Podgorica 16% 78% -62% 84% 22% Beograd 54% 54% 0% 46% 46% Ankara 77% 85% -8% 23% 15% Istanbul 76% 85% -9% 24% 15% Antalya 57% 85% -28% 43% 15% Diyarbakir 45% 85% -40% 55% 15%
4.1.4 Eligibility The total proportion of respondents (on the total of respondents that were asked the screening questions) that was ineligible for participation because they lived outside of the targeted city regions was 21% over all cities. This means an overall incidence rate of 79%. However, there are clear differences between the cities, as can be seen in the table below. In 23 cities, the proportion of screen-outs because of a residence outside of the target city is above 25%, and in seven it was above 50%, meaning that there was a higher chance that the respondent would not be eligible for participation than that they would be eligible.
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Quality of Life in European Cities Survey 2019
This can have several possible causes, ranging from a lacking reliability in the available source material that was used to link sample units to post codes (i.e., respondents actually live in another post code area than they have indicated according to the source), to difficulties among respondents to correctly state their postcode.
It should in any case be noted that a lower incidence rate in a city does not mean that the sample for that city is lacking quality in terms of representativity. Rather, it means that more gross sample is needed for these cities to reach the target number of completes. This, however, had no impact on the fieldwork planning.
City % ineligible Cluj-Napoca 62.5% Oulu / Uleåborg 59.1% Tirana 58.1% Ankara 57.8% Verona 57.4% Torino 55.3% Bologna 51.6% Valletta 48.7% Istanbul 46.5% Helsinki / Helsingfors 43.3% Roma 38.1% Antwerpen 37.5% Liège 33.1% Oviedo 32.5% Ljubljana 31.3% Miskolc 29.1% Madrid 28.9% Piatra Neamţ 28.3% Málaga 28.1% Barcelona 27.4% Amsterdam 25.4% Bucureşti 25.3% Diyarbakir 25.1% Skopje 24.8% Palermo 24.6% Rīga 24.2% Kraków 23.4% Warszawa 23.1% Gdańsk 22.2% Białystok 21.1% Rotterdam 21.1% Beograd 20.0%
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Quality of Life in European Cities Survey 2019
Graz 19.1% Bratislava 18.9% Burgas 18.8% Bordeaux 18.5% Sofia 17.9% Marseille 17.9% Belfast 17.0% Ostrava 17.0% Bruxelles / Brussel 16.8% Rostock 16.8% Budapest 16.7% København 16.7% Strasbourg 16.6% Oslo 16.4% Aalborg 16.4% Athina 16.3% Antalya 15.8% Groningen 15.7% Paris 15.6% München 14.7% Malmö 14.6% Dortmund 14.1% Praha 14.0% Hamburg 13.7% Braga 13.7% Lefkosia 13.7% Essen 13.4% Cardiff 13.4% Lille 13.2% Napoli 13.0% Wien 12.7% Zagreb 12.2% Stockholm 11.8% Vilnius 11.4% Glasgow 11.3% Tyneside conurbation 10.3% Rennes 9.9% Dublin 9.8% Irakleio 9.8% Leipzig 9.1% Košice 8.6% Podgorica 8.2% Luxembourg 7.6% Directorate-General for Regional and Urban Policy 2020 39
Quality of Life in European Cities Survey 2019
Tallinn 7.3% Berlin 6.9% London 6.7% Genève 6.0% Zürich 4.6% Lisboa 4.5% Manchester 4.4% Reykjavík 3.0%
5 Fieldwork performance analysis
5.1 Interview validation
During the fieldwork, a series of checks were performed once per week on all interviews to verify their quality. This was done via the following parameters:
- interview length: interviews were flagged if their length was below 50% of the average length for interviews in a country.10 For instance, if the average interview length in a country is 10 minutes, interviews shorter than 5 minutes are flagged.
- straightlining: interviews were flagged if they showed straightlining in 4 of the following multi-item questions (i.e., have the same response in all items): Q1 (10 items), Q2 (7), Q4 (4) , Q6 (5), Q13 (5). For Q1 and Q2, straightlining on all but one of the items (i.e., 9 in Q1 and 6 in Q2 also counts as straightlining, given the high number of items.
- item non-response: interviews were flagged if there is a non-response (i.e., answering “don’t know” or refusing to answer) on at least 30% of the substantive questions and 50% of the socio-demo background questions. Separately, interviews are also flagged if there is a 75% non-response rate in the large question blocks Q1 and Q2.
Any interview that was flagged for 2 of the above checks was selected for removal. Suspicious interviews were assessed and treated (i.e., excluded and replaced) during the fieldwork itself, so that no interviews had to me removed from the final sample.
The table below gives an overview of the interviews removed based on these checks, per city (in cities not mentioned no cases had to be removed).
city Count % Graz 3 0.3% Praha 1 0.1% Aalborg 3 0.3% Helsinki / 1 0.1% Helsingfors Strasbourg 1 0.1%
10 The separate calculation per country is meant to take into account natural interview length differences between countries due to language and cultural differences.
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Quality of Life in European Cities Survey 2019
Rostock 3 0.3% Dublin 3 0.4% Rotterdam 1 0.1% Braga 1 0.1% Lisboa 1 0.1% Madrid 3 0.2% Oviedo 2 0.1% Manchester 1 0.1% Reykjavík 1 0.1% Genève 4 0.5% Zürich 2 0.2% Podgorica 1 0.1% Beograd 1 0.1%
5.2 Interview breakoffs
The table below shows the main different parts of the questionnaire, each time with the break-off percentage (as proportion of the total group of people that terminated the interview before the end). In other words, the table shows at which points in the survey respondents were most likely to quit. It stands out from this overview that the likelihood to break off the interview is at its highest during the Q3 question block. This is not surprising, since Q3 is the third consecutive item block after Q1 and Q2, together comprising of 23 questions. In total, just over half of the interview break-offs occurs before the start of Q4. From Q4 on, the break-off probability decreases again, but with a spike in Q6 – which is again a 5-item question block.
It is notable that once the background questions are reached, almost all respondents reach the end of the interview – only 1.5% of the break-offs occurs during the socio-demographic background questions.
question break-off cumulative (block) percentage screener 0.2% 0.2% Q1 7.7% 7.9% Q2 17.0% 24.9% Q3 26.6% 51.5% Q4 10.4% 61.9% Q5 6.7% 68.6% Q6 16.0% 84.6% Q7-Q12 9.4% 94.0% Q13 4.5% 98.5% Q14-end 1.5% 100.0%
5.3 Item non-response
The next table below shows the 10 question items with the highest non-response rate.
non-response question (%) Directorate-General for Regional and Urban Policy 2020 41
Quality of Life in European Cities Survey 2019
Q13.5 There is corruption in my local public 20.20% administration Q3.3 Is the city where you live a good place or not a good place to live for the following groups? Gay or lesbian 15.38% people. Q4.2 On the whole, are you very satisfied, fairly satisfied, not very satisfied or not at all satisfied with 15.35% your personal job situation Q1.7 Generally speaking, please tell me if you are very satisfied, rather satisfied, rather unsatisfied or very 13.91% unsatisfied with each of the following issues in your city or area. - Schools and other educational facilities. Q1.3 Generally speaking, please tell me if you are very satisfied, rather satisfied, rather unsatisfied or very unsatisfied with each of the following issues in your city 13.37% or area. - Sport facilities such as sport fields and indoor sports halls. Q2.2 It is easy to find a good job in my city. 11.04% Q13.4 Information and services of my local public 10.33% administration can be easily accessed online Q13.1 Is the city where you live a good place or not a good place to live for the following groups? Immigrants from 9.88% other countries. Q2.5 It is easy to find good housing in my city at a 9.59% reasonable price. Q3.4 Is the city where you live a good place or not a good place to live for the following groups? Racial and ethnic 9.45% minorities. Q15 In general, how is your health? 8.03%
It is likely that the main reason for the higher non-response in these items is the fact that some respondents feel that they do not have enough knowledge of, or experience with, the topics of these questions. For instance, people who do not use a city’s educational or sport facilities may not be able to tell whether they are satisfied with them in response to questions Q1.3 and Q1.7, respectively. Similarly, if they have no (recent) experience with searching for a job or a house, they might conclude that they don’t know whether it is easy to find one in their city (cf. questions Q2.2 and Q2.5, respectively).
Two special cases are the following:
- Q4.2 is asked to all respondents, also those who are retired and still in school. For the latter groups, the response is coded as “don’t know” during the interview, and will after the fieldwork be recoded to “not applicable”.
- Q15 discusses the respondent’s health, which is a special category of personal data (see 2.3.4). The sensitivity of the question is in itself already likely to increase non-response. In addition to that, GDPR requires an explicit consent verification before the question can be asked. 5% of respondents opted out after being asked consent in Q15a, and another 3% chose not to answer the question after agreeing to hear it – bringing the total non- response rate for Q15 to 8%.
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Quality of Life in European Cities Survey 2019
5.4 Response rates
5.4.1 Response rates in the 2019 Perception Survey The technical report contains an overview per city of the response rate, calculated according to AAPOR guidelines. Specifically, the Technical Report contains the following figures:
AAPOR response rate type 1. This is the most conservative response rate type. It represents the number of complete interviews (700 in all cities) as a percentage of the total working not-ineligible sample that was used in the fieldwork. With ‘not- ineligible’ we mean all respondents that were not confirmed ineligible – a large part of this being people that refuse to participate and for which eligibility could not be confirmed. Ineligible respondents are not taken into account for the response rate calculation because they do not belong to the target
AAPOR response rate type 3. This response rate figure considers partial interviews also as successful interviews (in the sense that responses were gathered), thus counting them together with complete interviews in the calculation of the response rate.
AAPOR response rate type 4. This response rate type also counts partial interviews as successful. In addition to that, it makes an assumption about the eligibility of those respondents that could not be screened (i.e., that were not reached of refused to participate before the screening questions could be asked). This is calculated by adding to the calculation a factor that assumes the proportion in the full sample that was actually ineligible (and should thus not be included in the response rate calculation). This factor is the ratio of confirmed eligible vs. confirmed ineligible respondents, as measured by the screening questions.
The below table shows AAPOR response rate type 4 per city. This response rate calculation gives the best idea of how many sample units that could have led to a complete interview actually did, precisely because it includes an approximation of how much of the base sample was actually not eligible.
The response rate in most cities was below the 4% that was estimated at the beginning of the project, most often ranging between 2 and 4%. In some cities, particularly those in Turkey (12.2% on average) and Romania (10.5%), the response rate was notably higher.
City AAPOR RR4 Graz 2.8% Wien 1.7% Antwerpen 3.7% Bruxelles / Brussel 3.1% Liège 2.9% Burgas 2.8% Sofia 2.3% Zagreb 1.8% Lefkosia 2.5% Ostrava 3.2% Praha 3.5%
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Quality of Life in European Cities Survey 2019
Aalborg 2.3% København 2.4% Tallinn 2.9% Helsinki / 2.6% Helsingfors Oulu / Uleåborg 2.6% Bordeaux 2.4% Lille 2.6% Marseille 2.1% Rennes 1.9% Strasbourg 2.5% Paris 2.2% Berlin 2.9% Dortmund 2.3% Essen 2.7% Hamburg 2.9% Leipzig 2.6% München 3.5% Rostock 2.4% Athina 2.6% Irakleio 2.4% Budapest 3.0% Miskolc 3.6% Dublin 2.5% Bologna 4.8% Napoli 8.2% Palermo 5.3% Roma 4.5% Torino 4.5% Verona 5.2% Vilnius 3.5% Luxembourg 2.7% Riga 4.0% Valletta 4.3% Amsterdam 3.6% Groningen 3.7% Rotterdam 2.7% Bialystok 3.9% Gdansk 4.6% Kraków 4.1% Warszawa 3.9% Braga 4.8% Lisboa 4.3% Bucuresti 6.2%
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Cluj-Napoca 9.7% Piatra Neamt 11.3% Bratislava 3.1% Košice 2.5% Ljubljana 3.2% Barcelona 2.9% Madrid 2.9% Málaga 3.2% Oviedo 3.5% Malmö 2.6% Stockholm 2.5% Belfast 2.5% Cardiff 2.3% Glasgow 2.2% London 2.6% Manchester 2.2% Tyneside 2.1% conurbation Reykjavík 3.1% Oslo 2.8% Genève 2.3% Zürich 1.7% Tirana 8.0% Skopje 6.9% Podgorica 1.8% Beograd 6.9% Ankara 11.5% Istanbul 11.9% Antalya 12.6% Diyarbakir 12.6%
5.4.2 Impact and significance of response rates While the response rate achieved in the 2019 Perception Survey can be considered low in absolute terms (only about 2 in every 100 phone numbers in the gross sample resulting in a complete interview), the response rate is not significantly below what is nowadays often seen in CATI surveys. The decline of landline connections and the shifting habits of phone use (towards chat message applications like Whatsapp and video messaging, rather than phone conversations), and increased used of number recognition and number blocking on mobile phones, makes reaching respondents ever more difficult. This is a challenge that concerns all CATI surveys.
A general objective when organizing surveys is indeed to maximize response rates, this both from the perspective of fieldwork efficiency as well as maintaining data quality by avoiding non-response bias. It is the latter (non-response bias) that is sometimes seen as the biggest risk related to declining response rates.
Non-response bias is the phenomenon where survey data are affected by non-response because the opinions from those that participated in the survey are significantly different Directorate-General for Regional and Urban Policy 2020 45
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from those that did not respond. It needs to be emphasized, however, that the relationship between response rates and potential non-response bias is a complex one, and that low non-response is a necessary but not a sufficient condition of non-response bias. Specifically, non-response bias only applies to individual variables (rather than surveys as a whole), and only if there is a non-zero correlation between a given variable and the likelihood that someone participates in the survey. It also needs to be taken into account that any non-response bias that is related to variables that are also used for weighting (i.e., when younger respondents are harder to reach) is already balanced out – of course only to a certain level of skews in these variables – by the weighting.
Academic studies of non-response fall into two types, Absolute Non-Response studies and Relative Non-Response studies. Absolute Non-Response studies compare survey estimates to good estimates of a “true” value of a variable, normally from the Census to look at total non-response bias. Relative non-response bias studies assess how survey estimates change with increasing fieldwork effort (e.g. number of contact attempts, extent of reissuing). There are two key academic meta-analysis studies:
• Groves and Peycheva (2008) 11 conducted a meta-analysis of Absolute Non- Response in 59 studies (covering 959 estimates). While they found examples of large non-response bias existing, they also found that there was a very low correlation between non-response bias and response rates, and greater variation within studies than between them. They argue for the importance of finding theories that link unit non-response to non-response bias and make a distinction between missing respondents that don’t introduce bias and those that do. An example would be young men, living on their own, and whether they play sport. These type of households tend to be underrepresented in household surveys. Weighting can help account for this, but only if those who are interviewed are similar to those who are not. If, for example, not enough fieldwork effort is made and those who are out playing sport several time a week are not contacted, it could be assumed that bias would remain after weighting. This, however, is taking into account explicitly in the Perception Survey fieldwork setup by conducting multiple contact attempts and by spreading these attempts over different times of the day and the week – thus succeeding in keeping the skew towards older people relatively low.
• Sturgis et al (2016) 12 examined relative non-response bias and fieldwork effort in 541 non-demographic variables in six surveys. They conclude that “response rate appears to have only a weak association with non-response bias”.
In short, it is important to remember that while non-response bias does occur, it is important to be aware of the relative importance of response rates in the overall set of factors that eventually determine survey outcomes. With respect to survey fieldwork organization and effective use of the available resources, it needs to be considered on a survey-by-survey basis whether extra fieldwork effort to increase response rates or to reach a certain pre-set target in this regards is the right strategy, given that its likely impact will be low.
6 Data comparison 2019-2015
11 https://academic.oup.com/poq/article-abstract/72/2/167/1920564/The-Impact-of-Nonresponse-Rates-on- Nonresponse
12 https://academic.oup.com/poq/article-abstract/81/2/523/2676922
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Annex 2 contains a table showing the differences between the 2019 and the 2015 results of the Perception Survey (overall, combined for all cities). This comparison was made to verify whether the changes in the sample design, the screening procedure and the weighting of the data had any impact on the data that would cause deviations that go beyond what could be considered normal variance or trend changes between two waves.
7 Recommendations for future waves
- The 2019 Perception Survey applied a new sampling approach that relied on the availability of publicly available geolocation data linked to phone numbers to identify eligible mobile phone sample. In evaluation of this approach, 2 major conclusions can be drawn. First, in most cities the approach proved successful in building a sample that was at the same time big enough (enough numbers could be gathered to yield the target amount of mobile number interviews in the final sample) and accurate enough (in most cities a clear majority of the selected units proved indeed to be eligible, greatly increasing calling efficiency). Second, building a mobile phone sample in this way is time-intensive, and in the scheduling of a future wave – should the same method be applied – enough time should be foreseen to build a mobile sample in this way.
- Measured per age group, the deviations from the targets are quite small. There is, however, a slight skew away from younger people (-35). This may in part be due to a higher proportion of landline numbers in the sample, though as said the landline skew is only moderate in the final sample. More importantly, it is also the case that younger people are simply harder to reach over the phone, even via mobile phones. While the results of the 2019 Perception Survey do not show reason for great concern yet, there is a possibility that this trend in CATI surveys will continue. In the longer term, it is therefore advisable to look into the possibilities that other survey modes (e.g., web and smartphone) and other recruitment modes (e.g., via social media) have to offer. This should of course be accompanied with an analysis of mode and sample source biases and the effects that they can have on comparability.
- Already during the pilot, it became clear that the questionnaire has some elements that create a heightened risk of observation issues. First, the sequence of big grid questions in the beginning of the survey puts a big burden on the respondents, as is demonstrated by the fact that we can observe a big dropout around the Q2-Q4 block. It should be noted that while a dropout is an issue mostly for the fieldwork efficiency, with less impact on the data quality itself (because the data from terminated interviews are not counted), it is also indicative of a risk that responses to these questions from respondents that remain in the interview (and are thus included in the results) are also of lower quality. This can be solved by either making the questionnaire more focussed (i.e. shorter), or by splitting the grids in smaller parts and spreading them over the interview, so that the cognitive load of the interview is distributed more evenly.
Second, trying to measure respondents’ occupation in a way that is comparable with universe statistics is notoriously difficult to do efficiently in a CATI survey. If comparison with benchmark statistics is necessary, coding occupation according to ISCO codes is necessary. However, identifying the right ISCO category for a respondent’s given occupation is complex, and accuracy of this task can be considerably increased by using an approach with multiple questions. Within the present design, however, this might be difficult, since the questionnaire is already quite long. If this is to remain a background variable in future waves, and if comparison against universe statistics is deemed necessary, we advise that it is still asked by using one question, as is presently done but with giving enough examples to interviewers on how to code the most common types of occupations. However, if the occupation data are only needed for comparison between cities and for research purposes without comparison to a wider universe, it can be considered to use questions with response categories that are easier to grasp for
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respondents. An example could be for instance the categories used in the Market Monitoring Survey over the past years:
What is your current occupation?
1. Self-employed
2. Manager
3. Other white collar
4. Blue collar
5. Student
6. House-person and other not in employment
7. Seeking a job
8. Retired
- Any future wave should always identify the best screening option per city to identify whether respondents live in the target region. This cannot always be done via postcode, but as the Lisboa example (see 2.3.1) shows, it is also not always easy to ask for local regions. Moreover, as administrative and statistical boundaries, as well as postcode systems, change, it needs to be determined for each new wave whether previously used screening questions can still work. Local expertise is indispensable here.
- English was offered in all cities as an optional language to take the interview in, in order to include as much as possible immigrant populations that might not (yet) speak the local language well enough to be able (or feel comfortable) to do the interview in that language. However, it was only very rarely used – the most in Paris (7 times) and Luxembourg (9 times). To the extent that it is easy for fieldwork agencies to offer this option, it is worth considering keeping this option. This is most likely in centrally organised fieldwork, working from a small number of hubs. In case of heavily decentralized fieldwork, with local agencies in each country, offering English might require extra investment from these local centres that will not pay off, given the rare use of the option by respondents.
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Annex 1. Final questionnaire Introduction text Language. [PROG: SINGLE RESPONSE] Code the respondent language (DO NOT READ OUT)
1 Albanian show if country_sample = 32 (AL) or 33 (MK) 2 Bulgarian show if country_sample = 3 (BG)
3 Catalan show if country_sample = 26 (ES) 4 Croatian show if country_sample = 4 (HR) 5 Czech show if country_sample = 6 (CZ) 6 Danish show if country_sample = 7 (DK) 7 Dutch show if country_sample = 2 (BE) or 20 (NL) 8 English Always show 9 Estonian show if country_sample = 8 (EE) 10 Finnish show if country_sample = 9 (FI) 11 French show if country_sample = 2 (BE) or 10 (FR) or 17 (LU) 12 German show if country_sample = 1 (AT) or 11 (DE) or 17 (LU) or 31 (CH) 13 Greek show if country_sample = 5 (CY) or 12 (EL) 14 Hungarian show if country_sample = 13 (HU) 15 Icelandic show if country_sample = 29 (IS) 16 Italian show if country_sample = 15 (IT) or 31 (CH) 17 Latvian show if country_sample = 18 (LV) 18 Lithuanian show if country_sample = 16 (LT) 19 Macedonian show if country_sample = 33 (MK) 20 Maltese show if country_sample = 19 (MT) 21 Montenegrin show if country_sample = 34 (ME) 22 Norwegian show if country_sample = 30 (NO) 23 Poland show if country_sample = 21 (PL) 24 Portugal show if country_sample = 22 (PT) 25 Romanian show if country_sample = 23 (RO) 26 Russian show if country_sample = 8 (EE) or 18 (LV) 27 Serbian show if country_sample = 35 (RS) 28 Slovakian show if country_sample = 24 (SK) 29 Slovenian show if country_sample = 25 (SI) 30 Spanish show if country_sample = 26 (ES) 31 Swedish show if country_sample = 27 (SE) 32 Turkish show if country_sample = 36 (TR)
Intro1. [PROG: TEXT; Show if sample_country is not 28 (UK)]
Quality of Life in European Cities Survey 2019
Good morning/afternoon/evening. My name is XXX and I’m calling on behalf of Ipsos, a market research firm. We are conducting a large study for the European Commission on how people experience their life in the city.
[PROG: if SampleType = 1 (mobile): continue to Intro_consentmob; if SampleType = 2 (fixed): continue to D0.]
Intro2. [PROG: TEXT; Show if sample_country = 28 (UK)]
Good morning/afternoon/evening. My name is XXX and I’m calling on behalf of Ipsos, a market research firm. We are conducting a large study for an international public body on how people experience their life in the city.
Interviewer Instruction: if the respondent asks for which public body the study is conducted, you can mention the European Commission.
[PROG: if SampleType = 1 (mobile): continue to Intro_consent; if SampleType = 2 (fixed): continue to D0.]
D0. [PROG: SINGLE RESPONSE; Show if SampleType = 2 (fixed)] Please can I speak to the person aged 15 or older within your household whose birthday it was most recently?
1. Yes 2. Person is not available. 99. No, refusal
[PROG: IF D0 = 99 : Screen out]
D0b. [PROG: SINGLE RESPONSE; Show if D0 = 2] When would be a good moment to call back to this person?
[PROG: Show appointment screen]
Intro1_2. [PROG: TEXT; Show if D0 = 1 and if sample_country is not 28 (UK)]
Interviewer instruction: repeat introduction if a new respondent comes to the line: Intro1. Good morning/afternoon/evening. My name is XXX and I’m calling on behalf of Ipsos, a market research firm. We are conducting a large study for the European Commission on how people experience their life in the city.)
Intro2_2. [PROG: TEXT; Show if D0 = 1 and if sample_country is 28 (UK)] Directorate-General for Regional and Urban Policy 2020 50
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Interviewer instruction: repeat introduction if a new respondent comes to the line: Intro2.Good morning/afternoon/evening. My name is XXX and I’m calling on behalf of Ipsos, a market research firm. We are conducting a large study for an international public body on how people experience their life in the city.)
Interviewer Instruction: if the respondent asks for which public body the study is conducted, you can mention the European Commission.
Introconsent. [PROG: TEXT; Show to all]
The survey will take about 10 minutes. We guarantee you that all your answers will remain anonymous, and that no personal data will be shared in any way.
Before we start, I just want to clarify that participation in the survey is voluntary and you can change your mind at any time. Are you happy to proceed with the interview? Only read IF NECESSARY: If you would like to read the Privacy Notice beforehand you can access it online at https://survey.ipsos.be/privacynoticeQoLCities.pdf
Screener
D1. [PROG: Quantity, 3 digits, range min. 0 – max. 115 + 999] What is your age?
999. Don’t know/No Answer/Refuses (DO NOT READ OUT)
D1_recode. [PROG: HIDDEN VARIABLE; recode the response from D1 into the corresponding age category]
1. 15-19 2. 20-24 3. 25-34 4. 35-44 5. 45-54 6. 55-64 7. 65-74 8. 75+ 999. Don’t know/No Answer/Refuses
[PROG: IF D1 < 15 : Screen out] [PROG: IF D1 = 999 : Screen out]
D2. [PROG: SINGLE RESPONSE] What is your sex? (DO NOT READ OUT, to be observed by interviewer) Directorate-General for Regional and Urban Policy 2020 51
Quality of Life in European Cities Survey 2019
1. Male 2. Female
D3a. [PROG: SINGLE RESPONSE; show if country_sample = 28 (UK), 14 (IE), 3 (BG), 23 (RO), 20 (NL)] Do you live in …
1 The city of Belfast show if City_sample = 2801 (Belfast) 2 The city of Lisburn show if City_sample = 2801 (Belfast) 3 The borough of Castlereagh show if City_sample = 2801 (Belfast) 4 The city of Cardiff show if City_sample = 2802 (Cardiff) 5 The city of Glasgow show if City_sample = 2803 (Glasgow) 6 The council area of East Dunbartonhsire show if City_sample = 2803 (Glasgow) 7 The council area of East Renfrewshire show if City_sample = 2803 (Glasgow) 8 The council area of Renfrewshire show if City_sample = 2803 (Glasgow) 9 Greater London show if City_sample = 2804 (London) 10 The city of Newcastle upon Tyne show if City_sample = 2806 (Tyneside Conurbation) 11 The borough of North Tyneside show if City_sample = 2806 (Tyneside Conurbation) 12 The borough of South Tyneside show if City_sample = 2806 (Tyneside Conurbation) 13 The metropolitan Borough of Gateshead show if City_sample = 2806 (Tyneside Conurbation) 14 Dublin County show if City_sample = 1401 (Dublin) 15 The city of Burgas show if City_sample = 301 (Burgas) 16 Sofia Capital Municipality show if City_sample = 302 (Sofia) 17 Municipiul Bucureşti show if City_sample = 2301 (Bucuresti) 18 Municipiul Cluj-Napoca show if City_sample = 2302 (Cluj Napoca) 19 Municipiul Piatra Neamţ show if City_sample = 2303 (Piatra Neamt) 20 De gemeente Amsterdam show if City_sample = 2001 (Amsterdam) 21 De gemeente Amstelveen show if City_sample = 2001 (Amsterdam) 22 De gemeente Diemen show if City_sample = 2001 (Amsterdam) 23 De gemeente ouder-Amstel show if City_sample = 2001 (Amsterdam) 24 De gemeente Rotterdam show if City_sample = 2003 (Rotterdam) 25 De gemeente Ablasserdam show if City_sample = 2003 (Rotterdam) 26 De gemeente Barendrecht show if City_sample = 2003 (Rotterdam) 27 De gemeente Capelle-aan-den-IJssel show if City_sample = 2003 (Rotterdam) 28 De gemeente Dordrecht show if City_sample = 2003 (Rotterdam) 29 De gemeente Hendrik-Ido-Ambacht show if City_sample = 2003 (Rotterdam) 30 De gemeente Krimpen aan den IJssel show if City_sample = 2003 (Rotterdam) 31 De gemeente Papendrecht show if City_sample = 2003 (Rotterdam) 32 De gemeente Ridderkerk show if City_sample = 2003 (Rotterdam) 33 De gemeente Schiedam show if City_sample = 2003 (Rotterdam) 34 De gemeente Vlaardingen show if City_sample = 2003 (Rotterdam) 35 De gemeente Zwijndrecht show if City_sample = 2003 (Rotterdam) 36 De gemeente Groningen show if City_sample = 2002 (Groningen) 37 Greater Manchester show if City_sample = 2805 (Manchester)
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No, I live somewhere else (DO NOT READ 98 OUT) Don’t know/No Answer/Refuses (DO 99 NOT READ OUT)
[PROG: IF D3a = 99 : Screen out] [PROG: IF D3a = 98 : Screen out and show message “I’m sorry, you do not live in the right region to participate in this survey.“ Note: it is important the screen out is done after D3a in order to collect their answers still in D3a for sample analysis purposes
D3b. [PROG: SINGLE RESPONSE; Drop down list; show if country_sample = 22 (PT)] In which Freguesia do you live? Interviewer instruction: Do not read out list
1 Adaúfe show if City_sample = 2201 (Braga) 2 Águas Livres show if City_sample = 2202 (Lisboa) 3 Ajuda show if City_sample = 2202 (Lisboa) 4 Alcabideche show if City_sample = 2202 (Lisboa) 5 Alcântara show if City_sample = 2202 (Lisboa) 6 Alfragide show if City_sample = 2202 (Lisboa) 7 Algés, Linda-a-Velha e Cruz Quebrada-Dafundo show if City_sample = 2202 (Lisboa) 8 Almada, Cova da Piedade, Pragal e Cacilhas show if City_sample = 2202 (Lisboa) 9 Alto do Seixalinho, Santo André e Verderena show if City_sample = 2202 (Lisboa) 10 Alvalade show if City_sample = 2202 (Lisboa) 11 Amora show if City_sample = 2202 (Lisboa) 12 Areeiro show if City_sample = 2202 (Lisboa) 13 Arentim e Cunha show if City_sample = 2201 (Braga) 14 Arroios show if City_sample = 2202 (Lisboa) 15 Avenidas Novas show if City_sample = 2202 (Lisboa) 16 Barcarena show if City_sample = 2202 (Lisboa) 17 Barreiro e Lavradio show if City_sample = 2202 (Lisboa) 18 Beato show if City_sample = 2202 (Lisboa) 19 Belém show if City_sample = 2202 (Lisboa) 20 Benfica show if City_sample = 2202 (Lisboa) 21 Braga (Maximinos, Sé e Cividade) show if City_sample = 2201 (Braga) 22 Braga (São José de São Lázaro e São João do Souto) show if City_sample = 2201 (Braga) 23 Braga (São Vicente) show if City_sample = 2201 (Braga) 24 Braga (São Vítor) show if City_sample = 2201 (Braga) 25 Bucelas show if City_sample = 2202 (Lisboa) 26 Cabreiros e Passos (São Julião) show if City_sample = 2201 (Braga) 27 Camarate, Unhos e Apelação show if City_sample = 2202 (Lisboa) 28 Campo de Ourique show if City_sample = 2202 (Lisboa) 29 Campolide show if City_sample = 2202 (Lisboa) Directorate-General for Regional and Urban Policy 2020 53
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30 Caparica e Trafaria show if City_sample = 2202 (Lisboa) 31 Carcavelos e Parede show if City_sample = 2202 (Lisboa) 32 Carnaxide e Queijas show if City_sample = 2202 (Lisboa) 33 Carnide show if City_sample = 2202 (Lisboa) 34 Cascais e Estoril show if City_sample = 2202 (Lisboa) 35 Celeirós, Aveleda e Vimieiro show if City_sample = 2201 (Braga) 36 Charneca de Caparica e Sobreda show if City_sample = 2202 (Lisboa) 37 Corroios show if City_sample = 2202 (Lisboa) 38 Costa da Caparica show if City_sample = 2202 (Lisboa) 39 Crespos e Pousada show if City_sample = 2201 (Braga) 40 Encosta do Sol show if City_sample = 2202 (Lisboa) 41 Escudeiros e Penso (Santo Estêvão e São Vicente) show if City_sample = 2201 (Braga) 42 Espinho show if City_sample = 2201 (Braga) 43 Esporões show if City_sample = 2201 (Braga) 44 Este (São Pedro e São Mamede) show if City_sample = 2201 (Braga) 45 Estrela show if City_sample = 2202 (Lisboa) 46 Falagueira-Venda Nova show if City_sample = 2202 (Lisboa) 47 Fanhões show if City_sample = 2202 (Lisboa) 48 Fernão Ferro show if City_sample = 2202 (Lisboa) 49 Ferreiros e Gondizalves show if City_sample = 2201 (Braga) 50 Figueiredo show if City_sample = 2201 (Braga) 51 Gualtar show if City_sample = 2201 (Braga) 52 Guisande e Oliveira (São Pedro) show if City_sample = 2201 (Braga) 53 Lamas show if City_sample = 2201 (Braga) 54 Laranjeiro e Feijó show if City_sample = 2202 (Lisboa) 104 Lisboa show if City_sample = 2202 (Lisboa) 55 Lomar e Arcos show if City_sample = 2201 (Braga) 56 Loures show if City_sample = 2202 (Lisboa) 57 Lousa show if City_sample = 2202 (Lisboa) 58 Lumiar show if City_sample = 2202 (Lisboa) 59 Marvila show if City_sample = 2202 (Lisboa) 60 Merelim (São Paio), Panoias e Parada de Tibães show if City_sample = 2201 (Braga) 61 Merelim (São Pedro) e Frossos show if City_sample = 2201 (Braga) 62 Mina de Água show if City_sample = 2202 (Lisboa) 63 Mire de Tibães show if City_sample = 2201 (Braga) 64 Misericórdia show if City_sample = 2202 (Lisboa) 65 Morreira e Trandeiras show if City_sample = 2201 (Braga) 66 Moscavide e Portela show if City_sample = 2202 (Lisboa) 67 Nogueira, Fraião e Lamaçães show if City_sample = 2201 (Braga) 68 Nogueiró e Tenões show if City_sample = 2201 (Braga) 69 Odivelas show if City_sample = 2202 (Lisboa) 70 Oeiras e São Julião da Barra, Paço de Arcos e Caxias show if City_sample = 2202 (Lisboa) 71 Olivais show if City_sample = 2202 (Lisboa) 72 Padim da Graça show if City_sample = 2201 (Braga)
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73 Palhais e Coina show if City_sample = 2202 (Lisboa) 74 Palmeira show if City_sample = 2201 (Braga) 75 Parque das Nações show if City_sample = 2202 (Lisboa) 76 Pedralva show if City_sample = 2201 (Braga) 77 Penha de França show if City_sample = 2202 (Lisboa) 78 Pontinha e Famões show if City_sample = 2202 (Lisboa) 79 Porto Salvo show if City_sample = 2202 (Lisboa) 80 Póvoa de Santo Adrião e Olival Basto show if City_sample = 2202 (Lisboa) 81 Priscos show if City_sample = 2201 (Braga) 82 Ramada e Caneças show if City_sample = 2202 (Lisboa) 83 Real, Dume e Semelhe show if City_sample = 2201 (Braga) 84 Ruilhe show if City_sample = 2201 (Braga) 85 Sacavém e Prior Velho show if City_sample = 2202 (Lisboa) 86 Santa Clara show if City_sample = 2202 (Lisboa) 87 Santa Iria de Azoia, São João da Talha e Bobadela show if City_sample = 2202 (Lisboa) 88 Santa Lucrécia de Algeriz e Navarra show if City_sample = 2201 (Braga) 89 Santa Maria Maior show if City_sample = 2202 (Lisboa) 90 Santo Antão e São Julião do Tojal show if City_sample = 2202 (Lisboa) 91 Santo António show if City_sample = 2202 (Lisboa) 92 Santo António da Charneca show if City_sample = 2202 (Lisboa) 93 Santo António dos Cavaleiros e Frielas show if City_sample = 2202 (Lisboa) 94 São Domingos de Benfica show if City_sample = 2202 (Lisboa) 95 São Domingos de Rana show if City_sample = 2202 (Lisboa) 96 São Vicente show if City_sample = 2202 (Lisboa) 97 Seixal, Arrentela e Aldeia de Paio Pires show if City_sample = 2202 (Lisboa) 98 Sequeira show if City_sample = 2201 (Braga) 99 Sobreposta show if City_sample = 2201 (Braga) 100 Tadim show if City_sample = 2201 (Braga) 101 Tebosa show if City_sample = 2201 (Braga) 102 Venteira show if City_sample = 2202 (Lisboa) 103 Vilaça e Fradelos show if City_sample = 2201 (Braga) 998 No, I live somewhere else (DO NOT READ OUT) 999 Don’t know/No Answer/Refuses (DO NOT READ OUT)
[PROG: IF D3b = 999 : Screen out] [PROG: IF D3b = 998 : Screen out and show message “I’m sorry, you do not live in the right region to participate in this survey.“ Note: it is important the screen out is done after D3b in order to collect their answers still in D3b for sample analysis purposes
D3c_1. [PROG: Open end box; range: min. 3 characters – max. 3 characters; show if country_sample = 19 (MT)] What is your postcode?
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Interviewer instruction: only use letters to record the postcode, no numbers
999. Don’t know/No Answer/Refuses (DO NOT READ OUT)
D3c_2. [PROG: Quantity; 3 digits; range: min 100 – max 999; show if country_sample = 29 (IS)] What is your postcode? Interviewer instruction: only use numbers to record the postcode, no spaces or hyphens
999. Don’t know/No Answer/Refuses (DO NOT READ OUT)
D3c_3. [PROG: Quantity; 4 digits; range: min 0000 – max 9999; show if country_sample = 30 (NO)] What is your postcode? Interviewer instruction: only use numbers to record the postcode, no spaces or hyphens
9999. Don’t know/No Answer/Refuses (DO NOT READ OUT)
D3c_4. [PROG: Quantity; 4 digits; range: min 1000 – max 9999; show if country_sample = 1 (AT), 2 (BE), 5 (CY), 7 (DK), 13 (HU), 17 (LU), 18 (LV), 25 (SI), 31 (CH), 32 (AL), 33 (MK))] What is your postcode? Interviewer instruction: only use numbers to record the postcode, no spaces or hyphens
9999. Don’t know/No Answer/Refuses (DO NOT READ OUT)
D3c_5. [PROG: Quantity; 5 digits; range: min 00000 – max 99999; show if country_sample = 8 (EE), 9 (FI), 10 (FR), 11 (DE), 15 (IT), 16 (LT), 21 (PL), 24 (SK), 26 (ES), 34 (ME), 35 (RS), 36 (TR)] What is your postcode? Interviewer instruction: only use numbers to record the postcode, no spaces or hyphens
99999. Don’t know/No Answer/Refuses (DO NOT READ OUT)
D3c_6. [PROG: Quantity; 5 digits; range: min 10000 – max 99999; show if country_sample = 4 (HR), 6 (CZ), 12 (EL), 27 (SE))] What is your postcode? Interviewer instruction: only use numbers to record the postcode, no spaces or hyphens
999999. Don’t know/No Answer/Refuses (DO NOT READ OUT)
[PROG: IF D3c_1 = 999 : Screen out] [PROG: IF D3c_2 = 999 : Screen out] [PROG: IF D3c_3 = 9999 : Screen out] [PROG: IF D3c_4 = 9999 : Screen out] [PROG: IF D3c_5 = 99999 : Screen out] [PROG: IF D3c_6 = 999999 : Screen out]
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[PROG: match input in D3c_1-6 against list “D3c – postcodes” in Annex. Only match against postcodes from sample country (columns A-B in annex list), not from other countries! If postcode does not match against a code in the list, go to D3d: Screen out and show message “I’m sorry, you do not live in the right region to participate in this survey.“ Note: it is important the screen out is done after D3c in order to collect their answers still in D3c for sample analysis purposes]
D3_cityrecode. [PROG: HIDDEN VARIABLE; recode D3c_1-6 into city_code value (see sheet D3C – postcodes in annex]
Note: labels of D3_cityrecode are identical to city_sample build the variable using the info from column I, F, B,E, C e.g. if D3c_4 (column I) = 1070 (column F) & Country_sample = 2 (Column B) D3_cityrecode = 202 (column E) Bruxelles/Brussel (column C)
IF country_sample = 28 (UK), 14 (IE), 3 (BG), 23 (RO), 20 (NL) or 22 (PT) autocode D3_cityrecode = city_sample
D3_lau_recode. [PROG: HIDDEN VARIABLE; recode D3c_1-6 into lau_code value (see sheet D3C – postcodes in annex]
Note: labels of D3_lau_recode are identical to lau_sample build the variable using the info from column I, F, B,D, G e.g. if D3c_4 (column I) = 1070 (column F) & Country_sample = 2 (Column B) D3_lau_recode = 202001 (column D) Anderlecht (column G)
+ IF country_sample = 28 (UK), 14 (IE), 3 (BG), 23 (RO), 20 (NL) or 22 (PT) use the below tables for the recode
D3c_lau_recode If D3a = = 1 2801001 2 2801002 3 2801002 4 2802001 5 2803002 6 2803001 7 2803003 8 2803004 Directorate-General for Regional and Urban Policy 2020 57
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10 2806001 11 2806002 12 2806003 13 2806004 15 301001 16 302001 17 2301001 18 2302001 19 2303001 20 2001002 21 2001001 22 2001003 23 2001004 24 2003009 25 2003001 26 2003002 27 2003003 28 2003004 29 2003005 30 2003006 31 2003007 32 2003008 33 2003010 34 2003011 35 2003012 36 2002001
Recode D3b into lau_code as follows:
D3_lau_recode if D3b = = 1 2201001 2 2202045 3 2202005 4 2202001 5 2202006 6 2202044 7 2202041 8 2202055 9 2202060 10 2202015 11 2202063 12 2202016 13 2201019 14 2202017 Directorate-General for Regional and Urban Policy 2020 58
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15 2202018 16 2202039 17 2202061 18 2202007 19 2202019 20 2202008 21 2201020 22 2201021 23 2201013 24 2201014 25 2202029 26 2201022 27 2202038 28 2202020 29 2202009 30 2202056 31 2202003 32 2202042 33 2202010 34 2202004 35 2201023 36 2202057 37 2202064 38 2202054 39 2201024 40 2202046 41 2201025 42 2201002 43 2201003 44 2201026 45 2202021 46 2202047 47 2202030 48 2202065 49 2201027 50 2201004 51 2201005 52 2201028 53 2201006 54 2202058 55 2201029 56 2202031 57 2202032 58 2202011 59 2202012 Directorate-General for Regional and Urban Policy 2020 59
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60 2201030 61 2201031 62 2202048 63 2201007 64 2202022 65 2201032 66 2202033 67 2201033 68 2201034 69 2202050 70 2202043 71 2202013 72 2201008 73 2202062 74 2201009 75 2202023 76 2201010 77 2202024 78 2202051 79 2202040 80 2202052 81 2201011 82 2202053 83 2201035 84 2201012 85 2202034 86 2202025 87 2202035 88 2201036 89 2202026 90 2202036 91 2202027 92 2202059 93 2202037 94 2202014 95 2202002 96 2202028 97 2202066 98 2201015 99 2201016 100 2201017 101 2201018 102 2202049 103 2201037
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104 No LAU recode
D4. [PROG: hidden empty variable] DEGURBA
Main Questionnaire
Q1. [PROG: SINGLE RESPONSE GRID] Generally speaking, please tell me if you are very satisfied, rather satisfied, rather unsatisfied or very unsatisfied with each of the following issues in your city or area.
Rows [PROG: Randomise items 1-10] 1. Public transport, for example the bus, tram or metro. 2. Health care services, doctors and hospitals. 3. Sport facilities such as sport fields and indoor sports halls. 4. Cultural facilities such as concert halls, theatres, museums and libraries. 5. Green spaces such as parks and gardens. 6. Public spaces such as markets, squares, pedestrian areas. 7. Schools and other educational facilities. 8. The quality of the air. 9. The noise level. 10. Cleanliness.
Columns 4. Very satisfied 3. Rather satisfied 2. Rather unsatisfied 1. Very unsatisfied 99. Don’t know/No Answer/Refuses (DO NOT READ OUT)
Q2. [PROG: SINGLE RESPONSE GRID] I will read you a few statements. Please tell me whether you strongly agree, somewhat agree, somewhat disagree or strongly disagree with each of these statements.
Rows [PROG: Randomise items 1-7; Treat 3-4 and 6-7 as fixed pairs: Make sure that item 4 always comes right after 3, and item 7 right after 6] 1. I'm satisfied to live in my city. 2. It is easy to find a good job in my city. 3. I feel safe walking alone at night in my city. 4. I feel safe walking alone at night in my neighbourhood. 5. It is easy to find good housing in my city at a reasonable price. 6. Generally speaking, most people in my city can be trusted. Directorate-General for Regional and Urban Policy 2020 61
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7. Generally speaking, most people in my neighbourhood can be trusted.
Columns 4. Strongly agree 3. Somewhat agree 2. Somewhat disagree 1. Strongly disagree 99. Don’t know/No Answer/Refuses (DO NOT READ OUT)
Q3. [PROG: SINGLE RESPONSE GRID] Is the city where you live a good place or not a good place to live for the following groups?
Rows [PROG: Randomise Rows; Keep item 1 always first, randomise items 2-6] 1. People in general. [PROG: Fixed] 2. Racial and ethnic minorities. 3. Gay or lesbian people. 4. Immigrants from other countries. 5. Young families with children. 6. Elderly people.
Columns 1. A good place to live 2. Not a good place to live 99. Don’t know/No Answer/Refuses (DO NOT READ OUT)
Q4. [PROG: SINGLE RESPONSE GRID] On the whole, are you very satisfied, fairly satisfied, not very satisfied or not at all satisfied with:
Rows [PROG: Randomise items 1-4] 1. The neighbourhood where you live 2. Your personal job situation. 3. The financial situation of your household. 4. The life you lead.
Columns 4. Very satisfied 3. Fairly satisfied 2. Not very satisfied 1. Not at all satisfied 99. Don’t know/No Answer/Refuses (DO NOT READ OUT)
Q5. [PROG: MULTIPLE RESPONSE; max. 2 responses allowed] On a typical day, which mode(s) of transport do you use most often?…
Interviewer instruction: allow 2 responses if offered spontaneously by the respondent, but do not probe if only 1 is given.
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1. Car 2. Motorcycle 3. Bicycle 4. Foot 5. Train 6. Urban public transport (bus, tram or metro) 7. Other 98. Do not commute [PROG: Single Response] 99. Don’t know/No Answer/Refuses (DO NOT READ OUT) [PROG: Single Response]
Q6. [PROG: SINGLE RESPONSE GRID] Thinking about public transport in your city, based on your experience or perceptions, please tell me whether you strongly agree, somewhat agree, somewhat disagree or strongly disagree with each of these statements. Public transport in your city is:
Rows [PROG: Randomise items 1-5] 1. Affordable 2. Safe 3. Easy to get 4. Frequent (comes often) 5. Reliable (comes when it says it will)
Columns 4. Strongly agree 3. Somewhat agree 2. Somewhat disagree 1. Strongly disagree 99. Don’t know/No Answer/Refuses (DO NOT READ OUT)
Q7. [PROG: SINGLE RESPONSE] In the city where you live, do you have confidence in the local police force?
1. Yes 2. No 99. Don’t know/No Answer/Refuses (DO NOT READ OUT)
Q8. [PROG: SINGLE RESPONSE] Within the last 12 months, was any money or property stolen from you or another household member in your city?
1. Yes 2. No 99. Don’t know/No Answer/Refuses (DO NOT READ OUT)
Q9. [PROG: SINGLE RESPONSE] Within the last 12 months, have you been assaulted or mugged in your city?
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1. Yes 2. No 99. Don’t know/No Answer/Refuses (DO NOT READ OUT)
Q10. [PROG: SINGLE RESPONSE] Within the last 12 months, would you say you had difficulties to pay your bills at the end of the month …
1. Most of the time 2. From time to time 3. Almost never/never 99. Don’t know/No Answer/Refuses (DO NOT READ OUT)
Q11. [PROG: SINGLE RESPONSE] Do you feel that if you needed material help (e.g. money, loan or an object) you could receive it from relatives, friends, neighbours or other persons you know?
1. Yes 2. No 99. Don’t know/No Answer/Refuses (DO NOT READ OUT)
Q12. [PROG: SINGLE RESPONSE] Do you feel that if you needed non-material help (e.g. somebody to talk to, help with doing something or collecting something) you could receive it from relatives, friends, neighbours or other persons you know?
1. Yes 2. No 99. Don’t know/No Answer/Refuses (DO NOT READ OUT)
Q13. [PROG: SINGLE RESPONSE GRID] I will read you a few statements about the local public administration in your city. Please tell me whether you strongly agree, somewhat agree, somewhat disagree or strongly disagree with each of these statements.
Rows [PROG: Randomise items 1-5] 1. I am satisfied with the amount of time it takes to get a request solved by my local public administration. 2. The procedures used by my local public administration are straightforward and easy to understand 3. The fees charged by my local public administration are reasonable 4. Information and services of my local public administration can be easily accessed online 5. There is corruption in my local public administration
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3. Somewhat agree 2. Somewhat disagree 1. Strongly disagree 99. Don’t know/No Answer/Refuses (DO NOT READ OUT)
Q14. [PROG: SINGLE RESPONSE] Compared to five years ago, would you say the quality of life in your city or area has:
1. Decreased 2. Stayed the same 3. Increased 99. Don’t know/No Answer/Refuses (DO NOT READ OUT)
Socio Demographic questions
D5. [PROG: SINGLE RESPONSE; insert answer list “D5 – Countries”as drop down] In which country were you born?
D6. [PROG: SINGLE RESPONSE] Have you ever lived in another city for at least 1 year?
1. Yes 2. No 99. Don’t know/No Answer/Refuses (DO NOT READ OUT)
D7. [PROG: Quantity; only if D6 = 1; min. 0; max. 115] How many years have you been living in your current city since last moving here?
Interviewer instruction: If respondent answers “less than 1 year”, code as 0
999. Don’t know/No Answer/Refuses (DO NOT READ OUT)
D9. [PROG: Quantity; min. 1; max. 15] How many people usually live in your household? Please include yourself.
D9b. [PROG: Quantity; only if D9 > 1; min.1.; max. = answer given in D9] How many of these are aged 15 and older? Please include yourself.
[PROG: autocode D9b = 1 if D9 = 1]
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D8. [PROG: SINGLE RESPONSE. ONLY IF D9 > 1] Which of the following best describes your household composition? With household, we mean all people that typically live with you in the same residence. Please include anyone who is temporarily away for work, study or vacation
[PROG: autocode D8 = 1 if D9 = 1]
1. One-person household [PROG: do not show. If D9 = 1, autocode D8 = 1] 2. Lone parent with at least one child aged less than 25 3. Lone parent with all children aged 25 or more 4. Couple without any child(ren) 5. Couple with at least one child aged less than 25 6. Couple with all children aged 25 or more 7. Other type of household 99. Don’t know/No Answer/Refuses (DO NOT READ OUT)
D10local. [PROG: SINGLE RESPONSE; insert answer list “D10 – education”; use the value and show “Educ categories ENGLISH” in the master questionnaire and the “Educ categories LOCAL” for the local translations] What is the highest level of education you have successfully completed?
Interviewer instruction: DO NOT READ OUT response options unless needed to proceed
99. Don’t know/No Answer/Refuses (DO NOT READ OUT)
D10ISCED. [PROG: HIDDEN VARIABLE; recode the response from D10local into the corresponding isced level as indicated in the column “isced code”]
1. Less than Primary education (ISCED 0) 2. Primary education (ISCED 1) 3. Lower secondary education (ISCED 2) 4. Upper secondary education (ISCED 3) 5. Post-secondary non-tertiary education (ISCED 4) 6. Short-cycle tertiary education (ISCED 5) 7. Bachelor or equivalent (ISCED 6) 8. Master or equivalent (ISCED 7) 9. Doctoral or equivalent (ISCED 8) 10. Don’t know/No Answer/Refuses
D11a. [PROG: SINGLE RESPONSE] Do you currently have a job? Interviewer instruction: Include employees, employers, self-employed and people working as a relative assisting on family business. DO NOT INCLUDE people in compulsory military service or full-time homemakers.
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1. Yes 2. No 99. Don’t know/No Answer/Refuses
D11. [PROG: SINGLE RESPONSE, DO NOT SHOW IF D11a = 1] Which of the following best describes your current working status?
1. At work as employee or employer/self-employed/relative assisting on family business [PROG: do not show. If D11a = 1, autocode D11 = 1] 2. Unemployed, not looking actively for a job 3. Unemployed, looking actively for a job 4. Retired 5. Unable to work due to long-standing health problems 6. In full-time education (at school, university, etc.) / student 7. Full-time homemaker/responsible for ordinary shopping and looking after home 8. Compulsory military or civilian service 9. Other 99. Don’t know/No Answer/Refuses (DO NOT READ OUT)
D12. [PROG: SINGLE RESPONSE; only ask if D11 =1] What is your current job? Interviewer instruction: DO NOT READ OUT response options unless needed to proceed. If respondent is unsure, ask to state their exact job/function and propose a suitable category. If a respondent is in the military, always code as “armed forces occupation”, regardless of their job within the military.
1. Manager 2. Professional 3. Technician and associate professional 4. Clerical support worker 5. Services and sales worker 6. Agricultural, forestry or fishery worker 7. Craft or related trade worker 8. Plant or machine operator or assembler 9. Elementary occupation 10. Armed forces occupation [PROG: autocode D12 = 10 if D11 = 8] 99. Don’t know/No Answer/Refuses (DO NOT READ OUT)
D13. [PROG: SINGLE RESPONSE; ask if D11 = 1 or D11 = 8] Which of the following best describes your job?
1. Full-time job 2. Part-time job 99. Don’t know/No Answer/Refuses (DO NOT READ OUT)
D14. [PROG: SINGLE RESPONSE; ask if SampleType = 2 (Fixed)] Do you personally own a mobile phone?
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1. Yes 2. No
[PROG: autocode D14 = 1 if SampleType = 1 (mobile sample)]
D15. [PROG: SINGLE RESPONSE; ask if SampleType = 1 (Mobile)] Do you have a landline phone in the household?
1. Yes 2. No
[PROG: autocode D15 = 1 if SampleType = 2 (fixed sample)]
Mobfix. [PROG: HIDDEN VARIABLE; recode the response from D14 and D15 into the corresponding category]
1. Fixed only: If (SampleType = 2 and D14 = 2) 2. Mobile only: if (SampleType = 1 and D15 = 2) 3. Mixed: if (SampleType = 2 and D14 = 1) OR or (SampleType = 1 and D15 = 1)
Q15a [PROG: SINGLE RESPONSE] The next question is about your health status. Please remember that all your responses will be treated confidentially. You do not have to answer this question if you do not want to. Are you happy to proceed?
1. Yes 2. No
Q15. [PROG: SINGLE RESPONSE, ask if Q15a=1] In general, how is your health?
[PROG: autocode Q15=99 if Q15a = 2]
5. Very good 4. Good 3. Fair (neither good or bad) 2. Bad 1. Very bad 99. Don’t know/No Answer/Refuses (DO NOT READ OUT)
Outro1. Only read IF NECESSARY: Thank you for taking the time to participate in this study. You can access the privacy notice here: https://survey.ipsos.be/privacynoticeQoLCities.pdf. This explains the purposes for
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processing your personal data as well as your rights under data protection regulations to access your personal data, withdraw consent, object to processing of your personal data and other required information.
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Annex 2. List of LAUs per city
Country City Name LAU CODE LAU LABEL AL Tirana AL0310 Durrës AL Tirana AL1150 Tiranë AL Tirana AL1151 Kamëz AT Wien 90001 Wien AT Graz 60101 Graz BE Bruxelles / Brussel 21001 Anderlecht BE Bruxelles / Brussel 21002 Auderghem / Oudergem BE Bruxelles / Brussel 21003 Berchem-Sainte-Agathe / Sint-Agatha-Berchem BE Bruxelles / Brussel 21004 Bruxelles / Brussel BE Bruxelles / Brussel 21005 Etterbeek BE Bruxelles / Brussel 21006 Evere BE Bruxelles / Brussel 21007 Forest / Vorst BE Bruxelles / Brussel 21008 Ganshoren BE Bruxelles / Brussel 21009 Ixelles / Elsene BE Bruxelles / Brussel 21010 Jette BE Bruxelles / Brussel 21011 Koekelberg BE Bruxelles / Brussel 21012 Molenbeek-Saint-Jean / Sint-Jans-Molenbeek BE Bruxelles / Brussel 21013 Saint-Gilles / Sint-Gillis BE Bruxelles / Brussel 21014 Saint-Josse-ten-Noode / Sint-Joost-ten-Node BE Bruxelles / Brussel 21015 Schaerbeek / Schaarbeek BE Bruxelles / Brussel 21016 Uccle / Ukkel BE Bruxelles / Brussel 21017 Watermael-Boitsfort / Watermaal-Bosvoorde BE Bruxelles / Brussel 21018 Woluwe-Saint-Lambert / Sint-Lambrechts-Woluwe BE Bruxelles / Brussel 21019 Woluwe-Saint-Pierre / Sint-Pieters-Woluwe BE Antwerpen 11002 Antwerpen / Anvers BE Liège 62003 Ans BE Liège 62015 Beyne-Heusay BE Liège 62038 Fléron BE Liège 62051 Herstal BE Liège 62063 Liège / Luik BE Liège 62093 Saint-Nicolas BE Liège 62096 Seraing BG Sofia 68134 София BG Burgas 07079 Бургас CH Zürich CH0054 Dietlikon CH Zürich CH0062 Kloten CH Zürich CH0066 Opfikon CH Zürich CH0069 Wallisellen CH Zürich CH0097 Rümlang CH Zürich CH0131 Adliswil CH Zürich CH0135 Kilchberg (ZH) CH Zürich CH0136 Langnau am Albis
Quality of Life in European Cities Survey 2019
CH Zürich CH0139 Rüschlikon CH Zürich CH0141 Thalwil CH Zürich CH0161 Zollikon CH Zürich CH0191 Dübendorf CH Zürich CH0200 Wangen-Brüttisellen CH Zürich CH0243 Dietikon CH Zürich CH0245 Oberengstringen CH Zürich CH0247 Schlieren CH Zürich CH0249 Unterengstringen CH Zürich CH0250 Urdorf CH Zürich CH0261 Zürich CH Genève CH6608 Carouge (GE) CH Genève CH6612 Chêne-Bougeries CH Genève CH6613 Chêne-Bourg CH Genève CH6617 Cologny CH Genève CH6621 Genève CH Genève CH6628 Lancy CH Genève CH6631 Onex CH Genève CH6633 Plan-les-Ouates CH Genève CH6634 Pregny-Chambésy CH Genève CH6640 Thônex CH Genève CH6641 Troinex CH Genève CH6643 Vernier CH Genève CH6645 Veyrier CY Lefkosia 1000 Λευκωσία CY Lefkosia 1010 Άγιος Δομέτιος CY Lefkosia 1011 Έγκωμη Λευκωσίας CY Lefkosia 1012 Στρόβολος CY Lefkosia 1013 Αγλαντζιά ή Αγλαγγιά CY Lefkosia 1021 Λακατάμεια CY Lefkosia 1022 Συνοικισμός Ανθούπολης CY Lefkosia 1023 Λατσιά ή Λακκιά CY Lefkosia 1024 Γέρι CZ Praha 554782 Praha CZ Ostrava 554821 Ostrava DE Berlin 11000000 Berlin, Stadt DE Hamburg 02000000 Hamburg, Freie und Hansestadt DE München 09162000 München, Landeshauptstadt DE Essen 05113000 Essen, Stadt DE Leipzig 14713000 Leipzig, Stadt DE Dortmund 05913000 Dortmund, Stadt DE Rostock 13003000 Rostock, Hansestadt DK København 101 København DK København 147 Frederiksberg
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DK København 153 Brøndby DK København 157 Gentofte DK København 159 Gladsaxe DK København 161 Glostrup DK København 163 Herlev DK København 165 Albertslund DK København 167 Hvidovre DK København 173 Lyngby-Taarbæk DK København 175 Rødovre DK København 183 Ishøj DK København 185 Tårnby DK København 187 Vallensbæk DK København 253 Greve DK Aalborg 851 Aalborg EE Tallinn 0784 Tallinn EL Athina 45010000 Ψευδοδημοτικη Κοινοτητα Αθηναίων EL Athina 45020000 Ψευδοδημοτικη Κοινοτητα Βύρωνος EL Athina 45030000 Ψευδοδημοτικη Κοινοτητα Γαλατσίου EL Athina 45040101 Δημοτική Κοινότητα Δάφνης EL Athina 45040201 Δημοτική Κοινότητα Υμηττού EL Athina 45050000 Ψευδοδημοτικη Κοινοτητα Ζωγράφου EL Athina 45060000 Ψευδοδημοτικη Κοινοτητα Ηλιουπόλεως EL Athina 45070000 Ψευδοδημοτικη Κοινοτητα Καισαριανής EL Athina 45080101 Δημοτική Κοινότητα Νέας Φιλαδελφείας EL Athina 45080201 Δημοτική Κοινότητα Νέας Χαλκηδόνος EL Athina 46010000 Ψευδοδημοτικη Κοινοτητα Αμαρουσίου EL Athina 46020000 Ψευδοδημοτικη Κοινοτητα Αγίας Παρασκευής EL Athina 46030000 Ψευδοδημοτικη Κοινοτητα Βριλησσίων EL Athina 46040000 Ψευδοδημοτικη Κοινοτητα Ηρακλείου EL Athina 46050101 Δημοτική Κοινότητα Κηφισιάς EL Athina 46050201 Δημοτική Κοινότητα Εκάλης EL Athina 46050301 Δημοτική Κοινότητα Νέας Ερυθραίας EL Athina 46060101 Δημοτική Κοινότητα Πεύκης EL Athina 46060201 Δημοτική Κοινότητα Λυκοβρύσεως EL Athina 46070000 Ψευδοδημοτικη Κοινοτητα Μεταμορφώσεως EL Athina 46080000 Ψευδοδημοτικη Κοινοτητα Νέας Ιωνίας EL Athina 46090101 Δημοτική Κοινότητα Χολαργού EL Athina 46090201 Δημοτική Κοινότητα Παπάγου EL Athina 46100101 Δημοτική Κοινότητα Μελισσίων EL Athina 46100201 Δημοτική Κοινότητα Νέας Πεντέλης EL Athina 46100301 Δημοτική Κοινότητα Πεντέλης EL Athina 46110101 Δημοτική Κοινότητα Ψυχικού EL Athina 46110201 Δημοτική Κοινότητα Νέου Ψυχικού EL Athina 46110301 Δημοτική Κοινότητα Φιλοθέης EL Athina 46120000 Ψευδοδημοτικη Κοινοτητα Χαλανδρίου Directorate-General for Regional and Urban Policy 2020 73
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EL Athina 47010000 Ψευδοδημοτικη Κοινοτητα Περιστερίου EL Athina 47020000 Ψευδοδημοτικη Κοινοτητα Αγίας Βαρβάρας EL Athina 47030101 Δημοτική Κοινότητα Αγίων Αναργύρων EL Athina 47030201 Δημοτική Κοινότητα Καματερού EL Athina 47040000 Ψευδοδημοτικη Κοινοτητα Αιγάλεω EL Athina 47050000 Ψευδοδημοτικη Κοινοτητα Ιλιου EL Athina 47060000 Ψευδοδημοτικη Κοινοτητα Πετρουπόλεως EL Athina 47070000 Ψευδοδημοτικη Κοινοτητα Χαϊδαρίου EL Athina 48010000 Ψευδοδημοτικη Κοινοτητα Καλλιθέας EL Athina 48020000 Ψευδοδημοτικη Κοινοτητα Αγίου Δημητρίου EL Athina 48030000 Ψευδοδημοτικη Κοινοτητα Αλίμου EL Athina 48040000 Ψευδοδημοτικη Κοινοτητα Γλυφάδας EL Athina 48050101 Δημοτική Κοινότητα Αργυρούπολης EL Athina 48050201 Δημοτική Κοινότητα Ελληνικού EL Athina 48060101 Δημοτική Κοινότητα Μοσχάτου EL Athina 48060201 Δημοτική Κοινότητα Ταύρου EL Athina 48070000 Ψευδοδημοτικη Κοινοτητα Νέας Σμύρνης EL Athina 48080000 Ψευδοδημοτικη Κοινοτητα Παλαιού Φαλήρου EL Athina 49010101 Δημοτική Κοινότητα Αχαρνών EL Athina 49010201 Δημοτική Κοινότητα Θρακομακεδόνων EL Athina 49020101 Δημοτική Κοινότητα Βούλας EL Athina 49020201 Δημοτική Κοινότητα Βάρης EL Athina 49020301 Δημοτική Κοινότητα Βουλιαγμένης EL Athina 49080201 Δημοτική Κοινότητα Γλυκών Νερών EL Athina 49090101 Δημοτική Κοινότητα Γέρακα EL Athina 49090201 Δημοτική Κοινότητα Ανθούσας EL Athina 49090301 Δημοτική Κοινότητα Παλλήνης EL Athina 50050101 Δημοτική Κοινότητα Άνω Λιοσίων EL Athina 50050201 Δημοτική Κοινότητα Ζεφυρίου EL Athina 51010000 Ψευδοδημοτικη Κοινοτητα Πειραιώς EL Athina 51020101 Δημοτική Κοινότητα Κερατσινίου EL Athina 51020201 Δημοτική Κοινότητα Δραπετσώνας EL Athina 51030000 Ψευδοδημοτικη Κοινοτητα Κορυδαλλού EL Athina 51040101 Δημοτική Κοινότητα Νικαίας EL Athina 51040201 Δημοτική Κοινότητα Αγίου Ιωάννου Ρέντη EL Irakleio 71010100 Ψευδοδημοτικη Κοινοτητα Ηρακλείου ES Madrid 28006 Alcobendas ES Madrid 28007 Alcorcón ES Madrid 28049 Coslada ES Madrid 28058 Fuenlabrada ES Madrid 28065 Getafe ES Madrid 28074 Leganés ES Madrid 28079 Madrid ES Madrid 28080 Majadahonda
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ES Madrid 28092 Móstoles ES Madrid 28106 Parla ES Madrid 28115 Pozuelo de Alarcón ES Madrid 28123 Rivas-Vaciamadrid ES Madrid 28127 Rozas de Madrid, Las ES Madrid 28130 San Fernando de Henares ES Madrid 28134 San Sebastián de los Reyes ES Barcelona 08003 Alella ES Barcelona 08015 Badalona ES Barcelona 08019 Barcelona ES Barcelona 08056 Castelldefels ES Barcelona 08073 Cornellà de Llobregat ES Barcelona 08077 Esplugues de Llobregat ES Barcelona 08089 Gavà ES Barcelona 08101 Hospitalet de Llobregat, L' ES Barcelona 08118 Masnou, El ES Barcelona 08125 Montcada i Reixac ES Barcelona 08126 Montgat ES Barcelona 08169 Prat de Llobregat, El ES Barcelona 08180 Ripollet ES Barcelona 08184 Rubí ES Barcelona 08187 Sabadell ES Barcelona 08194 Sant Adrià de Besòs ES Barcelona 08200 Sant Boi de Llobregat ES Barcelona 08205 Sant Cugat del Vallès ES Barcelona 08211 Sant Feliu de Llobregat ES Barcelona 08217 Sant Joan Despí ES Barcelona 08221 Sant Just Desvern ES Barcelona 08238 Sant Quirze del Vallès ES Barcelona 08245 Santa Coloma de Gramenet ES Barcelona 08252 Barberà del Vallès ES Barcelona 08266 Cerdanyola del Vallès ES Barcelona 08279 Terrassa ES Barcelona 08281 Teià ES Barcelona 08282 Tiana ES Barcelona 08301 Viladecans ES Barcelona 08904 Badia del Vallès ES Málaga 29067 Málaga ES Oviedo 33044 Oviedo FI Helsinki / Helsingfors 049 Espoo / Esbo FI Helsinki / Helsingfors 091 Helsinki / Helsingfors FI Helsinki / Helsingfors 092 Vantaa / Vanda FI Helsinki / Helsingfors 235 Kauniainen / Grankulla FI Oulu / Uleåborg 564 Oulu / Uleåborg FR Paris 75101 Paris 1er Arrondissement Directorate-General for Regional and Urban Policy 2020 75
Quality of Life in European Cities Survey 2019
FR Paris 75102 Paris 2e Arrondissement FR Paris 75103 Paris 3e Arrondissement FR Paris 75104 Paris 4e Arrondissement FR Paris 75105 Paris 5e Arrondissement FR Paris 75106 Paris 6e Arrondissement FR Paris 75107 Paris 7e Arrondissement FR Paris 75108 Paris 8e Arrondissement FR Paris 75109 Paris 9e Arrondissement FR Paris 75110 Paris 10e Arrondissement FR Paris 75111 Paris 11e Arrondissement FR Paris 75112 Paris 12e Arrondissement FR Paris 75113 Paris 13e Arrondissement FR Paris 75114 Paris 14e Arrondissement FR Paris 75115 Paris 15e Arrondissement FR Paris 75116 Paris 16e Arrondissement FR Paris 75117 Paris 17e Arrondissement FR Paris 75118 Paris 18e Arrondissement FR Paris 75119 Paris 19e Arrondissement FR Paris 75120 Paris 20e Arrondissement FR Paris 77055 Brou-sur-Chantereine FR Paris 77083 Champs-sur-Marne FR Paris 77108 Chelles FR Paris 77121 Collégien FR Paris 77139 Courtry FR Paris 77169 Émerainville FR Paris 77258 Lognes FR Paris 77294 Mitry-Mory FR Paris 77337 Noisiel FR Paris 77373 Pontault-Combault FR Paris 77390 Roissy-en-Brie FR Paris 77468 Torcy FR Paris 77479 Vaires-sur-Marne FR Paris 77514 Villeparisis FR Paris 78005 Achères FR Paris 78007 Aigremont FR Paris 78015 Andrésy FR Paris 78092 Bougival FR Paris 78123 Carrières-sous-Poissy FR Paris 78124 Carrières-sur-Seine FR Paris 78126 Celle-Saint-Cloud FR Paris 78133 Chambourcy FR Paris 78138 Chanteloup-les-Vignes FR Paris 78146 Chatou FR Paris 78158 Chesnay
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FR Paris 78165 Clayes-sous-Bois FR Paris 78168 Coignières FR Paris 78172 Conflans-Sainte-Honorine FR Paris 78190 Croissy-sur-Seine FR Paris 78208 Élancourt FR Paris 78251 Fourqueux FR Paris 78297 Guyancourt FR Paris 78311 Houilles FR Paris 78350 Louveciennes FR Paris 78358 Maisons-Laffitte FR Paris 78367 Mareil-Marly FR Paris 78372 Marly-le-Roi FR Paris 78382 Maurecourt FR Paris 78383 Maurepas FR Paris 78396 Mesnil-le-Roi FR Paris 78418 Montesson FR Paris 78423 Montigny-le-Bretonneux FR Paris 78481 Pecq FR Paris 78490 Plaisir FR Paris 78498 Poissy FR Paris 78502 Port-Marly FR Paris 78524 Rocquencourt FR Paris 78551 Saint-Germain-en-Laye FR Paris 78586 Sartrouville FR Paris 78621 Trappes FR Paris 78624 Triel-sur-Seine FR Paris 78642 Verneuil-sur-Seine FR Paris 78643 Vernouillet FR Paris 78644 Verrière FR Paris 78646 Versailles FR Paris 78650 Vésinet FR Paris 78674 Villepreux FR Paris 78686 Viroflay FR Paris 78688 Voisins-le-Bretonneux FR Paris 91021 Arpajon FR Paris 91027 Athis-Mons FR Paris 91044 Ballainvilliers FR Paris 91097 Boussy-Saint-Antoine FR Paris 91103 Brétigny-sur-Orge FR Paris 91114 Brunoy FR Paris 91122 Bures-sur-Yvette FR Paris 91136 Champlan FR Paris 91161 Chilly-Mazarin FR Paris 91174 Corbeil-Essonnes FR Paris 91182 Courcouronnes Directorate-General for Regional and Urban Policy 2020 77
Quality of Life in European Cities Survey 2019
FR Paris 91191 Crosne FR Paris 91201 Draveil FR Paris 91215 Épinay-sous-Sénart FR Paris 91216 Épinay-sur-Orge FR Paris 91225 Étiolles FR Paris 91228 Évry FR Paris 91235 Fleury-Mérogis FR Paris 91272 Gif-sur-Yvette FR Paris 91286 Grigny FR Paris 91312 Igny FR Paris 91326 Juvisy-sur-Orge FR Paris 91345 Longjumeau FR Paris 91347 Longpont-sur-Orge FR Paris 91363 Marcoussis FR Paris 91377 Massy FR Paris 91421 Montgeron FR Paris 91425 Montlhéry FR Paris 91432 Morangis FR Paris 91434 Morsang-sur-Orge FR Paris 91457 Norville FR Paris 91458 Nozay FR Paris 91471 Orsay FR Paris 91477 Palaiseau FR Paris 91479 Paray-Vieille-Poste FR Paris 91494 Plessis-Pâté FR Paris 91521 Ris-Orangis FR Paris 91549 Sainte-Geneviève-des-Bois FR Paris 91552 Saint-Germain-lès-Arpajon FR Paris 91553 Saint-Germain-lès-Corbeil FR Paris 91570 Saint-Michel-sur-Orge FR Paris 91573 Saint-Pierre-du-Perray FR Paris 91577 Saintry-sur-Seine FR Paris 91587 Saulx-les-Chartreux FR Paris 91589 Savigny-sur-Orge FR Paris 91600 Soisy-sur-Seine FR Paris 91645 Verrières-le-Buisson FR Paris 91657 Vigneux-sur-Seine FR Paris 91659 Villabé FR Paris 91661 Villebon-sur-Yvette FR Paris 91665 Ville-du-Bois FR Paris 91666 Villejust FR Paris 91667 Villemoisson-sur-Orge FR Paris 91685 Villiers-sur-Orge FR Paris 91687 Viry-Châtillon
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FR Paris 91689 Wissous FR Paris 91691 Yerres FR Paris 91692 Ulis FR Paris 92002 Antony FR Paris 92004 Asnières-sur-Seine FR Paris 92007 Bagneux FR Paris 92009 Bois-Colombes FR Paris 92012 Boulogne-Billancourt FR Paris 92014 Bourg-la-Reine FR Paris 92019 Châtenay-Malabry FR Paris 92020 Châtillon FR Paris 92022 Chaville FR Paris 92023 Clamart FR Paris 92024 Clichy FR Paris 92025 Colombes FR Paris 92026 Courbevoie FR Paris 92032 Fontenay-aux-Roses FR Paris 92033 Garches FR Paris 92035 Garenne-Colombes FR Paris 92036 Gennevilliers FR Paris 92040 Issy-les-Moulineaux FR Paris 92044 Levallois-Perret FR Paris 92046 Malakoff FR Paris 92047 Marnes-la-Coquette FR Paris 92048 Meudon FR Paris 92049 Montrouge FR Paris 92050 Nanterre FR Paris 92051 Neuilly-sur-Seine FR Paris 92060 Plessis-Robinson FR Paris 92062 Puteaux FR Paris 92063 Rueil-Malmaison FR Paris 92064 Saint-Cloud FR Paris 92071 Sceaux FR Paris 92072 Sèvres FR Paris 92073 Suresnes FR Paris 92075 Vanves FR Paris 92076 Vaucresson FR Paris 92077 Ville-d'Avray FR Paris 92078 Villeneuve-la-Garenne FR Paris 93001 Aubervilliers FR Paris 93005 Aulnay-sous-Bois FR Paris 93006 Bagnolet FR Paris 93007 Blanc-Mesnil FR Paris 93008 Bobigny FR Paris 93010 Bondy Directorate-General for Regional and Urban Policy 2020 79
Quality of Life in European Cities Survey 2019
FR Paris 93013 Bourget FR Paris 93014 Clichy-sous-Bois FR Paris 93015 Coubron FR Paris 93027 Courneuve FR Paris 93029 Drancy FR Paris 93030 Dugny FR Paris 93031 Épinay-sur-Seine FR Paris 93032 Gagny FR Paris 93033 Gournay-sur-Marne FR Paris 93039 Île-Saint-Denis FR Paris 93045 Lilas FR Paris 93046 Livry-Gargan FR Paris 93047 Montfermeil FR Paris 93048 Montreuil FR Paris 93049 Neuilly-Plaisance FR Paris 93050 Neuilly-sur-Marne FR Paris 93051 Noisy-le-Grand FR Paris 93053 Noisy-le-Sec FR Paris 93055 Pantin FR Paris 93057 Pavillons-sous-Bois FR Paris 93059 Pierrefitte-sur-Seine FR Paris 93061 Pré-Saint-Gervais FR Paris 93062 Raincy FR Paris 93063 Romainville FR Paris 93064 Rosny-sous-Bois FR Paris 93066 Saint-Denis FR Paris 93070 Saint-Ouen FR Paris 93071 Sevran FR Paris 93072 Stains FR Paris 93073 Tremblay-en-France FR Paris 93074 Vaujours FR Paris 93077 Villemomble FR Paris 93078 Villepinte FR Paris 93079 Villetaneuse FR Paris 94001 Ablon-sur-Seine FR Paris 94002 Alfortville FR Paris 94003 Arcueil FR Paris 94004 Boissy-Saint-Léger FR Paris 94011 Bonneuil-sur-Marne FR Paris 94015 Bry-sur-Marne FR Paris 94016 Cachan FR Paris 94017 Champigny-sur-Marne FR Paris 94018 Charenton-le-Pont FR Paris 94019 Chennevières-sur-Marne
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FR Paris 94021 Chevilly-Larue FR Paris 94022 Choisy-le-Roi FR Paris 94028 Créteil FR Paris 94033 Fontenay-sous-Bois FR Paris 94034 Fresnes FR Paris 94037 Gentilly FR Paris 94038 Haÿ-les-Roses FR Paris 94041 Ivry-sur-Seine FR Paris 94042 Joinville-le-Pont FR Paris 94043 Kremlin-Bicêtre FR Paris 94044 Limeil-Brévannes FR Paris 94046 Maisons-Alfort FR Paris 94047 Mandres-les-Roses FR Paris 94052 Nogent-sur-Marne FR Paris 94053 Noiseau FR Paris 94054 Orly FR Paris 94055 Ormesson-sur-Marne FR Paris 94056 Périgny FR Paris 94058 Perreux-sur-Marne FR Paris 94059 Plessis-Trévise FR Paris 94060 Queue-en-Brie FR Paris 94065 Rungis FR Paris 94067 Saint-Mandé FR Paris 94068 Saint-Maur-des-Fossés FR Paris 94069 Saint-Maurice FR Paris 94071 Sucy-en-Brie FR Paris 94073 Thiais FR Paris 94074 Valenton FR Paris 94075 Villecresnes FR Paris 94076 Villejuif FR Paris 94077 Villeneuve-le-Roi FR Paris 94078 Villeneuve-Saint-Georges FR Paris 94079 Villiers-sur-Marne FR Paris 94080 Vincennes FR Paris 94081 Vitry-sur-Seine FR Paris 95014 Andilly FR Paris 95018 Argenteuil FR Paris 95019 Arnouville FR Paris 95051 Beauchamp FR Paris 95060 Bessancourt FR Paris 95063 Bezons FR Paris 95088 Bonneuil-en-France FR Paris 95127 Cergy FR Paris 95176 Cormeilles-en-Parisis FR Paris 95183 Courdimanche Directorate-General for Regional and Urban Policy 2020 81
Quality of Life in European Cities Survey 2019
FR Paris 95197 Deuil-la-Barre FR Paris 95203 Eaubonne FR Paris 95205 Écouen FR Paris 95210 Enghien-les-Bains FR Paris 95218 Éragny FR Paris 95219 Ermont FR Paris 95252 Franconville FR Paris 95256 Frépillon FR Paris 95257 Frette-sur-Seine FR Paris 95268 Garges-lès-Gonesse FR Paris 95277 Gonesse FR Paris 95288 Groslay FR Paris 95306 Herblay FR Paris 95323 Jouy-le-Moutier FR Paris 95369 Margency FR Paris 95424 Montigny-lès-Cormeilles FR Paris 95426 Montlignon FR Paris 95427 Montmagny FR Paris 95428 Montmorency FR Paris 95450 Neuville-sur-Oise FR Paris 95476 Osny FR Paris 95491 Plessis-Bouchard FR Paris 95500 Pontoise FR Paris 95539 Saint-Brice-sous-Forêt FR Paris 95555 Saint-Gratien FR Paris 95563 Saint-Leu-la-Forêt FR Paris 95572 Saint-Ouen-l'Aumône FR Paris 95574 Saint-Prix FR Paris 95582 Sannois FR Paris 95585 Sarcelles FR Paris 95598 Soisy-sous-Montmorency FR Paris 95607 Taverny FR Paris 95637 Vauréal FR Paris 95680 Villiers-le-Bel FR Strasbourg 67043 Bischheim FR Strasbourg 67118 Eckbolsheim FR Strasbourg 67204 Hœnheim FR Strasbourg 67218 Illkirch-Graffenstaden FR Strasbourg 67267 Lingolsheim FR Strasbourg 67365 Ostwald FR Strasbourg 67447 Schiltigheim FR Strasbourg 67482 Strasbourg FR Bordeaux 33039 Bègles FR Bordeaux 33063 Bordeaux
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FR Bordeaux 33069 Bouscat FR Bordeaux 33075 Bruges FR Bordeaux 33119 Cenon FR Bordeaux 33162 Eysines FR Bordeaux 33167 Floirac FR Bordeaux 33192 Gradignan FR Bordeaux 33249 Lormont FR Bordeaux 33281 Mérignac FR Bordeaux 33318 Pessac FR Bordeaux 33522 Talence FR Bordeaux 33550 Villenave-d'Ornon FR Lille 59009 Villeneuve-d'Ascq FR Lille 59163 Croix FR Lille 59193 Emmerin FR Lille 59220 Faches-Thumesnil FR Lille 59247 Forest-sur-Marque FR Lille 59278 Hallennes-lez-Haubourdin FR Lille 59286 Haubourdin FR Lille 59299 Hem FR Lille 59328 Lambersart FR Lille 59332 Lannoy FR Lille 59343 Lesquin FR Lille 59346 Lezennes FR Lille 59350 Lille FR Lille 59360 Loos FR Lille 59367 Lys-lez-Lannoy FR Lille 59368 Madeleine FR Lille 59378 Marcq-en-Barœul FR Lille 59386 Marquette-lez-Lille FR Lille 59410 Mons-en-Barœul FR Lille 59421 Mouvaux FR Lille 59426 Neuville-en-Ferrain FR Lille 59507 Ronchin FR Lille 59508 Roncq FR Lille 59512 Roubaix FR Lille 59527 Saint-André-lez-Lille FR Lille 59566 Sequedin FR Lille 59585 Templemars FR Lille 59598 Toufflers FR Lille 59599 Tourcoing FR Lille 59636 Wambrechies FR Lille 59646 Wasquehal FR Lille 59648 Wattignies FR Lille 59650 Wattrelos FR Rennes 35238 Rennes Directorate-General for Regional and Urban Policy 2020 83
Quality of Life in European Cities Survey 2019
FR Marseille 13002 Allauch FR Marseille 13055 Marseille FR Marseille 13075 Plan-de-Cuques HR Zagreb 01333 Grad Zagreb HU Budapest 02112 Budapest 17. ker. HU Budapest 03179 Budapest 02. ker. HU Budapest 04011 Budapest 19. ker. HU Budapest 05467 Budapest 04. ker. HU Budapest 06026 Budapest 20. ker. HU Budapest 08208 Budapest 16. ker. HU Budapest 09566 Budapest 01. ker. HU Budapest 10214 Budapest 22. ker. HU Budapest 10700 Budapest 10. ker. HU Budapest 11314 Budapest 15. ker. HU Budapest 13189 Budapest 21. ker. HU Budapest 13392 Budapest 05. ker. HU Budapest 14216 Budapest 11. ker. HU Budapest 16337 Budapest 14. ker. HU Budapest 16586 Budapest 06. ker. HU Budapest 18069 Budapest 03. ker. HU Budapest 24299 Budapest 13. ker. HU Budapest 24697 Budapest 12. ker. HU Budapest 25405 Budapest 08. ker. HU Budapest 29285 Budapest 18. ker. HU Budapest 29586 Budapest 09. ker. HU Budapest 29744 Budapest 07. ker. HU Budapest 34139 Budapest 23. ker. HU Miskolc 30456 Miskolc IE Dublin 02001 Arran Quay A IE Dublin 02002 Arran Quay B IE Dublin 02003 Arran Quay C IE Dublin 02004 Arran Quay D IE Dublin 02005 Arran Quay E IE Dublin 02006 Ashtown A IE Dublin 02007 Ashtown B IE Dublin 02008 Ayrfield IE Dublin 02009 Ballybough A IE Dublin 02010 Ballybough B IE Dublin 02011 Ballygall A IE Dublin 02012 Ballygall B IE Dublin 02013 Ballygall C IE Dublin 02014 Ballygall D IE Dublin 02015 Ballymun A IE Dublin 02016 Ballymun B
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IE Dublin 02017 Ballymun C IE Dublin 02018 Ballymun D IE Dublin 02019 Ballymun E IE Dublin 02020 Ballymun F IE Dublin 02021 Beaumont A IE Dublin 02022 Beaumont B IE Dublin 02023 Beaumont C IE Dublin 02024 Beaumont D IE Dublin 02025 Beaumont E IE Dublin 02026 Beaumont F IE Dublin 02027 Botanic A IE Dublin 02028 Botanic B IE Dublin 02029 Botanic C IE Dublin 02030 Cabra East A IE Dublin 02031 Cabra East B IE Dublin 02032 Cabra East C IE Dublin 02033 Cabra West A IE Dublin 02034 Cabra West B IE Dublin 02035 Cabra West C IE Dublin 02036 Cabra West D IE Dublin 02037 Clontarf East A IE Dublin 02038 Clontarf East B IE Dublin 02039 Clontarf East C IE Dublin 02040 Clontarf East D IE Dublin 02041 Clontarf East E IE Dublin 02042 Clontarf West A IE Dublin 02043 Clontarf West B IE Dublin 02044 Clontarf West C IE Dublin 02045 Clontarf West D IE Dublin 02046 Clontarf West E IE Dublin 02047 Drumcondra South A IE Dublin 02048 Drumcondra South B IE Dublin 02049 Drumcondra South C IE Dublin 02050 Edenmore IE Dublin 02051 Finglas North A IE Dublin 02052 Finglas North B IE Dublin 02053 Finglas North C IE Dublin 02054 Finglas South A IE Dublin 02055 Finglas South B IE Dublin 02056 Finglas South C IE Dublin 02057 Finglas South D IE Dublin 02058 Grace Park IE Dublin 02059 Grange A IE Dublin 02060 Grange B IE Dublin 02061 Grange C Directorate-General for Regional and Urban Policy 2020 85
Quality of Life in European Cities Survey 2019
IE Dublin 02062 Grange D IE Dublin 02063 Grange E IE Dublin 02064 Harmonstown A IE Dublin 02065 Harmonstown B IE Dublin 02066 Inns Quay A IE Dublin 02067 Inns Quay B IE Dublin 02068 Inns Quay C IE Dublin 02069 Kilmore A IE Dublin 02070 Kilmore B IE Dublin 02071 Kilmore C IE Dublin 02072 Kilmore D IE Dublin 02073 Mountjoy A IE Dublin 02074 Mountjoy B IE Dublin 02075 North City IE Dublin 02076 North Dock A IE Dublin 02077 North Dock B IE Dublin 02078 North Dock C IE Dublin 02079 Phoenix Park IE Dublin 02080 Priorswood A IE Dublin 02081 Priorswood B IE Dublin 02082 Priorswood C IE Dublin 02083 Priorswood D IE Dublin 02084 Priorswood E IE Dublin 02085 Raheny-Foxfield IE Dublin 02086 Raheny-Greendale IE Dublin 02087 Raheny-St. Assam IE Dublin 02088 Rotunda A IE Dublin 02089 Rotunda B IE Dublin 02090 Whitehall A IE Dublin 02091 Whitehall B IE Dublin 02092 Whitehall C IE Dublin 02093 Whitehall D IE Dublin 02094 Chapelizod IE Dublin 02095 Cherry Orchard A IE Dublin 02096 Carna IE Dublin 02097 Cherry Orchard C IE Dublin 02098 Crumlin A IE Dublin 02099 Crumlin B IE Dublin 02100 Crumlin C IE Dublin 02101 Crumlin D IE Dublin 02102 Crumlin E IE Dublin 02103 Crumlin F IE Dublin 02104 Decies IE Dublin 02105 Drumfinn
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IE Dublin 02106 Inchicore A IE Dublin 02107 Inchicore B IE Dublin 02108 Kilmainham A IE Dublin 02109 Kilmainham B IE Dublin 02110 Kilmainham C IE Dublin 02111 Kimmage A IE Dublin 02112 Kimmage B IE Dublin 02113 Kimmage C IE Dublin 02114 Kimmage D IE Dublin 02115 Kimmage E IE Dublin 02116 Kylemore IE Dublin 02117 Mansion House A IE Dublin 02118 Mansion House B IE Dublin 02119 Merchants Quay A IE Dublin 02120 Merchants Quay B IE Dublin 02121 Merchants Quay C IE Dublin 02122 Merchants Quay D IE Dublin 02123 Merchants Quay E IE Dublin 02124 Merchants Quay F IE Dublin 02125 Pembroke East A IE Dublin 02126 Pembroke East B IE Dublin 02127 Pembroke East C IE Dublin 02128 Pembroke East D IE Dublin 02129 Pembroke East E IE Dublin 02130 Pembroke West A IE Dublin 02131 Pembroke West B IE Dublin 02132 Pembroke West C IE Dublin 02133 Rathfarnham IE Dublin 02134 Rathmines East A IE Dublin 02135 Rathmines East B IE Dublin 02136 Rathmines East C IE Dublin 02137 Rathmines East D IE Dublin 02138 Rathmines West A IE Dublin 02139 Rathmines West B IE Dublin 02140 Rathmines West C IE Dublin 02141 Rathmines West D IE Dublin 02142 Rathmines West E IE Dublin 02143 Rathmines West F IE Dublin 02144 Royal Exchange A IE Dublin 02145 Royal Exchange B IE Dublin 02146 Saint Kevin's IE Dublin 02147 South Dock IE Dublin 02148 Terenure A IE Dublin 02149 Terenure B IE Dublin 02150 Terenure C Directorate-General for Regional and Urban Policy 2020 87
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IE Dublin 02151 Terenure D IE Dublin 02152 Ushers A IE Dublin 02153 Ushers B IE Dublin 02154 Ushers C IE Dublin 02155 Ushers D IE Dublin 02156 Ushers E IE Dublin 02157 Ushers F IE Dublin 02158 Walkinstown A IE Dublin 02159 Walkinstown B IE Dublin 02160 Walkinstown C IE Dublin 02161 Wood Quay A IE Dublin 02162 Wood Quay B IE Dublin 03001 Ballinascorney IE Dublin 03002 Ballyboden IE Dublin 03003 Bohernabreena IE Dublin 03004 Clondalkin-Ballymount IE Dublin 03005 Clondalkin-Cappaghmore IE Dublin 03006 Clondalkin-Dunawley IE Dublin 03007 Clondalkin-Monastery IE Dublin 03008 Clondalkin-Moorfield IE Dublin 03009 Clondalkin-Rowlagh IE Dublin 03010 Clondalkin Village IE Dublin 03011 Edmondstown IE Dublin 03012 Firhouse-Ballycullen IE Dublin 03013 Firhouse-Knocklyon IE Dublin 03014 Firhouse Village IE Dublin 03015 Lucan-Esker IE Dublin 03016 Lucan Heights IE Dublin 03017 Lucan-St. Helens IE Dublin 03018 Newcastle IE Dublin 03019 Palmerston Village IE Dublin 03020 Palmerston West IE Dublin 03021 Rathcoole IE Dublin 03022 Rathfarnham-Ballyroan IE Dublin 03023 Rathfarnham-Butterfield IE Dublin 03024 Rathfarnham-Hermitage IE Dublin 03025 Rathfarnham-St. Enda's IE Dublin 03026 Rathfarnham Village IE Dublin 03027 Saggart IE Dublin 03028 Tallaght-Avonbeg IE Dublin 03029 Tallaght-Belgard IE Dublin 03030 Tallaght-Fettercairn IE Dublin 03031 Tallaght-Glenview IE Dublin 03032 Tallaght-Jobstown
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IE Dublin 03033 Tallaght-Killinardan IE Dublin 03034 Tallaght-Kilnamanagh IE Dublin 03035 Tallaght-Kiltipper IE Dublin 03036 Tallaght-Kingswood IE Dublin 03037 Tallaght-Millbrook IE Dublin 03038 Tallaght-Oldbawn IE Dublin 03039 Tallaght-Springfield IE Dublin 03040 Tallaght-Tymon IE Dublin 03041 Templeogue-Cypress IE Dublin 03042 Templeogue-Kimmage Manor IE Dublin 03043 Templeogue-Limekiln IE Dublin 03044 Templeogue-Orwell IE Dublin 03045 Templeogue-Osprey IE Dublin 03046 Templeogue Village IE Dublin 03047 Terenure-Cherryfield IE Dublin 03048 Terenure-Greentrees IE Dublin 03049 Terenure-St. James IE Dublin 04001 Airport IE Dublin 04002 Balbriggan Rural IE Dublin 04003 Balbriggan Urban IE Dublin 04004 Baldoyle IE Dublin 04005 Balgriffin IE Dublin 04006 Ballyboghil IE Dublin 04007 Balscadden IE Dublin 04008 Blanchardstown-Abbotstown IE Dublin 04009 Blanchardstown-Blakestown IE Dublin 04010 Blanchardstown-Coolmine IE Dublin 04011 Blanchardstown-Corduff IE Dublin 04012 Blanchardstown-Delwood IE Dublin 04013 Blanchardstown-Mulhuddart IE Dublin 04014 Blanchardstown-Roselawn IE Dublin 04015 Blanchardstown-Tyrrelstown IE Dublin 04016 Castleknock-Knockmaroon IE Dublin 04017 Castleknock-Park IE Dublin 04018 Clonmethan IE Dublin 04019 Donabate IE Dublin 04020 Dubber IE Dublin 04021 Garristown IE Dublin 04022 Hollywood IE Dublin 04023 Holmpatrick IE Dublin 04024 Howth IE Dublin 04025 Kilsallaghan IE Dublin 04026 Kinsaley IE Dublin 04027 Lucan North IE Dublin 04028 Lusk Directorate-General for Regional and Urban Policy 2020 89
Quality of Life in European Cities Survey 2019
IE Dublin 04029 Malahide East IE Dublin 04030 Malahide West IE Dublin 04031 Portmarnock North IE Dublin 04032 Portmarnock South IE Dublin 04033 Rush IE Dublin 04034 Skerries IE Dublin 04035 Sutton IE Dublin 04036 Swords-Forrest IE Dublin 04037 Swords-Glasmore IE Dublin 04038 Swords-Lissenhall IE Dublin 04039 Swords-Seatown IE Dublin 04040 Swords Village IE Dublin 04041 The Ward IE Dublin 04042 Turnapin IE Dublin 05001 Ballinteer-Broadford IE Dublin 05002 Ballinteer-Ludford IE Dublin 05003 Ballinteer-Marley IE Dublin 05004 Ballinteer-Meadowbroads IE Dublin 05005 Ballinteer-Meadowmount IE Dublin 05006 Ballinteer-Woodpark IE Dublin 05007 Ballybrack IE Dublin 05008 Blackrock-Booterstown IE Dublin 05009 Blackrock-Carysfort IE Dublin 05010 Blackrock-Central IE Dublin 05011 Blackrock-Glenomena IE Dublin 05012 Blackrock-Monkstown IE Dublin 05013 Blackrock-Newpark IE Dublin 05014 Blackrock-Seapoint IE Dublin 05015 Blackrock-Stradbrook IE Dublin 05016 Blackrock-Templehill IE Dublin 05017 Blackrock-Williamstown IE Dublin 05018 Cabinteely-Granitefield IE Dublin 05019 Cabinteely-Kilbogget IE Dublin 05020 Cabinteely-Loughlinstown IE Dublin 05021 Cabinteely-Pottery IE Dublin 05022 Churchtown-Castle IE Dublin 05023 Churchtown-Landscape IE Dublin 05024 Churchtown-Nutgrove IE Dublin 05025 Churchtown-Orwell IE Dublin 05026 Churchtown-Woodlawn IE Dublin 05027 Clonskeagh-Belfield IE Dublin 05028 Clonskeagh-Farranboley IE Dublin 05029 Clonskeagh-Milltown IE Dublin 05030 Clonskeagh-Roebuck
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IE Dublin 05031 Clonskeagh-Windy Arbour IE Dublin 05032 Dalkey-Avondale IE Dublin 05033 Dalkey-Bullock IE Dublin 05034 Dalkey-Coliemore IE Dublin 05035 Dalkey Hill IE Dublin 05036 Dalkey Upper IE Dublin 05037 Dundrum-Balally IE Dublin 05038 Dundrum-Kilmacud IE Dublin 05039 Dundrum-Sandyford IE Dublin 05040 Dundrum-Sweetmount IE Dublin 05041 Dundrum-Taney IE Dublin 05042 Dun Laoghaire-East Central IE Dublin 05043 Dun Laoghaire-Glasthule IE Dublin 05044 Dun Laoghaire-Glenageary IE Dublin 05045 Dun Laoghaire-Monkstown Farm IE Dublin 05046 Dun Laoghaire-Mount Town IE Dublin 05047 Dun Laoghaire-Sallynoggin East IE Dublin 05048 Dun Laoghaire-Sallynoggin South IE Dublin 05049 Dun Laoghaire-Sallynoggin West IE Dublin 05050 Dun Laoghaire-Sandycove IE Dublin 05051 Dun Laoghaire-Salthill IE Dublin 05052 Dun Laoghaire-West Central IE Dublin 05053 Foxrock-Beechpark IE Dublin 05054 Foxrock-Carrickmines IE Dublin 05055 Foxrock-Deansgrange IE Dublin 05056 Foxrock-Torquay IE Dublin 05057 Glencullen IE Dublin 05058 Killiney North IE Dublin 05059 Killiney South IE Dublin 05060 Shankill-Rathmichael IE Dublin 05061 Shankill-Rathsallagh IE Dublin 05062 Shankill-Shanganagh IE Dublin 05063 Stillorgan-Deerpark IE Dublin 05064 Stillorgan-Kilmacud IE Dublin 05065 Stillorgan-Leopardstown IE Dublin 05066 Stillorgan-Merville IE Dublin 05067 Stillorgan-Mount Merrion IE Dublin 05068 Stillorgan-Priory IE Dublin 05069 Tibradden IS Reykjavík 0000 Reykjavík IS Reykjavík 1000 Kópavogur IS Reykjavík 1100 Seltjarnarnes IS Reykjavík 1300 Garðabær IS Reykjavík 1400 Hafnarfjörður IS Reykjavík 1604 Mosfellsbær Directorate-General for Regional and Urban Policy 2020 91
Quality of Life in European Cities Survey 2019
IS Reykjavík 1606 Kjósarhreppur IT Roma 058091 Roma IT Napoli 063001 Acerra IT Napoli 063002 Afragola IT Napoli 063003 Agerola IT Napoli 063004 Anacapri IT Napoli 063005 Arzano IT Napoli 063006 Bacoli IT Napoli 063007 Barano d'Ischia IT Napoli 063008 Boscoreale IT Napoli 063009 Boscotrecase IT Napoli 063010 Brusciano IT Napoli 063011 Caivano IT Napoli 063012 Calvizzano IT Napoli 063013 Camposano IT Napoli 063014 Capri IT Napoli 063015 Carbonara di Nola IT Napoli 063016 Cardito IT Napoli 063017 Casalnuovo di Napoli IT Napoli 063018 Casamarciano IT Napoli 063019 Casamicciola Terme IT Napoli 063020 Casandrino IT Napoli 063021 Casavatore IT Napoli 063022 Casola di Napoli IT Napoli 063023 Casoria IT Napoli 063024 Castellammare di Stabia IT Napoli 063025 Castello di Cisterna IT Napoli 063026 Cercola IT Napoli 063027 Cicciano IT Napoli 063028 Cimitile IT Napoli 063029 Comiziano IT Napoli 063030 Crispano IT Napoli 063031 Forio IT Napoli 063032 Frattamaggiore IT Napoli 063033 Frattaminore IT Napoli 063034 Giugliano in Campania IT Napoli 063035 Gragnano IT Napoli 063036 Grumo Nevano IT Napoli 063037 Ischia IT Napoli 063038 Lacco Ameno IT Napoli 063039 Lettere IT Napoli 063040 Liveri IT Napoli 063041 Marano di Napoli IT Napoli 063042 Mariglianella
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IT Napoli 063043 Marigliano IT Napoli 063044 Massa Lubrense IT Napoli 063045 Melito di Napoli IT Napoli 063046 Meta IT Napoli 063047 Monte di Procida IT Napoli 063048 Mugnano di Napoli IT Napoli 063049 Napoli IT Napoli 063050 Nola IT Napoli 063051 Ottaviano IT Napoli 063052 Palma Campania IT Napoli 063053 Piano di Sorrento IT Napoli 063054 Pimonte IT Napoli 063055 Poggiomarino IT Napoli 063056 Pollena Trocchia IT Napoli 063057 Pomigliano d'Arco IT Napoli 063058 Pompei IT Napoli 063059 Portici IT Napoli 063060 Pozzuoli IT Napoli 063061 Procida IT Napoli 063062 Qualiano IT Napoli 063063 Quarto IT Napoli 063064 Ercolano IT Napoli 063065 Roccarainola IT Napoli 063066 San Gennaro Vesuviano IT Napoli 063067 San Giorgio a Cremano IT Napoli 063068 San Giuseppe Vesuviano IT Napoli 063069 San Paolo Bel Sito IT Napoli 063070 San Sebastiano al Vesuvio IT Napoli 063071 Sant'Agnello IT Napoli 063072 Sant'Anastasia IT Napoli 063073 Sant'Antimo IT Napoli 063074 Sant'Antonio Abate IT Napoli 063075 San Vitaliano IT Napoli 063076 Saviano IT Napoli 063077 Scisciano IT Napoli 063078 Serrara Fontana IT Napoli 063079 Somma Vesuviana IT Napoli 063080 Sorrento IT Napoli 063081 Striano IT Napoli 063082 Terzigno IT Napoli 063083 Torre Annunziata IT Napoli 063084 Torre del Greco IT Napoli 063085 Tufino IT Napoli 063086 Vico Equense IT Napoli 063087 Villaricca Directorate-General for Regional and Urban Policy 2020 93
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IT Napoli 063088 Visciano IT Napoli 063089 Volla IT Napoli 063090 Santa Maria la Carità IT Napoli 063091 Trecase IT Napoli 063092 Massa di Somma IT Torino 001272 Torino IT Palermo 082053 Palermo IT Bologna 037006 Bologna IT Verona 023091 Verona LT Vilnius 13 Vilniaus miesto savivaldybė LU Luxembourg 0304 Luxembourg LV Rīga 0010000 Rīga ME Podgorica 20176 Podgorica MK Skopje MK0080102 Skopje - Aerodrom MK Skopje MK0080301 Vizbegovo MK Skopje MK0080305 Skopje - Butel MK Skopje MK0080405 Indžikovo MK Skopje MK0080408 Singelić MK Skopje MK0080409 Skopje - Gazi Baba MK Skopje MK0080411 Stajkovci MK Skopje MK0080507 Skopje - Đorče Petrov MK Skopje MK0080802 Gorno Nerezi MK Skopje MK0080803 Skopje - Karpoš MK Skopje MK0080902 Skopje - Kisela Voda MK Skopje MK0080903 Usje MK Skopje MK0081111 Krušopek MK Skopje MK0081113 Qubin MK Skopje MK0081121 Skopje - Saraj MK Skopje MK0081123 Šiševo MK Skopje MK0081401 Skopje - Centar MK Skopje MK0081501 Skopje - Čair MK Skopje MK0081701 Gorno Orizari MK Skopje MK0081702 Skopje - Šuto Orizari MT Valletta MT01101 Valletta MT Valletta MT01103 Birgu MT Valletta MT01104 L-Isla MT Valletta MT01105 Bormla MT Valletta MT01108 Ħaż-Żabbar MT Valletta MT01117 Fgura MT Valletta MT01118 Floriana MT Valletta MT01129 Kalkara MT Valletta MT01133 Luqa MT Valletta MT01134 Marsa MT Valletta MT01145 Paola
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MT Valletta MT01157 Santa Luċija MT Valletta MT01162 Tarxien MT Valletta MT01165 Xgħajra MT Valletta MT01206 Ħal Qormi MT Valletta MT01214 Birkirkara MT Valletta MT01221 Gżira MT Valletta MT01227 Ħamrun MT Valletta MT01241 Msida MT Valletta MT01246 Pembroke MT Valletta MT01247 Pieta' MT Valletta MT01252 San Ġiljan MT Valletta MT01253 San Ġwann MT Valletta MT01258 Santa Venera MT Valletta MT01259 Sliema MT Valletta MT01260 Swieqi MT Valletta MT01261 Ta' Xbiex NL Amsterdam GM0362 Amstelveen NL Amsterdam GM0363 Amsterdam NL Amsterdam GM0384 Diemen NL Amsterdam GM0437 Ouder-Amstel NL Rotterdam GM0482 Alblasserdam NL Rotterdam GM0489 Barendrecht NL Rotterdam GM0502 Capelle aan den IJssel NL Rotterdam GM0505 Dordrecht NL Rotterdam GM0531 Hendrik-Ido-Ambacht NL Rotterdam GM0542 Krimpen aan den IJssel NL Rotterdam GM0590 Papendrecht NL Rotterdam GM0597 Ridderkerk NL Rotterdam GM0599 Rotterdam NL Rotterdam GM0606 Schiedam NL Rotterdam GM0622 Vlaardingen NL Rotterdam GM0642 Zwijndrecht NL Groningen GM0014 Groningen NO Oslo 0301 Oslo kommune PL Warszawa 1007141286501 Warszawa PL Kraków 1001121216101 Kraków PL Gdańsk 1004221436101 Gdańsk PL Białystok 1006201376101 Białystok PT Lisboa 110501 Alcabideche PT Lisboa 110506 São Domingos de Rana PT Lisboa 110507 Carcavelos e Parede PT Lisboa 110508 Cascais e Estoril PT Lisboa 110601 Ajuda PT Lisboa 110602 Alcântara PT Lisboa 110607 Beato Directorate-General for Regional and Urban Policy 2020 95
Quality of Life in European Cities Survey 2019
PT Lisboa 110608 Benfica PT Lisboa 110610 Campolide PT Lisboa 110611 Carnide PT Lisboa 110618 Lumiar PT Lisboa 110621 Marvila PT Lisboa 110633 Olivais PT Lisboa 110639 São Domingos de Benfica PT Lisboa 110654 Alvalade PT Lisboa 110655 Areeiro PT Lisboa 110656 Arroios PT Lisboa 110657 Avenidas Novas PT Lisboa 110658 Belém PT Lisboa 110659 Campo de Ourique PT Lisboa 110660 Estrela PT Lisboa 110661 Misericórdia PT Lisboa 110662 Parque das Nações PT Lisboa 110663 Penha de França PT Lisboa 110664 Santa Clara PT Lisboa 110665 Santa Maria Maior PT Lisboa 110666 Santo António PT Lisboa 110667 São Vicente PT Lisboa 110702 Bucelas PT Lisboa 110705 Fanhões PT Lisboa 110707 Loures PT Lisboa 110708 Lousa PT Lisboa 110726 Moscavide e Portela PT Lisboa 110727 Sacavém e Prior Velho PT Lisboa 110728 Santa Iria de Azoia, São João da Talha e Bobadela PT Lisboa 110729 Santo Antão e São Julião do Tojal PT Lisboa 110730 Santo António dos Cavaleiros e Frielas PT Lisboa 110731 Camarate, Unhos e Apelação PT Lisboa 111002 Barcarena PT Lisboa 111009 Porto Salvo PT Lisboa 111012 Algés, Linda-a-Velha e Cruz Quebrada-Dafundo PT Lisboa 111013 Carnaxide e Queijas PT Lisboa 111014 Oeiras e São Julião da Barra, Paço de Arcos e Caxias PT Lisboa 111512 Alfragide PT Lisboa 111513 Águas Livres PT Lisboa 111514 Encosta do Sol PT Lisboa 111515 Falagueira-Venda Nova PT Lisboa 111516 Mina de Água PT Lisboa 111517 Venteira PT Lisboa 111603 Odivelas PT Lisboa 111608 Pontinha e Famões
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PT Lisboa 111609 Póvoa de Santo Adrião e Olival Basto PT Lisboa 111610 Ramada e Caneças PT Lisboa 150303 Costa da Caparica PT Lisboa 150312 Almada, Cova da Piedade, Pragal e Cacilhas PT Lisboa 150313 Caparica e Trafaria PT Lisboa 150314 Charneca de Caparica e Sobreda PT Lisboa 150315 Laranjeiro e Feijó PT Lisboa 150407 Santo António da Charneca PT Lisboa 150409 Alto do Seixalinho, Santo André e Verderena PT Lisboa 150410 Barreiro e Lavradio PT Lisboa 150411 Palhais e Coina PT Lisboa 151002 Amora PT Lisboa 151005 Corroios PT Lisboa 151006 Fernão Ferro PT Lisboa 151007 Seixal, Arrentela e Aldeia de Paio Pires PT Braga 030301 Adaúfe PT Braga 030312 Espinho PT Braga 030313 Esporões PT Braga 030315 Figueiredo PT Braga 030319 Gualtar PT Braga 030322 Lamas PT Braga 030325 Mire de Tibães PT Braga 030330 Padim da Graça PT Braga 030331 Palmeira PT Braga 030334 Pedralva PT Braga 030336 Priscos PT Braga 030338 Ruilhe PT Braga 030349 Braga (São Vicente) PT Braga 030351 Braga (São Vítor) PT Braga 030354 Sequeira PT Braga 030355 Sobreposta PT Braga 030356 Tadim PT Braga 030357 Tebosa PT Braga 030363 Arentim e Cunha PT Braga 030364 Braga (Maximinos, Sé e Cividade) PT Braga 030365 Braga (São José de São Lázaro e São João do Souto) PT Braga 030366 Cabreiros e Passos (São Julião) PT Braga 030367 Celeirós, Aveleda e Vimieiro PT Braga 030368 Crespos e Pousada PT Braga 030369 Escudeiros e Penso (Santo Estêvão e São Vicente) PT Braga 030370 Este (São Pedro e São Mamede) PT Braga 030371 Ferreiros e Gondizalves PT Braga 030372 Guisande e Oliveira (São Pedro) PT Braga 030373 Lomar e Arcos PT Braga 030374 Merelim (São Paio), Panoias e Parada de Tibães Directorate-General for Regional and Urban Policy 2020 97
Quality of Life in European Cities Survey 2019
PT Braga 030375 Merelim (São Pedro) e Frossos PT Braga 030376 Morreira e Trandeiras PT Braga 030377 Nogueira, Fraião e Lamaçães PT Braga 030378 Nogueiró e Tenões PT Braga 030379 Real, Dume e Semelhe PT Braga 030380 Santa Lucrécia de Algeriz e Navarra PT Braga 030381 Vilaça e Fradelos RO Bucureşti 179132 Municipiul Bucureşti RO Cluj-Napoca 54975 Municipiul Cluj-Napoca RO Piatra Neamţ 120726 Municipiul Piatra Neamţ RS Beograd 70106 Belgrade - Voždovac RS Beograd 70114 Belgrade - Vračar RS Beograd 70149 Belgrade - Zvezdara RS Beograd 70157 Belgrade - Zemun RS Beograd 70181 Belgrade - Novi Beograd RS Beograd 70203 Belgrade - Palilula RS Beograd 70211 Belgrade - Rakovica RS Beograd 70220 Belgrade - Savski venac RS Beograd 70246 Belgrade - Stari grad RS Beograd 70254 Belgrade - Čukarica RS Beograd 80314 Pančevo SE Stockholm 0123 Järfälla SE Stockholm 0126 Huddinge SE Stockholm 0127 Botkyrka SE Stockholm 0136 Haninge SE Stockholm 0138 Tyresö SE Stockholm 0162 Danderyd SE Stockholm 0163 Sollentuna SE Stockholm 0180 Stockholm SE Stockholm 0182 Nacka SE Stockholm 0183 Sundbyberg SE Stockholm 0184 Solna SE Stockholm 0186 Lidingö SE Malmö 1280 Malmö SI Ljubljana 061 Ljubljana SK Bratislava 528595 Bratislava - mestská časť Staré Mesto SK Bratislava 529311 Bratislava - mestská časť Podunajské Biskupice SK Bratislava 529320 Bratislava - mestská časť Ružinov SK Bratislava 529338 Bratislava - mestská časť Vrakuňa SK Bratislava 529346 Bratislava - mestská časť Nové Mesto SK Bratislava 529354 Bratislava - mestská časť Rača SK Bratislava 529362 Bratislava - mestská časť Vajnory SK Bratislava 529371 Bratislava - mestská časť Devínska Nová Ves SK Bratislava 529389 Bratislava - mestská časť Dúbravka
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SK Bratislava 529397 Bratislava - mestská časť Karlova Ves SK Bratislava 529401 Bratislava - mestská časť Devín SK Bratislava 529419 Bratislava - mestská časť Lamač SK Bratislava 529427 Bratislava - mestská časť Záhorská Bystrica SK Bratislava 529435 Bratislava - mestská časť Čunovo SK Bratislava 529443 Bratislava - mestská časť Jarovce SK Bratislava 529460 Bratislava - mestská časť Petržalka SK Bratislava 529494 Bratislava - mestská časť Rusovce SK Košice 598119 Košice - mestská časť Kavečany SK Košice 598127 Košice - mestská časť Ťahanovce SK Košice 598151 Košice - mestská časť Sever SK Košice 598186 Košice - mestská časť Staré Mesto SK Košice 598194 Košice - mestská časť Lorinčík SK Košice 598208 Košice - mestská časť Pereš SK Košice 598216 Košice - mestská časť Myslava SK Košice 598224 Košice - mestská časť Západ SK Košice 598682 Košice - mestská časť Dargovských hrdinov SK Košice 599018 Košice - mestská časť Košická Nová Ves SK Košice 599093 Košice - mestská časť Barca SK Košice 599786 Košice - mestská časť Šebastovce SK Košice 599794 Košice - mestská časť Krásna SK Košice 599816 Košice - mestská časť Nad jazerom SK Košice 599824 Košice - mestská časť Juh SK Košice 599841 Košice - mestská časť Šaca SK Košice 599859 Košice - mestská časť Poľov SK Košice 599875 Košice - mestská časť Sídlisko Ťahanovce SK Košice 599883 Košice - mestská časť Sídlisko KVP SK Košice 599891 Košice - mestská časť Džungľa SK Košice 599913 Košice - mestská časť Vyšné Opátske SK Košice 599972 Košice - mestská časť Luník IX TR Ankara TR6001 Altindag TR Ankara TR6002 Çankaya TR Ankara TR6003 Etimesgut TR Ankara TR6005 Keçiören TR Ankara TR6006 Mamak TR Ankara TR6007 Sincan TR Ankara TR6008 Yenimahalle TR Ankara TR6025 Pursaklar TR Antalya TR7017 Kepez TR Antalya TR7018 Konyaalti TR Antalya TR7019 Muratpasa TR Diyarbakir TR21014 Baglar TR Diyarbakir TR21015 Kayapinar TR Diyarbakir TR21016 Sur TR Diyarbakir TR21017 Yenisehir Directorate-General for Regional and Urban Policy 2020 99
Quality of Life in European Cities Survey 2019
TR Istanbul TR16001 Nilüfer TR Istanbul TR16002 Osmangazi TR Istanbul TR16003 Yildirim TR Istanbul TR16005 Gemlik TR Istanbul TR16006 Gürsu TR Istanbul TR16008 Inegöl TR Istanbul TR16012 Kestel TR Istanbul TR34002 Avcilar TR Istanbul TR34003 Bagcilar TR Istanbul TR34004 Bahçelievler TR Istanbul TR34005 Bakirköy TR Istanbul TR34006 Bayrampasa TR Istanbul TR34007 Besiktas TR Istanbul TR34008 Beykoz TR Istanbul TR34009 Beyoglu TR Istanbul TR34011 Esenler TR Istanbul TR34012 Eyüp TR Istanbul TR34013 Fatih TR Istanbul TR34014 Gaziosmanpasa TR Istanbul TR34015 Güngören TR Istanbul TR34016 Kadiköy TR Istanbul TR34017 Kagithane TR Istanbul TR34018 Kartal TR Istanbul TR34019 Küçükçekmece TR Istanbul TR34020 Maltepe TR Istanbul TR34021 Pendik TR Istanbul TR34022 Sariyer TR Istanbul TR34023 Sisli TR Istanbul TR34024 Tuzla TR Istanbul TR34025 Ümraniye TR Istanbul TR34026 Üsküdar TR Istanbul TR34027 Zeytinburnu TR Istanbul TR34028 Büyükçekmece TR Istanbul TR34030 Silivri TR Istanbul TR34031 Sultanbeyli TR Istanbul TR34033 Atasehir TR Istanbul TR34034 Çekmeköy TR Istanbul TR34035 Sancaktepe TR Istanbul TR34036 Sultangazi TR Istanbul TR34037 Arnavutköy TR Istanbul TR34038 Basaksehir TR Istanbul TR34039 Beylikdüzü TR Istanbul TR34040 Esenyurt TR Istanbul TR41001 Gebze
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TR Istanbul TR41002 Gölcük TR Istanbul TR41004 Karamürsel TR Istanbul TR41005 Körfez TR Istanbul TR41006 Derince TR Istanbul TR41007 Basiskele TR Istanbul TR41008 Çayirova TR Istanbul TR41009 Darica TR Istanbul TR41010 Dilovasi TR Istanbul TR41011 Izmit TR Istanbul TR41012 Kartepe TR Istanbul TR54005 Hendek TR Istanbul TR54013 Adapazari TR Istanbul TR54014 Arifiye TR Istanbul TR54015 Erenler TR Istanbul TR54016 Serdivan TR Istanbul TR59000 Tekirdag Merkez TR Istanbul TR59001 Çerkezköy TR Istanbul TR59002 Çorlu TR Istanbul TR59005 Marmaraereglisi TR Istanbul TR77000 Yalova Merkez TR Istanbul TR77001 Altinova TR Istanbul TR77004 Çiftlikköy TR Istanbul TR81000 Düzce Merkez TR Istanbul TR81002 Cumayeri TR Istanbul TR81003 Çilimli TR Istanbul TR81005 Gümüsova UK London E09000001 City of London UK London E09000002 Barking and Dagenham UK London E09000003 Barnet UK London E09000004 Bexley UK London E09000005 Brent UK London E09000006 Bromley UK London E09000007 Camden UK London E09000008 Croydon UK London E09000009 Ealing UK London E09000010 Enfield UK London E09000011 Greenwich UK London E09000012 Hackney UK London E09000013 Hammersmith and Fulham UK London E09000014 Haringey UK London E09000015 Harrow UK London E09000016 Havering UK London E09000017 Hillingdon UK London E09000018 Hounslow UK London E09000019 Islington Directorate-General for Regional and Urban Policy 2020 101
Quality of Life in European Cities Survey 2019
UK London E09000020 Kensington and Chelsea UK London E09000021 Kingston upon Thames UK London E09000022 Lambeth UK London E09000023 Lewisham UK London E09000024 Merton UK London E09000025 Newham UK London E09000026 Redbridge UK London E09000027 Richmond upon Thames UK London E09000028 Southwark UK London E09000029 Sutton UK London E09000030 Tower Hamlets UK London E09000031 Waltham Forest UK London E09000032 Wandsworth UK London E09000033 Westminster UK Glasgow S30000015 East Dunbartonshire UK Glasgow S30000019 Glasgow City UK Glasgow S30000020 East Renfrewshire UK Glasgow S30000021 Renfrewshire UK Manchester E08000001 Bolton UK Manchester E08000002 Bury UK Manchester E08000003 Manchester UK Manchester E08000004 Oldham UK Manchester E08000005 Rochdale UK Manchester E08000006 Salford UK Manchester E08000007 Stockport UK Manchester E08000008 Tameside UK Manchester E08000009 Trafford UK Manchester E08000010 Wigan UK Cardiff W06000015 Cardiff UK Belfast UKN06 Belfast UK Belfast UKN14 Lisburn and Castlereagh Tyneside UK conurbation E08000021 Newcastle upon Tyne Tyneside UK conurbation E08000022 North Tyneside Tyneside UK conurbation E08000023 South Tyneside Tyneside UK conurbation E08000037 Gateshead
Directorate-General for Regional and Urban Policy 2020 102