The map shows trends in citizen perception of the city’s efforts to engage climate change for the period of 2009-2012. Data is represented as percentage change in reference to 2009.

The data are based on interviews of randomly selected citizens implemented within the framework of the perception survey on the quality of life in European cities. The data are based on surveys/interviews of randomly selected citizens.

The information is provided as a ‘synthetic index’. From the perception survey data, an index was calculated by subtracting the negative answers from the positive ones, and dividing the result by the total number of answers (‘very much agree’ and ‘quite agree’ are counted as positive answers, whereas ‘very much disagree’ and ‘quite disagree’ are negative answers). This initial index has a minimum of − 1 and a maximum of + 1. To make it easier to use, the index was then multiplied by 50, and 50 was then added to the result. The resulting index covers values between 0 and 100. A value above 50 means positive answers predominate; below 50, there are more negative answers.

The map shows trends in citizen perception of the city’s efforts to engage climate change for the period of 2012-2015.

Data is represented as percentage change in reference to 2012.

The data are based on interviews of randomly selected citizens implemented within the framework of the perception survey on the quality of life in European cities. The data are based on surveys/interviews of randomly selected citizens.

The information is provided as a ‘synthetic index’. From the perception survey data, an index was calculated by subtracting the negative answers from the positive ones, and dividing the result by the total number of answers (‘very much agree’ and ‘quite agree’ are counted as positive answers, whereas ‘very much disagree’ and ‘quite disagree’ are negative answers). This initial index has a minimum of − 1 and a maximum of + 1. To make it easier to use, the index was then multiplied by 50, and 50 was then added to the result. The resulting index covers values between 0 and 100. A value above 50 means positive answers predominate; below 50, there are more negative answers.

The map shows the normalised trend for gross domestic product at current market prices by NUTS3 regions for the period between the first and last available year. The GDP is the most widely used measure for the overall size of an economy. The dataset only includes NUTS3 regions which intersect with urban audit cities. More accurately, the indicator is calculated by dividing the total difference between the first and last available year by the count of years between the first and last available year. The approach to take an average change for varying reference years rather than taking fixed periods was selected to increase coverage.

The map shows the normalised trend in share of population aged 15 to 64, qualified at tertiary level (ISCED 5-6), living in cities, represented in [%] of the total population for the period between the first and last available year. More accurately, the indicator is calculated by dividing the total difference between the first and last available year by the count of years between the first and last available year. The approach to take an average change for varying reference years rather than taking fixed periods was selected to increase coverage.

In addition to individual levels and people’s abilities to take up knowledge, the indicator can also serve as a proxy showing the abundance of universities and other higher education institutions that create new knowledge, transfer knowledge and foster innovation, also relevant for adaptation in general and for the city.

The map shows the normalised trend in share of population aged 15 to 64, qualified at tertiary level (ISCED 5-6), living in cities, represented in [%] of the total population for the period between the first and last available year. More accurately, the indicator is calculated by dividing the total difference between the first and last available year by the count of years between the first and last available year. The approach to take an average change for varying reference years rather than taking fixed periods was selected to increase coverage.

On average, older people are more sensitive to health risks posed by heat waves, for instance, or air pollution due to forest fires, than younger people. Also, they tend to be more stressed and less mobile in the event of disasters like flooding, forest fires or water scarcity. A higher percentage of elderly people in a city indicates a higher sensitivity of that city to climatic threats. Overall, the group of over 65s constituted about 17.5 % of the total population of Europe in 2011, but this share is expected to rise to 29.5 % by 2060. At the city level, some cities in northern Italy (Turin, Milan and Genoa) have high values, above 23 %; other major European cities start at a lower value, but have higher growth rates for elderly populations. Cities in northern Italy tend to have values above the country average. At the other end of the spectrum are cities in Bulgaria, France, Romania, southern Spain and the United Kingdom, where the share of elderly people tends to be lower than in suburban and rural areas.

Effective adaptation measures for reducing the actual impacts on elderly people include putting special registers and services in place to take extra and targeted care of this group

The map shows the share of population aged 64 and over for latest available year [%]. Due to differences in the availability of reference years, the indicator represents the value for the most recent available year. This allows to maximize coverage.

On average, older people are more sensitive to health risks posed by heat waves, for instance, or air pollution due to forest fires, than younger people. Also, they tend to be more stressed and less mobile in the event of disasters like flooding, forest fires or water scarcity. A higher percentage of elderly people in a city indicates a higher sensitivity of that city to climatic threats. Overall, the group of over 65s constituted about 17.5 % of the total population of Europe in 2011, but this share is expected to rise to 29.5 % by 2060. At the city level, some cities in northern Italy (Turin, Milan and Genoa) have high values, above 23 %; other major European cities start at a lower value, but have higher growth rates for elderly populations. Cities in northern Italy tend to have values above the country average. At the other end of the spectrum are cities in Bulgaria, France, Romania, southern Spain and the United Kingdom, where the share of elderly people tends to be lower than in suburban and rural areas.

Effective adaptation measures for reducing the actual impacts on elderly people include putting special registers and services in place to take extra and targeted care of this group in the event of extreme situations.

The map shows the changes of degree of green urban areas inside the urban morphological zones (UMZ) inside the core city (cc) between 2006 and 2012 in percent.

Green urban areas are extracted from the Urban Atlas product (reference year2012) for the core city UMZ. Following urban atlas classes were used for the calculation of green urban areas:

• Discontinuous Low Density Urban Fabric (S.L. : 10% - 30%)

• Discontinuous Very Low Density Urban Fabric (S.L. < 10%)

• Green urban areas

• Sports and leisure facilities

• Arable land (annual crops

• Permanent crops (vineyards, fruit trees, olive groves)

• Pastures

• Complex and mixed cultivation patterns

• Orchards

• Forests

• Herbaceous vegetation associations (natural grassland, moors…)

• Open spaces with little or no

The map shows the degree of green urban areas inside the urban morphological zones (UMZ) inside the core city (cc) for the year 2012 in percent.

Green urban areas are extracted from the Urban Atlas product (reference year2012) for the core city UMZ. Following urban atlas classes were used for the calculation of green urban areas:

• Discontinuous Low Density Urban Fabric (S.L. : 10% - 30%)

• Discontinuous Very Low Density Urban Fabric (S.L. < 10%)

• Green urban areas

• Sports and leisure facilities

• Arable land (annual crops

• Permanent crops (vineyards, fruit trees, olive groves)

• Pastures

• Complex and mixed cultivation patterns

• Orchards

• Forests

• Herbaceous vegetation associations (natural grassland, moors…)

• Open spaces with little or no vegetations

The map shows the normalised trend in the proportion of lonely pensioner households for the period between the first and last available year. More accurately, the indicator is calculated by dividing the total difference between the first and last available year by the count of years between the first and last available year. The approach to take an average change for varying reference years rather than taking fixed periods was selected to increase coverage.

The map shows the share of lonely pensioner households for latest available year [%]. Due to differences in the availability of reference years, the indicator represents the value for the most recent available year. This allows to maximize coverage.

The map shows the city population for period between first and last available reference year - based on EUROSTAT urban audit database

The map shows the city population density [inhab / Km²] for period between first and last available reference year - based on EUROSTAT urban audit database