This data set contains estimates of the base rates of 550 food safety-relevant food handling practices in European households. The data are representative for the population of private households in the ten European countries in which the SafeConsume Household Survey was conducted (Denmark, France, Germany, Greece, Hungary, Norway, Portugal, Romania, Spain, UK).

Sampling design

In each of the ten EU and EEA countries where the survey was conducted (Denmark, France, Germany, Greece, Hungary, Norway, Portugal, Romania, Spain, UK), the population under study was defined as the private households in the country. Sampling was based on a stratified random design, with the NUTS2 statistical regions of Europe and the education level of the target respondent as stratum variables. The target sample size was 1000 households per country, with selection probability within each country proportional to stratum size.

Fieldwork

The fieldwork was conducted between December 2018 and April 2019 in ten EU and EEA countries (Denmark, France, Germany, Greece, Hungary, Norway, Portugal, Romania, Spain, United Kingdom). The target respondent in each household was the person with main or shared responsibility for food shopping in the household. The fieldwork was sub-contracted to a professional research provider (Dynata, formerly Research Now SSI). Complete responses were obtained from altogether 9996 households.

Weights

In addition to the SafeConsume Household Survey data, population data from Eurostat (2019) were used to calculate weights. These were calculated with NUTS2 region as the stratification variable and assigned an influence to each observation in each stratum that was proportional to how many households in the population stratum a household in the sample stratum represented. The weights were used in the estimation of all base rates included in the data set.

Transformations

All survey variables were normalised to the [0,1] range before the analysis. Responses to food frequency questions were transformed into the proportion of all meals consumed during a year where the meal contained the respective food item. Responses to questions with 11-point Juster probability scales as the response format were transformed into numerical probabilities. Responses to questions with time (hours, days, weeks) or temperature (C) as response formats were discretised using supervised binning. The thresholds best separating between the bins were chosen on the basis of five-fold cross-validated decision trees. The binned versions of these variables, and all other input variables with multiple categorical response options (either with a check-all-that-apply or forced-choice response format) were transformed into sets of binary features, with a value 1 assigned if the respective response option had been checked, 0 otherwise.

Treatment of missing values

In many cases, a missing value on a feature logically implies that the respective data point should have a value of zero. If, for example, a participant in the SafeConsume Household Survey had indicated that a particular food was not consumed in their household, the participant was not presented with any other questions related to that food, which automatically results in missing values on all features representing the responses to the skipped questions. However, zero consumption would also imply a zero probability that the respective food is consumed undercooked. In such cases, missing values were replaced with a value of 0.

Percentage of valid votes (Representative European Election Statistics): Germany, cut-off date, parties, gender, age groups

The European Business Performance database describes the performance of the largest enterprises in the twentieth century. It covers eight countries that together consistently account for above 80 per cent of western European GDP: Great Britain, Germany, France, Belgium, Italy, Spain, Sweden, and Finland. Data have been collected for five benchmark years, namely on the eve of WWI (1913), before the Great Depression (1927), at the extremes of the golden age (1954 and 1972), and in 2000.The database is comprised of two distinct datasets. The Small Sample (625 firms) includes the largest enterprises in each country across all industries (economy-wide). To avoid over-representation of certain countries and sectors, countries contribute a number of firms that is roughly proportionate to the size of the economy: 30 firms from Great Britain, 25 from Germany, 20 from France, 15 from Italy, 10 from Belgium, Spain, and Sweden, and 5 from Finland. By the same token, a cap has been set on the number of financial firms entering the sample, so that they range between up to 6 for Britain and 1 for Finland.The second dataset, or Large Sample (1,167 firms), is made up of the largest firms per industry. Here industries are so selected as to take into account long-term technological developments and the rise of entirely new products and services. Firms have been individually classified using the two-digit ISIC Rev. 3.1 codes, then grouped under a manageable number of industries. To some extent and broadly speaking, the two samples have a rather distinct focus: the Small Sample is biased in favour of sheer bigness, whereas the Large Sample emphasizes industries.As far as size and performance indicators are concerned, total assets has been picked as the main size measure in the first three benchmarks, turnover in 1972 and 2000 (financial intermediaries, though, are ranked by total assets throughout the database). Performance is gauged by means of two financial ratios, namely return on equity and shareholders’ return, i.e. the percentage year-on-year change in share price based on year-end values. In order to smooth out volatility, at each benchmark performance figures have been averaged over three consecutive years (for instance, performance in 1913 reflects average performance in 1911, 1912, and 1913).All figures were collected in national currency and converted to US dollars at current year-average exchange rates.

Notes on the basis for this dataset: This dataset is based on a Eurostat dataset (ISOC_CI_CFP_CU):

Online data code:ISOC_CI_CFP_CU Source of data:Eurostat Last data update:10/05/2023 11:00 Last structure update:08/02/2021 23:00 Data navigation tree location: Science, technology, digital society > Digital economy and society > ICT usage in households and by individuals > Connection to the internet and computer use Cross cutting topics > Skills-related statistics > Skills supply - self-reported measures > Digital skills - ICT usage in households and by individuals > Internet and computer use

Header and data descritions of the filtered dataset: This filtered dataset contains the following headers and the corresponding data:

date [year in format yyyy form 2007 untill (and including) 2017 in reverse order; last line in the filtered dataset contains increase in percent-points] ATHN [Neutron Monitor in Athens, Greece, Europe; data: neutron detections per second averaged over a 1 year period] AT [ Austria , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] BE [ Belgium , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] BG [ Bulgaria , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] CY [ Cyprus , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] CZ [ Czechia , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] DE [ Germany , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] DK[ Denmark , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] EE [ Estonia , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] EL [ Greece , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] ES [ Spain , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] EU28 [all 28 member countries of the EU between 2007 and 2017, Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] FI [ Finland , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] FR [ France , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] HR [ Croatia , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] HU [ Hungary , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] IE [ Ireland , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] IT [ Italy , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] LT [ Lithuania , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] LU [ Luxembourg , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] LV [ Latvia , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] MT [ Malta , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] NL [ Netherlands , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] PL [ Poland , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] PT [ Portugal , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] RO [ Romania , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] SE [ Sweden , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] SI [ Slovenia , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] SK [ Slovakia , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points] UK [ United Kingdom , Europe; data: percent of individuals (no age restriction) who used a computer at least once within the previous three months; bottom line contains the increase between 2007 and 2017 in percent-points]

Obtaining the filtered dataset:

The non-financial Annual Sector Accounts (ASA) are compiled in accordance with the European System of Accounts (ESA 2010) and are transmitted by the EU Member States, EFTA Members (except Liechtenstein) following ESA2010 transmission programme (Table 8) established by the Regulation (EU) No 549/2013 of the European Parliament and of the Council of 21 May 2013 on the European system of national and regional accounts in the European Union, annexes A and B respectively).

The ASA encompass non-financial accounts that provide a description of the different stages of the economic process: production, generation of income, distribution of income, redistribution of income, use of income and non-financial accumulation. The ASA record the economic flows of institutional sectors in order to illustrate their economic behaviour and interactions between them. They also provide a list of balancing items that have high analytical value in their own right: value added, operating surplus and mixed income, balance of primary incomes, disposable income, saving, net lending / net borrowing. All of them but net lending / net borrowing, can be expressed in gross or net terms, i.e. with and without consumption of fixed capital that accounts for the use and obsolescence of fixed assets.

In terms of institutional sectors, a broad distinction is made between the domestic economy (ESA 2010 classification code S.1) and the rest of the world (S.2). Within S.1 and S.2, in turn, more detailed subsectors are distinguished as explained in more detail in section "3.2 Classification system".

Data are presented in the table "Non-financial transactions" (nasa_10_nf_tr).

The table contains data, as far as they are available, expressed in national currency and millions of euro in current prices.

In line with ESA2010 Transmission programme requirements data series start from 1995 (unless subject to voluntary transmission option and/or country specific derogations). Countries may transmit longer series on voluntary basis.

Available level of detail by sectors and transactions may also vary by country due to voluntary transmission of some items (as defined in ESA2010 transmission programme) and country specific derogations.

ASA collected according ESA2010 Transmission programme include selected data on employment (in persons and hours worked) by institutional sectors. However, as transmission of these variables is voluntary (except for the sector of General government), data availability may vary significantly across countries.

A set of key indicators, deemed meaningful for economic analysis, is available in the table "Key indicators" (nasa_10_ki) for most of the members of the European Economic Area (EEA), of the Euro area and EU.

Key ratios are derived from non-financial transactions as follows:

• Gross household saving rate (S.14_S.15): B8G/(B6G+D8rec-D8pay)*100;
• Gross investment rate of households (S.14_S.15): P51G/(B6G+D8rec-D8pay)*100;
• Gross investment rate of non-financial corporations (S.11): P51G/B1G*100;
• Gross profit share of non-financial corporations (S.11): B2G_B3G/B1G*100;
• Total investment to GDP ratio (S.1): P51G/B1GQ*100;
• Business investment to GDP ratio: (S.11_P51G+S.12_P51G)/B1GQ*100;
• Government investment to GDP ratio: S.13_P51G/B1GQ*100;
• Households investment to GDP ratio: (S.14_S.15_P51G)/B1GQ*100.

With the following transaction codes:

• B8G - Gross saving;
• B6G - Gross disposable income;
• D8rec / D8pay - the adjustment for the change in pension entitlements (receivable / payable);
• P51G - Gross fixed capital formation;
• B1G - Gross value added;
• B1GQ – Gross domestic product;
• B2G_B3G - Gross operating surplus/ mixed income.

In the above, all ratios are expressed in gross terms, i.e. before deduction of consumption of fixed capital.

The following key indicators are calculated in real or nominal terms:

• Real growth of household adjusted disposable income per capita (percentage change on previous period, S.14_S.15): B7G/(POP_NC*Price Deflator);
• Nominal growth of household adjusted disposable income per capita (percentage change on previous period, S.14_S.15): B7G/(POP_NC);
• Real growth of household actual consumption per capita (percentage change on previous period, S.14_S.15): P4/(POP_NC*Price Deflator).

With the following codes (the codes already described above have not been listed):

• B7G - Gross adjusted gross disposable income (adjusted for social transfers in kind);
• P4 - Actual final consumption (adjusted for social transfers in kind);
• POP_NC - Total population national concept (source:Quarterly national accounts, Eurobase domain namq_10_pe);
• Price deflator - Price index/implicit deflator calculated as CP_MEUR/CLV10_MEUR – both indicators refer to households and NPISH final consumption expenditure (P31_S14_S15) (source: Quarterly national accounts, Eurobase domain namq_10_gdp).

The following key indicators combine non-financial with financial accounts:

• Gross return on capital employed, before taxes, of non-financial corporations (S.11): [B2G_B3G/(AF2+AF3+AF4+AF5, liab)]*100;
• Net debt-to-income ratio, after taxes, of non-financial corporations (S.11): ([(AF2+AF3+AF4, liab)/(B4N-D5pay)]*100);
• Net return on equity, after taxes, of non-financial corporations (S.11): [(B4N-D5pay)/(AF5, liab)]*100
• Gross debt-to-income ratio of households (S.14_15): [(AF4, liab)/(B6G+D8net)]*100;
• Household net financial assets ratio (BF90/(B6G+D8net)).

With the following codes (the codes already described above have not been listed):

• B4N - Net entrepreneurial income;
• D5pay - Current taxes on income and wealth;
• AF2 - Currency and deposits;
• AF3 - Debt securities (excluding financial derivatives);
• AF4 - Loans;
• AF5 - Equity and investment fund shares;
• BF90 – Financial net worth.

"rec" means resources, that is transactions that add to the economic value of a given sector.

"pay" means "uses", that is transactions that reduce the economic value of a given sector.

"liab" refers to the stock of liabilities incurred by a given sector and recorded in the financial balance sheets.

See also the sector accounts dedicated website for more information.

The current growing interest in the growth of the Western European economies between the end of World War II and the first oil crisis of 1973 is primarily due to the end of the Cold War and the subsequent demand for solutions for the economic problems of Central and Eastern European transition countries. It was and is discussed to what extent we could learn from the successful rebuilding of the Western European economies. In this context one area of special interest is the reconstruction of West Germany, closely accompanied by the principle of the social market economy. The recollection of this principle, and the call for a new Marshall Plan imply the idea that the Western European post-war boom in essence can be traced to a successful economic policy. It is shown how this assumption can stand up to a theoretical and empirical analysis. Using the new growth theory and the cointegration analysis both national (eg social market economy and Planification (i.e. macroeconomic framework development planning)) and international explanations (eg the Marshall Plan) of the so called ‘golden age’ are examined. It turns out that the impact of economic policies on economic growth must be put into perspective. In contrast, the importance of the different economic conditions of the countries for the explication of their growth process is underlined. Variables, inter alia: - Investment behavior of industry - Production and Export industry - Exchange Rates - Structure of the economies Data focus: Foreign trade structure, external value (foreign wholesale prices), export volume, industrial production, capital stock, long-term development (income, investment rates, openness, exchange rates), patents (patent applications in Germany, France). List of tables in the database HISTAT ZA: - Investment rates in four European countries (1880-1995) - Net fixed assets of the industry in Germany (1950-1968) - Sectoral Gross capital expenditures in Germany (1960-1976) - Sectoral Gross investment in France (1949-1965) - Export volume index of France and the Federal Republic of Germany (1950-1973) - Export volume in millions of current U.S. dollars (1951-1990) - Weighted exchange rate index in indirect rate (1950-1973) - Index of industrial production in Europe and North America (1950-1973) - Construction and equipment investment in Germany (1950-1968) - Investment rates in four European countries (1880-1995) - Sectoral gross and net capital stock in France (1950-1970) - Sectoral gross and net capital stock, investment in France (1950-1969) - Percentage of the French colonies in the French total exports (1950-1973) - Openness of four European economies (1880-1994) - Annual patent applications in the United States (1963-1995) - Real per capita income in Europe and the United States (1870-1992) - Regional structure of the French export value (1896-1973) - French sector gross investment (1960-1976) - Exchange rates in four European countries (1891-1995) Territory of investigation: Germany, France, further OECD-states. Sources: Publications of the official French and German statistics, publications of the OECD, USA and further states; scientific journals.

Germany DE: Tariff Rate: Applied: Weighted Mean: Primary Products data was reported at 0.840 % in 2022. This records a decrease from the previous number of 1.340 % for 2021. Germany DE: Tariff Rate: Applied: Weighted Mean: Primary Products data is updated yearly, averaging 1.690 % from Dec 2000 (Median) to 2022, with 23 observations. The data reached an all-time high of 4.800 % in 2001 and a record low of 0.830 % in 2020. Germany DE: Tariff Rate: Applied: Weighted Mean: Primary Products data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Germany – Table DE.World Bank.WDI: Trade Tariffs. Weighted mean applied tariff is the average of effectively applied rates weighted by the product import shares corresponding to each partner country. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups and import weights. To the extent possible, specific rates have been converted to their ad valorem equivalent rates and have been included in the calculation of weighted mean tariffs. Import weights were calculated using the United Nations Statistics Division's Commodity Trade (Comtrade) database. Effectively applied tariff rates at the six- and eight-digit product level are averaged for products in each commodity group. When the effectively applied rate is unavailable, the most favored nation rate is used instead. Primary products are commodities classified in SITC revision 3 sections 0-4 plus division 68 (nonferrous metals).;World Bank staff estimates using the World Integrated Trade Solution system, based on tariff data from the United Nations Conference on Trade and Development's Trade and Development's Trade Analysis and Information System (TRAINS) database and global imports data from the United Nations Statistics Division's Comtrade database.;;The tariff data for the European Union (EU) apply to EU Member States in alignment with the EU membership for the respective countries/economies and years. In the context of the tariff data, the EU membership for a given country/economy and year is defined for the entire year during which the country/economy was a member of the EU (irrespective of the date of accession to or withdrawal from the EU within a given year). The tariff data for the EU are, thus, applicable to Belgium, France, Germany, Italy, Luxembourg, and the Netherlands (EU Member State(s) since 1958), Denmark and Ireland (EU Member State(s) since 1973), the United Kingdom (EU Member State(s) from 1973 until 2020), Greece (EU Member State(s) since 1981), Spain and Portugal (EU Member State(s) since 1986), Austria, Finland, and Sweden (EU Member State(s) since 1995), Czech Republic, Estonia, Cyprus, Latvia, Lithuania, Hungary, Malta, Poland, Slovakia, and Slovenia (EU Member State(s) since 2004), Romania and Bulgaria (EU Member State(s) since 2007), Croatia (EU Member State(s) since 2013). For more information, please revisit the technical note on bilateral applied tariff (https://wits.worldbank.org/Bilateral-Tariff-Technical-Note.html).

This indicator measures the share of GDP that is used for investment activities in the government, business and household sectors. It is defined as gross fixed capital formation (GFCF) expressed as a percentage of GDP. Gross fixed capital formation consists of resident producers’ investments, deducting disposals, in fixed assets during a given period. It also includes certain additions to the value of non-produced assets realized by producers or institutional units. Fixed assets are tangible or intangible assets produced as outputs from production processes that are used repeatedly, or continuously, for more than one year.

This dataset provides values for GDP reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

This dataset provides values for INTEREST RATE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

The Survey of Health, Ageing and Retirement in Europe (SHARE) is a multidisciplinary and cross-national panel database of micro data on health, socio-economic status and social and family networks of more than 30,000 individuals aged 50 or over. Eleven countries have contributed data to the 2004 SHARE baseline study. They are a balanced representation of the various regions in Europe, ranging from Scandinavia (Denmark and Sweden) through Central Europe (Austria, France, Germany, Switzerland, Belgium, and the Netherlands) to the Mediterranean (Spain, Italy and Greece). Further data have been collected in 2005-06 in Israel. Two 'new' EU member states - the Czech Republic and Poland - as well as Ireland have joined SHARE in 2006 and participated in the second wave of data collection in 2006-07. The survey’s third wave of data collection, SHARELIFE in 2008-09, will collect detailed retrospective life-histories in sixteen countries, with Slovenia joining in as a new member.The data are available to the entire research community for no costs via de SHARE website.

Data: SOEP v37, 2020, doi:10.5684/soep.core.v37eu Abstract of the referenced publication: Using data from the German Socio-Economic Panel, this article examines (1) the prevalence of vegetarians in Germany, (2) their social profile, and (3) dynamic features and short-term effects on subjective health of a vegetarian diet. As in many other Western countries, the prevalence of vegetarians and vegans in Germany is on an upward trend. In the period 2016-2020, about 7 percent of the Germans declared themselves as vegetarians (including vegans). The probability of being a vegetarian is higher among women, younger people, the better educated, those living in single households, residents of urban areas, and those who support the green political party. We observe considerable temporal stability of individual dietary patterns – mainly due to a dominant group of continuous non-vegetarians (almost 90 percent). We also test a special variant of the health-benefit hypothesis of a vegetarian diet. We find no support of this hypothesis when looking at short-term effects on individuals’ overall assessment of their personal health.

This dataset provides values for INFLATION RATE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

The European Union Statistics on Income and Living Conditions (EU-SILC) collects timely and comparable multidimensional microdata on income, poverty, social exclusion and living conditions.

The EU-SILC collection is a key instrument for providing information required by the European Semester (_[1]) and the European Pillar of Social Rights, and the main source of data for microsimulation purposes and flash estimates of income distribution and poverty rates.

AROPE remains crucial to monitor European social policies, especially to monitor the EU 2030 target on poverty and social exclusion. For more information, please consult EU social indicators.

The EU-SILC instrument provides two types of data:

• Cross-sectional data pertaining to a given time or a certain time period with variables on income, poverty, social exclusion and other living conditions.
• Longitudinal data pertaining to individual-level changes over time, observed periodically over four‐or more year rotation scheme (Annex III (2) of 2019/1700).

EU-SILC collects:

• annual variables,
• three-yearly modules,
• six-yearly modules,
• ad-hoc new policy needs modules,
• optional variables.

The variables collected are grouped by topic and detailed topic and transmitted to Eurostat in four main files (D-File, H-File, R-File and P-file).

The domain ‘Income and Living Conditions’ covers the following topics: persons at risk of poverty or social exclusion, income inequality, income distribution and monetary poverty, living conditions, material deprivation, and EU-SILC ad-hoc modules, which are structured into collections of indicators on specific topics.

In 2023, in addition to annual data, in EU-SILC were collected: the three yearly module on labour market and housing, the six yearly module on intergenerational transmission of advantages and disadvantages, housing difficulties, and the ad hoc subject on households energy efficiency.

Starting from 2021 onwards, the EU quality reports use the structure of the Single Integrated Metadata Structure (SIMS).

_{([1]) The European Semester is the European Union’s framework for the coordination and surveillance of economic and social policies.}

Abstract

The Urban Green Raster Germany is a land cover classification for Germany that addresses in particular the urban vegetation areas. The raster dataset covers the terrestrial national territory of Germany and has a spatial resolution of 10 meters. The dataset is based on a fully automated classification of Sentinel-2 satellite data from a full 2018 vegetation period using reference data from the European LUCAS land use and land cover point dataset. The dataset identifies eight land cover classes. These include Built-up, Built-up with significant green share, Coniferous wood, Deciduous wood, Herbaceous vegetation (low perennial vegetation), Water, Open soil, Arable land (low seasonal vegetation). The land cover dataset provided here is offered as an integer raster in GeoTiff format. The assignment of the number coding to the corresponding land cover class is explained in the legend file.

Data acquisition

The data acquisition comprises two main processing steps: (1) Collection, processing, and automated classification of the multispectral Sentinel 2 satellite data with the “Land Cover DE method”, resulting in the raw land cover classification dataset, NDVI layer, and RF assignment frequency vector raster. (2) GIS-based postprocessing including discrimination of (densely) built-up and loosely built-up pixels according NDVI threshold, and creating water-body and arable-land masks from geo-topographical base-data (ATKIS Basic DLM) and reclassification of water and arable land pixels based on the assignment frequency.

Data collection

Satellite data were searched and downloaded from the Copernicus Open Access Hub (https://scihub.copernicus.eu/).

The LUCAS reference and validation points were loaded from the Eurostat platform (https://ec.europa.eu/eurostat/web/lucas/data/database).

The processing of the satellite data was performed at the DLR data center in Oberpfaffenhofen.

GIS-based post-processing of the automatic classification result was performed at IOER in Dresden.

Value of the data

The dataset can be used to quantify the amount of green areas within cities on a homogeneous data base [5].

Thus it is possible to compare cities of different sizes regarding their greenery and with respect to their ratio of green and built-up areas [6].

Built-up areas within cities can be discriminated regarding their built-up density (dense built-up vs. built-up with higher green share).

Data description

A Raster dataset in GeoTIFF format: The dataset is stored as an 8 bit integer raster with values ranging from 1 to 8 for the eight different land cover classes. The nomenclature of the coded values is as follows: 1 = Built-up, 2=open soil; 3=Coniferous wood, 4= Deciduous wood, 5=Arable land (low seasonal vegetation), 6=Herbaceous vegetation (low perennial vegetation), 7=Water, 8=Built-up with significant green share. Name of the file ugr2018_germany.tif. The dataset is zipped alongside with accompanying files: *.twf (geo-referencing world-file), *.ovr (Overlay file for quick data preview in GIS), *.clr (Color map file).

A text file with the integer value assignment of the land cover classes. Name of the file: Legend_LC-classes.txt.

Experimental design, materials and methods

The first essential step to create the dataset is the automatic classification of a satellite image mosaic of all available Sentinel-2 images from May to September 2018 with a maximum cloud cover of 60 percent. Points from the 2018 LUCAS (Land use and land cover survey) dataset from Eurostat [1] were used as reference and validation data. Using Random Forest (RF) classifier [2], seven land use classes (Deciduous wood, Coniferous wood, Herbaceous vegetation (low perennial vegetation), Built-up, Open soil, Water, Arable land (low seasonal vegetation)) were first derived, which is methodologically in line with the procedure used to create the dataset "Land Cover DE - Sentinel-2 - Germany, 2015" [3]. The overall accuracy of the data is 93 % [4].

Two downstream post-processing steps served to further qualify the product. The first step included the selective verification of pixels of the classes arable land and water. These are often misidentified by the classifier due to radiometric similarities with other land covers; in particular, radiometric signatures of water surfaces often resemble shadows or asphalt surfaces. Due to the heterogeneous inner-city structures, pixels are also frequently misclassified as cropland.

To mitigate these errors, all pixels classified as water and arable land were matched with another data source. This consisted of binary land cover masks for these two land cover classes originating from the Monitor of Settlement and Open Space Development (IOER Monitor). For all water and cropland pixels that were outside of their respective masks, the frequencies of class assignments from the RF classifier were checked. If the assignment frequency to water or arable land was at least twice that to the subsequent class, the classification was preserved. Otherwise, the classification strength was considered too weak and the pixel was recoded to the land cover with the second largest assignment frequency.

Furthermore, an additional land cover class "Built-up with significant vegetation share" was introduced. For this purpose, all pixels of the Built-up class were intersected with the NDVI of the satellite image mosaic and assigned to the new category if an NDVI threshold was exceeded in the pixel. The associated NDVI threshold was previously determined using highest resolution reference data of urban green structures in the cities of Dresden, Leipzig and Potsdam, which were first used to determine the true green fractions within the 10m Sentinel pixels, and based on this to determine an NDVI value that could be used as an indicator of a significant green fraction within the built-up pixel. However, due to the wide dispersion of green fraction values within the built-up areas, it is not possible to establish a universally valid green percentage value for the land cover class of Built-up with significant vegetation share. Thus, the class essentially serves to the visual differentiability of densely and loosely (i.e., vegetation-dominated) built-up areas.

Acknowledgments

This work was supported by the Federal Institute for Research on Building, Urban Affairs and Spatial Development (BBSR) [10.06.03.18.101].The provided data has been developed and created in the framework of the research project “Wie grün sind bundesdeutsche Städte?- Fernerkundliche Erfassung und stadträumlich-funktionale Differenzierung der Grünausstattung von Städten in Deutschland (Erfassung der urbanen Grünausstattung)“ (How green are German cities?- Remote sensing and urban-functional differentiation of the green infrastructure of cities in Germany (Urban Green Infrastructure Inventory)). Further persons involved in the project were: Fabian Dosch (funding administrator at BBSR), Stefan Fina (research partner, group leader at ILS Dortmund), Annett Frick, Kathrin Wagner (research partners at LUP Potsdam).

References

[1] Eurostat (2021): Land cover / land use statistics database LUCAS. URL: https://ec.europa.eu/eurostat/web/lucas/data/database

[2] L. Breiman (2001). Random forests, Mach. Learn., 45, pp. 5-32

[3] M. Weigand, M. Wurm (2020). Land Cover DE - Sentinel-2—Germany, 2015 [Data set]. German Aerospace Center (DLR). doi: 10.15489/1CCMLAP3MN39

[4] M. Weigand, J. Staab, M. Wurm, H. Taubenböck, (2020). Spatial and semantic effects of LUCAS samples on fully automated land use/land cover classification in high-resolution Sentinel-2 data. Int J Appl Earth Obs, 88, 102065. doi: https://doi.org/10.1016/j.jag.2020.102065

[5] L. Eichler., T. Krüger, G. Meinel, G. (2020). Wie grün sind deutsche Städte? Indikatorgestützte fernerkundliche Erfassung des Stadtgrüns. AGIT Symposium 2020, 6, 306–315. doi: 10.14627/537698030

[6] H. Taubenböck, M. Reiter, F. Dosch, T. Leichtle, M. Weigand, M. Wurm (2021). Which city is the greenest? A multi-dimensional deconstruction of city rankings. Comput Environ Urban Syst, 89, 101687. doi: 10.1016/j.compenvurbsys.2021.101687

The value of exports of goods and services, as a percentage of the gross domestic product (GDP). Also available for goods and services separately.

This dataset provides values for GOVERNMENT DEBT TO GDP reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

This dataset describes the share of renewables in the energy production of France, Germany, and Sweden as reported by Ember, Enerdata, and World Bank. The dataset serves as an example for the data comparison feature of the LDM.

Germany DE: Share of Tariff Lines with Specific Rates: Manufactured Products data was reported at 0.040 % in 2022. This records a decrease from the previous number of 0.040 % for 2021. Germany DE: Share of Tariff Lines with Specific Rates: Manufactured Products data is updated yearly, averaging 0.031 % from Dec 2000 (Median) to 2022, with 23 observations. The data reached an all-time high of 0.247 % in 2020 and a record low of 0.015 % in 2008. Germany DE: Share of Tariff Lines with Specific Rates: Manufactured Products data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Germany – Table DE.World Bank.WDI: Trade Tariffs. Share of tariff lines with specific rates is the share of lines in the tariff schedule that are set on a per unit basis or that combine ad valorem and per unit rates. It shows the extent to which countries use tariffs based on physical quantities or other, non-ad valorem measures. Manufactured products are commodities classified in SITC revision 3 sections 5-8 excluding division 68.;World Bank staff estimates using the World Integrated Trade Solution system, based on data from United Nations Conference on Trade and Development's Trade Analysis and Information System (TRAINS) database.;;The tariff data for the European Union (EU) apply to EU Member States in alignment with the EU membership for the respective countries/economies and years. In the context of the tariff data, the EU membership for a given country/economy and year is defined for the entire year during which the country/economy was a member of the EU (irrespective of the date of accession to or withdrawal from the EU within a given year). The tariff data for the EU are, thus, applicable to Belgium, France, Germany, Italy, Luxembourg, and the Netherlands (EU Member State(s) since 1958), Denmark and Ireland (EU Member State(s) since 1973), the United Kingdom (EU Member State(s) from 1973 until 2020), Greece (EU Member State(s) since 1981), Spain and Portugal (EU Member State(s) since 1986), Austria, Finland, and Sweden (EU Member State(s) since 1995), Czech Republic, Estonia, Cyprus, Latvia, Lithuania, Hungary, Malta, Poland, Slovakia, and Slovenia (EU Member State(s) since 2004), Romania and Bulgaria (EU Member State(s) since 2007), Croatia (EU Member State(s) since 2013). For more information, please revisit the technical note on bilateral applied tariff (https://wits.worldbank.org/Bilateral-Tariff-Technical-Note.html).

Germany DE: Share of Tariff Lines with Specific Rates: Primary Products data was reported at 2.623 % in 2022. This records an increase from the previous number of 2.284 % for 2021. Germany DE: Share of Tariff Lines with Specific Rates: Primary Products data is updated yearly, averaging 3.299 % from Dec 2000 (Median) to 2022, with 23 observations. The data reached an all-time high of 26.653 % in 2020 and a record low of 2.135 % in 2016. Germany DE: Share of Tariff Lines with Specific Rates: Primary Products data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Germany – Table DE.World Bank.WDI: Trade Tariffs. Share of tariff lines with specific rates is the share of lines in the tariff schedule that are set on a per unit basis or that combine ad valorem and per unit rates. It shows the extent to which countries use tariffs based on physical quantities or other, non-ad valorem measures. Primary products are commodities classified in SITC revision 3 sections 0-4 plus division 68 (nonferrous metals).;World Bank staff estimates using the World Integrated Trade Solution system, based on data from United Nations Conference on Trade and Development's Trade Analysis and Information System (TRAINS) database.;;The tariff data for the European Union (EU) apply to EU Member States in alignment with the EU membership for the respective countries/economies and years. In the context of the tariff data, the EU membership for a given country/economy and year is defined for the entire year during which the country/economy was a member of the EU (irrespective of the date of accession to or withdrawal from the EU within a given year). The tariff data for the EU are, thus, applicable to Belgium, France, Germany, Italy, Luxembourg, and the Netherlands (EU Member State(s) since 1958), Denmark and Ireland (EU Member State(s) since 1973), the United Kingdom (EU Member State(s) from 1973 until 2020), Greece (EU Member State(s) since 1981), Spain and Portugal (EU Member State(s) since 1986), Austria, Finland, and Sweden (EU Member State(s) since 1995), Czech Republic, Estonia, Cyprus, Latvia, Lithuania, Hungary, Malta, Poland, Slovakia, and Slovenia (EU Member State(s) since 2004), Romania and Bulgaria (EU Member State(s) since 2007), Croatia (EU Member State(s) since 2013). For more information, please revisit the technical note on bilateral applied tariff (https://wits.worldbank.org/Bilateral-Tariff-Technical-Note.html).