Around 3.15 million people lived in the Athens metropolitan area, making it the largest metropolitan area in Greece in 2021. The second-most populated metropolitan area, Thessaloniki, had approximately 802,390 inhabitants. At the municipality level, Athens was the largest city in the country with 643,452 residents.

Actual value and historical data chart for Greece Population In The Largest City Percent Of Urban Population

Greece GR: Population in Largest City data was reported at 3,157,451.000 Person in 2017. This records a decrease from the previous number of 3,159,302.000 Person for 2016. Greece GR: Population in Largest City data is updated yearly, averaging 3,062,740.500 Person from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 3,187,197.000 Person in 2001 and a record low of 1,813,686.000 Person in 1960. Greece GR: Population in Largest City data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Greece – Table GR.World Bank.WDI: Population and Urbanization Statistics. Population in largest city is the urban population living in the country's largest metropolitan area.; ; United Nations, World Urbanization Prospects.; ;

Greece GR: Population in Largest City: as % of Urban Population data was reported at 37.274 % in 2017. This records a decrease from the previous number of 37.402 % for 2016. Greece GR: Population in Largest City: as % of Urban Population data is updated yearly, averaging 41.153 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 45.213 % in 1975 and a record low of 37.226 % in 2011. Greece GR: Population in Largest City: as % of Urban Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Greece – Table GR.World Bank.WDI: Population and Urbanization Statistics. Population in largest city is the percentage of a country's urban population living in that country's largest metropolitan area.; ; United Nations, World Urbanization Prospects.; Weighted average;

About this dataset

Context

This list ranks the 1 cities in the Major County, OK by Greek population, as estimated by the United States Census Bureau. It also highlights population changes in each city over the past five years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:

• 2019-2023 American Community Survey 5-Year Estimates
• 2014-2018 American Community Survey 5-Year Estimates
• 2009-2013 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Greek Population: This column displays the rank of city in the Major County, OK by their Greek population, using the most recent ACS data available.
• City: The City for which the rank is shown in the previous column.
• Greek Population: The Greek population of the city is shown in this column.
• % of Total City Population: This shows what percentage of the total city population identifies as Greek. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Major County Greek Population: This tells us how much of the entire Major County, OK Greek population lives in that city. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: This column displays the rank trend across the last 5 years.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

This study describes the development of a lightweight hybrid framework for context-aware platial information that integrates NLP techniques with Graph Neural Networks (GNNs) to extract and analyze geosemantic knowledge from textual and spatial data. The main purpose is to create a platial knowledge graph that represents cities not simply as locations on maps, but as multidimensional entities with spatial, cultural, historical, and social characteristics. More specifically, we construct a diverse platial knowledge graph where cities are represented as nodes connected to other cities based on semantic features derived from both linguistic content and spatial context.

About this dataset

Context

This list ranks the 1,450 cities in the Wisconsin by Greek population, as estimated by the United States Census Bureau. It also highlights population changes in each city over the past five years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:

• 2019-2023 American Community Survey 5-Year Estimates
• 2014-2018 American Community Survey 5-Year Estimates
• 2009-2013 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Greek Population: This column displays the rank of city in the Wisconsin by their Greek population, using the most recent ACS data available.
• City: The City for which the rank is shown in the previous column.
• Greek Population: The Greek population of the city is shown in this column.
• % of Total City Population: This shows what percentage of the total city population identifies as Greek. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Wisconsin Greek Population: This tells us how much of the entire Wisconsin Greek population lives in that city. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: This column displays the rank trend across the last 5 years.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

This dataset presents detailed 100-meter LCZ maps for the five most populated cities in Greece (Athens, Thessaloniki, Larissa, Patras, and Heraklion), along with key urban form parameters, such as building height, and height-to-width ratio. These data were developed using a hybrid GIS-based and remote sensing-based framework that incorporates ultra-high-resolution elevation models (0.8 m), supported by Copernicus Earth Observation and synthetic aperture radar (SAR) datasets. Special attention was given to ensure seamless integration with the Weather Research and Forecasting (WRF) model, enabling highly-resolved numerical weather analysis for the targeted urban areas. This was achieved by including in the dataset city-specific surface and urban canopy properties for each LCZ class, assigned through a Land Use / Land Cover (LU/LC) classification at 100 m spatial resolution in WRF compliant format. The LU/LC data were constructed by integrating the LCZ urban classes with CORINE land cover information for the natural areas.

The dataset is organized into three main directories: (i) Urban_Parameters, (ii) Local_Climate_Zones, and (iii) WRF_Input:(i) The Urban_Parameters directory contains 100-meter GeoTIFF maps of building heights (H) and height-to-width-ratios (H/W) for the five Greek cities., these are projected in UTM Zone 34N (EPSG:32634), except for Heraklion, which is provided in UTM Zone 35N (EPSG: 32635). The file naming convention follows the format: cityName_urbanParameter.tif (e.g., athens_buildingHeight.tif).(ii) The Local_Climate_Zones directory contains 100-meter GeoTIFF LCZ maps for five Greek cities (Athens, Thessaloniki, Larissa, Patras, and Heraklion). These maps include urban LCZ classes corresponding to LCZs 1 to 10 and LCZ E. Similar to the urban parameter’s maps, LCZs are projected in UTM Zone 34N, except for Heraklion (UTM Zone 35N). The file naming convention follows the format: cityName_lcz.tif (e.g., athens_lcz.tif). (iii) The WRF_Input directory is divided into three subdirectories:(a) CLC_LCZ_WRF_tif: This subdirectory contains merged CORINE Land Cover (CLC) and LCZ maps, forming a gridded Land Use / Land Cover (LU/LC) dataset, designed to be used as the input surface classes in WRF modelling. The maps are provided in GeoTIFF format in the same projections as the previous GeoTIFF files. The file naming convention follows the format: cityName_clc_lcz_WRF_version.tif (e.g., athens_clc_lcz_WRF_v1.tif).• Version v1: Applicable to WRF versions prior to WRF version 4.4.2, with LCZs numbered from 31 to 41 • Version v2: Applicable to WRF version 4.4.2. and later versions, with LCZs numbered from 51 to 61. (b) CLC_LCZ_WRF: This subdirectory includes the same LU/LC data as CLC_LCZ_WRF_tif, converted into binary format for compatibility with the WPS (WRF Pre-processing System) system and transformed into geographical coordinates. It is divided into further subdirectories named according to the format cityName_clc_lcz_WRF_version (e.g., athens_clc_lcz_WRF_v1) containing the binary files. The versioning of WRF follows the same logic as described above.(c) Urban_Parameters_WRF: This subdirectory contains city-specific surface and urban canopy properties (e.g., width of streets, building heights) for each LCZ class, stored in WRF compliant look-up tables (URBPARM_LCZ.TBL). The file naming convention for these files is: cityName_URBPARM_LCZ.TBL (e.g., athens_URBPARM_LCZ.TBL).

Greece is one of the countries most affected by the current financial crisis. Since 2010, when the Greek government and IMF/EU/ECB agreed on the largest loan ever received by a single country, Greece has seen sweeping transformations in the character of its polity and state functions. The Greek version of the crisis has produced a rupture in the modus operandi of the state in question and in its relationship with its citizenry. The main axis of this rupture is the systemic challenge and reconfiguration of the category 'public' - which of course includes public spaces. This research focuses on the newly emerging public socialities in reference to public urban spaces. The idea of emphasizing spontaneous and unspontaneous socio-spatial practices, public urban materialities and spatialities in the capital city of Greece appears as an ideal way in which to study the wide variety of the social consequences of the financial crisis.

The strategic development of Kalamata has been focused on two main areas: the restoration of the city's historic urban core and the revitalization of its waterfront, with recent efforts emphasizing the latter. The waterfront's increasing importance in urban development strategies highlights the city's commitment to take action against climate change, as being part of the EU Mission for 100 climate-neutral and smart cities network. The municipality envisions the waterfront as the primary direction for local development, aiming for it to be accessible, responsive, and significant, all while maintaining its unique local character. This paper showcases strategies employed in waterfront redevelopment using six case studies from Cyprus, Greece, Croatia, Denmark, Portugal and the USA. The case studies for Larnaca (Cyprus), Thessaloniki (Greece) and Rijeka (Croatia) were selected because of their proximity to Kalamata in the Mediterranean and their similar weather patterns, cultural traits and social practices. Like Kalamata, the cities of Vijle (Denmark), Viana do Castelo (Portgual) and Annapolis (USA) are small-to-medium sized urban areas, similar in terms of geography and demographics, that offer interesting lessons in sustainable community and economic development along waterfront areas.

The report starts with a brief overview land use, governance, and value capture, explaining why they are important for sustainable urban development. The second section introduces the concept and potential of land readjustment as a tool for urban transformation that can address various challenges such as urban sprawl, climate change, disaster recovery, and social inclusion. The third section presents a comparative analysis of land readjustment policies in different contexts, with a focus on the Japanese case, which is considered a global best practice, and the UN-Habitat's PILAR approach, which is designed for developing countries. Next the attention turns to Greece with a review of the legal framework and implementation process, and then a deep dive into a practical case in Kalamata. This medium-sized city successfully used land replotting for post-disaster reconstruction and urban expansion, with plans to apply the approach to support urban renewal and climate adaptation. The report concludes with key findings and recommendations for improving and expanding the use of land readjustment in Greece and other European countries.

The HEAT-ALARM Urban Morphology and Environment Database (UMED) provides ultra-high-resolution data that characterize the landscape, form and function of the five highest populated cities in Greece (Athens, Thessaloniki, Larissa, Patras, Heraklion) for integration in the Weather Research and Forecasting (WRF) model, versions 4.3-4.4.1 (Skamarock et al., 2021), in order to enable the highly-resolved numerical weather prediction (NWP) over the targeted urban areas as part of the weather forecasting component of the project's heat-health warning system (HHWS). These data include: (a) terrain elevation based on the 3-arc-sec (~ 90 m) horizontal grid resolution SRTM (shuttle radar topography mission) data, version 4.1 (Jarvis et al., 2008), (b) land use/land cover (LU/LC) at 100 m spatial resolution, constructed by combining CORINE land cover data (CLC – Feranec et al., 2016) and Local Climate Zone (LCZ – Stewart and Oke, 2012) classification, and (c) city-specific surface and urban canopy properties (e.g., width of streets, albedo of roofs) for each LCZ class, assigned in look-up tables (URBPARM_LCZ.TBL). Along with UMED, the GEOGRID.TBL.ARW_HEAT-ALARM is provided for assisting the WRF implementation with the above data.

Considering that such finely detailed data have been incorporated in modeling studies only in Athens so far (e.g. Giannaros et al., 2014; Agathangelidis et al., 2019; Giannaros et al., 2018, 2023), the development and distribution of UMED in the framework of HEAT-ALARM constitutes an important step towards the creation of an open-access national urban dataset in Greece that can be used for accurately describing and modeling the urban environments under various applications.

More information on the data generation and their use within the WRF-urban modeling framework can be found at: https://osf.io/p2qrd.

Compared to the previous version, this version includes minor bug fixes related to the look-up tables providing the surface and urban canopy properties (URBPARM_LCZ_HEAT-ALARM.zip).

References

Agathangelidis, I., Cartalis, C., Santamouris, M., 2019. Integrating Urban Form, Function, and Energy Fluxes in a Heat Exposure Indicator in View of Intra-Urban Heat Island Assessment and Climate Change Adaptation. Clim. . https://doi.org/10.3390/cli7060075

Feranec, J., Soukup, T., Hazeu, G., & Jaffrain, G. (Eds.), 2016. European Landscape Dynamics: CORINE Land Cover Data (1st ed.). CRC Press. https://doi.org/10.1201/9781315372860

Giannaros, C., Agathangelidis, I., Papavasileiou, G., Galanaki, E., Kotroni, V., Lagouvardos, K., Giannaros, T.M., Cartalis, C., Matzarakis, A., 2023. The extreme heat wave of July–August 2021 in the Athens urban area (Greece): Atmospheric and human-biometeorological analysis exploiting ultra-high resolution numerical modeling and the local climate zone framework. Sci. Total Environ. 857, 159300. https://doi.org/https://doi.org/10.1016/j.scitotenv.2022.159300

Giannaros, C., Nenes, A., Giannaros, T.M., Kourtidis, K., Melas, D., 2018. A comprehensive approach for the simulation of the Urban Heat Island effect with the WRF/SLUCM modeling system: The case of Athens (Greece). Atmos. Res. 201, 86–101. https://doi.org/https://doi.org/10.1016/j.atmosres.2017.10.015

Giannaros, T.M., Melas, D., Daglis, I.A., Keramitsoglou, I., 2014. Development of an operational modeling system for urban heat islands: An application to Athens, Greece. Nat. Hazards Earth Syst. Sci. 14, 347–358. https://doi.org/10.5194/nhess-14-347-2014

Jarvis, A., Reuter, H.I., Nelson, A., Guevara, E., 2008. Hole-filled SRTM for the globe, version 4, available from the CGIAR-CSI SRTM 90 m database (http://srtm.csi.cgiar.org)

Skamarock, W.C., Klemp, J.B., Dudhia, J., Gill, D.O., Liu, Z., Berner, J., Wang, W., Power, J.G., Duda, M.G., Backer, D.M., Huang, X.Y., 2021. A Description of the Advanced Research WRF Model Version 4.3. NCAR Technical Note (NCAR/TN-556+STR), Boulder, Colorando, USA. Available online: http://dx.doi.org/10.5065/1dfh-6p97

Stewart, I.D., Oke, T.R., 2012. Local climate zones for urban temperature studies. Bull. Am. Meteorol. Soc. 93, 1879–1900. https://doi.org/10.1175/BAMS-D-11-00019.1

The main object of the doctoral dissertation is the study of the spatio-social transformations that occur in the city of Athens from the beginning of the 1990s until today, ie after the transformation of Greece from a "sending country" to a "host and transit" country of immigrants. In the context of the so-called "new migration" to the country and an ever-increasing "ethnocultural diversity", the main research questions of the dissertation are specialized in three parts. The first part of the questions concerns the access of immigrants to the residence and, in particular, the "housing routes" that follow until the access to the home. Do immigrants have access to private housing and, if so, to what extent? What is the profile of the immigrant owners, what are the characteristics of the properties they buy and through what mechanisms do they manage to become owners? The second part of the questions concerns the geographies of immigrants' access to private housing or, more simply, their spatial distribution in the city of Athens. In which neighborhoods and in which buildings of the city do the immigrant owners settle and, mainly, in what spatial relationship with the Greek inhabitants? In more specific terms, what is the degree of ethno-racial housing segregation in the city of Athens? Finally, apart from the spatial relations between Greeks and immigrants, the third part of the questions concerns the social relations of international coexistence that develop in the neighborhoods of Athens and range between relations of friendship, trust, and solidarity, but also relations of conflict, racism and intolerance. All the above questions are investigated through the combination of quantitative and qualitative research methods, with the basic tools of a statistical sample survey in the archive of the Athens Mortgage Office and a series of 20 semi-structured interviews by immigrant owners.

Abstract

The Household Budget Survey (HBS) is a national survey collecting information from a representative sample of households, on household composition, members employment status, living conditions and, mainly, focusing on their members expenditure on goods and services as well as on their income. The expenditure information collected from households is very detailed.

The main purpose of the HBS is to determine in detail the household expenditure pattern in order to revise the Consumer Price Index. Moreover, the HBS is the most appropriate source in order to:

• Complete the available statistical data for the estimation of the total private consumption;
• Study the households expenditures and their structure in relation to their income and other economic, social and demographic characteristics;
• Analyze the changes in the living conditions of the households in comparison with the previous surveys;
• Study the relationship between households purchases and receipts in kind;
• Study low income limits in the different socio-economic categories and population groups;
• Study the changes in the nutritional habits of the households.

Geographic coverage

National coverage

Analysis unit

• Households,
• Individuals.

Kind of data

Sample survey data [ssd]

Frequency of data collection

The frequency of data collection is continual spread within the reference year.

Sampling procedure

The two-stage area stratified sampling was adopted for the HBS survey based on the rotational integrated design method and on the Population Census of 2011 as well. This method was judged as the most appropriate for both cross -sectional and longitudinal comparisons.

The primary sampling units (PSUs) are the areas (one or more unified city blocks), the secondary sampling units selected in each primary unit are the households and their members. In each Region (NUTS 2), the stratification of primary units was conducted by allocating the Municipalities and Communes according to the degree of urbanization (urban, semi-urban, and rural regions). Except for the two former Major City Agglomerations (Athens and Thessaloniki).

The Greater Athens Area was divided into 31 strata of about equal size (equal number of households) on the basis of the lists of city blocks of the Municipalities that constitute it and taking into consideration socio-economic criteria. Similarly, the Greater Thessaloniki Area was divided into 9 equally sized strata. The two former Major City Agglomerations account for 37% of the total population and for even larger percentages in certain socio-economic variables.

1st stage of sampling

In this stage, from any stratum (crossing of Region with the degree of urbanisation), primary units were drawn. The number of draws is approximately proportional to the population size of the stratum (number of households in the last population census of the year 2011).

2nd stage of sampling

In this stage from each primary sampling unit (selected area) the sample of secondary units (households) was selected. Actually, in the second stage we drew a systematic sample of dwellings. However, in most cases, one household corresponds to each dwelling. If in the selected dwelling lives more than one household, all of them were interviewed. The sampling frame containing the secondary units (households) in the selected sampling primary units was updated before the selection of households. The total number of the primary sampling units was 1023. Due to non-response, the actual total number of primary sampling units was 1004.

The survey was conducted on a sample of 6150 private households throughout the Country.

Mode of data collection

Face-to-face [f2f]

2,500 years ago, in August or September 480BCE, the Battle of Thermopylae saw a vastly outnumbered Greek force hold out against one of the largest armies ever assembled; in what has been romantically remembered as the last stand of the 300 Spartans. Background Around the turn of the fifth century BCE, the Achaemenid Empire (or the First Persian Empire) was the world's largest and most powerful kingdom, stretching from present-day China to the Balkans and North Africa. In 492 BCE, King Darius launched the first Persian invasion of Greece, as a means of punishing the Greek cities, who had previously aided an Ionian Greek rebellion against Darius in Asia Minor. After two years of war, an alliance of Greek states finally defeated the Persians at the Battle of Marathon in 490BCE. Ten years later, Darius' successor, Xerxes, sought revenge and launched the second Persian invasion of Greece, amassing one of the largest armies the world had ever seen. Ancient sources claimed that the Persian army numbered between 2.5 and four million, however most modern estimates claim that it was a few hundred thousand at most. As the Persian army swept southwards, most Greek states surrendered, although a small number united and sent their armies to head off the Persians at the narrow pass of Thermopylae, while their navies created a blockade at the straits of Artemisium. The build-up Modern estimates claim that there were around 7,000 Greek soldiers sent to defend the narrow pass at Thermopylae, which was described as a 15 meter stretch between sheer cliffs and treacherous waters. This force was led by the Spartans and their king***, Leonidas; Sparta had the only professional army in Greece, however the battle coincided with a period of sacred religious and Olympic celebrations when fighting was not permitted, meaning Sparta sent just 300 veteran warriors to Thermopylae. Other states also sent relatively small numbers for similar reasons, as well as their reluctance to send their armies so far to the north. Despite these vastly inferior numbers, the Greek style of fighting was much better suited to the terrain at Thermopylae. Persian armies favored mobile battles, using archers to inflict most damage before sending in a combination of lightly armored soldiers and cavalry units to clean up remaining forces. The Greeks, however, used heavily armored hoplites in compact, phalanx formations, and preferred close-quarters combat. Battle of Thermopylae When the Persians reached Thermopylae, Xerxes waited four days before attacking, assuming that the Greeks would disperse at the sight of his army; when they did not, Xerxes ordered them to surrender their weapons, to which Leonidas famously replied "molon labe" or "come and take them". Xerxes then launched his attack, however, his archers inflicted little damage against the heavily armored Greeks and their heavy, bronze shields. Persian foot soldiers and cavalry, including the professional, heavily armored "Immortals", then suffered heavily at the hands of the Greek defenders. It was only after two days of fighting when the Persians gained the upper hand; a local shepherd, seeking reward, led a Persian force around a hidden path in the mountains, where they outflanked the Greeks. Surrounded, Leonidas ordered most of his forces to fall back, while the Spartans, Thebans, Thespians and helots remained. Eventually, King Leonidas and the rear guard were defeated as they protected their comrades' retreat (there are some reports of the Thebans surrendering). Despite losing the battle, the actions of the Greeks delayed the Persian advance into Greece, and bought their comrades enough time to regroup. The Greek armies and navies were then able to organize further defenses, and eventually defeat the invasion at the battles of Salamis and Plataea. By winning this war, the Greeks prevented the Persians from extending their empire and influence across Europe, in a war that likely altered the course of human history and development for all time.

Context

The GeoNames geographical database contains over 10 million geographical names and consists of over 9 million unique features with 2.8 million populated places and 5.5 million alternate names. All features are categorized into one out of nine feature classes and further subcategorized into one out of 645 feature codes.

Content

The main 'geoname' table has the following fields :

• geonameid : integer id of record in geonames database
• name : name of geographical point (utf8) varchar(200)
• asciiname : name of geographical point in plain ascii characters, varchar(200)
• alternatenames : alternatenames, comma separated, ascii names automatically transliterated, convenience attribute from alternatename table, varchar(10000)
• latitude : latitude in decimal degrees (wgs84)
• longitude : longitude in decimal degrees (wgs84)
• feature class : see http://www.geonames.org/export/codes.html, char(1)
• feature code : see http://www.geonames.org/export/codes.html, varchar(10)
• country code : ISO-3166 2-letter country code, 2 characters
• cc2 : alternate country codes, comma separated, ISO-3166 2-letter country code, 200 characters
• admin1 code : fipscode (subject to change to iso code), see exceptions below, see file admin1Codes.txt for display names of this code; varchar(20)
• admin2 code : code for the second administrative division, a county in the US, see file admin2Codes.txt; varchar(80)
• admin3 code : code for third level administrative division, varchar(20)
• admin4 code : code for fourth level administrative division, varchar(20)
• population : bigint (8 byte int)
• elevation : in meters, integer
• dem : digital elevation model, srtm3 or gtopo30, average elevation of 3''x3'' (ca 90mx90m) or 30''x30'' (ca 900mx900m) area in meters, integer. srtm processed by cgiar/ciat.
• timezone : the iana timezone id (see file timeZone.txt) varchar(40)
• modification date : date of last modification in yyyy-MM-dd format

AdminCodes:

Most adm1 are FIPS codes. ISO codes are used for US, CH, BE and ME. UK and Greece are using an additional level between country and fips code. The code '00' stands for general features where no specific adm1 code is defined. The corresponding admin feature is found with the same countrycode and adminX codes and the respective feature code ADMx.

feature classes:

• A: country, state, region,...
• H: stream, lake, ...
• L: parks,area, ...
• P: city, village,...
• R: road, railroad
• S: spot, building, farm
• T: mountain,hill,rock,...
• U: undersea
• V: forest,heath,...

Acknowledgements

• Data Sources: http://www.geonames.org/data-sources.html

The HELIAD study has been conducted in Greece the city of in Larissa, Thessaly since 2009, and in Marousi, Athens since 2013. Over a thousand participants of a random sample completed the initial evaluation. The subjects were, on average, 73.4 years old, 60% of the sample were female, and most of the participants were poorly educated with an average of 5.41 years of education. The main previous occupations of the male participants were unskilled laborer, self-employed professional, farmer, and skilled laborer. Approximately half of the female participants described themselves as homemakers, whereas fewer than 20% worked as unskilled laborers and farmers. The majority of the participants were married. The baseline assessment of the cohort began in 2011, and the participants were re-evaluated approximately 3 years after the initial assessment.

GR：最大城市人口在12-01-2017达3,157,451.000人，相较于12-01-2016的3,159,302.000人有所下降。GR：最大城市人口数据按年更新，12-01-1960至12-01-2017期间平均值为3,062,740.500人，共58份观测结果。该数据的历史最高值出现于12-01-2001，达3,187,197.000人，而历史最低值则出现于12-01-1960，为1,813,686.000人。CEIC提供的GR：最大城市人口数据处于定期更新的状态，数据来源于World Bank，数据归类于全球数据库的希腊 – Table GR.World Bank.WDI：人口和城市化进程统计。

GR：最大城市人口占城市总人口的百分比在12-01-2017达37.274%，相较于12-01-2016的37.402%有所下降。GR：最大城市人口占城市总人口的百分比数据按年更新，12-01-1960至12-01-2017期间平均值为41.153%，共58份观测结果。该数据的历史最高值出现于12-01-1975，达45.213%，而历史最低值则出现于12-01-2011，为37.226%。CEIC提供的GR：最大城市人口占城市总人口的百分比数据处于定期更新的状态，数据来源于World Bank，数据归类于全球数据库的希腊 – 表 GR.世行.WDI：人口和城市化进程统计。

The annual number of passengers of the Athens' three-lined subway system remained almost stable between 2015 to 2019. In 2020, amid the COVID-19 pandemic, the ridership of the metro network dropped by ** percent, compared to 2019 levels. Athens is the capital and largest city in Greece.