Francisco was the most popular first name for boys registered in Portugal in 2024, with 1,270 registrations. Lourenço followed, with 1,040 newborn baby boys under this name, while Vicente and Tomás closed the podium, with 1,036 registrations each. The names for baby girls in Portugal were dominated, in 2024, by the name Maria, which was registered 4,295 times. Alice and Benedita followed at a distance, with an average of 980 registrations each. Sinking birth rates and rising life expectancy in Portugal and throughout Europe   Europe’s crude birth rate was 9.2 in 2022, having slumped when compared to previous decades. The low birth rates on the continent occurred simultaneously with an increasing life expectancy, which emphasizes the aging of the European population. Also in 2022, Portugal presented one of the continent’s lowest birth rates, namely 7.8, and the average age of women when giving birth to their first child has risen continuously over the last decade. However, since 2021 there has been a decrease. Decreasing population in Portugal, but boosting numbers of elderly people   The Portuguese population is expected to decrease during the upcoming decade. As of 2035, it is predicted that Portugal’s nationals will equal to less than 10 million, almost 2.9 million of which will be 65 years of age and older. This figure presents an increase of almost 700,000 senior citizens compared to the recorded figures of 2015.

Visualization of first name statistics of Statistik Austria. As part of the application, two or more male or female first names can be selected and compared with each other. It shows a line chart with the number of children to whom the selected names were assigned between 1984 and 2022, as well as an Austria map in which the districts are colored according to the popularity of the names.

Maria was the most popular first name for girls registered in Portugal in 2024, with almost 4,300 registrations. Following from afar was the name Alice, with 992 newborn baby girls under this name. In third place, Benedita was chosen 973 times by parents to name their newborn girls. The names for baby boys in Portugal were dominated, in 2024, by the name Francisco, which was registered 1,270 times. Lourenço, Vicente, and Tomás followed, with around 1,030 registrations each. Women pursue family achievements at an increasingly older age   In Portugal, the mean age of women on their first marriage has increased constantly during the last decade. As of 2023, women were 34.3 years old at the time they got married for the first time, which is an increase of half-year compared to 2022. The mean age with which women get married is higher than the average age at which they give birth to their first child. Even though this value has also increased during the past decade, it has been decreasing since 2021, and, by 2023, it was 30.2 years of age. Payment inequality to the disadvantage of women   Women working in Portugal face a gender pay gap (GPG) which puts them at a disadvantage when compared to men. From 2010 to 2021, the gender pay gap decreased by five percent in the country, which is a step forward in terms of the fight against gender inequality. Nevertheless, the GPG was still more than 13 percent in 2022, which means that Portuguese women received 13 percent lower salaries than their male counterparts. When it comes to the average monthly earnings by gender, the tendency of higher salaries for men is widespread throughout Europe. On average, in 2022, men in Portugal received around 2,090 U.S. dollars per month, while women were paid 1,900 U.S. dollars monthly.

The list of the most frequently given first names, separated by gender and broken down by district. All existing years of first name data are also available collectively at https://github.com/berlinonline/haeufige-vornamen-berlin.

Top 20 most popular boys' and girls' names.

Source agency: Northern Ireland Statistics and Research Agency

Designation: Official Statistics not designated as National Statistics

Language: English

Alternative title: Popular First Names (Northern Ireland)

The list of the most frequently given first names, separated by gender and broken down by district. In contrast to previous years, if there are several first names, the position is also given. The position does not allow any conclusions to be drawn about the given name. All existing years of first name data are also available collectively at https://github.com/berlinonline/haeufige-vornamen-berlin.

Top 100 most popular boys' and girls' names.

Source agency: Northern Ireland Statistics and Research Agency

Designation: Official Statistics not designated as National Statistics

Language: English

Alternative title: Babies First Names Bulletin (Northern Ireland)

Population Register (GR) statistics. The most popular names of boys in Lithuania, TOP 20, ie boy's name, year of birth, number of names. Geographical sample of data - the whole territory of the country.

Most popular names of newborns (City of Brussels)

Note: figures are only available from 5 occurrences.

See more detailed statistics via: Statbel

My contribution for prospective parents of Vienna. Maybe he helps with the name choice - at least if you are either looking for a very popular name or want to exclude exactly that. Content of the visualization: The 100 most popular girls' and boys' names per year since 2000 in Vienna.

The dataset contains the most commonly used female names of residents of the Municipality of Pisa in the year indicated. Lesser-used names are not given to ensure anonymity.

The dataset contains the most commonly used male names of residents of the Municipality of Pisa in the year indicated. Lesser-used names are not given to ensure anonymity.

The list of the most frequently given first names, separated by gender and broken down by district. All existing years of first name data are also available collectively at https://github.com/berlinonline/haeufige-vornamen-berlin.

The 60 most common given names (phonetically identical first names were partly summarised) in the state of Salzburg for newborns with Austrian citizenship (expired time series)

Analysis of ‘List of common forenames 2020 ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/ee41f6eb-8ced-4427-a200-d92434040ed3 on 13 January 2022.

--- Dataset description provided by original source is as follows ---

The list of the most frequently awarded forenames, broken down by gender and by district. In contrast to previous years, in the case of several forenames, the position is also indicated. The position does not allow any conclusions to be drawn as to the name of the call.

All the existing years of the previous name data are also available at https://github.com/berlinonline/haeufige-vornamen-berlin.

--- Original source retains full ownership of the source dataset ---

This data set is a harmonized collection of existing data from GBIF, the EU-Forest project and the LUCAS survey. It has about 3 million observations and is supplemented by variables (e.g. location accuracy, land cover type, canopy height, etc.) which enable precise filtering for specific user applications.

The RDS file is created from an sf-object and suitable for fast reading in the R-programming environment. The CSV.GZ file contains records as a table with Easting and Northing in Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035) and can be fed in a GIS after being unzipped.

The code producing this data set is publicly available on GitLab.

Variables:

• id = unique point identifier
• easting = x coordinate
• northing = y coordinate
• country = ISO country code
• species = Latin species name
• genus = genus name
• scientific_name = long species name
• gbif_taxon_key = taxon key from GBIF
• gbif_genus_key = genus key from GBIF
• taxon_rank = species or genus
• year = year of observation
• accessed_through = database through which data was accessed (GBIF, LUCAS, EU-Forest)
• dataset_info = data set name (individual sub-data-set)
• citation = DOI citation of the individual data set
• license = distribution license
• location_accuracy = spatial accuracy of observation (meters)
• flag_location_issue = known location issues present
• flag_date_issue = known date issues present
• eoo = Extent of occurrence (applying the concept of natural geographical range used for the EU-Forest data set (Mauri et al., 2017) to all other data points. 1 = point inside species range; 0 = point outside; NA = EOO polygon not available for this species)
• dbh = Diameter Breast Height (only recorded for observations from the EU-Forest data set (Mauri et al., 2017))
• lc1 = LUCAS land cover type 1 (only recorded for observations from LUCAS data)
• lc2 = LUCAS land cover type 2 (only recorded for observations from LUCAS data)
• landmask_country = land mask overlay 30 meters (NA = not on land)
• corine = CORINE 2018 land cover type (extracted from the 100 meter raster data set)
• nightlights = light pollution observed by VIIRS (proxy for remoteness / distance to human structures)
• canopy_height = canopy height derived from GEDI waveform LiDAR point data
• natura_2000 = Natura 2000 site code (if a point falls inside a protected area (GIS-layer) this variable contains the site identification code; all sites can be explored on an interactive map)
• freq_location = number of points with identical location (in some cases one location has multiple observation, differing in species and/or year. This may lead to difficulties in certain modeling tasks)
• geometry = point geometry in ETRS89 / LAEA Europe

See this detailed documentation for more insights into each variable.

If you would like to know more about the creation of this data set, see

1. the R-Markdown documenting the process (GitLab repository)
2. the talk at OpenGeoHub Summer School 2020 (Youtube)

Some advice: This data set is a puzzle with pieces from many different sources. Take some time to explore before including it in your work. Use summary statistics to see which variables have NAs and how many. Choose your filtering criteria wisely. For example, some points with the highest location accuracy have no record for the year of observations. You would exclude these, if "year > 1990" was your criteria.

This work has received funding from the European Union's the Innovation and Networks Executive Agency (INEA) under Grant Agreement Connecting Europe Facility (CEF) Telecom project 2018-EU-IA-0095 (https://ec.europa.eu/inea/en/connecting-europe-facility/cef-telecom/2018-eu-ia-0095).

Our Europe Zip Code Database offers comprehensive postal code data for spatial analysis, including postal and administrative areas for numerous European countries. This dataset contains accurate and up-to-date information on all administrative divisions, cities, and zip codes, making it an invaluable resource for various applications such as address capture and validation, map and visualization, reporting and business intelligence (BI), master data management, logistics and supply chain management, and sales and marketing. Our location data packages are available in various formats, including CSV, optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more. Product features include fully and accurately geocoded data, multi-language support with address names in local and foreign languages, comprehensive city definitions, and the option to combine map data with UNLOCODE and IATA codes, time zones, and daylight saving times. Companies choose our location databases for their enterprise-grade service, reduction in integration time and cost by 30%, and weekly updates to ensure the highest quality.

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The most common first names in 2013

Please note: for a correct view and use of this dataset it is advisable to consult it at original page on the Portal of the Municipality of Livorno. At the same address there are also, for the enabled datasets, additional access formats, the preview of the visualization via API call, the consultation of the fields in DCAT-AP IT format, the possibility to express an evaluation and comment on the dataset itself.

All resource formats available for this dataset can be downloaded as ZIP packages: inside the package sarà available the resource in the chosen format, complete with all the information on the metadata and the license associated with it.

Most used family names on the territory of the Municipality of Livorno. Dataset updated on 15 March 2016