In 2025, Italy’s resident population is estimated to be almost 59 million inhabitants. About one-sixth of them lived in Lombardy, the most populous region in the country. Lazio and Campania followed, with roughly 5.7 million and 5.6 million inhabitants, respectively. These figures are mainly driven by Rome and Naples, the administrative capitals of these regions, and two of the largest metropolitan areas in the country. Which region has the oldest population? The population in Italy has become older and older over the last years. The average age in the country is equal to 46.8 years, but in some regions this figure is even higher. Liguria records an average age of 49.6 years and has one of the lowest birth rates in the country. Demographic trends for the future Liguria’s case, however, is not an outlier. Italy is already the country with the highest share of old people in Europe. At the same time, the very low number of new births means that, despite an always-increasing life expectancy, the Italian population is declining. Indeed, projections estimate that the country will have five million fewer inhabitants by 2050.

WorldPop produces different types of gridded population count datasets, depending on the methods used and end application. An overview of the data can be found in Tatem et al, and a description of the modelling methods used found in Stevens et al. The 'Global per country 2000-2020' datasets represent the outputs from a project focused on construction of consistent 100m resolution population count datasets for all countries of the World for each year 2000-2020. These efforts necessarily involved some shortcuts for consistency. The 'individual countries' datasets represent older efforts to map populations for each country separately, using a set of tailored geospatial inputs and differing methods and time periods. The 'whole continent' datasets are mosaics of the individual countries datasets

WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00645

The population of Italy is getting older every year, becoming one of the oldest ones in the world. In 2025, the average age of the Italian population was 46.8 years, 3.4 years more than the average age registered in 2010. However, the age differs significantly depending on the region. According to the most recent data for 2025, the “oldest” citizens of the Italian peninsula live in the region of Liguria, with an average age 49.6 years, whereas the youngest are in Campania, 44.5 years on average. Women live longer than men The difference in the average age of the population can be observed not only on a regional basis, but also between genders. In 2021, Italian women were on average roughly three years older than men. When it comes to the life expectancy, data confirm the longevity of Italian women. In fact, females in Italy are expected to live on average about four years longer than men. The Old Continent In 2024, Europe was the continent with the highest share of population older than 65 years. Whereas the worldwide percentage of the population over 65 years was of ten percent, the percentage of elderly people in the Old Continent reached 20 percent.

Synthetic populations for regions of the World (SPW) | Italy

Dataset information

A synthetic population of a region as provided here, captures the people of the region with selected demographic attributes, their organization into households, their assigned activities for a day, the locations where the activities take place and thus where interactions among population members happen (e.g., spread of epidemics).

License

CC-BY-4.0

Acknowledgment

This project was supported by the National Science Foundation under the NSF RAPID: COVID-19 Response Support: Building Synthetic Multi-scale Networks (PI: Madhav Marathe, Co-PIs: Henning Mortveit, Srinivasan Venkatramanan; Fund Number: OAC-2027541).

Contact information

Henning.Mortveit@virginia.edu

Identifiers

Region name	Italy
Region ID	ita
Model	coarse
Version	0_9_0

Sources

Description	Name	Version	Url
Activity template data	World Bank	2021	https://data.worldbank.org
Administrative boundaries	ADCW	7.6	https://www.adci.com/adc-worldmap
Curated POIs based on OSM	SLIPO/OSM POIs		http://slipo.eu/?p=1551 https://www.openstreetmap.org/
Household data	IPUMS		https://international.ipums.org/international
Population count with demographic attributes	GPW	v4.11	https://sedac.ciesin.columbia.edu/data/set/gpw-v4-admin-unit-center-points-population-estimates-rev11

Files description

Base data files (ita_data_v_0_9.zip)

Filename	Description
ita_person_v_0_9.csv	Data for each person including attributes such as age, gender, and household ID.
ita_household_v_0_9.csv	Data at household level.
ita_residence_locations_v_0_9.csv	Data about residence locations
ita_activity_locations_v_0_9.csv	Data about activity locations, including what activity types are supported at these locations
ita_activity_location_assignment_v_0_9.csv	For each person and for each of their activities, this file specifies the location where the activity takes place

Derived data files

Filename	Description
ita_contact_matrix_v_0_9.csv	A POLYMOD-type contact matrix constructed from a network representation of the location assignment data and a within-location contact model.

Validation and measures files

Filename	Description
ita_household_grouping_validation_v_0_9.pdf	Validation plots for household construction
ita_activity_durations_{adult,child}_v_0_9.pdf	Comparison of time spent on generated activities with survey data
ita_activity_patterns_{adult,child}_v_0_9.pdf	Comparison of generated activity patterns by the time of day with survey data
ita_location_construction_0_9.pdf	Validation plots for location construction
ita_location_assignement_0_9.pdf	Validation plots for location assignment, including travel distribution plots
ita_ita_ver_0_9_0_avg_travel_distance.pdf	Choropleth map visualizing average travel distance
ita_ita_ver_0_9_0_travel_distr_combined.pdf	Travel distance distribution
ita_ita_ver_0_9_0_num_activity_loc.pdf	Choropleth map visualizing number of activity locations
ita_ita_ver_0_9_0_avg_age.pdf	Choropleth map visualizing average age
ita_ita_ver_0_9_0_pop_density_per_sqkm.pdf	Choropleth map visualizing population density
ita_ita_ver_0_9_0_pop_size.pdf	Choropleth map visualizing population size

Age and sex structures: WorldPop produces different types of gridded population count datasets, depending on the methods used and end application. An overview of the data can be found in Tatem et al, and a description of the modelling methods used found in Tatem et al and Pezzulo et al. The 'Global per country 2000-2020' datasets represent the outputs from a project focused on construction of consistent 100m resolution population count datasets for all countries of the World for each year 2000-2020 structured by male/female and 5-year age classes (plus a <1 year class). These efforts necessarily involved some shortcuts for consistency. The 'individual countries' datasets represent older efforts to map population age and sex counts for each country separately, using a set of tailored geospatial inputs and differing methods and time periods. The 'whole continent' datasets are mosaics of the individual countries datasets. WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).

This is a sample dataset for the Biodemography Workshop. Within this dataset, input files related to demographic statistics will be considered, specifically population by gender and by Nuts2 in Italy, as well as shapefiles for map creation. The variables to be analyzed include the ratio between male and female, and vice versa. The final output consists of two maps. The data source is Istat, which provides these with a CC BY license: 1-https://demo.istat.it/app/?i=POS&l=it 2-https://www.istat.it/it/archivio/222527 To conduct the analysis, the open-source software R-Studio was used. The data management methodology will also be outlined in a Data Management Plan, written using Overleaf, in which we will provide more detailed information.

The general version represents both the natural features of the territory and the main human settlements: reports on the main themes of transport (main road network, railways, ports, airports), hydrography (up to the 4th order of IGM, lakes and main reservoirs, glaciers), settlements (archaeological sites, historic buildings and imported religions), inhabited places distinct by inhabitants and administrative importance, as well as state and regional boundaries; the orography is represented with ipsometric shades, smoke and altitude points. The physical version represents the natural features of the territory: reports in more detail the themes of hydrography, the reliefs, the main geographical regions (masses and mountain ranges, highlands, valleys, plains, seas, gulfs, peaks, chiefs, islands and archipelagos) with their toponyomas, and reports the inhabited places up to the provincial capitals and state borders; the orography is represented with ipsometric shades, smoke and altitude points. The political version represents the main administrative aspects of the territory: reports the boundaries of State, Region and Province, the main inhabited areas up to the provincial capitals and municipalities with a population of more than 50 000 inhabitants (distinguished by administrative importance and by population groups), the place names for administrative units and the main geographical regions and natural forms of the territory; the orography is represented with smoke

In 2023, the Italian region which registered the highest fertility rate was Trentino-South Tyrol, where the average number of children born per female reached 1.42 infants. Over the last years, the fertility rate in Italy has constantly decreased, except for 2021 when a slight increase by 0.01 points was recorded. Fewer and fewer children born per womanThe average number of children born per female significantly varied from the middle of the twentieth century to present days. In 2017, Italian women were on average a mother of one child, whereas about seven decades earlier, females had on average at least two kids. The lowest fertility rates worldwide From the global perspective, Italy was one of the world's twenty countries with the lowest fertility rate in 2023. This figure in Taiwan reached only 1.07 children per woman, placing the country on top of the ranking.

New-ID: NBI16

Agro-ecological zones datasets is made up of AEZBLL08, AEZBLL09, AEZBLL10.

The Africa Agro-ecological Zones Dataset documentation

Files: AEZBLL08.E00 Code: 100025-011 AEZBLL09.E00 100025-012 AEZBLL10.E00 100025-013

Vector Members The E00 files are in Arc/Info Export format and should be imported with the Arc/Info command Import cover In-Filename Out-Filename.

The Africa agro-ecological zones dataset is part of the UNEP/FAO/ESRI Database project that covers the entire world but focuses on Africa. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. Most data for the database were provided by Food and Agriculture Organization (FAO), the Soil Resources, Management and Conservation Service Land and Water Development Division, Italy. The daset was developed by United Nations Environment Program (UNEP), Kenya. The base maps that were used were the UNESCO/FAO Soil Map of the world (1977) in Miller Oblated Stereographic projection, the Global Navigation and Planning Charts (various 1976-1982) and the National Geographic Atlas of the World (1975). basemap and the source maps. The digitizing was done with a spatial resolution of 0.002 inches. The maps were then transformed from inch coordinates to latitude/longitude degrees. The transformation was done by an unpublished algorithm (by US Geological Survey and ESRI) to create coverages for one-degree graticules. This edit step required appending the country boundaries from Administrative Unit map and then producing the computer plot.

Contact: UNEP/GRID-Nairobi, P O Box 30552 Nairobi, Kenya FAO, Soil Resources, Management and Conservation Service, 00100, Rome, Italy ESRI, 380 New York Street, Redlands, CA 92373, USA

The AEZBLL08 data covers North-West of African continent The AEZBLL09 data covers North-East of African continent The AEZBLL10 data covers South of African continent

References:

ESRI. Final Report UNEP/FAO world and Africa GIS data base (1984). Internal Publication by ESRI, FAO and UNEP

FAO/UNESCO. Soil Map of the World (1977). Scale 1:5000000. UNESCO, Paris

Defence Mapping Agency. Global Navigation and Planning Charts for Africa (various dates:1976-1982). Scale 1:5000000. Washington DC.

G.M. Grosvenor. National Geographic Atlas of the World (1975). Scale 1:8500000. National Geographic Society, Washington DC.

FAO. Statistical Data on Existing Animal Units by Agro-ecological Zones for Africa (1983). Prepared by Todor Boyadgiev of the Soil Resources, Management and Conservation Services Division.

FAO. Statistical Data on Existing and Potential Populations by Agro-ecological Zones for Africa (1983). Prepared by Marina Zanetti of the Soil Resources, Management and Conservation Services Division. FAO. Report on the Agro-ecological Zones Project. Vol.I (1978), Methodology & Result for Africa. World Soil Resources No.48.

Source : UNESCO/FAO Soil Map of the World, scale 1:5000000 Publication Date : Dec 1984 Projection : Miller Type : Polygon Format : Arc/Info Export non-compressed Related Datasets : All UNEP/FAO/ESRI Datasets, Landuse (100013/05, New-ID: 05 FAO Irrigable Soils Datasets and Water balance (100050/53)

The European Copernicus Coastal Flood Awareness System (ECFAS) project will contribute to the evolution of the Copernicus Emergency Monitoring Service by demonstrating the technical and operational feasibility of a European Coastal Flood Awareness System. Specifically, ECFAS will provide a much-needed solution to bolster coastal resilience to climate risk and reduce population and infrastructure exposure by monitoring and supporting disaster preparedness, two factors that are fundamental to damage prevention and recovery if a storm hits.

The ECFAS Proof-of-Concept development will run from January 2021-December 2022. The ECFAS project is a collaboration between Istituto Universitario di Studi Superiori IUSS di Pavia (Italy, ECFAS Coordinator), Mercator Ocean International (France), Planetek Hellas (Greece), Collecte Localisation Satellites (France), Consorzio Futuro in Ricerca (Italy), Universitat Politecnica de Valencia (Spain), University of the Aegean (Greece), and EurOcean (Portugal), and is funded by the European Commission H2020 Framework Programme within the call LC-SPACE-18-EO-2020 - Copernicus evolution: research activities in support of the evolution of the Copernicus services.

This project has received funding from the European Union’s Horizon 2020 programme

Description of the containing files inside the Dataset.

The dataset was divided at European country level, except the Adriatic area which was extracted as a region and not on a country level due to the small size of the countries. The buffer zone of each data was 10km inland in order to be correlated with the new Copernicus product Coastal Zone LU/LC.

Specifically, the dataset includes the new Coastal LU/LC product which was implemented by the EEA and became available at the end of 2020. Additional information collected in relation to the location and characteristics of transport (road and railway) and utility networks (power plants), population density and time variability. Furthermore, some of the publicly available datasets that were used in CEMS related to the abovementioned assets were gathered such as OpenStreetMap (building footprints, road and railway network infrastructures), GeoNames (populated places but also names of administrative units, rivers and lakes, forests, hills and mountains, parks and recreational areas, etc.), the Global Human Settlement Layer (GHS) and Global Human Settlement Population Grid (GHS-POP) generated by JRC. Also, the dataset contains 2 layers with statistics information regarding the population of Europe per sex and age divided in administrative units at NUTS level 3. The first layers includes information fro the whole Europe and the second layer has only the information regaridng the population at the Coastal area. Finally, the dataset includes the global database of Floods protection standars. Below there are tables which present the dataset.

Copernicus Land Monitoring Service	Resolution	Comment
Coastal LU/LC	1:10.000	A Copernicus hotspot product to monitor landscape dynamics in coastal zones
EU-Hydro - Coastline	1:30.000	EU-Hydro is a dataset for all European countries providing the coastline
Natura 2000	1: 100000	A Copernicus hotspot product to monitor important areas for nature conservation
European Settlement Map	10m	A spatial raster dataset that is mapping human settlements in Europe
Imperviousness Density	10m	The percentage of sealed area
Impervious Built-up	10m	The part of the sealed surfaces where buildings can be found
Grassland 2018	10m	A binary grassland/non-grassland product
Tree Cover Density 2018	10m	Level of tree cover density in a range from 0-100%

Joint Research Center	Resolution	Comment
Global Human Settlement Population Grid GHS-POP)	250m	Residential population estimates for target year 2015
GHS settlement model layer (GHS-SMOD)	1km	The GHS Settlement Model grid delineates and classify settlement typologies via a logic of population size, population and built-up area densities
GHS-BUILT	10m	Built-up grid derived from Sentinel-2 global image composite for reference year 2018
ENACT 2011 Population Grid (ENACT-POP R2020A)	1km	The ENACT is a population density for the European Union that take into account major daily and monthly population variations
JRC Open Power Plants Database (JRC-PPDB-OPEN)	-	Europe’s open power plant database
GHS functional urban areas (GHS-FUA R2019A)	1km	City and its commuting zone (area of influence of the city in terms of labour market flows)
GHS Urban Centre Database (GHS-UCDB R2019A)	1km	Urban Centres defined by specific cut-off values on resident population and built-up surface

Additional Data	Resolution	Comment
Open Street Map (OSM)	-	BF, Transportation Network, Utilities Network, Places of Interest
CEMS	-	Data from Rapid Mapping activations in Europe
GeoNames	-	Populated places, Adm. units, Hydrography, Forests, Hills/Mountains, Parks, etc.
Global Administrative Areas	-	Administrative areas of all countries, at all levels of sub-division
NUTS3 Population Age/Sex Group	-	Eurostat population by age ansd sex statistics interesected with the NUTS3 Units
FLOPROS		A global database of FLOod PROtection Standards, which comprises information in the form of the flood return period associated with protection measures, at different spatial scales

Disclaimer:

ECFAS partners provide the data "as is" and "as available" without warranty of any kind. The ECFAS partners shall not be held liable resulting from the use of the information and data provided.

This project has received funding from the Horizon 2020 research and innovation programme under grant agreement No. 101004211

As of December 2024, Lombardy was the region in Italy hosting the largest share of immigrants, followed by Emilia-Romagna, Lazio, and Piedmont. Lombardy is the region with the highest number of inhabitants in the country. The north Italian region has ten million residents, around one sixth of the total national population, and was housing 18,200 immigrants. The Mediterranean route to Europe  In 2020, 955 migrants died or went missing in the Italian Central Mediterranean Sea in the attempt to reach Europe. In 2024, 66,317 people arrived at the Italian shores, 91,300 individuals less compared to 2023. Death and missing cases still represent a serious hazard for the people who want to reach Italy from North Africa. Racism on the rise in Italy Race-related violence is strictly correlated with immigration. According to 2020 data, the cases of racial physical violence increased, in particular between 2016 and 2018. Over these three years, the cases of body violence ranged from 24 to 127 attacks. Similarly, insults, threats, and harassment became more widespread. Between 2017 and 2019, the cases grew from 88 to 206, while only in the first three months of 2020 there were 53 episodes of racist insults, threats, and harassment.

In 1938, the year before the outbreak of the Second world War, the countries with the largest populations were China, the Soviet Union, and the United States, although the United Kingdom had the largest overall population when it's colonies, dominions, and metropole are combined. Alongside France, these were the five Allied "Great Powers" that emerged victorious from the Second World War. The Axis Powers in the war were led by Germany and Japan in their respective theaters, and their smaller populations were decisive factors in their defeat. Manpower as a resource In the context of the Second World War, a country or territory's population played a vital role in its ability to wage war on such a large scale. Not only were armies able to call upon their people to fight in the war and replenish their forces, but war economies were also dependent on their workforce being able to meet the agricultural, manufacturing, and logistical demands of the war. For the Axis powers, invasions and the annexation of territories were often motivated by the fact that it granted access to valuable resources that would further their own war effort - millions of people living in occupied territories were then forced to gather these resources, or forcibly transported to work in manufacturing in other Axis territories. Similarly, colonial powers were able to use resources taken from their territories to supply their armies, however this often had devastating consequences for the regions from which food was redirected, contributing to numerous food shortages and famines across Africa, Asia, and Europe. Men from annexed or colonized territories were also used in the armies of the war's Great Powers, and in the Axis armies especially. This meant that soldiers often fought alongside their former-enemies. Aftermath The Second World War was the costliest in human history, resulting in the deaths of between 70 and 85 million people. Due to the turmoil and destruction of the war, accurate records for death tolls generally do not exist, therefore pre-war populations (in combination with other statistics), are used to estimate death tolls. The Soviet Union is believed to have lost the largest amount of people during the war, suffering approximately 24 million fatalities by 1945, followed by China at around 20 million people. The Soviet death toll is equal to approximately 14 percent of its pre-war population - the countries with the highest relative death tolls in the war are found in Eastern Europe, due to the intensity of the conflict and the systematic genocide committed in the region during the war.

The world's Jewish population has had a complex and tumultuous history over the past millennia, regularly dealing with persecution, pogroms, and even genocide. The legacy of expulsion and persecution of Jews, including bans on land ownership, meant that Jewish communities disproportionately lived in urban areas, working as artisans or traders, and often lived in their own settlements separate to the rest of the urban population. This separation contributed to the impression that events such as pandemics, famines, or economic shocks did not affect Jews as much as other populations, and such factors came to form the basis of the mistrust and stereotypes of wealth (characterized as greed) that have made up anti-Semitic rhetoric for centuries. Development since the Middle Ages The concentration of Jewish populations across the world has shifted across different centuries. In the Middle Ages, the largest Jewish populations were found in Palestine and the wider Levant region, with other sizeable populations in present-day France, Italy, and Spain. Later, however, the Jewish disapora became increasingly concentrated in Eastern Europe after waves of pogroms in the west saw Jewish communities move eastward. Poland in particular was often considered a refuge for Jews from the late-Middle Ages until the 18th century, when it was then partitioned between Austria, Prussia, and Russia, and persecution increased. Push factors such as major pogroms in the Russian Empire in the 19th century and growing oppression in the west during the interwar period then saw many Jews migrate to the United States in search of opportunity.

Based on a comparison of coronavirus deaths in 210 countries relative to their population, Peru had the most losses to COVID-19 up until July 13, 2022. As of the same date, the virus had infected over 557.8 million people worldwide, and the number of deaths had totaled more than 6.3 million. Note, however, that COVID-19 test rates can vary per country. Additionally, big differences show up between countries when combining the number of deaths against confirmed COVID-19 cases. The source seemingly does not differentiate between "the Wuhan strain" (2019-nCOV) of COVID-19, "the Kent mutation" (B.1.1.7) that appeared in the UK in late 2020, the 2021 Delta variant (B.1.617.2) from India or the Omicron variant (B.1.1.529) from South Africa.

The difficulties of death figures

This table aims to provide a complete picture on the topic, but it very much relies on data that has become more difficult to compare. As the coronavirus pandemic developed across the world, countries already used different methods to count fatalities, and they sometimes changed them during the course of the pandemic. On April 16, for example, the Chinese city of Wuhan added a 50 percent increase in their death figures to account for community deaths. These deaths occurred outside of hospitals and went unaccounted for so far. The state of New York did something similar two days before, revising their figures with 3,700 new deaths as they started to include “assumed” coronavirus victims. The United Kingdom started counting deaths in care homes and private households on April 29, adjusting their number with about 5,000 new deaths (which were corrected lowered again by the same amount on August 18). This makes an already difficult comparison even more difficult. Belgium, for example, counts suspected coronavirus deaths in their figures, whereas other countries have not done that (yet). This means two things. First, it could have a big impact on both current as well as future figures. On April 16 already, UK health experts stated that if their numbers were corrected for community deaths like in Wuhan, the UK number would change from 205 to “above 300”. This is exactly what happened two weeks later. Second, it is difficult to pinpoint exactly which countries already have “revised” numbers (like Belgium, Wuhan or New York) and which ones do not. One work-around could be to look at (freely accessible) timelines that track the reported daily increase of deaths in certain countries. Several of these are available on our platform, such as for Belgium, Italy and Sweden. A sudden large increase might be an indicator that the domestic sources changed their methodology.

Where are these numbers coming from?

The numbers shown here were collected by Johns Hopkins University, a source that manually checks the data with domestic health authorities. For the majority of countries, this is from national authorities. In some cases, like China, the United States, Canada or Australia, city reports or other various state authorities were consulted. In this statistic, these separately reported numbers were put together. For more information or other freely accessible content, please visit our dedicated Facts and Figures page.

In 2024, Monaco was the European country estimated to have the highest fertility rate. The country had a fertility rate of 2.1 children per woman. Other small countries such as Gibraltar or Montenegro also came towards the top of the list for 2024, while the large country with the highest fertility rate was France, with 1.64 children per woman. On the other hand, Ukraine had the lowest fertility rate, averaging around one child per woman.

The smallest country in the world is Vatican City, with a landmass of just **** square kilometers (0.19 square miles). Vatican City is an independent state surrounded by Rome. Vatican City is not the only small country located inside Italy. San Marino is another microstate, with a land area of ** square kilometers, making it the fifth-smallest country in the world. Many of these small nations have equally small populations, typically less than ************** inhabitants. However, the population of Singapore is almost *** million, and it is the twentieth smallest country in the world with a land area of *** square kilometers. In comparison, Jamaica is almost eight times larger than Singapore, but has half the population.

This statistic shows the estimated number of Muslims living in different European countries as of 2016. Approximately **** million Muslims were estimated to live in France, the most of any country listed. Germany and the United Kingdom also have large muslim populations with **** million and **** million respectively.