In 2025, Moscow was the largest city in Europe with an estimated urban agglomeration of 12.74 million people. The French capital, Paris, was the second largest city in 2025 at 11.35 million, followed by the capitals of the United Kingdom and Spain, with London at 9.84 million and Madrid at 6.81 million people. Istanbul, which would otherwise be the largest city in Europe in 2025, is excluded as it is only partially in Europe, with a sizeable part of its population living in Asia. Europe’s population is almost 750 million Since 1950, the population of Europe has increased by approximately 200 million people, increasing from 550 million to 750 million in these seventy years. Before the turn of the millennium, Europe was the second-most populated continent, before it was overtaken by Africa, which saw its population increase from 228 million in 1950 to 817 million by 2000. Asia has consistently had the largest population of the world’s continents and was estimated to have a population of 4.6 billion. Europe’s largest countries Including its territory in Asia, Russia is by far the largest country in the world, with a territory of around 17 million square kilometers, almost double that of the next largest country, Canada. Within Europe, Russia also has the continent's largest population at 145 million, followed by Germany at 83 million and the United Kingdom at almost 68 million. By contrast, Europe is also home to various micro-states such as San Marino, which has a population of just 30 thousand.

As of 2025, Tokyo-Yokohama in Japan was the largest world urban agglomeration, with 37 million people living there. Delhi ranked second with more than 34 million, with Shanghai in third with more than 30 million inhabitants.

By 1800, London had grown to be the largest city in Western Europe with just under one million inhabitants. Paris was now the second largest city, with over half a million people, and Naples was the third largest city with 450 thousand people. The only other cities with over two hundred thousand inhabitants at this time were Vienna, Amsterdam and Dublin. Another noticeable development is the inclusion of many more northern cities from a wider variety of countries. The dominance of cities from France and Mediterranean countries was no longer the case, and the dispersal of European populations in 1800 was much closer to how it is today, more than two centuries later.

A dataset of grid level (500m by 500m) accessibility indicators measured in peak traffic and free flow conditions for all cities (represented by the Functional Urban Area) with more than 250 thousand people in EU27, the UK, Switzerland and Norway.

The two files contains respectively a map of driving distances around major and minor cities in Europe. The distance is given by range of 5 km from 5 to 45 km in a resolution of 100m*100m. The encoding is uint8. The metropolitan areas considered in this study are those from this study of functional urban area definition. The corresponding geofile is available at this adress. Most of the cities with more than 50'000 habitants are included, some cities have been removed or added depending on the local context. As the geofile contains only polygones of the metropolitan areas and not the city point, the points form cities with the number of habitants have been dowloaded from Natural Data and then selected by comparison with the polygones. Cities with more than 20'000 have also been selected as commuting center for rural areas. Only the cities outside the metropolitan commuting zones are included. The calculation of driving distances around centers have been performed with OpenRouteService (personal API key required, free of charge).

The average for 2023 based on 27 countries was 74.4 percent. The highest value was in Belgium: 98.19 percent and the lowest value was in Slovakia: 54.03 percent. The indicator is available from 1960 to 2023. Below is a chart for all countries where data are available.

This repository contains data described in the article "Data on different types of green spaces and their accessibility in the seven largest urban regions in Finland". More details of the data are available in the upcoming data description article (manuscript can be found in this repository).

Abstract: Access to good quality green spaces in urban regions is vital for the well-being of citizens. In this article, we present data on green space quality and path distances to different types of green spaces. The path distances represent green space accessibility using active travel modes (walking, cycling). The path distances were calculated using the pedestrian street network across the seven largest urban regions in Finland. We derived the green space typology from the Urban Atlas Data that is available across functional urban areas in Europe and enhanced it with national data on water bodies, conservation areas and recreational facilities and routes from Finland. We extracted the walkable street network from OpenStreetMap and calculated shortest paths to different types of green spaces using open-source Python programming tools. Network distances were calculated up to ten kilometers from each green space edge and the distances were aggregated into a 250 m x 250 m statistical grid that is interoperable with various statistical data from Finland. The geospatial data files representing the different types of green spaces, network distances across the seven urban regions, as well as the processing and analysis scripts are shared in an open repository. These data offer actionable information about green space accessibility in Finnish city regions and support the integration of green space quality and active travel modes into further research and planning activities.

Related research article:

Viinikka, A., Tiitu, M., Heikinheimo, V., Halonen, J. I., Nyberg, E., & Vierikko, K. (2023). Associations of neighborhood-level socioeconomic status, accessibility, and quality of green spaces in Finnish urban regions. Applied Geography, 157, 102973. https://doi.org/10.1016/j.apgeog.2023.102973

The city of Paris in France had an estimated gross domestic product of 757.6 billion Euros in 2021, the most of any European city. Paris was followed by the spanish capital, Madrid, which had a GDP of 237.5 billion Euros, and the Irish capital, Dublin at 230 billion Euros. Milan, in the prosperous north of Italy, had a GDP of 228.4 billion Euros, 65 billion euros larger than the Italian capital Rome, and was the largest non-capital city in terms of GDP in Europe. The engine of Europe Among European countries, Germany had by far the largest economy, with a gross domestic product of over 4.18 trillion Euros. The United Kingdom or France have been Europe's second largest economy since the 1980s, depending on the year, with forecasts suggesting France will overtake the UK going into the 2020s. Germany however, has been the biggest European economy for some time, with five cities (Munich, Berlin, Hamburg, Stuttgart and Frankfurt) among the 15 largest European cities by GDP. Europe's largest cities In 2023, Moscow was the largest european city, with a population of nearly 12.7 million. Paris was the largest city in western Europe, with a population of over 11 million, while London was Europe's third-largest city at 9.6 million inhabitants.

This statistic shows the largest urban settlements in the Netherlands in 2021. In 2021, around 1.13 million people lived in Amsterdam, making it the largest city in the Netherlands. Population of the Netherlands With the global financial crisis in 2008 as well as the Euro zone crisis, many countries in Europe suffered a great economic impact. In spite of the crisis, the Netherlands maintained a stable economy over the past decade. The country's unemployment rate, for example, has been kept at a relatively low level in comparison to other countries in Europe also affected by the economic crisis. In 2014, Spain had an unemployment rate of more than 25 percent. The Netherlands' population has also seen increases in growth in comparison to previous years, with the figures slowly decreasing since 2011. As a result of the increase in population, the degree of urbanization - which is the share of the population living in urban areas - has increased, while the size of the labor force in the Netherlands has been relatively stable over the past decade. The population density of inhabitants per square kilometer in the Netherlands has also increased. Large cities in the Netherlands have experienced the impact of the population density growth and increase in the size of the labor force first hand. Three cities in the Netherlands have over half a million residents (as can be seen above). Additionally, more and more visitors are coming to the kingdom: The number of tourists in the Netherlands has increased significantly since 2001, a change which has also impacted the country's metropolises. Due to its location and affordable accommodation prices, the country’s tourism industry is developing and the largest cities in the Netherlands are taking advantage of it.

Introduction: Access to the healthcare system when patients are vulnerable and living outside metropolitan areas can be challenging. Our objective was to explore healthcare system satisfaction of urban and rural inhabitants depending on financial and health vulnerabilities.Methods: Repeated cross-sectional data from 353,523 European citizens (2002–2016). Multivariable associations between rural areas, vulnerability factors and satisfaction with the healthcare system were assessed with linear mixed regressions and adjusted with sociodemographic and control factors.Results: In unadjusted analysis, the people who lived in houses in the countryside and those who lived in the suburbs were the most satisfied with the healthcare system. In the adjusted model, residents living in big cities had the highest satisfaction. Financial and health vulnerabilities were associated with less satisfaction with the healthcare system, with a different effect according to the area of residence: the presence of health vulnerability was more negatively correlated with the healthcare system satisfaction of big city inhabitants, whereas financial vulnerability was more negatively correlated with the satisfaction of those living in countryside homes.Conclusion: Vulnerable residents, depending on their area of residence, may require special attention to increase their satisfaction with the healthcare system.

Tap waters were collected from major metropolitan areas of the western United States. Tap waters were sampled between 2012-2015 from seven metropolitan areas: Los Angeles-Long Beach-Santa Ana (CA), Phoenix-Mesa-Glendale (AZ), Salt Lake City (UT), San Diego-Carlsbad-San Marcos (CA), San Francisco-Oakland-Fremont (CA), San Jose-Sunnyvale-Santa Clara (CA), and Riverside-San Bernardino-Ontario (CA). These areas represent some of the most populous in the US and employ a diversity of water management practices. Here hydrogen (d2H) and oxygen (d18O) isotope values along with strontium isotope ratios (87Sr/86Sr) and element abundances were measured. d2H and d18O of 2039 tap waters were measured following Tipple et al., 2017 (Water Research, 119, 212-224). 87Sr/86Sr and elemental compositions of 820 and 806 waters were analyzed following Tipple et al., 2018 (Scientific Reports, 8, 2224), respectively. The purpose of these data was to assess spatial, temporal, and climatic dynamics in isotope and elemental geochemistry of tap waters. We found that the isotope and elemental geochemistry of tap waters corresponded to the water sources (e.g., transported water, local surface water, groundwater, etc.) and management practices (e.g., storage in open reservoirs, mixing, etc.) for discrete areas within the larger metropolitan areas.

In the year 1500, the share of Western Europe's population living in urban areas was just six percent, but this rose to 31 percent by the end of the 19th century. Despite this drastic change, development was quite slow between 1500 and 1800, and it was not until the industrial revolution when there was a spike in urbanization. As Britain was the first region to undergo the industrial revolution, from around the 1760s until the 1840s, these areas were the most urbanized in Europe by 1890. The Low Countries Prior to the 19th century, Belgium and the Netherlands had been the most urbanized regions due to the legacy of their proto-industrial areas in the medieval period, and then the growth of their port cities during the Netherlands' empirical expansion (Belgium was a part of the Netherlands until the 1830s). Belgium was also quick to industrialize in the 1800s, and saw faster development than its larger, more economically powerful neighbors, France and Germany. Least-urban areas Ireland was the only Western European region with virtually no urbanization in the 16th and 17th century, but the industrial growth of Belfast and Dublin (then major port cities of the British Empire) saw this change by the late-1800s. The region of Scandinavia was the least-urbanized area in Western Europe by 1890, but it saw rapid economic growth in Europe during the first half of the following century.

The dataset and the validation are fully described in a Nature Scientific Data Descriptor https://www.nature.com/articles/s41597-019-0265-5

If you want to use this dataset in an interactive environment, then use this link https://mybinder.org/v2/gh/GeographerAtLarge/TravelTime/HEAD

The following text is a summary of the information in the above Data Descriptor.

The dataset is a suite of global travel-time accessibility indicators for the year 2015, at approximately one-kilometre spatial resolution for the entire globe. The indicators show an estimated (and validated), land-based travel time to the nearest city and nearest port for a range of city and port sizes.

The datasets are in GeoTIFF format and are suitable for use in Geographic Information Systems and statistical packages for mapping access to cities and ports and for spatial and statistical analysis of the inequalities in access by different segments of the population.

These maps represent a unique global representation of physical access to essential services offered by cities and ports.

The datasets travel_time_to_cities_x.tif (where x has values from 1 to 12) The value of each pixel is the estimated travel time in minutes to the nearest urban area in 2015. There are 12 data layers based on different sets of urban areas, defined by their population in year 2015 (see PDF report).

travel_time_to_ports_x (x ranges from 1 to 5)

The value of each pixel is the estimated travel time to the nearest port in 2015. There are 5 data layers based on different port sizes.

Format Raster Dataset, GeoTIFF, LZW compressed Unit Minutes

Data type Byte (16 bit Unsigned Integer)

No data value 65535

Flags None

Spatial resolution 30 arc seconds

Spatial extent

Upper left -180, 85

Lower left -180, -60 Upper right 180, 85 Lower right 180, -60 Spatial Reference System (SRS) EPSG:4326 - WGS84 - Geographic Coordinate System (lat/long)

Temporal resolution 2015

Temporal extent Updates may follow for future years, but these are dependent on the availability of updated inputs on travel times and city locations and populations.

Methodology Travel time to the nearest city or port was estimated using an accumulated cost function (accCost) in the gdistance R package (van Etten, 2018). This function requires two input datasets: (i) a set of locations to estimate travel time to and (ii) a transition matrix that represents the cost or time to travel across a surface.

The set of locations were based on populated urban areas in the 2016 version of the Joint Research Centre’s Global Human Settlement Layers (GHSL) datasets (Pesaresi and Freire, 2016) that represent low density (LDC) urban clusters and high density (HDC) urban areas (https://ghsl.jrc.ec.europa.eu/datasets.php). These urban areas were represented by points, spaced at 1km distance around the perimeter of each urban area.

Marine ports were extracted from the 26th edition of the World Port Index (NGA, 2017) which contains the location and physical characteristics of approximately 3,700 major ports and terminals. Ports are represented as single points

The transition matrix was based on the friction surface (https://map.ox.ac.uk/research-project/accessibility_to_cities) from the 2015 global accessibility map (Weiss et al, 2018).

Code The R code used to generate the 12 travel time maps is included in the zip file that can be downloaded with these data layers. The processing zones are also available.

Validation The underlying friction surface was validated by comparing travel times between 47,893 pairs of locations against journey times from a Google API. Our estimated journey times were generally shorter than those from the Google API. Across the tiles, the median journey time from our estimates was 88 minutes within an interquartile range of 48 to 143 minutes while the median journey time estimated by the Google API was 106 minutes within an interquartile range of 61 to 167 minutes. Across all tiles, the differences were skewed to the left and our travel time estimates were shorter than those reported by the Google API in 72% of the tiles. The median difference was −13.7 minutes within an interquartile range of −35.5 to 2.0 minutes while the absolute difference was 30 minutes or less for 60% of the tiles and 60 minutes or less for 80% of the tiles. The median percentage difference was −16.9% within an interquartile range of −30.6% to 2.7% while the absolute percentage difference was 20% or less in 43% of the tiles and 40% or less in 80% of the tiles.

This process and results are included in the validation zip file.

Usage Notes The accessibility layers can be visualised and analysed in many Geographic Information Systems or remote sensing software such as QGIS, GRASS, ENVI, ERDAS or ArcMap, and also by statistical and modelling packages such as R or MATLAB. They can also be used in cloud-based tools for geospatial analysis such as Google Earth Engine.

The nine layers represent travel times to human settlements of different population ranges. Two or more layers can be combined into one layer by recording the minimum pixel value across the layers. For example, a map of travel time to the nearest settlement of 5,000 to 50,000 people could be generated by taking the minimum of the three layers that represent the travel time to settlements with populations between 5,000 and 10,000, 10,000 and 20,000 and, 20,000 and 50,000 people.

The accessibility layers also permit user-defined hierarchies that go beyond computing the minimum pixel value across layers. A user-defined complete hierarchy can be generated when the union of all categories adds up to the global population, and the intersection of any two categories is empty. Everything else is up to the user in terms of logical consistency with the problem at hand.

The accessibility layers are relative measures of the ease of access from a given location to the nearest target. While the validation demonstrates that they do correspond to typical journey times, they cannot be taken to represent actual travel times. Errors in the friction surface will be accumulated as part of the accumulative cost function and it is likely that locations that are further away from targets will have greater a divergence from a plausible travel time than those that are closer to the targets. Care should be taken when referring to travel time to the larger cities when the locations of interest are extremely remote, although they will still be plausible representations of relative accessibility. Furthermore, a key assumption of the model is that all journeys will use the fastest mode of transport and take the shortest path.

Urban sprawl has resulted in the permanent presence of large mammal species in urban areas, leading to human–wildlife conflicts. Wild boar Sus scrofa are establishing a permanent presence in many cities in Europe, with the largest German urban population occurring in Berlin. Despite their relatively long-term presence, there is little knowledge of colonization processes, dispersal patterns or connectivity of Berlin's populations, hampering the development of effective management plans. We used 13 microsatellite loci to genotype 387 adult and subadult wild boar from four urban forests, adjacent built-up areas and the surrounding rural forests. We applied genetic clustering algorithms to analyse the population genetic structure of the urban boar. We used approximate Bayesian computation to infer the boar's colonization history of the city. Finally, we used assignment tests to determine the origin of wild boar hunted in the urban built-up areas. The animals in three urban forests formed distinct genetic clusters, with the remaining samples all being assigned to one rural population. One urban cluster was founded by individuals from another urban cluster rather than by rural immigrants. The wild boar that had been harvested within urban built-up areas was predominantly assigned to the rural cluster surrounding the urban area, rather than to one of the urban clusters. Synthesis and applications. Our results are likely to have an immediate impact on management strategies for urban wild board populations in Berlin, because they show that there are not only distinct urban clusters, but also ongoing source–sink dynamics between urban and rural areas. It is therefore essential that the neighbouring Federal States of Berlin and Brandenburg develop common hunting plans to control the wild boar population and reduce conflicts in urban areas.

The regional statistics interface enables the computerised retrieval of statistical data in Greater Helsinki’s Aluesarjat (in Finnish) statistical database in different file formats, such as XLSX, CSV, JSON and JSON-stat.

The Regional series statistical database contains statistical time series on several regional phenomena. The statistics are mostly annual, the longest population series starting from 1962. The database consists of three entities: statistical area data of the cities of Helsinki, Espoo and Vantaa as well as municipal and regional statistics on Greater Helsinki and the Uusimaa region. ̈ The database provides the latest information available. Some data is updated once a year, some on a quarterly basis.

The statistics of the database are described in more detail in the data collections Regional series and Historical statistics.

Topics

• Housing
• Historical statistics
• Education
• Construction
• Income level
• Labour market and employment
• Population

In addition to the contents of the Aluesarjat statistical database, the interface also provides access to the Aluesarjat archive database, Wellness statistics, Nordstat database and Helsinki environmental statistics.

Instructions have been prepared on the use of the PX-Web API, which should be consulted before using the interface:

• How to use the City of Helsinki PX-Web API
• Statistics Finland’s instructions for use in Finnish
• Statistics Sweden’s instructions for use

Thematic area - Air

Name of Indicator - Ambient air quality in urban areas

DPSIR - S = states

Indicator type - A = descriptive indicator

Definition of the indicator The indicator shows the annual average concentration of NO2 and SO2 and numbers of the days with exceed of NO2 and SO2 limit value during a year in urban area (3 biggest cities).

Units - µg/m3

Policy relevance of the indicator Legal framework includes: Law #1515 from 16.06.1993 on environment protection, Law #1422 from 17.12.1997 on air protection, Law #1536 from 25.02.1998 on hydrometeorological activity.

Strategic framework includes: Environmental Strategy (Government Decision #301 from 24th April 2014), Association agreement between Republic of Moldova and European Union (Law #112 from 02.07.2014).

Targets Environmental strategy establishes as a target reduction of pollutants emissions into the atmosphere by 30% by 2023.

Key question What is the trend in the concentration of NO2 and SO2 in urban area?

Assessment

In urban area, the main source of NO2 is the cars traffic, especially diesel cars. Also, the concentrations of NO2 are dependent on season and meteorological conditions.

According to monitoring data, in Chisinau, capital city, the annual average concentration of NO2 is the highest and has a stabile ascendant trend, the highest concentration of NO2 being registered in 2011 – 58,2 µg/m3 (see Figure #1). In Balti, starting with 2011, a descending trend is registered, for 2014 the annual average concentration of NO2 being 22,9 µg/m3. Tiraspol registers an ascendant trend.

Figure #2 shows the number of the days with exceeded of maximum allowable concentration (daily average). So, for Chisinau situation is alarming, where, in the period of 2010-2014, more than 200 days with exceeded of MAC (daily average) have been registered. In Balti, in the period 2010-2012 there were registered a growing trend, but starting with 2013 it goes down, last data showing a number of 39 days with exceeded of MAC (daily average). For Tiraspol, during the observed period, only 2013-2014 registered more than 50 days with exceeded of MAC, all other years registered less than 10 days with exceeded of MAC.

The main sources of SO2 are fixed sources (major thermal power plants, small and medium size boilers for coal combustion in urban environments). The main anthropogenic sources include coal and oil combustion. Also, SO2 emissions depend on season and meteorological conditions. So, during the heating period/winter emissions of SO2 will be higher that other period of the year.
Table #1 shows that the situation with the SO2 annual average concentration by cities is not as alarming as in case with NO2. Figure #3 shows a descendent trend of SO2 annual average concentration for all 3 cities, the highest annual average concentration being recorded in 2006 in Balti - 41 µg/m3.

Only in Balti were registered in 2005-2006 days with exceeded of MAC (daily average) of SO2. For Chisinau and Tiraspol no days with exceeded MAC have been registered (see Figure #4).

Key messages In the period 2005-2014 for NO2 was registered a negative trend, with a weak fluctuation, while for SO2 the trend is positive. There is not a continue monitoring, monitoring is sporadically (3 times per day).

Data coverage - 2005-2014

Data source - Ministry of Environment, State Hydrometeorological Service.

Methodology: Monitoring of the air quality in urban areas is performing using manual technical approach at the fixed monitoring stations (6 stations in Chisinau, 2 in Balti and 3 in Tiraspol). Samples are collected by approved programs 3 times during 24 hours (07.00, 13.00, 19.00). For estimation of air quality in the territory of Moldova, we use the Guidance “Руководство по контролю загрязнения атмосферы РД 52.04.186-89 б Москва 1991 г.”, approved in ’90ns by Ministry of Health. The target value was approved by Ministry of Health because is related only to human health, but do not contain the value related to vegetation, ecosystem and other aspect, stipulated in EU Directive 2008/50. To estimate the air pollution in the Republic of Moldova are in force the next approved norms: - Maximum allowable concentration (maximum momentary) - MACmm, which represents the approved concentration norms during short time - 20 minutes and is 85 µg/m3 (for NO2) and 500 µg/m3 (for SO2), - Maximum allowable concentration (daily average) – MACda, , which represents the approved concentration norms during 24 hours and represents 40 µg/m3 (for NO2) and 50 µg/m3 (for SO2). In the normative acts of Moldova there is not in force the principle of annual concentration. For annual concentration is used the same approved daily norms and represents 40 µg/m3 (for NO2) and 50 µg/m3 (for SO2).

Reporting obligations: Annual National Report Daily and monthly bulletins
Advertisement on high level of air pollution.

Recommendations:

Background: Situated at the crossroads of Asia, Middle East and Europe, Turkey has an ethnically diverse population of over 78 milllion people. Aim: To investigate the population genetics and potential differences in the autosomal short tandem repeat (STR) polymorphisms across all the major geographic regions and largest metropolitan province of Turkey within the context of the Near Eastern/European genetic landscape. Subjects and methods: Samples from a total of 5299 unrelated individuals were analysed at 10 common [D2S1338, D3S1358, D8S1179, D16S539, D18S51, D19S433, D21S11, FGA, TH01, vWA] and five new European Standard Set (ESS) core autosomal STR loci [D1S1656, D2S441, D10S1248, D12S391, D22S1045]. Results: Allele frequencies, statistical parameters of forensic interest and population differentiation tests were calculated for nine population datasets corresponding to the seven major geographic regions, the largest metropolitan province, and a combined dataset for the entire country. Cumulative results confirmed the presence of significant differences among these nine autosomal datasets themselves and with those from the nearby populations, therefore justifying the differential use of these separate datasets on a case-by-case basis in forensic investigations. Conclusion: This collection of autosomal STR population datasets comprises the largest and most comprehensive of its kind from Turkey so far.

Smart City Market Outlook

The global smart city market size was estimated at $500 billion in 2023 and is projected to reach $3 trillion by 2032, growing at a compound annual growth rate (CAGR) of 23%. This remarkable growth is driven by rapid urbanization, technological advancements, and increasing government initiatives aimed at sustainable development. The convergence of IoT, AI, and data analytics is playing a pivotal role in transforming urban landscapes into interconnected, efficient ecosystems.

One of the primary growth factors of the smart city market is the accelerated pace of urbanization. With more than half of the world’s population now residing in urban areas, cities face increasing pressure to improve infrastructure and services. Smart city technologies offer solutions for efficient resource management, enhanced public safety, and improved quality of life. The need for effective urban planning and sustainable development is pushing governments to adopt smart city initiatives at an unprecedented rate.

Advancements in technology, particularly in IoT, AI, and big data, are significantly contributing to the smart city market's expansion. IoT sensors and devices facilitate real-time data collection, enabling cities to monitor and manage resources such as water, electricity, and waste more efficiently. AI and data analytics are used to interpret this data, providing actionable insights that help in optimizing urban operations, reducing costs, and enhancing citizen services. The integration of these technologies is creating a symbiotic relationship between the digital and physical worlds, driving the evolution of smart cities.

Government support and initiatives are also major catalysts for the growth of the smart city market. Various governments around the world are investing heavily in smart city projects to address urban challenges such as traffic congestion, pollution, and energy consumption. For instance, the European Union has earmarked substantial funding for smart city projects under its Horizon 2020 program. Similarly, countries like China and India have launched extensive smart city missions aimed at transforming urban areas into technologically advanced, sustainable habitats.

Regionally, North America and Europe are leading the smart city market, owing to their advanced technological infrastructure and significant government investments. However, Asia Pacific is expected to exhibit the highest growth rate during the forecast period. Rapid urbanization, coupled with increasing government initiatives in countries like China, India, and Japan, is driving the smart city market in this region. Latin America and the Middle East & Africa are also showing promising growth, supported by improving economic conditions and increasing focus on sustainable development.

Component Analysis

The smart city market is segmented into three primary components: hardware, software, and services. Each of these components plays a crucial role in enabling and enhancing the various functionalities of a smart city. Hardware components include sensors, smart meters, and communication devices, among others. These devices are essential for collecting real-time data from various urban environments, which is then used to monitor and manage city operations.

Software solutions are integral to the smart city market as they provide the platforms and applications needed to analyze and interpret the data collected by hardware devices. These software solutions enable various functions such as traffic management, energy management, and public safety. They also offer predictive analytics capabilities, which help city administrators anticipate and mitigate potential issues before they escalate. The increasing complexity and volume of data generated by smart cities necessitate robust software solutions to manage and analyze this data effectively.

Services are another critical component of the smart city market. These include consulting services, system integration, and managed services, which are essential for the successful implementation and operation of smart city projects. Consulting services help cities identify their specific needs and design customized smart city solutions. System integration services ensure that various hardware and software components work seamlessly together, while managed services provide ongoing support and maintenance to ensure the smooth functioning of smart city systems.

The hardware segment is expected to account for a significant share of the smart city market, driv

Coefficient estimates, standard errors (SE) and p-values estimated from the GLM model predicting the probability of dying given SARS-CoV-2 infection in Germany and Italy.

NOTE: After the reform of social welfare and health care structures in 2023, responsibility for organising social welfare and health care services was transferred to the wellbeing services counties, and the Kuusikko cooperation ended in June 2023 for social welfare and health care services. For this reason, the dataset ends with 2021 data.

Social assistance data for the six largest cities in 2005-2021. Social assistance covers both customers (households and persons) and costs.

Since the beginning of the Kuusikko work, social assistance has been included in the comparisons. Operations started already in 1994 between three cities in the Helsinki metropolitan area, when the first comparison of the number of customers and costs of social assistance was made on the basis of 1993 data. The first five-city income support report was made from 1995 data when Turku and Tampere joined the work. The actual Kuusikko was reached in 2005, when Oulu also participated in income support comparisons.

In 2011, the six cities published the first report on adult social work in the six largest cities. Adult social work reports have focused more on describing the operating environment, operating methods and customer base than other Kuusikko reports. More numerical data have also been added to the 2019 report. However, in the absence of a national definition of adult social work and due to municipality-specific differences in the structures and processes of adult social work, the information available is not always fully comparable between municipalities.

Following the transfer of the granting and payment of basic social assistance to Kela in 2017, the reports on adult social work and social assistance previously published as separate reports have been merged into a single publication. The reports on adult social work and social assistance have been produced in cooperation with the expert working group on adult social work in the six largest cities.

The six cities are made up of the six most populous cities in Finland. In order of population, the six cities include Helsinki, Espoo, Tampere, Vantaa, Oulu and Turku. The Kuusikko working groups compare the health and social services of cities, employment services and early childhood education and care services. Data on customer numbers, performances, personnel and costs are mainly compiled from municipalities' own information systems and financial statements. Urban experts agree on the most uniform definitions for data collection and implement the data collection in practice.