Based on a wide variety of categories, the top major global smart cities were ranked using an index score, where a top index score of ** was possible. Scores were based on various different categories including transport and mobility, sustainability, governance, innovation economy, digitalization, living standard, and expert perception. In more detail, the index also includes provision of smart parking and mobility, recycling rates, and blockchain ecosystem among other factors that can improve the standard of living. In 2019, Zurich, Switzerland was ranked first, achieving an overall index score of ****. Spending on smart city technology is projected to increase in the future.

Smart city applications Smart cities use data and digital technology to improve the quality of life, while changing the nature and economics of infrastructure. However, the definition of smart cities can vary widely and is based on the dynamic needs of a cities’ citizens. Mobility seems to be the most important smart city application for many cities, especially in European cities. For example, e-hailing services are available in most leading smart cities. The deployment of smart technologies that will incorporate mobility, utilities, health, security, and housing and community engagement will be important priorities in the future of smart cities.

In 2022, the leading global digital city on the index ranking shown here was Copenhagen with a score of ****. Seoul, Beijing, Amsterdam, and Singapore rounded out the top 5 for the best digital cities.

In 2024, Canberra, the capital city of Australia, ranked ***** in the global Smart City Index while topping the list among the reported Asia-Pacific cities. Contrastingly, Manila, the capital city of the Philippines, ranked ***** in the Smart City Index globally.

This statistic illustrates the index scores of the leading smart cities in the U.S. in 2019. At that time, Washington, D.C. was ranked second, achieving an overall index score of ****. Scores were based on various different categories including transport and mobility, sustainability, governance, innovation economy, digitalization, living standard, cyber security and expert perception.

This dataset contains data collected during a study "Transparency of open data ecosystems in smart cities: Definition and assessment of the maturity of transparency in 22 smart cities" (Sustainable Cities and Society (SCS), vol.82, 103906) conducted by Martin Lnenicka (University of Pardubice), Anastasija Nikiforova (University of Tartu), Mariusz Luterek (University of Warsaw), Otmane Azeroual (German Centre for Higher Education Research and Science Studies), Dandison Ukpabi (University of Jyväskylä), Visvaldis Valtenbergs (University of Latvia), Renata Machova (University of Pardubice).

This study inspects smart cities’ data portals and assesses their compliance with transparency requirements for open (government) data by means of the expert assessment of 34 portals representing 22 smart cities, with 36 features.

It being made public both to act as supplementary data for the paper and in order for other researchers to use these data in their own work potentially contributing to the improvement of current data ecosystems and build sustainable, transparent, citizen-centered, and socially resilient open data-driven smart cities.

Purpose of the expert assessment The data in this dataset were collected in the result of the applying the developed benchmarking framework for assessing the compliance of open (government) data portals with the principles of transparency-by-design proposed by Lněnička and Nikiforova (2021)* to 34 portals that can be considered to be part of open data ecosystems in smart cities, thereby carrying out their assessment by experts in 36 features context, which allows to rank them and discuss their maturity levels and (4) based on the results of the assessment, defining the components and unique models that form the open data ecosystem in the smart city context.

Methodology Sample selection: the capitals of the Member States of the European Union and countries of the European Economic Area were selected to ensure a more coherent political and legal framework. They were mapped/cross-referenced with their rank in 5 smart city rankings: IESE Cities in Motion Index, Top 50 smart city governments (SCG), IMD smart city index (SCI), global cities index (GCI), and sustainable cities index (SCI). A purposive sampling method and systematic search for portals was then carried out to identify relevant websites for each city using two complementary techniques: browsing and searching. To evaluate the transparency maturity of data ecosystems in smart cities, we have used the transparency-by-design framework (Lněnička & Nikiforova, 2021)*. The benchmarking supposes the collection of quantitative data, which makes this task an acceptability task. A six-point Likert scale was applied for evaluating the portals. Each sub-dimension was supplied with its description to ensure the common understanding, a drop-down list to select the level at which the respondent (dis)agree, and a comment to be provided, which has not been mandatory. This formed a protocol to be fulfilled on every portal. Each sub-dimension/feature was assessed using a six-point Likert scale, where strong agreement is assessed with 6 points, while strong disagreement is represented by 1 point. Each website (portal) was evaluated by experts, where a person is considered to be an expert if a person works with open (government) data and data portals daily, i.e., it is the key part of their job, which can be public officials, researchers, and independent organizations. In other words, compliance with the expert profile according to the International Certification of Digital Literacy (ICDL) and its derivation proposed in Lněnička et al. (2021)* is expected to be met. When all individual protocols were collected, mean values and standard deviations (SD) were calculated, and if statistical contradictions/inconsistencies were found, reassessment took place to ensure individual consistency and interrater reliability among experts’ answers. *Lnenicka, M., & Nikiforova, A. (2021). Transparency-by-design: What is the role of open data portals?. Telematics and Informatics, 61, 101605 *Lněnička, M., Machova, R., Volejníková, J., Linhartová, V., Knezackova, R., & Hub, M. (2021). Enhancing transparency through open government data: the case of data portals and their features and capabilities. Online Information Review.

Test procedure (1) perform an assessment of each dimension using sub-dimensions, mapping out the achievement of each indicator (2) all sub-dimensions in one dimension are aggregated, and then the average value is calculated based on the number of sub-dimensions – the resulting average stands for a dimension value - eight values per portal (3) the average value from all dimensions are calculated and then mapped to the maturity level – this value of each portal is also used to rank the portals.

Description of the data in this data set Sheet#1 "comparison_overall" provides results by portal Sheet#2 "comparison_category" provides results by portal and category Sheet#3 "category_subcategory" provides list of categories and its elements

Format of the file .xls

Licenses or restrictions CC-BY

For more info, see README.txt

In 2024, **** Saudi Arabian cities listed among the leading 100 smart cities globally. ****** listed 25th, making it the smartest city in the Kingdom. Meanwhile, Al-Khobar ranked 99th globally and ***** within the country in the same year.

As of 2024, Seattle was rated the best prepared city in the United States for a smart city future, with an index score of ****. Miami and Austin followed, with **** and ****, respectively. All the top three cities ranked best in their connectivity and infrastructure preparedness.

This dataset supports the study "Machine Learning-Based Prediction of Smart City Development Index in Indonesia." It contains lightly pre-processed indicators from 98 cities and regencies across Indonesia between 2019 and 2021.

Data was collected from official sources such as BPS-Statistics Indonesia and regional open data portals. It includes 17 variables relevant to smart city dimensions, covering aspects such as economy, infrastructure, education, public health, and access to services.

The dataset has undergone basic preprocessing in the form of missing value imputation only. No normalization, transformation, or feature engineering has been applied.

This dataset is intended for reproducible machine learning research and urban analytics. It is released under the CC BY 4.0 license.

We included how the framework is implemented and the results of data analysis in our paper.

In 2025, Seoul was ranked **** among smart cities worldwide according to multiple indicators covering existing infrastructure, technological services, and categories under the Human Development Index (HDI). This was **** places higher than in the previous year. The capital city of South Korea has risen in global smart city rankings almost every survey year since 2019.

🇬🇧 영국

RESUMO As reflexões sobre a abertura dos dados em projetos de cidades inteligentes são consideradas relevantes no contexto da evolução da chamada Sociedade da Informação. Este estudo buscou considerar os aspectos ligados às questões de transparência e privacidade, para verificar como os dados são tratados em projetos de cidades inteligentes, ou seja, como tem sido a governança de municípios com projetos de cidades inteligentes no tocante à abertura dos dados e como estas iniciativas lidam com a privacidade dos dados de seus cidadãos. Além disso, o estudo considera e analisa os rankings “Connected Smart Cities, The Global Power City Index e Cities in Motion Index”, servindo como modelos e indicadores para que se possa considerar uma cidade como “inteligente”. O município de Aparecida de Goiânia possui um projeto de Cidade Inteligente em andamento, diante desta prerrogativa, foi escolhido como o estudo de caso da pesquisa. Os dados foram coletados por meio de entrevista, que possibilitou inferência ampla sobre o projeto. É possível afirmar que a prefeitura de Aparecida de Goiânia com seu projeto de cidade inteligente valoriza a abertura dos dados governamentais e considera a privacidade de seus cidadãos na concepção do projeto, além de prever o incremento na infraestrutura de rede e armazenamento de dados, aspectos ligados à guarda dos dados para livre acesso. As cidades inteligentes possuem características distintas para uma evolução eficiente da sociedade garantindo métodos capazes de proteger as informações dos cidadãos sem que percam o acesso. *************************************************************************************************** ABSTRACT Reflections on the openness of data in smart city projects are considered relevant in the context of the evolution of the so-called Information Society. This study sought to consider aspects related to transparency and privacy issues, to verify how data is processed in intelligent city projects, ie, how municipalities have been governed by intelligent city projects in terms of data openness and how these initiatives deal with the privacy of their citizens' data. In addition, the study considers and analyzes the rankings "Connected Smart Cities, The Global Power City Index and Cities in Motion Index", serving as models and indicators for a city to be considered as "smart". The municipality of Aparecida de Goiânia has an Intelligent City project underway, faced with this prerogative, was chosen as the case study of the research. The data were collected through an interview, which allowed a broad inference about the project. It is possible to affirm that the city hall of Aparecida de Goiânia with its intelligent city project values the opening of government data and considers the privacy of its citizens in the conception of the project, besides predicting the increase in the network infrastructure and data storage, related aspects data for free access. Intelligent cities have distinct characteristics for an efficient evolution of society, guaranteeing methods capable of protecting citizens' information without losing access.

Im Jahr 2024 war München im deutschen Großstadtvergleich die Stadt mit dem höchsten Smart City Index. Der Indexwert betrug 88.3 Verglichen wurden alle 81 Städte Deutschlands mit mehr als 100.000 Einwohnern.

2017 Smart Cities Index lavet af Easypark

In 2019, Amsterdam was ranked first in the Benelux, achieving an overall index score of 7.55, whereas second ranked city Brussels (Belgium) had an index of 6.43. Scores were based on various different categories including transport and mobility, sustainability, governance, innovation economy, digitalization, living standard, cyber security and expert perception.

The dataset is an index to digitized historical death certificates from 1862-1948 from all 5 NYC boroughs. Details about certificates in DORIS's collection and their digitization status can be found on our website (https://a860-historicalvitalrecords.nyc.gov/digital-vital-records).

Based on the Office for National Statistics’ delineation of the scope of the digital economy industry, this paper selects indicators from five industrial dimensions: digital product manufacturing, digital product service, digital technology application, digital factor drive and digital efficiency improvement, and constructs an evaluation system to measure the development level of China’s digital economy at the provincial level. It is found that there is a wide gap in the development of China’s provincial digital economy, with the eastern coastal provinces and cities having a high level of digital economy development. The coupling and coordination model was then applied to examine the interrelationships between the five industrial dimensions of the digital economy, and it was found that most of the coupling and coordination relationships of the five industrial dimensions are at the stage of medium-high coupling and low coupling and coordination, and each province and city has different coupling and coordination characteristics. The numerical evaluation results provide an intuitive understanding of the differences and deficiencies in the development of the digital economy in different regions, and serve as a reference for the medium and long-term digital economy development planning of provinces and municipalities as well as the whole country. In the future, the state should invest more in the digital economy in the central and western regions, and each province should cultivate and develop the digital economy in accordance with its own local conditions.

In the context of global climate change, green development has become the main goal of smart city construction. Most existing research suggests that smart cities will enhance the level of the green total factor productivity (GTFP) in cities. However, this study found that smart cities will reduce the level of green total factor production in the short term and increase it in the long term. Based on this, this article selects three batches of smart cities in China from 2013 to 2019, and uses the Malmquist index model, common frontier function, and panel data method to analyze the GTFP model in the early stage of smart city construction in China. The study found that: (1) the GTFP of the three batches of smart cities in the early stage of construction was less than 1 and showed a downward trend, indicating that smart cities will reduce the GTFP level of cities in the short term. (2) Technical efficiency is the main reason for the decline of GTFP in the early stage of smart city construction and the rise of GTFP in the medium and long term. Specifically, there is a U-shaped relationship between the technological efficiency of smart cities and their GTFP. For every 1% increase in technical efficiency in the later stages of smart cities, GTFP increases by 47.3%. (3) The GTFP in the process of smart city construction shows a trend of decreasing in the early stage and increasing in the middle and later stages. The GTFP level in the later stage of smart cities is greater than 1 and shows a fluctuating upward trend, indicating that smart cities will improve the city’s GTFP level in the long run. In view of this, we should attach importance to ecological protection in the early stage of smart city construction and take effective measures to reduce carbon emissions during this period. During this period, policies such as taxation can be implemented to encourage companies to adopt cleaner production technologies, strengthen the exchange of green technologies between cities, accelerate the flow of green knowledge, reduce redundant construction of information infrastructure, and thus minimize the decline in GTFP in the early stages of smart city construction. This study provides policy recommendations and decision-making references for further promoting the construction of new green and smart cities worldwide.

United States - Consumer Price Index for All Urban Consumers: Computers, Peripherals, and Smart Home Assistants in U.S. City Average was 35.20200 Index Dec 2007=100 in May of 2025, according to the United States Federal Reserve. Historically, United States - Consumer Price Index for All Urban Consumers: Computers, Peripherals, and Smart Home Assistants in U.S. City Average reached a record high of 155.70000 in January of 2005 and a record low of 33.94200 in December of 2024. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Consumer Price Index for All Urban Consumers: Computers, Peripherals, and Smart Home Assistants in U.S. City Average - last updated from the United States Federal Reserve on July of 2025.

Layers used in this map include: ACS data by block and tract relating to internet access across multiple attribute dimensions, including age, race, income, and education. Population and related demographics data of population by census tractNeighborhoods dataPublic facilities locations data (schools, libraries, and other locations where high-speed internet can be accessed)Availability of internet infrastructure by service providerIndex values based on composites from national survey methodologies: created by CBG Communication as part of the Vancouver Digital Inclusion Project. City of Vancouver Equity Index