In 2024, London was the most attractive city worldwide according to the Global Power City Index (GCPI), with a score of ******. New York City and Tokyo followed with ****** and ****** points respectively. The Global Power City Index (GPCI) provides a ranking of global cities based on the following criteria: economy, research and development, cultural interaction, livability, environment, and accessibility. It is an assessment of city's power to attract people, businesses and capital from all over the world.

In 2023, Paris was the most livable city worldwide according to the Global Power City Index (GCPI), with *** points. Furthermore, Madrid was the second most livable city with ***** points, while Tokyo was the third with ***** points.

The criteria taken into consideration include, among others, costs and ease of living, number of retail shops and restaurants, and availability of medical services.

A list of some key resources for comparing London with other world cities.

European Union/Eurostat, Urban Audit

Arcadis, Sustainable cities index

AT Kearney, Global Cities Index

McKinsey, Urban world: Mapping the economic power of cities

Knight Frank, Wealth report

OECD, Better Life Index

UNODC, Statistics on drugs, crime and criminal justice at the international level

Economist, Hot Spots

Economist, Global Liveability Ranking and Report August 2014

Mercer, Quality of Living Reports

PWC, Cities of opportunity

BCG, Decoding Global Talent

Forbes, World's most influential cities

Mastercard, Global Destination Cities Index

Numbeo, Database of user contributed data

In 2023, the capital city of Manila in the Philippines ranked 70 out of 156 cities for the Global Cities Index Ranking - two places lower than the previous year. The ranking is determined by totaling the weighted averages of five dimensions - business activity, human capital, information exchange, cultural experience, and political engagement.

Due to rapid urbanization over the past 20 years, many newly developed areas have lagged in socio-economic maturity, creating an imbalance with older cities and leading to the rise of "ghost cities". However, the complexity of socio-economic factors has hindered global studies from measuring this phenomenon. To address this gap, a unified framework based on urban vitality theory and multi-source data is proposed to measure the Ghost City Index (GCI), which has been validated using various data sources. The study encompasses 8,841 natural cities worldwide with areas exceeding 5 km², categorizing each into new urban areas (developed after 2005) and old urban areas (developed before 2005). Urban vitality was gauged using the density of road networks, points of interest (POIs), and population density with 1 km resolution across morphological, functional, and social dimensions. By comparing urban vitality in new and old urban areas, we quantify the GCI globally using the theory of urban vitality for the first time. The results reveal that the vitality of new urban areas is 7.69% that of old ones. The top 5% (442) of cities were designated as ghost cities, a finding mirrored by news media and other research. This study sheds light on strategies for sustainable global urbanization, crucial for the United Nations' Sustainable Development Goals.The code file gives the calculation process of data respectively, and the excel file gives the obtained data. For the explanation of the fields in “citypoint.shp”, please refer to the Supplementary Information of the paper (https://doi.org/10.1016/j.habitatint.2025.103350).Ref: Zhang, Y., Tu, T., & Long, Y. (2025). Inferring ghost cities on the globe in newly developed urban areas based on urban vitality with multi-source data. Habitat International, 158, 103350. https://doi.org/10.1016/j.habitatint.2025.103350

Official ranking of 173 global cities based on stability, healthcare, culture, education, and infrastructure by the Economist Intelligence Unit

In 2024, New York was the most attractive city worldwide in the research and development (R&D) category according to the Global Power City Index (GCPI), with ***** points. Out of the top ten cities within the R&D category, five are located within the United States, while the other five are located across Europe (London and Paris) and Asia (Tokyo, Seoul, and Hong Kong.) The criteria taken into consideration include, among others, the number of scientists working in the R&A industry, availability of R&A funding, and the number of launched start-ups.

1. Global_1028_cities_Gini_trend.shpThis data represents the annual dynamic and trends of the Gini index for global 1028 cities in 2000-2018 using the WorldPop population and Landsat-derived greenspace mappings. Key attributes in the shapefile data:(1) Trend: temporal trend of Gini index;(2) P: P-value at a statistically significant level of 0.05;(3) Y2000-Y2018: Annual Gini index for year 2000 to 2018;(4) SouthNorth: Categories of Global South or Global North;(5) Continent: Categories of continents;(6) lat/long: Latitude/longitude of the city centriod;(7) urbanArea: urban areas in Km2.2. Global_1028_cities_Gini_thres20_trend.shpThis data represents the annual dynamic and trends of the Gini index (threshold = 0.20) for global 1028 cities in 2000-2018 using the WorldPop population and Landsat-derived greenspace mappings, together with threshold-based linear unmixing classification approach Key attributes in the shapefile data:(1) Trend_20: temporal trend of Gini index;(2) P_20: P-value at a statistically significant level of 0.05;(3) Y2000-Y2018: Annual Gini index for year 2000 to 2018;(4) SouthNorth: Categories of Global South or Global North;(5) Continent: Categories of continents;(6) lat/long: Latitude/longitude of the city centriod;(7) urbanArea: urban areas in Km2.3. Global_1028_cities_Gini_thres30_trend.shpThis data represents the annual dynamic and trends of the Gini index (threshold = 0.30) for global 1028 cities in 2000-2018 using the WorldPop population and Landsat-derived greenspace mappings, together with threshold-based linear unmixing classification approach Key attributes in the shapefile data:(1) Trend_30: temporal trend of Gini index;(2) P_30: P-value at a statistically significant level of 0.05;(3) Y2000-Y2018: Annual Gini index for year 2000 to 2018;(4) SouthNorth: Categories of Global South or Global North;(5) Continent: Categories of continents;(6) lat/long: Latitude/longitude of the city centriod;(7) urbanArea: urban areas in Km2.4. Global_1028_cities_Gini_thres40_trend.shpThis data represents the annual dynamic and trends of the Gini index (threshold = 0.40) for global 1028 cities in 2000-2018 using the WorldPop population and Landsat-derived greenspace mappings, together with threshold-based linear unmixing classification approach Key attributes in the shapefile data:(1) Trend_40: temporal trend of Gini index;(2) P_40: P-value at a statistically significant level of 0.05;(3) Y2000-Y2018: Annual Gini index for year 2000 to 2018;(4) SouthNorth: Categories of Global South or Global North;(5) Continent: Categories of continents;(6) lat/long: Latitude/longitude of the city centriod;(7) urbanArea: urban areas in Km2.5. Global_1028_cities_Gini_thres50_trend.shpThis data represents the annual dynamic and trends of the Gini index (threshold = 0.50) for global 1028 cities in 2000-2018 using the WorldPop population and Landsat-derived greenspace mappings, together with threshold-based linear unmixing classification approach Key attributes in the shapefile data:(1) Trend_50: temporal trend of Gini index;(2) P_50: P-value at a statistically significant level of 0.05;(3) Y2000-Y2018: Annual Gini index for year 2000 to 2018;(4) SouthNorth: Categories of Global South or Global North;(5) Continent: Categories of continents;(6) lat/long: Latitude/longitude of the city centriod;(7) urbanArea: urban areas in Km2.

Dynamic social media content, such as Twitter messages, can be used to examine individuals’ beliefs and perceptions. By analyzing Twitter messages, this study examines how Twitter users exchanged and recognized toponyms (city names) for different cities in the United States. The frequency and variety of city names found in their online conversations were used to identify the unique spatiotemporal patterns of “geographical awareness” for Twitter users. A new analytic method, Knowledge Discovery in Cyberspace for Geographical Awareness (KDCGA), is introduced to help identify the dynamic spatiotemporal patterns of geographic awareness among social media conversations. Twitter data were collected across 50 U.S. cities. Thousands of city names around the world were extracted from a large volume of Twitter messages (over 5 million tweets) by using the Twitter Application Programming Interface (APIs) and Python language computer programs. The percentages of distant city names (cities located in distant states or other countries far away from the locations of Twitter users) were used to estimate the level of global geographical awareness for Twitter users in each U.S. city. A Global awareness index (GAI) was developed to quantify the level of geographical awareness of Twitter users from within the same city. Our findings are that: (1) the level of geographical awareness varies depending on when and where Twitter messages are posted, yet Twitter users from big cities are more aware of the names of international cities or distant US cities than users from mid-size cities; (2) Twitter users have an increased awareness of other city names far away from their home city during holiday seasons; and (3) Twitter users are more aware of nearby city names than distant city names, and more aware of big city names rather than small city names.

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.

Every tweet in the first dataset includes at least one name of a large city in the U.S. or elsewhere. The second dataset does not include city names outside the U.S., but contains the names of small, mid-sized, and large cities in the U.S.Two datasets of tweets.

In 2024, Copenhagen was the most environmentally friendly city worldwide according to the Global Power City Index (GCPI), with ***** points. Stockholm followed with ****points, while Vienna came third with *****. Eight out of the ten top cities are located in Europe.The criteria taken into consideration include, among others, sustainability, air quality and comfort, and urban environment.

CN: GDP Index: PY=100: TI: Real Estate: Hebei: Shijiazhuang data was reported at 104.200 Prev Year=100 in 2021. This records an increase from the previous number of 101.800 Prev Year=100 for 2020. CN: GDP Index: PY=100: TI: Real Estate: Hebei: Shijiazhuang data is updated yearly, averaging 108.750 Prev Year=100 from Dec 2002 (Median) to 2021, with 18 observations. The data reached an all-time high of 126.100 Prev Year=100 in 2011 and a record low of 101.800 Prev Year=100 in 2020. CN: GDP Index: PY=100: TI: Real Estate: Hebei: Shijiazhuang data remains active status in CEIC and is reported by Shijiazhuang Municipal Bureau of Statistics. The data is categorized under China Premium Database’s National Accounts – Table CN.AE: Gross Domestic Product: Prefecture Level City: Index: TI: Real Estate.

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

CN: GDP Index: PY=100: TI: Real Estate: Shanghai data was reported at 99.700 Prev Year=100 in 2023. This records a decrease from the previous number of 100.900 Prev Year=100 for 2022. CN: GDP Index: PY=100: TI: Real Estate: Shanghai data is updated yearly, averaging 104.500 Prev Year=100 from Dec 2001 (Median) to 2023, with 23 observations. The data reached an all-time high of 127.000 Prev Year=100 in 2009 and a record low of 70.700 Prev Year=100 in 2010. CN: GDP Index: PY=100: TI: Real Estate: Shanghai data remains active status in CEIC and is reported by Shanghai Municipal Bureau of Statistics. The data is categorized under China Premium Database’s National Accounts – Table CN.AE: Gross Domestic Product: Prefecture Level City: Index: TI: Real Estate.

This dataset includes many indexes of global cities. The variables of congestion level, skyscraper index, whether a city was bombed in WWII (World War II), and global cities’ population are key variables. (1) The congestion level data were collected from TOMTOM company. The congestion level data includes five indexes in 2004 which are “Congestion level”, “Morning peak Congestion level”, “Evening peak Congestion level”, “Highways Congestion level”, “Non-highways Congestion level”, and two indexes in 2020 which are “Time lost per year” and “Congestion level”. (2) The data of skyscraper index is calculated using the data of building height from the Council on Tall Buildings and Urban Habitat, from which we can obtain accurate data on the number of buildings taller than 150 m. With these data, we constructed an index of skyscrapers taller than 150 m in a city. A building receives a score of 1.5 if it is taller than 150 m and shorter than 200 m, 2.0 if it is between 200 m and 300 m, and so on. Then, we summed the scores for skyscrapers in the city as the “skyscraper index” of the city. (3) The data of whether a city was bombed in WWII is dummy variable, if the urban area of a city was bombed in WWII, it is 1, and 0 otherwise. The authors consulted various historical files and determined the value. (4) The data of global cities’ population, as well as the area and density of the city, are on the city-level, and were collected from the website of the cities or countries’ statistics department. These indicators are good measures of the level of congestion, urban spatial structure, instrumental variable (IV) for urban spatial structure, and urban population in global cities, and can be reused in other analysis.

Column D shows the population in each city based on the 2010 census. Column E indicates the rank of population in each city. Column F represents the number of tweets collected in each of the 50 home cities in Fig 1. Column G shows the number of tweets containing city names outside the U.S. divided by the total number of tweets. Column H is GAI multiply by 100000. Column I is normalized GAI that ranges between 0 and 1.* represents the biggest top 10 cities by population.Global Awareness Index (GAI) at 50 cities in the U.S.

According to the latest results of IFDAQ's Global Cities Consumer IPX (Index), by 2030 Paris is expected to become the leading city for fashion, with an index value of ***** points. IFDAQ's forecast for 2030 put New York and London in the second and third place, respectively.The index measures global cities taking into account factors such as GDP, brand presence, wealth, consumption and creative power.

CN: GDP Index: PY=100: TI: Real Estate: Jiangsu: Yangzhou data was reported at 103.200 Prev Year=100 in 2021. This records an increase from the previous number of 101.800 Prev Year=100 for 2018. CN: GDP Index: PY=100: TI: Real Estate: Jiangsu: Yangzhou data is updated yearly, averaging 104.450 Prev Year=100 from Dec 2012 (Median) to 2021, with 8 observations. The data reached an all-time high of 124.900 Prev Year=100 in 2013 and a record low of 99.000 Prev Year=100 in 2014. CN: GDP Index: PY=100: TI: Real Estate: Jiangsu: Yangzhou data remains active status in CEIC and is reported by Yangzhou Municipal Bureau of Statistics. The data is categorized under China Premium Database’s National Accounts – Table CN.AE: Gross Domestic Product: Prefecture Level City: Index: TI: Real Estate.

CN: GDP Index: PY=100: TI: Education: Hebei: Shijiazhuang data was reported at 104.300 Prev Year=100 in 2021. This records a decrease from the previous number of 111.300 Prev Year=100 for 2020. CN: GDP Index: PY=100: TI: Education: Hebei: Shijiazhuang data is updated yearly, averaging 109.600 Prev Year=100 from Dec 2005 (Median) to 2021, with 15 observations. The data reached an all-time high of 122.000 Prev Year=100 in 2006 and a record low of 103.200 Prev Year=100 in 2010. CN: GDP Index: PY=100: TI: Education: Hebei: Shijiazhuang data remains active status in CEIC and is reported by Shijiazhuang Municipal Bureau of Statistics. The data is categorized under China Premium Database’s National Accounts – Table CN.AE: Gross Domestic Product: Prefecture Level City: Index: TI: Education.