According to the Hurun Global Rich List 2025, the city with the highest number of billionaires in 2025 was New York. In detail, *** billionaires resided in the American city. Furthermore, ** billionaires lived in London, while Shanghai had a billionaire population of ** individuals. New York was the only city in the world with more than 100 billionaires that year. Mega-cities of the world A large number of the world’s billionaires are concentrated in a select number of the world’s mega-cities. This has as much to do with the location of their wealth, business interests, and further earning potential, as does the quality of life in those cities. A look at the most significant industries in the global billionaire production line helps to explain the prominence of the traditional capitals of global business including New York, London and Hong Kong. The place of many Chinese cities on the list can in part be explained by the strong performance of industrial conglomerates from the country in recent years. Economic growth in China While New York is the city with the highest number of billionaires, China now boasts the most billionaires of any country in the world. However, ***** of the top ten wealthiest billionaires still came from the United States as of 2025.

A dataset listing the richest zip codes in New York per the most current US Census data, including information on rank and average income.

A dataset listing New York counties by population for 2024.

The objective of this data collection was to examine inequalities of wealth and the geographic distribution of wealthy individuals in late 18th- and early 19th-century New York and to investigate wealth in relationship to occupation and location. For this study, the entire set of tax assessment records and United States Census records for New York City were computerized and occupational status was added for all entries. The collection addresses topics such as social class structure, demographic factors, occupational status and geographic distribution, property values and geographic distribution, and the relationship of these factors to the political system. Units of analysis were individual property owners and renters for the tax assessment data and heads of households for the census data. Data collected included the individual's name, address, occupation, sex, and race, the type, quantity, and value of real and personal property, and the type and occupancy of the structure at the address. Occupational data from city directories were used to supplement the tax and census data.

In 2021, the per capita income in San Francisco city was at 80,383 U.S. dollars. San Francisco was followed in this regard by Seattle and Washington, D.C. The most populated cities in the U.S. are ranked by per capita income in this statistic. While New York, New York had the highest population, San Francisco had the highest per capita income in 2021. The median household income in San Francisco in 2020 was 119,136 dollars, the highest among the most populated cities in the United States.

In 2023, the median household income in New York amounted to 81,600 U.S. dollars. This is an increase from the previous year, when the median household income in the state amounted to 75,910 U.S. dollars. The median household income for the United States can be accessed here.

"Neighborhood Financial Health (NFH) Digital Mapping and Data Tool provides neighborhood financial health indicator data for every neighborhood in New York City. DCWP's Office of Financial Empowerment (OFE) also developed NFH Indexes to present patterns in the data within and across neighborhoods. NFH Index scores describe relative differences between neighborhoods across the same indicators; they do not evaluate neighborhoods against fixed standards. OFE intends for the NFH Indexes to provide an easy reference tool for comparing neighborhoods, and to establish patterns in the relationship of NFH indicators to economic and demographic factors, such as race and income. Understanding these connections is potentially useful for uncovering systems that perpetuate the racial wealth gap, an issue with direct implications for OFE’s mission to expand asset building opportunities for New Yorkers with low and moderate incomes. This data tool was borne out of the Collaborative for Neighborhood Financial Health, a community-led initiative designed to better understand how neighborhoods influence the financial health of their residents.

This statistic shows the number of the super-rich, or Ultra-High-Net-Worth, persons in the United States in 2014, sorted by city. New York has the largest concentration of super-rich individuals; about 8,655 UHNW (Ultra High Net Worth) people are living in the metro area.

The codes attached are used to support our study. Each of these codes is exported from ArcMap where they were constructed using ModelBuilder.Our study area focuses on New York City, which provides a data-rich urban environment with extreme variations in local population density and diverse types of input data in which to construct multiple methods. In this study area we can then compare the efficacy of multiple methodologies, which employ a strong binary mask paired with a density variable directly derived from the binary mask. We test the following methodologies:

1. Land areas binary mask

2. Building footprint binary mask

3. Building footprint binary mask and area density variable

4. Building footprints binary mask and volume density variable

5. Residential building footprint binary mask

6. Residential building footprint binary mask and area density variable

7. Residential building footprint binary mask and volume density variable

Cities in the United States dominate the list of cities with the highest number of ulta high net worth individuals (UHNWI) in 2023. This comes as no surprise as it is the country with the highest number of UHNWIs. New York had 16,630 individuals with a net worth exceeding 30 million U.S. dollars, followed by Hong Kong with 12,545 individuals.

Includes the error tables, ESRI ArcMap document, accompanying ESRI Geodatabase, ESRI Toolkit and the Python scripts/codes used in the analysis. The error tables are by Census Block for each tested method as well as the calculated grouped error statistics.Our study area focuses on New York City, which provides a data-rich urban environment with extreme variations in local population density and diverse types of input data in which to construct multiple methods. In this study area we can then compare the efficacy of multiple methodologies, which employ a strong binary mask paired with a density variable directly derived from the binary mask. We test the following methodologies:1. Land areas binary mask2. Building footprint binary mask3. Building footprint binary mask and area density variable4. Building footprints binary mask and volume density variable5. Residential building footprint binary mask6. Residential building footprint binary mask and area density variable7. Residential building footprint binary mask and volume density variable

Graph and download economic data for Income Inequality in New York County, NY (2020RATIO036061) from 2010 to 2023 about New York County, NY; inequality; New York; NY; income; and USA.

This statistic shows the top ten cities in the world with the largest amount of wealth held by billionaires from the finance and investment industry in 2015. New York had the largest amount of wealth in this regard in 2015, 67 billionaires held a shared total of 287 billion U.S. dollars in wealth.

The Flood Vulnerability Index (FVI) assesses the distribution of vulnerability to flooding across NYC in order to guide flood resilience policies and programs. Vulnerability contains three components: exposure to a hazard, susceptibility to harm from the exposure, and capacity to recover (Cutter et al., 2009). There are six hazard-specific FVIs, one for each of the six different flood hazard scenarios, which include current and two future storm surge scenarios and current and two future tidal flooding scenarios. Exposures vary for different types of flooding and different scenarios within each flood type.

Each FVI consists of two component sub-indices: an exposure index and an index that reflects susceptibility to harm and capacity to recover. The exposure index is different in each FVI in order to capture the different exposures to each of the flood hazard scenarios. The sub-index that reflects susceptibility to harm and capacity to recover -- the Flood Susceptibility to Harm and Recovery Index (FSHRI) -- is the same for each FVI. It aggregates 12 socio-economic indicators correlated with various types of hardships that people may suffer due to flooding and different dimensions of ability to recover.

For additional information, please visit this link.

CO: Rural Population Living in Areas Where Elevation is Below 5 Meters: % of Total Population data was reported at 0.561 % in 2015. This records a decrease from the previous number of 0.571 % for 2000. CO: Rural Population Living in Areas Where Elevation is Below 5 Meters: % of Total Population data is updated yearly, averaging 0.571 % from Dec 1990 (Median) to 2015, with 3 observations. The data reached an all-time high of 0.579 % in 1990 and a record low of 0.561 % in 2015. CO: Rural Population Living in Areas Where Elevation is Below 5 Meters: % of Total Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Colombia – Table CO.World Bank.WDI: Environmental: Land Use, Protected Areas and National Wealth. Rural population below 5m is the percentage of the total population, living in areas where the elevation is 5 meters or less.;Center for International Earth Science Information Network - CIESIN - Columbia University, and CUNY Institute for Demographic Research - CIDR - City University of New York. 2021. Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates, Version 3. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/d1x1-d702.;Weighted average;

This is the official repository of the CIM-WV dataset. For technical details, please refer to:Li, M., Yeh, A. G. & Xue, F. (2023). CIM-WV: A 2D semantic segmentation dataset of rich window view contents in high-rise, high-density Hong Kong based on photorealistic City Information Models. Urban Informatics, 1-24.This study was supported in part by the Department of Science and Technology of Guangdong Province (GDST) (2020B1212030009, 2023A1515010757) and the University of Hong Kong (203720465).Overview of CIM-WVThis paper presents a City Information Model (CIM)-generated Window View (CIM-WV) dataset comprising 2,000 annotated images collected in the high-rise, high-density urban areas of Hong Kong. 1) Window view images of CIM-WV depict diversified urban scenes of Hong Kong at different locations, elevations, and orientations2) The CIM-WV includes seven semantic labels, i.e., building, sky, vegetation, road, waterbody, vehicle, and terrain.In addition, we provide variants of DeepLab V3+ models trained on CIM-WV, real window view images, Google Earth CIM-generated window view images from New York, and Google Earth CIM-generated window view images from Singapore, respectively.You can modify the source code here to use the trained DeepLab V3+ models. Contribution1) CIM-WV is the first public CIM-generated photorealistic window view dataset with rich semantics. 2) Comparative analysis shows a more accurate window view assessment using deep learning from CIM-WV than deep transfer learning from ground-level views.3) For urban researchers and practitioners, our publicly accessible deep learning models trained on CIM-WV enable novel multi-source window view-based urban applications including precise real estate valuation, improvement of built environment, and window view-related urban analytics.Please cite our paper and dataset, if you find our work useful for your research and practices. Many thanks.For any inquiries, please feel free to contact Maosu at maosulee@connect.hku.hk or Dr. Frank at xuef@hku.hk.

The number of billionaires in Pakistan is forecast to reach **** in 2026. In 2016, there were just ***** individuals whose net worth exceeded *** billion U.S. dollars in Pakistan.

Leading billionaire cities

According to the Hurun Global Rich List 2022, Beijing had the most billionaires in 2022. In total, *** billionaires lived in China's capital. Furthermore, *** billionaires resided in Shanghai, while *** were in New York. Many of the world's billionaires are concentrated in a few megacities. A look at the primary industries of billionaires globally helps to explain the importance of traditional global business capitals such as New York, London, and Hong Kong. The inclusion of Chinese cities on the list can be explained partly by the country's industrial conglomerates' strong performance in recent years.

The effect of ******** on the wealth of billionaires

Elon Musk was the billionaire whose fortune grew the most due to the COVID-19 pandemic. From September 2019 to September 2022, Elon Musk increased his net worth by ***** billion US dollars. Google’s Larry Page added the second highest value to his net worth during the period under consideration, with an increase of **** billion dollars. In contrast, Facebook founder Mark Zuckerberg’s net worth decreased by nearly ** billion US dollars during the same time.

In 2023, the GDP of the New York metro area amounted to *** trillion chained 2017 U.S. dollars. This is an increase from 2021, when the GDP of the New York metro area was **** trillion dollars. New York CityThe New York metro area’s GDP has steadily risen in the last two decades from *** trillion U.S. dollars in 2001 to **** trillion U.S. dollars in 2023. In September 2023, the New York- Newark-Jersey City area had an unemployment rate of *** percent. It also had the highest population in the country in 2022 at ***** million people. New York City’s economy is one of the greatest in the country and is home to many Fortune 500 companies, including Big Pharma’s Bristol-Myers Squibb. Industries such as media, real estate, fashion and entertainment are some of the most prominent in the area. The finance industry in New York City, also known as Wall Street, is one of the leading financial centers of the world and houses the New York Stock Exchange and NASDAQ. The region is also home to one of the largest trading industries in the country at the Port of New York and New Jersey. This port includes a large estuary, regional airports, and a plethora of rail and road networks. Silicon Alley is one of the country’s largest technology industry hubs, including internet, telecommunications, and biotechnology. In 2022, there were some ****** business establishments in the region that focused on professional, scientific, and technical services.

Abstract (en): This multi-method project sought to gain a better understanding of the commercial sexually exploited children (CSEC) population, particularly its size, characteristics, needs, and geographic spread in New York City. It represents a first attempt to understand the CSEC population in a major metropolitan area and to examine a concerted institutional effort to meet its needs. Three forms of data were collected in the project: questionnaire data, interview data, and network data. The project used Respondent Driven Sampling (RDS) to identify commercial sexually exploited children (CSEC) in New York City. Interviews were conducted with 230 youths between January 2006 and December 2007. Quantitative surveys regarding the frequency and quality of cross-stakeholder communication were administered at the beginning of the evaluation and one year later. For the purpose of trend analysis of CSEC related offenses, research staff obtained citywide arrest and prosecution data on child prostitution, exploitation, and solicitation of a minor. The New York City Criminal Justice Agency (CJA) provided arrest data for arrestees under 19 years of age in all five boroughs of New York City from January 1, 1998 through December 31, 2006. The study had two primary goals. The first was to provide a reliable and ethnographically rich description of the local commercial sexually exploited children (CSEC) population, including its size, characteristics, experiences, and service needs. The second was to evaluate the local demonstration project, documenting its major initiatives, achievements, and obstacles. In achiving these goals, the study also sought to identify lessons for other jurisdictions interested in replicating efforts like those in New York City. Three distinct forms of data were collected in the project: statistical and coded data in the form of a questionnaire, narrative and quantitative data in the form of open ended questions whose answers were transcribed, and network data derived from the sampling chains themselves and the "special seed" data used to provide information of network cycle length and tree overlaps not normally available in RDS methods. The project used Respondent Driven Sampling (RDS) to identify a representative sample of commercial sexually exploited children (CSEC) in New York City. Dataset 1 (CSEC Interview data) includes data collected from 230 youths between January 2006 and December 2007. When a potential research subject contacted the project by telephone, the research team completed a brief initial eligibility assessment (questions on age and CSEC involvement) and set a time and place for the interview to be completed. Respondents were paid twenty dollars (cash or gift certificate) for completing the interview, and researchers were trained to provide the respondents with an opportunity to seek or get help. Respondents who completed the interview process were given 'coupons' to distribute to others they knew who meet the research criteria to help with further recruitment.Semi-structured stakeholder interviews were conducted twice: at the beginning of the evaluation period and one year later. The main purposes were to gain a better understanding of the history and nature of Coalition Against the Sexual Exploitation of Children (CASEC) and to obtain stakeholder perceptions of the project's strengths, weaknesses, accomplishments, and challenges (The semi-structured Stakeholder interviews are not available as part of this data collection). At both the initial and follow-up interviews, quantitative surveys regarding the frequency and quality of cross-stakeholder communication (Dataset 2, Stakeholder Communication and Satisfaction Data, n=16) were administered.For the purpose of trend analysis of CSEC related offense, research staff obtained citywide arrest and prosecution data on child prostitution, exploitation, and solicitation of a minor. For defendants ages 16 and older, the New York State Division of Criminal Justice Services (DCJS) provide the researchers with comprehensive arrest, disposition and sentencing data for all five New York City boroughs from January 1, 1982 - December 31, 2006 (Dataset 3 CSEC Explotation and Solicitation Case Data, n = 2212 and Dataset 4 CSEC Child Prostitution Case Data, n = 6928). The New York City Criminal Justice Agency (CJA) provided arrest data for arrestees under 19 years of age in all five boroughs of New York City from January 1, 1998 through December 31, ...

This statistic presents the estimated net worth of the 15 wealthiest media entrepreneurs in the United States in 2013. Michael Bloomberg, the founder of Bloomberg L.P. and Mayor of New York City, was the wealthiest media entrepreneur of the United States in 2013 with an estimated net worth of 31 billion dollars. In 2012, his wealth amounted to around 25 billion.

Media entrepreneurs – additional information

One of the world’s wealthiest media entrepreneurs, David Geffen was, as of March 2014, also one of the world’s leading art collectors with a collection valued at 1.1 billion U.S. dollars. Geffen’s wealth comes from his career as a producer and a film studio executive. In 1994 he founded DreamWorks SKG alongside Jeffrey Katzenberg and Steven Spielberg. DreamWorks was the studio behind blockbuster films such as ‘Saving Private Ryan’, ‘Gladiator’ and ‘Shrek’.

The social media industry has also created a selection of successful and wealthy entrepreneurs, as demonstrated in a ranking of the richest social media entrepreneurs, as of June 2014 by net worth. Larry Page and Sergey Brin, the founders of Google both top the list in first and second place with a personal net worth of 31.6 and 31.2 billion U.S. respectively. In third place is the founder of Facebook Marc Zuckerberg with an estimated personal net worth of 29.8 billion U.S. dollars.

However, Page, Brin and Zuckerberg do not top the list of the world’s richest internet billionaires of 2013. That title belonged to Jeff Bezos, the founder of Amazon who as of March 2013 had an estimated personal net worth of 25.2 billion U.S. dollars. Bezos founded Amazon in 1994 and in August 2013 he purchased The Washington Post for a reported 250 million U.S. dollars, in cash.