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 megacities. This has as much to do with the location of their wealth, business interests, and further earning potential as it does with 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 recently. 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 the 20 richest counties in New York for 2024, including information on rank, county, population, average income, and median income.

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.

"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.

Cities in the United States dominate the list of cities with the highest number of ultra-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 ****** individuals with a net worth exceeding ** million U.S. dollars, followed by Hong Kong with ****** individuals.

NYC Agency Social Media Engagement

Social Media Interaction Metrics of NYC Agencies 2011-2012

By Anton Cheng [source]

About this dataset

New York City Agency Social Media Engagement Data

This dataset provides an extensive analysis of the social media engagement of various New York City agencies. It presents comprehensive insights into the digital impact, interaction and reach these agencies have had across several popular social media and digital platforms. The data offers a snapshot of agency presence within the online community over time, tentatively providing patterns that could be instrumental in designing better communication strategies.

The data fields in this rich dataset include Agency, Platform, Url, 'Date Sampled' and 'Likes/Followers/Visits/Downloads.' Each record stands for a snapshot on a given day (Date Sampled) revealing the digital footprint of NYC's agency pages.

Agency refers to the specific department or sector within NYC's municipal structure that is represented on these platforms. This field gives us an opportunity to study how diverse sections of governance employ social media differently, maybe even uniquely.

Platform” designates which particular social media or digital platform was utilised by each NYC agency. Potential examples encompass global giants such as Facebook, Twitter, Instagram as well as others; offering valuable knowledge about which platforms are more effective for public sector engagement initiatives or reach a bigger audience base.

Next is Url; it denotes the unique web address directing users to each specific page owned by particular NYC agencies across different platforms.

Lastly is “Likes/Followers/Visits/Downloads,” wherein lies actual numerical fascination! These may comprise likes given by users on platform based posts, followers gained (for instance: Twitter), visits made onto web pages or downloads facilitated from them - depending upon what functionality does operationalized platform provide for user-interaction engagement metrics visibility. These raw numbers explicitly represent interaction intensity level occurring at these interfaces between common Nathaniels and administrations served thereforth!

Step into this exciting world reflecting how technology aids public administration in our contemporary era - delve into this dataset & uncover stories snd dynamics it enfolds!

How to use the dataset

How to use this dataset

This data set provides a rich base for many kinds of analysis. Here are some suggestions:

Benchmarking Agency Engagement

If you are interested in understanding the social media engagement and digital footprint of agencies, this dataset can provide important insights. By analyzing likes, followers, visits, and downloads across different platforms, you can benchmark each agency's outreach efforts.

Analyzing Social Media Performance Over Time

By using the 'Date Sampled' field provided in this dataset, analysts can understand how an agency's social media performance varies over time. For example, they might identify seasonal trends or measure the impact of particular campaigns.

Relationship between Platforms and Engagement

By comparing 'Likes/Followers/Visits/Downloads' across different platforms (for example Twitter versus Facebook versus Instagram), one could draw conclusions about where specific types of content or messages get more engagement.This could help agencies understand where to best concentrate their digital marketing efforts.

Building Prediction Models

With a sufficient amount of additional data on other potential influencers on social media engagement (such as news events, promotional activities etc.), these figures could be used to build machine learning models predicting future social media engagement metrics based on historical data.

Enhancing with External Data

To allow more detailed analysis you might want to combine this data with other relevant datasets. Some ideas include: - Agencies’ total budgets: Are agencies with larger budgets better at engaging with public via social networks? - Population demographics: Can we see a correlation between an agency’s online popularity and the age distribution in NYC?

Remember: While working with this kind of data stock check frequently that your use case aligns with kaggle terms & conditions regarding privacy respect.

• Load your preferred coding language’s CSV reading library.
• Download ‘NYC_Social_Media_Usage.csv’.
• Read the csv file using your coding language’s CSV reading function.
• Proceed to perform analysis on the data. &g...

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.

Out of all 50 states, New York had the highest per-capita real gross domestic product (GDP) in 2024, at 92,341 U.S. dollars, followed closely by Massachusetts. Mississippi had the lowest per-capita real GDP, at 41,603 U.S. dollars. While not a state, the District of Columbia had a per capita GDP of more than 210,780 U.S. dollars. What is real GDP? A country’s real GDP is a measure that shows the value of the goods and services produced by an economy and is adjusted for inflation. The real GDP of a country helps economists to see the health of a country’s economy and its standard of living. Downturns in GDP growth can indicate financial difficulties, such as the financial crisis of 2008 and 2009, when the U.S. GDP decreased by 2.5 percent. The COVID-19 pandemic had a significant impact on U.S. GDP, shrinking the economy 2.8 percent. The U.S. economy rebounded in 2021, however, growing by nearly six percent. Why real GDP per capita matters Real GDP per capita takes the GDP of a country, state, or metropolitan area and divides it by the number of people in that area. Some argue that per-capita GDP is more important than the GDP of a country, as it is a good indicator of whether or not the country’s population is getting wealthier, thus increasing the standard of living in that area. The best measure of standard of living when comparing across countries is thought to be GDP per capita at purchasing power parity (PPP) which uses the prices of specific goods to compare the absolute purchasing power of a countries currency.