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

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

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

This dataset contains data from California resident tax returns filed with California adjusted gross income and self-assessed tax listed by zip code. This dataset contains data for taxable years 1992 to the most recent tax year available.

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

The median house prices in the most expensive zip codes in New England, United States ranged from 1.9 to 2.8 million U.S. dollars. Boston (zip code 02199) was the most expensive in New England with a median house price of 2.8 million U.S. dollars. Nevertheless, that was more affordable than in the ten zip codes with the highest median house price in the entire United States.

IntroductionOur study explores how New York City (NYC) communities of various socioeconomic strata were uniquely impacted by the COVID-19 pandemic.MethodsNew York City ZIP codes were stratified into three bins by median income: high-income, middle-income, and low-income. Case, hospitalization, and death rates obtained from NYCHealth were compared for the period between March 2020 and April 2022.ResultsCOVID-19 transmission rates among high-income populations during off-peak waves were higher than transmission rates among low-income populations. Hospitalization rates among low-income populations were higher during off-peak waves despite a lower transmission rate. Death rates during both off-peak and peak waves were higher for low-income ZIP codes.DiscussionThis study presents evidence that while high-income areas had higher transmission rates during off-peak periods, low-income areas suffered greater adverse outcomes in terms of hospitalization and death rates. The importance of this study is that it focuses on the social inequalities that were amplified by the pandemic.

The median house price in 94027, Atherton, California, was about 8.3 million U.S. dollars. This made it the most expensive zip code in the United States in 2023. 11962 Sagaponack, N.Y., was the runner-up with a median house price of about 8.1 million U.S. dollars. Of the 10 most expensive zip codes in the United States in 2026, six were in California.

This layer shows census tracts that meet the following definitions: Census tracts with median household incomes at or below 80 percent of the statewide median income or with median household incomes at or below the threshold designated as low income by the Department of Housing and Community Development’s list of state income limits adopted under Healthy and Safety Code section 50093 and/or Census tracts receiving the highest 25 percent of overall scores in CalEnviroScreen 4.0 or Census tracts lacking overall scores in CalEnviroScreen 4.0 due to data gaps, but receiving the highest 5 percent of CalEnviroScreen 4.0 cumulative population burden scores or Census tracts identified in the 2017 DAC designation as disadvantaged, regardless of their scores in CalEnviroScreen 4.0 or Lands under the control of federally recognized Tribes.

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

Spotzi's Income dataset for the United States offers valuable insights into the intricacies of yearly income at various levels. This dataset is meticulously curated, presenting a detailed analysis of total income, types of household earnings, and the critical aspect of whether households are above the poverty level. This dataset is available at Census Block level, and allows for a holistic understanding of the economic landscape at both regional and national scales.

What is included?

Each data variable is presented as a percentage of the total population within each selected area. Please see below for a complete list of available data variables:

Income

• Individual Yearly Income: Under 10K, 10K-20K, 20K-35K, 35K-50K, 50K-100K, 100K+
• Individual Yearly Income - Female: Under 10K, 10K-20K, 20K-35K, 35K-50K, 50K-100K, 100K+
• Individual Yearly Income - Male: Under 10K, 10K-20K, 20K-35K, 35K-50K, 50K-100K, 100K+
• Household Income Earning Status: Earns Income, Does Not Earn Income
• Households Below Poverty Level

Household Earnings By Type

• Salary
• Interest Dividends
• Retirement Income
• Self-Employment

• • Individual Yearly Income: Marketers can leverage individual income data to tailor their strategies based on the financial capacities of their target audience. For example, luxury brands may target individuals with higher income brackets, while budget-conscious brands may focus on those with lower income levels.
  • Household Earning by Type: Marketers can use this data to understand the sources of household income, allowing for targeted campaigns. For example, financial services may tailor promotions based on the types of income earned, offering retirement planning services to those with significant retirement income.

• This demographic data is typically available at the census block level. These blocks are smaller, more detailed units designed for statistical purposes, enabling a more precise analysis of population, housing, and demographic data. Census blocks may vary in size and shape but are generally more localized compared to ZIP codes.

  Still looking for demographic data at the postal code level? Contact sales.

• There are numerous other census data datasets available for the United States, covering a wide range of demographics. These include information on:

  • Commuting
  • Educational Attainment
  • Employment
  • Families
  • Finance
  • Housing

This feature dataset contains a snapshot of all King County parcels from September 2020, with all of the "additional relevant criteria" data used in Method 2 of the LCI opportunity area determination described below.There are two methods by which a property may qualify as being in an opportunity area:Method 1. Property meets all three of the following "specified criteria" in King County code 26.12.003.(a) Areas "located in a census tract in which the median household income is in the lowest one-third for median household income for census tracts in King County; (b) "located in a ZIP code in which hospitalization rates for asthma, diabetes, and heart disease are in the highest one-third for ZIP codes in King County; and (c) "are within the Urban Growth Boundary and do not have a publicly owned and accessible park or open space within one-quarter mile of a residence, or are outside the Urban Growth Boundary and do not have a publicly owned and accessible park or open space within two miles of a residence." (King County Code 26.12.003)Data results related to Method 1 are shown in the LCI Opportunity Areas dataset on the King County GIS Open Data site. In this dataset, the parcels where the "CriteriaAllYN" column is equal to "Y" also represents those parcels.Method 2. If a property does not qualify under Method #1, a project may qualify if: "the project proponent or proponents can demonstrate, and the advisory committee determines, that residents living in the area, or populations the project is intended to serve, disproportionately experience limited access to public open spaces and experience demonstrated hardships including, but not limited to, low income, poor health and social and environmental factors that reflect a lack of one or more conditions for a fair and just society as defined as "determinants of equity" in KCC 2.10.210." (King County Code 26.12.003)Conservation Futures (CFT) values the use of multiple sources of data and information to demonstrate that a property is in an opportunity area. Applicants are welcome to provide additional criteria and data sources not identified in this report to demonstrate that a property is in an opportunity area. These sources are provided in the document here: Understanding the Data Report.

The median house prices in the most expensive zip codes in California reached as high as 8.3 million U.S dollars. Atherton (94027), had the most expensive median house price, followed by Santa Barbara (93108), and Beverly Hills (90210). Six of the ranked zip codes were among the top ten most expensive zip codes in the United States in 2023.

Small business transactions and revenue data aggregated from several credit card processors, collected by Womply and compiled by Opportunity Insights. Transactions and revenue are reported based on the ZIP code where the business is located.

Data provided for CT (FIPS code 9), MA (25), NJ (34), NY (36), and RI (44).

Data notes from Opportunity Insights: Seasonally adjusted change since January 2020. Data is indexed in 2019 and 2020 as the change relative to the January index period. We then seasonally adjust by dividing year-over-year, which represents the difference between the change since January observed in 2020 compared to the change since January observed since 2019. We account for differences in the dates of federal holidays between 2019 and 2020 by shifting the 2019 reference data to align the holidays before performing the year-over-year division.

Small businesses are defined as those with annual revenue below the Small Business Administration’s thresholds. Thresholds vary by 6 digit NAICS code ranging from a maximum number of employees between 100 to 1500 to be considered a small business depending on the industry.

County-level and metro-level data and breakdowns by High/Middle/Low income ZIP codes have been temporarily removed since the August 21st 2020 update due to revisions in the structure of the raw data we receive. We hope to add them back to the OI Economic Tracker soon.

More detailed documentation on Opportunity Insights data can be found here: https://github.com/OpportunityInsights/EconomicTracker/blob/main/docs/oi_tracker_data_documentation.pdf

A dataset listing the 20 richest cities in Virginia for 2024, including information on rank, city, county, population, average income, and median income.

Abstract: Food insecurity occurs when a household lacks consistent access to food and is more prevalent in ethnic and racial minoritized populations. While there has been a proliferation of research linking food insecurity to obesity, these findings are mixed. It may be helpful to consider some additional geographic factors that may be associated with both factors including socioeconomic status and grocery store density. The purpose of the current study aimed to examine spatial relationships between food insecurity and SES/store density and BMI and SES/store density in a diverse sample of adolescents and young adults across two studies in a large, urban city. GIS analysis revealed that participants with the highest food insecurity (larger symbols) tend to live in the zip codes with the lowest median income. There did not appear to be clear a relationship between food insecurity and store density. Participants with the highest BMI tend to live in zip codes with lower median income and participants with higher BMI tended to live further away from downtown, which has the highest concentration of grocery stores in the city. Our findings may help to inform future interventions and policy approaches to addressing both obesity and food insecurity in areas of higher prevalence.

This repository contains data for a data science class exercise.Students: This exercise is about income mobility over three generations: grandparents (g1), parents (g2), and children (g3). Your task is to predict log income in generation 3 using data on log incomes in generations 1 and 2. Additional predictors available include education in each generation, race as reported by the grandparent (g1), and sex of the respondent in g3.The data you will use are in for_students.zip.learning.csv contains 1,365 observations for which the outcome g3_log_income is recordedholdout_public.csv contains 1,365 observations for which the outcome g3_log_income is NAYour task is to build a predictive model using learning.csv. Then, make predictions for the cases in holdout_public.csv.Here are some details about the variables in the data. All cases are from the cross-sectional Survey Research Sample of the PSID. In each generation, we took each respondent's annual income over several surveys from age 30 to 45, adjusted to 2022 dollars, and took the average. We truncated the data to the range from $5,000 to $448,501.10, where the bottom code is arbitrary and the top code is what we believe to be the lowest PSID top code over the series (in 1978), converted to 2022 dollars. Education is the first report at ages 30-45, coded as less than high school, high school, some college, or 4+ years of college. We merged the data together across generations using the PSID Family Identification Mapping System 3-generation prospective linkage file. See for_replication.zip for code to produce these data as well as a log file noting sample restrictions.We are trusting the students to not open the instructor data, which contains the outcomes you are trying to predict. You could peek of course, but that would be no fun! We are trusting you not to peek.Instructors: The file for_instructors.zip contains the true holdout outcomes in holdout_private.csv. You can use these to evaluate students' predictive performance (as long as you trust that they have not peeked).For those replicating: The file for_replication.zip contains the directory structure and code that produced this exercise from raw files downloaded from the PSID.

I use income, demographic, and solar PV installation data to test whether wealth inequality has an effect on Solar PV usage within the State of Arizona. I find that there is no statistically significant evidence that a higher proportion of low income households within a given zip code results in a decreased installation rate of solar PV. Rather, as the proportion of individuals earning between $50,000-75,000 dollars a year or $100,000-$200,000 dollars per year rises, the number of solar PV installations increase. Finally, I find there is a positive correlation between the median age of a zip code and solar PV installations.

This statistic displays the top 10 of the richest municipalities in the Netherlands in 2021. Bloemendaal was the richest municipality in the country that year. Around 29.7 percent of households in Bloemendaal had one million euros worth in wealth or more.

A dataset listing the 20 richest cities in South Carolina for 2024, including information on rank, city, county, population, average income, and median income.