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

This annual study provides selected income and tax items classified by State, ZIP Code, and the size of adjusted gross income. These data include the number of returns, which approximates the number of households; the number of personal exemptions, which approximates the population; adjusted gross income; wages and salaries; dividends before exclusion; and interest received. Data are based who reported on U.S. Individual Income Tax Returns (Forms 1040) filed with the IRS. SOI collects these data as part of its Individual Income Tax Return (Form 1040) Statistics program, Data by Geographic Areas, ZIP Code Data.

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

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

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

Wealth Segmentation of U.S. ZIP Codes Based on IRS Data

This dataset provides a wealth-tier classification of U.S. ZIP codes for high income brackets using IRS income data and multivariate KMeans clustering. It can help with regional targeting, CRM enrichment, market analysis, or any data science task that benefits from understanding high income distribution across the U.S.

💡 Source

• IRS SOI ZIP Code Data (open source)
• Aggregated across AGI brackets (stub 3–6)

🧠 What’s Inside

Each row represents a ZIP code with:

• AGI (A00100), Total Income (A00200)
• Capital Gains, Business Income, Tax Paid
• Cluster assignment (0–2)
• Wealth Tier label: Low, Medium, or High

The cluster assignments are refined using distance to cluster centroids in normalized feature space to improve accuracy.

💼 Use Cases

• Segmenting markets for B2B/B2C outreach
• CRM lead enrichment
• Territory planning and resource allocation
• Visualization and dashboard overlays

📬 Contact

Created by Namrata Nyamagoudar(LinkedIn) for open-source analysis and enrichment use cases.

Data SourcesAmerican Community Survey (ACS):Conducted by: U.S. Census BureauDescription: The ACS is an ongoing survey that provides detailed demographic and socio-economic data on the population and housing characteristics of the United States.Content: The survey collects information on various topics such as income, education, employment, health insurance coverage, and housing costs and conditions.Frequency: The ACS offers more frequent and up-to-date information compared to the decennial census, with annual estimates produced based on a rolling sample of households.Purpose: ACS data is essential for policymakers, researchers, and communities to make informed decisions and address the evolving needs of the population.CDC/ATSDR Social Vulnerability Index (SVI):Created by: ATSDR’s Geospatial Research, Analysis & Services Program (GRASP)Utilized by: CDCDescription: The SVI is designed to identify and map communities that are most likely to need support before, during, and after hazardous events.Content: SVI ranks U.S. Census tracts based on 15 social factors, including unemployment, minority status, and disability, and groups them into four related themes. Each tract receives rankings for each Census variable and for each theme, as well as an overall ranking, indicating its relative vulnerability.Purpose: SVI data provides insights into the social vulnerability of communities at both the tract and zip code levels, helping public health officials and emergency response planners allocate resources effectively.Utilization and IntegrationBy integrating data from both the ACS and the SVI, this dataset enables an in-depth analysis and understanding of various socio-economic and demographic indicators at the census tract level. This integrated data is valuable for research, policymaking, and community planning purposes, allowing for a comprehensive understanding of social and economic dynamics across different geographical areas in the United States.ApplicationsTargeted Interventions: Facilitates the development of targeted interventions to address the needs of vulnerable populations within specific zip codes.Resource Allocation: Assists emergency response planners in allocating resources more effectively based on community vulnerability at the zip code level.Research: Provides a rich dataset for academic and applied research in socio-economic and demographic studies at a granular zip code level.Community Planning: Supports the planning and development of community programs and initiatives aimed at improving living conditions and reducing vulnerabilities within specific zip code areas.Note: Due to limitations in the data environment, variable names may be truncated. Refer to the provided table for a clear understanding of the variables. CSV Variable NameShapefile Variable NameDescriptionStateNameStateNameName of the stateStateFipsStateFipsState-level FIPS codeState nameStateNameName of the stateCountyNameCountyNameName of the countyCensusFipsCensusFipsCounty-level FIPS codeState abbreviationStateFipsState abbreviationCountyFipsCountyFipsCounty-level FIPS codeCensusFipsCensusFipsCounty-level FIPS codeCounty nameCountyNameName of the countyAREA_SQMIAREA_SQMITract area in square milesE_TOTPOPE_TOTPOPPopulation estimates, 2013-2017 ACSEP_POVEP_POVPercentage of persons below poverty estimateEP_UNEMPEP_UNEMPUnemployment Rate estimateEP_HBURDEP_HBURDHousing cost burdened occupied housing units with annual income less than $75,000EP_UNINSUREP_UNINSURUninsured in the total civilian noninstitutionalized population estimate, 2013-2017 ACSEP_PCIEP_PCIPer capita income estimate, 2013-2017 ACSEP_DISABLEP_DISABLPercentage of civilian noninstitutionalized population with a disability estimate, 2013-2017 ACSEP_SNGPNTEP_SNGPNTPercentage of single parent households with children under 18 estimate, 2013-2017 ACSEP_MINRTYEP_MINRTYPercentage minority (all persons except white, non-Hispanic) estimate, 2013-2017 ACSEP_LIMENGEP_LIMENGPercentage of persons (age 5+) who speak English "less than well" estimate, 2013-2017 ACSEP_MUNITEP_MUNITPercentage of housing in structures with 10 or more units estimateEP_MOBILEEP_MOBILEPercentage of mobile homes estimateEP_CROWDEP_CROWDPercentage of occupied housing units with more people than rooms estimateEP_NOVEHEP_NOVEHPercentage of households with no vehicle available estimateEP_GROUPQEP_GROUPQPercentage of persons in group quarters estimate, 2014-2018 ACSBelow_5_yrBelow_5_yrUnder 5 years: Percentage of Total populationBelow_18_yrBelow_18_yrUnder 18 years: Percentage of Total population18-39_yr18_39_yr18-39 years: Percentage of Total population40-64_yr40_64_yr40-64 years: Percentage of Total populationAbove_65_yrAbove_65_yrAbove 65 years: Percentage of Total populationPop_malePop_malePercentage of total population malePop_femalePop_femalePercentage of total population femaleWhitewhitePercentage population of white aloneBlackblackPercentage population of black or African American aloneAmerican_indianamerican_iPercentage population of American Indian and Alaska native aloneAsianasianPercentage population of Asian aloneHawaiian_pacific_islanderhawaiian_pPercentage population of Native Hawaiian and Other Pacific Islander aloneSome_othersome_otherPercentage population of some other race aloneMedian_tot_householdsmedian_totMedian household income in the past 12 months (in 2019 inflation-adjusted dollars) by household size – total householdsLess_than_high_schoolLess_than_Percentage of Educational attainment for the population less than 9th grades and 9th to 12th grade, no diploma estimateHigh_schoolHigh_schooPercentage of Educational attainment for the population of High school graduate (includes equivalency)Some_collegeSome_collePercentage of Educational attainment for the population of Some college, no degreeAssociates_degreeAssociatesPercentage of Educational attainment for the population of associate degreeBachelor’s_degreeBachelor_sPercentage of Educational attainment for the population of Bachelor’s degreeMaster’s_degreeMaster_s_dPercentage of Educational attainment for the population of Graduate or professional degreecomp_devicescomp_devicPercentage of Household having one or more types of computing devicesInternetInternetPercentage of Household with an Internet subscriptionBroadbandBroadbandPercentage of Household having Broadband of any typeSatelite_internetSatelite_iPercentage of Household having Satellite Internet serviceNo_internetNo_internePercentage of Household having No Internet accessNo_computerNo_computePercentage of Household having No computerThis table provides a mapping between the CSV variable names and the shapefile variable names, along with a brief description of each variable.

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.

Abstract

ADI: An index of socioeconomic status for communities. Dataset ingested directly from BigQuery.

Documentation

The Area Deprivation Index (ADI) can show where areas of deprivation and affluence exist within a community. The ADI is calculated with 17 indicators from the American Community Survey (ACS) having been well-studied in the peer-reviewed literature since 2003, and used for 20 years by the Health Resources and Services Administration (HRSA). High levels of deprivation have been linked to health outcomes such as 30-day hospital readmission rates, cardiovascular disease deaths, cervical cancer incidence, cancer deaths, and all-cause mortality. The 17 indicators from the ADI encompass income, education, employment, and housing conditions at the Census Block Group level.

The ADI is available on BigQuery for release years 2018-2020 and is reported as a percentile that is 0-100% with 50% indicating a "middle of the nation" percentile. Data is provided at the county, ZIP, and Census Block Group levels. Neighborhood and racial disparities occur when some neighborhoods have high ADI scores and others have low scores. A low ADI score indicates affluence or prosperity. A high ADI score is indicative of high levels of deprivation. Raw ADI scores and additional statistics and dataviz can be seen in this ADI story with a BroadStreet free account.

Dataset source: https://help.broadstreet.io/article/adi/

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

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

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

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

There is ongoing debate about whether the relationship between income and pro-social behaviour depends on economic inequality. Studies investigating this question differ in their conclusions but are consistent in measuring inequality at aggregated geographic levels (i.e. at the state, region, or country-level). I hypothesise that local, more immediate manifestations of inequality are important for driving pro-social behaviour, and test the interaction between income and inequality at a much finer geographical resolution than previous studies. I first analyse the charitable giving of US households using ZIP-code level measures of inequality and data on tax deductible charitable donations reported to the IRS. I then examine whether the results generalise using a large-scale UK household survey and neighbourhood-level inequality measures. In both samples I find robust evidence of a significant interaction effect, albeit in the opposite direction as that which has been previously postulated–higher income individuals behave more pro-socially rather than less when local inequality is high.

BackgroundHypertension is the most common primary diagnosis associated with postpartum readmissions within 42 days of delivery hospitalization. In the United States, nearly half of the cases of eclampsia, a severe form of preeclampsia, develop during the postpartum period, and the postpartum onset of hypertensive disorders of pregnancy, like antepartum hypertension poses long-term health risks to pregnant individuals, including an increased likelihood of developing overall cardiovascular disease, coronary heart disease, heart failure, and chronic hypertension. In this paper, we estimate the trends in the incidence of readmissions for postpartum hypertension within 42 days of delivery discharge in the US, disaggregated by median household income.Methods and findingsUsing National Readmissions Database, we calculated the readmission rates for postpartum hypertension, both overall and stratified by ZIP Code median household income for each year between 2010 and 2019. We also calculated the percentage change and average annual growth rate (AAGR) in the rate of readmissions for postpartum hypertension between 2010 and 2019 for each income group. We then used a logistic regression model to compare the temporal changes in readmission for postpartum hypertension between the lowest and the highest income quartiles. The estimated incidence of postpartum hypertension readmissions doubled for all the income groups between 2010 and 2019 (0.36% vs. 0.8%). While the incidence of postpartum hypertension cases was higher among the lowest-income quartile, the increase in postpartum hypertension readmissions between 2010 and 2019 was greater in the highest-income quartile. Moreover, the incidence of postpartum hypertension readmissions rose faster in pregnant patients without a history of hypertension compared to those with a history of hypertension (AAGR 8.3% vs. 5.1%).ConclusionThe increasing postpartum hypertension readmission burden suggests rising future health risks among mothers and a growing cost burden to the U.S. healthcare system. The higher rate of increase in postpartum hypertension readmissions among people without a history of hypertension calls for blood pressure checking in the postpartum period for all patients regardless of risk status.