A global database of Census Data that provides an understanding of population distribution at administrative and zip code levels over 55 years, past, present, and future.

Leverage up-to-date census data with population trends for real estate, market research, audience targeting, and sales territory mapping.

Self-hosted commercial demographic dataset curated based on trusted sources such as the United Nations or the European Commission, with a 99% match accuracy. The global Census Data is standardized, unified, and ready to use.

Use cases for the Global Census Database (Consumer Demographic Data)

• Ad targeting

• B2B Market Intelligence

• Customer analytics

• Real Estate Data Estimations

• Marketing campaign analysis

• Demand forecasting

• Sales territory mapping

• Retail site selection

• Reporting

• Audience targeting

Census data export methodology

Our consumer demographic data packages are offered in CSV format. All Demographic data are optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more.

Product Features

• Historical population data (55 years)

• Changes in population density

• Urbanization Patterns

• Accurate at zip code and administrative level

• Optimized for easy integration

• Easy customization

• Global coverage

• Updated yearly

• Standardized and reliable

• Self-hosted delivery

• Fully aggregated (ready to use)

• Rich attributes

Why do companies choose our demographic databases

• Standardized and unified demographic data structure

• Seamless integration in your system

• Dedicated location data expert

Note: Custom population data packages are available. Please submit a request via the above contact button for more details.

The Consumer Expenditure Survey (CE) program provides a continuous and comprehensive flow of data on the buying habits of American consumers, including data on their expenditures, income, and consumer unit (families and single consumers) characteristics. These data are used widely in economic research and analysis, and in support of revisions of the Consumer Price Index.

The CE program is comprised of two separate components (each with its own survey questionnaire and independent sample), the Diary Survey and the quarterly Interview Survey (ICPSR 36237). This data collection contains the Diary Survey component, which was designed to obtain data on frequently purchased smaller items, including food, housing, apparel and services, transportation, entertainment, and out-of-pocket health care costs. Each consumer unit (CU) recorded its expenditures in a diary for two consecutive 1-week periods. Although the diary was designed to collect information on expenditures that could not be easily recalled over time, respondents were asked to report all expenses (except overnight travel) that the CU incurred during the survey week.

The 2013 Diary Survey release contains five sets of data files (FMLD, MEMD, EXPD, DTBD, DTID), and one processing file (DSTUB). The FMLD, MEMD, EXPD, DTBD, and DTID files are organized by the quarter of the calendar year in which the data were collected. There are four quarterly datasets for each of these files.

The FMLD files contain CU characteristics, income, and summary level expenditures; the MEMD files contain member characteristics and income data; the EXPD files contain detailed weekly expenditures at the Universal Classification Code (UCC) level; the DTBD files contain the CU's reported annual income values or the mean of the five imputed income values in the multiple imputation method; and the DTID files contain the five imputed income values. Please note that the summary level expenditure and income information on the FMLD files permit the data user to link consumer spending, by general expenditure category, and household characteristics and demographics on one set of files.

The DSTUB file provides the aggregation scheme used in the published consumer expenditure tables. The DSTUB file is further explained in Section III.F.6. 'Processing Files' of the Diary Survey Users' Guide. A second documentation guide, the 'Users' Guide to Income Imputation,' includes information on how to appropriately use the imputed income data.

Demographic and family characteristics data include age, sex, race, marital status, and CU relationships for each CU member. Income information was also collected, such as wage, salary, unemployment compensation, child support, and alimony, as well as information on the employment of each CU member age 14 and over.

The unpublished integrated CE data tables produced by the BLS are available to download through NADAC (click on 'Other' in the Dataset(s) section). The tables show average and percentile expenditures for detailed items, as well as the standard error and coefficient of variation (CV) for each spending estimate. The BLS unpublished integrated CE data tables are provided as an easy-to-use tool for obtaining spending estimates. However, users are cautioned to read the BLS explanatory letter accompanying the tables. The letter explains that estimates of average expenditures on detailed spending items (such as leisure and art-related categories) may be unreliable due to so few reports of expenditures for those items.

In late 2020, Hispanic and African American consumers each accounted for nearly a tenth all Amazon retail spending in the United States. Meanwhile, white consumers led the list, representing over 70 percent of the e-commerce platform's consumer spending share.

This statistic shows the results of a survey in which respondents were asked if they have bought free-from foods or if they might buy them in future in the United Kingdom (UK) in 2015. A majority of both groups have no food intolerance or allergy sufferers in the household at 56 and 71 percent respectively, followed by those who are sufferers.

This statistic shows the share of consumers that purchase organic foods in the United States in 2016, by ethnicity. According to the survey, a 79 percent share of Caucasian consumers in the U.S. purchase organic products.

The chart provides an insightful analysis of the estimated sales amounts for Consumer Electronics stores across various platforms. Custom Cart stands out, generating a significant portion of sales with an estimated amount of $1.25T, which is 95.79% of the total sales in this category. Following closely, Shopify accounts for $29.03B in sales, making up 2.22% of the total. WooCommerce also shows notable performance, contributing $9.15B to the total sales, representing 0.70%. This data highlights the sales dynamics and the varying impact of each platform on the Consumer Electronics market.

Explore the statistics for Consumer Electronics eCommerce in 2025, including store count by region and platform, estimated sales amount by platform and region, products sold by platform and region, and total app spend by platform and region. Gain insights into regional preferences, market penetration, consumer trends, and technological investments within the Consumer Electronics sector. Discover the leading regions and platforms, as well as the dynamics of sales and product volumes. Stay informed about the evolving landscape of Consumer Electronics online stores for a comprehensive understanding of the market.

Delving into the Consumer Electronics sector, our data presents a revealing look at store distribution by region, highlighting regional preferences and market penetration in this niche. United States leads with 35.24K stores, which is 33.89% of the total. United Kingdom follows, contributing 10.18K stores, which is 9.79% of the total. India comes third, with 6.33K stores, making up 6.08% of the total.

Population Size Class A (over 1,500,000) - Consumer Price Index for All Urban Consumers: Other Services in Size Class A was 314.53500 Index Dec 1986=100 in February of 2022, according to the United States Federal Reserve. Historically, Population Size Class A (over 1,500,000) - Consumer Price Index for All Urban Consumers: Other Services in Size Class A reached a record high of 314.53500 in February of 2022 and a record low of 100.00000 in December of 1986. Trading Economics provides the current actual value, an historical data chart and related indicators for Population Size Class A (over 1,500,000) - Consumer Price Index for All Urban Consumers: Other Services in Size Class A - last updated from the United States Federal Reserve on March of 2025.

During a survey carried out in November 2021 among marketers from 10 countries worldwide, 52 percent stated their organizations used past purchases to define target consumer segments. Consumer demographics, such as age, gender, income, or location, were used most often, named by 60 percent of respondents.

Population Size Class A (over 1,500,000) - Consumer Price Index for All Urban Consumers: Nondurables Less Food and Apparel in Size Class A was 292.70800 Index Dec 1986=100 in February of 2022, according to the United States Federal Reserve. Historically, Population Size Class A (over 1,500,000) - Consumer Price Index for All Urban Consumers: Nondurables Less Food and Apparel in Size Class A reached a record high of 292.70800 in February of 2022 and a record low of 100.00000 in December of 1986. Trading Economics provides the current actual value, an historical data chart and related indicators for Population Size Class A (over 1,500,000) - Consumer Price Index for All Urban Consumers: Nondurables Less Food and Apparel in Size Class A - last updated from the United States Federal Reserve on March of 2025.

Population Size Class A (over 1,500,000) - Consumer Price Index for All Urban Consumers: Durables in Size Class A was 114.45800 Index Dec 1986=100 in February of 2022, according to the United States Federal Reserve. Historically, Population Size Class A (over 1,500,000) - Consumer Price Index for All Urban Consumers: Durables in Size Class A reached a record high of 121.20000 in March of 1997 and a record low of 93.74900 in September of 2018. Trading Economics provides the current actual value, an historical data chart and related indicators for Population Size Class A (over 1,500,000) - Consumer Price Index for All Urban Consumers: Durables in Size Class A - last updated from the United States Federal Reserve on March of 2025.

Abstract

The Consumer Expenditure Survey (CE) program provides a continuous and comprehensive flow of data on the buying habits of American consumers. These data are used widely in economic research and analysis, and in support of revisions of the Consumer Price Index. To meet the needs of users, the Bureau of Labor Statistics (BLS) produces population estimates for consumer units (CUs) of average expenditures in news releases, reports, issues, and articles in the Monthly Labor Review. Tabulated CE data are also available on the Internet and by facsimile transmission (See Section XV. APPENDIX 4). The microdata are available online at http://www/bls.gov/cex/pumdhome.htm.

These microdata files present detailed expenditure and income data for the Diary component of the CE for 2002. They include weekly expenditure (EXPD) and annual income (DTBD) files. The data in EXPD and DTBD files are categorized by a Universal Classification Code (UCC). The advantage of the EXPD and DTBD files is that with the data classified in a standardized format, the user may perform comparative expenditure (income) analysis with relative ease. The FMLD and MEMD files present data on the characteristics and demographics of CUs and CU members. The summary level expenditure and income information on the FMLD files permits the data user to link consumer spending, by general expenditure category, and household characteristics and demographics on one set of files.

Estimates of average expenditures in 2002 from the Diary survey, integrated with data from the Interview survey, are published in Consumer Expenditures in 2002. A list of recent publications containing data from the CE appears at the end of this documentation.

The microdata files are in the public domain and with appropriate credit, may be reproduced without permission. A suggested citation is: "U.S. Department of Labor, Bureau of Labor Statistics, Consumer Expenditure Survey, Diary Survey, 2002".

Analysis unit

Consumer Units

Kind of data

Sample survey data [ssd]

Sampling procedure

Samples for the CE are national probability samples of households designed to be representative of the total U. S. civilian population. Eligible population includes all civilian noninstitutional persons. The first step in sampling is the selection of primary sampling units (PSUs), which consist of counties (or parts thereof) or groups of counties. The set of sample PSUs used for the 2002 sample is composed of 105 areas. The design classifies the PSUs into four categories: • 31 "A" certainty PSUs are Metropolitan Statistical Areas (MSA's) with a population greater than 1.5 million. • 46 "B" PSUs, are medium-sized MSA's. • 10 "C" PSUs are nonmetropolitan areas that are included in the CPI. • 18 "D" PSUs are nonmetropolitan areas where only the urban population data will be included in the CPI.

The sampling frame (that is, the list from which housing units were chosen) for the 2002 survey is generated from the 1990 Population Census 100-percent-detail file. The sampling frame is augmented by new construction permits and by techniques used to eliminate recognized deficiencies in census coverage. All Enumeration Districts (ED's) from the Census that fail to meet the criterion for good addresses for new construction, and all ED's in nonpermit-issuing areas are grouped into the area segment frame. To the extent possible, an unclustered sample of units is selected within each PSU. This lack of clustering is desirable because the sample size of the Diary Survey is small relative to other surveys, while the intraclass correlations for expenditure characteristics are relatively large. This suggests that any clustering of the sample units could result in an unacceptable increase in the within-PSU variance and, as a result, the total variance. Each selected sample unit is requested to keep two 1-week diaries of expenditures over consecutive weeks. The earliest possible day for placing a diary with a household is predesignated with each day of the week having an equal chance to be the first of the reference week. The diaries are evenly spaced throughout the year. During the last 6 weeks of the year, however, the Diary Survey sample is supplemented to twice its normal size to increase the reporting of types of expenditures unique to the holidays.

STATE IDENTIFIER Since the CE is not designed to produce state-level estimates, summing the consumer unit weights by state will not yield state population totals. A CU's basic weight reflects its probability of selection among a group of primary sampling units of similar characteristics. For example, sample units in an urban nonmetropolitan area in California may represent similar areas in Wyoming and Nevada. Among other adjustments, CUs are post-stratified nationally by sex-age-race. For example, the weights of consumer units containing a black male, age 16-24 in Alabama, Colorado, or New York, are all adjusted equivalently. Therefore, weighted population state totals will not match population totals calculated from other surveys that are designed to represent state data. To summarize, the CE sample was not designed to produce precise estimates for individual states. Although state-level estimates that are unbiased in a repeated sampling sense can be calculated for various statistical measures, such as means and aggregates, their estimates will generally be subject to large variances. Additionally, a particular state-population estimate from the CE sample may be far from the true state-population estimate.

INTERPRETING THE DATA Several factors should be considered when interpreting the expenditure data. The average expenditure for an item may be considerably lower than the expenditure by those CUs that purchased the item. The less frequently an item is purchased, the greater the difference between the average for all consumer units and the average of those purchasing. (See Section V.B. for ESTIMATION OF TOTAL AND MEAN EXPENDITURES). Also, an individual CU may spend more or less than the average, depending on its particular characteristics. Factors such as income, age of family members, geographic location, taste and personal preference also influence expenditures. Furthermore, even within groups with similar characteristics, the distribution of expenditures varies substantially. Expenditures reported are the direct out-of-pocket expenditures. Indirect expenditures, which may be significant, may be reflected elsewhere. For example, rental contracts often include utilities. Renters with such contracts would record no direct expense for utilities, and therefore, appear to have no utility expenses. Employers or insurance companies frequently pay other costs. CUs with members whose employers pay for all or part of their health insurance or life insurance would have lower direct expenses for these items than those who pay the entire amount themselves. These points should be considered when relating reported averages to individual circumstances.

Mode of data collection

Computer Assisted Personal Interview [capi]

Population Size Class D (under 50,000) - Consumer Price Index for All Urban Consumers: Gasoline, Unleaded Regular in Size Class D (DISCONTINUED) was 195.14200 Index 1982-84=100 in July of 2017, according to the United States Federal Reserve. Historically, Population Size Class D (under 50,000) - Consumer Price Index for All Urban Consumers: Gasoline, Unleaded Regular in Size Class D (DISCONTINUED) reached a record high of 285.83300 in January of 2012 and a record low of 69.70000 in July of 1986. Trading Economics provides the current actual value, an historical data chart and related indicators for Population Size Class D (under 50,000) - Consumer Price Index for All Urban Consumers: Gasoline, Unleaded Regular in Size Class D (DISCONTINUED) - last updated from the United States Federal Reserve on December of 2022.

Population Size Class D (under 50,000) - Consumer Price Index for All Urban Consumers: Rent of Primary Residence in Size Class D (DISCONTINUED) was 262.87700 Index 1982-84=100 in July of 2017, according to the United States Federal Reserve. Historically, Population Size Class D (under 50,000) - Consumer Price Index for All Urban Consumers: Rent of Primary Residence in Size Class D (DISCONTINUED) reached a record high of 262.87700 in July of 2017 and a record low of 102.90000 in January of 1984. Trading Economics provides the current actual value, an historical data chart and related indicators for Population Size Class D (under 50,000) - Consumer Price Index for All Urban Consumers: Rent of Primary Residence in Size Class D (DISCONTINUED) - last updated from the United States Federal Reserve on March of 2025.

More than half of consumers belonging to Generation Z bought something on social media platforms, according to a survey in 2024. Almost a third of overall consumers bought on social media platforms. The consumer experience In a 2023 survey, Facebook and Instagram were the social media platforms offering the best shopping experience. To gain deeper insights into the elements constituting a satisfactory social commerce shopping journey from the user's viewpoint, key factors shaping consumers' heightened engagement with social commerce included, but were not limited to, deals and discounts, seamless purchasing processes, exclusive offers, and increased availability of customer reviews. Social shopping destinations Facebook is the leading social commerce platform globally, except among Gen Z, who favor Instagram and TikTok. However, the types of social media accounts that shoppers followed and purchased from varied by age group. Gen Z and Millennials predominantly bought from brand accounts, with Gen Z also showing a preference for social media influencers. Conversely, Gen X and Boomers preferred purchasing from trusted retailer accounts.

Sourcing accurate and up-to-date geodemographic data across Asia and MENA has historically been difficult for retail brands looking to expand their store networks in these regions. Either the data does not exist or it isn't readily accessible or updated regularly.

GapMaps uses known population data combined with billions of mobile device location points to provide highly accurate and globally consistent geodemographic datasets across Asia and MENA at 150m x 150m grid levels in major cities and 1km grids outside of major cities.

With this information, brands can get a detailed understanding of who lives in a catchment, where they work and their spending potential which allows you to:

• Better understand your customers
• Identify optimal locations to expand your retail footprint
• Define sales territories for franchisees
• Run targeted marketing campaigns.

Premium geodemographics data for Asia and MENA includes the latest estimates (updated annually) on:

1. Population (how many people live in your local catchment)
2. Demographics (who lives within your local catchment)
3. Worker population (how many people work within your local catchment)
4. Consuming Class and Premium Consuming Class (who can can afford to buy goods & services beyond their basic needs and /or shop at premium retailers)
5. Retail Spending (Food & Beverage, Grocery, Apparel, Other). How much are consumers spending on retail goods and services by category.

Primary Use Cases for GapMaps Geodemographic Data:

1. Retail (eg. Fast Food/ QSR, Cafe, Fitness, Supermarket/Grocery)
2. Customer Profiling: get a detailed understanding of the demographic profile of your customers, where they work and their spending potential
3. Analyse your trade areas at a granular 150m x 150m grid levels using all the key metrics
4. Site Selection: Identify optimal locations for future expansion and benchmark performance across existing locations.
5. Target Marketing: Develop effective marketing strategies to acquire more customers.

6. Integrate GapMaps demographic data with your existing GIS or BI platform to generate powerful visualizations.

7. Commercial Real-Estate (Brokers, Developers, Investors, Single & Multi-tenant O/O)

8. Tenant Recruitment

9. Target Marketing

10. Market Potential / Gap Analysis

11. Marketing / Advertising (Billboards/OOH, Marketing Agencies, Indoor Screens)

12. Customer Profiling

13. Target Marketing

14. Market Share Analysis

Population Size Class D (under 50,000) - Consumer Price Index for All Urban Consumers: Recreation Services in Size Class D (DISCONTINUED) was 115.81900 Index Dec 2009=100 in December of 2017, according to the United States Federal Reserve. Historically, Population Size Class D (under 50,000) - Consumer Price Index for All Urban Consumers: Recreation Services in Size Class D (DISCONTINUED) reached a record high of 118.26000 in September of 2017 and a record low of 95.02500 in August of 2011. Trading Economics provides the current actual value, an historical data chart and related indicators for Population Size Class D (under 50,000) - Consumer Price Index for All Urban Consumers: Recreation Services in Size Class D (DISCONTINUED) - last updated from the United States Federal Reserve on March of 2025.

Population Size Class A (over 1,500,000) - Consumer Price Index for All Urban Consumers: Recreation Commodities in Size Class A was 75.78900 Index Dec 2009=100 in January of 2021, according to the United States Federal Reserve. Historically, Population Size Class A (over 1,500,000) - Consumer Price Index for All Urban Consumers: Recreation Commodities in Size Class A reached a record high of 99.22700 in January of 2010 and a record low of 75.10000 in January of 2020. Trading Economics provides the current actual value, an historical data chart and related indicators for Population Size Class A (over 1,500,000) - Consumer Price Index for All Urban Consumers: Recreation Commodities in Size Class A - last updated from the United States Federal Reserve on February of 2025.

Descriptive statistics of predicted factor scores of the latent variables.