Aggregated and anonymized purchase data from consumer credit and debit card spending. Spending is reported based on the ZIP code where the cardholder lives, not the ZIP code where transactions occurred. Data from Affinity Solutions, compiled by Opportunity Insights. Update Frequency: Weekly Date Range: January 13th until the most recent date available. Data Frequency: Data is daily until the final two weeks of the series, and the daily data is presented as a 7 day lookback moving average. For the final two weeks of the series, the data is weekly and presented as weekly data points. Index Period: January 4th - January 31st Indexing Type: 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.

Consumer spending data offers key insights into an audience's private and household expenditure on various goods and services. This data reveals the value of goods and services purchased by USA's residents and breaks down their spending into food, fashion, lifestyle spending, and more.

Spotzi's consumer spending data provides insights into the total expenditure in each area of the USA. To facilitate regional comparisons, the data is also available in the form of an index, where an index value of 100 corresponds to the national average.

In the USA, this data is available at 5-digit postal code level.

This map shows the average amount spent on meals away from home at restaurants or other per household in the U.S. in 2020 in a multiscale map (by country, state, county, ZIP Code, tract, and block group).The pop-up is configured to include the following information for each geography level:Average annual spending on meals at restaurants per householdAverage annual spending on all food away from home per householdAverage annual spending on food by meal typeThis map shows Esri's 2020 U.S. Consumer Spending Data in Census 2010 geographies. The map adds increasing level of detail as you zoom in, from state, to county, to ZIP Code, to tract, to block group data.Esri's 2020 U.S. Consumer Spending database provides the details about which products and services consumers buy, including total dollars spent, average amount spent per household, and a Spending Potential Index. Esri's Consumer Spending database identifies hundreds of items in more than 15 categories, including apparel, food and beverage, financial, entertainment and recreation, and household goods and services. See Consumer Spending database to view the methodology statement and complete variable list.Additional Esri Resources:Esri DemographicsU.S. 2020/2025 Esri Updated DemographicsEssential demographic vocabularyPermitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

Consumer spending data offers key insights into an audience's private and household expenditure on various goods and services. This data reveals the value of goods and services purchased by South Korean residents and breaks down their spending into food, fashion, lifestyle spending, and more.

Spotzi's consumer spending data provides insights into the total Euro expenditure in each area of South Korea. To facilitate regional comparisons, the data is also available in the form of an index, where an index value of 100 corresponds to the national average.

In South Korea, this data is available at 5-digit postal code level.

RUNS APP OF SAME NAMEThis map shows the average amount spent on water and public services per household in the U.S. in 2018 in a multiscale map (by country, state, county, ZIP Code, tract, and block group).The pop-up is configured to include the following information for each geography level:Average annual spent per household on water and public servicesAverage annual spending per household on other water and public services such as sewage, trash, and maintenanceThis map shows Esri's 2018 U.S. Consumer Spending Data in Census 2010 geographies. The map adds increasing level of detail as you zoom in, from state, to county, to ZIP Code, to tract, to block group data.Esri's 2018 U.S. Consumer Spending database details which products and services consumers buy, including total dollars spent, average amount spent per household, and a Spending Potential Index. Esri's Consumer Spending database identifies hundreds of items in more than 15 categories, including apparel, food and beverage, financial, entertainment and recreation, and household goods and services. See Consumer Spending database to view the methodology statement and complete variable list.Additional Esri Resources:Esri DemographicsU.S. 2018/2023 Esri Updated DemographicsEssential demographic vocabulary

Consumer spending data offers key insights into an audience's private and household expenditure on various goods and services. This data reveals the value of goods and services purchased by Austrian residents and breaks down their spending into food, fashion, lifestyle spending, and more.

Spotzi's consumer spending data provides insights into the total Euro expenditure in each area of Austria. To facilitate regional comparisons, the data is also available in the form of an index, where an index value of 100 corresponds to the national average.

In Austria, this data is available at 4-digit postal code level.

Please do upvote if you love the work.♥️🥰 For more related datasets: https://www.kaggle.com/datasets/rajatsurana979/fifafcmobile24 https://www.kaggle.com/datasets/rajatsurana979/most-streamed-spotify-songs-2023 https://www.kaggle.com/datasets/rajatsurana979/comprehensive-credit-card-transactions-dataset https://www.kaggle.com/datasets/rajatsurana979/hotel-reservation-data-repository https://www.kaggle.com/datasets/rajatsurana979/percent-change-in-consumer-spending https://www.kaggle.com/datasets/rajatsurana979/fast-food-sales-report/data

Aggregated and anonymized purchase data from consumer credit and debit card spending. Spending is reported based on the ZIP code where the cardholder lives, not the ZIP code where transactions occurred. Data from Affinity Solutions, compiled by Opportunity Insights.

Update Frequency: Weekly Date Range: January 13th until the most recent date available.

Data Frequency: Data is daily until the final two weeks of the series, and the daily data is presented as a 7-day lookback moving average. For the final two weeks of the series, the data is weekly and presented as weekly data points.

Index Period: January 4th - January 31st

Indexing Type: 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.

For dataset column description, please refer to column description

Consumer spending data offers key insights into an audience's private and household expenditure on various goods and services. This data reveals the value of goods and services purchased by Hungarian residents and breaks down their spending into food, fashion, lifestyle spending, and more.

Spotzi's consumer spending data provides insights into the total Euro expenditure in each area of Hungary. To facilitate regional comparisons, the data is also available in the form of an index, where an index value of 100 corresponds to the national average.

In Hungary, this data is available at both municipality and 4-digit postal code level.

Consumer spending data offers key insights into an audience's private and household expenditure on various goods and services. This data reveals the value of goods and services purchased by Serbian residents and breaks down their spending into food, fashion, lifestyle spending, and more.

Spotzi's consumer spending data provides insights into the total Euro expenditure in each area of Serbia. To facilitate regional comparisons, the data is also available in the form of an index, where an index value of 100 corresponds to the national average.

In Serbia, this data is available at both the street and 5-digit postal code level.

This map layer shows the average amount spent on meals away from home at restaurants or other per household in the U.S. in 2016 in a multiscale map (by country, state, county, ZIP Code, tract, and block group).The pop-up is configured to include the following information for each geography level:Average annual spending for meals at restaurants per householdAverage annual spending on all food away from home per householdAverage annual spending for food by meal typeThis map shows Esri's 2016 U.S. Consumer Spending Data in Census 2010 geographies. The map adds increasing level of detail as you zoom in, from state, to county, to ZIP Code, to tract, to block group data.Esri's 2016 U.S. Consumer Spending database provides the details about which products and services consumers buy, including total dollars spent, average amount spent per household, and a Spending Potential Index. Esri's Consumer Spending database identifies hundreds of items in more than 15 categories, including apparel, food and beverage, financial, entertainment and recreation, and household goods and services. See Consumer Spending database to view the methodology statement and complete variable list.Additional Esri Resources:Esri DemographicsU.S. 2016/2021 Esri Updated DemographicsEssential demographic vocabulary

This service offers Esri's Retail MarketPlace database for the United States which measures retail market supply and demand. The data is modeled from the Census of Retail Trade by the US Census Bureau, Infogroup business data, and statistics from the US Bureau of Labor Statistics.

All attributes are available at all geography levels: country, state, county, tract, block group, ZIP code, place, county subdivision, congressional district, core-based statistical area (CBSA), and designated market area (DMA).

Over 2,300 attributes measuring likely demand for a wide variety of products and services in retail categories including food and drink, automotive, electronics, appliances, health, and personal care. The database provides a direct comparison between retail sales and consumer spending by industry and measures the gap between supply and demand.

To view ArcGIS Online items using this service, including the terms of use, visit http://goto.arcgisonline.com/demographics9/USA_Retail_Marketplace_2019.

Consumer spending data offers key insights into an audience's private and household expenditure on various goods and services. This data reveals the value of goods and services purchased by Slovakian residents and breaks down their spending into food, fashion, lifestyle spending, and more.

Spotzi's consumer spending data provides insights into the total Euro expenditure in each area of Slovakia. To facilitate regional comparisons, the data is also available in the form of an index, where an index value of 100 corresponds to the national average.

In Slovakia, this data is available at both the street and 5-digit postal code level.

Research on the economic burden of air pollution has focused primarily on its macroeconomic impact. However, as some studies have found that air pollution can lead to avoidance behavior–for example, reducing the time spent outdoors–we hypothesize that it can also influence consumer spending activity. We combine high frequency data on ozone and fine particulate pollution with daily consumer spending in brick-and-mortar retail in 129 postal codes in Spain during 2014 to estimate the association between the two. Using a linear fixed effects model, we find that a 1-standard deviation increase in ozone concentration (20.97 μg/m3) is associated with 3.9 percent decrease in consumer spending (95% CI: -0.066, -0.012; p0.10). Further, we do not observe a sufficiently strong bounce-back in consumer spending in the day–or even the week–following higher ozone concentration. Also, we find that the relationship between ozone concentration and consumer spending is heterogeneous, with those aged below 25 and those aged 45 or above exhibiting stronger negative association. This research informs policymakers about a plausibly unaccounted cost of ambient air pollution, even at concentrations lower than the WHO air quality guideline for short-term exposure.

This layer shows the market opportunity for grocery stores in the U.S. in 2017 in a multiscale map (by country, state, county, ZIP Code, tract, and block group). The map uses the Leakage/Surplus Factor, an indexed value that represents opportunity (leakage), saturation (surplus), or balance within a market. This map focuses on the opportunity for grocery stores (NAICS 4451). The pop-up is configured to include the following information for each geography level:Count of grocery stores - NAICS 4451Total annual NAICS 4451 sales (supply)Total annual NAICS 4451 sales potential (demand)Market Opportunity for NAICS 4451 (expressed as an index)Total annual supply and demand for various food industriesFood and Beverage Stores - NAICS 445Specialty Food Stores - NAICS 4452Beer/Wine/Liquor Stores - NAICS 4453Esri's Leakage/Surplus Factor measures the balance between the volume of retail sales (supply) generated by retail businesses and the volume of retail potential (demand) produced by household spending on retail goods within the same industry. The factor enables a one-step comparison of supply against demand, and a simple way to identify business opportunity. Leakage implies that potential sales are "leaking" from an area, while surplus implies a saturation within a given area. The values range from -100 to +100, with a value of 0 representing a balanced market. See the Leakage/Surplus Factor Data Note for more information. Esri's 2017 Retail MarketPlace (RMP) database provides a direct comparison between retail sales and consumer spending by industry and measures the gap between supply and demand. This database includes retail sales by industry to households and retail potential or spending by households. The Retail MarketPlace data helps organizations accurately measure retail activity by trade area and compare retail sales to consumer spending by NAICS industry classification. See Retail MarketPlace Database to view the methodology statement, supported geography levels, and complete variable list. Additional Esri Resources:Esri DemographicsU.S. 2017/2022 Esri Updated DemographicsEssential demographic vocabularyEsri's arcgis.com demographic map layers

Consumer expenditure data to 2036 broken down by London borough, post code sectors, and industry sectors.

1 - Aggregated Borough Base

2 - Aggregated Postal Base Greater London

3 - Aggregated Regional Base

The Aggregated category contains spending data on the following sectors:
• Convenience
• Comparison – Bulky
• Comparison - Not Bulky
• DIY
• Gardening
• Accommodation Services
• Restaurants and Cafes
• Takeaway / Snack Spending
• On Licence (i.e. Pubs & Wine Bars)
• Leisure
• Other Goods and Services
• Other Spending (Mostly Household related, Health and Education)

4 - Detailed Borough Base

5 - Detailed Regional Base

The detailed category contains spending data on sectors including:
• Food
• Non-alcoholic beverages
• Alcoholic beverages
• Tobacco
• Clothing and footwear
• Actual rentals for housing
• Imputed rentals for housing
• Maintenance and repair of the dwelling
• Water supply and miscellaneous services relating to the
• Electricity, gas & other fuels
• Furniture & Textiles
• Household Goods and Services
• Medical Products
• Medical Services
• Purchase of vehicles
• Operation of personal transport equipment
• Transport services
• Postal services
• Telecommunications Services
• Audio-visual
• Other major durables for recreation and culture
• Other recreational items and equipment

More information on GLA website

The regression of consumer spending on air pollution.

Research on the economic burden of air pollution has focused primarily on its macroeconomic impact. However, as some studies have found that air pollution can lead to avoidance behavior–for example, reducing the time spent outdoors–we hypothesize that it can also influence consumer spending activity. We combine high frequency data on ozone and fine particulate pollution with daily consumer spending in brick-and-mortar retail in 129 postal codes in Spain during 2014 to estimate the association between the two. Using a linear fixed effects model, we find that a 1-standard deviation increase in ozone concentration (20.97 μg/m3) is associated with 3.9 percent decrease in consumer spending (95% CI: -0.066, -0.012; p0.10). Further, we do not observe a sufficiently strong bounce-back in consumer spending in the day–or even the week–following higher ozone concentration. Also, we find that the relationship between ozone concentration and consumer spending is heterogeneous, with those aged below 25 and those aged 45 or above exhibiting stronger negative association. This research informs policymakers about a plausibly unaccounted cost of ambient air pollution, even at concentrations lower than the WHO air quality guideline for short-term exposure.

Retirement Notice: This item is in mature support as of June 2023 and will be retired in December 2025. This map shows the average amount spent on water and public services per household in the U.S. in 2022 in a multiscale map (by country, state, county, ZIP Code, tract, and block group).The pop-up is configured to include the following information for each geography level:Average annual spent per household on water and public servicesAverage annual spending per household on other water and public services such as sewage, trash, and maintenance Permitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

This dataset contains the information of more than 5000 customers, based on the points that each customer has earned, a loan is offered to them. The features are: Age: Customer's age in completed years Experience: Years of professional experience Income: Annual income of the customer Zip code: home address Zip code Family: Family size of customer CCAvg: Spending on credit cards per month Education: Education level (Undergraduate=1, Graduate= 2, Advanced=3) Mortgage: Value of house mortgage if any Personal_loan: Did this customer accept the personal loan offered in the last campaign? Security_account: Does the customer have a securities account with this bank? Cd_account: Does the customer have a certificate of deposit (CD) account with this bank? Online: Does the customer use internet banking facilities? Creditcard: Does the customer use a credit card issued by Universal Bank?

This dataset have 5000 row and 14 columns and personal Loan is target. other features are: • id : Customer ID • age : Customer's age in completed years • experience : years of professional experience • income : Annual income of the customer • zip_code : Home Address ZIP code. • family : Family size of the customer • ccavg : Avg. spending on credit cards per month • education : Education Level. Undergrad Graduate Advanced/Professional • mortgage : Value of house mortgage if any. • personal_loan : Did this customer accept the personal loan offered in the last campaign? • securities_account : Does the customer have a securities account with the bank? • cd_account : Does the customer have a certificate of deposit (CD) account with the bank? • online : Does the customer use internet banking facilities? • creditcard : D