Consumer Spending in the United States increased to 16291.80 USD Billion in the first quarter of 2025 from 16273.20 USD Billion in the fourth quarter of 2024. This dataset provides the latest reported value for - United States Consumer Spending - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F4560787%2F1bf7d8acca3f6ca6adbae87c95df1f33%2F1_MIXrCZ0QAVp6qoElgWea-A.jpg?generation=1697784111548502&alt=media" alt="">

Data is the new oil, and this dataset is a wellspring of knowledge waiting to be tapped😷!

Don't forget to upvote and share your insights with the community. Happy data exploration!🥰

** 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

Description: Welcome to the world of credit card transactions! This dataset provides a treasure trove of insights into customers' spending habits, transactions, and more. Whether you're a data scientist, analyst, or just someone curious about how money moves, this dataset is for you.

Features: - Customer ID: Unique identifiers for every customer. - Name: First name of the customer. - Surname: Last name of the customer. - Gender: The gender of the customer. - Birthdate: Date of birth for each customer. - Transaction Amount: The dollar amount for each transaction. - Date: Date when the transaction occurred. - Merchant Name: The name of the merchant where the transaction took place. - Category: Categorization of the transaction.

Why this dataset matters: Understanding consumer spending patterns is crucial for businesses and financial institutions. This dataset is a goldmine for exploring trends, patterns, and anomalies in financial behavior. It can be used for fraud detection, marketing strategies, and much more.

Acknowledgments: We'd like to express our gratitude to the contributors and data scientists who helped curate this dataset. It's a collaborative effort to promote data-driven decision-making.

Let's Dive In: Explore, analyze, and visualize this data to uncover the hidden stories in the world of credit card transactions. We look forward to seeing your innovative analyses, visualizations, and applications using this dataset.

The San Francisco Controller's Office maintains a database of budgetary data that appears in summarized form in each Annual Appropriation Ordinance (AAO). This data is presented on the Budget report hosted at http://openbook.sfgov.org, and is also available in this dataset in CSV format. New data is added on an annual basis when the AAO is published for each new fiscal year. Data is available from fiscal year 2010 forward. The City and County of San Francisco's budget is a two-year plan for how the City government will spend money with available resources. In the budget process, a budget is proposed by the Mayor, and then modified and approved by the Board of Supervisors as the Appropriation Ordinance. Each year, the City will update the Budget for the upcoming fiscal year and also set a budget for the subsequent fiscal year, which will be updated and approved in the following year. Enterprise departments do not submit a budget for the second year of the two year budget; rather, estimates of enterprise department budgets in the second year of the budget are incorporated into high-level spending and revenue figures. This dataset and the Appropriation Ordinance departmental views answer the question "How much does each department spend?". To show how much is spent by departments from the General Fund we make the following adjustments to the regular revenues and fund balance & reserves: + Transfers from one department to another (leaving out transfers within the same department) + Recoveries from one department to another (leaving out recoveries within the same department) - GF spent in other funds (this is deducted from GF Sources and added to the other fund's Sources) This is the gross total. By removing the transfers and recoveries that go from one department to the another we see the same net total that is in the Appropriation Ordinance Consolidated Schedule of Sources and Uses. Note that the amount added for transfers into the General Fund that move from one department to another is different than the amount deducted to eliminate the double counting caused by transfers. Transfer Adjustments: To meet accounting needs, money can be moved from one fund or department to another. For example, Public Works provides building maintenance services for the Fire Department for which the Fire Department pays Public Works. To solve this double counting problem, this dataset shows a reduction of $100,000 called Transfer Adjustments (Citywide) to the budgeted spending & revenue for the department providing the service. This lets the dataset display both the gross total of activity for both departments and the net total use of City and County revenues. In the example above, the money is moving both between departments, from Fire to Public Works, and between funds, from General Fund Operating to General Fund Works Orders/Overhead. Transfer Adjustments (Citywide): -Transfer Adjustments (Citywide) are used when money is moved from one department to another. These are deducted from the gross total to create the net total. -Transfer Adjustments are included in the gross total when they are within the same department. A separate sub-object is used to distinguish departmental Transfer Adjustments from Transfer Adjustments (Citywide). -Transfer Adjustments (Citywide) may differ from transfer adjustment lines in other public reports as a result of different approaches used to report transfers; however, the net total will remain the same across this dataset, the Mayor's Budget Book, and the Appropriations Ordinance, with limited exceptions due to error corrections and different methodologies used to present net totals. For more information, contact us. An example of a Transfer Adjustment within a department would be Public Works overhead allocations. Overhead costs cannot easily be isolated to a direct service or unit and so are allocated across those units using accepted accounting methods. Central m

This dataset provides values for PERSONAL SPENDING reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Medicare Spending per Beneficiary

Detailed Hospital Expense Breakdown

By Health [source]

About this dataset

This file allows healthcare executives and analysts to make informed decisions regarding how well continued improvements are being made over time so that they can understand how efficient they are fulfilling treatments while staying within budgetary constraints. Additionally, it’ll also help them map out trends amongst different hospitals and spot anomalies that could indicate areas where decisions should be reassessed as needed

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For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset can provide valuable insights into how Medicare is spending per patient at specific hospitals in the United States. It can be used to gain a better understanding of the types of services covered under Medicare, and to what extent those services are being used. By comparing the average Medicare spending across different hospitals, users can also gain insight into potential disparities in care delivery or availability.

To use this dataset, first identify which hospital you are interested in analyzing. Then locate the row for that hospital in the dataset and review its associated values: value, footnote (optional), and start/end dates (optional). The Value column refers to how much Medicare spends on each particular patient; this is a numerical value represented as a decimal number up to 6 decimal places. The Footnote (optional) provides more information about any special circumstances that may need attention when interpreting the value data points. Finally, if Start Date and End Date fields are present they will specify over what timeframe these values were aggregated over.

Once all relevant data elements have been reviewed successively for all hospitals of interest then comparison analysis among them can be conducted based on Value, Footnote or Start/End dates as necessary to answer specific research questions or formulate conclusions about how Medicare is spending per patient at various hospitals nationwide

Research Ideas

• Developing a cost comparison tool for hospitals that allows patients to compare how much Medicare spends per patient across different hospitals.
• Creating an algorithm to help predict Medicare spending at different facilities over time and build strategies on how best to manage those costs.
• Identifying areas in which a hospital can save money by reducing unnecessary spending in order to reduce overall Medicare expenses

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: Medicare_hospital_spending_per_patient_Medicare_Spending_per_Beneficiary_Additional_Decimal_Places.csv | Column name | Description | |:---------------|:--------------------------------------------------------------------------------------| | Value | The amount of Medicare spending per patient for a given hospital or region. (Numeric) | | Footnote | Any additional notes or information related to the value. (Text) | | Start_Date | The start date of the period for which the value applies. (Date) | | End_Date | The end date of the period for which the value applies. (Date) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Health.

According to a survey conducted in 2023, many college students surveyed said their biggest expense was food. Specifically, ********* of college students said they spend the most on food in a typical month.

Survey of Household Spending (SHS), average household spending, Canada, regions and provinces.

Consumer Spending in Serbia decreased to 1019908.50 RSD Million in the first quarter of 2025 from 1161254.80 RSD Million in the fourth quarter of 2024. This dataset provides - Serbia Consumer Spending- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Context

This dataset was created from the online retail dataset found here https://www.kaggle.com/roshansharma/online-retail. This has had some processing for customer segmentation so it can be used for nice visualisation of the data.

Content

The following variables are used: | Variable | Description | | --- | --- | |**CustomerID**| This is the same CustomerID field as in the online retail dataset found in the link above and can be linked to this dataset.| |**Frequency**|This is how many times a customer purchased.| |**Recency**|This is how many days ago a customer made a purchase. This is adjusted to reference a point in time.| |**Monetary** |This is how much a customer spent in total. Their total Lifetime monetary value.| |**rankF**|This is the Frequency value divided into different ranges from 1 to 5 using the cut function in R. (5 = lots of visits, 1 = very low visits)| |**rankR**|This is the Recency value divided into different ranges from 1 to 5 using the cut function in R and then flipped. (5 = very Recent, 1 = ages ago) | |**rankM**|This is the Monetary value divided into different ranges from 1 to 5 using the cut function in R. (5 = High spender, 1 = low spender) | |**groupRFM**| The group RFM is a value combining the rankR, rankF and rankM. This uses 1 digit per rank (ie 1 rankR, 2 rankF, 5 rankM would be 125 Group)| |**Country**|This is the customer delivery country from the original online retail dataset.| |**Customer_Segment**| A customer segment is added to give a more human description of the customer and therefore can be treated differently. These segments are listed below.|

Customer Segments

The customer segments below detail the description of the customers from their details processed in the RFM analysis. | Customer Segment | Segment Description | | --- | --- | |**Champions** | Bought recently buy often and spend the most | |**Loyal Customers**|Spend good money Responsive to promotions| |**Potential Loyalist**|Recent customers spent good amount, bought more than once| |**Recent High Spender**|Recent customers not frequent but spend some| |**New Customers**|Bought more recently but not often| |**Promising**|Recent shoppers but haven’t spent much| |**Need Attention**|Above average recency frequency & monetary values| |**About To Sleep**|Below average recency frequency & monetary values| |**At Risk**|Spent big money purchased often but long time ago| |**Can’t Lose Them**|Made big purchases and often but long time ago| |**Hibernating**|Low spenders low frequency purchased long time ago| |**Lost**|Lowestrecency frequency & monetary scores|

Acknowledgements

Thank you to the owners of the online retail dataset. https://www.kaggle.com/roshansharma

Inspiration

The online retail dataset is a great set for finding anomalies and doing some interesting reports, however RFM analysis allows you to treat clusters of data in the same way which is suitable for marketing teams etc.

RFM analysis is a straight forward analytical process that can be achieved by clustering but a more manual process is good as you can adjust these figures to get more even groups. I will post my R code for this and link shortly.| | | | | --- | --- | | | | | | | --- | --- | | | |

A. SUMMARY This dataset contains data from financial statements of state committees that (1) contribute to or (2) receive funds from a San Francisco committee which was Primarily Formed for a local election, or (3) filed a Late Reporting Period statement with the SFEC during the 90 days before an election. The search period for financial statements begins two years before an election and runs through the next semi-annual filing deadline. The dataset currently filters by the elections of 2024-03-05 and 2024-11-05. B. HOW THE DATASET IS CREATED During an election period, an automated script runs nightly to examine filings by Primarily Formed San Francisco committees. If a primarily formed committee reports accepting money from or giving money to a second committee, that second committee's ID number is added to a filter list. If a committee electronically files a late reporting period form with the San Francisco Ethics Commission, the committee's ID number is also included in the filter list. The filter list is used in a second step that looks for filings by committees that file with the San Francisco Ethics Commission or the California Secretary of State. This dataset shows the committees that file with the California Secretary of State. The data comes from a nightly export of the Secretary of State's database. A second dataset includes Non-Primarily Formed committees that file with the San Francisco Ethics Commission. C. UPDATE PROCESS This dataset is rewritten nightly based on data derived from campaign filings. The update script runs automatically on a timer during the 90 days before an election. Refer to the "Data Last Updated" date in the section "About This Dataset" on the landing page to see when the script last ran successfully. D. HOW TO USE THIS DATASET Transactions from all FPPC Form 460 schedules are presented together, refer to the Form Type to differentiate. Transactions with a Form Type of D, E, F, G, H, F496, or F497P2 represent expenditures or money spent by the committee. Transactions with Form Type A, B1, C, I, F496P3, and F497P1 represent receipts or money taken in by the committee. Refer to the instructions for Forms 460, 496, and 497 for more details. Transactions on Form 460 Schedules D, F, G, and H are also reported on Schedule E. When doing summary statistics use care not to double count expenditures. Transactions from FPPC Form 496 and Form 497 filings are also in this dataset. Transactions that were reported on these forms are also reported on the Form 460 at the next filing deadline. If a 460 filing deadline has passed and the committee has filed a campaign statement, transactions on filings from the late reporting period should be disregarded. This dataset only shows transactions from the most recent filing version. Committee's amendments overwrite filings which come before in sequence. Campaign Committees are required to file statements according to a schedule set out by the California Fair Political Practices Commission. Depending on timing, transactions which have occurred may not be listed as they might not have been reported yet. E. RELATED DATASETS <a href=

Survey of Household Spending (SHS), average household spending on detailed food categories.

Analysis of ‘Customer Personality Analysis’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/imakash3011/customer-personality-analysis on 21 November 2021.

--- Dataset description provided by original source is as follows ---

Context

Problem Statement

Customer Personality Analysis is a detailed analysis of a company’s ideal customers. It helps a business to better understand its customers and makes it easier for them to modify products according to the specific needs, behaviors and concerns of different types of customers.

Customer personality analysis helps a business to modify its product based on its target customers from different types of customer segments. For example, instead of spending money to market a new product to every customer in the company’s database, a company can analyze which customer segment is most likely to buy the product and then market the product only on that particular segment.

Content

Attributes

People

• ID: Customer's unique identifier
• Year_Birth: Customer's birth year
• Education: Customer's education level
• Marital_Status: Customer's marital status
• Income: Customer's yearly household income
• Kidhome: Number of children in customer's household
• Teenhome: Number of teenagers in customer's household
• Dt_Customer: Date of customer's enrollment with the company
• Recency: Number of days since customer's last purchase
• Complain: 1 if customer complained in the last 2 years, 0 otherwise

Products

• MntWines: Amount spent on wine in last 2 years
• MntFruits: Amount spent on fruits in last 2 years
• MntMeatProducts: Amount spent on meat in last 2 years
• MntFishProducts: Amount spent on fish in last 2 years
• MntSweetProducts: Amount spent on sweets in last 2 years
• MntGoldProds: Amount spent on gold in last 2 years

Promotion

• NumDealsPurchases: Number of purchases made with a discount
• AcceptedCmp1: 1 if customer accepted the offer in the 1st campaign, 0 otherwise
• AcceptedCmp2: 1 if customer accepted the offer in the 2nd campaign, 0 otherwise
• AcceptedCmp3: 1 if customer accepted the offer in the 3rd campaign, 0 otherwise
• AcceptedCmp4: 1 if customer accepted the offer in the 4th campaign, 0 otherwise
• AcceptedCmp5: 1 if customer accepted the offer in the 5th campaign, 0 otherwise
• Response: 1 if customer accepted the offer in the last campaign, 0 otherwise

Place

• NumWebPurchases: Number of purchases made through the company’s web site
• NumCatalogPurchases: Number of purchases made using a catalogue
• NumStorePurchases: Number of purchases made directly in stores
• NumWebVisitsMonth: Number of visits to company’s web site in the last month

Target

Need to perform clustering to summarize customer segments.

Solution

You can take help from following link to know more about the approach to solve this problem. Visit this URL

Inspiration

happy learning....

Hope you like this dataset please don't forget to like this dataset

--- Original source retains full ownership of the source dataset ---

IoTeX is a decentralized crypto system, a new generation of blockchain platform for the development of the Internet of things (IoT). The project team is sure that the users do not have such an application that would motivate to implement the technology of the Internet of things in life. And while this will not be created, people will not have the desire to spend money and time on IoT. The developers of IoTeX decided to implement not the application itself, but the platform for creation. It is through the platform that innovative steps in the space of the Internet of things will be encouraged. Learn more... This dataset is one of many crypto datasets that are available within the Google Cloud Public Datasets . As with other Google Cloud public datasets, you can query this dataset for free, up to 1TB/month of free processing, every month. Watch this short video to learn how to get started with the public datasets. Want to know how the data from these blockchains were brought into BigQuery, and learn how to analyze the data? 瞭解詳情

Government Spending in Brazil decreased to 57326.04 BRL Million in the first quarter of 2025 from 61109.86 BRL Million in the fourth quarter of 2024. This dataset provides - Brazil Government Spending - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The housing affordability measure illustrates the relationship between income and housing costs. A household that spends 30% or more of its collective monthly income to cover housing costs is considered to be “housing cost-burden[ed].”[1] Those spending between 30% and 49.9% of their monthly income are categorized as “moderately housing cost-burden[ed],” while those spending more than 50% are categorized as “severely housing cost-burden[ed].”[2]

How much a household spends on housing costs affects the household’s overall financial situation. More money spent on housing leaves less in the household budget for other needs, such as food, clothing, transportation, and medical care, as well as for incidental purchases and saving for the future.

The estimated housing costs as a percentage of household income are categorized by tenure: all households, those that own their housing unit, and those that rent their housing unit.

Throughout the period of analysis, the percentage of housing cost-burdened renter households in Champaign County was higher than the percentage of housing cost-burdened homeowner households in Champaign County. All three categories saw year-to-year fluctuations between 2005 and 2023, and none of the three show a consistent trend. However, all three categories were estimated to have a lower percentage of housing cost-burdened households in 2023 than in 2005.

Data on estimated housing costs as a percentage of monthly income was sourced from the U.S. Census Bureau’s American Community Survey (ACS) 1-Year Estimates, which are released annually.

As with any datasets that are estimates rather than exact counts, it is important to take into account the margins of error (listed in the column beside each figure) when drawing conclusions from the data.

Due to the impact of the COVID-19 pandemic, instead of providing the standard 1-year data products, the Census Bureau released experimental estimates from the 1-year data in 2020. This includes a limited number of data tables for the nation, states, and the District of Columbia. The Census Bureau states that the 2020 ACS 1-year experimental tables use an experimental estimation methodology and should not be compared with other ACS data. For these reasons, and because data is not available for Champaign County, no data for 2020 is included in this Indicator.

For interested data users, the 2020 ACS 1-Year Experimental data release includes a dataset on Housing Tenure.

[1] Schwarz, M. and E. Watson. (2008). Who can afford to live in a home?: A look at data from the 2006 American Community Survey. U.S. Census Bureau.

[2] Ibid.

Sources: U.S. Census Bureau; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (17 October 2024).; U.S. Census Bureau; American Community Survey, 2022 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (22 September 2023).; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (30 September 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (10 June 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (10 June 2021).;U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2016 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (14 September 2017).; U.S. Census Bureau; American Community Survey, 2015 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (19 September 2016).; U.S. Census Bureau; American Community Survey, 2014 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2013 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2012 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2011 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2010 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2009 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2008 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; 16 March 2016).; U.S. Census Bureau; American Community Survey, 2007 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2006 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2005 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).

Government Spending in the United States decreased to 3990.60 USD Billion in the first quarter of 2025 from 3996.30 USD Billion in the fourth quarter of 2024. This dataset provides the latest reported value for - United States Government Spending - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

A. SUMMARY This dataset contains data from financial statements of campaign committees that file with the San Francisco Ethics Commission and (1) contribute to or (2) receive funds from a San Francisco committee which was Primarily Formed for a local election, or (3) filed a Late Reporting Period statement with the SFEC. Financial statements are included for a committee if they meet any of the three criteria for each election included in the search parameters and are not primarily formed for the election. The search period for financial statements begins two years before an election and runs through the next semi-annual filing deadline. The dataset currently filters by the elections of 2024-03-05 and 2024-11-05. B. HOW THE DATASET IS CREATED During an election period an automated script runs nightly to examine filings by Primarily Formed San Francisco committees. If a primarily formed committee reports accepting money from or giving money to a second committee, that second committee's ID number is added to a filter list. If a committee electronically files a late reporting period form with the San Francisco Ethics Commission, the committee's ID number is also included in the filter list. The filter list is used in a second step that looks for filings by committees that file with the San Francisco Ethics Commission or the California Secretary of State. This dataset shows the output of the second step for committees that file with the San Francisco Ethics Commission. The data comes from a nightly search of the Ethics Commission campaign database. A second dataset includes committees that file with the Secretary of State. C. UPDATE PROCESS This dataset is rewritten nightly and is based on data derived from campaign filings. The update script runs automatically on a timer during the 90 days before an election. Refer to the "Data Last Updated" date in the section "About This Dataset" on the landing page to see when the script last ran successfully. D. HOW TO USE THIS DATASET Transactions from all FPPC Form 460 schedules are presented together, refer to the Form Type to differentiate. Transactions from FPPC Form 461 and Form 465 filings are presented together, refer to the Form Type to differentiate. Transactions with a Form Type of D, E, F, G, H, F461P5, F465P3, F496, or F497P2 represent expenditures, or money spent by the committee. Transactions with Form Type A, B1, C, I, F496P3, and F497P1 represent receipts, or money taken in by the committee. Refer to the instructions for Forms 460, 496, and 497 for more details. Transactions on Form 460 Schedules D, F, G, and H are also reported on Schedule E. When doing summary statistics use care not to double count expenditures. Transactions from FPPC Form 496 and Form 497 filings are presented in this dataset. Transactions that were reported on these forms are also reported on the Form 460 at the next filing deadline. If a 460 filing deadline has passed and the committee has filed a campaign statement, transactions on 496/497 filings from the late reporting period should be disregarded. This dataset only shows transactions from the most recent filing version. Committee amendments overwrite filings which come before in sequence. Campaign Committees are required to file statements according to a schedule set out by the C

SafeGraph is just a data company. That's all we do.SafeGraph Places for ArcGIS is a subset of SafeGraph Places. SafeGraph Places is a points-of-interest (POI) dataset with business listing, building footprint, visitor insights, & foot-traffic data for every place people spend money in the U.S.The complete SafeGraph Places dataset has ~ 5.4 million points-of-interest in the USA and is updated monthly (to reflect store openings & closings).Here, for free on this listing, SafeGraph offers a subset of attributes from SafeGraph Places: POI business listing information and POI locations (building centroids).Columns in this dataset:safegraph_place_idparent_safegraph_place_idlocation_namesafegraph_brand_idsbrandstop_categorystreet_addresscitystatezip_codeNAICS codeGeometry Point data. Latitude and longitude of building centroid.For data definitions and complete documentation visit SafeGraph Developer and Data Scientist Docs.For statistics on the dataset, see SafeGraph Places Summary Statistics.Data is available as a hosted Feature Service to easily integrate with all ESRI products in the ArcGIS ecosystem.Want More? Want this POI data for use outside of ArcGIS Online? Want POI data for Canada? Want POI building footprints (Geometry)?Want more detailed category information (Core Places)?Want phone numbers or operating hours (Core Places)?Want POI visitor insights & foot-traffic data (Places Patterns)?To see more, preview & download all SafeGraph Places, Patterns, & Geometry data from SafeGraph’s Data Bar.Or drop us a line! Your data needs are our data delights. Contact: support-esri@safegraph.comView Terms of Use

A. SUMMARY This dataset contains data from financial statements of state committees that (1) contribute to or (2) receive funds from a San Francisco committee which was Primarily Formed for a local election, or (3) filed a Late Reporting Period statement with the SFEC during the 90 days before an election. The search period for financial statements begins two years before an election and runs through the next semi-annual filing deadline. The dataset currently filters by the elections of 2024-03-05 and 2024-11-05.

B. HOW THE DATASET IS CREATED During an election period, an automated script runs nightly to examine filings by Primarily Formed San Francisco committees. If a primarily formed committee reports accepting money from or giving money to a second committee, that second committee's ID number is added to a filter list. If a committee electronically files a late reporting period form with the San Francisco Ethics Commission, the committee's ID number is also included in the filter list. The filter list is used in a second step that looks for filings by committees that file with the San Francisco Ethics Commission or the California Secretary of State.

This dataset shows the committees that file with the California Secretary of State. The data comes from a nightly export of the Secretary of State's database. A second dataset includes Non-Primarily Formed committees that file with the San Francisco Ethics Commission.

C. UPDATE PROCESS This dataset is rewritten nightly based on data derived from campaign filings. The update script runs automatically on a timer during the 90 days before an election. Refer to the "Data Last Updated" date in the section "About This Dataset" on the landing page to see when the script last ran successfully.

D. HOW TO USE THIS DATASET Transactions from all FPPC Form 460 schedules are presented together, refer to the Form Type to differentiate.

Transactions with a Form Type of D, E, F, G, H, F496, or F497P2 represent expenditures or money spent by the committee. Transactions with Form Type A, B1, C, I, F496P3, and F497P1 represent receipts or money taken in by the committee. Refer to the instructions for Forms 460, 496, and 497 for more details.

Transactions on Form 460 Schedules D, F, G, and H are also reported on Schedule E. When doing summary statistics use care not to double count expenditures.

Transactions from FPPC Form 496 and Form 497 filings are also in this dataset. Transactions that were reported on these forms are also reported on the Form 460 at the next filing deadline. If a 460 filing deadline has passed and the committee has filed a campaign statement, transactions on filings from the late reporting period should be disregarded.

This dataset only shows transactions from the most recent filing version. Committee's amendments overwrite filings which come before in sequence.

Campaign Committees are required to file statements according to a schedule set out by the California Fair Political Practices Commission. Depending on timing, transactions which have occurred may not be listed as they might not have been reported yet.

E. RELATED DATASETS

• Campaign Finance - Local Non-Focal Committees - Committees that file with the SFEC and have contributed to a committee primarily formed for this election
• Campaign Finance - Transactions - All transactions e-filed with the SFEC
• Campaign Finance - Filers - All entities that e-file with the SFEC
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