In 2024, Target Corporation's food and beverage product segment generated sales of approximately 23.8 billion U.S. dollars. In contrast, the hardline segment, which include electronics, toys, entertainment, sporting goods, and luggage, registered sales of 15.8 billion U.S. dollars. Target Corporation had revenues amounting to around 106.6 billion U.S. dollars that year.
This data set consists of 11 areas identified as Target Neighborhoods in Universal Solicitation of Broadband (USB). Data Limitations: Data accuracy is limited as of the date of publication and by the methodology and accuracy of the original sources. The City shall not be liable for any costs related to, or in reliance of, the data contained in these datasets.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Analysis of ‘Do You Know Where America Stands On Guns?’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/poll-quiz-gunse on 28 January 2022.
--- Dataset description provided by original source is as follows ---
This folder contains the data behind the quiz Do You Know Where America Stands On Guns?
guns-polls.csv
contains the list of polls about guns that we used in our quiz. All polls have been taken after February 14, 2018, the date of the school shooting in Parkland, Florida.The data is available under the Creative Commons Attribution 4.0 International License and the code is available under the MIT License. If you do find it useful, please let us know.
Source: https://github.com/fivethirtyeight/data
This dataset was created by FiveThirtyEight and contains around 100 samples along with End, Republican Support, technical information and other features such as: - Start - Support - and more.
- Analyze Question in relation to Url
- Study the influence of Population on Pollster
- More datasets
If you use this dataset in your research, please credit FiveThirtyEight
--- Original source retains full ownership of the source dataset ---
Techsalerator’s News Event Data in Latin America offers a detailed and extensive dataset designed to provide businesses, analysts, journalists, and researchers with an in-depth view of significant news events across the Latin American region. This dataset captures and categorizes key events reported from a wide array of news sources, including press releases, industry news sites, blogs, and PR platforms, offering valuable insights into regional developments, economic changes, political shifts, and cultural events.
Key Features of the Dataset: Comprehensive Coverage:
The dataset aggregates news events from numerous sources such as company press releases, industry news outlets, blogs, PR sites, and traditional news media. This broad coverage ensures a wide range of information from multiple reporting channels. Categorization of Events:
News events are categorized into various types including business and economic updates, political developments, technological advancements, legal and regulatory changes, and cultural events. This categorization helps users quickly locate and analyze information relevant to their interests or sectors. Real-Time Updates:
The dataset is updated regularly to include the most recent events, ensuring users have access to the latest news and can stay informed about current developments. Geographic Segmentation:
Events are tagged with their respective countries and regions within Latin America. This geographic segmentation allows users to filter and analyze news events based on specific locations, facilitating targeted research and analysis. Event Details:
Each event entry includes comprehensive details such as the date of occurrence, source of the news, a description of the event, and relevant keywords. This thorough detailing helps in understanding the context and significance of each event. Historical Data:
The dataset includes historical news event data, enabling users to track trends and perform comparative analysis over time. This feature supports longitudinal studies and provides insights into how news events evolve. Advanced Search and Filter Options:
Users can search and filter news events based on criteria such as date range, event type, location, and keywords. This functionality allows for precise and efficient retrieval of relevant information. Latin American Countries Covered: South America: Argentina Bolivia Brazil Chile Colombia Ecuador Guyana Paraguay Peru Suriname Uruguay Venezuela Central America: Belize Costa Rica El Salvador Guatemala Honduras Nicaragua Panama Caribbean: Cuba Dominican Republic Haiti (Note: Primarily French-speaking but included due to geographic and cultural ties) Jamaica Trinidad and Tobago Benefits of the Dataset: Strategic Insights: Businesses and analysts can use the dataset to gain insights into significant regional developments, economic conditions, and political changes, aiding in strategic decision-making and market analysis. Market and Industry Trends: The dataset provides valuable information on industry-specific trends and events, helping users understand market dynamics and emerging opportunities. Media and PR Monitoring: Journalists and PR professionals can track relevant news across Latin America, enabling them to monitor media coverage, identify emerging stories, and manage public relations efforts effectively. Academic and Research Use: Researchers can utilize the dataset for longitudinal studies, trend analysis, and academic research on various topics related to Latin American news and events. Techsalerator’s News Event Data in Latin America is a crucial resource for accessing and analyzing significant news events across the region. By providing detailed, categorized, and up-to-date information, it supports effective decision-making, research, and media monitoring across diverse sectors.
Techsalerator’s News Event Data in North America offers a comprehensive and detailed dataset designed to provide businesses, analysts, journalists, and researchers with a thorough view of significant news events across North America. This dataset captures and categorizes major events reported from a diverse range of news sources, including press releases, industry news sites, blogs, and PR platforms, providing valuable insights into regional developments, economic shifts, political changes, and cultural events.
Key Features of the Dataset: Extensive Coverage:
The dataset aggregates news events from a wide array of sources, including company press releases, industry-specific news outlets, blogs, PR sites, and traditional media. This broad coverage ensures a diverse range of information from multiple reporting channels. Categorization of Events:
News events are categorized into various types such as business and economic updates, political developments, technological advancements, legal and regulatory changes, and cultural events. This categorization helps users quickly find and analyze information relevant to their interests or sectors. Real-Time Updates:
The dataset is updated regularly to include the most current events, ensuring that users have access to up-to-date news and can stay informed about recent developments as they happen. Geographic Segmentation:
Events are tagged with their respective countries and territories within North America. This geographic segmentation allows users to filter and analyze news events based on specific locations, facilitating targeted research and analysis. Event Details:
Each event entry includes comprehensive details such as the date of occurrence, source of the news, a description of the event, and relevant keywords. This thorough detailing helps users understand the context and significance of each event. Historical Data:
The dataset includes historical news event data, enabling users to track trends and conduct comparative analysis over time. This feature supports longitudinal studies and provides insights into how news events evolve. Advanced Search and Filter Options:
Users can search and filter news events based on criteria such as date range, event type, location, and keywords. This functionality allows for precise and efficient retrieval of relevant information. North American Countries and Territories Covered: Countries: Canada Mexico United States Territories: American Samoa (U.S. territory) French Polynesia (French overseas collectivity; included for regional relevance) Guam (U.S. territory) New Caledonia (French special collectivity; included for regional relevance) Northern Mariana Islands (U.S. territory) Puerto Rico (U.S. territory) Saint Pierre and Miquelon (French overseas territory; geographically close to North America and included for regional comprehensiveness) Wallis and Futuna (French overseas collectivity; included for regional relevance) Benefits of the Dataset: Strategic Insights: Businesses and analysts can use the dataset to gain insights into significant regional developments, economic conditions, and political changes, aiding in strategic decision-making and market analysis. Market and Industry Trends: The dataset provides valuable information on industry-specific trends and events, helping users understand market dynamics and identify emerging opportunities. Media and PR Monitoring: Journalists and PR professionals can track relevant news across North America, enabling them to monitor media coverage, identify emerging stories, and manage public relations efforts effectively. Academic and Research Use: Researchers can utilize the dataset for longitudinal studies, trend analysis, and academic research on various topics related to North American news and events. Techsalerator’s News Event Data in North America is a crucial resource for accessing and analyzing significant news events across the continent. By providing detailed, categorized, and up-to-date information, it supports effective decision-making, research, and media monitoring across diverse sectors.
Listing of SONYMA target areas by US Census Bureau Census Tract or Block Numbering Area (BNA). The State of New York Mortgage Agency (SONYMA) targets specific areas designated as ‘areas of chronic economic distress’ for its homeownership lending programs. Each state designates ‘areas of chronic economic distress’ with the approval of the US Secretary of Housing and Urban Development (HUD). SONYMA identifies its target areas using US Census Bureau census tracts and block numbering areas. Both census tracts and block numbering areas subdivide individual counties. SONYMA also relates each of its single-family mortgages to a specific census tract or block numbering area. New York State identifies ‘areas of chronic economic distress’ using census tract numbers. 26 US Code § 143 (current through Pub. L. 114-38) defines the criteria that the Secretary of Housing and Urban Development uses in approving designations of ‘areas of chronic economic distress’ as: i) the condition of the housing stock, including the age of the housing and the number of abandoned and substandard residential units, (ii) the need of area residents for owner-financing under this section, as indicated by low per capita income, a high percentage of families in poverty, a high number of welfare recipients, and high unemployment rates, (iii) the potential for use of owner-financing under this section to improve housing conditions in the area, and (iv) the existence of a housing assistance plan which provides a displacement program and a public improvements and services program. The US Census Bureau’s decennial census last took place in 2010 and will take place again in 2020. While the state designates ‘areas of chronic economic distress,’ the US Department of Housing and Urban Development must approve the designation. The designation takes place after the decennial census.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
POLITISKY24 (Political Stance Analysis on Bluesky for 2024) is a first-of-its-kind dataset for stance detection, focused on the 2024 U.S. presidential election. It designed for target-specific user-level stance detection and contains 16,044 user-target stance pairs centered on two key political figures, Kamala Harris and Donald Trump. In addition, this dataset includes detailed metadata, such as complete user posting histories and engagement graphs (likes, reposts, and quotes).
Stance labels were generated using a robust and evaluated pipeline that integrates state-of-the-art Information Retrieval (IR) techniques with Large Language Models (LLMs), offering confidence scores, reasoning explanations, and text spans for each label. With an LLM-assisted labeling accuracy of 81%, POLITISKY24 provides a rich resource for the target-specific stance detection task. This dataset enables the exploration of Bluesky platform, paving the way for deeper insights into political opinions and social discourse, and addressing gaps left by traditional datasets constrained by platform policies.
In the uploaded files:
The file user_post_history_dataset.parquet
includes the posting history of 8,561 active Bluesky users who have shared content related to American politics.
The file user_post_list_for_stance_detection.parquet
contains a list of up to 1,000 recent English-language post IDs per user, intended for use in the stance detection task.
The file user_network_dataset.parquet
captures users’ interactions through likes, reposts, and quotes.
The file human_annotated_validation_user_stance_dataset.parquet
contains human-annotated stance labels for 445 validation users toward Trump and Harris, resulting in a total of 890 user-target pairs. The labels are divided into three stances: 1 (favor), 2 (against), and 3 (neither).
The file llm_annotated_validation_user_stance_dataset.parquet
contains stance labels annotated by an LLM for the same 445 validation users toward Trump and Harris, also totaling 890 user-target pairs. In addition to stance labels, each pair includes an explanation of the reasoning, the source tweets, spans from the source tweets used in the reasoning, and a confidence score.
The file llm_annotated_full_user_stance_dataset.parquet
is similar to the above LLM-annotated validation file but covers all dataset users excluding the validation set. It provides stance labels for 8,022 users toward Trump and Harris, totaling 16,044 user-target pairs.
The file human_annotated_validation_stance_relevancy_dataset (post-target entity pairs).parquet
contains human-annotated stance labels for 175 validation posts toward Trump and Harris, resulting in 350 post-target pairs. The labels are divided into three stances: 1 (favor), 2 (against), and 3 (neither).
The file human_annotated_validation_stance_relevancy_dataset (query-post stance relevancy pairs).parquet
contains 700 query-post stance relevancy pairs derived from the post-target entity pairs.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Analysis of ‘US non-voters poll data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/us-non-voters-poll-datae on 28 January 2022.
--- Dataset description provided by original source is as follows ---
This dataset contains the data behind Why Many Americans Don't Vote.
Data presented here comes from polling done by Ipsos for FiveThirtyEight, using Ipsos’s KnowledgePanel, a probability-based online panel that is recruited to be representative of the U.S. population. The poll was conducted from Sept. 15 to Sept. 25 among a sample of U.S. citizens that oversampled young, Black and Hispanic respondents, with 8,327 respondents, and was weighted according to general population benchmarks for U.S. citizens from the U.S. Census Bureau’s Current Population Survey March 2019 Supplement. The voter file company Aristotle then matched respondents to a voter file to more accurately understand their voting history using the panelist’s first name, last name, zip code, and eight characters of their address, using the National Change of Address program if applicable. Sixty-four percent of the sample (5,355 respondents) matched, although we also included respondents who did not match the voter file but described themselves as voting “rarely” or “never” in our survey, so as to avoid underrepresenting nonvoters, who are less likely to be included in the voter file to begin with. We dropped respondents who were only eligible to vote in three elections or fewer. We defined those who almost always vote as those who voted in all (or all but one) of the national elections (presidential and midterm) they were eligible to vote in since 2000; those who vote sometimes as those who voted in at least two elections, but fewer than all the elections they were eligible to vote in (or all but one); and those who rarely or never vote as those who voted in no elections, or just one.
The data included here is the final sample we used: 5,239 respondents who matched to the voter file and whose verified vote history we have, and 597 respondents who did not match to the voter file and described themselves as voting "rarely" or "never," all of whom have been eligible for at least 4 elections.
If you find this information useful, please let us know.
License: Creative Commons Attribution 4.0 International License
Source: https://github.com/fivethirtyeight/data/tree/master/non-voters
This dataset was created by data.world's Admin and contains around 6000 samples along with Race, Q27 6, technical information and other features such as: - Q4 6 - Q8 3 - and more.
- Analyze Q10 3 in relation to Q8 6
- Study the influence of Q6 on Q10 4
- More datasets
If you use this dataset in your research, please credit data.world's Admin
--- Original source retains full ownership of the source dataset ---
The United States Geological Survey (USGS) - Science Analytics and Synthesis (SAS) - Gap Analysis Project (GAP) manages the Protected Areas Database of the United States (PAD-US), an Arc10x geodatabase, that includes a full inventory of areas dedicated to the preservation of biological diversity and to other natural, recreation, historic, and cultural uses, managed for these purposes through legal or other effective means (www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/science/protected-areas). The PAD-US is developed in partnership with many organizations, including coordination groups at the [U.S.] Federal level, lead organizations for each State, and a number of national and other non-governmental organizations whose work is closely related to the PAD-US. Learn more about the USGS PAD-US partners program here: www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/science/pad-us-data-stewards. The United Nations Environmental Program - World Conservation Monitoring Centre (UNEP-WCMC) tracks global progress toward biodiversity protection targets enacted by the Convention on Biological Diversity (CBD) through the World Database on Protected Areas (WDPA) and World Database on Other Effective Area-based Conservation Measures (WD-OECM) available at: www.protectedplanet.net. See the Aichi Target 11 dashboard (www.protectedplanet.net/en/thematic-areas/global-partnership-on-aichi-target-11) for official protection statistics recognized globally and developed for the CBD, or here for more information and statistics on the United States of America's protected areas: www.protectedplanet.net/country/USA. It is important to note statistics published by the National Oceanic and Atmospheric Administration (NOAA) Marine Protected Areas (MPA) Center (www.marineprotectedareas.noaa.gov/dataanalysis/mpainventory/) and the USGS-GAP (www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/science/pad-us-statistics-and-reports) differ from statistics published by the UNEP-WCMC as methods to remove overlapping designations differ slightly and U.S. Territories are reported separately by the UNEP-WCMC (e.g. The largest MPA, "Pacific Remote Islands Marine Monument" is attributed to the United States Minor Outlying Islands statistics). At the time of PAD-US 2.1 publication (USGS-GAP, 2020), NOAA reported 26% of U.S. marine waters (including the Great Lakes) as protected in an MPA that meets the International Union for Conservation of Nature (IUCN) definition of biodiversity protection (www.iucn.org/theme/protected-areas/about). USGS-GAP released PAD-US 3.0 Statistics and Reports in the summer of 2022. The relationship between the USGS, the NOAA, and the UNEP-WCMC is as follows: - USGS manages and publishes the full inventory of U.S. marine and terrestrial protected areas data in the PAD-US representing many values, developed in collaboration with a partnership network in the U.S. and; - USGS is the primary source of U.S. marine and terrestrial protected areas data for the WDPA, developed from a subset of the PAD-US in collaboration with the NOAA, other agencies and non-governmental organizations in the U.S., and the UNEP-WCMC and; - UNEP-WCMC is the authoritative source of global protected area statistics from the WDPA and WD-OECM and; - NOAA is the authoritative source of MPA data in the PAD-US and MPA statistics in the U.S. and; - USGS is the authoritative source of PAD-US statistics (including areas primarily managed for biodiversity, multiple uses including natural resource extraction, and public access). The PAD-US 3.0 Combined Marine, Fee, Designation, Easement feature class (GAP Status Code 1 and 2 only) is the source of protected areas data in this WDPA update. Tribal areas and military lands represented in the PAD-US Proclamation feature class as GAP Status Code 4 (no known mandate for biodiversity protection) are not included as spatial data to represent internal protected areas are not available at this time. The USGS submitted more than 51,000 protected areas from PAD-US 3.0, including all 50 U.S. States and 6 U.S. Territories, to the UNEP-WCMC for inclusion in the WDPA, available at www.protectedplanet.net. The NOAA is the sole source of MPAs in PAD-US and the National Conservation Easement Database (NCED, www.conservationeasement.us/) is the source of conservation easements. The USGS aggregates authoritative federal lands data directly from managing agencies for PAD-US (https://ngda-gov-units-geoplatform.hub.arcgis.com/pages/federal-lands-workgroup), while a network of State data-stewards provide state, local government lands, and some land trust preserves. National nongovernmental organizations contribute spatial data directly (www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/science/pad-us-data-stewards). The USGS translates the biodiversity focused subset of PAD-US into the WDPA schema (UNEP-WCMC, 2019) for efficient aggregation by the UNEP-WCMC. The USGS maintains WDPA Site Identifiers (WDPAID, WDPA_PID), a persistent identifier for each protected area, provided by UNEP-WCMC. Agency partners are encouraged to track WDPA Site Identifier values in source datasets to improve the efficiency and accuracy of PAD-US and WDPA updates. The IUCN protected areas in the U.S. are managed by thousands of agencies and organizations across the country and include over 51,000 designated sites such as National Parks, National Wildlife Refuges, National Monuments, Wilderness Areas, some State Parks, State Wildlife Management Areas, Local Nature Preserves, City Natural Areas, The Nature Conservancy and other Land Trust Preserves, and Conservation Easements. The boundaries of these protected places (some overlap) are represented as polygons in the PAD-US, along with informative descriptions such as Unit Name, Manager Name, and Designation Type. As the WDPA is a global dataset, their data standards (UNEP-WCMC 2019) require simplification to reduce the number of records included, focusing on the protected area site name and management authority as described in the Supplemental Information section in this metadata record. Given the numerous organizations involved, sites may be added or removed from the WDPA between PAD-US updates. These differences may reflect actual change in protected area status; however, they also reflect the dynamic nature of spatial data or Geographic Information Systems (GIS). Many agencies and non-governmental organizations are working to improve the accuracy of protected area boundaries, the consistency of attributes, and inventory completeness between PAD-US updates. In addition, USGS continually seeks partners to review and refine the assignment of conservation measures in the PAD-US.
MealMe provides comprehensive grocery and retail SKU-level product data, including real-time pricing, from the top 100 retailers in the USA and Canada. Our proprietary technology ensures accurate and up-to-date insights, empowering businesses to excel in competitive intelligence, pricing strategies, and market analysis.
Retailers Covered: MealMe’s database includes detailed SKU-level data and pricing from leading grocery and retail chains such as Walmart, Target, Costco, Kroger, Safeway, Publix, Whole Foods, Aldi, ShopRite, BJ’s Wholesale Club, Sprouts Farmers Market, Albertsons, Ralphs, Pavilions, Gelson’s, Vons, Shaw’s, Metro, and many more. Our coverage spans the most influential retailers across North America, ensuring businesses have the insights needed to stay competitive in dynamic markets.
Key Features: SKU-Level Granularity: Access detailed product-level data, including product descriptions, categories, brands, and variations. Real-Time Pricing: Monitor current pricing trends across major retailers for comprehensive market comparisons. Regional Insights: Analyze geographic price variations and inventory availability to identify trends and opportunities. Customizable Solutions: Tailored data delivery options to meet the specific needs of your business or industry. Use Cases: Competitive Intelligence: Gain visibility into pricing, product availability, and assortment strategies of top retailers like Walmart, Costco, and Target. Pricing Optimization: Use real-time data to create dynamic pricing models that respond to market conditions. Market Research: Identify trends, gaps, and consumer preferences by analyzing SKU-level data across leading retailers. Inventory Management: Streamline operations with accurate, real-time inventory availability. Retail Execution: Ensure on-shelf product availability and compliance with merchandising strategies. Industries Benefiting from Our Data CPG (Consumer Packaged Goods): Optimize product positioning, pricing, and distribution strategies. E-commerce Platforms: Enhance online catalogs with precise pricing and inventory information. Market Research Firms: Conduct detailed analyses to uncover industry trends and opportunities. Retailers: Benchmark against competitors like Kroger and Aldi to refine assortments and pricing. AI & Analytics Companies: Fuel predictive models and business intelligence with reliable SKU-level data. Data Delivery and Integration MealMe offers flexible integration options, including APIs and custom data exports, for seamless access to real-time data. Whether you need large-scale analysis or continuous updates, our solutions scale with your business needs.
Why Choose MealMe? Comprehensive Coverage: Data from the top 100 grocery and retail chains in North America, including Walmart, Target, and Costco. Real-Time Accuracy: Up-to-date pricing and product information ensures competitive edge. Customizable Insights: Tailored datasets align with your specific business objectives. Proven Expertise: Trusted by diverse industries for delivering actionable insights. MealMe empowers businesses to unlock their full potential with real-time, high-quality grocery and retail data. For more information or to schedule a demo, contact us today!
WARNING: This is a pre-release dataset and its fields names and data structures are subject to change. It should be considered pre-release until the end of March 2025. The schema changed in February 2025 - please see below. We will post a roadmap of upcoming changes, but service URLs and schema are now stable. For deployment status of new services in February 2025, see https://gis.data.ca.gov/pages/city-and-county-boundary-data-status. Additional roadmap and status links at the bottom of this metadata.
Purpose
County boundaries along with third party identifiers used to join in external data. Boundaries are from the California Department of Tax and Fee Administration (CDTFA). These boundaries are the best available statewide data source in that CDTFA receives changes in incorporation and boundary lines from the Board of Equalization, who receives them from local jurisdictions for tax purposes. Boundary accuracy is not guaranteed, and though CDTFA works to align boundaries based on historical records and local changes, errors will exist. If you require a legal assessment of boundary location, contact a licensed surveyor.
This dataset joins in multiple attributes and identifiers from the US Census Bureau and Board on Geographic Names to facilitate adding additional third party data sources. In addition, we attach attributes of our own to ease and reduce common processing needs and questions. Finally, coastal buffers are separated into separate polygons, leaving the land-based portions of jurisdictions and coastal buffers in adjacent polygons. This layer removes the coastal buffer polygons. This feature layer is for public use.
Related Layers
This dataset is part of a grouping of many datasets:
Point of Contact
California Department of Technology, Office of Digital Services, odsdataservices@state.ca.gov
Field and Abbreviation Definitions
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Retail Sales in the United States decreased 0.90 percent in May of 2025 over the previous month. This dataset provides - U.S. December Retail Sales Increased More Than Forecast - actual values, historical data, forecast, chart, statistics, economic calendar and news.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Analysis of ‘🍕 Pizza restaurants and Pizzas on their Menus’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/pizza-restaurants-and-pizzas-on-their-menuse on 13 February 2022.
--- Dataset description provided by original source is as follows ---
About this Data
This is a list of over 3,500 pizzas from multiple restaurants provided by Datafiniti's Business Database. The dataset includes the category, name, address, city, state, menu information, price range, and more for each pizza restaurant.
Note that this is a sample of a large dataset. The full dataset is available through Datafiniti.
What You Can Do with this Data
You can use this data to discover how much you can expect to pay for pizza across the country. E.g.:
- What are the least and most expensive cities for pizza?
- What is the number of restaurants serving pizza per capita (100,000 residents) across the U.S.?
- What is the median price of a large plain pizza across the U.S.?
- Which cities have the most restaurants serving pizza per capita (100,000 residents)?
Data Schema
A full schema for the data is available in our support documentation.
About Datafiniti
Datafiniti provides instant access to web data. We compile data from thousands of websites to create standardized databases of business, product, and property information. Learn more.
Interested in the Full Dataset?
Get this data and more by creating a free Datafiniti account or requesting a demo.
This dataset was created by Datafiniti and contains around 10000 samples along with Longitude, Price Range Max, technical information and other features such as: - Date Updated - Categories - and more.
- Analyze Date Added in relation to Province
- Study the influence of Price Range Min on Address
- More datasets
If you use this dataset in your research, please credit Datafiniti
--- Original source retains full ownership of the source dataset ---
WARNING: This is a pre-release dataset and its fields names and data structures are subject to change. It should be considered pre-release until the end of 2024. Expected changes:
Purpose
County and incorporated place (city) boundaries along with third party identifiers used to join in external data. Boundaries are from the authoritative source the California Department of Tax and Fee Administration (CDTFA), altered to show the counties as one polygon. This layer displays the city polygons on top of the County polygons so the area isn"t interrupted. The GEOID attribute information is added from the US Census. GEOID is based on merged State and County FIPS codes for the Counties. Abbreviations for Counties and Cities were added from Caltrans Division of Local Assistance (DLA) data. Place Type was populated with information extracted from the Census. Names and IDs from the US Board on Geographic Names (BGN), the authoritative source of place names as published in the Geographic Name Information System (GNIS), are attached as well. Finally, coastal buffers are removed, leaving the land-based portions of jurisdictions. This feature layer is for public use.
Related Layers
This dataset is part of a grouping of many datasets:
Point of Contact
California Department of Technology, Office of Digital Services, odsdataservices@state.ca.gov
Field and Abbreviation Definitions
Accuracy
CDTFA"s source data notes the following about accuracy:
City boundary changes and county boundary line adjustments filed with the Board of Equalization per Government Code 54900. This GIS layer contains the boundaries of the unincorporated county and incorporated cities within the state of California. The initial dataset was created in March of 2015 and was based on the State Board of Equalization tax rate area boundaries. As of April 1, 2024, the maintenance of this dataset is provided by the California Department of Tax and Fee Administration for the purpose of determining sales and use tax rates. The boundaries are continuously being revised to align with aerial imagery when areas of conflict are discovered between the original boundary provided by the California State Board of Equalization and the boundary made publicly available by local, state, and federal government. Some differences may occur between actual recorded boundaries and the boundaries used for sales and use tax purposes. The boundaries in this map are representations of taxing jurisdictions for the purpose of determining sales and use tax rates and should not be used to determine precise city or county boundary line locations. COUNTY = county name; CITY = city name or unincorporated territory; COPRI =
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Analysis of ‘🎗️ Cancer Rates by U.S. State’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/cancer-rates-by-u-s-statee on 13 February 2022.
--- Dataset description provided by original source is as follows ---
In the following maps, the U.S. states are divided into groups based on the rates at which people developed or died from cancer in 2013, the most recent year for which incidence data are available.
The rates are the numbers out of 100,000 people who developed or died from cancer each year.
Incidence Rates by State
The number of people who get cancer is called cancer incidence. In the United States, the rate of getting cancer varies from state to state.
*Rates are per 100,000 and are age-adjusted to the 2000 U.S. standard population.
‡Rates are not shown if the state did not meet USCS publication criteria or if the state did not submit data to CDC.
†Source: U.S. Cancer Statistics Working Group. United States Cancer Statistics: 1999–2013 Incidence and Mortality Web-based Report. Atlanta (GA): Department of Health and Human Services, Centers for Disease Control and Prevention, and National Cancer Institute; 2016. Available at: http://www.cdc.gov/uscs.
Death Rates by State
Rates of dying from cancer also vary from state to state.
*Rates are per 100,000 and are age-adjusted to the 2000 U.S. standard population.
†Source: U.S. Cancer Statistics Working Group. United States Cancer Statistics: 1999–2013 Incidence and Mortality Web-based Report. Atlanta (GA): Department of Health and Human Services, Centers for Disease Control and Prevention, and National Cancer Institute; 2016. Available at: http://www.cdc.gov/uscs.
Source: https://www.cdc.gov/cancer/dcpc/data/state.htm
This dataset was created by Adam Helsinger and contains around 100 samples along with Range, Rate, technical information and other features such as: - Range - Rate - and more.
- Analyze Range in relation to Rate
- Study the influence of Range on Rate
- More datasets
If you use this dataset in your research, please credit Adam Helsinger
--- Original source retains full ownership of the source dataset ---
This dataset includes all 7 metro counties that have made their parcel data freely available without a license or fees.
This dataset is a compilation of tax parcel polygon and point layers assembled into a common coordinate system from Twin Cities, Minnesota metropolitan area counties. No attempt has been made to edgematch or rubbersheet between counties. A standard set of attribute fields is included for each county. The attributes are the same for the polygon and points layers. Not all attributes are populated for all counties.
NOTICE: The standard set of attributes changed to the MN Parcel Data Transfer Standard on 1/1/2019.
https://www.mngeo.state.mn.us/committee/standards/parcel_attrib/parcel_attrib.html
See section 5 of the metadata for an attribute summary.
Detailed information about the attributes can be found in the Metro Regional Parcel Attributes document.
The polygon layer contains one record for each real estate/tax parcel polygon within each county's parcel dataset. Some counties have polygons for each individual condominium, and others do not. (See Completeness in Section 2 of the metadata for more information.) The points layer includes the same attribute fields as the polygon dataset. The points are intended to provide information in situations where multiple tax parcels are represented by a single polygon. One primary example of this is the condominium, though some counties stacked polygons for condos. Condominiums, by definition, are legally owned as individual, taxed real estate units. Records for condominiums may not show up in the polygon dataset. The points for the point dataset often will be randomly placed or stacked within the parcel polygon with which they are associated.
The polygon layer is broken into individual county shape files. The points layer is provided as both individual county files and as one file for the entire metro area.
In many places a one-to-one relationship does not exist between these parcel polygons or points and the actual buildings or occupancy units that lie within them. There may be many buildings on one parcel and there may be many occupancy units (e.g. apartments, stores or offices) within each building. Additionally, no information exists within this dataset about residents of parcels. Parcel owner and taxpayer information exists for many, but not all counties.
This is a MetroGIS Regionally Endorsed dataset.
Additional information may be available from each county at the links listed below. Also, any questions or comments about suspected errors or omissions in this dataset can be addressed to the contact person at each individual county.
Anoka = http://www.anokacounty.us/315/GIS
Caver = http://www.co.carver.mn.us/GIS
Dakota = http://www.co.dakota.mn.us/homeproperty/propertymaps/pages/default.aspx
Hennepin = https://gis-hennepin.hub.arcgis.com/pages/open-data
Ramsey = https://www.ramseycounty.us/your-government/open-government/research-data
Scott = http://opendata.gis.co.scott.mn.us/
Washington: http://www.co.washington.mn.us/index.aspx?NID=1606
This dataset contains forecasted emissions factors for electricity generation and transportation. These factors are used to convert activity data to particulate matter 2.5 (PM2.5) emissions and metric ton of carbon dioxide equivalent (mTCO2e). This dataset can be applied to "Forecast of Emissions and PM 2.5 Reductions from City Actions" and the "Forecast of Citywide Emissions" dataset to convert from activity data to emissions. For any additional detail please refer to section 6 of the New York City Climate Budgeting Technical Appendices (https://www.nyc.gov/assets/omb/downloads/pdf/exec24-nyccbta.pdf). This dataset is going to be updated once a year during the Executive Budget.
You can find the complete collection of Climate Budget data by clicking here.
The American Trends Panel (ATP), created by "https://www.pewresearch.org/" Target="_blank">Pew Research Center, is a nationally representative panel of randomly selected U.S. adults. Panelists participate via self-administered web surveys. Panelists who do not have internet access at home are provided with a tablet and wireless internet connection. Interviews are conducted in both English and Spanish. The panel is being managed by "https://www.ipsos.com/en" Target="_blank">Ipsos.
The "https://www.pewresearch.org/science/dataset/american-trends-panel-wave-114/" Target="_blank">ATP Wave 114 was conducted from September 13 to 18, 2022. A total of 10,588 panelists responded out of 11,687 who were sampled for a response rate of 91 percent. The cumulative response rate accounting for nonresponse to the recruitment surveys and attrition is 3 percent. The break-off rate among panelists who logged on to the survey and completed at least one item is 1 percent. The margin of sampling error for the full sample of 10,588 respondents is plus or minus 1.5 percentage points.
The ATPW114 addresses topics of COVID-19, scientists and religion.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Unemployment Rate in the United States decreased to 4.10 percent in June from 4.20 percent in May of 2025. This dataset provides the latest reported value for - United States Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.
Listing of SONYMA target areas by US Census Bureau Census Tract or Block Numbering Area (BNA). The State of New York Mortgage Agency (SONYMA) targets specific areas designated as ‘areas of chronic economic distress’ for its homeownership lending programs. Each state designates ‘areas of chronic economic distress’ with the approval of the US Secretary of Housing and Urban Development (HUD). SONYMA identifies its target areas using US Census Bureau census tracts and block numbering areas. Both census tracts and block numbering areas subdivide individual counties. SONYMA also relates each of its single-family mortgages to a specific census tract or block numbering area. New York State identifies ‘areas of chronic economic distress’ using census tract numbers. 26 US Code § 143 (current through Pub. L. 114-38) defines the criteria that the Secretary of Housing and Urban Development uses in approving designations of ‘areas of chronic economic distress’ as: i) the condition of the housing stock, including the age of the housing and the number of abandoned and substandard residential units, (ii) the need of area residents for owner-financing under this section, as indicated by low per capita income, a high percentage of families in poverty, a high number of welfare recipients, and high unemployment rates, (iii) the potential for use of owner-financing under this section to improve housing conditions in the area, and (iv) the existence of a housing assistance plan which provides a displacement program and a public improvements and services program. The US Census Bureau’s decennial census last took place in 2010 and will take place again in 2020. While the state designates ‘areas of chronic economic distress,’ the US Department of Housing and Urban Development must approve the designation. The designation takes place after the decennial census.
In 2024, Target Corporation's food and beverage product segment generated sales of approximately 23.8 billion U.S. dollars. In contrast, the hardline segment, which include electronics, toys, entertainment, sporting goods, and luggage, registered sales of 15.8 billion U.S. dollars. Target Corporation had revenues amounting to around 106.6 billion U.S. dollars that year.