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These are economic models in Make and Use formats with variations of one and two-region versions where the one region is just a U.S. state of interest (SoI) and the two-region version include both the SoI and Rest of the U.S. (RoUS). Inudstry and Commodity output vectors are also provided. Models are available representing annual totals for each year for each state from 2012 to 2017. Variations for "Domestic" forms of models are available. See the associated publication, also available without fees in PubMed, for details. These models were created with stateior v0.1.0 (https://github.com/USEPA/stateior/releases/tag/0.1.0). and can be used in that R software. See https://github.com/USEPA/stateior/tree/0.1.0 for usage details. The provided data link reveals many R Data Format (.RDS) files that can be read into R, along with metadata files in JSON format that provide information on provenance of the data. File names corresponded with the definitions in the associated data dictionary (for two-region files) and the associated supporting link (for one-region files). Other files are precursors to the one and two-region models with data that are used in the model building process and can be read into R. All model files corresponding to the associated publication have the the text "0.1.0" in the filename, for example "Census_StateExport_2013_0.1.0.rds". Each file contains all states for the year in the file name with a year is included. This dataset is associated with the following publication: Li, M., J. Ferreira, C.D. Court, D. Meyer, M. Li, and W.W. Ingwersen. StateIO - Open Source Economic Input-Output Models for the 50 States of the United States of America. International Regional Science Review. SAGE Publications, THOUSAND OAKS, CA, USA, 46(4): 428-481, (2023).

These data represent an update to the dataset, "State IO Two-Region Economic Input-Output Models for 50 U.S. States 2012-2017" based on the methods described by Li et al. (2022). They are an update to that dataset published with an expanded time series. These models were produced with the stateior R package, v0.4.0. Excel files (50 in total) are provided for two region (State of Interest and Rest of U.S.) Make and Use tables for each U.S. State IO model for years 2012-2023. Additional data files supporting this release including all intermediate and final products in native R format (.RDS) and can be opened directly in R software or through the stateior package. See the stateior github page for more details. https://dmap-data-commons-ord.s3.amazonaws.com/index.html#stateio/ All values are in current dollar years (e.g "Make 2012" is the Make table in 2012 USD in a given model). For a description of the methods used and survey of results see the Addendum 1 on the EPA Science Inventory page for the original publication. Please cite this dataset as: Young, Ben, Julie Chen, Jorge Vendries, and Wesley Ingwersen. 2025. “StateIO v0.4.0 Two-Region Economic Input-Output Models for 50 U.S. States: 2012-2023.” Data.gov. https://doi.org/10.23719/1532211. This dataset is associated with the following publication: Li, M., J. Ferreira, C.D. Court, D. Meyer, M. Li, and W.W. Ingwersen. StateIO - Open Source Economic Input-Output Models for the 50 States of the United States of America. International Regional Science Review. SAGE Publications, THOUSAND OAKS, CA, USA, 46(4): 428-481, (2023).

• This dataset contains the population data of USA for the year 2020.
• This data is provided by the USA Government officially on their website. > I'll be updating this dataset annually !! Website Link : https://datausa.io/

United States BoP: IO: CA: Exp: Primary Income (PI) data was reported at 10.523 USD bn in Mar 2018. This records a decrease from the previous number of 10.580 USD bn for Dec 2017. United States BoP: IO: CA: Exp: Primary Income (PI) data is updated quarterly, averaging 7.973 USD bn from Mar 2003 (Median) to Mar 2018, with 61 observations. The data reached an all-time high of 10.602 USD bn in Sep 2017 and a record low of 5.336 USD bn in Mar 2006. United States BoP: IO: CA: Exp: Primary Income (PI) data remains active status in CEIC and is reported by Bureau of Economic Analysis. The data is categorized under Global Database’s USA – Table US.JB011: Balance of Payments: BPM6: International Organizations and Unallocated.

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United States BoP: IO: CA: Imp: Primary Income (PI) data was reported at 10.034 USD bn in Mar 2018. This records an increase from the previous number of 9.233 USD bn for Dec 2017. United States BoP: IO: CA: Imp: Primary Income (PI) data is updated quarterly, averaging 5.635 USD bn from Mar 2003 (Median) to Mar 2018, with 61 observations. The data reached an all-time high of 10.034 USD bn in Mar 2018 and a record low of 4.831 USD bn in Mar 2011. United States BoP: IO: CA: Imp: Primary Income (PI) data remains active status in CEIC and is reported by Bureau of Economic Analysis. The data is categorized under Global Database’s USA – Table US.JB011: Balance of Payments: BPM6: International Organizations and Unallocated.

United States BoP: IO: CA: Exp: GS: Gds: GM: Other data was reported at 0.000 USD mn in Mar 2018. This stayed constant from the previous number of 0.000 USD mn for Dec 2017. United States BoP: IO: CA: Exp: GS: Gds: GM: Other data is updated quarterly, averaging 0.000 USD mn from Mar 2003 to Mar 2018, with 57 observations. United States BoP: IO: CA: Exp: GS: Gds: GM: Other data remains active status in CEIC and is reported by Bureau of Economic Analysis. The data is categorized under Global Database’s USA – Table US.JB011: Balance of Payments: BPM6: International Organizations and Unallocated.

Our dataset provides detailed and precise insights into the business, commercial, and industrial aspects of any given area in the USA (Including Point of Interest (POI) Data and Foot Traffic. The dataset is divided into 150x150 sqm areas (geohash 7) and has over 50 variables. - Use it for different applications: Our combined dataset, which includes POI and foot traffic data, can be employed for various purposes. Different data teams use it to guide retailers and FMCG brands in site selection, fuel marketing intelligence, analyze trade areas, and assess company risk. Our dataset has also proven to be useful for real estate investment.- Get reliable data: Our datasets have been processed, enriched, and tested so your data team can use them more quickly and accurately.- Ideal for trainning ML models. The high quality of our geographic information layers results from more than seven years of work dedicated to the deep understanding and modeling of geospatial Big Data. Among the features that distinguished this dataset is the use of anonymized and user-compliant mobile device GPS location, enriched with other alternative and public data.- Easy to use: Our dataset is user-friendly and can be easily integrated to your current models. Also, we can deliver your data in different formats, like .csv, according to your analysis requirements. - Get personalized guidance: In addition to providing reliable datasets, we advise your analysts on their correct implementation.Our data scientists can guide your internal team on the optimal algorithms and models to get the most out of the information we provide (without compromising the security of your internal data).Answer questions like: - What places does my target user visit in a particular area? Which are the best areas to place a new POS?- What is the average yearly income of users in a particular area?- What is the influx of visits that my competition receives?- What is the volume of traffic surrounding my current POS?This dataset is useful for getting insights from industries like:- Retail & FMCG- Banking, Finance, and Investment- Car Dealerships- Real Estate- Convenience Stores- Pharma and medical laboratories- Restaurant chains and franchises- Clothing chains and franchisesOur dataset includes more than 50 variables, such as:- Number of pedestrians seen in the area.- Number of vehicles seen in the area.- Average speed of movement of the vehicles seen in the area.- Point of Interest (POIs) (in number and type) seen in the area (supermarkets, pharmacies, recreational locations, restaurants, offices, hotels, parking lots, wholesalers, financial services, pet services, shopping malls, among others). - Average yearly income range (anonymized and aggregated) of the devices seen in the area.Notes to better understand this dataset:- POI confidence means the average confidence of POIs in the area. In this case, POIs are any kind of location, such as a restaurant, a hotel, or a library. - Category confidences, for example"food_drinks_tobacco_retail_confidence" indicates how confident we are in the existence of food/drink/tobacco retail locations in the area. - We added predictions for The Home Depot and Lowe's Home Improvement stores in the dataset sample. These predictions were the result of a machine-learning model that was trained with the data. Knowing where the current stores are, we can find the most similar areas for new stores to open.How efficient is a Geohash?Geohash is a faster, cost-effective geofencing option that reduces input data load and provides actionable information. Its benefits include faster querying, reduced cost, minimal configuration, and ease of use.Geohash ranges from 1 to 12 characters. The dataset can be split into variable-size geohashes, with the default being geohash7 (150m x 150m).

United States BoP: IO: CA: Imp: Secondary Income data was reported at 3.521 USD bn in Mar 2018. This records a decrease from the previous number of 4.871 USD bn for Dec 2017. United States BoP: IO: CA: Imp: Secondary Income data is updated quarterly, averaging 3.160 USD bn from Mar 2003 (Median) to Mar 2018, with 61 observations. The data reached an all-time high of 7.801 USD bn in Sep 2016 and a record low of 1.069 USD bn in Sep 2004. United States BoP: IO: CA: Imp: Secondary Income data remains active status in CEIC and is reported by Bureau of Economic Analysis. The data is categorized under Global Database’s USA – Table US.JB011: Balance of Payments: BPM6: International Organizations and Unallocated.

United States BoP: IO: CA: Exp: PI: Investment Income (II) data was reported at 9.555 USD bn in Mar 2018. This records a decrease from the previous number of 9.620 USD bn for Dec 2017. United States BoP: IO: CA: Exp: PI: Investment Income (II) data is updated quarterly, averaging 6.931 USD bn from Mar 2003 (Median) to Mar 2018, with 61 observations. The data reached an all-time high of 9.644 USD bn in Sep 2017 and a record low of 4.439 USD bn in Mar 2006. United States BoP: IO: CA: Exp: PI: Investment Income (II) data remains active status in CEIC and is reported by Bureau of Economic Analysis. The data is categorized under Global Database’s USA – Table US.JB011: Balance of Payments: BPM6: International Organizations and Unallocated.

United States Foreign LT Sec: UH: International Organizations (IO) data was reported at 88.789 USD bn in Apr 2018. This records an increase from the previous number of 87.598 USD bn for Mar 2018. United States Foreign LT Sec: UH: International Organizations (IO) data is updated monthly, averaging 56.464 USD bn from Dec 2011 (Median) to Apr 2018, with 77 observations. The data reached an all-time high of 88.789 USD bn in Apr 2018 and a record low of 45.512 USD bn in Jan 2012. United States Foreign LT Sec: UH: International Organizations (IO) data remains active status in CEIC and is reported by US Department of Treasury. The data is categorized under Global Database’s USA – Table US.Z046: Foreign Long Term Securities by US Holders: By Country.

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Context

This dataset has been cleaned and prepared from the U.S. Census Bureau and can be found here: https://www.census.gov/data/tables/2013/demo/2009-2013-lang-tables.html. Further details can be found here: https://www.census.gov/data/developers/data-sets/language-stats.html. We only cleaned data for the state level. There is data for the nation, county, and 'core-based statistical area' levels if you are interested in looking at and cleaning that data.

The original tables had data split into a new tab for each state and wasn't conducive to data analysis. We consolidated all of the information into one table and put it into a tidy format.

The dataset has each of the 50 states, plus Washington, D.C. and Puerto Rico.

Content

The dataset has the following columns: - Group: Character - Subgroup: Character - Language: Character - State: Character - Speakers: Number (Integer) - Margin of Error - English Speakers: Number (Integer) - nonEnglishSpeakers: Number (Integer) - Margin of Error - NonEnglishSpeakers: Number (Integer)

Acknowledgements

This dataset was cleaned for a Data Visualization class I took in Fall 2020. Here is the link to the final project: https://datavis-fall-2020-team.github.io/uslanguages.github.io/

Here is a link to our repository: https://github.com/DataVis-Fall-2020-Team/uslanguages.github.io

Inspiration

The questions we originally sought to answer were: - Which languages are spoken in the U.S.? - Where are these languages spoken within the U.S.? - Which states have the most language diversity? - Which foreign language speakers are most fluent in English? - How have the languages spoken changed over time?

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Comprehensive dataset containing 26,933 verified Museum businesses in United States with complete contact information, ratings, reviews, and location data.

Gross Domestic Product (GDP) of the United States (US) both nominal and real on an annual and quarterly basis. Annual data is provided since 1930 and quarterly data since 1947. Both total GDP (level...

A dataset of public corporate filings (such as annual reports, quarterly reports, and ad-hoc disclosures) for DATA I/O CORP (DAIO), provided by FinancialReports.eu.

Comprehensive dataset containing 124,203 verified Construction company businesses in United States with complete contact information, ratings, reviews, and location data.

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