USHAP (USHighAirPollutants) is one of the series of long-term, full-coverage, high-resolution, and high-quality datasets of ground-level air pollutants for the United States. It is generated from the big data (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence by considering the spatiotemporal heterogeneity of air pollution.

This is the big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 1 km (i.e., D1K, M1K, and Y1K) ground-level Black Carbon (BC) dataset in the United States from 2000 to 2020. Our daily BC estimates agree well with ground measurements with an average cross-validation coefficient of determination (CV-R2) of 0.80 and normalized root-mean-square error (NRMSE) of 0.60, respectively.

All the data will be made public online once our paper is accepted, and if you want to use the USHighBC dataset for related scientific research, please contact us (Email: weijing_rs@163.com; weijing@umd.edu).

• Wei, J., Wang, J., Li, Z., Kondragunta, S., Anenberg, S., Wang, Y., Zhang, H., Diner, D., Hand, J., Lyapustin, A., Kahn, R., Colarco, P., da Silva, A., and Ichoku, C. Long-term mortality burden trends attributed to black carbon and PM2.5 from wildfire emissions across the continental USA from 2000 to 2020: a deep learning modelling study. The Lancet Planetary Health, 2023, 7, e963–e975. https://doi.org/10.1016/S2542-5196(23)00235-8

More air quality datasets of different air pollutants can be found at: https://weijing-rs.github.io/product.html

Locations of known and historic fish ponds on the island of Hawaii (Big Island). For some fish ponds, data includes their condition, ownership, and references used to map them.

The statistic shows the success rate of various big data initiatives as of 2019, according to a survey of industry-leading firms, primarily in the United States. As of that time, 59.5 percent of respondents reported having seen measurable results from big data initiatives to decrease expenses.

This EnviroAtlas dataset categorizes land cover into structural elements (e.g. core, edge, connector, etc.). It depicts core areas of natural land cover, core fragmentation, and patterns of connectivity among core patches. Water is treated as missing in this dataset; waterbodies are masked out and not included in the analysis with the development and natural land cover classes. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

This dataset is called the Gridded SSURGO (gSSURGO) Database and is derived from the Soil Survey Geographic (SSURGO) Database. SSURGO is generally the most detailed level of soil geographic data developed by the National Cooperative Soil Survey (NCSS) in accordance with NCSS mapping standards. The tabular data represent the soil attributes, and are derived from properties and characteristics stored in the National Soil Information System (NASIS). The gSSURGO data were prepared by merging traditional SSURGO digital vector map and tabular data into a Conterminous US-wide extent, and adding a Conterminous US-wide gridded map layer derived from the vector, plus a new value added look up (valu) table containing "ready to map" attributes. The gridded map layer is offered in an ArcGIS file geodatabase raster format. The raster and vector map data have a Conterminous US-wide extent. The raster map data have a 30 meter cell size. Each cell (and polygon) is linked to a map unit identifier called the map unit key. A unique map unit key is used to link to raster cells and polygons to attribute tables, including the new value added look up (valu) table that contains additional derived data. The value added look up (valu) table contains attribute data summarized to the map unit level using best practice generalization methods intended to meet the needs of most users. The generalization methods include map unit component weighted averages and percent of the map unit meeting a given criteria. The Gridded SSURGO dataset was created for use in national, regional, and state-wide resource planning and analysis of soils data. The raster map layer data can be readily combined with other national, regional, and local raster layers, e.g., National Land Cover Database (NLCD), the National Agricultural Statistics Service (NASS) Crop Data Layer, or the National Elevation Dataset (NED).

This statistic depicts the leading benefits of big data usage in the United States as of August 2013, according to agencies and brand executives. As of 2013, 64 percent of agency respondents and 63 percent of marketer respondents reported "developing greater insight into the customer experience across all types of media, and then crafting a strategy that turns this understanding into positive results" to be the major benefit.

SLIDO-4.5 is an Esri ArcGIS version 10.7 file geodatabase which can be downloaded here: https://www.oregon.gov/dogami/slido/Pages/data.aspx The geodatabase contains two feature datasets (a group of datasets within the geodatabase) containing six feature classes total, as well as two raster data sets, one individual table, and two individual feature classes. The original studies vary widely in scale, scope and focus which is reflected in the wide range of accuracy, detail, and completeness with which landslides are mapped. In the future, we propose a continuous update of SLIDO. These updates should take place: 1) each time DOGAMI publishes a new GIS dataset that contains landslide inventory or susceptibility data or 2) at the end of each winter season, a common time for landslide occurrences in Oregon, which will include recent historic landslide point data. In order to keep track of the updates, we will use a primary release number such as Release 4.0 along with a decimal number identifying the update such as 4.5.

A 10-meter resolution land surface digital elevation model (DEM) grayscale hillshade for Big Island in Hawaii derived from United States Geological Survey (USGS) 1/3 arc-second DEM quadrangles. For the related dataset containing numeric elevation values for this image layer, see http://pacioos.org/metadata/usgs_dem_10m_bigisland.html

The Economic Census is the U.S. Government's official five-year measure of American business and the economy. It is conducted by the U.S. Census Bureau, and response is required by law. In October through December 2012, forms were sent out to nearly 4 million businesses, including large, medium and small companies representing all U.S. locations and industries. Respondents were asked to provide a range of operational and performance data for their companies.

About this dataset

Context

The dataset tabulates the Big Run population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Big Run across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Big Run was 633, a 0.16% decrease year-by-year from 2022. Previously, in 2022, Big Run population was 634, a decline of 0.94% compared to a population of 640 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Big Run decreased by 47. In this period, the peak population was 680 in the year 2000. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Big Run is shown in this column.
• Year on Year Change: This column displays the change in Big Run population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Big Run Population by Year. You can refer the same here

The statistic shows the biggest barriers to the adoption of big data among corporations as of 2019, according to a survey of industry-leading firms, primarily in the United States but also worldwide. As of that time, 40.3 percent of respondents suggested that big data adoption was held up by a Lack of organizational alignment or agility.

The U.S. Fish and Wildlife Service Corporate Master Table (CMT) is the official source of Service organization codes and related information. Information in the CMT includes, but is not limited to, organization codes, organization names, Federal Budget Management System (FBMS), cost center codes, fire unit identifiers, program names, mailing and physical/shipping addresses, telephone and fax numbers as well as latitude and longitude coordinates. The CMT enables all Service automated systems to utilize a corporate data set of known quality, eliminating the workload required to maintain each system's data set, and thereby facilitating data sharing. Other customers for the CMT are Service personnel who maintain directories, communicate with Congress and with the Public, maintain World Wide Web sites, etc. These spatial data were created using the information in the CMT. The CMT contains location information on all the offices within the Service that have an organization code. Unstaffed offices and some other facilities may not be included. The latitude and longitude points used are usually the location of the main administrative site. The latitude and longitude data is not completely verified but is the best we have at this time. This data set is intended to give an overview of where USFWS has stations across the United States and Territories, including locations outside the 50 states. It is not intended to be the exact location of every USFWS office. The CMT is primarily used for accounting purposes and therefore one location in the CMT can represent many different offices. Some points are duplicates where a station, most usually an Ecological Field Office, may be associated with more than one USFWS program. This data is updated from an internal authoritative source every night at 2:30am EST.For a direct link to the official Enterprise Geospatial dataset and metadata: https://ecos.fws.gov/ServCat/Reference/Profile/60076.Dataset contact: fwsgis@fws.gov

About this dataset

Context

The dataset tabulates the Big Stone township population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Big Stone township across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Big Stone township was 290, a 0.34% decrease year-by-year from 2022. Previously, in 2022, Big Stone township population was 291, a decline of 0% compared to a population of 291 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Big Stone township increased by 37. In this period, the peak population was 292 in the year 2020. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Big Stone township is shown in this column.
• Year on Year Change: This column displays the change in Big Stone township population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Big Stone township Population by Year. You can refer the same here

This is a tiled collection of the 3D Elevation Program (3DEP) and is one meter resolution. The 3DEP data holdings serve as the elevation layer of The National Map, and provide foundational elevation information for earth science studies and mapping applications in the United States. Scientists and resource managers use 3DEP data for hydrologic modeling, resource monitoring, mapping and visualization, and many other applications. The elevations in this DEM represent the topographic bare-earth surface. USGS standard one-meter DEMs are produced exclusively from high resolution light detection and ranging (lidar) source data of one-meter or higher resolution. One-meter DEM surfaces are seamless within collection projects, but, not necessarily seamless across projects. The spatial reference used for tiles of the one-meter DEM within the conterminous United States (CONUS) is Universal Transverse Mercator (UTM) in units of meters, and in conformance with the North American Datum of 1983 ...

Note:- Only publicly available data can be worked upon.

Discover a world of legal insights made easy with APISCRAPY's USA Court Data, USA Litigation Data, and US County Legal Data services. We've made sure our services are straightforward and accessible for everyone, from legal professionals to researchers and businesses.

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Whether you're a legal professional looking for detailed court data, a researcher analyzing legal trends, or a business wanting affordable access to US County Legal Data, APISCRAPY's services are designed to meet your needs. Choose APISCRAPY for straightforward and cost-effective legal data services, where accuracy and simplicity come together for your success.

About this dataset

Context

The dataset tabulates the Big Bend population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Big Bend across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Big Bend was 1,501, a 0.07% increase year-by-year from 2022. Previously, in 2022, Big Bend population was 1,500, a decline of 0.07% compared to a population of 1,501 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Big Bend increased by 261. In this period, the peak population was 1,501 in the year 2021. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Big Bend is shown in this column.
• Year on Year Change: This column displays the change in Big Bend population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Big Bend Population by Year. You can refer the same here

Area burned is an important variable for measuring wildfire activity. In the western United States (US), the timing and magnitude of area burned can be associated with meteorological and human activity to find the drivers of wildfire activity, but this type of research is dependent on the spatial and temporal resolution of available wildfire datasets. The Western US MTBS-Interagency (WUMI2) database is a dataset of wildfire events in the western United States (US) larger than 1 km2 for 1984 to 2020. WUMI2 includes the important Monitoring Trends in Burned Severity (MTBS) project (Eidenshink et al., 2007)—a Landsat satellite-based dataset of large fires (>4.04 km2)—and adds small (>1 to 4.04 km2) and large fires from government agency databases, including from the Fire Program Analysis (FPA) fire-occurrence database (Short et al., 2022). We performed extensive quality control to merge the datasets together and remove errors. The result is a western US-wide dataset with accurate fir..., Version WUMI2 Updated August 1, 2024: Our WUMI2 fire database consists of 21,693 western US fire events from 1984 through 2020. A text file (west_US_fires_1984-2020_WUMI2.txt) provides a list of each fire event, including the fire’s name, discovery date, point location, total area burned, and forested area burned (see the corresponding readme.txt file for column labels). We also include NetCDF files of the 1-km map of forest fractional coverage (forest_type_frac.nc) and the 1-km maps of monthly burned area over 1984–2020 (burnarea_1984-2020_WUMI2.nc). Fires included in this database are from the Monitoring Trends in Burned Severity Product (MTBS) (Eidenshink et al., 2007), the Fire Program Analysis fire-occurrence database (FPA FOD 6th edition) of interagency fires (Short, 2022), and interagency fires from local databases (CalFire, ST/C&L, TRIBE), and interagency fires from government agency databases (BIA, BLM, BOR, DOD, DOE, NPS, FWS, FS, NPS). More information on methodology can ..., The name of this version of the database is WUMI2. When using this database, please cite the following databases: Juang, C. S., Williams, A. P., Abatzoglou, J. T., Balch, J. K., Hurteau, M. D., & Moritz, M. A. (2022). Rapid growth of large forest fires drives the exponential response of annual forest-fire area to aridity in the western United States. Geophysical Research Letters, 49, e2021GL097131. https://doi.org/10.1029/2021GL097131. Eidenshink, J., Schwind, B., Brewer, K., Zhu, Z. L., Quayle, B., & Howard, S. (2007). A project for monitoring trends in burn severity. Fire ecology, 3, 3-21. https://doi.org/10.4996/fireecology.0301003 Short, Karen C. 2022. Spatial wildfire occurrence data for the United States, 1992-2020 [FPA_FOD_20221014]. 6th Edition. Fort Collins, CO: Forest Service Research Data Archive. https://doi.org/10.2737/RDS-2013-0009.6, # Western US MTBS-Interagency (WUMI) wildfire dataset

Dataset: Western US MTBS-Interagency (WUMI) Wildfire database

Version: WUMI2

Authors: Caroline S. Juang, A. Park Williams

Format: TXT

Last updated: 08/01/2024

DOI: https://doi.org/10.5061/dryad.sf7m0cg72

Description of the data

Our WUMI2 fire database consists of 21,693 western US fire events from 1984 through 2020. A text file (west_US_fires_1984-2020_WUMI2.txt) provides a list of each fire event, including the fire’s name, discovery date, point location, total area burned, and forested area burned (see the corresponding** readme.txt file for column labels). We also include NetCDF files of the 1-km map of forest fractional coverage (forest_type_frac.nc) and the 1-km maps of monthly burned area over 1984–2020 (burnarea_1984-2020_WUMI2.nc).** Fires included in this database are from the Monitoring Trends in Burned Severity Product (MTBS) ([Eidens...

About this dataset

Context

The dataset tabulates the Big Falls population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Big Falls across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2022, the population of Big Falls was 59, a 0.00% decrease year-by-year from 2021. Previously, in 2021, Big Falls population was 59, a decline of 1.67% compared to a population of 60 in 2020. Over the last 20 plus years, between 2000 and 2022, population of Big Falls decreased by 26. In this period, the peak population was 85 in the year 2000. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2022

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2022)
• Population: The population for the specific year for the Big Falls is shown in this column.
• Year on Year Change: This column displays the change in Big Falls population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Big Falls Population by Year. You can refer the same here

Envestnet®| Yodlee®'s Online Purchase Data (Aggregate/Row) Panels consist of de-identified, near-real time (T+1) USA credit/debit/ACH transaction level data – offering a wide view of the consumer activity ecosystem. The underlying data is sourced from end users leveraging the aggregation portion of the Envestnet®| Yodlee®'s financial technology platform.

Envestnet | Yodlee Consumer Panels (Aggregate/Row) include data relating to millions of transactions, including ticket size and merchant location. The dataset includes de-identified credit/debit card and bank transactions (such as a payroll deposit, account transfer, or mortgage payment). Our coverage offers insights into areas such as consumer, TMT, energy, REITs, internet, utilities, ecommerce, MBS, CMBS, equities, credit, commodities, FX, and corporate activity. We apply rigorous data science practices to deliver key KPIs daily that are focused, relevant, and ready to put into production.

We offer free trials. Our team is available to provide support for loading, validation, sample scripts, or other services you may need to generate insights from our data.

Investors, corporate researchers, and corporates can use our data to answer some key business questions such as: - How much are consumers spending with specific merchants/brands and how is that changing over time? - Is the share of consumer spend at a specific merchant increasing or decreasing? - How are consumers reacting to new products or services launched by merchants? - For loyal customers, how is the share of spend changing over time? - What is the company’s market share in a region for similar customers? - Is the company’s loyal user base increasing or decreasing? - Is the lifetime customer value increasing or decreasing?

Additional Use Cases: - Use spending data to analyze sales/revenue broadly (sector-wide) or granular (company-specific). Historically, our tracked consumer spend has correlated above 85% with company-reported data from thousands of firms. Users can sort and filter by many metrics and KPIs, such as sales and transaction growth rates and online or offline transactions, as well as view customer behavior within a geographic market at a state or city level. - Reveal cohort consumer behavior to decipher long-term behavioral consumer spending shifts. Measure market share, wallet share, loyalty, consumer lifetime value, retention, demographics, and more.) - Study the effects of inflation rates via such metrics as increased total spend, ticket size, and number of transactions. - Seek out alpha-generating signals or manage your business strategically with essential, aggregated transaction and spending data analytics.

Use Cases Categories (Our data provides an innumerable amount of use cases, and we look forward to working with new ones): 1. Market Research: Company Analysis, Company Valuation, Competitive Intelligence, Competitor Analysis, Competitor Analytics, Competitor Insights, Customer Data Enrichment, Customer Data Insights, Customer Data Intelligence, Demand Forecasting, Ecommerce Intelligence, Employee Pay Strategy, Employment Analytics, Job Income Analysis, Job Market Pricing, Marketing, Marketing Data Enrichment, Marketing Intelligence, Marketing Strategy, Payment History Analytics, Price Analysis, Pricing Analytics, Retail, Retail Analytics, Retail Intelligence, Retail POS Data Analysis, and Salary Benchmarking

1. Investment Research: Financial Services, Hedge Funds, Investing, Mergers & Acquisitions (M&A), Stock Picking, Venture Capital (VC)

2. Consumer Analysis: Consumer Data Enrichment, Consumer Intelligence

3. Market Data: AnalyticsB2C Data Enrichment, Bank Data Enrichment, Behavioral Analytics, Benchmarking, Customer Insights, Customer Intelligence, Data Enhancement, Data Enrichment, Data Intelligence, Data Modeling, Ecommerce Analysis, Ecommerce Data Enrichment, Economic Analysis, Financial Data Enrichment, Financial Intelligence, Local Economic Forecasting, Location-based Analytics, Market Analysis, Market Analytics, Market Intelligence, Market Potential Analysis, Market Research, Market Share Analysis, Sales, Sales Data Enrichment, Sales Enablement, Sales Insights, Sales Intelligence, Spending Analytics, Stock Market Predictions, and Trend Analysis

About this dataset

Context

The dataset tabulates the Big Flats town population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Big Flats town across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Big Flats town was 7,521, a 0.75% decrease year-by-year from 2022. Previously, in 2022, Big Flats town population was 7,578, a decline of 1.21% compared to a population of 7,671 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Big Flats town increased by 241. In this period, the peak population was 7,801 in the year 2013. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Big Flats town is shown in this column.
• Year on Year Change: This column displays the change in Big Flats town population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Big Flats town Population by Year. You can refer the same here