First, we would like to thank the wildland fire advisory group. Their wisdom and guidance helped us build the dataset as it currently exists. This dataset is comprised of two different zip files. Zip File 1: The data within this zip file are composed of two wildland fire datasets. (1) A merged dataset consisting of 40 different wildfire and prescribed fire layers. The original 40 layers were all freely obtained from the internet or provided to the authors free of charge with permission to use them. The merged layers were altered to contain a consistent set of attributes including names, IDs, and dates. This raw merged dataset contains all original polygons many of which are duplicates of the same fire. This dataset also contains all the errors, inconsistencies, and other issues that caused some of the data to be excluded from the combined dataset. Care should be used when working with this dataset as individual records may contain errors that can be more easily identified in the ...

A dataset to advance the study of life-cycle interactions of biomedical and socioeconomic factors in the aging process. The EI project has assembled a variety of large datasets covering the life histories of approximately 39,616 white male volunteers (drawn from a random sample of 331 companies) who served in the Union Army (UA), and of about 6,000 African-American veterans from 51 randomly selected United States Colored Troops companies (USCT). Their military records were linked to pension and medical records that detailed the soldiers������?? health status and socioeconomic and family characteristics. Each soldier was searched for in the US decennial census for the years in which they were most likely to be found alive (1850, 1860, 1880, 1900, 1910). In addition, a sample consisting of 70,000 men examined for service in the Union Army between September 1864 and April 1865 has been assembled and linked only to census records. These records will be useful for life-cycle comparisons of those accepted and rejected for service. Military Data: The military service and wartime medical histories of the UA and USCT men were collected from the Union Army and United States Colored Troops military service records, carded medical records, and other wartime documents. Pension Data: Wherever possible, the UA and USCT samples have been linked to pension records, including surgeon''''s certificates. About 70% of men in the Union Army sample have a pension. These records provide the bulk of the socioeconomic and demographic information on these men from the late 1800s through the early 1900s, including family structure and employment information. In addition, the surgeon''''s certificates provide rich medical histories, with an average of 5 examinations per linked recruit for the UA, and about 2.5 exams per USCT recruit. Census Data: Both early and late-age familial and socioeconomic information is collected from the manuscript schedules of the federal censuses of 1850, 1860, 1870 (incomplete), 1880, 1900, and 1910. Data Availability: All of the datasets (Military Union Army; linked Census; Surgeon''''s Certificates; Examination Records, and supporting ecological and environmental variables) are publicly available from ICPSR. In addition, copies on CD-ROM may be obtained from the CPE, which also maintains an interactive Internet Data Archive and Documentation Library, which can be accessed on the Project Website. * Dates of Study: 1850-1910 * Study Features: Longitudinal, Minority Oversamples * Sample Size: ** Union Army: 35,747 ** Colored Troops: 6,187 ** Examination Sample: 70,800 ICPSR Link: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06836

The deposited data allows replication of the statistical analysis and figures in "Warfare and Economic Inequality: Evidence from Preindustrial Germany (c. 1400-1800)". The question the project adresses is simple: What was the impact of military conflict on economic inequality? I argue that ordinary military conflicts increased local economic inequality. Warfare raised the financial needs of towns in preindustrial times, leading to more resource extraction from the population. This resource extraction happened via inequality-promoting channels, such as regressive taxation. Only in truly major wars might inequality-reducing destruction outweigh inequality-promoting extraction and reduce inequality. To test this argument I construct a novel panel dataset combining information about economic inequality in 75 localities, and more than 700 conflicts over four centuries. I find that the many ordinary conflicts — paradigmatic of life in the preindustrial world — were continuous reinforcers of economic inequality. I confirm that the Thirty Years’ War was indeed a great equaliser, but this was an exception and not the rule. Rising inequality is an underappreciated negative externality in times of conflict.

This dataset is extracted from https://en.wikipedia.org/wiki/List_of_countries_by_population_in_1800. Context: There s a story behind every dataset and heres your opportunity to share yours.Content: What s inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too. Acknowledgements:We wouldn t be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.Inspiration: Your data will be in front of the world s largest data science community. What questions do you want to see answered?

The dataset was generated for the South Central Plains EPA level III ecoregion which extends through eastern Texas, northwestern Louisiana, southwest Arkansas, and a small portion of southeastern Oklahoma covering approximately 15.2 ha. Contained in the data set are land change causes that occurred between 2001 to 2006 such as forest harvest, surficial mining, and cropland expansion. Only those pixels (30-meter resolution) that have changed during the time period have their cause classified, otherwise no change is indicated between 2001 and 2006. In general, the process to create the data combined an automated and manual interpretation approach of spatial data to correctly identify land change causes. In the approach, available spatial data were analyzed using an algorithm-based process of aggregation, validation, and attribution (AVA). Data that could not be validated as to their land change cause in the algorithm, were manually interpreted using historical imagery provided by Google Earth, Landsat satellite data, or high-resolution orthoimagery from National Agricultural Imagery Program (NAIP).

The world population surpassed eight billion people in 2022, having doubled from its figure less than 50 years previously. Looking forward, it is projected that the world population will reach nine billion in 2038, and 10 billion in 2060, but it will peak around 10.3 billion in the 2080s before it then goes into decline. Regional variations The global population has seen rapid growth since the early 1800s, due to advances in areas such as food production, healthcare, water safety, education, and infrastructure, however, these changes did not occur at a uniform time or pace across the world. Broadly speaking, the first regions to undergo their demographic transitions were Europe, North America, and Oceania, followed by Latin America and Asia (although Asia's development saw the greatest variation due to its size), while Africa was the last continent to undergo this transformation. Because of these differences, many so-called "advanced" countries are now experiencing population decline, particularly in Europe and East Asia, while the fastest population growth rates are found in Sub-Saharan Africa. In fact, the roughly two billion difference in population between now and the 2080s' peak will be found in Sub-Saharan Africa, which will rise from 1.2 billion to 3.2 billion in this time (although populations in other continents will also fluctuate). Changing projections The United Nations releases their World Population Prospects report every 1-2 years, and this is widely considered the foremost demographic dataset in the world. However, recent years have seen a notable decline in projections when the global population will peak, and at what number. Previous reports in the 2010s had suggested a peak of over 11 billion people, and that population growth would continue into the 2100s, however a sooner and shorter peak is now projected. Reasons for this include a more rapid population decline in East Asia and Europe, particularly China, as well as a prolongued development arc in Sub-Saharan Africa.

Simulated QCD jets from the Simulated QCD 1400-1800 Dataset of the CMS 2011 Open Data reprocessed into the MOD HDF5 format. Jets are provided at generator (truth) level in the GEN files and after GEANT4 detector simulation in the SIM files (which also contain associated GEN jets to facilitate studies involving both types of jets). Jets are selected from the hardest two anti-kT R=0.5 jets in events passing the Jet300 High Level Trigger (only relevant for SIM) and are required to have \(p_T^\text{jet}>375\) GeV, where \(p_T^\text{jet}\) includes a jet energy correction factor (again, only relevant for SIM). GEN jets contain truth-level particles with kinematic and PDG ID information, and SIM jets contain Particle Flow Candidates (PFCs) with kinematic, PDG ID, and vertex information. Additionally, jets have metadata describing their kinematics and provenance in the original CMS AOD files.

For additional details about the dataset, please see the accompanying paper, Exploring the Space of Jets with CMS Open Data. There, jets were further restricted to have \(|\eta^\text{jet}|<1.9\) to ensure tracking coverage and (in the case of SIM) have "medium" quality to reject fake jets.

The supported method for downloading, reading, and using this dataset is through the EnergyFlow Python package, which has additional documentation about how to read and use this and related datasets. Should any problems be encountered, please submit an issue on GitHub.

For reference, the other corresponding datasets of simulated jets available on Zenodo are:

• SIM/GEN QCD Jets 170-300 GeV
• SIM/GEN QCD Jets 300-470 GeV
• SIM/GEN QCD Jets 470-600 GeV
• SIM/GEN QCD Jets 600-800 GeV
• SIM/GEN QCD Jets 800-1000 GeV
• SIM/GEN QCD Jets 1000-1400 GeV
• SIM/GEN QCD Jets 1400-1800 GeV
• SIM/GEN QCD Jets 1800-\(\infty\) GeV

There is an associated dataset of jets recorded by the CMS detector available on Zenodo:

• CMS 2011A Jets, pT > 375 GeV

Abstract This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. Polygon data delineating modelled statistical flood extent with an Average Recurrence Interval (ARI) of 100 years. For historical/actual flood extents, refer to 'Historic_extent' layer. Also known as the 1 in 100 year flood layer, it is used, among other things, in the creation of 'Land Subject to Inundation' areas as used in …Show full descriptionAbstract This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. Polygon data delineating modelled statistical flood extent with an Average Recurrence Interval (ARI) of 100 years. For historical/actual flood extents, refer to 'Historic_extent' layer. Also known as the 1 in 100 year flood layer, it is used, among other things, in the creation of 'Land Subject to Inundation' areas as used in Planning Scheme Zones. The 1 in 100 year data is not restricted. This data is part of a group of layers depicting a range of statistical ARI extents. Current layers include 5, 10, 20, 30, 50, 100, 200, 500, 1000 year intervals, each in a separate dataset. The layer called EXTENT_PMF represents areas of 'probable maximum flood' and is also part of this group. The data is statistically derived using hydrological models, historic flood extents and heights. Purpose Mainly used for municipal planning and risk assessment. The EXTENT_100Y_ARI layer is deemed the most appropriate to use for determining areas at risk of flooding. This layer directly inputs into the Land Subject to Inundation overlay. (LSIO) Dataset History Lineage: Primary Positional Accuracy: Precision: 5m to 100m Initial data, flagged as 'modified = 20000101' varies in accuracy, and should be treated with caution, particularly at scales less than 1:25,000. Data with 'modified' values later than 20000101 are quite accurate and mostly sourced from flood studies. This data is suitable to use at township and parcel level. Reliability field provides clues to the accuracy, where a value of 1 is best and 3 is worst. Attribute Accuracy: Attributes are verified and should be accurate. Overall reliability of the source material is indicated in RELIABILITY field, where 'HIGH' is good and 'LOW' is poor quality source information. Logical Consistency: Attributes are consistent with other related layers e.g. flood height contours Data Source: Flood data dates back to mid 1800s and historically has been predominantly located in DNRE Floodplain Management. Some data is located in Water Authorities. Completeness: Floodplain Management Unit mapping conventions on definitions of flood mapping height data will be followed. Additional Metadata: Recommend liaison with Floodplain Management Unit to clarify use of this layer Refer to mapping reports for each major data capture effort to be kept at DNRE Floodplain Management Unit. Dataset Citation Victorian Department of Environment and Primary Industries (2014) Victoria - 1 in 100 Year Flood Extent. Bioregional Assessment Source Dataset. Viewed 05 October 2018, http://data.bioregionalassessments.gov.au/dataset/6e59ed35-3fde-48e3-8135-eb05263ce4aa.

This is a polygon layer derived from a 1:500,000-scale map showing the original, pre-settlement vegetation cover in Wisconsin. The original vegetation cover data was digitized in 1990 from a 1976 map created from land survey notes written in the mid-1800s when Wisconsin was first surveyed. Linework representing lakes and other hydrographic areas in other data sets were subsequently merged with the original vegetation cover data set to more closely match the source map.This digital version of the original vegetation cover map can be used to identify regional changes in land cover since the time when the state was first surveyed. The data is not intended for landscape-scale analysis.All 72 counties of Wisconsin are represented. Islands in Lake Michigan and Lake Superior belonging to Wisconsin counties are also represented.Associated Lookup Tables *these can also be found in the open data item under Download / Additional ResourcesLUC_Level_CodesVeg_Type_Codes

From the mid-sixteenth century, Italian Protomedico tribunals, Colleges of Physicians or Health Offices (jurisdiction varied from state to state) required ‘charlatans’ to submit their wares for inspection and, upon approval, pay a licence fee in order set up a stage from which to perform and sell them. As far as the medical magistracies were concerned, charlatans – or quacks, empirics, mountebanks, itinerant pedlars, whatever we wish to call them – had a definable identity. They constituted a specific trade or occupation. In this context, the Italian term ciarlatano lost some of its bite, becoming less a term of abuse and more a generic, bureaucratic label, identifying a category of healer. The word had a more precise meaning, fewer figurative connotations than it would acquire in English. More importantly, it was a label, the charlatans used themselves. The licensing regime in place in early modern Italy allows us unparalleled opportunities when it comes to the investigation of suspect but generally tolerated categories like charlatans. It was the ongoing attempt to regulate the activity of charlatans which provides us with the raw material for this Database and for the book associated with it, David Gentilcore’s Medical Charlatanism in Early Modern Italy (Oxford University Press, 2006; ISBN 0199245355) Main Topics: The licensing procedure - from initial application by the charlatan to the issuing of a licence - provides us with a wealth of information about them and the phenomenon of which they were part. Each complete licence tells us the charlatan's name and place of origin, his stage name or alias, the nature of his practice/activity, licences and/or 'privileges' from other States (if any), the remedies he wished to sell, and (sometimes) their ingredients. A database of such information can thus tell us as much about individuals and medicines as it can about broader trends in the history of early modern Europe. Itemising some 1,600 licences, issued to over a thousand of different charlatans the length and breadth of Italy, over a period of over two and a half centuries, the Italian Charlatans Database comes as close as it is possible to get in our attempt to understand charlatans and charlatanism 'from the inside'. The data has been divided in to three tables: representing the Charlatans (Charlatans), licences awarded to the Charlatans (Licences) and the remedies each licence allowed them to sell (LicenceToSell). Two appended documents offer further information relevant to this third table. Appendix One: Translation of remedy ingredients assists in the case of information supplied in the original Italian, by providing information on the ingredients and their purported uses and benefits. Appendix Two: Index of remedies with ingredients gives lists of ingredients for some of the main licensed remedies referred to in the Database.

The Anthropogenic Biomes of the World, Version 2: 1800 data set describes anthropogenic transformations within the terrestrial biosphere caused by sustained direct human interaction with ecosystems, including agriculture and urbanization circa 1800. Potential natural vegetation biomes, such as tropical rainforests or grasslands, are based on global vegetation patterns related to climate and geology. Anthropogenic transformation within each biome is approximated using population density, agricultural intensity (cropland and pasture) and urbanization. This data set is part of a time series for the years 1700, 1800, 1900, and 2000 that provides global patterns of historical transformation of the terrestrial biosphere during the Industrial Revolution.

Dataset of 3D reconstructions of the foraminifer Elphidium clavatum (marine protist with a calcite shell) acquired at the Beamline BL 47XU, SPring-8 synchrotron facility (Japan). A voxel size of 0.5 µm was used. In total, 124 specimens of Elphidium clavatum were scanned. For each specimen are available: a collection of raw images ("cropped" folder), a collection of binary images ("mask" folder), a 3D reconstruction (STL file), and two snapshot images of the 3D reconstruction. Sediment cores were collected in 2013 during a cruise with R/V Skagerak at Öresund station DV, north of the Island of Ven (55°55.59′ N, 12°42.66′ E). From the sediment core, 16 sediment layers were selected, representing the last 200 years (i.e., roughly the years ~2013, ~2010, ~2005, ~2002, ~1993, ~1986, ~1978, ~1960, ~1939, ~1923, ~1906, ~1890, ~1873, ~1857, ~1840, and ~1807). Between five to ten Elphidium clavatum specimens were selected from each layer. The dataset is part of the study exploring 3D time series of microfossils recording environmental conditions in the Baltic Sea entrance from the period early industrial (the 1800s) to present-day (the 2010s). The size of the dataset is 57 GB. Please contact the main author for further details. 124 specimens of Elphidium clavatum from 16 sediment layers were scanned. For each specimen, the following files are available: a collection of raw images in TIF format ("cropped" folder), a collection of binary images in TIF format ("mask" folder), a 3D reconstruction in STL format, and two snapshot images of the 3D reconstruction in TIF or PNG format. A voxel size of 0.5 µm was used. The data for each specimen is stored in a folder named as follows: DV(sediment depth in cm)-sp(specimen number)-(estimated year), e.g., “DV1-sp2-2013” (sediment depth: 1 cm, specimen 2, estimated year 2013). Examples of suitable software for handling the files include ImageJ and MeshLab. Total number of files: 69,652 (plus a readme file with documentation) Total number of folders: 390 Dataset size: 57,1 GB

Description and NotesDescription: Methane concentration from the Greenland NEEM-2011-S1 Ice Core from 71 to 408m depth (~270-1961 CE). Methane concentrations analysed online by laser spectrometer (SARA, Spectroscopy by Amplified Resonant Absorption, developed at Laboratoire Interdisciplinaire de Physique, Grenoble, France) on gas extracted from an ice core processed using a continuous melter system (Desert Research Institute). Methane data have a 5 second integration time (raw data acquisition rate 0.6 Hz). Analytical precision, from Allan Variance test, is 0.9 ppb (2 sigma). Long-term reproducibility is 2.6% (2 sigma). Gaps in the record are due to problems during online analysis. Online analysis conducted August-September 2011.Note: Lat-Long provided is for main NEEM borehole. The NEEM-2011-S1 core was drilled 200 m distance away in 2011 to 410 m depth.Methane concentrations are reported on NOAA2004 scale (instrument calibrated on dry synthetic air standards).A correction factor of 1.079 has been applied to all data to correct for methane dissolution in melted ice core sample prior to gas extraction. Correction factor calculated using empirical data (concentrations not aligned/tied to existing discrete methane measurements).Additional methods description provided in: Stowasser, C., Buizert, C., Gkinis, V., Chappellaz, J., Schupbach, S., Bigler, M., Fain, X., Sperlich, P., Baumgartner, M., Schilt, A., Blunier, T., 2012. Continuous measurements of methane mixing ratios from ice cores. Atmos. Meas. Tech. 5, 999-1013. Morville, J., Kassi, S., Chenevier, M., Romanini, D., 2005. Fast, low-noise, mode bymode, cavity-enhanced absorption spectroscopy by diode-laser self-locking. Appl. Phys. B Lasers Opt. 80, 1027-01038.* NEEM (North Greenland Eemian Ice Drilling) project information http://neem.dk/ NEEM-2011-S1 CH4 outliers.Data points removed from dataset according to specified cut-off value.Please refer to Rhodes et al. (2013) for full discussion of origins outlying data points. Briefly, these high frequency features are not artifacts of the continuous method and have been replicated by traditional discrete analyses. Comparison to chemistry measurements suggests they are related to biological in situ production of methane.

Underway surface air temperature and sea water temperature were collected aboard the Skelton Castle while in route from England to Bombay India as part of the East India Company during the dates 28 February 1800 to 3 June 1800. The data were prepared by one Mr. R. Perrins on behalf of Sir Anthony Carlisle as part of a study "to determine whether fishes possess any other temperature than that of the water in which they live." A table containing the data was found in Nicholson's "Journal of Natural Philosophy", published in 1804.

The IUPAC Solubility Data Series Volumes 1-103 (1979 to 2014) are made available online. Volumes 1-19 and 39-65 are available as scanned and OCR'd single PDF files. Volumes 20-38 are available with each volume indexed and sectioned into multiple PDF files that can browsed using an online table of contents. Volumes 66-103 are available through links to the document at the Journal of Physical and Chemical Reference Data. The electronic part of the database contains 67,500 solubility measurements, compiled from 18 volumes of the IUPAC Solubility Data Series. There are about 1800 chemical substances in the database and 5200 systems, of which 473 have been critically evaluated. The database has over 1800 references.

Topographic sheets (t-sheets) produced by the National Ocean Service (NOS) during the 1800s provide the position of past shorelines. The shoreline data can be vectorized into a geographic information system (GIS) and compared to modern shoreline data to calculate estimates of long-term shoreline rates of change. Many t-sheets were scanned and digitized by the National Oceanic and Atmospheric Administration (NOAA) and are available on the NOAA Shoreline website (https://shoreline.noaa.gov/data/datasheets/t-sheets.html). However, some t-sheets were not scanned by NOAA and are only available via the National Archives and Records Administration (NARA). The data included within this data release were previously unavailable or not published in digital format. These data were produced to provide a more comprehensive record of shoreline position for Fire Island and Great South Bay, New York, to aid geologic and coastal hazards studies. This data release includes previously unavailable georeferenced t-sheets and digital vector shorelines for the Fire Island and Great South Bay, New York, coastline from 1834, 1838, and 1874/1875. The original t-sheets were scanned by the NARA-authorized vendor and sent to the Unites States Geological Survey St. Petersburg Coastal and Marine Science Center (USGS SPCMSC) as non-georeferenced digital raster files. Upon arrival at the SPCMSC, USGS staff performed the following procedures: rasters were georeferenced, projected to a modern datum, and shorelines were digitized to create a vector polyline depicting the historical shoreline position. The t-sheets included in this data release are: 1) T-479a, T-479b, T-1 (Parts 2 and 3) (1834); 2) T-58 (Parts 1 and 2) (1838); 3) T-1374a, T-1374b, T-1375a, T-1375b (1874); and 4) T-1402 (1875). All shorelines, including the ocean-facing barrier island shoreline, back-barrier island shoreline, mainland and islands were digitized. Please read the full metadata for details on data collection, dataset variables, and data quality.

The USGS has produced a comprehensive database of digital vector shorelines by compiling shoreline positions from pre-existing historical shoreline databases and by generating historical and modern shoreline data. Shorelines are compiled by state and generally correspond to one of four time periods: 1800s, 1920s-1930s, 1970s, and 1998-2002. These shorelines were used to calculate long-term and short-term change rates in a GIS using the Digital Shoreline Analysis System (DSAS) version 3.0; An ArcGIS extension for calculating shoreline change: U.S. Geological Survey Open-File Report 2005-1304, Thieler, E.R., Himmelstoss, E.A., Zichichi, J.L., and Miller, T.M. Shoreline vectors derived from historic sources (first three time periods) represent the high water line (HWL) at the time of the survey, whereas modern shorelines (final time period) represent the mean high water line (MHW). Changing the shoreline definition from a proxy-based physical feature that is uncontrolled in terms of an elevation datum (HWL) to a datum-based shoreline defined by an elevation contour (MHW) has important implications with regard to inferred changes in shoreline position and calculated rates of change. This proxy-datum offset is particularly important when averaging shoreline change rates alongshore. Since the proxy-datum offset is a bias, virtually always acting in the same direction, the error associated with the apparent shoreline change rate shift does not cancel during averaging and it is important to quantify the bias in order to account for the rate shift. The shoreline change rates presented in this report have been calculated by accounting for the proxy-datum bias.

This metadata record describes two metrics that quantitatively measure the impact of reservoir storage on every flowline in the NHDPlus version 2 data suite (NHDPlusV2) for the conterminous United States. These metrics are computed for every 10 years from 1800 - 2015. The first metric (DamIndex_EROM.zip) estimates reservoir storage intensity in units of days based on reservoir storage in a contributing area normalized by the mean annual streamflow. This metric indicates the duration of storage impact upstream from each stream segment relative to the typical flow condition. In addition, this metric provides an assessment of the potential influence of a dam on average and low flows because the metric estimates the number of days of flow that can be sustained by contributing area storage alone, without additional water or groundwater input. The second metric (DamIndex_PMC.zip) represents the degree of regulation of a river reach based on upstream reservoir storage relative to the 30-year average annual precipitation, as well as the upstream dam and watershed areas. This second metric provides an estimate of the capacity of the contributing area to store precipitation and is oriented to understanding how peak flows may be affected by dams throughout the flow network; this metric is dimensionless. Reservoir storage, construction date and location data were obtained from the US Army Corps of Engineers' National Inventory of Dams (NID, 2018). Also, the dataset in this data release includes dam locations addressed to NHDPlusv2 (Final_NID_2018.zip). These calculations are based on the maximum NID storage , which indicates the maximum amount of water that can be stored behind each dam and therefore may overestimate the true reservoir storage impacts.

About this dataset

Context

The dataset tabulates the Warren 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 Warren 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 Warren town was 1,800, a 1.52% increase year-by-year from 2022. Previously, in 2022, Warren town population was 1,773, an increase of 0.74% compared to a population of 1,760 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Warren town increased by 483. In this period, the peak population was 1,800 in the year 2023. The numbers suggest that the population has not reached its peak yet and is showing a trend of further growth. 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 Warren town is shown in this column.
• Year on Year Change: This column displays the change in Warren 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 Warren town Population by Year. You can refer the same here

About this dataset

Context

The dataset tabulates the Cedar Point 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 Cedar Point 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 Cedar Point was 1,800, a 0.67% increase year-by-year from 2021. Previously, in 2021, Cedar Point population was 1,788, an increase of 1.30% compared to a population of 1,765 in 2020. Over the last 20 plus years, between 2000 and 2022, population of Cedar Point increased by 965. In this period, the peak population was 1,800 in the year 2022. The numbers suggest that the population has not reached its peak yet and is showing a trend of further growth. 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 Cedar Point is shown in this column.
• Year on Year Change: This column displays the change in Cedar Point 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 Cedar Point Population by Year. You can refer the same here