What is the relationship between environment and democracy? The framework of cultural evolution suggests that societal development is an adaptation to ecological threats. Pertinent theories assume that democracy emerges as societies adapt to ecological factors such as higher economic wealth, lower pathogen threats, less demanding climates, and fewer natural disasters. However, previous research confused within-country processes with between-country processes and erroneously interpreted between-country findings as if they generalize to within-country mechanisms. In this article, we analyze a time-series cross-sectional dataset to study the dynamic relationship between environment and democracy (1949-2016), accounting for previous misconceptions in levels of analysis. By separating within-country processes from between-country processes, we find that the relationship between environment and democracy not only differs by countries but also depends on the level of analysis. Economic wealth predicts increasing levels of democracy in between-country comparisons, but within-country comparisons show that democracy declines as countries become wealthier over time. This relationship is only prevalent among historically wealthy countries but not among historically poor countries, whose wealth also increased over time. By contrast, pathogen prevalence predicts lower levels of democracy in both between-country and within-country comparisons. Our longitudinal analyses identifying temporal precedence reveal that not only reductions in pathogen prevalence drive future democracy, but also democracy reduces future pathogen prevalence and increases future wealth. These nuanced results contrast with previous analyses using narrow, cross-sectional data. As a whole, our findings illuminate the dynamic process by which environment and democracy shape each other.

Methods Our Time-Series Cross-Sectional data combine various online databases. Country names were first identified and matched using R-package “countrycode” (Arel-Bundock, Enevoldsen, & Yetman, 2018) before all datasets were merged. Occasionally, we modified unidentified country names to be consistent across datasets. We then transformed “wide” data into “long” data and merged them using R’s Tidyverse framework (Wickham, 2014). Our analysis begins with the year 1949, which was occasioned by the fact that one of the key time-variant level-1 variables, pathogen prevalence was only available from 1949 on. See our Supplemental Material for all data, Stata syntax, R-markdown for visualization, supplemental analyses and detailed results (available at https://osf.io/drt8j/).

ARCTAS_Merge_P3B-Aircraft_Data contains pre-generated merge data files for the P-3B aircraft during the Arctic Research of the Composition of the Troposphere from Aircraft & Satellites (ARCTAS) mission. Data collection for this product is complete.The Arctic is a critical region in understanding climate change. The responses of the Arctic to environmental perturbations such as warming, pollution, and emissions from forest fires in boreal Eurasia and North America include key processes such as the melting of ice sheets and permafrost, a decrease in snow albedo, and the deposition of halogen radical chemistry from sea salt aerosols to ice. ARCTAS was a field campaign that explored environmental processes related to the high degree of climate sensitivity in the Arctic. ARCTAS was part of NASA’s contribution to the International Global Atmospheric Chemistry (IGAC) Polar Study using Aircraft, Remote Sensing, Surface Measurements, and Models of Climate, Chemistry, Aerosols, and Transport (POLARCAT) Experiment for the International Polar Year 2007-2008.ARCTAS had four primary objectives. The first was to understand long-range transport of pollution to the Arctic. Pollution brought to the Arctic from northern mid-latitude continents has environmental consequences, such as modifying regional and global climate and affecting the ozone budget. Prior to ARCTAS, these pathways remained largely uncertain. The second objective was to understand the atmospheric composition and climate implications of boreal forest fires; the smoke emissions from which act as an atmospheric perturbation to the Arctic by impacting the radiation budget and cloud processes and contributing to the production of tropospheric ozone. The third objective was to understand aerosol radiative forcing from climate perturbations, as the Arctic is an important place for understanding radiative forcing due to the rapid pace of climate change in the region and its unique radiative environment. The fourth objective of ARCTAS was to understand chemical processes with a focus on ozone, aerosols, mercury, and halogens. Additionally, ARCTAS sought to develop capabilities for incorporating data from aircraft and satellites related to pollution and related environmental perturbations in the Arctic into earth science models, expanding the potential for those models to predict future environmental change.ARCTAS consisted of two, three-week aircraft deployments conducted in April and July 2008. The spring deployment sought to explore arctic haze, stratosphere-troposphere exchange, and sunrise photochemistry. April was chosen for the deployment phase due to historically being the peak in the seasonal accumulation of pollution from northern mid-latitude continents in the Arctic. The summer deployment sought to understand boreal forest fires at their most active seasonal phase in addition to stratosphere-troposphere exchange and summertime photochemistry.During ARCTAS, three NASA aircrafts, the DC-8, P-3B, and BE-200, conducted measurements and were equipped with suites of in-situ and remote sensing instrumentation. Airborne data was used in conjunction with satellite observations from AURA, AQUA, CloudSat, PARASOL, CALIPSO, and MISR.The ASDC houses ARCTAS aircraft data, along with data related to MISR, a satellite instrument aboard the Terra satellite which provides measurements that provide information about the Earth’s environment and climate.

SEAC4RS_Merge_Data are pre-generated merge data files collected during the Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEA4CRS) airborne field study. This product contains merged data products collected from instruments onboard the DC-8 and ER-2 aircrafts. Data collection for this product is complete.Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) airborne field study was conducted in August and September of 2013. The field operation was based in Houston, Texas. The primary SEAC4RS science objectives are: to determine how pollutant emissions are redistributed via deep convection throughout the troposphere; to determine the evolution of gases and aerosols in deep convective outflow and the implications for UT/LS chemistry; to identify the influences and feedbacks of aerosol particles from anthropogenic pollution and biomass burning on meteorology and climate through changes in the atmospheric heat budget (i.e., semi-direct effect) or through microphysical changes in clouds (i.e., indirect effects); and lastly, to serve as a calibration and validation test bed for future satellite instruments and missions.The airborne observational data were collected from three aircraft platforms: the NASA DC-8, ER-2, and SPEC LearJet. Both the NASA DC-8 and ER-2 aircraft were instrumented for comprehensive in-situ and remote sensing measurements of the trace gas, aerosol properties, and cloud properties. In addition, radiative fluxes and meteorological parameters were also recorded. The NASA DC-8 was mostly responsible for tropospheric sampling, while the NASA ER-2 was operating in the lower stratospheric regime. The SPEC LearJet was dedicated to in-situ cloud characterizations. To accomplish the science objectives, the flight plans were designed to investigate the influence of biomass burning and pollution, their temporal evolution, and ultimately, impacts on meteorological processes which can, in turn, feedback on regional air quality. With respect to meteorological feedbacks, the opportunity to examine the impact of polluting aerosols on cloud properties and dynamics was of particular interest.

PEM-Tropics-A_Merge_Data is the merge data collected during the Pacific Exploratory Mission (PEM) Tropics A suborbital campaign. Data collection for this product is complete.From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those was PEM, which intended to improve the scientific understanding of human influence on tropospheric chemistry. Part of the PEM field campaigns were focused on the tropical Pacific region (PEM-Tropics) which was recognized as a “very large chemical vessel.” The overarching science objective was to assess the anthropogenic impact on tropospheric oxidizing power. A secondary objective was to investigate the impact of atmospheric sulfur chemistry, including oxidation of marine biogenic emission of dimethyl sulfide (DMS) on aerosol loading and radiative effect, which is of critical importance in the assessment of global climate change. The PEM-Tropics mission was conducted in two phases to contrast the influence of biomass burning in the dry season and the “relatively clean” wet season. The first, PEM-Tropics A, was carried out during the end of the dry season (August-September 1996), and the second, PEM-Topics B, was conducted during the wet season (March-April 1999). To accomplish its objectives, PEM-Tropics enlisted the NASA DC-8 and P-3B aircrafts to carry out longitudinal and latitudinal surveys at various altitudes as well as vertical profile sampling across the Pacific basin. Both aircrafts were equipped with in-situ instruments measuring hydroperoxyl radicals (HOx), ozone (O3), photochemical precursors (including, reactive nitrogen species and non-methane hydrocarbon species), and intermediate products (e.g., hydrogen peroxide (H2O2), formaldehyde (CH2O), and acetic acid (CH3OOH). The P3-B in-situ instrument payload also included a direct measurement of hydroxyl (OH) for both missions, while the OH and hydroperoxyl radical (HO2) measurements were added to DC-8 aircraft for PEM-Tropics B. Taking advantage of its excellent low altitude capability, the P-3B was instrumented with a comprehensive sulfur measurement package and conducted pseudo-Lagragian sampling for evaluating DMS oxidation chemistry, including measurements of DMS, sulfur dioxide (SO2), sulfuric acid (H2SO4), and methylsulfonic acid (MSA) as well as the first airborne measurement of dimethyl sulfoxide (DMSO) during PEM-Tropics B. More importantly, it was the first time that DMS (the source), OH and O3 (primary oxidants), and products (DMSO, MSA, H2SO4, SO2) were measured simultaneously aboard an aircraft in the tropical pacific. These observations, specifically DMSO, presented a substantial challenge to the DMS oxidation kinetics to this day. The DC-8 aircraft was equipped with the Differential Absoprtion Lidar (DIAL) during PEM-Tropics A, and the differential absorption lidars DIAL and LASE during PEM-Tropics B. These lidars provided real-time information for fine tuning the flight tracks to capture sampling opportunities. The lidar data products themselves provide valuable information of vertical profiles of ozone as well as aerosol and water vapor in tropical Pacific Furthermore, both aircrafts were fitted with instruments for aerosol composition and microphysical property measurements. Detailed description related to the motivation, implementation, and instrument payloads are available in the PEM-Tropics A overview paper and the PEM-Tropics B overview paper. Most of the publications based on PEM-Tropics A and B observations are available in the Journal of Geophysical Research special issues: Pacific Exploratory Mission-Tropics A and NASA Global Tropospheric Experiment Pacific Exploratory Mission in the Tropics Phase B: Measurement and Analyses (PEM-Tropics B), while other publications such as Nowak et al. (2001) were published prior to the special issues.

(Includes MeSH 2023 and 2024 changes) The MeSH 2025 Update - Combine Report lists new Entry Combinations. These are cases where a new, precoordinated Descriptor has been created to replace an existing Descriptor / Qualifier combination. This report includes MeSH changes from previous years, starting from 2023.

Geospatial analyses of human-environment interactions are challenged by the multi-scale, multi-dimensional nature of human-environment systems. Research in such contexts must often rely on integrating multiple, independently produced data sources, which presents heterogenous data qualities and interoperability challenges. Understanding data quality and transparency becomes increasingly important in these contexts, and multi‐granularity and context specific spatial data quality indicators are needed. We develop a data pedigree system that accounts for multiple data quality aspects, geospatial ambiguities that may hinder interoperability, and the fitness-for-use of each data source for indicating causal linkages between human activities and environmental change. We demonstrate its application to a particularly challenging and data sparse case study of identifying the location and timing of transnational cocaine trafficking, or ‘narco-trafficking’, in Central America with five spatial and temporal data quality indicators: geographic clarity, geographic interpretation, provenance, temporal specificity, and narco-trafficking certainty. The proposed data pedigree system provides a systematic and coherent analytical framework for interoperability, comparison, and corroboration of fragmented and incomplete data, which are needed to support advanced geospatial analyses, such as causal inference techniques. The study demonstrates the transferability and operationalization of the data pedigree system for examining complex human-environment interactions, especially those influenced by illicit economies.

This dataset provides information about the number of properties, residents, and average property values for Combine Drive cross streets in Waterford, CA.

Data for the two empirical applications. In the first application, we combine predictive densities of GDP growth for the euro area (EA), produced by individual professional forecasters participating in the ECB Survey of Professional Forecasters (SPF), with the histograms for each forecaster in the panel, publicly available here. The sub-folder data/ECB-SPF contains the csv files for the EA-SPF vintages.

In the second application, we forecast US inflation using a set of autoregressive distributed lag (ADL) regression models. For the different macroeconomic variables in the ADL regressions, we rely on the popular FRED-QD dataset provided by the Federal Reserve Bank of St. Louis, publicly available here. The sub-folder data/US-FRED contains two .rda files: one for the one-step ahead inflation forecasting exercise CPIAUCSL_qoq_Q_nhor1.rda and one for the four-step-ahead exercise CPIAUCSL_qoq_Q_nhor4.rda.

Data and code for peer-reviewed article published in International Economic Review. Paper published online February 27, 2025. When citing this dataset, please also cite the associated article. A sample Publication Citation is provided below.

About this dataset

Context

The dataset tabulates the population of Combine by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Combine across both sexes and to determine which sex constitutes the majority.

Key observations

There is a slight majority of female population, with 50.29% of total population being female. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Combine is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Combine total population. 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 Combine Population by Race & Ethnicity. You can refer the same here

This data set contains DLR Falcon 1 Second Data Merge data collected during the Deep Convective Clouds and Chemistry Experiment (DC3) from 29 May 2012 through 14 June 2012. These merges were created using data in the NASA DC3 archive as of September 25, 2013. In most cases, variable names have been kept identical to those submitted in the raw data files. However, in some cases, names have been changed (e.g., to eliminate duplication). Units have been standardized throughout the merge. In addition, a "grand merge" has been provided. This includes data from all the individual merged flights throughout the mission. This grand merge will follow the following naming convention: "dc3-mrg01-falcon_merge_YYYYMMdd_R1_thruYYYYMMdd.ict" (with the comment "_thruYYYYMMdd" indicating the last flight date included). This data set is in ICARTT format. Please see the header portion of the data files for details on instruments, parameters, quality assurance, quality control, contact information, and data set comments.

This data set contains NSF/NCAR GV HIAPER 1 Second Data Merge data collected during the Deep Convective Clouds and Chemistry Experiment (DC3) from 18 May 2012 through 30 June 2012. These are updated merges from the NASA DC3 archive that were made available on 13 June 2014. In most cases, variable names have been kept identical to those submitted in the raw data files. However, in some cases, names have been changed (e.g., to eliminate duplication). Units have been standardized throughout the merge. No "grand merge" has been provided for the 1-second data on the GV aircraft due to its prohibitive size. In most cases, downloading the individual merge files for each day and simply concatenating them should suffice. This data set is in ICARTT format. Please see the header portion of the data files for details on instruments, parameters, quality assurance, quality control, contact information, and data set comments.

This data set contains NASA DC-8 1 Second Data Merge data collected during the Deep Convective Clouds and Chemistry Experiment (DC3) from 18 May 2012 through 22 June 2012. These merges are an updated version that were provided by NASA. In most cases, variable names have been kept identical to those submitted in the raw data files. However, in some cases, names have been changed (e.g., to eliminate duplication). Units have been standardized throughout the merge. No "grand merge" has been provided for the 1-second data on the DC8 aircraft due to its prohibitive size (~1.5GB). In most cases, downloading the individual merge files for each day and simply concatenating them should suffice. This data set is in ICARTT format. Please see the header portion of the data files for details on instruments, parameters, quality assurance, quality control, contact information, and data set comments. For more information on the updates to this dataset, please see the readme file.

analyze the survey of income and program participation (sipp) with r if the census bureau's budget was gutted and only one complex sample survey survived, pray it's the survey of income and program participation (sipp). it's giant. it's rich with variables. it's monthly. it follows households over three, four, now five year panels. the congressional budget office uses it for their health insurance simulation . analysts read that sipp has person-month files, get scurred, and retreat to inferior options. the american community survey may be the mount everest of survey data, but sipp is most certainly the amazon. questions swing wild and free through the jungle canopy i mean core data dictionary. legend has it that there are still species of topical module variables that scientists like you have yet to analyze. ponce de león would've loved it here. ponce. what a name. what a guy. the sipp 2008 panel data started from a sample of 105,663 individuals in 42,030 households. once the sample gets drawn, the census bureau surveys one-fourth of the respondents every four months, over f our or five years (panel durations vary). you absolutely must read and understand pdf pages 3, 4, and 5 of this document before starting any analysis (start at the header 'waves and rotation groups'). if you don't comprehend what's going on, try their survey design tutorial. since sipp collects information from respondents regarding every month over the duration of the panel, you'll need to be hyper-aware of whether you want your results to be point-in-time, annualized, or specific to some other period. the analysis scripts below provide examples of each. at every four-month interview point, every respondent answers every core question for the previous four months. after that, wave-specific addenda (called topical modules) get asked, but generally only regarding a single prior month. to repeat: core wave files contain four records per person, topical modules contain one. if you stacked every core wave, you would have one record per person per month for the duration o f the panel. mmmassive. ~100,000 respondents x 12 months x ~4 years. have an analysis plan before you start writing code so you extract exactly what you need, nothing more. better yet, modify something of mine. cool? this new github repository contains eight, you read me, eight scripts: 1996 panel - download and create database.R 2001 panel - download and create database.R 2004 panel - download and create database.R 2008 panel - download and create database.R since some variables are character strings in one file and integers in anoth er, initiate an r function to harmonize variable class inconsistencies in the sas importation scripts properly handle the parentheses seen in a few of the sas importation scripts, because the SAScii package currently does not create an rsqlite database, initiate a variant of the read.SAScii function that imports ascii data directly into a sql database (.db) download each microdata file - weights, topical modules, everything - then read 'em into sql 2008 panel - full year analysis examples.R< br /> define which waves and specific variables to pull into ram, based on the year chosen loop through each of twelve months, constructing a single-year temporary table inside the database read that twelve-month file into working memory, then save it for faster loading later if you like read the main and replicate weights columns into working memory too, merge everything construct a few annualized and demographic columns using all twelve months' worth of information construct a replicate-weighted complex sample design with a fay's adjustment factor of one-half, again save it for faster loading later, only if you're so inclined reproduce census-publish ed statistics, not precisely (due to topcoding described here on pdf page 19) 2008 panel - point-in-time analysis examples.R define which wave(s) and specific variables to pull into ram, based on the calendar month chosen read that interview point (srefmon)- or calendar month (rhcalmn)-based file into working memory read the topical module and replicate weights files into working memory too, merge it like you mean it construct a few new, exciting variables using both core and topical module questions construct a replicate-weighted complex sample design with a fay's adjustment factor of one-half reproduce census-published statistics, not exactly cuz the authors of this brief used the generalized variance formula (gvf) to calculate the margin of error - see pdf page 4 for more detail - the friendly statisticians at census recommend using the replicate weights whenever possible. oh hayy, now it is. 2008 panel - median value of household assets.R define which wave(s) and spe cific variables to pull into ram, based on the topical module chosen read the topical module and replicate weights files into working memory too, merge once again construct a replicate-weighted complex sample design with a...

ARISE_Merge_Data_1 is the Arctic Radiation - IceBridge Sea & Ice Experiment (ARISE) 2014 pre-generated aircraft (C-130) merge data files. This product is a result of a joint effort of the Radiation Sciences, Cryospheric Sciences and Airborne Sciences programs of the Earth Science Division in NASA's Science Mission Directorate in Washington. Data collection is complete.ARISE was NASA's first Arctic airborne campaign designed to take simultaneous measurements of ice, clouds and the levels of incoming and outgoing radiation, the balance of which determined the degree of climate warming. Over the past few decades, an increase in global temperatures led to decreased Arctic summer sea ice. Typically, Arctic sea ice reflects sunlight from the Earth. However, a loss of sea ice means there is more open water to absorb heat from the sun, enhancing warming in the region. More open water can also cause the release of more moisture into the atmosphere. This additional moisture could affect cloud formation and the exchange of heat from Earth’s surface to space. Conducted during the peak of summer ice melt (August 28, 2014-October 1, 2014), ARISE was designed to study and collect data on thinning sea ice, measure cloud and atmospheric properties in the Arctic, and to address questions about the relationship between retreating sea ice and the Arctic climate. During the campaign, instruments on NASA’s C-130 aircraft conducted measurements of spectral and broadband radiative flux profiles, quantified surface characteristics, cloud properties, and atmospheric state parameters under a variety of Arctic atmospheric and surface conditions (e.g. open water, sea ice, and land ice). When possible, C-130 flights were coordinated to fly under satellite overpasses. The primary aerial focus of ARISE was over Arctic sea ice and open water, with minor coverage over Greenland land ice. Through these efforts, the ARISE field campaign helped improve cloud and sea ice computer modeling in the Arctic.

Replication Package for "Understanding the Strength of the Dollar" by Zhengyang Jiang, Robert J. Richmond, and Tony Zhang

The code in this replication package imports the raw data, constructs data panels by merging this data, estimates the models, and builds all of the tables in the paper. The file Run.R contains a sequential list of programs that need to be run for replication. All code is written in R.

Version 4 release notes: Adds data for 2017.Adds rows that submitted a zero-report (i.e. that agency reported no hate crimes in the year). This is for all years 1992-2017. Made changes to categorical variables (e.g. bias motivation columns) to make categories consistent over time. Different years had slightly different names (e.g. 'anti-am indian' and 'anti-american indian') which I made consistent. Made the 'population' column which is the total population in that agency. Version 3 release notes: Adds data for 2016.Order rows by year (descending) and ORI.Version 2 release notes: Fix bug where Philadelphia Police Department had incorrect FIPS county code. The Hate Crime data is an FBI data set that is part of the annual Uniform Crime Reporting (UCR) Program data. This data contains information about hate crimes reported in the United States. The data sets here combine all data from the years 1992-2015 into a single file. Please note that the files are quite large and may take some time to open.Each row indicates a hate crime incident for an agency in a given year. I have made a unique ID column ("unique_id") by combining the year, agency ORI9 (the 9 character Originating Identifier code), and incident number columns together. Each column is a variable related to that incident or to the reporting agency. Some of the important columns are the incident date, what crime occurred (up to 10 crimes), the number of victims for each of these crimes, the bias motivation for each of these crimes, and the location of each crime. It also includes the total number of victims, total number of offenders, and race of offenders (as a group). Finally, it has a number of columns indicating if the victim for each offense was a certain type of victim or not (e.g. individual victim, business victim religious victim, etc.). All the data was downloaded from NACJD as ASCII+SPSS Setup files and read into R using the package asciiSetupReader. All work to clean the data and save it in various file formats was also done in R. For the R code used to clean this data, see here. https://github.com/jacobkap/crime_data. The only changes I made to the data are the following. Minor changes to column names to make all column names 32 characters or fewer (so it can be saved in a Stata format), changed the name of some UCR offense codes (e.g. from "agg asslt" to "aggravated assault"), made all character values lower case, reordered columns. I also added state, county, and place FIPS code from the LEAIC (crosswalk) and generated incident month, weekday, and month-day variables from the incident date variable included in the original data. The zip file contains the data in the following formats and a codebook: .dta - Stata.rda - RIf you have any questions, comments, or suggestions please contact me at jkkaplan6@gmail.com.

Russia Number of Combines per 1000 Ha of Sown Area: AE: Combine Harvesters data was reported at 2.000 Unit in 2018. This stayed constant from the previous number of 2.000 Unit for 2017. Russia Number of Combines per 1000 Ha of Sown Area: AE: Combine Harvesters data is updated yearly, averaging 4.050 Unit from Dec 1990 (Median) to 2018, with 28 observations. The data reached an all-time high of 6.600 Unit in 1990 and a record low of 2.000 Unit in 2018. Russia Number of Combines per 1000 Ha of Sown Area: AE: Combine Harvesters data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Agriculture Sector – Table RU.RII001: Number of Agricultural Machines.

The Amazon-ULC Data Package, available as an R package, provides Urban Land Cover (ULC) classifications for selected cities in the Brazilian Amazon. The study areas cover approximately 1,200 km², including the municipal seats of Altamira (153 km²), Cametá (44 km²), Marabá (164 km²), Santarém (143 km²), and part of the Metropolitan Area of Belém (614 km²), all located in the state of Pará.These land cover maps have significant value in urban planning for Amazonian cities, as they can aid in monitoring urban sprawl, restricting construction in environmental protection areas, assisting in urban zoning, and identifying high-density areas, among other uses. Our classification model used images from the WPM sensor of the CBERS-4A satellite, and combined the GEOBIA approach, data mining techniques, and the random machine learning algorithm.

This collection contains the data used in the Multi-Criteria Analysis Shell for Spatial Decision Support (MCAS-S) software tool. From the Data menu, explore and download individual supplementary layers, or download the entire datapack. The Multi-Criteria Analysis Shell for Spatial Decision Support (MCAS-S) is a software tool developed by the Australian Bureau of Agricultural and Resource Economics and Sciences that enables multi-criteria analysis (MCA) using spatial data. It is a powerful, easy-to-use and flexible decision-support tool that promotes: - framework for assessing options - common metric for classifying, ranking and weighting of the data - tools to compare, combine and explore spatial data - live-update of alternative scenarios and trade-offs.

About this dataset

Context

The dataset tabulates the data for the Combine, TX population pyramid, which represents the Combine population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Combine, TX, is 35.5.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Combine, TX, is 24.3.
• Total dependency ratio for Combine, TX is 59.8.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Combine, TX is 4.1.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the Combine population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Combine for the selected age group is shown in the following column.
• Population (Female): The female population in the Combine for the selected age group is shown in the following column.
• Total Population: The total population of the Combine for the selected age group is shown in the following column.

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 Combine Population by Age. You can refer the same here