The original public land survey for the Umatilla National Forest was completed primarily between 1879 and 1887. Notes from these General Land Office (GLO) surveys provide the earliest systematically recorded information about species composition for national forest system lands in the Blue Mountains of northeastern Oregon and southeastern Washington. Using these historical records we were able to obtain GLO survey point locations as well as tree species and size information for trees found in the survey process. Point locations are included as a shapefile, and trees species and diameter are provided in tabular format.The GLO survey notes serve as a data source for characterizing presettlement vegetation conditions for the Umatilla National Forest.Data were published on 08/06/2020. On 11/05/2020 the metadata were updated to include reference to the article associated with these data (Hanberry et al. 2020).

About this dataset

Context

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

Key observations

There is a majority of female population, with 54.76% of total population being female. Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 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 Marked Tree is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Marked Tree 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 Marked Tree Population by Gender. You can refer the same here

It is a comprehensive collection of tree species observed and documented during a botanical survey. This dataset includes a list of various tree species found in specific regions, providing valuable insights into local biodiversity. Each entry represents a unique tree species identified during the survey, contributing to the study of flora and ecosystems.

Related links:

Shuvo, Shuvo Kumar Basak (2025), “Treevill: National Botanical Garden Unique & Rare Tree Argument Dataset ”, Mendeley Data, V1, doi: 10.17632/t7rwzgbfdd.1

https://doi.org/10.34740/KAGGLE/DSV/10582625

https://doi.org/10.34740/KAGGLE/DSV/10579609

https://doi.org/10.34740/KAGGLE/DSV/10579122

Treevill: N.B.G. Unique & Rare Raw Dataset - Access, Collaboration, and Paid Services Policy

I, Shuvo Kumar Basak, have created and curated the Treevill: N.B.G. Unique & Rare Raw Dataset, which consists of images of unique and rare tree species collected from the National Botanical Garden of Bangladesh. This dataset is freely available for research, educational, and non-commercial purposes.

Free Access to the Dataset: The Treevill: N.B.G. Unique & Rare Raw Dataset is available free of charge to all individuals and organizations for educational and research use. This is to support the advancement of knowledge and studies related to biodiversity, machine learning, and related fields.

Future Collaboration and Data Requests: While the dataset is provided free of charge, I encourage individuals and organizations to contact me directly if they need access to additional related data, further assistance, or if they plan on expanding their research in the future.

If you require any new data or specific related datasets, feel free to reach out to me, Shuvo Kumar Basak, for collaboration. I am happy to assist with additional data collection, cleaning, resizing, or other related services at a reasonable cost.

Paid Services - Hire for Data Collection: If you or your organization need custom data collection or wish to obtain related datasets beyond what is included in this collection, I offer a paid service to gather new data according to your specific requirements. This includes: Custom data collection for other tree species or related botanical data.

Data cleaning, resizing, and preprocessing to make the data ready for analysis.

Please contact me for a custom quote based on your specific needs. I will work with you to provide high-quality, tailored datasets to support your research, project, or business needs. Terms and Conditions: The dataset is intended for academic, research, and non-commercial purposes only. Redistribution or commercial use of the dataset without prior written consent is not permitted. Proper attribution to Shuvo Kumar Basak as the creator of the dataset should be provided when using the dataset in publications, projects, or other works.

**More Dataset:: ** https://www.kaggle.com/shuvokumarbasak4004/datasets

…………………………………..Note for Researchers Using the dataset………………………………………………………………………

This dataset was created by Shuvo Kumar Basak. If you use this dataset for your research or academic purposes, please ensure to cite this dataset appropriately. If you have published your research using this dataset, please share a link to your paper. Good Luck.

This script is used to analyze tree canopy and its change from 2006 to 2011 in Washington, D.C. with in American Community Survey (ACS) boundaries. The script will automatically read a small *.csv file (52kb) into memory and analyze in R. To download the file directly use the first link below. Rows correspond to block groups, data types using the R nomenclature shown in parentheses, the fields (columns) are: [1] "OBJECTID" - created by ArcGIS, unique (integer) [2] "AREAKEY" - the US Census Bureau FIPS code, the unique identifier for joining to other ACS/Census data (factor) [3] "EHHMEDINC" - Median Household Income in $'s (integer) [4] "Shape_Leng" - The length of the perimeter of the block group in meters (num) [5] "Shape_Area" - The area of the block group polygon in square meters (num) [6] "PctCanArea" - The percent of the block group that is covered by the sum of tree canopy datasets three categories 1) no change, 2) loss, and 3) gain. No change indicates that the tree canopy has not changed substantially from 2006 to 2011. Loss indicates that tree canopy was removed from 2006 to 2011. Gain indicates that new tree canopy was established between 2006 and 2011. The canopy data are described using the Letters from the SAL link provided below (num) [7] "PctNo_Chan" - The proportion of "PctCanArea" that is from the no change class (num) [8] "PctLoss" - The proportion of "PctCanArea" that is from the loss class (num) [9] "PctGain"- The proportion of "PctCanArea" that is from the gain class (num) [10] "IncomeQuan" - The median household income from "EHHMEDINC" categorized into quintiles (factor)

Abstract

The main objective of this survey is to provide a comprehensive picture about the tree horticulture sector in the Palestinian Territory, and so provide the interested people and policy makers and planner with reliable data that is needed to develop this strategic sector.

The other objectives for this survey are: 1. Define the demographic properties of the tree horticulture holders. 2. Define the demographic properties of the tree horticulture holdings 3. Define the pattern of land use for the tree horticulture holdings. 4. Define the tree horticulture crops. 5. Define the production and productivity of the horticulture trees in the Palestinian Territory. 6. Define a marketing tree horticulture crops. 7. Define of applications in the tree horticulture holdings. 8. Define available indicators related to the tree horticulture holdings employment. 9. Define the damages of the tree horticulture sectors due to the Israeli measures

Geographic coverage

All of the Palestinian Territory

Analysis unit

Agricultural holding

Universe

Agricultural Holding

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample is a one-stage stratified simple random sample. The sample size is 5,024 agricultural holdings in all of the Palestinian Territory, and it is large enough to obtain reliable estimates on a regional level (north, middle, south of the West Bank) in addition to Gaza Strip. The sampling frame consisted of enumerated agricultural holdings in the agricultural holdings enumeration activity which was implemented by PCBS in 2004. The agricultural holdings meet the criteria of the agricultural holding had been separated, according to the definition of type of agricultural holding

Sample Size The sample size is 5,024 agricultural holdings in all of the Palestinian Territory, and it is large enough to obtain reliable estimates on a regional level (North, Middle, South of the West Bank, and Gaza Strip) in addition to crop type.

Sampling deviation

-

Mode of data collection

Face-to-face [f2f]

Research instrument

The tree horticulture questionnaire was designed depending on the recommendations of the Food and Agriculture Organization of the United Nations (FAO), also benefiting from the international experience in field of design the questionnaires, in addition, the pilot was conducted during the period 08/07- 11/07/2006 to check the project documents including the questionnaire, in which the questionnaire was changed and developed to suit the tree horticulture sector in the Palestinian Territory.

The questionnaire for the Tree Horticulture Survey 2006 consists of eleven sections as follows: 1. Section One: identification data of the agricultural holder and holding 2. Section Two: demographic properties of the agricultural holders, concerning sex, age and other related indicators. 3. Section Three: regarding agricultural holding, the type, legal status, main source of irrigation water, main source of extension, and other related indicators. 4. Section Four: regarding land use, the total area of the agricultural holding, the treatment of chemical and organic fertilizers, pesticides and treatment area, and other related indicators 5. Section Five: regarding the tree horticulture crops, the type, area and other related indicators 6. Section Six: regarding the production of tree horticulture crops, rainfed or irrigated, and other related indicators. 7. Section Seven: the number of horticulture trees by age. 8. Section Eight: the costs of production requirements for the trees horticulture. 9. Section Nine: the marketing of tree horticulture crops. 10. Section Ten: the agricultural applications for tree horticulture holdings. 11. Section Eleven: agricultural labor, in terms of sex, status, and other related indicators. 12. Section Twelve: damage to agricultural as aresult of Israeli attacks, in terms of size, number, and other relevant indicators

Cleaning operations

Preparation of Data Entry Programme: At this stage the data entry program was prepared using the ACCESS package. Data entry screens were designed. Also, rules of entry were established in a manner that guarantees successful entry of questionnaires and queries to check data after each entry. These queries examine the variables on the questionnaire level.

• Organization and Management of Data Entry: The Information Systems and Computer Directorate prepared the data entry program; the directorate also supervised the data entry process and applied the required validation rules to edit the data. The directorate was also responsible to select and train the data entry personnel.

• Data Entry Personnel Training: Before the start of the data entry, data entry personnel were trained on the use of the data entry program and then how to enter data into the computerized database.

• Editing of the Entered Data: There are specific rules for data editing which connect the different indicators of the questionnaires logically with each other, so that errors in any questionnaire are treated immediately and data re-entered to get clean data.

Response rate

·Response rate 92.5%

Sampling error estimates

Statistical Errors Data of this survey affected by statistical errors due to use the sample, Therefore, the emergence of certain differences from the real values expect obtained through censuses. It had been calculated variation of the most important indicators exists and the facility with the report. and the dissemination levels of the data were particularized at the regional level in the West Bank (North, Middle, South) and Gaza Strip, due to the sample design and the variance calculations for the different indicators.

Non-Statistical Errors Non-statistical errors are probable in all stages of the project, during data collection or processing. This is referred to as non-response errors, response errors, interviewing errors, and data entry errors. To avoid errors and reduce their effects, great efforts were made to train the fieldworkers intensively. They were trained in how to carry out the interview, what to discuss and what to avoid, carrying out a pilot survey and practical and theoretical training during the training course.

Data appraisal

-

Results of the statistical analysis of change in percentage of trees by size class among all three time periods.

PurposeTo provide empirically-supported thresholds for step-based intensity (i.e., peak 30-min cadence; average of the top 30 steps/min in a day) and steps/day in relation to cardiometabolic health outcomes.MethodsReceiver operating characteristic curve analysis was applied to the National Health and Nutrition Examination Survey (NHANES) 2005–2006 accelerometer-derived step data to determine steps/day and peak 30-min cadence as risk screening values (i.e., thresholds) for fasting glucose, body mass index, waist circumference, high blood pressure, triglycerides, and HDL cholesterol. Thresholds for peak 30-min cadence and steps/day were derived that, when exceeded, classify the absence of each cardiometabolic risk factor. Additionally, logistic regression models that included the influence of age and smoking were developed using the sample weights, primary sampling units (PSUs), and stratification variables provided by the NHANES survey. Finally, a decision tree analysis was performed to delineate criteria for at-risk versus healthy populations using cadence bands.ResultsPeak 30-min cadence thresholds across cardiometabolic outcomes ranged from 66–72 steps/min. Steps/day thresholds ranged from 4325–6192 steps/day. Higher thresholds were observed in men compared to women. In men, higher steps/day thresholds were observed in age ranges of 30–39, while in women, higher thresholds were observed in the age-range 50–59 years. Decision trees for classifying being at low risk for metabolic syndrome contained one risk-free leaf at higher cadence bands, specifically for any time accumulated at ≥120 steps/min.ConclusionsMinimum thresholds representing absence of cardiometabolic risk range from 4325–6192 steps/day and 66–72 steps/min for peak 30-min cadence. Any time accumulated at ≥120 steps/min was associated with an absence of cardiometabolic risk. Although based on cross-sectional data, these thresholds represent potentially important and clinically interpretable daily physical activity goals.

Context

US Forest Service Forest Inventory and Analysis National Program.

The Forest Inventory and Analysis (FIA) Program of the U.S. Forest Service provides the information needed to assess America's forests.

https://www.fia.fs.fed.us/

Content

As the Nation's continuous forest census, our program projects how forests are likely to appear 10 to 50 years from now. This enables us to evaluate whether current forest management practices are sustainable in the long run and to assess whether current policies will allow the next generation to enjoy America's forests as we do today.

FIA reports on status and trends in forest area and location; in the species, size, and health of trees; in total tree growth, mortality, and removals by harvest; in wood production and utilization rates by various products; and in forest land ownership.

The Forest Service has significantly enhanced the FIA program by changing from a periodic survey to an annual survey, by increasing our capacity to analyze and publish data, and by expanding the scope of our data collection to include soil, under story vegetation, tree crown conditions, coarse woody debris, and lichen community composition on a subsample of our plots. The FIA program has also expanded to include the sampling of urban trees on all land use types in select cities.

For more details, see: https://www.fia.fs.fed.us/library/database-documentation/current/ver70/FIADB%20User%20Guide%20P2_7-0_ntc.final.pdf

Fork this kernel to get started with this dataset.

Acknowledgements

https://www.fia.fs.fed.us/

https://cloud.google.com/blog/big-data/2017/10/get-to-know-your-trees-us-forest-service-fia-dataset-now-available-in-bigquery

FIA is managed by the Research and Development organization within the USDA Forest Service in cooperation with State and Private Forestry and National Forest Systems. FIA traces it's origin back to the McSweeney - McNary Forest Research Act of 1928 (P.L. 70-466). This law initiated the first inventories starting in 1930.

Banner Photo by @rmorton3 from Unplash.

Inspiration

Estimating timberland and forest land acres by state.

https://cloud.google.com/blog/big-data/2017/10/images/4728824346443776/forest-data-4.png" alt="enter image description here"> https://cloud.google.com/blog/big-data/2017/10/images/4728824346443776/forest-data-4.png

These datasets represent inputs into spatial and temporal models associated with a published study assessing trends and vulnerabilities in the mutalism between whitebark pine (Pinus albicaulis) and Clark’s nutcracker (Nucifraga columbiana, CLNU) in national parks of the North Cascades and Sierra Nevada regions. The study used avian point count data and summary information on whitebark pine abundance and distribution. This reference provides the two datasets contributed to the temporal modeling analysis within the North Cascades region, specifically Mount Rainier National Park (MORA), as Clark’s nutcracker trends were not discernable at North Cascades National Park (NOCA). For data associated with national parks within the Sierra Nevada region, including Yosemite National Park (YOSE) and Sequoia and Kings Canyon National Parks (SEKI), please refer to: https://irma.nps.gov/DataStore/Reference/Profile/2278594. 1) Whitebark pine data from 29 0.04-hectare circular plots representing eight stands in Mount Rainier National Park, with survey data from 2004, 2007 (9 plots), 2009 and 2015. Basic plot data used to model relationships with Clark’s Nutcracker abundance estimates and trends are summarized by plot and year, including the number of live trees, average live tree diameter, live tree infection rates, and basic site characteristics (elevation, slope and aspect). For modeling and analytical purposes, the data set also includes filled records for plot-year combinations with no survey data, represented in the data as ‘NA’. Data were summarized by plot and year because Clark’s nuctracker density was modeled at the park scale according to park-scale tree metrics, accounting for both plot and stand effects on those tree metrics. 2) Avian survey data represent the count of Clark's nutcracker individuals at observed at point-count monitoring stations, 2005-2016. Avian survey covariates include: ambient noise level, observer, date, hour, presence of forest cover, presence of dense vegetation cover, elevation, aspect, and slope. For modeling and analytical purposes, the data set also includes non-detect records and filled records for transect-year combinations with no survey data, represented in the data as ‘NA’. Data were derived from the highest of three elevation strata, because Clark’s nutcracker were almost never observed in lower strata.

This dataset contains all the witness tree data available for Six Rivers Nationa Forest that was collected through the public land survey in the 1880s. Witness tree data were extracted from the handwritten archive into digital datasets for this analysis. Surveyors commented sporadically about rock type, soil texture, land features, and shrub cover in the original notes, but these details were not transcribed. Although the vast majority of trees were recorded with distance, direction, species, and stem diameter information, surveyors' notes contained three distinct types of omitted/absent data. Surveyors reported ‘no trees within limits’ when trees were present but were too far away for measurement. They reported ‘pits impractical’ when field conditions prevented physical demarcation of witness trees. Lastly, some entries were blank without explanation. These distinctions were recorded into as ‘NTWL’, ‘PI’, and ‘NA’, respectively.

About this dataset

Context

The dataset tabulates the population of Marked Tree by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Marked Tree. The dataset can be utilized to understand the population distribution of Marked Tree by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Marked Tree. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Marked Tree.

Key observations

Largest age group (population): Male # 55-59 years (146) | Female # 25-29 years (136). 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.

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

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.

Variables / Data Columns

• Age Group: This column displays the age group for the Marked Tree population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Marked Tree is shown in the following column.
• Population (Female): The female population in the Marked Tree is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Marked Tree for each age group.

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 Marked Tree Population by Gender. You can refer the same here

Subject characteristics of NHANES data after processing.

The dataset entitled “Heritage Trees in Jiaozuo, Henan Province” was generated through a systematic field survey of registered heritage trees in Jiaozuo City, Henan Province, China. Data were collected by trained survey teams using standard forestry measurement tools, including measuring tapes for tree circumference, laser rangefinders for tree height and crown spread, and GPS devices for geolocation. Each tree was examined and recorded in situ to ensure accuracy, with follow-up verification of species identification based on botanical references and expert consultations. Data processing involved standardizing species names, cross-checking protection classifications against official records, and formatting the dataset into a tabular structure for subsequent analysis.The dataset covers heritage trees within Jiaozuo City and provides spatial information at the district level. Geographic coordinates (longitude and latitude) were recorded with a spatial resolution sufficient for site-level mapping, generally within ±10 meters accuracy depending on GPS conditions. Temporal information is associated with the “Years” field, which indicates either the estimated or documented age of the tree in years. The dataset reflects the status of trees at the time of survey (most recently updated in 2020).The table consists of multiple records, each corresponding to one individual tree or a small group of co-occurring trees (indicated by the “Tree count” field). Each record contains the following columns:Tree No.: Unique identifier assigned to each heritage tree.Chinese name: Vernacular name of the species in Chinese.Family & Genus: Taxonomic classification at family and genus levels.Protection level: Legal or administrative classification of heritage tree protection.Years: Estimated or documented age of the tree (years).Tree height (m): Height of the tree measured in meters.Circumference (cm): Trunk circumference measured at standard breast height, in centimeters.Crown (m): Crown width (canopy spread) measured in meters.Growth status: Qualitative description of tree health and vitality.Location: Description of the specific site where the tree is located (e.g., village, temple, roadside).Habitat type: Type of surrounding environment (e.g., urban park, rural landscape, temple grounds).District: Administrative district within Jiaozuo where the tree is found.Longitude / Latitude: Geospatial coordinates recorded in decimal degrees.Tree count: Number of individual trees if more than one occurs together (e.g., twin trees).Measurement units are explicitly provided where applicable (meters, centimeters, years). For categorical descriptors such as growth status and habitat type, entries are qualitative and standardized for comparability.Some records may have missing values, particularly where tree age could not be precisely determined or where GPS reception was limited. In such cases, fields are left blank or annotated with standard placeholders. Potential errors may arise from measurement limitations (e.g., tree height estimation in dense canopy conditions) or from natural variability in tree form, but overall measurement uncertainty is minimal and within standard forestry survey tolerances.The dataset is stored in a widely used tabular format (CSV/Excel), compatible with common software such as Microsoft Excel, R, and Python (pandas). This ensures accessibility and ease of integration into further ecological, conservation, or spatial analyses. No specialized or proprietary software is required to use the dataset.This dataset provides a comprehensive inventory of heritage trees in Jiaozuo, Henan Province, with both biological and spatial attributes, offering a valuable foundation for ecological research, cultural heritage conservation, and urban planning initiatives.

This data set includes in one data file the common names, base diameters, and calculated tree masses for almost 3,000 trees on a 5 hectare plot (20 x 2,500 m) located in the Ducke Reserve near Manaus, Brazil in the central Amazon. Measurements were taken during October-December 1999. All diameter measurements were taken at 1.3 meters in height (DBH), or above the buttresses or other stem anomalies. Forest structure characteristics such as biomass density, stem density, diameter class distribution, and taxonomic information at the family and perhaps genus level, can be derived from these data.

This data publication contains a shapefile that provides tree percentages for historical beech and oak forests of Indiana. Data provided include the estimated percentage of beech, oak, ash, hickory, and sugar maple found in these forests, between 1799 and 1946, for each township in Indiana. These data were recovered, using unique GIS methods, from historical tree survey information stored in bar chart figures in Potzger et al. (1956) which presented the approximately 214,500 trees surveyed during that time period. The methods, tabular data, and findings are presented in Hanberry (2018).This purpose of this project was to 1) determine if a unique geographic information system (GIS) method could be used to reclaim information available in published figures, when associated raw data are not available, and also to generate a GIS layer for data provided in Potzger et al. (1956).

This is the data measured by intelligent Sen recorder, which is about the data of the self stability of the instrument, the measured data and the corresponding standard values of the total station.

Geographic coverage

The NHFS is focused on forest proximate households. Therefore, the sample is limited to enumeration areas which fall within 2.5km of the nearest forest, as defined using Metria and Geoville (2019) land cover data. The final sample includes enumeration areas from all 15 of Liberia's counties, but excludes urban areas of Montserrado.

Analysis unit

Household; Community

Universe

All EAs within 2.5 kilometers of forests except for the EAs from the urban part of the Montserrado county.

Kind of data

Sample survey data [ssd]

Sampling procedure

Given the focus of the NHFS on the population living in close proximity to forests4, a first step was to clearly define forest for the purposes of the survey. Building on the national definition of forest used in Liberia, and modifying it in order to minimize the impact of small urban forests and facilitate survey operations, the NHFS employed the following definition:

Forest = area with at least 30 percent tree canopy cover, with trees higher than 5 meters and at least 50 hectares in size

The forest cover was determined using high-resolution forest cover data produced in 2019 based on satellite information on forest cover in Liberia for 2015.6 All EAs within 2.5 kilometers of forests identified with this definition were deemed eligible for inclusion in the NHFS.7 EAs from the Montserrado county (part of Greater Monrovia) were excluded from the sample universe due to the high rate of urbanization. However, rural parts of Montserrado county were included in the sample universe.

Based on the forest definition defined above, the distance from each EA in the country (except urban Montserrado) to the nearest forest was computed. That distance was subsequently used to assign each EA to one of the following strata: S1 (less than 2km from forest); S2 (two to 7 km from forest); S3 (7 to 15 km from forest).

Following strata classification, a total of 250 EAs were selected through a Probability Proportional to Size (PPS) sampling approach within each stratum, with the following purposeful allocation across strata: 90 EAs in S1; 90 EAs in S2; 70 EAs in S3.8 The measure of size for each EA was based on the total number of households listed in the 2008 PHC.

Following the selection of the 250 sample EAs, a listing of households was conducted in each sample EA to provide the sampling frame for the second stage selection of households. Random sampling was used to select 12 households from the household listing for each sample EA.

The original sample design provided a total household sample size of 3,000 (250 EAs with 12 households sampled per EA), data from 14 households are missing or unusable, representing 0.05 percent of the sample and resulting in a final sample of 2,986 households. Similarly, data from 5 of the community questionnaires were missing or unusable, resulting in a total sample of 245 community questionnaires. The final sample of 2,986 households is distributed across counties.

Upon post-data collection analysis, it was discovered that the initial variable that was used to stratify EAs by distance to forest was incorrectly computed. Despite thorough attempts to understand the nature and source of the error, it was determined that a mechanical error must have occurred during the process of the distance calculations. This error rendered the stratification incorrect. Therefore, the stratification by distance to forest has been abandoned and the sample weighted to reflect only geographic clusters, not distance to forest. This was determined to be the most appropriate way forward following consultation with sampling experts.

The resulting sample, therefore, is weighted to reflect all EAs in Liberia (with the exception of urban Montserrado) that fall within 2.5 km of the nearest forest, which was the upper bound of the distances for the selected EAs.

Sampling deviation

Please refer to the Basic Information Document found in the External Resources section.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The NHFS survey consisted of: 1. A HH questionnaire, administered to 12 selected HHs in each enumeration area, and 2. A community questionnaire, administered to a group of members from the EA.

Each questionnaire was administered using computer-assisted personal interviewing (CAPI) with CSPro3 software.

Cleaning operations

The data cleaning process was done in several stages over the course of fieldwork and through preliminary analysis. The first stage of data cleaning was conducted by the field-based teams during the interview itself utilizing error messages generated by the CSPro application when a response did not fit the rules for a particular question. For questions that flagged an error, the enumerators were expected to record a comment within the questionnaire to explain to their supervisor the reason for the error and confirming that they double checked the response with the respondent.

The second stage occurred during the review of the questionnaire by the supervisors. Prior to sharing data with LISGIS HQ, the supervisor was to review the interviewers. Depending on the outcome, the supervisors can either approve or reject the case. If rejected, the case goes back to the respective enumerator and a re-visit to the household may be necessary. Additional errors were compiled into error reports by the World Bank and LISGIS HQ that were regularly sent to the teams and then corrected based on re-visits to the household.

The last stage involved a comprehensive review of the final raw data following the first and second stage cleaning, after data collection completion. Every variable was examined individually for (1) consistency with other sections and variables, (2) out of range responses, and (3) outliers. However, special care was taken to avoid making strong assumptions when resolving potential errors. Some minor errors remain in the data where the diagnosis and/or solution were unclear to the data cleaning team.

The first and the second stage of the cleaning activities were led by LISGIS and the World Bank provided technical assistance. The third stage of data cleaning was performed by the World Bank team exclusively.

1. Data Collection LocationThe Changbai Mountain deciduous Korean pine forest comprehensive observation site (central geographic coordinates: 128.0956E, 42.4030N, elevation 784 m) is located in Erdaobaihe Town, Antu County, Yanbian Korean Autonomous Prefecture, Jilin Province.2. Data Collection MethodsThe Changbai Mountain Forest Ecosystem Research Station, following the "Observation Indicators and Standards for Terrestrial Ecosystem Biology," further divides the long-term monitoring plot of 40 m × 40 m into secondary plots of 5 m × 5 m, totaling 64. For convenience and research needs, the monitoring plot is referred to as the Level I plot for tree layer observation, and the secondary plot is referred to as the Level II plot for shrub layer and herbaceous layer observation. - Tree layer observation: Investigate the diameter at breast height, height, and cover of each tree in the Level I plot. - Shrub layer observation: Mechanically sample and conduct long-term observation on 17 fixed Level II plots. Set up a 2 m × 2 m small plot in each selected Level II plot to investigate the height and cover of each shrub (clump). - Herbaceous layer observation: Conducted within the Level II plots selected for shrub layer investigation. Set up a 1 m × 1 m small plot in each selected Level II plot for inter-annual observation of the herbaceous layer. If necessary, all herbaceous plants can be removed for observation to investigate the height and cover of each herbaceous plant (clump) within the small plot.- Epiphyte observation: Investigate the category of epiphytes on each tree in the Level I plot. - Liana observation: Investigate the base diameter and length of lianas within the Level I plot.3. Data ProcessingData processing includes checking and completing original record information, data entry and verification, and data statistical analysis.The specific statistical analysis methods are as follows: - Tree layer: Based on individual tree surveys, statistics are calculated by Level II plot and species: number of individuals, average diameter, average height, and biomass calculated using models (including stem dry weight, branch dry weight, leaf dry weight, fruit (flower) dry weight, bark dry weight, aerial root dry weight, aboveground total dry weight, and underground total dry weight). Based on the results of individual tree surveys by species, statistics are calculated by Level II plot: species number, dominant species, average height of dominant species, density, aboveground total dry weight, and underground total dry weight. - Shrub layer: Based on species surveys by Level II plot, statistics are calculated by plot: number of individual plants (clumps), average height, biomass calculated using models (including branch dry weight, leaf dry weight, aboveground total dry weight, and underground total dry weight), species number, dominant species, average height of dominant species, density, aboveground total dry weight, and underground total dry weight. - Herbaceous layer: Based on species surveys by Level II plot, statistics are calculated by plot: number of individual plants (clumps), average height, aboveground total dry weight, species number, dominant species, average height of dominant species, density, aboveground total dry weight, and underground total dry weight (underground sampling plot 1 m × 1 m × 0.25 m). - Epiphytes: Based on the survey of epiphytes on each tree, statistics are calculated by Level II plot and species: number of individual plants (clumps). - Liana: Based on the survey within the Level I plot, statistics are calculated by Level II plot and species: number of individual plants (clumps), average base diameter, and average height.4. Database CompositionThe data set is stored in Excel format, including eight sheets. Sheet1 is for the composition of tree species in the Changbai Mountain deciduous Korean pine forest, with a total of 269 records, including indicators as shown in Table 2; Sheet2 is for the composition of shrub species, with a total of 66 records, including indicators as shown in Table 3; Sheet3 is for the composition of herbaceous species, with a total of 193 records, including indicators as shown in Table 4; Sheet4 is for the community characteristics of the tree layer, with a total of 118 records, including indicators as shown in Table 5; Sheet5 is for the community characteristics of the shrub layer, with a total of 32 records, including indicators as shown in Table 6; Sheet6 is for the community characteristics of the herbaceous layer, with a total of 32 records, including indicators as shown in Table 7; Sheet7 is for the species composition of epiphytes, with a total of 130 records, including indicators as shown in Table 8; Sheet8 is for the composition of liana species, with a total of 65 records, including indicators as shown in Table 9.5. Data Quality Control and AssessmentThe quality control of this data set follows the relevant monitoring specifications of the "Observation Indicators and Standards for Terrestrial Ecosystem Biology," with field surveys conducted by technicians with rich experience and professional skills, and the survey data is reviewed and verified by scientific researchers to ensure the scientific and accurate nature of the data.Specific measures are as follows: - During field surveys: The observation time for the species composition and community characteristics of the Changbai Mountain deciduous Korean pine forest is mid-August (the peak of plant growth). Standardized measurement tools and methods are used for data collection, such as using the same model of measuring instruments to measure tree diameter, plant height, and liana base diameter to reduce measurement errors. Plant species identification, common names, and scientific names are based on the Plant Smart database. For plant species that cannot be determined on-site, photos should be taken and specimens collected for indoor analysis and identification. Field survey data records are checked by both the investigator and the recorder to ensure the accuracy of the data. - Data entry: Paper data is transformed into electronic data, with one person entering and another verifying to ensure the accuracy of the data entry. - Quality control and assessment: Quality control methods include threshold checks (comparing monitoring data with historical data over the years, verifying data that exceeds the historical data threshold range, deleting outliers or marking explanations), consistency checks (such as different order of magnitude compared to other measurement values), etc. Quality assessment is carried out by plotting dynamic graphs based on annual or seasonal units and comparing data from the same period.

This dataset documents pre- and post-fire tree composition of stands along the current range edge of lodgepole pine (Pinus contorta ssp. latifolia) in the Yukon Territory. The objective of the study was to evaluate whether pine populations at the range edge appear to be expanding in association with fire disturbance.

This dataset has been published as: Jill F. Johnstone and F. Stuart Chapin, 2003. Non-equilibrium succession dynamics indicate continued northern migration of lodgepole pine. Global Change Biology, 9(10): 1401-1409.

Pre-fire diameter and stem counts of trees judged to be alive at the time of burning, based on belt-transect surveys.

About this dataset

Context

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

Key observations

There is a majority of male population, with 55.76% of total population being male. Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 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 Lone Tree is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Lone Tree 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 Lone Tree Population by Gender. You can refer the same here