Tinctures were the top selling CBD product in the United States in 2021. Tinctures represented a market value of nearly one billion U.S. dollars in that year. The second most popular product category was topicals at a value of over 800 million dollars.

The market value of CBD-infused beverages in the Untied States is predicted to grow substantially over the coming years. By 2023, the market is expected to be valued at 1.4 billion U.S. dollars, over 16 times greater than its 2018 size.

Expert industry market research on the CBD Product Manufacturing in the US (2005-2030). Make better business decisions, faster with IBISWorld's industry market research reports, statistics, analysis, data, trends and forecasts.

According a survey conducted in April 2022, 62 percent of U.S. adults using cannabidiol stated they took CBD for anxiety and stress. A further 60 percent indicated pain relief as their reason for using the supplement.

This is a graphical polygon dataset which depicts concentrated downtown retail, service office and mixed uses in the existing downtown business district. Major/regional shopping centers are permitted, but urban design standards are required in order to maintain a neighborhood commercial scale, to promote pedestrian activity and to maintain the unique character of the center.

The CBD Oil Market estimated size and share is projected to exceed USD 26.95 billion by 2034, with a forecasted CAGR of 12.7% during the period.

In recent years, the United States CBD salves market has emerged as a dynamic segment within the broader cannabis industry, appealing to consumers seeking natural pain relief and therapeutic benefits. CBD salves, infused with cannabidiol extracted from hemp, are topical applications praised for their potential to so

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

CBD Beverages Market is predicted to reach $22.18 billion by 2030 with a CAGR of 27.1% from 2025 to 2030.

DATA OVERVIEW

Mapped attributes are

• above ground biomass, AGB;
• downed wood biomass, i.e., the sum of coarse and fine woody debris, DWB;
• canopy bulk density, CBD;
• canopy height,CH;
• canopy base height, CBH;
• and canopy fuel load, CFL.

Models were fit using auxiliary information that included lidar data from 20 acquisitions in Oregon and climate data. Measurements in plots of the Forest Inventory and Analysis program (FIA) were used to obtain plot-level ground observations for predictive modeling. Tree and transect measurements in FIA plots were respectively used to obtain plot-level values of AGB and DWB. To obtain plot-level values of CBD, CH, CBH and CFL, tree measurements in FIA plots were processed with FuelCalc. Plot level auxiliary variables were obtained intersecting the axiliary information layers with the FIA plots. Predictive models were random forest models in which a parametric component was added to model the error variance. The error variance was modeled as a power function of the predictive value and was used to produce uncertainty maps. A different model was fit for each variable and the resulting models were used to obtain maps of synthetic predictions for all areas covered by the 20 lidar acquisitions. The modeled error variance was used to generate uncertainty maps for the predictions of each response variable. Model accuracy was assessed globally (for the entire dataset) and separately for each one of the 20 lidar acquisitions included in the dataset.

Results from the accuracy assessment can be found in Appendix A and Appendix B of Mauro et al. (2021).

Each variable has two associated maps. These maps are named using the following convention where VARIABLE is the acronym for each variable (AGB, DWB, CBD, CH, CBH or CFL):

Predictions of forest attributes:

VARIABLE.tif

Standard deviation of modeled errors:

SD_VARIABLE.tif

### There are two additional rasters. The first one, year.tif is necessary to obtain the reference year for each lidar acquisition. The second one, forest_mask.tif provides a forest vs non-forest mask. Forested areas are coded as 1s and non-forested areas with no-datas. This mask is a resampled subset of the PALSAR JAXA 2014 ‘New global 25m-resolution PALSAR mosaic and forest/non-forest map (2007-2010) - version 1’ from the Japan Aerospace Exploration Agency Earth Observation Research Center (www.eorc.jaxa.jp/ALOS/en/palsar_fnf/fnf_index.htm). Its reference year is 2009. Models to predict forest attributes were created using ground observations in forested areas. For many applications it is advisable to use the provided mask to excluded non-forested areas from analyses. This can be done, for example, multiplying the desired raster by the forest mask. Exceptions to this may occur in relatively open forested lands where the mask eliminates areas that actually sustain forest. In those areas, the use of an add-hoc forest mask might be more appropriate. ### Reference year: year.tif ### Forest mask: forest_mask.tif ###

UNITS:

For a given variable, both predictions and standard deviation of model errors have the same units. These units are:

• Variable (Abreviation): Units

• Above ground biomass (AGB): Mg/ha

• Downed wood biomass (DWB):Mg/ha

• Canopy bulk density (CBD): Kg/m3 (Kilogram per cubic meter)

• Canopy height (CH): m

• Canopy base height (CBH): m

  Canopy fuel load (CFL):Mg/ha

COORDINATE REFERENCE SYSTEM:

The reference system for all maps is EPSG 5070

USAGE

These data are made freely available to the public and the scientific community in the belief that their wide dissemination will lead to greater understanding and new scientific insights.

Please include the following citation in any publication that uses these data:

Mauro, F., Hudak, A.T., Fekety, P.A., Frank, B., Temesgen, H., Bell, D.M., Gregory, M.J., McCarley, T.R., 2021. Regional Modeling of Forest Fuels and Structural Attributes Using Airborne Laser Scanning Data in Oregon. Remote Sensing 13. https://doi.org/10.3390/rs13020261

About this dataset

Context

The dataset tabulates the population of Pawnee City by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Pawnee City. The dataset can be utilized to understand the population distribution of Pawnee City by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Pawnee City. 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 Pawnee City.

Key observations

Largest age group (population): Male # 10-14 years (46) | Female # 0-4 years (56). 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 Pawnee City population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Pawnee City is shown in the following column.
• Population (Female): The female population in the Pawnee City 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 Pawnee City 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 Pawnee City Population by Gender. You can refer the same here

The market for cannabidiol (CBD) in Canada was worth 446 million U.S. dollars in 2022. By 2026, the market is expected to have grown to 452.4 million dollars.

The purpose of this data package is to offer demographic data for U.S. cities. The data sources are multiple, the most important one being the U.S. Census Bureau, American Community Survey. In this case, the data was organized by the Big Cities Health Coalition (BCHC). Others are the New York City Department of City Planning and Department of Parks and Recreation, data being available through the NYC Open Data.

About this dataset

Context

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

Key observations

There is a slight majority of female population, with 52.89% 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 Plain City is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Plain City 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 Plain City Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Willow City by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Willow City. The dataset can be utilized to understand the population distribution of Willow City by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Willow City. 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 Willow City.

Key observations

Largest age group (population): Male # 70-74 years (11) | Female # 70-74 years (8). 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.

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 Willow City population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Willow City is shown in the following column.
• Population (Female): The female population in the Willow City 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 Willow City 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 Willow City Population by Gender. You can refer the same here

Considered as one of the world’s biodiversity rich countr ies, Papua New Guinea ranks among the megadiverse countries and the last frontiers for

biodiversity conservation. This land of diversity hosts 6

8% of the global

species, hosts one

sixth of known languages, and rivals Borneo, the Amazon and the Congo in t erms of biodiversity wealth. PNG comprises the eastern half of the largest tropical island on earth, along with hundreds of smaller outlying islands, and its land mass only occupying less than 0.5% of the world’s total. Its diverse interior consists of s pectacular highland valleys, grasslands, vast expanses of rainforest, ancient swamps and mangroves. Primary rainforest covers around 75% of the country, its interior consists of undulating mountain ranges and grassy lowlands that rise to Mt.Wilhelm, the hi ghest summit at 3,600m. Crisscrossing the country’s surface, acting as a lifeline in terms of sustenance and access are a collection of thousands of waterways, the largest of which are Sepik, Purari, Markham, Strickland, Kikori and Fly Rivers.

The Environment Protection Authority (EPA) monitors South Australian airsheds in order to assess the air quality and provide information that can be used to guide management decisions. This data set contains validated particle pollution data for the Adelaide CBD region. This site was commissioned in April 2014.

About this dataset

Context

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

Key observations

There is a majority of female population, with 55.18% 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 Rainbow City is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Rainbow City 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 Rainbow City Population by Race & Ethnicity. You can refer the same here

These data include the individual responses for the City of Tempe Annual Community Survey conducted by ETC Institute. This dataset has two layers and includes both the weighted data and unweighted data. Weighting data is a statistical method in which datasets are adjusted through calculations in order to more accurately represent the population being studied. The weighted data are used in the final published PDF report.These data help determine priorities for the community as part of the City's on-going strategic planning process. Averaged Community Survey results are used as indicators for several city performance measures. The summary data for each performance measure is provided as an open dataset for that measure (separate from this dataset). The performance measures with indicators from the survey include the following (as of 2023):1. Safe and Secure Communities1.04 Fire Services Satisfaction1.06 Crime Reporting1.07 Police Services Satisfaction1.09 Victim of Crime1.10 Worry About Being a Victim1.11 Feeling Safe in City Facilities1.23 Feeling of Safety in Parks2. Strong Community Connections2.02 Customer Service Satisfaction2.04 City Website Satisfaction2.05 Online Services Satisfaction Rate2.15 Feeling Invited to Participate in City Decisions2.21 Satisfaction with Availability of City Information3. Quality of Life3.16 City Recreation, Arts, and Cultural Centers3.17 Community Services Programs3.19 Value of Special Events3.23 Right of Way Landscape Maintenance3.36 Quality of City Services4. Sustainable Growth & DevelopmentNo Performance Measures in this category presently relate directly to the Community Survey5. Financial Stability & VitalityNo Performance Measures in this category presently relate directly to the Community SurveyMethods:The survey is mailed to a random sample of households in the City of Tempe. Follow up emails and texts are also sent to encourage participation. A link to the survey is provided with each communication. To prevent people who do not live in Tempe or who were not selected as part of the random sample from completing the survey, everyone who completed the survey was required to provide their address. These addresses were then matched to those used for the random representative sample. If the respondent’s address did not match, the response was not used. To better understand how services are being delivered across the city, individual results were mapped to determine overall distribution across the city. Additionally, demographic data were used to monitor the distribution of responses to ensure the responding population of each survey is representative of city population. Processing and Limitations:The location data in this dataset is generalized to the block level to protect privacy. This means that only the first two digits of an address are used to map the location. When they data are shared with the city only the latitude/longitude of the block level address points are provided. This results in points that overlap. In order to better visualize the data, overlapping points were randomly dispersed to remove overlap. The result of these two adjustments ensure that they are not related to a specific address, but are still close enough to allow insights about service delivery in different areas of the city. The weighted data are used by the ETC Institute, in the final published PDF report.The 2023 Annual Community Survey report is available on data.tempe.gov or by visiting https://www.tempe.gov/government/strategic-management-and-innovation/signature-surveys-research-and-dataThe individual survey questions as well as the definition of the response scale (for example, 1 means “very dissatisfied” and 5 means “very satisfied”) are provided in the data dictionary.Additional InformationSource: Community Attitude SurveyContact (author): Adam SamuelsContact E-Mail (author): Adam_Samuels@tempe.govContact (maintainer): Contact E-Mail (maintainer): Data Source Type: Excel tablePreparation Method: Data received from vendor after report is completedPublish Frequency: AnnualPublish Method: ManualData Dictionary

About this dataset

Context

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

Key observations

There is a majority of female population, with 55.46% 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 Sac City is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Sac City 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 Sac City Population by Gender. You can refer the same here