About this dataset

Context

The dataset tabulates the East Bay township population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for East Bay township. The dataset can be utilized to understand the population distribution of East Bay township by age. For example, using this dataset, we can identify the largest age group in East Bay township.

Key observations

The largest age group in East Bay Township, Michigan was for the group of age 60 to 64 years years with a population of 1,205 (10.35%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in East Bay Township, Michigan was the 85 years and over years with a population of 221 (1.90%). 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

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the East Bay township is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of East Bay township 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 East Bay township Population by Age. You can refer the same here

About this dataset

Context

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

Key observations

In 2023, the population of East Bay township was 11,694, a 0.16% increase year-by-year from 2022. Previously, in 2022, East Bay township population was 11,675, a decline of 0.06% compared to a population of 11,682 in 2021. Over the last 20 plus years, between 2000 and 2023, population of East Bay township increased by 1,804. In this period, the peak population was 11,694 in the year 2023. The numbers suggest that the population has not reached its peak yet and is showing a trend of further growth. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

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

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

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

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

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

About this dataset

Context

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

Key observations

In 2022, the population of East Bay township was 11,693, a 0.09% increase year-by-year from 2021. Previously, in 2021, East Bay township population was 11,683, an increase of 0.67% compared to a population of 11,605 in 2020. Over the last 20 plus years, between 2000 and 2022, population of East Bay township increased by 1,803. In this period, the peak population was 11,693 in the year 2022. The numbers suggest that the population has not reached its peak yet and is showing a trend of further growth. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

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

Data Coverage:

• From 2000 to 2022

Variables / Data Columns

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

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

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

VITAL SIGNS INDICATOR Population (LU1)

FULL MEASURE NAME Population estimates

LAST UPDATED October 2019

DESCRIPTION Population is a measurement of the number of residents that live in a given geographical area, be it a neighborhood, city, county or region.

DATA SOURCES U.S Census Bureau: Decennial Census No link available (1960-1990) http://factfinder.census.gov (2000-2010)

California Department of Finance: Population and Housing Estimates Table E-6: County Population Estimates (1961-1969) Table E-4: Population Estimates for Counties and State (1971-1989) Table E-8: Historical Population and Housing Estimates (2001-2018) Table E-5: Population and Housing Estimates (2011-2019) http://www.dof.ca.gov/Forecasting/Demographics/Estimates/

U.S. Census Bureau: Decennial Census - via Longitudinal Tract Database Spatial Structures in the Social Sciences, Brown University Population Estimates (1970 - 2010) http://www.s4.brown.edu/us2010/index.htm

U.S. Census Bureau: American Community Survey 5-Year Population Estimates (2011-2017) http://factfinder.census.gov

U.S. Census Bureau: Intercensal Estimates Estimates of the Intercensal Population of Counties (1970-1979) Intercensal Estimates of the Resident Population (1980-1989) Population Estimates (1990-1999) Annual Estimates of the Population (2000-2009) Annual Estimates of the Population (2010-2017) No link available (1970-1989) http://www.census.gov/popest/data/metro/totals/1990s/tables/MA-99-03b.txt http://www.census.gov/popest/data/historical/2000s/vintage_2009/metro.html https://www.census.gov/data/datasets/time-series/demo/popest/2010s-total-metro-and-micro-statistical-areas.html

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) All legal boundaries and names for Census geography (metropolitan statistical area, county, city, and tract) are as of January 1, 2010, released beginning November 30, 2010, by the U.S. Census Bureau. A Priority Development Area (PDA) is a locally-designated area with frequent transit service, where a jurisdiction has decided to concentrate most of its housing and jobs growth for development in the foreseeable future. PDA boundaries are current as of August 2019. For more information on PDA designation see http://gis.abag.ca.gov/website/PDAShowcase/.

Population estimates for Bay Area counties and cities are from the California Department of Finance, which are as of January 1st of each year. Population estimates for non-Bay Area regions are from the U.S. Census Bureau. Decennial Census years reflect population as of April 1st of each year whereas population estimates for intercensal estimates are as of July 1st of each year. Population estimates for Bay Area tracts are from the decennial Census (1970 -2010) and the American Community Survey (2008-2012 5-year rolling average; 2010-2014 5-year rolling average; 2013-2017 5-year rolling average). Estimates of population density for tracts use gross acres as the denominator.

Population estimates for Bay Area PDAs are from the decennial Census (1970 - 2010) and the American Community Survey (2006-2010 5 year rolling average; 2010-2014 5-year rolling average; 2013-2017 5-year rolling average). Population estimates for PDAs are derived from Census population counts at the tract level for 1970-1990 and at the block group level for 2000-2017. Population from either tracts or block groups are allocated to a PDA using an area ratio. For example, if a quarter of a Census block group lies with in a PDA, a quarter of its population will be allocated to that PDA. Tract-to-PDA and block group-to-PDA area ratios are calculated using gross acres. Estimates of population density for PDAs use gross acres as the denominator.

Annual population estimates for metropolitan areas outside the Bay Area are from the Census and are benchmarked to each decennial Census. The annual estimates in the 1990s were not updated to match the 2000 benchmark.

The following is a list of cities and towns by geographical area: Big Three: San Jose, San Francisco, Oakland Bayside: Alameda, Albany, Atherton, Belmont, Belvedere, Berkeley, Brisbane, Burlingame, Campbell, Colma, Corte Madera, Cupertino, Daly City, East Palo Alto, El Cerrito, Emeryville, Fairfax, Foster City, Fremont, Hayward, Hercules, Hillsborough, Larkspur, Los Altos, Los Altos Hills, Los Gatos, Menlo Park, Mill Valley, Millbrae, Milpitas, Monte Sereno, Mountain View, Newark, Pacifica, Palo Alto, Piedmont, Pinole, Portola Valley, Redwood City, Richmond, Ross, San Anselmo, San Bruno, San Carlos, San Leandro, San Mateo, San Pablo, San Rafael, Santa Clara, Saratoga, Sausalito, South San Francisco, Sunnyvale, Tiburon, Union City, Vallejo, Woodside Inland, Delta and Coastal: American Canyon, Antioch, Benicia, Brentwood, Calistoga, Clayton, Cloverdale, Concord, Cotati, Danville, Dixon, Dublin, Fairfield, Gilroy, Half Moon Bay, Healdsburg, Lafayette, Livermore, Martinez, Moraga, Morgan Hill, Napa, Novato, Oakley, Orinda, Petaluma, Pittsburg, Pleasant Hill, Pleasanton, Rio Vista, Rohnert Park, San Ramon, Santa Rosa, Sebastopol, Sonoma, St. Helena, Suisun City, Vacaville, Walnut Creek, Windsor, Yountville Unincorporated: all unincorporated towns

U.S. Census Bureau QuickFacts statistics for East Bay township, Grand Traverse County, Michigan. QuickFacts data are derived from: Population Estimates, American Community Survey, Census of Population and Housing, Current Population Survey, Small Area Health Insurance Estimates, Small Area Income and Poverty Estimates, State and County Housing Unit Estimates, County Business Patterns, Nonemployer Statistics, Economic Census, Survey of Business Owners, Building Permits.

About this dataset

Context

The dataset tabulates the East Bay township population by year. The dataset can be utilized to understand the population trend of East Bay township.

Content

The dataset constitues the following datasets

• East Bay Township, Michigan Population Dataset: Yearly Figures, Population Change, and Percent Change Analysis

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/.

A. SUMMARY Medical provider confirmed COVID-19 cases and confirmed COVID-19 related deaths in San Francisco, CA aggregated by several different geographic areas and normalized by 2016-2020 American Community Survey (ACS) 5-year estimates for population data to calculate rate per 10,000 residents. On September 12, 2021, a new case definition of COVID-19 was introduced that includes criteria for enumerating new infections after previous probable or confirmed infections (also known as reinfections). A reinfection is defined as a confirmed positive PCR lab test more than 90 days after a positive PCR or antigen test. The first reinfection case was identified on December 7, 2021. Cases and deaths are both mapped to the residence of the individual, not to where they were infected or died. For example, if one was infected in San Francisco at work but lives in the East Bay, those are not counted as SF Cases or if one dies in Zuckerberg San Francisco General but is from another county, that is also not counted in this dataset. Dataset is cumulative and covers cases going back to 3/2/2020 when testing began. Geographic areas summarized are: 1. Analysis Neighborhoods 2. Census Tracts 3. Census Zip Code Tabulation Areas B. HOW THE DATASET IS CREATED Addresses from medical data are geocoded by the San Francisco Department of Public Health (SFDPH). Those addresses are spatially joined to the geographic areas. Counts are generated based on the number of address points that match each geographic area. The 2016-2020 American Community Survey (ACS) population estimates provided by the Census are used to create a rate which is equal to ([count] / [acs_population]) * 10000) representing the number of cases per 10,000 residents. C. UPDATE PROCESS Geographic analysis is scripted by SFDPH staff and synced to this dataset daily at 7:30 Pacific Time. D. HOW TO USE THIS DATASET San Francisco population estimates for geographic regions can be found in a view based on the San Francisco Population and Demographic Census dataset. These population estimates are from the 2016-2020 5-year American Community Survey (ACS). Privacy rules in effect To protect privacy, certain rules are in effect: 1. Case counts greater than 0 and less than 10 are dropped - these will be null (blank) values 2. Death counts greater than 0 and less than 10 are dropped - these will be null (blank) values 3. Cases and deaths dropped altogether for areas where acs_population < 1000 Rate suppression in effect where counts lower than 20 Rates are not calculated unless the case count is greater than or equal to 20. Rates are generally unstable at small numbers, so we avoid calculating them directly. We advise you to apply the same approach as this is best practice in epidemiology. A note on Census ZIP Code Tabulation Areas (ZCTAs) ZIP Code Tabulation Areas are special boundaries created by the U.S. Census based on ZIP Codes developed by the USPS. They are not, however, the same thing. ZCTAs are areal representations of routes. Read how the Census develops ZCTAs on their website. Row included for Citywide case counts, incidence rate, and deaths A single row is included that has the Citywide case counts and incidence rate. This can be used for comparisons. Citywide will capture all cases regardless of address quality. While some cases cannot be mapped to sub-areas like Census Tracts, ongo

Population-adjusted prevalence of antibodies from COVID-19 vaccination in Round 3 within race/ethnicity and age groups and prevalence differences between non-White and White individuals.

Characteristics of participants at each round of the study compared to study region population.

Objective: The purpose of this study was to examine whether a common, non-invasive, muscular fitness field test was a better predictor of bone strength compared to body mass. Methods: Hierarchical multiple regression analyses were used to determine the amount of variance that peak power explained for bone strength of the tibia compared to body mass. Peak power was estimated from maximal vertical jump height using Sayer’s equation. Peripheral quantitative computed tomography scans were used to assess bone strength measures. Results: Peak power (ꞵ=0.541, p<0.001) contributed more to the unique variance in bone strength index for compression compared to body mass (ꞵ=-0.102, p=0.332). For polar strength strain index, the beta coefficient for body mass remained significant (ꞵ=0.257, p<0.006), however, peak power’s contribution was similar (ꞵ=0.213, p= 0.051). Conclusion: Compared to body mass, peak power was a better predictor for trabecular bone strength but similar to body mass for cortical bone strength. These data provide additional support for the development of a vertical jump test as a simple, objective, valid and reliable measure to monitor bone strength among youth and adult populations. Methods Recruitment and Participant Characteristics: A convenience sample of 142 participants (79 F, 63 M) (13.3% African American/Black, 17.9% Latina/o, 28.6% White, 27.6% Asian/Pacific Islander, 1.0% American Indian or Alaskan Native and 11.7% Mixed Race or Unknown) was recruited for this observational, cross-sectional study, from the faculty, staff, and students at a mid-sized regional university. Participants were recruited through flyers, emails to the university community, and word-of-mouth advertisement. Participants received no compensation for participation. A general health and demographic survey was completed by all participants prior to the start of data collection to determine age, sex, and ethnicity of the participants. Participants were excluded if they had a history of any diseases that might influence bone health (endocrine diseases, gastrointestinal disorders, and eating disorders), were under 18 years of age, smoked, or were pregnant. All participants were informed of the risks and benefits of the study and provided written informed consent. The study was approved by the California State University, East Bay Institutional Review Board (IRB) (CSUEB-IRB-2016-223-F). The study was pre-registered at the Center for Open Science OSF (DOI: 10.17605/OSF.IO/B5QZC). Peripheral quantitative computed tomography (pQCT) (XCT 2000 Stratec Medizintechnik, Pforzheim, Germany) scans were used to assess bone strength measures of the dominant tibia. Maximal jump height was measured using a Vertec™ (JUMPUSA.com, Sunnyvale, CA).

About this dataset

Context

The dataset tabulates the data for the East Bay Township, Michigan population pyramid, which represents the East Bay township population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 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 East Bay Township, Michigan, is 16.5.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for East Bay Township, Michigan, is 33.6.
• Total dependency ratio for East Bay Township, Michigan is 50.1.
• Potential support ratio, which is the number of youth (working age population) per elderly, for East Bay Township, Michigan is 3.0.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 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 East Bay township population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the East Bay township for the selected age group is shown in the following column.
• Population (Female): The female population in the East Bay township for the selected age group is shown in the following column.
• Total Population: The total population of the East Bay township 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 East Bay township Population by Age. You can refer the same here

Along the East Asian-Australasian flyway (EAAF), waterbirds are threatened by a wide range of human activities. Studies have shown that wintering populations of many species have declined in Australia and Japan; however, long term data along China’s coast are limited. In this study, we analyzed data collected from monthly bird surveys to quantify population trends of wintering waterbirds from 1998 to 2017 in the Deep Bay area, South China. Of the 42 species studied, 12 declined, while nine increased significantly. Phylogenetic comparative analysis revealed that population trends were negatively correlated to reliance on the Yellow Sea and body size. Further, waterbird species breeding in Southern Siberia declined more than those breeding in East Asia. These findings, coupled with a relatively high number of increasing species, support the continual preservation of wetlands in the Deep Bay area. This study provides another case study showing that data collected from wintering sites provide insights on the threats along migratory pathway and inform conservation actions. As such, we encourage population surveys in the EAAF to continue, particularly along the coast of China.

Abstract

Chakaria Health and Demographic Surveillance System (CHDSS), located on the south-eastern coast of the Bay of Bengal, is one of the field sites of International Centre for Diarrhoeal Disease Research, Bangladesh (ICDDRB). The HDSS was established in 1999 covering 183 villages of 166,405 individuals living in 26,979 households.

In CHDSS since 2004 data on socio-demographic and health indicators including birth, death, migration, marriage, maternal health, education and employment have been systematically collected and recorded from 7,042 households, randomly chosen from the total of 26,979 households through quarterly household visits by a team of surveillance workers (SWs) with supervision from a team of two supervisors. In 2011 data collection system was modified choosing 49 villages randomly from a total of 183 which were divided into 14 work areas and 14 SWs were recruited from their residing areas. Most of the households included in the system prior to this modification were also included in the new system. The modification of the system has resulted the visit by SWs almost double the number of households, saving time spent on travel in comparison with the previous system. In addition, the modification allowed the possibility of estimating migration as the system includes complete villages.

Currently, surveillance covers 82,160 individuals (16,272 households). The primary objective of CHDSS is to monitor the changes in socio-demographic indicators, inequalities in health and impact of public health interventions. This dataset contains rounds 1 to 26 of demographic surveillance data covering the period from 2 Feb 2004 to 31 December 2014.

Geographic coverage

Chakaria is one of the 500 upazilas (sub-districts) in Bangladesh. It is located between latitudes 21o34' and 21o55' North and longitudes 91o54' and 92o13' East in the southeastern coast of the Bay of Bengal. Administratively, it is under Cox's Bazar district with an estimated population of 511,861 in 2013. The highway from Chittagong to Cox's Bazar passes through Chakaria. The east side of Chakaria is hilly, while on the west side towards the Bay of Bengal is lowland. The Chakaria HDSS covered 11 unions: Baraitali, Kaiarbil, Bheola Manik Char, Paschim Boro Bheola, Shaharbil, Kakara, Harbang, Purba Boro Bheola, Surajpur Manikpur, Konakhali, and Dhemoshia.

Analysis unit

Individual

Universe

Currently, the study covers 82,029 individuals (16,624 households) in Chakaria HDSS area.

Kind of data

Event history data

Frequency of data collection

Collection of data from households on a quarterly basis

Sampling procedure

This dataset is related to the demographic surveillance population as a whole

Sampling deviation

Not applicable

Mode of data collection

Proxy Respondent [proxy]

Research instrument

• Child Health Profile form
• Death form
• Household Characteristics form
• DSS data collection blank form
• Abolished form
• Correction form
• Listing form
• Migration form

Cleaning operations

All the filled questionnaires were manually checked for completeness and for any inconsistencies. Subsequently, computer-based data editing procedures were applied to ensure the quality of data.

Response rate

On an average the response rate is 99.99% in all rounds over the years.

Sampling error estimates

Not applicable

Data appraisal

CentreId MetricTable QMetric Illegal Legal Total Metric RunDate
BD013 MicroDataCleaned Starts 135810 2017-06-29 12:12 BD013 MicroDataCleaned Transitions 0 339739 339739 0. 2017-06-29 12:12 BD013 MicroDataCleaned Ends 135810 2017-06-29 12:12 BD013 MicroDataCleaned SexValues 339739 2017-06-29 12:12 BD013 MicroDataCleaned DoBValues 339739 2017-06-29 12:13

About this dataset

Context

The dataset tabulates the population of East Bay township by race. It includes the population of East Bay township across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of East Bay township across relevant racial categories.

Key observations

The percent distribution of East Bay township population by race (across all racial categories recognized by the U.S. Census Bureau): 93.39% are white, 1.79% are Black or African American, 0.14% are American Indian and Alaska Native, 0.27% are Asian and 4.42% are multiracial.

Content

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

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the East Bay township
• Population: The population of the racial category (excluding ethnicity) in the East Bay township is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of East Bay township 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 East Bay township Population by Race & Ethnicity. You can refer the same here

Worldwide, cetaceans are impacted by human activities, and those populations that occur in shallow-nearshore habitats are particularly vulnerable. We present the results of the first long-term study on the responses of a coastal population of endangered Irrawaddy dolphins to widespread habitat changes. We particularly investigated their responses in terms of distribution and abundance. Boat-based, line-transect surveys were conducted during 12 discrete survey periods in 7 survey years spanning a 15-year period (totaling 78 days and 4,630 km of effort) in Balikpapan Bay, East Kalimantan, Indonesia. Irrawaddy dolphins were sighted on 136 occasions. Through DISTANCE analysis, a decrease in population density in the inner Bay area was observed from 0.45 dolphins/km2 in 2000–2001 (CV = 24%) to 0.34 and 0.32 dolphins/km2 in 2008 and 2015 (CV = 31% and 25%). A shift in distribution was noted between the periods 2000–2002 and 2008–2015 with significantly lower occurrence in the lower Bay segment compared to upper Bay segments. No sightings were made in the outer Bay area in later years, which coincided with increased shipping traffic in these areas. A peak in stranding events in 2016 and 2018 followed extremely high phenol levels within Bay waters in 2015 and a large-scale oil spill in 2018. The mean annual mortality rates of 0.67 Irrawaddy dolphins/year is unsustainable based on the lower potential biological removal (PBR) values for best abundance estimates of 2015 (Ndistance = 45 and Nmark–recapture = 73). Other threats to local dolphins include unsustainable fishing, underwater noise caused by construction, particularly piling activities. The research helped to identify Balikpapan Bay as an Important Marine Mammal Area by the IUCN MMPA Taskforce. Serious concerns remain for the concrete plans to move Indonesia’s capital city to the area north of the Bay, in terms of increased shipping traffic and harbor construction in the upper Bay segments that represent primary dolphin habitat. We recommend that protected areas be assigned for marine mammals and artisanal fisheries and shipping traffic and piling activities be excluded from these areas. We also recommend a legislated requirement of a mitigation protocol compulsory for piling and seismic activities within Indonesia.

INDICATOR DEFINITION Count of all adult females, fully weaned pups and dead pups hauled out on, or close to, the day of maximum cow numbers, set for October 15.

TYPE OF INDICATOR There are three types of indicators used in this report: 1.Describes the CONDITION of important elements of a system; 2.Show the extent of the major PRESSURES exerted on a system; 3.Determine RESPONSES to either condition or changes in the condition of a system.

This indicator is one of: CONDITION

RATIONALE FOR INDICATOR SELECTION Elephant seals from Macquarie Island are long distance foragers who can utilise the Southern Ocean both west as far as Heard Island and east as far as the Ross Sea. Thus their populations reflect foraging conditions across a vast area.

The slow decline in their numbers (-2.3% annually from 1988-1993) suggests that their ocean foraging has been more difficult in recent decades. Furthermore, interactions with humans are negligible due to the absence of significant overlap in their diet with commercial fisheries. This suggests that changes in 'natural' ocean conditions may have altered aspects of prey availability. It is clear that seal numbers are changing in response to ocean conditions but at the moment these conditions cannot be specified.

DESIGN AND STRATEGY FOR INDICATOR MONITORING PROGRAM Spatial Scale: Five beaches on Macquarie Island (lat54 degrees 37' 59.9' S, long 158 degrees 52' 59.9' E): North Head to Aurora Point; Aurora Point to Caroline Cove; Garden Cove to Sandy Bay; Sandy Bay to Waterfall Bay; Waterfall Bay to Hurd Point.

Frequency: Annual census on 15th October

Measurement Technique: Monitoring the Southern Elephant Seal population on Macquarie island requires a one day whole island adult female census on October 15 and a daily count of cow numbers, fully weaned pups and dead pups on the west and east isthmus beaches throughout October.

Daily cow counts during October, along the isthmus beaches close to the Station, provide data to identify exactly the day of maximum numbers. The isthmus counts are recorded under the long-established (since 1950) harem names. Daily counts allow adjustment to the census totals if the day of maximum numbers of cows ashore happens to fall on either side of October 15. Personnel need to be dispersed around the island by October 15 so that all beaches are counted for seals on that day. This has been achieved successfully for the last 15 years.

On the day of maximum haul out (around 15th October) the only Elephant seals present are cows, their young pups and adult males. The three classes can be readily distinguished and counted accurately. Lactating pups are not counted, their numbers are provided by the cow count on a 1:1 proportion. The combined count of cows, fully weaned pups and dead pups provides an index of pup production.

The count of any group is made until there is agreement between counts to better than +/- 5%. Thus there is always a double count as a minimum; the number of counts can reach double figures when a large group is enumerated. The largest single group on Macquarie Island is that at West Razorback with greater than 1,000 cows; Multiple counts are always required there.

RESEARCH ISSUES Much research has been done already to acquire demographic data so that population models can be produced. Thus there will be predicted population sizes for elephant seals on Macquarie Island in 2002 onwards and the annual censuses will allow these predictions to be tested against the actual numbers. The censuses are also a check on the population status of this endangered species.

LINKS TO OTHER INDICATORS

Geographic coverage

The 1985 census covered the so-called white areas of South Africa, i.e. the areas in the former four provinces of the Cape, the Orange Free State, Transvaal, and Natal. It also covered the so-called National States of KwaZulu, Kangwane, Gazankulu, Lebowa, Qwaqwa, and Kwandebele. The 1985 South African census excluded the areas of the Transkei, Bophutatswana, Ciskei, and Venda.

The 1985 Census dataset contains 9 data files. These refer to Development Regions demarcated by the South African Government according to their socio-economic conditions and development needs. These Development Regions are labeled A to J (there is no Region I, presumably because Statistics SA felt an "I" could be confused with the number 1). The 9 data files in the 1985 Census dataset refer to the following areas:

DEV REGION AREA COVERED A Western Cape Province including Walvis Bay B Northern Cape C Orange Free State and Qwaqwa D Eastern Cape/Border E Natal and Kwazulu F Eastern Transvaal, KaNgwane and part of the Simdlangentsha district of Kwazulu G Northern Transvaal, Lebowa and Gazankulu H PWV area, Moutse and KwaNdebele J Western Transvaal

Analysis unit

The units of analysis under observation in the South African census 1985 are households and individuals

Universe

The South African census 1985 census covered the provinces of the Cape, the Orange Free State, Transvaal, and Nata and the so-called National States of KwaZulu, Kangwane, Gazankulu, Lebowa, Qwaqwa, and Kwandebele. The 1985 South African census excluded the areas of the Transkei, Bophutatswana, Ciskei, and Venda.

Kind of data

Census/enumeration data [cen]

Sampling procedure

Although the census was meant to cover all residents of the so called white areas of South Africa, in 88 areas door-to-door surveys were not possible and the population in these areas was enumerated by means of a sample survey conducted by the Human Sciences Research Council.

Mode of data collection

Face-to-face [f2f]

Research instrument

The1985 population census questionnaire was administered to each household and collected information on household and area type, and information on household members, including relationship within household, sex, age, marital status, population group, birthplace, country of citizenship, level of education, occupation, identity of employer and the nature of economic activities

Data appraisal

UNDER-ENUMERATION: The following under-enumeration figures have been calculated for the 1985 census. Estimated percentage distribution of undercount by race according to the HSRC: Percent undercount
Whites 7.6%
Blacks in the “RSA” 20.4% Blacks in the “National States” 15.1% Coloureds 1.0% Asians 4.6%

description: The federal and state endangered California clapper rail, Rallus longirostris obsoletus. is a species that, until very recently, was on the verge of extinction. This secretive marsh bird's decline began over 100 years ago in the pristine marshes of San Francisco Bay (Bay) and the California coast (Fig. 1). In the earlier part of this century, the rail was found as far north as Humboldt Bay pd as far south as Morro Bay (Gill 1979) (Fig. 2). In the early 80s, the last known pair of rails outside of the Bay was seen at Elkhorn Slough in Monterey County. During the first half of this century, exploitation of the Bay's natural resources, including unrestricted filling and diking of the tidal marshes, began shrinking the rail's habitat in San Pablo Bay, Central and South San Francisco Bay from over 51,000 hectares to less than 9,000 hectares that now remain today (Dedrick 1993). The cumulative effects from this continued loss of critical habitat, combined with recent threats from increased predation, probable contamination, and other stresses associated with expanding urban growth, has created a crisis for our bay's indigenous rail. After the rail was listed as Endangered under the authority of the Endangered Species Act by the U.S. Fish and Wildlife Service (Service) in 1970, censuses of the population in the Bay were initiated. In the early 1970s, Gill estimated the total California clapper rail population at 4200 to 6000 individuals (1979). Surveys for the rail continued into the 80s (Moss 1980), with Harvey providing an estimate of 1200-1500 rails in 1981. The survey by Harvey was more accurate than the Gill estimate because an actual count was made, as compared to an average density which Gill applied to all suitable habitat. Subsequent censuses were sporadic and incomplete (Harvey 1987) until the Service, led by the San Francisco Bay National Wildlife Refuge (Refuge) began winter high tide surveys of South San Francisco Bay (South Bay) in 1988 (Foerster 1989). The Service began to suspect that the rail was in serious decline after the Refuge conducted a thorough survey of major South Bay marshes in the winter of 1988-89 and estimated a total population of only 700 rails. It was discovered that populations of rails in marshes on the east side of the bay were suffering the greatest declines and that predation by non-native predators was implicated as a primary factor (Foerster 1989). This hypothesis was confirmed by data collected by the Refuge and subsequently an Environmental Assessment and Predator Management Plan was implemented in March 1991 (Foerster and Takekawa 1991). Since 1988, the Refuge has continued to conduct annual winter high tide surveys of South Bay rail populations and some San Pablo Bay (North Bay) subpopulations (Figs. 2 and 3), with the assistance of the California Department of Fish and Game (CDFG) and other local organizations such as the San Francisco Bay Bird Observatory. This report summarizes data collected between November 1989 and January 1993, encompassing four annual winter surveys. During the last two years, the Refuge also initiated research into several factors which were implicated in rail population decline. The factors which were identified as significantly affecting rail survival included predation by non-native predators (Foerster and Takekawa 1991), and high levels of heavy metals in prey species (Lonzarich, et al. 1992). Continued analysis of these factors by the Service will culminate in a several reports to be released in late 1994.; abstract: The federal and state endangered California clapper rail, Rallus longirostris obsoletus. is a species that, until very recently, was on the verge of extinction. This secretive marsh bird's decline began over 100 years ago in the pristine marshes of San Francisco Bay (Bay) and the California coast (Fig. 1). In the earlier part of this century, the rail was found as far north as Humboldt Bay pd as far south as Morro Bay (Gill 1979) (Fig. 2). In the early 80s, the last known pair of rails outside of the Bay was seen at Elkhorn Slough in Monterey County. During the first half of this century, exploitation of the Bay's natural resources, including unrestricted filling and diking of the tidal marshes, began shrinking the rail's habitat in San Pablo Bay, Central and South San Francisco Bay from over 51,000 hectares to less than 9,000 hectares that now remain today (Dedrick 1993). The cumulative effects from this continued loss of critical habitat, combined with recent threats from increased predation, probable contamination, and other stresses associated with expanding urban growth, has created a crisis for our bay's indigenous rail. After the rail was listed as Endangered under the authority of the Endangered Species Act by the U.S. Fish and Wildlife Service (Service) in 1970, censuses of the population in the Bay were initiated. In the early 1970s, Gill estimated the total California clapper rail population at 4200 to 6000 individuals (1979). Surveys for the rail continued into the 80s (Moss 1980), with Harvey providing an estimate of 1200-1500 rails in 1981. The survey by Harvey was more accurate than the Gill estimate because an actual count was made, as compared to an average density which Gill applied to all suitable habitat. Subsequent censuses were sporadic and incomplete (Harvey 1987) until the Service, led by the San Francisco Bay National Wildlife Refuge (Refuge) began winter high tide surveys of South San Francisco Bay (South Bay) in 1988 (Foerster 1989). The Service began to suspect that the rail was in serious decline after the Refuge conducted a thorough survey of major South Bay marshes in the winter of 1988-89 and estimated a total population of only 700 rails. It was discovered that populations of rails in marshes on the east side of the bay were suffering the greatest declines and that predation by non-native predators was implicated as a primary factor (Foerster 1989). This hypothesis was confirmed by data collected by the Refuge and subsequently an Environmental Assessment and Predator Management Plan was implemented in March 1991 (Foerster and Takekawa 1991). Since 1988, the Refuge has continued to conduct annual winter high tide surveys of South Bay rail populations and some San Pablo Bay (North Bay) subpopulations (Figs. 2 and 3), with the assistance of the California Department of Fish and Game (CDFG) and other local organizations such as the San Francisco Bay Bird Observatory. This report summarizes data collected between November 1989 and January 1993, encompassing four annual winter surveys. During the last two years, the Refuge also initiated research into several factors which were implicated in rail population decline. The factors which were identified as significantly affecting rail survival included predation by non-native predators (Foerster and Takekawa 1991), and high levels of heavy metals in prey species (Lonzarich, et al. 1992). Continued analysis of these factors by the Service will culminate in a several reports to be released in late 1994.

About this dataset

Context

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

Key observations

There is a slight majority of female population, with 50.98% 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 East Bay township is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of East Bay township 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 East Bay township Population by Race & Ethnicity. You can refer the same here

MU abbreviations are as in Table 1. Solid lines demarcate the four major clusters discovered by Paetkau et al., 1999 [4], which correspond to our Structure results for K = 4. From left to right, these are: the Hudson Complex, the Canadian Arctic Archipelago, Norwegian Bay, and the Polar Basin. Dotted lines denote the west–east clusters within the Basin and the Archipelago detected by K-means clustering in GenoDive. These six clusters include additional east–west substructure within the Archipelago and within the Polar Basin. DS is an admixture zone showing affinity for both Hudson Complex and the Archipelago, with southern samples tending to belong to the Hudson Complex cluster and northern samples tending to belong to the Eastern Archipelago cluster. LP has been excluded from all comparisons because it deviates significantly from Hardy–Weinberg equilibrium. For mitochondrial DNA, MC, VM, and NW were omitted because sample sizes were too small (i.e., N ≤ 3, k = 1) to accurately estimate haplotype frequencies.