Draft dataset for Bay Area Census website prototype. Includes census 2000 population breakdown by age, sex and race.

About this dataset

Context

The dataset tabulates the Non-Hispanic population of San Francisco County by race. It includes the distribution of the Non-Hispanic population of San Francisco County across various race categories as identified by the Census Bureau. The dataset can be utilized to understand the Non-Hispanic population distribution of San Francisco County across relevant racial categories.

Key observations

Of the Non-Hispanic population in San Francisco County, the largest racial group is White alone with a population of 313,559 (44.60% of the total Non-Hispanic population).

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 (for Non-Hispanic) for the San Francisco County
• Population: The population of the racial category (for Non-Hispanic) in the San Francisco County is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of San Francisco County total Non-Hispanic 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 San Francisco County Population by Race & Ethnicity. You can refer the same here

Racial diversity is measured by a diversity index that is calculated using United States Census racial and ethnic population characteristics from the PL-94 data file. The diversity index is a quantitative measure of the distribution of the proportion of five major ethnic populations (non-Hispanic White, non-Hispanic Black, Asian and Pacific Islander, Hispanic, and Two or more races). The index ranges from 0 (low diversity meaning only one group is present) to 1 (meaning an equal proportion of all five groups is present). The diversity score for the United States in 2010 is 0.60. The diversity score for the San Francisco Bay Region is 0.84. Within the region, Solano (0.89) and Alameda (0.90) Counties are the most diverse and the remaining North Bay (0.55 - 0.64) Counties are the least diverse.

About this dataset

Context

The dataset presents the median household income across different racial categories in San Francisco township. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to gain insights into economic disparities and trends and explore the variations in median houshold income for diverse racial categories.

Key observations

Based on our analysis of the distribution of San Francisco township population by race & ethnicity, the population is predominantly White. This particular racial category constitutes the majority, accounting for 95.31% of the total residents in San Francisco township. Notably, the median household income for White households is $133,763. Interestingly, White is both the largest group and the one with the highest median household income, which stands at $133,763.

https://i.neilsberg.com/ch/san-francisco-township-mn-median-household-income-by-race.jpeg" alt="San Francisco township median household income diversity across racial categories">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 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 of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in San Francisco township.
• Median household income: Median household income, adjusting for inflation, presented in 2022-inflation-adjusted dollars

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 San Francisco township median household income by race. You can refer the same here

About this dataset

Context

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

Key observations

The percent distribution of San Francisco County population by race (across all racial categories recognized by the U.S. Census Bureau): 40.49% are white, 5.09% are Black or African American, 0.66% are American Indian and Alaska Native, 34.97% are Asian, 0.38% are Native Hawaiian and other Pacific Islander, 7.75% are some other race and 10.66% 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 San Francisco County
• Population: The population of the racial category (excluding ethnicity) in the San Francisco County is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of San Francisco County 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 San Francisco County Population by Race & Ethnicity. You can refer the same here

Draft dataset for Bay Area Census website prototype. Includes census 2020 housing data. Contains housing by occupancy and vacancy status.

About this dataset

Context

The dataset tabulates the San Francisco Hispanic or Latino population. It includes the distribution of the Hispanic or Latino population, of San Francisco, by their ancestries, as identified by the Census Bureau. The dataset can be utilized to understand the origin of the Hispanic or Latino population of San Francisco.

Key observations

Among the Hispanic population in San Francisco, regardless of the race, the largest group is of Mexican origin, with a population of 65,170 (48.90% of the total Hispanic population).

Content

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

Origin for Hispanic or Latino population include:

• Mexican
• Puerto Rican
• Cuban
• Other Hispanic or Latino

Variables / Data Columns

• Origin: This column displays the origin for Hispanic or Latino population for the San Francisco
• Population: The population of the specific origin for Hispanic or Latino population in the San Francisco is shown in this column.
• % of Total Hispanic Population: This column displays the percentage distribution of each Hispanic origin as a proportion of San Francisco total Hispanic or Latino 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 San Francisco Population by Race & Ethnicity. You can refer the same here

Relative concentration of the Northern California region's American Indian population. The variable AIANALN records all individuals who select American Indian or Alaska Native as their SOLE racial identity in response to the Census questionnaire, regardless of their response to the Hispanic ethnicity question. Both Hispanic and non-Hispanic in the Census questionnaire are potentially associated with American Indian / Alaska Native race alone. IMPORTANT: this self reported ancestry and Tribal membership are distinct identities and one does not automatically imply the other. These data should not be interpreted as a distribution of "Tribal people." Numerous Rancherias in the Northern California region account for the wide distribution of very to extremely high concentrations of American Indians outside the San Francisco Bay Area.

"Relative concentration" is a measure that compares the proportion of population within each Census block group data unit that identify as American Indian / Alaska Native alone to the proportion of all people that live within the 1,207 block groups in the Northern California RRK region that identify as American Indian / Alaska native alone. Example: if 5.2% of people in a block group identify as AIANALN, the block group has twice the proportion of AIANALN individuals compared to the Northern California RRK region (2.6%), and more than three times the proportion compared to the entire state of California (1.6%). If the local proportion is twice the regional proportion, then AIANALN individuals are highly concentrated locally.

Draft dataset for Bay Area Census website prototype. Includes census 2020 households data. Households by marriage, family, and ownership status, as well as householder sex and size.

Abstract

The Greater Bay Area Cancer Registry (GBACR), in compliance with California state law, gathers information about all cancers diagnosed or treated in a nine-county area (Alameda, Contra Costa, Marin, Monterey, San Benito, San Francisco, San Mateo, Santa...

Documentation

PHS does NOT host these data. This listing is information only.

The Greater Bay Area Cancer Registry (GBACR), in compliance with California state law, gathers information about all cancers diagnosed or treated in a nine-county area (Alameda, Contra Costa, Marin, Monterey, San Benito, San Francisco, San Mateo, Santa Clara and Santa Cruz). This information is obtained from medical records provided by hospitals, doctors\342\200\231 offices, and other related facilities.

The information, stored under secure conditions with strict regulations that protect confidentiality, helps the GBACR understand cancer occurrence and survival in the Greater Bay Area. For each patient, the information includes basic demographic facts like age, gender, and race/ethnicity, as well as cancer type, extent of disease, treatment and survival. Combined over the diverse Bay Area population, this information gives the GBACR and all users an opportunity to learn how such characteristics may be related to cancer causes, mortality, care and prevention.

In addition to its local use, information collected by the GBACR becomes part of state and federal population-based registries whose mission is to monitor cancer occurrence at the state and national levels, respectively. Data from the GBACR have contributed to the National Cancer Institute’s Surveillance, Epidemiology and End Results (SEER) program since 1973. The nine counties are also part of the statewide California Cancer Registry (CCR), which conducts essential monitoring of cancer occurrence and survival in California.

GBACR data are of the highest quality, as recognized by national and international registry standard-setting organizations, including SEER, the National Program for Cancer Registries, and the North American Association for Central Cancer Registries (NAACCR).

The CPIC has also started collecting data on environmenal factors. These data are available in the The California Neighborhoods Data System. This a new resource for examining the impact of neighborhood characteristics on cancer incidence and outcomes in populations includes a compilation of existing geospatial and other secondary data for characterizing contextual factors

A summary and description of social and built environment data and measures in the California Neighborhoods Data System (2010) can be found here: Social and Built Environment Data and Measures

More information about this new data source can be found here: The California Neighborhoods Data System

Patient characteristics All reported cancer cases in the state of California.

Data overview Data categories Socioeconomic status Racial/ethnic composition Immigration/acculturation characteristics Racial/ethnic residential segregation Population density Urbanicity (Rural/Urban) Housing Businesses Commuting Street connectivity Parks Farmers Markets Traffic density Crime Tapestry Segmentation

Notes To apply for these data, you can see instructions here: https://www.ccrcal.org/retrieve-data/data-for-researchers/how-to-request-ccr-data/

This dataset contains counts of live births for California counties based on information entered on birth certificates. Final counts are derived from static data and include out of state births to California residents, whereas provisional counts are derived from incomplete and dynamic data. Provisional counts are based on the records available when the data was retrieved and may not represent all births that occurred during the time period.

The final data tables include both births that occurred in California regardless of the place of residence (by occurrence) and births to California residents (by residence), whereas the provisional data table only includes births that occurred in California regardless of the place of residence (by occurrence). The data are reported as totals, as well as stratified by parent giving birth's age, parent giving birth's race-ethnicity, and birth place type. See temporal coverage for more information on which strata are available for which years.

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%

Population monitoring is essential to assess, manage and protect threatened species. Although the South Pacific loggerhead turtle subpopulation is classified as critically endangered by the IUCN, monitoring data are scarce. This study reports the results of the first long-term monitoring of the nesting population of loggerhead turtles held by Bwärä Tortues Marines on La Roche Percée beach, New Caledonia. From 2006 to 2020, Capture Mark Recapture was used to identify nesting individuals. Time and nesting success were recorded on site. A total of 452 different females were observed and tagged over 14 years. The number of different nesting individuals observed each year showed a significant increase along the timeframe of the study. A remigration interval of 3.34 years was observed and the overall nesting success was 59.02%. This study also reports the inter-nesting intervals, monthly and hourly variabilities in the visits at the nesting site. The conservation actions led by Bwärä Tortues Marines seem to be correlated with a higher nesting success. This study provides encouraging results and highlights the need to pursue the monitoring and conservation actions implemented by Bwärä Tortues Marines. Further management recommendations are also provided. Methods Overview Over 14 years (2006-2020), daily patrols were implemented throughout each nesting season (from November to March). The timing and activities of the loggerhead turtles were monitored. Study site The study was conducted on La Roche Percée, a sandy beach in the Bay of La Roche Percée, New Caledonia (-21.612831, 165.463286). This site is included in a marine protected area registered under the natural reserve status (Decree n° 33-1993/APS 1993 and 293-99/PS, 1999). The beach is oriented South-west and is 2.5 km in length starting from a stand-up rock at the North-western end, to the mouth of the Néra river at the South-eastern end. The Néra river brings dark sediments directly to the beach of La Roche Percée resulting in darker sand compared to nearby beaches. La Roche Percée bay faces a large break in the barrier reef surrounding the area, allowing waves but also marine megafauna to enter the lagoon. At high tide, the beach is 20m (meters) wide in the middle and 100m wide at the northern and southern ends. During the hot and humid season (November to March) the beach can be submerged by water due to extreme weather events. The only access to La Roche Percée is a road over a dam on its northern shore – blocking the northern bank of the Nera river. Vegetation near the road partially protects the beach from anthropogenic light sources (cars and houses) and there are no street lights in this area. Sand replenishment work took place in 2011 (during the time of this study but not during the nesting season) at the vegetation margin to widen the beach and counter beach erosion. The sand was taken from the Néra river but was not washed and sieved causing its compaction on some parts of the beach, resulting in the inability of turtles to dig in these areas. This compacted area was high on the beach at the margin of the vegetation and was partially covered with sand and vines (Ipomoea pescaprae). The bay is also famous for being one of the most important surf spots in New Caledonia and is one of the main leisure sites, attracting many locals and tourists all year long. Although pets are prohibited inside the reserve following a government decree put in place in 2009, dogs are regularly seen on the beach. In the bay, a second beach named La Baie des Tortues (i.e. Turtle Bay, -21.60655, 165.45478, 280m in length = 1/5 of La Roche Percée) is located 100m north of La Roche Percée beach. The beaches are separated by a high rocky spit. This beach was not monitored during the night as the steep and slippery path leading to it would represent a safety hazard for the team working without light. Monitoring and data collection The walking patrol covered the 2.5km of the study site, walking back and forth between the two ends of the beach. Each turtle activity on the beach of La Roche Percée was recorded from November 2006 to March 2020 (14 nesting seasons) by members of Bwärä Tortues Marines. Two patrol sessions were conducted on the beach every day during the nesting seasons: (1) in the evening, usually conducted from 8 pm to 1 am (i.e. night shift), which enabled monitoring of the nesting females encountered on the beach, (2) in the morning (i.e. morning shift) starting at dawn, for 2 to 5 hours, and allowing an exhaustive count of all nesting activities throughout the night. The duration of a patrol could vary if a turtle or a track was detected by a member of Bwärä Tortues Marine before or after the usual hours. For example, if a turtle was spotted at 00:50 am the patrol team stayed on site until the female completed her nesting cycle. Sometimes, turtles crawled on the beach one after the other and the patrol team had to work until dawn. Only extreme weather conditions such as rain downpours or cyclonic alerts led to the cancellation of a patrol (on average eight patrols per year).
During the night shift, one or two teams composed of an eco-guard and trained volunteers walked without light alongside the high-water line, searching for turtle tracks. The number of teams deployed depended on the number of volunteers available. When a track was noticed, the team stopped and remained immobile while trying to locate the individual. The team followed the track in a way which meant they were unnoticed by the turtle (e.g., crawling). Observers were able to determine the precise nesting phase by seeing or hearing the turtle. The nesting phases include: ascending the beach, making a body pit (multiple body pit attempts could be undertaken before the next stage), digging the egg chamber, laying eggs, filling the egg chamber, covering the body pit, and returning to the surf (for precise descriptions of the nesting phases see Hailman & Elowson, 1992). To avoid disturbance, the turtles were approached after the beginning of the egg-laying phase, from the back and without light. During the data collection, a red light was eventually used for a short time only and never toward the head of the turtle. If the individual was found returning to the sea, the data were gathered while the turtle stopped to breathe. Data collection included: individual identification and carapace length measure, date, time, location, and nesting phase. The morning shift consisted mainly of recording all the activities that happened during the late part of the night. It always started at dawn to maximize the chances of meeting the last turtles of the night, to protect those individuals from potential disturbances (i.e. beach visitors or dogs), and to be sure to record all turtle activities. A later start could have led to the loss of data due to rising tides, strong winds, or beach users who could have erased the tracks. Activities were defined using the sand cues as above. The date, time, and location of the activity were recorded. Each track (crawling tracks, body pit, and mound) was then wiped off so as not to count it a second time during the following night and to hide the nest. Individuals were identified using Capture-Mark-Recapture (CMR). Every studied individual was tagged with a titanium tag (Titanium Turtle Tag, Stockbrands, Australia) on the trailing edge of the front flipper, in the skin between the first and the second scales adjacent to the axilla following recommendations published in Limpus (1992). During the first five seasons, most of the individuals were tagged on both left and right front flippers, following which it was reduced to one tag, placed on the left flipper, to minimize individuals' stress. Following the CMR protocol, if turtles did not have a tag, they were tagged and considered as Captures (C). Individuals already tagged were considered as Recaptures (R). Individuals could be recaptured from earlier in the same nesting season or from previous nesting seasons. Individual tags were read at the end of the egg-laying phase or during the next phases to minimize disturbance. A few turtles were tagged before this study and observed between 2006 and 2020 (n=9, Limpus, Boyle & Sunderland, 2006). They were included as recaptures in the results. In 2011/2012, the titanium tags were substituted with Passive Integrated Transponder (PIT) tags (Animal Electronic I.D. Systems, Australia). After one season the PIT tag project was considered too intrusive and no longer used. Only one female was tagged with a titanium tag during this season. Each individual studied by the team was measured using the minimum Curved Carapace Length method (Wyneken, 2001) and physical anomalies were recorded. The location of the nest or the nesting attempt was recorded during night and morning sifts. A triangulation method was used to record location from the season 2006/2007 to the season 2016/2017. First, electric poles along the road allowed the team to obtain an approximate location of the nest. Then, precise location of the nest was obtained using triangulation made with salient field cues (e.g., tree, rocks). Since 2017/2018 onwards GPS were used. If the turtle had already left the beach (i.e. back to the sea), its activity was recorded using visual cues on the sand. The activity was considered a “nesting success” when the pit was filled with disturbed sand, indicating that the individual went through all the nesting phases and laid its eggs (Hailman & Elowson, 1992). The activity was considered as an aborted nesting attempt (also called “false crawl”) when the cues showed either (1) an attempt: the individual went through the first phases of the nesting process but stopped before laying its egg, during either the body pit or the digging of the chamber steps, or (2) a turnaround: the individual ascended the beach, stopped, turned around and returned to the sea (continuous