About this dataset

Context

The dataset tabulates the Morro Bay 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 Morro Bay 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 Morro Bay was 10,589, a 0.93% decrease year-by-year from 2022. Previously, in 2022, Morro Bay population was 10,688, a decline of 0.93% compared to a population of 10,788 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Morro Bay increased by 200. In this period, the peak population was 10,788 in the year 2021. The numbers suggest that the population has already reached its peak and is showing a trend of decline. 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 Morro Bay is shown in this column.
• Year on Year Change: This column displays the change in Morro Bay 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 Morro Bay Population by Year. You can refer the same here

About this dataset

Context

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

Key observations

The percent distribution of Morro Bay population by race (across all racial categories recognized by the U.S. Census Bureau): 79.74% are white, 0.28% are Black or African American, 0.78% are American Indian and Alaska Native, 5.07% are Asian, 6.18% are some other race and 7.95% 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 Morro Bay
• Population: The population of the racial category (excluding ethnicity) in the Morro Bay is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Morro Bay 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 Morro Bay Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset tabulates the Half Moon Bay 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 Half Moon Bay 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 Half Moon Bay was 11,176, a 1.80% decrease year-by-year from 2021. Previously, in 2021, Half Moon Bay population was 11,381, a decline of 3.26% compared to a population of 11,765 in 2020. Over the last 20 plus years, between 2000 and 2022, population of Half Moon Bay decreased by 202. In this period, the peak population was 12,627 in the year 2018. The numbers suggest that the population has already reached its peak and is showing a trend of decline. 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 Half Moon Bay is shown in this column.
• Year on Year Change: This column displays the change in Half Moon Bay 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 Half Moon Bay Population by Year. You can refer the same here

VITAL SIGNS INDICATOR Poverty (EQ5)

FULL MEASURE NAME The share of the population living in households that earn less than 200 percent of the federal poverty limit

LAST UPDATED December 2018

DESCRIPTION Poverty refers to the share of the population living in households that earn less than 200 percent of the federal poverty limit, which varies based on the number of individuals in a given household. It reflects the number of individuals who are economically struggling due to low household income levels.

DATA SOURCE U.S Census Bureau: Decennial Census http://www.nhgis.org (1980-1990) http://factfinder2.census.gov (2000)

U.S. Census Bureau: American Community Survey Form C17002 (2006-2017) http://api.census.gov

METHODOLOGY NOTES (across all datasets for this indicator) The U.S. Census Bureau defines a national poverty level (or household income) that varies by household size, number of children in a household, and age of householder. The national poverty level does not vary geographically even though cost of living is different across the United States. For the Bay Area, where cost of living is high and incomes are correspondingly high, an appropriate poverty level is 200% of poverty or twice the national poverty level, consistent with what was used for past equity work at MTC and ABAG. For comparison, however, both the national and 200% poverty levels are presented.

For Vital Signs, the poverty rate is defined as the number of people (including children) living below twice the poverty level divided by the number of people for whom poverty status is determined. Poverty rates do not include unrelated individuals below 15 years old or people who live in the following: institutionalized group quarters, college dormitories, military barracks, and situations without conventional housing. The household income definitions for poverty change each year to reflect inflation. The official poverty definition uses money income before taxes and does not include capital gains or noncash benefits (such as public housing, Medicaid, and food stamps). For the national poverty level definitions by year, see: https://www.census.gov/hhes/www/poverty/data/threshld/index.html For an explanation on how the Census Bureau measures poverty, see: https://www.census.gov/hhes/www/poverty/about/overview/measure.html

For the American Community Survey datasets, 1-year data was used for region, county, and metro areas whereas 5-year rolling average data was used for city and census tract.

To be consistent across metropolitan areas, the poverty definition for non-Bay Area metros is twice the national poverty level. Data were not adjusted for varying income and cost of living levels across the metropolitan areas.

This portion of the data release presents orthomosaic images of the Whale's Tail Marsh region of South San Francisco Bay, CA. The orthomosaics have resolutions of 2.5 centimeters per pixel and were derived from structure-from-motion (SfM) processing of repeat aerial imagery collected from fixed-wing aircraft. The raw imagery used to create these elevation models was acquired from an approximate altitude of 427 meters (1,400 feet) above ground level (AGL), using a Hasselblad A6D-100c camera fitted with an HC 80 lens, resulting in a nominal ground-sample-distance (GSD) of 2.5 centimeters per pixel. The acquisition flight lines were designed to provide approximately 50 percent overlap between adjacent flight lines (sidelap), with approximately 70 percent overlap between sequential images along the flight line (forelap). Survey control was established using an onboard camera-synchronized dual-frequency GPS system as well as ground control points (GCPs) distributed throughout the survey area and measured using survey-grade post-processed kinematic (PPK) GPS. Both the data from the onboard GPS and from the GPS used to measure the GCPs were post-processed using a nearby Continuously Operating Reference Station (CORS) station operated by the National Geodetic Survey (NGS). Structure-from-motion processing of these data was conducted using a "4D" processing workflow in which imagery from each of the different acquisition dates were co-aligned in order to increase relative spatial precision between the final data products. The resulting orthomosaics have been formatted as cloud optimized GeoTIFFs with internal overviews and masks to facilitate cloud-based queries and display.

Diet composition can be influenced by age- and sex-related factors including an individual’s morphology, social status, and acquired skills; however, specialization may only be necessary when competition is intensified by high population densities or increased energetic demands. The western sandpiper is a small (22-35 grams) migratory shorebird that exhibits female-biased sexual size dimorphism with a 5 percent greater body size and a 15 percent longer bill in females compared to males. It is considered a generalist with a diverse diet that includes benthic invertebrates and biofilm – a thin layer of microphytobenthos, bacteria, and detritus encased in a polysaccharide-rich matrix of extracellular polymeric substances that forms on the surface of mudflats at low tide. In San Francisco Bay, CA, USA, western sandpipers are one of the most abundant shorebird species foraging on tidal mudflats throughout the non-breeding season. Stable carbon and nitrogen isotope data and were collected from western sandpipers and their potential prey on the Dumbarton shoal, an intertidal mudflat on the southwestern side of San Francisco Bay that supports a high biomass of benthic invertebrates and biofilm consumed by western sandpipers. Morphometric data were used to assign sexes and age classes to western sandpipers.

This study is the first comprehensive publication of tidal datums and extreme tides for San Francisco Bay (Bay) since the United States Army Corps of Engineers (USACE) published itsSan Francisco Bay Tidal Stage vs. Frequency Study in 1984 (USACE 1984). The USACE study was groundbreaking at the time of publication, presenting tidal datums and the “100-year tide” elevation for 53 locations around the Bay. The purpose of this study is to update and expand on the USACE study and to present daily and extreme tidal information for more than 900 locations along the Bay shoreline. Tidal datums, described further in Section 2 , are standard elevations defined by a certain phase of the tide (e.g., mean high tide, mean low tide). A tidal datum is used as a reference to measure and define local water levels, and as such is specific to local hydrodynamic processes and is not easily extended from one area to another without substantiating measurements or analysis. Many industries and activities rely on tidal datums, including shipping and navigation, coastal flood management, coastal development, and wetland restoration. Extreme tidal elevations are estimated for less-frequent extreme tides (e.g., 2-year tides to 500-year tides [tides with a 50.0 percent to 0.2 percent annual chance of occurrence, respectively]). Knowledge of the 100-year tide, or the water elevation with a 1 percent annual chance of occurrence, is critical for shoreline planning, floodplain management, and sea level rise (SLR) adaptation efforts. This study presents detailed daily and extreme tide information for the entirety of the Bay shoreline. This data set will support floodplain management efforts; shoreline vulnerability and risk analyses; shoreline engineering, design, and permitting; ecological studies; and appropriate sea level rise adaptation planning. The goal of this study is to provide data that support a wide-range of planning efforts around the Bay, particularly as communities seek to understand—and begin to adapt to—rising sea levels. You can access the full report at: http://www.adaptingtorisingtides.org/wp-content/uploads/2016/05/20160429.SFBay_Tidal-Datums_and_Extreme_Tides_Study.FINAL_.pdf.

VITAL SIGNS INDICATOR Vulnerability to Sea Level Rise (EN11)

FULL MEASURE NAME Share of population living in zones at risk from various sea level rise forecast scenarios

LAST UPDATED July 2017

DESCRIPTION Vulnerability to sea level rise refers to the share of the historical and current Bay Area population located in areas at risk from forecasted sea level rise over the coming decades. Given that there are varying forecasts for the heightened high tides (i.e., mean highest high water mark), projected sea level impacts are presented for six scenarios ranging from a one foot rise to six feet. A neighborhood is considered vulnerable to sea level rise when at least 10 percent of its land area is forecasted to be inundated by peak high tides in the coming years. The dataset includes at-risk population and population share data for the region, counties, and neighborhoods.

DATA SOURCE San Francisco Bay Conservation and Development Commission/Metropolitan Transportation Commission ART (Adaption to Rising Tides) Bay Area Sea Level Rise Analysis and Mapping Project 2017 Sea Level Rise Maps http://www.adaptingtorisingtides.org/project/regional-sea-level-rise-mapping-and-shoreline-analysis/

U.S. Census Bureau 1990-2010 Decennial Census http://factfinder2.census.gov

U.S. Census Bureau 2015 American Community Survey http://factfinder2.census.gov

CONTACT INFORMATION vitalsigns.info@mtc.ca.gov

METHODOLOGY NOTES (across all datasets for this indicator) Projected areas of inundation were developed by BCDC and NOAA at one-foot intervals ranging from one foot to four feet of sea level rise. Regional and local sea level rise analysis is based on data from BCDC’s ART (Adapting to Rising Tides) Bay Area Sea Level Rise and Mapping Project. This data reflects the most up-to-date and detailed sea level rise mapping for the Bay Area. Sea level rise analysis for metro areas is based on national sea level rise mapping from NOAA, which is best for metro-to-metro comparison. To determine the impacts on historical and current populations, inundation areas were overlaid on a U.S. Census shapefile of 2010 Census tracts using Census Bureau population data.

Because census tracts can extend beyond the coastline, the baseline scenario of zero feet was used to determine existing sea level coverage of census tracts. Sea level rise refers to the change from this level. The area of the tract was determined by measuring the component of the tract area not currently under water. This area, rather than the total tract area, was used as the denominator to determine the percentage of the census tract that is inundated under future sea level rise projection scenarios. When at least 10 percent of tract land area is inundated with a given sea level, its residents are considered to be affected by sea level rise.

For the purpose of this analysis, SLR scenarios were assumed not to reflect periodic inundation due to extreme weather events, which may lead to an even greater share of residents affected on a less frequent basis. Prior to the impacts from sea level rise, neighborhoods will experience temporary flooding from extreme weather events which can create significant damage to homes and neighborhoods. It should be noted that by directly reviewing maps and tools through the ART (Adapting to Rising Tides) program, regular inundation sea level rise and temporary flooding from extreme weather events are both available. More information on this approach is available here: http://www.adaptingtorisingtides.org/project/regional-sea-level-rise-mapping-and-shoreline-analysis/

Sea level rise analysis for metro areas reflects local, as opposed to global, sea level rise. Recent data has shown sea level is rising faster in the southeast region of the United States. Regional differences in the rate of sea level rise. More information and data related to the rate of sea level rise for different coastal regions is available here: https://oceanservice.noaa.gov/facts/sealevel-global-local.html

Sediment analysis of cores collected in San Francisco Bay, San Pablo Bay, and the Carquinez Strait and Suisun Bay, California from 1990 to 2016 for percent sand. Handwritten core logs, x-radiographs, and the cores themselves, were examined for the presence of sand.

About this dataset

Context

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

Key observations

Of the Non-Hispanic population in Half Moon Bay, the largest racial group is White alone with a population of 6,947 (85.73% 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 Half Moon Bay
• Population: The population of the racial category (for Non-Hispanic) in the Half Moon Bay is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Half Moon Bay 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 Half Moon Bay Population by Race & Ethnicity. You can refer the same here

Water samples were collected in South San Francisco Bay adjacent to Whale’s Tail South marsh on three days from June through December 2021 to analyze for suspended-sediment concentration and the percent of sand and fines in suspended sediment.

This dataset tracks annual white student percentage from 2019 to 2023 for Bay Area Technology vs. California and Bay Area Technology School District

This dataset tracks annual black student percentage from 2019 to 2023 for Bay Area Technology vs. California and Bay Area Technology School District

This dataset tracks annual asian student percentage from 2019 to 2023 for Bay Area Technology vs. California and Bay Area Technology School District

This dataset tracks annual white student percentage from 1991 to 2023 for Morro Bay High School vs. California and San Luis Coastal Unified School District

This data release includes 9 GeoTIFF rasters that represent percent cover of intertidal microbial biofilm on the mudflats of South San Francisco Bay, CA between June 2020, and June 2022. Rasters follow the naming scheme biofilmPC_YYYYMMDD_10m.tif, where “biofilmPC” describes the dataset, YYYYMMDD is the date of the image, and 10m is the spatial resolution of the raster. Raster data products were derived from 10m Sentinel-2 Muti-Spectral Imager (MSI) imagery, collected and maintained by the European Space Agency (ESA) and published for public access on Copernicus Data Hub (https://scihub.copernicus.eu/dhus/#/home). The area of interest includes the Eden Landing Ecological Reserve and all tidally exposed mudflats within the bounds of San Francisco Bay proper, south of Hayward, CA. Maps report the percent cover of biofilm ranging from 0 to 1 and were generated via a series of processing steps in Python and VIPER tools in ENVI. The no data value is 0, reporting anywhere where there is no biofilm as a 0 value. The study area was constrained using a vector mask, then water and vegetated areas were recognized and removed using a series of indices, leaving only the mudflat areas. Data was imported to ENVI and run through Multiple Endmember Spectral Mixture Analysis (MESMA) in VIPER tools using a percent cover endmember library generated from an in-situ dataset. The accuracy of the raster data products was evaluated by comparing raster values to 43 reference data points taken around South San Francisco Bay, using linear regression. The R squared of the regression between raster percent cover values and in situ reference data was 53%. Accuracy was influenced by differences in scale between image data and in-situ reference data and possible error in image georectification.

About this dataset

Context

The dataset tabulates the Morro Bay 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 Morro Bay. The dataset can be utilized to understand the population distribution of Morro Bay by age. For example, using this dataset, we can identify the largest age group in Morro Bay.

Key observations

The largest age group in Morro Bay, CA was for the group of age 55 to 59 years years with a population of 973 (9.08%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Morro Bay, CA was the 10 to 14 years years with a population of 285 (2.66%). 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 Morro Bay is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Morro Bay 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 Morro Bay Population by Age. You can refer the same here

These datasets provide information on plant alpha, beta, and gamma diversity, and plant species abundance at several spatial scales for tidal wetlands along a salinity gradient in the San Francisco Bay-Delta and an impounded brackish wetland complex in Suisun Marsh, California. Files include diversity metrics calculated at the patch, site, and region scales, average percent cover of wetland dominant plants at the patch scale, and average percent cover of all wetland plants at the site scale.

This dataset tracks annual hispanic student percentage from 1991 to 2023 for Morro Bay High School vs. California and San Luis Coastal Unified School District

This dataset was developed/compiled for use in the San Francisco Bay Area Upland Habitat Goals Project, a Project used to identify a Conservation Lands Network (CLN) for biodiversity preservation to inform conservation investments and lasting cooperative conservation partnerships. The Conservation Lands Network GIS Database is the primary output of the Project. The data depicts the spatially explicit CLN that is recommended for the nine county San Francisco Bay Area Region, California.