Comprehensive demographic dataset for Tahoe Park South, Sacramento, CA, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.

Comprehensive demographic dataset for Country Park South, Sacramento, CA, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.

Comprehensive demographic dataset for Sunnywoods South, Sacramento, CA, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.

About this dataset

Context

This list ranks the 7 cities in the Sacramento County, CA by South African population, as estimated by the United States Census Bureau. It also highlights population changes in each city over the past five years.

Content

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

• 2019-2023 American Community Survey 5-Year Estimates
• 2014-2018 American Community Survey 5-Year Estimates
• 2009-2013 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by South African Population: This column displays the rank of city in the Sacramento County, CA by their South African population, using the most recent ACS data available.
• City: The City for which the rank is shown in the previous column.
• South African Population: The South African population of the city is shown in this column.
• % of Total City Population: This shows what percentage of the total city population identifies as South African. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Sacramento County South African Population: This tells us how much of the entire Sacramento County, CA South African population lives in that city. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: This column displays the rank trend across the last 5 years.

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/.

FBI National Incident-Based Reporting System (FBI NIBRS) crime data for Highway Patrol: South Sacramento Area Office (State Police) in California, including incidents, statistics, demographics, and detailed incident information.

Comprehensive demographic dataset for Cambridge South, Sacramento, CA, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.

The 1998 Dress Rehearsal was conducted as a prelude to the United States Census of Population and Housing, 2000, in the following locations: (1) Columbia, South Carolina, and surrounding areas, including the town of Irmo and the counties of Chester, Chesterfield, Darlington, Fairfield, Kershaw, Lancaster, Lee, Marlboro, Newberry, Richland, and Union, (2) Sacramento, California, and (3) Menominee County, Wisconsin, including the Menominee American Indian Reservation. This collection contains map files showing various levels of geography (in the form of Census Tract Outline Maps, Voting District/State Legislative District Outline Maps, and County Block Maps), TIGER/Line digital files, and Corner Point files for the Census 2000 Dress Rehearsal sites. The Corner Point data files contain the bounding latitude and longitude coordinates for each individual map sheet of the 1998 Dress Rehearsal Public Law (P.L.) 94-171 map products. These files include a sheet identifier, minimum and maximum longitude, minimum and maximum latitude, and the map scale (integer value) for each map sheet. The latitude and longitude coordinates are in decimal degrees and expressed as integer values with six implied decimal places. There is a separate Corner Point File for each of the three map types: County Block Map, Census Tract Outline Map, and Voting District/State Legislative District Outline Map. Each of the three map file types is provided in two formats: Portable Document Format (PDF), for viewing, and Hewlett-Packard Graphics Language (HP-GL) format, for plotting. The County Block Maps show the greatest detail and the most complete set of geographic information of all the maps. These large-scale maps depict the smallest geographic entities for which the Census Bureau presents data -- the census blocks -- by displaying the features that delineate them and the numbers that identify them. These maps show the boundaries, names, and codes for American Indian/Alaska Native areas, county subdivisions, places, census tracts, and, for this series, the geographic entities that the states delineated in Phase 2, Voting District Project, of the Redistricting Data Program. The HP-GL version of the County Block Maps is broken down into index maps and map sheets. The map sheets cover a small area, and the index maps are composed of multiple map sheets, showing the entire area. The intent of the County Block Map series is to provide a map for each county on the smallest possible number of map sheets at the maximum practical scale, dependent on the area size of the county and the density of the block pattern. The latter affects the display of block numbers and feature identifiers. The Census Tract Outline Maps show the boundaries and numbers of census tracts, and name the features underlying the boundaries. These maps also show the boundaries and names of counties, county subdivisions, and places. They identify census tracts in relation to governmental unit boundaries. The mapping unit is the county. These large-format maps are produced to support the P.L. 94-171 program and all other 1998 Dress Rehearsal data tabulations. The Voting District/State Legislative District Outline Maps show the boundaries and codes for voting districts as delineated by the states in Phase 2, Voting District Project, of the Redistricting Data Program. The features underlying the voting district boundaries are shown, as well as the names of these features. Additionally, for states that submit the information, these maps show the boundaries and codes for state legislative districts and their underlying features. These maps also show the boundaries of and names of American Indian/Alaska Native areas, counties, county subdivisions, and places. The scale of the district maps is optimized to keep the number of map sheets for each area to a minimum, but the scale and number of map sheets will vary by the area size of the county and the voting districts and state legislative districts delineated by the states. The Census 2000 Dress Rehearsal TIGER/Line Files consist of line segments representing physical features and governmental and statistical boundaries. The files contain information distributed over a series of record types for the spatial objects of a county. These TIGER/Line Files are an extract of selected geographic and cartographic information from the Census TIGER (Topological

Comprehensive demographic dataset for South Natomas, Sacramento, CA, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.

Financial overview and grant giving statistics of South Sacramento Food Bank and Community Service

This resource is a member of a series. The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) System (MTS). The MTS represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The All Roads shapefile includes all features within the MTS Super Class "Road/Path Features" distinguished where the MAF/TIGER Feature Classification Code (MTFCC) for the feature in the MTS that begins with "S". This includes all primary, secondary, local neighborhood, and rural roads, city streets, vehicular trails (4wd), ramps, service drives, alleys, parking lot roads, private roads for service vehicles (logging, oil fields, ranches, etc.), bike paths or trails, bridle/horse paths, walkways/pedestrian trails, and stairways.

Financial overview and grant giving statistics of South Sacramento Interfaith Partnership

About this dataset

Context

This list ranks the 6 cities in the Sac County, IA by South African population, as estimated by the United States Census Bureau. It also highlights population changes in each city over the past five years.

Content

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

• 2019-2023 American Community Survey 5-Year Estimates
• 2014-2018 American Community Survey 5-Year Estimates
• 2009-2013 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by South African Population: This column displays the rank of city in the Sac County, IA by their South African population, using the most recent ACS data available.
• City: The City for which the rank is shown in the previous column.
• South African Population: The South African population of the city is shown in this column.
• % of Total City Population: This shows what percentage of the total city population identifies as South African. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Sac County South African Population: This tells us how much of the entire Sac County, IA South African population lives in that city. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: This column displays the rank trend across the last 5 years.

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/.

Comprehensive demographic dataset for South Land Park, Sacramento, CA, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.

Financial overview and grant giving statistics of South Sacramento Elk Grove District Council Society Of St Vncnt De Pau

Comprehensive demographic dataset for South Country, Sacramento, CA, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.

Financial overview and grant giving statistics of South Sacramento Christian Center Church

The USGS Central Region Energy Team assesses oil and gas resources of the United States. The onshore and State water areas of the United States comprise 71 provinces. Within these provinces, Total Petroleum Systems are defined and Assessment Units are defined and assessed. Each of these provinces is defined geologically, and most province boundaries are defined by major geologic changes. The Sacramento Basin Province is located in the Northern Central Valley, California, encompassing all or parts of Shasta, Tehana, Glenn, Butte, Colusa, Lake, Yuba, Sutter, Placer, Yolo, Napa, Sacramento, Amador, San Joaquin, Solano, Contra Costa, Calaveras, Alameda, and Stanislaus counties in California. The main population centers within the study area are Sacramento, Stockton, Modesto, Elk Grove, Yuba City, Chico, and Red Bluff, California. The main highways, I-5 and I-80, generally traverse the area from north to south and east to west respectively. The Sacramento River and its tributaries drai ...

We use Monte Carlo methods which draw parameters from Bayesian posterior distributions to generate a distribution of population size estimates and trajectories, thus giving managers a fuller accounting of the uncertainty in the population status. We then propagate this population estimate and its associated uncertainty into a model using Monte Carlo methods to assess the impact of fishing bycatch on the species. We show that the population is below the recovery goal of 3,000 adults. The current total population estimate (including juveniles) is approximately 10,000 fish. Our model finds that fishing bycatch pressure reduces an otherwise assumed stable population by a median value of 0.4% per year, which could impede the recovery of the species. Fisheries bycatch is only one of many threats this population faces, and future work is needed to assess how other threats, such as spawning habitat alteration through dams and water diversions, may affect this population’s trajectory. The framework presented here is suitable for further data integration or modular expansion to incorporate the cumulative effects of challenges facing green sturgeon recovery. Methods We assessed the number of spawning green sturgeon adults in annual surveys of the Sacramento River, CA, USA from the Irvine Finch Boat Ramp (river kilometer 320, just west of Chico) up to Redding (river kilometer 480) (Fig. 1). Acoustic tag data and egg mat studies have confirmed that this is the extent of the spawning grounds (Poytress et al. 2013; Thomas et al. 2014). We surveyed any site along that section of the river with depths greater than 5 m (Erickson et al. 2002). Generally, we see green sturgeon in approximately 40 sites. This section of the river provides the vast majority (effectively all) of the southern DPS green sturgeon spawning locations. 2.3 Spawner survey A detailed description of the methods is published by Mora et al. (2015) and is only briefly described here. The survey has taken place continuously since 2010. There are three phases of the survey conducted over three weeks in mid-June. In phase one, a survey crew drifts downstream over the deepest parts of the channel with a depth sounder. The crew contour maps areas of the river with depths greater than 5 m using a sonar system. The survey generally finds approximately 70 areas with a depth over 5 m within the study area (Fig. 1). The crew marks locations with spawner observations in the last 5 years for an automatic revisit in phase three. In phase two, the crew uses a Dual frequency IDentification SONar (DIDSON; Sound Metrics, Bellevue, Washington) video camera to scan for green sturgeon during 3 passes at sites without spawners in the previous 5 years. In phase three, the crew visits all sites where sturgeon have been seen in the past 5 years as well as any new ones added during phase two. At each site, the crew makes 7 passes recording DIDSON footage. The DIDSON footage from phase three is reviewed in random order three times and counts are combined into an estimate of the number of sturgeon at each site location. The sum of counts from all sites is the total number of spawners observed. 2.4 Life table, IPM, and sensitivity analysis 2.4.1 Literature parameters Both the IPM and life table models need some parameters describing demographics, behavior, and physiology. We took a subset of these parameters directly from the literature (Appendix S1). All these parameters are for the northern DPS green sturgeon as similar data is unavailable for the southern population. 3.4.2 Length vs. age relationship We used the age vs length data for southern DPS green sturgeon on the Sacramento River from supplement 1 of Ulaski and Quist (2021). We fit these data with Bayesian regression in R using JAGS (packages used rjags, purr, ggplot, dplyr, patchwork, viridis, minpack.lm, ggextra, mcmcplots, and furrr) (Plummer 2003; R Core Team 2015; RStudio Team 2015; Wickham 2016b; Elzhov et al. 2016; Wickham 2016a; Curtis 2018; Wickham et al. 2018; Garnier 2018; Attali & Baker 2019; Pedersen 2019; Vaughan & Dancho 2021). The model and priors are as follows:

L ~ normal(Μ_L, Τ) Μ_L = L_∞ (1-e^((-k(A-t_0)))) L_(∞ ) ~ normal(μ = 190 cm, τ = 0.05 (1/cm)) k ~ gamma(ϕ = 1, θ = 0.2 (1/yrs)) t_0 ~ normal(μ = -3 yrs, τ = 0.001 (1/yrs)) Τ ~ gamma(ϕ = 0.001, θ = 0.001 (1/cm))

Eq. 1

where L is the length, ML is the mean of the length distribution, T is the precision of the length distribution, L∞ is the asymptotic length, k is the growth coefficient, A is age in years, and t0 is agee at zero length. Priors are loosely informed by data from northern green sturgeon (Adair et al. 1983; Farr et al. 2002). We ran three MCMC chains with 1000 adaptation steps and 20000 burn-in steps and saved 10000 samples per chain at 90% thinning. Each chain had random starting values based on draws from the prior distributions. All chains converged based on visual inspection or running means. We compared these results to fits for the northern DPS of green sturgeon as previous population estimates used that data (Beamesderfer et al. 2007; Mora et al. 2018). 3.4.3 Annual survival Appendix S1 presents an estimate of mortality based on a catch curve analysis from a fishery in the Columbia Estuary. We used the telemetry data from the sturgeon on our system to calculate annual mortality. We then used these two values to bookend the estimate of annual mortality in the life table model and IPM. We used data from the BARD for all green sturgeon tags from 2007-2018. We only used data where length was labeled as either total length or fork length. We converted all lengths to fork lengths and all lengths reported in this manuscript are fork lengths. We used the mean parameters from Eq. 1 to convert the lengths to ages. We grouped the data in 5-year bins to reduce noise. Instantaneous mortality is equal to the slope of the change in counts with age after natural-log transformation. We calculated the slope of the descending arm and converted it from instantaneous mortality to annual survival using (annual survival) = exp(-instantaneous mortality) (Ricker 1975). Subsequent calculations involving survival drew mortality from a uniform distribution over the range between the Columbia River and this estimate. 3.4.4 Probability of being an adult Rather than using published ages of maturity or the maturity curve implied by the Beamesderfert al. (2007) cohort model, we based the timing of maturity on data specific to the southern population. We calculated the probability that fish of a certain length are adults (i.e. potential spawners) using the same raw data set from the BARD. We flagged fish as adults if they were marked as “mature”, “adult,” or “eggs” (meaning they were caught with eggs) or if detections were above river kilometer 320 (the bottom of the spawning ground) (note the BARD uses a different river kilometer 0, thus river kilometer 320 equates to 410 in the BARD). Only tagging-year records were used so that length and maturity data were contemporaneous. We grouped individuals by sex into females and others (males and unknown). We obtained two separate estimates of the probability of maturation with length using Bayesian logistic regression. The first estimate was a sex-specific hierarchical model fit divided between females and others in which the sexes shared a common slope but had separate intercepts. We used the female parameters from this model in the sensitivity analysis calculation because that analysis needed fecundity. The second estimate was a fit with all the data for use in the population estimate, which needed the total number of spawners. The models are as follows:

P ~ Bernoulli(Μ_A) Μ_A = (1+e^(-(b_0+b_1 L)))^(-1) b_(1 ) ~ normal(μ = 0 (1/cm), τ = 10^(-12) cm) b_0 ~ normal(μ = 0, τ = 10^(-12))

Eq. 2

where P is the probability of being an adult, MA is the mean of the probability distribution, L is the length, b0 is the intercept and b1 is the slope. Thus, in the hierarchical model, females and others share the b1 term but have separate b0 terms. We used broad normal priors (Eq. 2). We ran three chains with 1000 adaptation steps and 20000 burn-in steps and saved 10000 samples at 90% thinning per chain. Each chain had random starting values based on draws from the prior distributions. All chains converged. We compared these results to fits for the northern population of green sturgeon as previous population estimates used that data (Beamesderfer et al. 2007; Mora et al. 2018). 3.4.5 Spawning interval distribution The population estimate portion of the IPM needs the distribution of spawning intervals for all adults and the sensitivity analysis requires the average spawning interval of females. To calculate these data, we took all detections from the BARD above river kilometer 320 for months during the spawning season (March - September). We removed any detections from the same year the fish was tagged as well as any detections for fish without a detected outmigration between upstream records. For each fish, we then found both the interval between tagging and the first return and, if available, the interval between the first and second return. We divided these into two groups (all fish and females). We then constructed a distribution showing the fraction of fish that have a return interval greater than each interval and calculated the average return interval. We compared these results to fits for the northern population of green sturgeon as previous population estimates used that data (Beamesderfer et al. 2007; Mora et al. 2018). 3.4.6 Calculate the fraction of the population that is adults and spawners We then sampled from the estimated distributions of survival, maturation, and spawning interval described above to make 10,000 life tables (the average parameter values converged at approximately 8,000 samples) from which we calculated the fraction of the population that is

Comprehensive demographic dataset for South City Farms, Sacramento, CA, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.

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.