CDFW BIOS GIS Dataset, Contact: Steve Stone, Description: This dataset depicts the general boundaries of the California Central Valley Steelhead DPS distinct population segment (DPS) under the U.S. Endangered Species Act, as well as the historical population structure of the species.

CDFW BIOS GIS Dataset, Contact: Charleen Gavette, Description: Depiction of Biogeographic Population Groups (BPG) within the South-Central California Coast Steelhead Distict Population Segments (DPS).

VITAL SIGNS INDICATOR Migration (EQ4)

FULL MEASURE NAME Migration flows

LAST UPDATED December 2018

DESCRIPTION Migration refers to the movement of people from one location to another, typically crossing a county or regional boundary. Migration captures both voluntary relocation – for example, moving to another region for a better job or lower home prices – and involuntary relocation as a result of displacement. The dataset includes metropolitan area, regional, and county tables.

DATA SOURCE American Community Survey County-to-County Migration Flows 2012-2015 5-year rolling average http://www.census.gov/topics/population/migration/data/tables.All.html

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) Data for migration comes from the American Community Survey; county-to-county flow datasets experience a longer lag time than other standard datasets available in FactFinder. 5-year rolling average data was used for migration for all geographies, as the Census Bureau does not release 1-year annual data. Data is not available at any geography below the county level; note that flows that are relatively small on the county level are often within the margin of error. The metropolitan area comparison was performed for the nine-county San Francisco Bay Area, in addition to the primary MSAs for the nine other major metropolitan areas, by aggregating county data based on current metropolitan area boundaries. Data prior to 2011 is not available on Vital Signs due to inconsistent Census formats and a lack of net migration statistics for prior years. Only counties with a non-negligible flow are shown in the data; all other pairs can be assumed to have zero migration.

Given that the vast majority of migration out of the region was to other counties in California, California counties were bundled into the following regions for simplicity: Bay Area: Alameda, Contra Costa, Marin, Napa, San Francisco, San Mateo, Santa Clara, Solano, Sonoma Central Coast: Monterey, San Benito, San Luis Obispo, Santa Barbara, Santa Cruz Central Valley: Fresno, Kern, Kings, Madera, Merced, Tulare Los Angeles + Inland Empire: Imperial, Los Angeles, Orange, Riverside, San Bernardino, Ventura Sacramento: El Dorado, Placer, Sacramento, Sutter, Yolo, Yuba San Diego: San Diego San Joaquin Valley: San Joaquin, Stanislaus Rural: all other counties (23)

One key limitation of the American Community Survey migration data is that it is not able to track emigration (movement of current U.S. residents to other countries). This is despite the fact that it is able to quantify immigration (movement of foreign residents to the U.S.), generally by continent of origin. Thus the Vital Signs analysis focuses primarily on net domestic migration, while still specifically citing in-migration flows from countries abroad based on data availability.

Well construction data for 11,917 domestic and 2,390 public-supply wells in the Central Valley were compiled as part of the U.S. Geological Survey (USGS) National Water Quality Assessment Project (NAWQA) and California State Water Resources Control Board (SWRCB) Groundwater Ambient Monitoring and Assessment Program Priority Basin Project (GAMA-PBP). Data were compiled for wells reported in the USGS National Water Information System (NWIS) database and from well information reported to the SWRCB Department of Drinking Water (SWRCB-DDW). Driller’s log data were transcribed from scanned images of well completion reports filed with California Department of Water Resources (DWR). The wells reported in this data release were filtered by water use to select domestic and public-supply wells and omit other water uses. The compilation was then assumed to be representative of the total population of domestic and public-supply wells in the Central Valley. The wells in the compilation were constructed between 1911 and 2008 but are not grouped or separated by date. The data were used to produce two point data sets containing well location and construction information (depth from land surface to the top and bottom of the well screen, hereafter well-screen tops and bottoms; and screen length), and 12 interpolated GIS raster surfaces created by using Empirical Bayesian Kriging on a 1600 by 1600 meter (1 square-mile) grid. The tables are also included in csv format. The 12 rasters comprise predicted values for well screen tops and bottoms and their 10th and 90th quantile values. The interpolated surfaces may also be used to calculate volumes of water-supply in the Central Valley defined by the well-screen tops and bottoms.

About this dataset

Context

The dataset tabulates the Portola Valley population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of Portola Valley. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

Key observations

The largest age group was 65 years and over with a poulation of 1,837 (42.43% of the total population). 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 cohorts:

• Under 18 years
• 18 to 64 years
• 65 years and over

Variables / Data Columns

• Age Group: This column displays the age cohort for the Portola Valley population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in Portola Valley is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the Portola Valley 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 Portola Valley Population by Age. You can refer the same here

Outmigrating Central Valley Chinook Salmon

Access this data on Dryad (DOI: 10.5061/dryad.280gb5mxx)

This Dryad entry contains data files and scripts used for analyses in Thompson et al. 2024 (Evolutionary Applications). Briefly, the study examines outmigration characteristics of juvenile Chinook salmon collecte d at Chipps Island in the Sacramento/San Joaquine Delta by assigning each sample to a population of origin. The analysis is broken down into three parts: 1) leave-one-out analyses to develop a population assignme nt method and evaluate its expected efficacy; 2) validation of the assignment method using an independent dataset of known-origin samples; 3) population assignment of unknown-origin juvenile samples collected at Chipps Island (and obtained from a tissue archive). This Dryad entry contains files and scripts necessary for that analysis, as well as the resulting output files.

Data Files

The following are descriptions of all data files uploaded here.

(Note: the sin...

The Waterfowl Program annually monitors the waterfowl breeding population in California for conservation and to establish hunting frameworks within the Pacific Flyway, 1992-2022. Data are collected from both a fixed-wing and helicopter with 2 observers in each. The survey is conducted about the third week in April in the Central Valley and second week in May in NE CA. The state is divided into 9 strata for sampling with transects/segments in each stratum. Species, sex, and breeding status collected. Survey has been conducted since 1955 however a revised survey was developed in 1992 and continues annually. The project is funded by Pittman-Roberston Grant and State Duck Stamp funds. This data and metadata were submitted by California Department of Fish and Wildlife (CDFW) Staff though the Data Management Plan (DMP) framework with the id: DMP000455. For more information, please visit https://wildlife.ca.gov/Data/Sci-Data.

This file is an example data set from the Central Valley of California from a drought study corresponding to “recent non-drought conditions” (Scenario 1 in Petrie et al., in review). In 2014, following an 8-year period with 7 below-normal to critically-dry water years, the bioenergetic model TRUEMET was used to assess the impacts of drought on wintering waterfowl habitat and bioenergetics in the Central Valley of California. The goal of the study was to assess whether available foraging habitats could provide enough food to support waterfowl populations (ducks and geese) under a variety of climate and population level scenarios. This information could then be used by managers to adapt their waterfowl habitat management plans to drought conditions. The study area spanned the Central Valley and included the Sacramento Valley in the north, the San Joaquin Valley in the south, and Suisun Marsh and Sacramento-San Joaquin River Delta (Delta) east of San Francisco Bay. The data set consists of two foraging guilds (ducks and geese/swans) and five forage types: harvested corn, rice (flooded), rice (unflooded), wetland invertebrates and wetland moist soil seeds. For more background on the data set, see Petrie et al. in review.

The California Central Valley steelhead (Oncorhynchus mykiss) has declined precipitously since Euro-American colonization and has been listed as threatened under the United States Endangered Species Act since 1998. Hatchery-origin fish now dominate the population and hatchery management is a key listing factor. However, scant release metric information is available. We compiled a time series of O. mykiss hatchery release data for all four Central Valley hatcheries releasing O. mykiss between 1948 and 2017. The biocomplexity of released fish has declined since the early 1980s. Individuals have been released at increasingly similar numbers, biomass, body sizes, times, and locations over time. Moreover, yearling fish have been released at larger sizes, leading to the near-exclusive release of age-1 smolts in February and March since the late 1990s and early 2000s. Pervasive reductions in release portfolios have likely occurred for other hatchery-supported Pacific salmonid stocks throughout the Pacific Rim region. In an increasingly variable environment, such reductions in intraspecific diversity could significantly affect population stability and resilience. Methods Release & Return Database Compilation

We compiled release data from 135 annual reports provided by the California Department of Fish and Wildlife (CDFW) for state-operated hatcheries. The state hatchery annual reports spanned one fiscal year from 1 July to 30 June of the following year: NFH (54 reports, 1956–57 to 2009–10), FRFH (40 reports, 1969–70 to 2009–10), and MRFH (41 reports, 1964–65 to 2007–08; no releases were reported for 1976–77, 1988–89, or 2001–02). From 1988 to 2001, release records were obtained for MRFH from the California Hatchery Scientific Review Group (CA HSRG 2012, Appendix VI) instead of annual reports because the former dataset is more detailed and complete. From 2001–2017 for the FRFH and 2003–2017 for the NFH and MRFH, an electronic dataset from CDFW's statewide inventory system was used instead of annual reports or California Hatchery Scientific Review Group data (CA HSRG 2012, Appendix VIII). Hatchery release data were considered “draft” or non-finalized from 1994–2017 for the NFH, 1992–2017 for the FRFH, and 1988–2017 for the MRFH. Release data for the CNFH from 1948 to winter 1975 were obtained from an electronic database provided by the U.S. Fish and Wildlife Service (USFWS) and data from spring 1975 to 2017 were obtained from the Regional Mark Information System (RMIS, http://www.rmpc.org/, retrieved on 21 May 2020).
The basic reporting unit for all data sources was a cohort of similarly sized fish released together in a stated release location over a specified time (hereafter referred to as a “release group”). Information about brood year (same as emergence year for winter-run O. mykiss), total number of fish planted, average or range of fish size(s)-at-release (usually expressed as fish·lb-1), release timing (from single days to months), and descriptions and (or) geographic coordinates of release locations were reported for release groups. Total numbers of returning O. mykiss trapped at state hatcheries were compiled from annual reports and the electronic database provided by CDFW.

Resulting databases: CV_Steelhead_Hatchery_Release_Database.csv

Objective 1: Temporal trends in juvenile releases Number released: We present annual release data according to the California ‘water year’ (WY; 1 October to 30 September) since this period is more relevant to the Central Valley O. mykiss life cycle than calendar years (e.g., upstream migration in fall and winter, spawning and emergence in winter, rearing in spring and summer [Williams 2006]). When the release period spanned two WYs (5.1% of total number released, 90.5% of which occurred before WY 1976), the WY possessing the larger share of the release period was assigned as the release WY. In rare instances when the release period spanned two WYs and was split equally between WYt and WYt+1 (0.2% of all releases), WYt+1 was set as the release year. A three-year moving average was applied to the annual numerical release data to highlight longer-term trends.

Data file used: CVSH_totmill.csv

Biomass released: Release group biomass was calculated as the product of total number of fish released and mean fish mass for that release group. The mean annual fish mass-at-release for a hatchery was used as the mean mass-at-release for any release group’s missing weight, length, or life history stage-at-release information (3.8% of all releases). A three year moving average was also applied to the annual biomass release data to smooth the time series data.

Data file used: CVSH_biom.csv

Release timing: We analyzed release timing at the monthly scale because release day of month was missing for 63.5% of all releases. The release period usually occurred within the same calendar month (80.3% of all releases) but occasionally a range of calendar months were reported (16.7% of all releases). In limited cases, only release year was reported (3.0% of all releases). Due to these inconsistencies, we restrict all release timing and growth rate analyses to cases when the release start and end months were the same.

Data file used: CVSH_RelTot_revised.csv

Release location: Geographic coordinates of release locations and river km distances from the releasing hatchery to the release location were obtained from Sturrock et al. (2019) (92.0% of all releases), RMIS (1.4% of all releases), or the electronic database provided by CDFW (0.7% of all releases). An additional 5.7% of release location coordinates and hatchery distances were newly determined using the methods described by Sturrock et al. (2019). Coordinates and distances are not available for 0.2% of all released fish due to insufficient descriptions of release site locations.

Data file used: CVSH_violin_hatchdist.csv

Size-at-release: Fish sizes were reported as mean mass (75.2% of all releases) or length (19.5% of total) for each release group. To facilitate dataset comparisons, length-at-release was converted to mass-at-release (and vice versa) according to the following relationship for Sacramento River O. mykiss (Hallock et al. 1961):
ln(M) = ln (8.80 ∙ 10 ―6) + 3.06 ∙ ln(FL)
Where mass is in measured grams and fork length is measured in millimeters. Note that this (M) (FL) relationship was determined for fish with FLs equal to or larger than 325 mm but predicts masses for smaller fish within 5% deviation from a similar length-weight transformation reported for California Central Coast O. mykiss (Huber 2018; 53–442 mm FL range; R2=0.99) across nearly the entire O. mykiss size range encountered in this investigation (97.0% of all hatchery fish with reported lengths smaller than 325 mm FL were larger than 53 mm FL). When size ranges were reported, the midpoint was assigned as the mean length or mass for the release group. Occasionally missing size data could be gleaned from written descriptions of the release group’s life history stage (e.g., “fry”, “fingerlings”, “yearlings”). In these cases (1.5% of all releases), the midpoint of the life stage-at-release mass range (see “Life-stage-at-release” below) was used.

Data file used: CVSH_mass_at_release_violin_data.csv

Age-at-release: All age information was estimated based on an assumed 1 February spawn date (Satterthwaite et al. 2010) and, therefore, should be considered apparent ages. Age analyses were restricted to cases when the release group beginning and end months of release are identical (80.3% of all releases). Apparent ages were estimated as the difference between release month midpoint and 1 February of the brood year.

Data file used: CVSH_age_at_rel_violin_data.csv

Life-stage-at-release: We explored both coarse- and fine-scale trends in the composition of life- stages-at-release. We first classified O. mykiss as sub-yearling (y-) or yearling or older fish (y+). We followed hatchery program guidelines (CA HSRG 2012, Appendix VIII) and assumed O. mykiss became yearlings once they grew to 71.2 g (~180 mm FL). We further classified life history stage-at-release diversity according to fish sizes and standardized nomenclature guidelines (IEP Steelhead PWT 1998). “Yolk-sac fry” were defined as fish with masses <0.3 g; “fry”: ≥0.3 to 1.4 g; “parr”: ≥1.4 g to <26.3 g; “silvery parr”: ≥26.3 g to <71.2 g; “small smolts”: ≥71.2 g to <131.6 g; “large smolts”: 131.6 g to <219.6g; “subadults”: ≥219.6 g to <954.0 g; and “adults”: ≥954.0 g. For cases when size data were missing but life-stage-at-release was described, “fed fry” were assumed to be fry, “fingerlings” were assumed to be parr, “advanced fingerlings” were assumed to be silvery parr, and “smolts” were assumed to be small smolts.Data files used: CVSH_fig8_lifehist.csv and CVSH_propyrling.csv Objective 2: Temporal variation in juvenile release metrics Life stage diversity: We characterized life stage diversity by calculating the Reciprocal Simpson’s Index (RSI; Simpson 1949) for each hatchery and all hatcheries combined per release year. The RSI measures the evenness of a community and ranges from 0 (all life stages were equally represented in every release group) to 1 (all fish were planted at the same life stage).

Data file used: CVSH_DI.csv

Interannual variation in release metrics: To investigate interannual variation in release practices, we divided the 70-year time series (1948-2017) into seven 10-year intervals and calculated the decadal coefficient of variation (CV) for six metrics associated with hatchery releases summarized CV10 annually at each hatchery and for all O. mykiss hatchery programs combined. The metrics examined included (1) total number released, (2) total biomass released, (3) mean release month, (4) mean release distance downstream of hatchery, (5)

San Joaquin Valley Subsidence Analysis README.
Written: Joel Dudas, 3/12/2017. Amended: Ben Brezing, 4/2/2019. DWR’s Division of Engineering Geodetic Branch received a request in 1/2017 from Jeanine Jones to produce a graphic of historic subsidence in the entirety of the San Joaquin Valley. The task was assigned to the Mapping & Photogrammetry Office and the Geospatial Data Support Section to complete by early February. After reviewing the alternatives, the decision was made to produce contours from the oldest available set of quad maps for which there was reasonable certainty about quality and datum, and to compare that to the most current Valley-wide DEM. For the first requirement, research indicated that the 1950’s vintage quad maps for the Valley were the best alternative. Prior quad map editions are uneven in quality and vintage, and the actual control used for the contour lines was extremely suspect. The 1950’s quads, by contrast, were produced primarily on the basis of 1948-1949 aerial photography, along with control corresponding to that period, and referenced to the National Geodetic Vertical Datum of 1929. For the current set, the most recent Valley-wide dataset that was freely available, in the public domain, and of reasonable accuracy was the 2005 NextMap SAR acquisition (referenced to NAVD88). The primary bulk of the work focused on digitizing the 1950’s contours. First, all of the necessary quads were downloaded from the online USGS quad source https://ngmdb.usgs.gov/maps/Topoview/viewer/#4/41.13/-107.51. Then the entire staff of the Mapping & Photogrammetry Lab (including both the Mapping Office and GDDS staff) proceeded to digitize the contours. Given the short turnaround time constraint and limited budget, certain shortcuts occurred in contour development. While efforts were made to digitize accurately, speed really was important. Contours were primarily focused only on agricultural and other lowland areas, and so highlands were by and large skipped. The tight details of contours along rivers, levees, and hillsides was skipped and/or simplified. In some cases, only major contours were digitized. The mapping on the source quads itself varied….in a few cases on spot elevations on benchmarks were available in quads. The contour interval sometimes varied, even within the quad sheet itself. In addition, because 8 different people were creating the contours, variability exists in the style and attention to detail. It should be understood that given the purpose of the project (display regional subsidence patterns), that literal and precise development of the historic contour sets leaves some things to be desired. These caveats being said, the linework is reasonably accurate for what it is (particularly given that the contours of that era themselves were mapped at an unknown and varying actual quality). The digitizers tagged the lines with Z values manually entered after linework that corresponded to the mapped elevation contours. Joel Dudas then did what could be called a “rough” QA/QC of the contours. The individual lines were stitched together into a single contour set, and exported to an elevation raster (using TopoToRaster in ArcGIS 10.4). Gross blunders in Z values were corrected. Gaps in the coverage were filled. The elevation grid was then adjusted to NAVD88 using a single adjustment for the entire coverage area (2.5’, which is a pretty close average of values in this region). The NextMap data was extracted for the area, and converted into feet. The two raster sets were fixed to the same origin point. The subsidence grid was then created by subtracting the old contour-derived grid from the NextMAP DEM. The subsidence grid that includes all of the values has the suffix “ALL”. Then, to improve the display fidelity, some of the extreme values (above +5’ and below -20’*) were filtered out of the dataset, and the subsidence grid was regenerated for these areas and suffixed with “cut.” The purpose of this cut was to extract some of the riverine and hilly areas that produced more extreme values and other artifacts purely due to the analysis approach (i.e. not actual real elevation change). * - some of the areas with more than 20 feet of subsidence were omitted from this clipping, because they were in heavily subsided areas and may be “real subsidence.”The resulting subsidence product should be perceived in light of the above. Some of the collar of the San Joaquin Valley shows large changes, but that is simply due to the analysis method. Also, individual grid cells may or may not be comparing the same real features. Errors are baked into both comparison datasets. However, it is important to note that the large areas of subsidence in the primary agriculture area agree fairly well with a cruder USGS subsidence map of the Valley based on extensometer data. We have confidence that the big picture story these results show us is largely correct, and that the magnitudes of subsidence are somewhat reasonable. The contour set can serve as the baseline to support future comparisons using more recent or future data as it becomes available. It should be noted there are two key versions of the data. The “Final Deliverables” from 2/2017 were delivered to support the initial Public Affairs press release. Subsequent improvements were made in coverage and blunder correction as time permitted (it should be noted this occurred in the midst of the Oroville Dam emergency) to produce the final as of 3/12/2017. Further improvements in overall quality and filtering could occur in the future if time and needs demand it.
Update (4/3/2019, Ben Brezing): The raster was further smoothed to remove artifacts that result from comparing the high resolution NextMAP DEM to the lower resolution DEM that was derived from the 1950’s quad map contours. The smoothing was accomplished by removing raster cells with values that are more than 0.5 feet different than adjacent cells (25 meter cell size), as well as the adjacent cells. The resulting raster was then resampled to a raster with 100 meter cell size using cubic resampling technique and was then converted to a point feature class. The point feature class was then interpolated to a raster with 250 meter cell size using the IDW technique, a fixed search radius of 1250 meters and power=2. The resulting raster was clipped to a smaller extent to remove noisier areas around the edges of the Central Valley while retaining coverage for the main area of interest.

We develop a Bayesian model-based approach to finite population estimation accounting for spatial dependence. Our innovation here is a framework that achieves inference for finite population quantities in spatial process settings. A key distinction from the small area estimation setting is that we analyze finite populations referenced by their geographic coordinates (point-referenced data). Specifically, we consider a two-stage sampling design in which the primary units are geographic regions, the secondary units are point-referenced locations, and the measured values are assumed to be a partial realization of a spatial process. Traditional geostatistical models do not account for variation attributable to finite population sampling designs, which can impair inferential performance. On the other hand, design-based estimates will ignore the spatial dependence in the finite population. This motivates the introduction of geostatistical processes that will enable inference at arbitrary locations in our domain of interest.We demonstrate using simulation experiments that process-based finite population sampling models considerably improve model fit and inference over models that fail to account for spatial correlation. Furthermore, the process based models offer richer inference with spatially interpolated maps over the entire region. We reinforce these improvements and demonstrate scalable inference for groundwater Nitrate levels in the population of California Central Valley wells by offering estimates of mean Nitrate levels and their spatially interpolated maps.

CDFW BIOS GIS Dataset, Contact: Steve Stone, Description: This dataset depicts the general boundaries of the Central California Coast Coho Salmon evolutionarily significant unit (ESU) (i.e., a distinct population segment (DPS) under the U.S. Endangered Species Act) as well as the historical population structure of the species.

This geodataabase provides an estimate to the spatial distribution of potential historical habitat for California Coastal Chinook Salmon, Central California Coast Coho Salmon, Northern California Steelhead and Central California Coast Steelhead. Intrinsic potential measures the potential for development of favorable habitat characteristics as a function of the underlying geomorphic and hydrological attributes, as determined through a Digital Elevation Model (DEM) and mean annual precipitation grid. The model does not predict the actual distribution of "good'' habitat, but rather the potential for that habitat to occur, nor does the model predict abundance or productivity. Additionally, the model does not predict current conditions, but rather those patterns expected under pristine conditions as related through the input data. Thus, IP provides a tool for examining the historical distribution of habitat among and within watersheds, a proxy for population size and structure, and a useful template for examining the consequences of recent anthropogenic activity at landscape scales.

The dataset 'ds181_Chinook_ln' is a product of the CalFish Adult Salmonid Abundance Database. Data in this shapefile are collected from stream sections or reaches where Chinook population monitoring occurs and that are best represented by linear features. Some escapement monitoring locations are logically represented by point features, such as dams and hatcheries. See the companion point feature shapefile 'ds180_Chinook_pnts' for information collected from point locations.The CalFish Abundance Database contains a comprehensive collection of anadromous fisheries abundance information. Beginning in 1998, the Pacific States Marine Fisheries Commission, the California Department of Fish and Game, and the National Marine Fisheries Service, began a cooperative project aimed at collecting, archiving, and entering into standardized electronic formats, the wealth of information generated by fisheries resource management agencies and tribes throughout California.The data format provides for sufficient detail to convey the relative accuracy of each population trend index record yet is simple and straight forward enough to be suited for public use. For those interested in more detail the database offers hyperlinks to digital copies of the original documents used to compile the information. In this way the database serves as an information hub directing the user to additional supporting information. This offers utility to field biologists and others interested in obtaining information for more in-depth analysis. Hyperlinks, built into the spatial data attribute tables used in the BIOS and CalFish I-map viewers, open the detailed index data archived in the on-line CalFish database application. The information can also be queried directly from the database via the CalFish Tabular Data Query. Once the detailed annual trend data are in view, another hyperlink opens a digital copy of the document used to compile each record.During 2010, as a part of the Central Valley Chinook Comprehensive Monitoring Plan, the CalFish Salmonid Abundance Database was reorganized and updated. CalFish provides a central location for sharing Central Valley Chinook salmon escapement estimates and annual monitoring reports to all stakeholders, including the public. Annual Chinook salmon in-river escapement indices that were, in many cases, eight to ten years behind are now current though 2009. In some cases, multiple datasets were consolidated into a single, more comprehensive, dataset to more closely reflect how data are reported in the California Department of Fish and Game standard index, Grandtab.Extensive data are currently available in the CalFish Abundance Database for California Chinook, coho, and steelhead. Major data categories include adult abundance population estimates, actual fish and/or carcass counts, counts of fish collected at dams, weirs, or traps, and redd counts. Harvest data has also been compiled for many streams.This CalFish Abundance Database shapefile was generated from fully routed 1:100,000 hydrography. In a few cases streams had to be added to the hydrography dataset in order to provide a means to create shapefiles to represent abundance data associated with them. Streams added were digitized at no more than 1:24,000 scale based on stream line images portrayed in 1:24,000 Digital Raster Graphics (DRG).The features in this layer represent the location for which abundance data records apply. In many cases there are multiple datasets associated with the same location, and so, features may overlap. Please view the associated datasets for detail regarding specific features. In CalFish these are accessed through the "link" field that is visible when performing an identify or query operation. A URL string is provided with each feature in the downloadable data which can also be used to access the underlying datasets.The Chinook data that is available from the CalFish website is actually mirrored from the StreamNet website where the CalFish Abundance Database's tabular data is currently stored. Additional information about StreamNet may be downloaded at http://www.streamnet.org. Complete documentation for the StreamNet database may be accessed at http://http://www.streamnet.org/def.html

VITAL SIGNS INDICATOR Migration (EQ4)

FULL MEASURE NAME Migration flows

LAST UPDATED December 2018

DESCRIPTION Migration refers to the movement of people from one location to another, typically crossing a county or regional boundary. Migration captures both voluntary relocation – for example, moving to another region for a better job or lower home prices – and involuntary relocation as a result of displacement. The dataset includes metropolitan area, regional, and county tables.

DATA SOURCE American Community Survey County-to-County Migration Flows 2012-2015 5-year rolling average http://www.census.gov/topics/population/migration/data/tables.All.html

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) Data for migration comes from the American Community Survey; county-to-county flow datasets experience a longer lag time than other standard datasets available in FactFinder. 5-year rolling average data was used for migration for all geographies, as the Census Bureau does not release 1-year annual data. Data is not available at any geography below the county level; note that flows that are relatively small on the county level are often within the margin of error. The metropolitan area comparison was performed for the nine-county San Francisco Bay Area, in addition to the primary MSAs for the nine other major metropolitan areas, by aggregating county data based on current metropolitan area boundaries. Data prior to 2011 is not available on Vital Signs due to inconsistent Census formats and a lack of net migration statistics for prior years. Only counties with a non-negligible flow are shown in the data; all other pairs can be assumed to have zero migration.

Given that the vast majority of migration out of the region was to other counties in California, California counties were bundled into the following regions for simplicity: Bay Area: Alameda, Contra Costa, Marin, Napa, San Francisco, San Mateo, Santa Clara, Solano, Sonoma Central Coast: Monterey, San Benito, San Luis Obispo, Santa Barbara, Santa Cruz Central Valley: Fresno, Kern, Kings, Madera, Merced, Tulare Los Angeles + Inland Empire: Imperial, Los Angeles, Orange, Riverside, San Bernardino, Ventura Sacramento: El Dorado, Placer, Sacramento, Sutter, Yolo, Yuba San Diego: San Diego San Joaquin Valley: San Joaquin, Stanislaus Rural: all other counties (23)

One key limitation of the American Community Survey migration data is that it is not able to track emigration (movement of current U.S. residents to other countries). This is despite the fact that it is able to quantify immigration (movement of foreign residents to the U.S.), generally by continent of origin. Thus the Vital Signs analysis focuses primarily on net domestic migration, while still specifically citing in-migration flows from countries abroad based on data availability.

The U.S. Geological Survey (USGS) is developing SPARROW models (SPAtially Related Regressions On Watershed Attributes) to assess the transport of contaminants (for example, nutrients) through the Pacific drainages of the United States (the Columbia River basin; the coastal drainages of Washington, Oregon, and California; the Klamath River basin; the Central Valley of California, and the west slopes of the Sierra Nevada Mountains). SPARROW relates instream water quality measurements to spatially referenced characteristics of watersheds, including contaminant sources and the factors influencing terrestrial and aquatic transport. The number of people with on-site wastewater treatment (primarily septic tanks) is a potential factor affecting nutrient delivery to streams. The spatial data set “Population with On-Site Wastewater Treatment within the Pacific Drainages of the United States (2010)" represents the number of people that did not have access to centralized municipal wastewater treatment in 2010. This data set was created by disaggregating census block populations to developed land and retaining those populations that were outside of the service boundaries for municipal wastewater treatment plants.

About this dataset

Context

The dataset tabulates the Grass Valley population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of Grass Valley. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

Key observations

The largest age group was 18 to 64 years with a poulation of 7,194 (50.93% of the total population). 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 cohorts:

• Under 18 years
• 18 to 64 years
• 65 years and over

Variables / Data Columns

• Age Group: This column displays the age cohort for the Grass Valley population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in Grass Valley is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the Grass Valley 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 Grass Valley Population by Age. You can refer the same here

The GIS layer "Range_extent_15" is a simple polyline representing the geographic distribution of the southern sea otter (Enhydra lutris nereis) in mainland California, based on data collected during the spring 2015 range-wide census. The USGS range-wide sea otter census has been undertaken twice a year since 1982, once in May and once in October, using consistent methodology involving both ground-based and aerial-based counts. The spring census is considered more accurate than the fall count, and provides the primary basis for gauging population trends by State and Federal management agencies. Sea otter distribution in California (the mainland range) is considered to comprise a band of potential habitat stretching along the coast of California, and bounded to the north and south by range limits defined as "the points farthest from the range center at which 5 or more otters are counted within a 10km contiguous stretch of coastline (as measured along the 10m bathymetric contour) during the two most recent spring censuses, or at which these same criteria were met in the previous year".

About this dataset

Context

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

Key observations

The percent distribution of Portola Valley population by race (across all racial categories recognized by the U.S. Census Bureau): 79.60% are white, 0.23% are Black or African American, 9.38% are Asian, 0.90% are some other race and 9.89% 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 Portola Valley
• Population: The population of the racial category (excluding ethnicity) in the Portola Valley is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Portola Valley 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 Portola Valley Population by Race & Ethnicity. You can refer the same here