20 year Projected Urban Growth scenarios. Base year is 2000. Projected year in this dataset is 2020.

By 2020, most forecasters agree, California will be home to between 43 and 46 million residents-up from 35 million today. Beyond 2020 the size of California's population is less certain. Depending on the composition of the population, and future fertility and migration rates, California's 2050 population could be as little as 50 million or as much as 70 million. One hundred years from now, if present trends continue, California could conceivably have as many as 90 million residents.

Where these future residents will live and work is unclear. For most of the 20th Century, two-thirds of Californians have lived south of the Tehachapi Mountains and west of the San Jacinto Mountains-in that part of the state commonly referred to as Southern California. Yet most of coastal Southern California is already highly urbanized, and there is relatively little vacant land available for new development. More recently, slow-growth policies in Northern California and declining developable land supplies in Southern California are squeezing ever more of the state's population growth into the San Joaquin Valley.

How future Californians will occupy the landscape is also unclear. Over the last fifty years, the state's population has grown increasingly urban. Today, nearly 95 percent of Californians live in metropolitan areas, mostly at densities less than ten persons per acre. Recent growth patterns have strongly favored locations near freeways, most of which where built in the 1950s and 1960s. With few new freeways on the planning horizon, how will California's future growth organize itself in space? By national standards, California's large urban areas are already reasonably dense, and economic theory suggests that densities should increase further as California's urban regions continue to grow. In practice, densities have been rising in some urban counties, but falling in others.

These are important issues as California plans its long-term future. Will California have enough land of the appropriate types and in the right locations to accommodate its projected population growth? Will future population growth consume ever-greater amounts of irreplaceable resource lands and habitat? Will jobs continue decentralizing, pushing out the boundaries of metropolitan areas? Will development densities be sufficient to support mass transit, or will future Californians be stuck in perpetual gridlock? Will urban and resort and recreational growth in the Sierra Nevada and Trinity Mountain regions lead to the over-fragmentation of precious natural habitat? How much water will be needed by California's future industries, farms, and residents, and where will that water be stored? Where should future highway, transit, and high-speed rail facilities and rights-of-way be located? Most of all, how much will all this growth cost, both economically, and in terms of changes in California's quality of life?

Clearly, the more precise our current understanding of how and where California is likely to grow, the sooner and more inexpensively appropriate lands can be acquired for purposes of conservation, recreation, and future facility siting. Similarly, the more clearly future urbanization patterns can be anticipated, the greater our collective ability to undertake sound city, metropolitan, rural, and bioregional planning.

Consider two scenarios for the year 2100. In the first, California's population would grow to 80 million persons and would occupy the landscape at an average density of eight persons per acre, the current statewide urban average. Under this scenario, and assuming that 10% percent of California's future population growth would occur through infill-that is, on existing urban land-California's expanding urban population would consume an additional 5.06 million acres of currently undeveloped land. As an alternative, assume the share of infill development were increased to 30%, and that new population were accommodated at a density of about 12 persons per acre-which is the current average density of the City of Los Angeles. Under this second scenario, California's urban population would consume an additional 2.6 million acres of currently undeveloped land. While both scenarios accommodate the same amount of population growth and generate large increments of additional urban development-indeed, some might say even the second scenario allows far too much growth and development-the second scenario is far kinder to California's unique natural landscape.

This report presents the results of a series of baseline population and urban growth projections for California's 38 urban counties through the year 2100. Presented in map and table form, these projections are based on extrapolations of current population trends and recent urban development trends. The next section, titled Approach, outlines the methodology and data used to develop the various projections. The following section, Baseline Scenario, reviews the projections themselves. A final section, entitled Baseline Impacts, quantitatively assesses the impacts of the baseline projections on wetland, hillside, farmland and habitat loss.

This dataset consists of modeled projections of land use and land cover and population for the State of California for the period 1970-2101. For the 1970-2001 period, we used the USGS's LUCAS model to "backcast" LULC, beginning with the 2001 initial conditions and ending with 1970. For future projections, the model was initialized in 2001 and run forward on an annual time step to 2100. In total 5 simulations were run with 10 Monte Carlo replications of each simulation. The simulations include: 1) Historical backcast from 2001-1970, 2) Business-as-usual (BAU) projection from 2001-2101, and 3) three modified BAU projections based on California Department of Finance population projections based on high, medium, and low growth rates.

The California State Places Boundary data.

This dataset offers high-resolution boundary definitions, which allow users to analyze and visualize California’s state limits within mapping and spatial analysis projects.

The shapefile is part of a ZIP archive containing multiple related files that together define and support the boundary data. These files include:

.shp (Shape): This is the core file containing the vector data for California’s Places boundaries, representing the geographic location and geometry of the state outline.

.shx (Shape Index): A companion index file for the .shp file, allowing for quick spatial queries and efficient data access.

.dbf (Attribute Table): A database file that stores attribute data linked to the geographic features in the .shp file, such as area identifiers or classification codes, in a tabular format compatible with database applications.

.prj (Projection): This file contains projection information, specifying the coordinate system and map projection used for the data, essential for aligning it accurately on maps.

.cpg (Code Page): This optional file indicates the character encoding for the attribute data in the .dbf file, which is useful for ensuring accurate text representation in various software.

.sbn and .sbx (Spatial Index): These files serve as a spatial index for the shapefile, allowing for faster processing of spatial queries, especially for larger datasets.

.xml (Metadata): A metadata file in XML format, often following FGDC or ISO standards, detailing the dataset’s origin, structure, and usage guidelines, providing essential information about data provenance and quality.

Scenario-based simulation model projections of land use change, ecosystem carbon stocks, and ecosystem carbon fluxes for the State of California from 2001-2101 using the SyncroSim software framework, see http://doc.syncrosim.com/index.php?title=Reference_Guide for software documentation. We explored four land-use scenarios and two radiative forcing scenarios (e.g. Representative Concentration Pathways; RCPs) as simulated by four earth system models (i.e. climate models). Results can be used to understand the drivers of change in ecosystem carbon storage over short, medium, and long (e.g. 100 year) time intervals. See Sleeter et al. (2019) Global Change Biology (doi: 10.1111/gcb.14677) for detailed descriptions of scenarios.

This dataset consists of modeled projections of land use and land cover for the State of California for the period 2001-2101. The Land Use and Carbon Scenario Simulator (LUCAS) model was initialized in 2001 and run forward on an annual time step to 2100. In total 9 simulations were run with 10 Monte Carlo replications of each simulation. Two base scenarios were selected from Sleeter et al., 2017 (http://onlinelibrary.wiley.com/doi/10.1002/2017EF000560/full) for analysis, including a "business-as-usual" (BAU) land use scenario and a scenario based on "medium" population projections. For each base scenario we ran three alternative conservation scenarios where we simulated conversion of lands into conservation easements. The three conservation easement scenarios simulated conversion of 1) 120 km2/yr for 15 years, 2) 120 km2/yr for 30 years, and 3) 240 km2/yr for 30 years. All easement conversions were set to begin in 2020 and extend for their stated duration. In addition to the conservation easement scenarios, we also ran a variant of the BAU land use scenario where current Williamson Act lands were removed from the simulation of future conditions.

This is a polygon layer based upon the traffic impact fee (TIF) zones within the county. This data is maintained by the El Dorado County's Surveyor's Department GIS Division (EDC GIS).Spatial Reference Projected Coordinate System: NAD 1983 (2011) State Plane California II FIPS 0402 US Feet (Zone II) Projection: Lambert Conformal Conic Geographic Coordinate System: NAD 1983 (2011)

This dataset provides long-term occupational employment projections for the state of California across various industries. It offers insights into job growth, industry trends, and workforce demand over a 10-year horizon.

Why is this dataset useful? 1. Job Market Analysis – Identify which jobs and industries are expected to grow or decline. Workforce Planning – Helps businesses, policymakers, and educators align training programs with future job demand. 2. Predictive Modeling – Use this dataset for time-series forecasting, job demand predictions, and labor market analytics.

Data Details: - Timeframe: 2022-2032 - Geography: State of California - Industries Covered: Technology, Healthcare, Retail, Manufacturing, Finance, and more.

Columns: 1. Area Type – Specifies the geographic classification (e.g., state-level or regional). 2. Area Name – The name of the geographic region (e.g., California, specific labor market regions). 3. Period – The timeframe of the projection, typically from the base year to the projected year (e.g., 2022-2032). 4. SOC Level – The level of the Standard Occupational Classification (SOC) system used for job categorization. 5. Standard Occupational Classification (SOC) – A unique code representing a specific occupation based on the SOC system. 6. Occupational Title – The official job title corresponding to the SOC code. 7. Base Year Employment Estimate – The estimated number of jobs for the occupation in the base year (e.g., 2022). 8. Projected Year Employment Estimate – The expected number of jobs for the occupation in the projected year (e.g., 2032). 9. Numeric Change – The absolute difference in employment between the base year and projected year. 10. Percentage Change – The percentage increase or decrease in employment over the projection period. 11. Exits – Estimated number of workers leaving the occupation due to retirement or career changes. 12. Transfers – Estimated number of workers transferring into or out of an occupation. 13. Total Job Openings – The sum of exits, transfers, and new job creation, representing the total expected openings. 14. Median Hourly Wage – The median hourly wage for the occupation. 15. Median Annual Wage – The median annual wage for the occupation. 16. Entry Level Education – The typical minimum education required for the occupation (e.g., high school diploma, bachelor's degree). 17. Work Experience – The amount of prior work experience typically needed for the occupation. 18. Job Training – The type of on-the-job training required for entry into the occupation.

Potential Use Cases: ✔ Career Guidance – Helps individuals choose high-growth career paths. ✔ Economic Research – Understand how employment trends impact the economy. ✔ Machine Learning Models – Build predictive models for workforce demand.

If you find this dataset useful, please upvote! Your support encourages more high-quality datasets.

This is a polygon layer based upon the jurisdictional boundaries of each district. This data is owned and maintained by the El Dorado County's Surveyor's Department GIS Division (EDC GIS).Spatial Reference Projected Coordinate System: NAD 1983 (2011) State Plane California II FIPS 0402 US Feet (Zone II) Projection: Lambert Conformal Conic Geographic Coordinate System: NAD 1983 (2011)

This is a polygon layer based upon 40 foot elevation contours for the county. This data is owned and maintained by the El Dorado County's Surveyor's Department GIS Division (EDC GIS). Sourced from U.S. Geological Survey. Contours derived from the National Elevation Dataset.Spatial Reference Projected Coordinate System: NAD 1983 (2011) State Plane California II FIPS 0402 US Feet (Zone II) Projection: Lambert Conformal Conic Geographic Coordinate System: NAD 1983 (2011)

This is a polygon layer based upon the jurisdictional boundaries of each zone of benefit. This data is owned and maintained by the El Dorado County's Surveyor's Department GIS Division (EDC GIS).Spatial Reference Projected Coordinate System: NAD 1983 (2011) State Plane California II FIPS 0402 US Feet (Zone II) Projection: Lambert Conformal Conic Geographic Coordinate System: NAD 1983 (2011)

This is a polygon layer based upon the jurisdictional boundaries of each district. This data is owned and maintained by the El Dorado County's Surveyor's Department GIS Division (EDC GIS).Spatial Reference Projected Coordinate System: NAD 1983 (2011) State Plane California II FIPS 0402 US Feet (Zone II) Projection: Lambert Conformal Conic Geographic Coordinate System: NAD 1983 (2011)

This data includes the locations of the MT data collected in and around the Coso Geothermal field that covered the West Flank area. These are the data that the 3D MT models were created from that were discussed in Phase 1 of the West Flank FORGE project.

The projected coordinate system is NAD 1927 State Plane California IV FIPS 0404 and the Projection is Lambert Conformal Conic. Units are in feet.

This dataset contains projections of coastal cliff retreat and associated uncertainty across Northern California for future scenarios of sea-level rise (SLR) to include 25, 50, 75, 100, 125, 150, 175, 200, 250, 300, and 500 centimeters (cm) of SLR by the year 2100 and cover coastline from the Golden Gate Bridge to the California-Oregon state border. Present-day cliff-edge positions used as the baseline for projections are also included. Projections were made using numerical models and field observations such as historical cliff retreat rate, nearshore slope, coastal cliff height, and mean annual wave power, as part of Coastal Storm Modeling System (CoSMoS). See cited references and methods for more detail.

This is a polygon layer based upon the jurisdictional boundaries of each district. This data is owned and maintained by the El Dorado County's Surveyor's Department GIS Division (EDC GIS).Spatial Reference Projected Coordinate System: NAD 1983 (2011) State Plane California II FIPS 0402 US Feet (Zone II) Projection: Lambert Conformal Conic Geographic Coordinate System: NAD 1983 (2011)

To aid applicants with quantification and monetization of benefits of proposed water storage projects per Chapter 8 of Proposition 1 (Water Code section 79750 et. seq.), the California Water Commission (Commission) developed a Technical Reference which was released in August 2016.

These data and model products are companion information to the Technical Reference and were developed to assist applicants for funding under the Water Storage Investment Program (WSIP). The WSIP required applicants for public funding to analyze their proposed projects using climate and sea level conditions for California projected at years 2030 and 2070.

The data and model products were developed for the following climate and sea level conditions:

• Without-Project 2030 Future Conditions – Year 2030 future condition with projected climate and sea level conditions for a thirty-year period centered at 2030 (climate period 2016-2045)

• Without-Project 2070 Future Conditions – Year 2070 future condition with projected climate and sea level conditions for a thirty-year period centered at 2070 (climate period 2056-2085)

• 1995 Historical Temperature-detrended Conditions (reference) – Year 1995 historical condition with climate and sea level conditions for a thirty-year period centered at 1995 (reference climate period 1981-2010)  

  California Water Commission

The California Water Commission consists of nine members appointed by the Governor and confirmed by the State Senate. Seven members are chosen for their expertise related to the control, storage, and beneficial use of water and two are chosen for their knowledge of the environment. The Commission provides a public forum for discussing water issues, advises the Director of the Department of Water Resources on matters within the Department’s jurisdiction, approves rules and regulations, and monitors and reports on the construction and operation of the State Water Project. Proposition 1: The Water Quality, Supply, and Infrastructure Improvement Act approved by voters in 2014, gave the Commission new responsibilities regarding the distribution of public funds set aside for the public benefits of water storage projects, and developing regulations for the quantification and management of those benefits. In 2018, the Commission approved maximum conditional funding amounts for eight projects in the Water Storage Investment Program.

The California State Boundary data from the US Census Bureau's 2023 MAF/TIGER database provides detailed geographic boundary data designed for use in Geographic Information System applications.

This dataset offers high-resolution boundary definitions, which allow users to analyze and visualize California’s state limits within mapping and spatial analysis projects.

The shapefile is part of a ZIP archive containing multiple related files that together define and support the boundary data. These files include:

.shp (Shape): This is the core file containing the vector data for California’s boundary, representing the geographic location and geometry of the state outline.

.shx (Shape Index): A companion index file for the .shp file, allowing for quick spatial queries and efficient data access.

.dbf (Attribute Table): A database file that stores attribute data linked to the geographic features in the .shp file, such as area identifiers or classification codes, in a tabular format compatible with database applications.

.prj (Projection): This file contains projection information, specifying the coordinate system and map projection used for the data, essential for aligning it accurately on maps.

.cpg (Code Page): This optional file indicates the character encoding for the attribute data in the .dbf file, which is useful for ensuring accurate text representation in various software.

.sbn and .sbx (Spatial Index): These files serve as a spatial index for the shapefile, allowing for faster processing of spatial queries, especially for larger datasets.

.xml (Metadata): A metadata file in XML format, often following FGDC or ISO standards, detailing the dataset’s origin, structure, and usage guidelines, providing essential information about data provenance and quality.

This comprehensive set of files ensures compatibility with most GIS software and allows users to perform a wide range of spatial analyses with detailed information on California’s boundary as defined by the U.S. Census Bureau's 2023 MAF/TIGER database.

This dataset includes one file for each of the 51 counties that were collected, as well as a CA_Merged file with the parcels merged into a single file.Note – this data does not include attributes beyond the parcel ID number (PARNO) – that will be provided when available, most likely by the state of California.DownloadA 1.6 GB zipped file geodatabase is available for download - click here.DescriptionA geodatabase with parcel boundaries for 51 (out of 58) counties in the State of California. The original target was to collect data for the close of the 2013 fiscal year. As the collection progressed, it became clear that holding to that time standard was not practical. Out of expediency, the date requirement was relaxed, and the currently available dataset was collected for a majority of the counties. Most of these were distributed with minimal metadata.The table “ParcelInfo” includes the data that the data came into our possession, and our best estimate of the last time the parcel dataset was updated by the original source. Data sets listed as “Downloaded from” were downloaded from a publicly accessible web or FTP site from the county. Other data sets were provided directly to us by the county, though many of them may also be available for direct download. Â These data have been reprojected to California Albers NAD84, but have not been checked for topology, or aligned to county boundaries in any way. Tulare County’s dataset arrived with an undefined projection and was identified as being California State Plane NAD83 (US Feet) and was assigned by ICE as that projection prior to reprojection. Kings County’s dataset was delivered as individual shapefiles for each of the 50 assessor’s books maintained at the county. These were merged to a single feature class prior to importing to the database.The attribute tables were standardized and truncated to include only a PARNO (APN). The format of these fields has been left identical to the original dataset. The Data Interoperablity Extension ETL tool used in this process is included in the zip file. Where provided by the original data sources, metadata for the original data has been maintained. Please note that the attribute table structure changes were made at ICE, UC Davis, not at the original data sources.Parcel Source InformationCountyDateCollecDateCurrenNotesAlameda4/8/20142/13/2014Download from Alamenda CountyAlpine4/22/20141/26/2012Alpine County PlanningAmador5/21/20145/14/2014Amador County Transportation CommissionButte2/24/20141/6/2014Butte County Association of GovernmentsCalaveras5/13/2014Download from Calaveras County, exact date unknown, labelled 2013Contra Costa4/4/20144/4/2014Contra Costa Assessor’s OfficeDel Norte5/13/20145/8/2014Download from Del Norte CountyEl Dorado4/4/20144/3/2014El Dorado County AssessorFresno4/4/20144/4/2014Fresno County AssessorGlenn4/4/201410/13/2013Glenn County Public WorksHumboldt6/3/20144/25/2014Humbodt County AssessorImperial8/4/20147/18/2014Imperial County AssessorKern3/26/20143/16/2014Kern County AssessorKings4/21/20144/14/2014Kings CountyLake7/15/20147/19/2013Lake CountyLassen7/24/20147/24/2014Lassen CountyLos Angeles10/22/201410/9/2014Los Angeles CountyMadera7/28/2014Madera County, Date Current unclear likely 7/2014Marin5/13/20145/1/2014Marin County AssessorMendocino4/21/20143/27/2014Mendocino CountyMerced7/15/20141/16/2014Merced CountyMono4/7/20144/7/2014Mono CountyMonterey5/13/201410/31/2013Download from Monterey CountyNapa4/22/20144/22/2014Napa CountyNevada10/29/201410/26/2014Download from Nevada CountyOrange3/18/20143/18/2014Download from Orange CountyPlacer7/2/20147/2/2014Placer CountyRiverside3/17/20141/6/2014Download from Riverside CountySacramento4/2/20143/12/2014Sacramento CountySan Benito5/12/20144/30/2014San Benito CountySan Bernardino2/12/20142/12/2014Download from San Bernardino CountySan Diego4/18/20144/18/2014San Diego CountySan Francisco5/23/20145/23/2014Download from San Francisco CountySan Joaquin10/13/20147/1/2013San Joaquin County Fiscal year close dataSan Mateo2/12/20142/12/2014San Mateo CountySanta Barbara4/22/20149/17/2013Santa Barbara CountySanta Clara9/5/20143/24/2014Santa Clara County, Required a PRA requestSanta Cruz2/13/201411/13/2014Download from Santa Cruz CountyShasta4/23/20141/6/2014Download from Shasta CountySierra7/15/20141/20/2014Sierra CountySolano4/24/2014Download from Solano Couty, Boundaries appear to be from 2013Sonoma5/19/20144/3/2014Download from Sonoma CountyStanislaus4/23/20141/22/2014Download from Stanislaus CountySutter11/5/201410/14/2014Download from Sutter CountyTehama1/16/201512/9/2014Tehama CountyTrinity12/8/20141/20/2010Download from Trinity County, Note age of data 2010Tulare7/1/20146/24/2014Tulare CountyTuolumne5/13/201410/9/2013Download from Tuolumne CountyVentura11/4/20146/18/2014Download from Ventura CountyYolo11/4/20149/10/2014Download from Yolo CountyYuba11/12/201412/17/2013Download from Yuba County

This data series provides tabular output from a series of modeling simulations for the State of California. The methods and results of this research are described in detail in Sleeter et al. (2019). We used the LUCAS model to project changes in ecosystem carbon balance resulting from land use and land use change, climate change, and ecosystem disturbances such as wildfire and drought. The model was run at a 1-km spatial resolution on an annual timestep. We simulated 32 unique scenarios, consisting of 4 land-use scenarios and 2 radiative forcing scenarios as simulated by 4 global climate models. For each scenario, we ran 100 Monte Carlo realizations of the model. Additional details describing the modeling effort can be found in the Global Change Biology paper. Results presented here have been aggregated from the individual cell level to either ecoregion or state-wide summaries.

This dataset contains the predicted prices of the asset California is a failed state over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

This dataset contains 30-year rolling average of annual average precipitation across all four models and two greenhouse gas (RCP) scenarios in the four model ensemble. The year identified for a 30 year rolling average is the mid-point of the 30-year average. eg. The year 2050 includes the values from 2036 to 2065.

The downscaling and selection of models for inclusion in ten and four model ensembles is described in 'https://www.energy.ca.gov/sites/default/files/2019-11/Projections_CCCA4-CEC-2018-006_ADA.pdf#page=11' rel='nofollow ugc'>Pierce et al. 2018, but summarized here. Thirty two global climate models (GCMs) were identified to meet the modeling requirements. From those, ten that closely simulate California’s climate were selected for additional analysis ('https://www.energy.ca.gov/sites/default/files/2019-11/Projections_CCCA4-CEC-2018-006_ADA.pdf#page=11' rel='nofollow ugc'>Table 1, Pierce et al. 2018) and to form a ten model ensemble. From the ten model ensemble, four models, forming a four model ensemble, were identified to provide coverage of the range of potential climate outcomes in California. The models in the four model ensemble and their general climate projection for California are:

• HadGEM2-ES (warm/dry),
• CanESM2 (average),
• CNRM-CM5 (cooler/wetter),
• and MIROC5 the model least like the others to improve coverage of the range of outcomes.

These data were downloaded from Cal-Adapt and prepared for use within CA Nature by California Natural Resource Agency and ESRI staff.

Cal-Adapt. (2018). LOCA Derived Data [GeoTIFF]. Data derived from LOCA Downscaled CMIP5 Climate Projections. Cal-Adapt website developed by University of California at Berkeley’s Geospatial Innovation Facility under contract with the California Energy Commission. Retrieved from https://cal-adapt.org/

Pierce, D. W., J. F. Kalansky, and D. R. Cayan, (Scripps Institution of Oceanography). 2018. Climate, Drought, and Sea Level Rise Scenarios for the Fourth California Climate Assessment. California’s Fourth Climate Change Assessment, California Energy Commission. Publication Number: CNRA-CEC-2018-006.