Background - Interest in developing alternative sources of renewable energy to reduce dependence on oil has increased in recent years. Some sources of renewable energy being considered will include power generation infrastructure and support activities located within continental shelf waters, and potentially within deeper waters off the U.S. Pacific coast and beyond state waters (i.e., outside three nautical miles). Currently, the Bureau of Ocean Energy Management (BOEM) is considering renewable energy proposals off the coast of Oregon, California, and Hawaii. From 1999–2002, the U.S. Geological Survey (USGS) and Humboldt State University (HSU) worked with BOEM (formely known as the Minerals Management Service, MMS) to conduct a multi-year study that quantified the at-sea distribution of seabirds and marine mammals. The aerial at-sea survey team flew over 55,000 kilometers and counted 485,000 seabirds (67 species) and 64,000 marine mammals (19 species). The study provided resource managers with updated information on distribution and abundance patterns and compared results with information from the late 1970s to early 1980s (Briggs et al. 1981, Briggs et al. 1987, see Mason et al. 2007). The California Department of Fish and Game (CDFG; now CA Department of Fish and Wildlife, CADFW) and U.S. Navy also provided significant matching funds. Oceanographic Context - USGS-HSU surveys began in May 1999, immediately following the strong 1997–1998 El Niño event. The 1999–2002 period featured a series of cold-water, La Niña events which led some researchers to postulate that the California Current System (CCS) had undergone a fundamental climate shift, on the scale of those documented in the 1920s, mid 1940s, and mid 1970s (Schwing et al. 2002). Generally, La Niña events have corresponded with stronger than normal upwelling in the CCS, and during this period, resulted in the greatest 4-yr mean upwelling index value on record (Schwing et al. 2002). La Niñas often follow El Niños, and seabird community composition (i.e., relative species-specific abundances) in any given year off southern California, is subject to variability caused by shifts in distribution among both warm- and cool-water affiliated species (Hyrenbach and Veit 2003). In contrast to the Mason et al. (2007) surveys, Briggs et al. (1987) conducted surveys during 1975–1983, coincident with another climate shift—from cold to warm conditions throughout the CCS (Mantua et al. 1997). Briggs et al. surveyed north of Point Conception during 1980–1983, after the transition to warmer water conditions occurred in the CCS. Acknowledgements - This project was funded by BOEM through an Interagency Agreement with the U.S. Geological Survey. The authors of these GIS data require that data users contact them regarding intended use and to assist with understanding limitations and interpretation. Aerial survey fieldwork in 1999-2002 was conducted jointly by the U.S. Geological Survey (Western Ecological Research Center, California: Principal Investigators J.Y, Takekawa and D. Orthmeyer; Key Project Staff: J. Adams, J. Ackerman, W.M. Perry, J.J. Felis, and J.L. Lee) and Humboldt State University (Department of Wildlife, Arcata, California; Principal Investigators: R.T. Golightly and H.R. Carter; Project Leader: G. McChesney; Key Project Staff: J. Mason and W. McIver). Major project cooperators who actively participated in aerial at-sea surveys include the Minerals Management Service (M. Pierson, M. McCrary), California Department of Fish and Wildlife (P. Kelly), and the U.S. Navy (S. Schwartz, T. Keeney). For additional acknowledgments, see Mason et al. (2007). These data are associated with the following publication: Mason, J.W., McChesney, G.J., McIver, W.R., Carter, H.R., Takekawa, J.Y., Golightly, R.T., Ackerman, J.T., Orthmeyer, D.L., Perry, W.M., Yee, J.L. and Pierson, M.O. 2007. At-sea distribution and abundance of seabirds off southern California: a 20-Year comparison. Cooper Ornithological Society, Studies in Avian Biology Vol. 33. References - Briggs, K.T., E.W. Chu, D.B. Lewis, W.B. Tyler, R.L. Pitman, and G.L. Hunt Jr. 1981. Summary of marine mammal and seabird surveys of the Southern California Bight area 1975–1978. Volume III. Investigators’ reports. Part III. USDI Bureau of Land Management BLM/YN/SR-81/01-04 (PB81-248197) and University of California, Institute of Marine Sciences, Santa Cruz, CA. Briggs, K.T., W.B. Tyler, D.B. Lewis, and D.R. Carlson. 1987. Bird communities at sea off California: 1975–1983. Studies in Avian Biology 11. Schwing, F.B., T. Murphree, and P.M. Green. 2002. The Northern Oscillation Index (NOI): a new climate index for the northeast Pacific. Progress in Oceanography 53: 115-139. Hyrenbach, K.D. and R.R. Veit. 2003. Ocean warming and seabird communities of the southern California Current System (1987–98): response at multiple temporal scales. Deep Sea Research Part II: Topical Studies in Oceanography 50: 2537-2565. Mantua, N.J., Hare, S.R., Zhang, Y., Wallace, J.M. and Francis, R.C. 1997. A Pacific interdecadal climate oscillation with impacts on salmon production. Bulletin of the American Meteorological Society, 78: 1069-1079. ESRI. ArcGIS Desktop: Release 10. Redlands, CA: Environmental Systems Research Institute. Mason, J.W., McChesney, G.J., McIver, W.R., Carter, H.R., Takekawa, J.Y., Golightly, R.T., Ackerman, J.T., Orthmeyer, D.L., Perry, W.M., Yee, J.L. and Pierson, M.O. 2007. At-sea distribution and abundance of seabirds off southern California: a 20-Year comparison. Cooper Ornithological Society, Studies in Avian Biology Vol. 33.

The goal of this project was to develop tree-ring based reconstructions of streamflow and precipitation for southern California. These reconstructions, along with existing reconstructions for northern and central California and an updated reconstruction of the Colorado River, provide information about statewide and regional drought for the past millennium.

For a series of studies on the ecosystem service values of chaparral in Southern California, we developed a raster data layer providing an ecological unit classification of the Southern California landscape. This raster dataset is at a 30 meter pixel resolution and partitions the landscape into 37 different ecological unit types. This dataset was derived through a GIS-based cluster analysis of 10 different physiographic variables, namely soil suborder type, terrain geomorphon type, flow accumulation, slope, solar irradiation, annual precipitation, annual minimum temperature, actual evapotranspiration, and climatic water deficit. This partitioning was based on physiographic variables rather than vegetation types because of the wish to have the ecological units reflect biophysical characteristics rather than the historical land use patterns that may influence vegetation. The cluster analysis was performed across a set of 10,000 points randomly placed on a GIS layer stack for the 10 variables. These random points were grouped into 37 discrete clusters using an algorithm called partitioning around medoids. This assignment of points to clusters was then used to train a random forest classifier, which in turn was run across the GIS stack to produce the output raster layer.

This dataset is described in the following book chapter publication:

Underwood, Emma C., Allan D. Hollander, Patrick R. Huber, and Charlie Schrader-Patton. 2018. “Mapping the Value of National Forest Landscapes for Ecosystem Service Provision.” In Valuing Chaparral, 245–70. Springer Series on Environmental Management. Springer, Cham. https://doi.org/10.1007/978-3-319-68303-4_9.

Methods Summary of Methods for Developing Ecological Units in Southern California

Allan Hollander and Emma Underwood, University of California Davis.

1) Compiling GIS layers. These data were compiled from a variety of sources and resolutions (Table 1) for the southern California study area (see Methods_figure_1.png for the study area). The original resolution of these raster layers ran from 10 meters to 270 meters, and resampling was conducted so all analyses were performed at a 30 meter raster resolution. We decided not to include vegetation in the data stack as the aim was to capture biophysical characteristics and vegetation will reflect current landscape history and land use patterns (e.g. fire history, type conversion from shrubland, or agricultural use). Lakes and reservoirs were omitted from the subsequent analysis. Data compiled:

a) Soil suborders. This was a discretely-classified raster layer with 22 soil suborder classes included in the southern California region. This was derived from the gridded Soil Survey Geographic Database (gSSURGO, available at http://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/geo/?cid=nrcs142p2_053628). This product is a rasterization at a 10-meter resolution of the county-scale SSURGO data published by the USDA Natural Resources Conservation Service.

b) Terrain geomorphons. This raster layer derives from a DEM surface and classifies the landscape into 10 discrete landform types, examples being ridges, slopes, hollows, and valleys. The algorithm for geomorphon classification uses a pattern recognition approach based on line of sight analysis (Jasiewisc and Stepinski 2013). This layer was created from a 30 meter DEM in GRASS 7.0.0, using the extension r.geomorphon (https://grass.osgeo.org/grass70/manuals/addons/r.geomorphon.html).

c) Annualized solar irradiation. This layer uses the r.sun model available in GRASS 7.0.0 (https://grass.osgeo.org/grass70/manuals/r.sun.html) which calculates direct, diffuse, and reflected solar irradiation for a given day, location, topography, and atmospheric conditions. This layer was created from a 30 meter DEM and assumes clear-sky conditions. To estimate the total annual irradiation, the model was run for every 15th day and these values were integrated over the year.

d) Flow accumulation. This layer is another product of 30 meter DEM data and measures the upslope area in pixel count that conceivably drains into a given pixel. This was calculated using the accumulation option in the GRASS 7.0.0 command r.watershed (https://grass.osgeo.org/grass70/manuals/r.watershed.html)

e) Slope. This was derived from 30 meter DEM data using the GRASS 7.0.0 command r.slope.aspect, and is measured in degrees.

f) Annual precipitation. This layer came from the 2014 Basin Characterization Model (BCM) for California (Flint et al. 2013) and gives the average annual precipitation between 1981 and 2010 at a 270-meter resolution.

g) Annual minimum temperature. This layer also came from BCM (Flint et al. 2013) and gives the average annual minimum temperature between 1981 and 2010 at a 270-meter resolution. Minimum temperature was included in the set of climate variables to represent montane winter conditions.

h) Climatic water deficit. This layer also came from the BCM (Flint et al. 2013) and gives the average climatic water deficit between 1981 and 2010 at a 270-meter resolution. The two evapotranspiration variables (climatic water deficit and actual evapotranspiration) are included in this set because they are strong drivers of vegetation distribution (Stephenson 1998).

i) Actual evapotranspiration. This layer also came from the BCM (Flint et al. 2013) and gives the average actual evapotranspiration between 1981 and 2010 at a 270-meter resolution.

Table 1. Summary of GIS data stack

    LAYER


    ORIGINAL SOURCE


    ORIGINAL RESOLUTION


    THEME








    Soil suborders


    gSSURGO


    10 meters


    Soil type




    Terrain geomorphons


    Digital elevation model


    30 meters


    Geomorphometry




    Solar irradiation


    Digital elevation model


    30 meters


    Energy balance




    Flow accumulation


    Digital elevation model


    30 meters


    Geomorphometry




    Slope


    Digital elevation model


    30 meters


    Geomorphometry




    Annual precipitation


    Basin Characterization Model


    270 meters


    Climate




    Annual min temperature


    Basin Characterization Model


    270 meters


    Climate




    Climatic water deficit


    Basin Characterization Model


    270 meters


    Climate




    Actual evapotranspiration


    Basin Characterization Model


    270 meters


    Climate

2) Generating 10,000 random points. A mask was imposed to limit analyses to the 35,158 square study area and 10,000 random points were generated to create a data table of the values of each GIS layer at each of the random points. This data table was the basis for sorting the random points into a limited number of clustered types. The first step in doing this is calculating in multivariate space the distance with respect to these environmental variables each random point is from every other point, in other words creating a dissimilarity matrix.

3) Assigning weights to variables. Because the 9 environmental variables use completely different metrics and are a combination of numerical and categorical types, calculating an environmental distance between any two of these random points requires some weighting to be assigned to each of the environmental variables to sum up their relative distances. A subanalysis to determine these weightings used a subset of the study area, the Santa Clara River watershed. Since these ecological units are intended to summarize a diverse set of ecological services, we chose three different proxy variables from the GIS data available for this area to represent biomass, hydrological response, and biodiversity. These proxies included mean annual MODIS Enhanced Vegetation Index (EVI) value for biomass, recharge for hydrological response, and habitat type in the California Wildlife Habitat Relations (CWHR) classification for biodiversity.

The MODIS EVI data was derived by averaging over the 2000-2014 period the maximum EVI value in a single year. The MODIS index used was MOD13Q1 (https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table/mod13q1) at a 250 meter resolution, available at 16-day intervals.


The hydrological recharge data were extracted from the 2014 Basin Characterization Model (Flint et al. 2013) at 270 meter resolution.


The CWHR habitat type came from the 2015 FRAP vegetation layer (FVEG15_1, from http://frap.fire.ca.gov/data/frapgisdata-sw-fveg_download), available at a 30 meter resolution.

a) We used random forest regression and classification (Hastie et al. 2009) to determine a ranking of importance values of these predictor variables using random forest regression for EVI and recharge and random forest classification for the habitat type. These were calculated using the randomForest package in R (Liaw and Wiener 2002).

b) We then averaged these three sets of importance values to create an overall set of weightings to enter into the dissimilarity matrix (Table 2).

Table 2. Weightings for each variable to reflect their relative importance to the ecological units

    VARIABLE NAME


    WEIGHT




    Precipitation


    1.00




    Annual minimum temperature


    0.600




    Slope


    0.507




    Climatic water deficit


    0.413




    Annualized solar radiation


    0.404




    Soil suborder

The UCSB Interdisciplinary Research Collaboratory, in conjunction with the UC Davis Library, other partner organizations, and contributions from the general public, is creating a publicly accessible version American Viticultural Areas boundaries. This dataset is UCSB's contribution. Using the text descriptions from the ATPF Code of regulations, we built this data from the official descriptions. This dataset will provide growers, vintners, and wine researchers with an important tool as they examine the scientific, economic and historical aspects of viticulture in California.https://www.ttb.gov/wine/ava.shtmlhttps://www.wineinstitute.org/resources/avas

This dataset represents the state of knowledge about the distribution of rocky shores along the Southern California coastline as of 1974. The data series is comprised of three overlapping polyline themes. The other two themes represent "sandy" and "cobble/other" shores.

The purpose of this project was to create digital, GIS format versions of the Southern California coastline hardcopy maps produced by the U.S. Department of the Interior, Bureau of Land Management, Pacific Continental Shelf Office, Los Angeles, prepared by William E. Grant (Manager) and printed by the U.S. Government Printing Office in 1974.

The original data was presented in hard copy format and, according to a disclosure on the map itself, the "visual graphic has been carefully prepared from existing sources. However, the Beareau of Land Management, U.S.D.I. does not guarantee the accuracy to the extent of responsibility or liability for reliance thereon. This is a special visual graphic overprint and is not to be used for navigational purposes." These non digital data were presented at a scale of 1:500,000. For the current project, these data were scanned, georeferenced (GCS_NAD83) and traced in ArcMap 8.3 software to produce polyline representations of the shoreline types. Data covers the shorelines from the US/Mexico border, north to California's Point Conception, including San Miguel, Santa Rosa, Santa Cruz, San Nicholas, Santa Catalina and San Clemente Islands

Data digitized from Channel Islands Area Map created by the US Department of the Interior Beurea of Land Management, Pacific Continental Shelf Office, 1974. University of California Santa Barbara library call number: 9507, .N2446, 1974, .US, graphic #10.

This dataset represents the state of knowledge about the distribution of shores classified as "cobble" or "other" along the Southern California coastline as of 1974. The complete data series is comprised of three overlapping polyline themes. The other two themes represent "rocky" and "sandy" shores.

The purpose of this project was to create digital, GIS format versions of the Southern California coastline hardcopy maps produced by the U.S. Department of the Interior, Bureau of Land Management, Pacific Continental Shelf Office, Los Angeles, prepared by William E. Grant (Manager) and printed by the U.S. Government Printing Office in 1974.

The original data was presented in hard copy format and, according to a disclosure on the map itself, the "visual graphic has been carefully prepared from existing sources. However, the Beareau of Land Management, U.S.D.I. does not guarantee the accuracy to the extent of responsibility or liability for reliance thereon. This is a special visual graphic overprint and is not to be used for navigational purposes." These non digital data were presented at a scale of 1:500,000. For the current project, these data were scanned, georeferenced (GCS_NAD83) and traced in ArcMap 8.3 software to produce polyline representations of the shoreline types. Data covers the shorelines from the US/Mexico border, north to California's Point Conception, including San Miguel, Santa Rosa, Santa Cruz, San Nicholas, Santa Catalina and San Clemente Islands.

Data digitized from Channel Islands Area Map created by the US Department of the Interior Beurea of Land Management, Pacific Continental Shelf Office, 1974. University of California Santa Barbara library call number: 9507, .N2446, 1974, .US, graphic #10.

This dataset represents the state of knowledge about the distribution of sandy shores along the Southern California coastline as of 1974. The data series is comprised of three overlapping polyline themes. The other two themes represent "rocky" and "cobble/other" shores.

The purpose of this project was to create digital, GIS format versions of the Southern California coastline hardcopy maps produced by the U.S. Department of the Interior, Bureau of Land Management, Pacific Continental Shelf Office, Los Angeles, prepared by William E. Grant (Manager) and printed by the U.S. Government Printing Office in 1974.

The original data was presented in hard copy format and, according to a disclosure on the map itself, the "visual graphic has been carefully prepared from existing sources. However, the Beareau of Land Management, U.S.D.I. does not guarantee the accuracy to the extent of responsibility or liability for reliance thereon. This is a special visual graphic overprint and is not to be used for navigational purposes." These non digital data were presented at a scale of 1:500,000. For the current project, these data were scanned, georeferenced (GCS_NAD83) and traced in ArcMap 8.3 software to produce polyline representations of the shoreline types. Data covers the shorelines from the US/Mexico border, north to California's Point Conception, including San Miguel, Santa Rosa, Santa Cruz, San Nicholas, Santa Catalina and San Clemente Islands

Data digitized from Channel Islands Area Map created by the US Department of the Interior Beurea of Land Management, Pacific Continental Shelf Office, 1974. University of California Santa Barbara library call number: 9507, .N2446, 1974, .US, graphic #10.

This dataset provides 2018 Healthy Places Index (HPI) scores for each census tract in California as calculated by the Public Health Alliance of Southern California. The HPI is comprised of 25 individual indicators organized in 8 policy action areas (domains) of economy, education, healthcare access, housing, neighborhoods, clean environment, transportation, and social environment. Read the Healthy Places Index to learn more about index interpretation. Information like this may be useful for studying public health equity across areas of different socioeconomic demographics.Spatial Extent: CaliforniaSpatial Unit: Census TractCreated: 2018Updated: n/aSource: Public Health Alliance of Southern CaliforniaContact Telephone: Contact Email: PHASoCal@PHI.orgSource Link: https://healthyplacesindex.org/data-reports/

The Eel River CZO operates on several spatial scales from a zero order hillslope to the entire Eel River on the north coast of California. Rivendell, Angelo, Sagehorn, South Fork, and Eel River GIS boundaries. GIS polygon shapefiles. All files are in geographic projection (Lat/Long) with a datum of WGS84.

The watershed boundaries are from USGS Watershed Boundary Dataset (WBD) http://nhd.usgs.gov/wbd.html. Rivendell and Angelo boundaries are created from LiDAR by the CZO. Sagehorn Ranch is a privately held, active commercial ranch with no public access. Please contact the CZO if you are interested in data from Sagehorn Ranch.

Shapefiles

Eel River Watershed (drainage area 9534 km^2): Entire eel river. Greatest extent of CZO research.

South Fork Eel Watershed (drainage area 1784 km^2).

Angelo Reserve Boundary (30.0 km^2): Angelo Coast Range Reserve is a University of California Natural Reserve System protected land. It is the central focus of CZO research. http://angelo.berkeley.edu

Sagehorn Ranch Boundary (21.1 km^2): Sagehorn Ranch is a private ranch with active cattle raising. The owners have allowed the CZO to place instrumentation on their lands. Access is only by explicit agreement by owners.

Rivendell Cachement (0.0076 km^2): Rivendell is a small, heavily instrumented hillslope within the Angelo Reserve. It has roughly 700 instruments deployed as of 2016. Data is online at http://sensor.berkeley.edu

This submission is from a master's group thesis project at The Bren School of Environmental Science & Management at the University of California, Santa Barbara, and contains the final written report and associated datasets. The graduate student researchers who completed this project include: Meghan Fletcher, Alyssa Kibbe, Grace Kumaishi, Anna Talken, and Nikole Vannest.

The California landscape has been fragmented by urban development, infrastructure, and agriculture. Maintaining connectivity between areas of wildlife habitat is important for the viability of many long-ranging species, such as the mountain lion (Puma concolor). Mountain lion populations are highly susceptible to habitat fragmentation, and face reduced access to resources and decreased genetic diversity. This study explores the habitat connectivity between the Jack and Laura Dangermond Preserve (JLDP), a 24,460 acre protected property owned by The Nature Conservancy (TNC), and neighboring protected areas to identify potential pathways of movement for mountain lions along the Central and Southern California coast. In this project, we: 1) determine regional connectivity and least cost paths between core habitats by modeling suitable mountain lion habitat, 2) estimate mountain lion habitat use and movement on JLDP by performing a site-level suitability and corridor analysis and 3) create a short film focused on highlighting our research, the role that JLDP plays in conservation, and the importance of habitat connectivity. The results of our project show that JLDP contains suitable habitat for mountain lions and may play a positive role in coastal connectivity. When considering the connectivity between JLDP and other regional protected areas, our analyses indicate that urbanized coastal regions act as barriers to mountain lions and contain pinch points that channelize movement. These results can guide TNC in developing management strategies for protecting mountain lions on JLDP and in the surrounding region.

Analyses were conducted using ArcGIS, Google Earth Engine, MaxENT, Circuitscape, and Omniscape. The project began in April 2021 and ended in June 2022. Methods Data was collected from open source data acquired using Google Earth Engine and Esri ArcOnline from the following sources: NASA, USGS, JPL-CalTech, Conservation Science Partners, CalFish, US Census and CalFire. It was processed using Esri ArcMap, ArcGIS Pro, Maxent, Omniscape via Jupyter Notebook and the Linkage Mapper Toolkit within ArcMap.

This dataset provides estimated percentages of children with special health care needs whose parents experienced stress from parenting and estimated percentages of children with special health care needs who repeated a grade in school. Information like this may be useful for studying children and disability.Spatial Extent: Southern California (Imperial, Kern, Los Angeles, Orange, Riverside, San Bernardino, San Diego, and Ventura County)Spatial Unit: CityCreated: Updated: n/aSource: U.S. Department of Health and Human Services (2011-2012 National Survey of Children's Health)Contact Person: Division of Services for Children with Special Health NeedsContact Phone: 301-443-8860Source Link: https://mchb.hrsa.gov/data/national-surveys

Authors: Rima Habre , Mariam Girguis , Robert Urman , Scott Fruin , Fred Lurmann ,Martin Shafer, Patrick Gorski , Meredith Franklin , Rob McConnell , Ed Avol, Frank GillilandClick here for full access.

This dataset provides estimated percentages of public school students in grades 7, 9, 11, and non-traditional programs (community day schools or continuation education) who were in physical fights at school in the previous year from 2015-2017. Some school districts are left blank because there were either too little samples to be considered representative or there was no data available. Information like this may be useful for studying children and mental health.Spatial Extent: Southern California (Imperial, Kern, Los Angeles, Orange, Riverside, San Bernardino, San Diego, and Ventura County)Spatial Unit: 2015 School DistrictsCreated: 2018Updated: n/aSource: California Department of Education (2015-2017 California Healthy Kids Survey)Contact Person: Coordinated School Health and Safety OfficeContact Email: hchan@cde.ca.govSource Link: https://calschls.org/reports-data/legacy/

This dataset provides estimated percentages of public school students in grades 7, 9, 11, and non-traditional programs (community day schools or continuation education) who had mean rumors or lies spread about them on the internet by other students in the previous year from 2015-2017. Some school districts are left blank because there were either too little samples to be considered representative or there was no data available. Information like this may be useful for studying children and mental health.Spatial Extent: Southern California (Imperial, Kern, Los Angeles, Orange, Riverside, San Bernardino, San Diego, and Ventura County)Spatial Unit: 2015 School DistrictsCreated: 2018Updated: n/aSource: California Department of Education (2015-2017 California Healthy Kids Survey)Contact Person: Coordinated School Health and Safety OfficeContact Email: hchan@cde.ca.govSource Link: https://calschls.org/reports-data/legacy/

This dataset provides estimated percentages of children ages 0-17 with special health care needs with and without insurance coverage, with adequate and inadequate insurance coverage, and with consistent and inconsistent insurance coverage. Information like this may be useful for studying children and disability.Spatial Extent: Southern California (Imperial, Kern, Los Angeles, Orange, Riverside, San Bernardino, San Diego, and Ventura County)Spatial Unit: CityCreated: Updated: n/aSource: U.S. Department of Health and Human Services (2011-2012 National Survey of Children's Health)Contact Person: Division of Services for Children with Special Health NeedsContact Phone: 301-443-8860Source Link: https://mchb.hrsa.gov/data/national-surveys

This dataset provides the number of domestic violence-related calls for assistance in 2018. Domestic violence is defined according to California Penal Code 13700. Information like this may be useful for studying safety and abuse.Spatial Extent: Southern California (Imperial, Kern, Los Angeles, Orange, Riverside, San Bernardino, San Diego, and Ventura County)Spatial Unit: CityCreated: 2018Updated: n/aSource: California Department of Justice (Criminal Justice Statistics Center)Contact Person: Open Justice InitiativeContact Email: openjustice@doj.ca.govSource Link: https://openjustice.doj.ca.gov/exploration/crime-statistics/domestic-violence-related-calls-assistance

The CA School Campus Database (CSCD) is a GIS data set that contains detailed outlines of the lands used by public schools for educational purposes. It includes campus boundaries of schools with kindergarten through 12th grade instruction, as well as colleges, universities, and public community colleges. Merged three school datasets together, combining the footprints of primary and secondary schools, community colleges, and universities. Dissolved all schools into one feature.

National Risk Index Version: March 2023 (1.19.0)A Heat Wave is a period of abnormally and uncomfortably hot and unusually humid weather typically lasting two or more days with temperatures outside the historical averages for a given area. Annualized frequency values for Heat Waves are in units of event-days per year.The National Risk Index is a dataset and online tool that helps to illustrate the communities most at risk for 18 natural hazards across the United States and territories: Avalanche, Coastal Flooding, Cold Wave, Drought, Earthquake, Hail, Heat Wave, Hurricane, Ice Storm, Landslide, Lightning, Riverine Flooding, Strong Wind, Tornado, Tsunami, Volcanic Activity, Wildfire, and Winter Weather. The National Risk Index provides Risk Index values, scores and ratings based on data for Expected Annual Loss due to natural hazards, Social Vulnerability, and Community Resilience. Separate values, scores and ratings are also provided for Expected Annual Loss, Social Vulnerability, and Community Resilience. For the Risk Index and Expected Annual Loss, values, scores and ratings can be viewed as a composite score for all hazards or individually for each of the 18 hazard types.Sources for Expected Annual Loss data include: Alaska Department of Natural Resources, Arizona State University’s (ASU) Center for Emergency Management and Homeland Security (CEMHS), California Department of Conservation, California Office of Emergency Services California Geological Survey, Colorado Avalanche Information Center, CoreLogic’s Flood Services, Federal Emergency Management Agency (FEMA) National Flood Insurance Program, Humanitarian Data Exchange (HDX), Iowa State University's Iowa Environmental Mesonet, Multi-Resolution Land Characteristics (MLRC) Consortium, National Aeronautics and Space Administration’s (NASA) Cooperative Open Online Landslide Repository (COOLR), National Earthquake Hazards Reduction Program (NEHRP), National Oceanic and Atmospheric Administration’s National Centers for Environmental Information (NCEI), National Oceanic and Atmospheric Administration's National Hurricane Center, National Oceanic and Atmospheric Administration's National Weather Service (NWS), National Oceanic and Atmospheric Administration's Office for Coastal Management, National Oceanic and Atmospheric Administration's National Geophysical Data Center, National Oceanic and Atmospheric Administration's Storm Prediction Center, Oregon Department of Geology and Mineral Industries, Pacific Islands Ocean Observing System, Puerto Rico Seismic Network, Smithsonian Institution's Global Volcanism Program, State of Hawaii’s Office of Planning’s Statewide GIS Program, U.S. Army Corps of Engineers’ Cold Regions Research and Engineering Laboratory (CRREL), U.S. Census Bureau, U.S. Department of Agriculture's (USDA) National Agricultural Statistics Service (NASS), U.S. Forest Service's Fire Modeling Institute's Missoula Fire Sciences Lab, U.S. Forest Service's National Avalanche Center (NAC), U.S. Geological Survey (USGS), U.S. Geological Survey's Landslide Hazards Program, United Nations Office for Disaster Risk Reduction (UNDRR), University of Alaska – Fairbanks' Alaska Earthquake Center, University of Nebraska-Lincoln's National Drought Mitigation Center (NDMC), University of Southern California's Tsunami Research Center, and Washington State Department of Natural Resources.Data for Social Vulnerability are provided by the Centers for Disease Control (CDC) Agency for Toxic Substances and Disease Registry (ATSDR) Social Vulnerability Index, and data for Community Resilience are provided by University of South Carolina's Hazards and Vulnerability Research Institute’s (HVRI) 2020 Baseline Resilience Indicators for Communities.The source of the boundaries for counties and Census tracts are based on the U.S. Census Bureau’s 2021 TIGER/Line shapefiles. Building value and population exposures for communities are based on FEMA’s Hazus 6.0. Agriculture values are based on the USDA 2017 Census of Agriculture.

Initial tsunami modeling was performed by the University of Southern California (USC) Tsunami Research Center funded through the California Emergency Management Agency (CalEMA) by the National Tsunami Hazard Mitigation Program. The tsunami modeling process utilized the MOST (Method of Splitting Tsunamis) computational program (Version 0), which allows for wave evolution over a variable bathymetry and topography used for the inundation mapping (Titov and Gonzalez, 1997; Titov and Synolakis, 1998). The bathymetric/topographic data that were used in the tsunami models consist of a series of nested grids. Near-shore grids with a 3 arc-second (75- to 90-meters) resolution or higher, were adjusted to "Mean High Water" sea-level conditions, representing a conservative sea level for the intended use of the tsunami modeling and mapping. A suite of tsunami source events was selected for modeling, representing realistic local and distant earthquakes and hypothetical extreme undersea, near-shore landslides (Table 1). Local tsunami sources that were considered include offshore reverse-thrust faults, restraining bends on strike-slip fault zones and large submarine landslides capable of significant seafloor displacement and tsunami generation. Distant tsunami sources that were considered include great subduction zone events that are known to have occurred historically (1960 Chile and 1964 Alaska earthquakes) and others which can occur around the Pacific Ocean "Ring of Fire." In order to enhance the result from the 75- to 90-meter inundation grid data, a method was developed utilizing higher-resolution digital topographic data (3- to 10-meters resolution) that better defines the location of the maximum inundation line (U.S. Geological Survey, 1993; Intermap, 2003; NOAA, 2004). The location of the enhanced inundation line was determined by using digital imagery and terrain data on a GIS platform with consideration given to historic inundation information (Lander, et al., 1993). This information was verified, where possible, by field work coordinated with local county personnel. The accuracy of the inundation line shown on these maps is subject to limitations in the accuracy and completeness of available terrain and tsunami source information, and the current understanding of tsunami generation and propagation phenomena as expressed in the models. Thus, although an attempt has been made to identify a credible upper bound to inundation at any location along the coastline, it remains possible that actual inundation could be greater in a major tsunami event. This map does not represent inundation from a single scenario event. It was created by combining inundation results for an ensemble of source events affecting a given region. For this reason, all of the inundation region in a particular area will not likely be inundated during a single tsunami event.

Race is a social and historical construct, and the racial categories counted by the census change over time so the process of constructing stable racial categories for 120 years out of census data required complex and imperfect decisions. We created a set of 5 racial/ethnic categories that enabled us to can see changes over this time period even as census categories changed over time. Since race is a social construction and the US Census Bureau used different categories over the 20th century to count populations, these maps offer only a partial window into this complex and contested process. For 1900-1940, we digitized the Enumeration District (ED) linework and processed IPUMS NHGIS (University of Minnesota) data to produce layers for each decade that display the most important racial/ethnic groups residing in Inland Southern California in the early 20th century. For 1960-2020, we processed IPUMS race data and used their census tract geometries. We do not yet have data for 1950.We used historical research on early 20th century southern California to construct historic racial categories for 1900-1940 from the IPUMS full count data, which allowed us to track groups that were not formally classified as racial groups in some census decades like Mexican, but which were important racial categories in southern California. Detailed explanation of how we constructed these categories and the rationale we used for the decisions we made can be found here. We tried to preserve some of the distinctive categories we used in different decades in the popups (so for instance you will see Mexican in early decades and Hispanic in later ones, but we renamed others into broader categories like Spanish Surname population in 1960-1970). We only included here on this data categories that allowed at least some comparison across this long time space so excluded some categories like other or multi-ethnic that only appeared periodically in the data. You can see the ways we grouped available data by downloading this data dictionary. You can also find some more explanation of these categories in our Mapping Race in the IE 1900-2020 Experience Builder which presents accessible maps for non GIS experts. If you are interested in mapping some of the other racial or ethnic groups in the early 20th century, you can explore and map the full range of variables we have created in the People's History of the IE IE_ED1900-1940 Race Hispanic Marriage and Age Feature layer. Suggested Citation: Tilton, Jennifer, Lisa Benvenuti, Tessa VanRy & Ashley Roman. People's History Race Dot Density Map 1900-2020. A People's History of the Inland Empire Census Project using IPUMS Data. Program in Race and Ethnic Studies University of Redlands, Center for Spatial Studies University of Redlands, UCR Public History. 2025.