Produced collectively by tsunami modelers, geologic hazard mapping specialists, and emergency planning scientists from the University of Southern California (USC) Tsunami Research Center, CGS, and Cal OES. The Tsunami Inundation Maps for Emergency Planning cover all low-lying, populated areas along the State’s coastline. Coordinated by Cal OES, these inudation maps are developed for at risk areas to tsunamis in California and represent a combination of the maximum considered tsunamis for each area.

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.

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 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 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

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.

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.

Version InformationThe data is updated annually with fire perimeters from the previous calendar year.Firep23_1 was released in May 2024. Two hundred eighty four fires from the 2023 fire season were added to the database (21 from BLM, 102 from CAL FIRE, 72 from Contract Counties, 19 from LRA, 9 from NPS, 57 from USFS and 4 from USFW). The 2020 Cottonwood fire, 2021 Lone Rock and Union fires, as well as the 2022 Lost Lake fire were added. USFW submitted a higher accuracy perimeter to replace the 2022 River perimeter. A duplicate 2020 Erbes fire was removed. Additionally, 48 perimeters were digitized from an historical map included in a publication from Weeks, d. et al. The Utilization of El Dorado County Land. May 1934, Bulletin 572. University of California, Berkeley. There were 2,132 perimeters that received updated attribution, the bulk of which had IRWIN IDs added. The following fires were identified as meeting our collection criteria, but are not included in this version and will hopefully be added in the next update: Big Hill #2 (2023-CAHIA-001020). YEAR_ field changed to a short integer type. San Diego CAL FIRE UNIT_ID changed to SDU (the former code MVU is maintained in the UNIT_ID domains). COMPLEX_INCNUM renamed to COMPLEX_ID and is in process of transitioning from local incident number to the complex IRWIN ID. Perimeters managed in a complex in 2023 are added with the complex IRWIN ID. Those previously added will transition to complex IRWIN IDs in a future update.If you would like a full briefing on these adjustments, please contact the data steward, Kim Wallin (kimberly.wallin@fire.ca.gov), CAL FIRE FRAP._CAL FIRE (including contract counties), USDA Forest Service Region 5, USDI Bureau of Land Management & National Park Service, and other agencies jointly maintain a fire perimeter GIS layer for public and private lands throughout the state. The data covers fires back to 1878. Current criteria for data collection are as follows:CAL FIRE (including contract counties) submit perimeters ≥10 acres in timber, ≥50 acres in brush, or ≥300 acres in grass, and/or ≥3 damaged/ destroyed residential or commercial structures, and/or caused ≥1 fatality.All cooperating agencies submit perimeters ≥10 acres._Discrepancies between wildfire perimeter data and CAL FIRE Redbook Large Damaging FiresLarge Damaging fires in California were first defined by the CAL FIRE Redbook, and has changed over time, and differs from the definition initially used to define wildfires required to be submitted for the initial compilation of this digital fire perimeter data. In contrast, the definition of fires whose perimeter should be collected has changed once in the approximately 30 years the data has been in existence. Below are descriptions of changes in data collection criteria used when compiling these two datasets. To facilitate comparison, this metadata includes a summary, by year, of fires in the Redbook, that do not appear in this fire perimeter dataset. It is followed by an enumeration of each “Redbook” fire missing from the spatial data. Wildfire Perimeter criteria:~1991: 10 acres timber, 30 acres brush, 300 acres grass, damages or destroys three residence or one commercial structure or does $300,000 worth of damage 2002: 10 acres timber, 50 acres brush, 300 acres grass, damages or destroys three or more structures, or does $300,000 worth of damage~2010: 10 acres timber, 30 acres brush, 300 acres grass, damages or destroys three or more structures (doesn’t include out building, sheds, chicken coops, etc.)Large and Damaging Redbook Fire data criteria:1979: Fires of a minimum of 300 acres that burn at least: 30 acres timber or 300 acres brush, or 1500 acres woodland or grass1981: 1979 criteria plus fires that took ,3000 hours of California Department of Forestry and Fire Protection personnel time to suppress1992: 1981 criteria plus 1500 acres agricultural products, or destroys three residence or one commercial structure or does $300,000 damage1993: 1992 criteria but “three or more structures destroyed” replaces “destroys three residence or one commercial structure” and the 3,000 hours of California Department of Forestry personnel time to suppress is removed2006: 300 acres or larger and burned at least: 30 acres of timber, or 300 acres of brush, or 1,500 acres of woodland, or 1,500 acres of grass, or 1,500 acres of agricultural products, or 3 or more structures destroyed, or $300,000 or more dollar damage loss.2008: 300 acres and largerYear# of Missing Large and Damaging Redbook Fires197922198013198115198261983319842019855219861219875619882319898199091991219921619931719942219959199615199791998101999720004200152002162003520042200512006112007320084320093201022011020124201322014720151020162201711201862019220203202102022020230Total488Enumeration of fires in the Redbook that are missing from Fire Perimeter data. Three letter unit code follows fire name.1979-Sylvandale (HUU), Kiefer (AEU), Taylor(TUU), Parker#2(TCU), PGE#10, Crocker(SLU), Silver Spur (SLU), Parkhill (SLU), Tar Springs #2 (SLU), Langdon (SCU), Truelson (RRU), Bautista (RRU), Crocker (SLU), Spanish Ranch (SLU), Parkhill (SLU), Oak Springs(BDU), Ruddell (BDF), Santa Ana (BDU), Asst. #61 (MVU), Bernardo (MVU), Otay #20 1980– Lightning series (SKU), Lavida (RRU), Mission Creek (RRU), Horse (RRU), Providence (RRU), Almond (BDU), Dam (BDU), Jones (BDU), Sycamore (BDU), Lightning (MVU), Assist 73, 85, 138 (MVU)1981– Basalt (LNU), Lightning #25(LMU), Likely (MNF), USFS#5 (SNF), Round Valley (TUU), St. Elmo (KRN), Buchanan (TCU), Murietta (RRU), Goetz (RRU), Morongo #29 (RRU), Rancho (RRU), Euclid (BDU), Oat Mt. (LAC & VNC), Outside Origin #1 (MVU), Moreno (MVU)1982- Duzen (SRF), Rave (LMU), Sheep’s trail (KRN), Jury (KRN), Village (RRU), Yuma (BDF)1983- Lightning #4 (FKU), Kern Co. #13, #18 (KRN)1984-Bidwell (BTU), BLM D 284,337, PNF #115, Mill Creek (TGU), China hat (MMU), fey ranch, Kern Co #10, 25,26,27, Woodrow (KRN), Salt springs, Quartz (TCU), Bonanza (BEU), Pasquel (SBC), Orco asst. (ORC), Canel (local), Rattlesnake (BDF)1985- Hidden Valley, Magic (LNU), Bald Mt. (LNU), Iron Peak (MEU), Murrer (LMU), Rock Creek (BTU), USFS #29, 33, Bluenose, Amador, 8 mile (AEU), Backbone, Panoche, Los Gatos series, Panoche (FKU), Stan #7, Falls #2 (MMU), USFS #5 (TUU), Grizzley, Gann (TCU), Bumb, Piney Creek, HUNTER LIGGETT ASST#2, Pine, Lowes, Seco, Gorda-rat, Cherry (BEU), Las pilitas, Hwy 58 #2 (SLO), Lexington, Finley (SCU), Onions, Owens (BDU), Cabazon, Gavalin, Orco, Skinner, Shell, Pala (RRU), South Mt., Wheeler, Black Mt., Ferndale, (VNC), Archibald, Parsons, Pioneer (BDU), Decker, Gleason(LAC), Gopher, Roblar, Assist #38 (MVU)1986– Knopki (SRF), USFS #10 (NEU), Galvin (RRU), Powerline (RRU), Scout, Inscription (BDU), Intake (BDF), Assist #42 (MVU), Lightning series (FKU), Yosemite #1 (YNP), USFS Asst. (BEU), Dutch Kern #30 (KRN)1987- Peach (RRU), Ave 32 (TUU), Conover (RRU), Eagle #1 (LNU), State 767 aka Bull (RRU), Denny (TUU), Dog Bar (NEU), Crank (LMU), White Deer (FKU), Briceburg (LMU), Post (RRU), Antelope (RRU), Cougar-I (SKU), Pilitas (SLU) Freaner (SHU), Fouts Complex (LNU), Slides (TGU), French (BTU), Clark (PNF), Fay/Top (SQF), Under, Flume, Bear Wallow, Gulch, Bear-1, Trinity, Jessie, friendly, Cold, Tule, Strause, China/Chance, Bear, Backbone, Doe, (SHF) Travis Complex, Blake, Longwood (SRF), River-II, Jarrell, Stanislaus Complex 14k (STF), Big, Palmer, Indian (TNF) Branham (BLM), Paul, Snag (NPS), Sycamore, Trail, Stallion Spring, Middle (KRN), SLU-864 1988- Hwy 175 (LNU), Rumsey (LNU), Shell Creek (MEU), PG&E #19 (LNU), Fields (BTU), BLM 4516, 417 (LMU), Campbell (LNF), Burney (SHF), USFS #41 (SHF), Trinity (USFS #32), State #837 (RRU), State (RRU), State (350 acres), RRU), State #1807, Orange Co. Asst (RRU), State #1825 (RRU), State #2025, Spoor (BDU), State (MVU), Tonzi (AEU), Kern co #7,9 (KRN), Stent (TCU), 1989– Rock (Plumas), Feather (LMU), Olivas (BDU), State 1116 (RRU), Concorida (RRU), Prado (RRU), Black Mt. (MVU), Vail (CNF)1990– Shipman (HUU), Lightning 379 (LMU), Mud, Dye (TGU), State 914 (RRU), Shultz (Yorba) (BDU), Bingo Rincon #3 (MVU), Dehesa #2 (MVU), SLU 1626 (SLU)1991- Church (HUU), Kutras (SHF)1992– Lincoln, Fawn (NEU), Clover, fountain (SHU), state, state 891, state, state (RRU), Aberdeen (BDU), Wildcat, Rincon (MVU), Cleveland (AEU), Dry Creek (MMU), Arroyo Seco, Slick Rock (BEU), STF #135 (TCU)1993– Hoisington (HUU), PG&E #27 (with an undetermined cause, lol), Hall (TGU), state, assist, local (RRU), Stoddard, Opal Mt., Mill Creek (BDU), Otay #18, Assist/ Old coach (MVU), Eagle (CNF), Chevron USA, Sycamore (FKU), Guerrero, Duck1994– Schindel Escape (SHU), blank (PNF), lightning #58 (LMU), Bridge (NEU), Barkley (BTU), Lightning #66 (LMU), Local (RRU), Assist #22 & #79 (SLU), Branch (SLO), Piute (BDU), Assist/ Opal#2 (BDU), Local, State, State (RRU), Gilman fire 7/24 (RRU), Highway #74 (RRU), San Felipe, Assist #42, Scissors #2 (MVU), Assist/ Opal#2 (BDU), Complex (BDF), Spanish (SBC)1995-State 1983 acres, Lost Lake, State # 1030, State (1335 acres), State (5000 acres), Jenny, City (BDU), Marron #4, Asist #51 (SLO/VNC)1996- Modoc NF 707 (Ambrose), Borrego (MVU), Assist #16 (SLU), Deep Creek (BDU), Weber (BDU), State (Wesley) 500 acres (RRU), Weaver (MMU), Wasioja (SBC/LPF), Gale (FKU), FKU 15832 (FKU), State (Wesley) 500 acres, Cabazon (RRU), State Assist (aka Bee) (RRU), Borrego, Otay #269 (MVU), Slaughter house (MVU), Oak Flat (TUU)1997- Lightning #70 (LMU), Jackrabbit (RRU), Fernandez (TUU), Assist 84 (Military AFV) (SLU), Metz #4 (BEU), Copperhead (BEU), Millstream, Correia (MMU), Fernandez (TUU)1998- Worden, Swift, PG&E 39 (MMU), Chariot, Featherstone, Wildcat, Emery, Deluz (MVU), Cajalco Santiago (RRU)1999- Musty #2,3 (BTU), Border # 95 (MVU), Andrews,

This dataset provides estimated percentages on levels of perceived school safety among public school students in grades 7, 9, 11, and non-traditional programs (community day schools or continuation education). 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, education, 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/

Link to landing page referenced by identifier. Service Protocol: Link to landing page referenced by identifier. Link Function: information-- dc:identifier.

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 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 Federal Emergency Management Agency (FEMA) Federal Insurance Rate Map (FIRM) guidelines do not currently exist for conducting and incorporating tsunami hazard assessments that reflect the substantial advances in tsunami research achieved in the last two decades; this conclusion is the result of two FEMA-sponsored workshops and the associated Tsunami Focused Study (Chowdhury and others, 2005). Therefore, as part of FEMA's Map Modernization Program, a Tsunami Pilot Study was carried out in the Seaside/Gearhart, Oregon, area to develop an improved Probabilistic Tsunami Hazard Analysis (PTHA) methodology and to provide recommendations for improved tsunami hazard assessment guidelines (Tsunami Pilot Study Working Group, 2006). The Seaside area was chosen because it is typical of many coastal communities in the section of the Pacific Coast from Cape Mendocino to the Strait of Juan de Fuca, and because State agencies and local stakeholders expressed considerable interest in mapping the tsunami threat to this area. The study was an interagency effort by FEMA, U.S. Geological Survey, and the National Oceanic and Atmospheric Administration (NOAA), in collaboration with the University of Southern California, Middle East Technical University, Portland State University, Horning Geoscience, Northwest Hydraulics Consultants, and the Oregon Department of Geological and Mineral Industries. We present the spatial (geographic information system, GIS) data from the pilot study in standard GIS formats and provide files for visualization in Google Earth, a global map viewer.

[Summary provided by the USGS.]

National Risk Index Version: March 2023 (1.19.0)The National Risk Index Counties feature layer contains county-level data for the Risk Index, Expected Annual Loss, Social Vulnerability, and Community Resilience.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.

Researchers working with the Global Land Analysis & Discovery (GLAD) at University of Maryland, College Park, Maryland and Advanced Rapid Imaging and Analysis (ARIA) and Observational Products for End-Users from Remote Sensing Analysis (OPERA) teams at NASA's Jet Propulsion Laboratory, Pasadena, California, created maps of surface disturbance due to ongoing wildfires in Los Angeles County in January 2025 using the OPERA Disturbance Alert from Harmonized Landsat Sentinel-2 (DIST-ALERT-HLS) products.The results posted here are preliminary results, primarily intended to aid the field response and people who wanted to have a rough first look at the area impacted by wildfire. The ARIA-share website has always focused on posting preliminary results as fast as possible for urgent response. All information is provisional for use under emergency response guidelines. These data are provided with absolutely no warranty of any kind. Use at your own risk.OPERA DIST-ALERT-HLSThe Disturbance product (DIST) maps per pixel vegetation disturbance (specifically, vegetation cover loss) from the Harmonized Landsat Sentinel-2 (HLS) scenes. We provide the (1) maximum vegetation anomaly value (VEG-ANOM-MAX) layer and (2) vegetation disturbance status (VEG-DIST-STATUS). All provided layers have been filtered to include only disturbance detected following the start of the Greater Los Angeles wildfires (starting Jan. 7, 2025). Images are provided from January 12, 2025. The images are provided as GeoTIFF files.VEG-ANOM-MAX:Difference between historical and current year observed vegetation cover at the date of maximum decrease (vegetation loss of 0-100%). This layer can be used to threshold vegetation disturbance per a given sensitivity (e.g. disturbance of >=20% vegetation cover loss). The sum of the historical percent vegetation and the anomaly value will be the vegetation cover estimate for the current year.VEG-DIST-STATUS:Indication of vegetation cover loss (vegetation disturbance). The status label is based on the maximum anomaly value, confidence level, and whether it is ongoing or finished. First means the pixel has had an anomaly detection but no subsequent observations whether anomalous or not. ''Provisional'' means there have been two consecutive disturbance detections but not yet with high confidence. ''Confirmed'' means that vegetation disturbance is detected with high confidence. The label ''finished'' is applied to confirmed disturbances that have had two consecutive no-anomaly observations or one 15 days or more after the last anomaly detection. If a new disturbance is detected, it will overwrite those in a ''finished'' state. These labels are reported for both above and below the 50% disturbance threshold based on the maximum anomaly value.Satellite/Sensor:MultiSpectral Instrument (MSI) on European Space Agency's (ESA) Copernicus Sentinel-2A/B satellites and Operational Land Imager 2 (OLI-2) on NASA's Landsat 8/9 satellite.Resolution:30 metersThe DIST-ALERT-HLS products included here have these flags:255 represents ''No Data'' and is based on the Fmask layer of the source HLS granule.OPERA DIST-ALERT-HLS data availability:The post-processed products are available to download at https://aria-share.jpl.nasa.gov/20250113-GreaterLosAngeles_Fires/DIST-HLS/The OPERA DIST-HLS products have been in production since January 2022, are freely distributed to the public via NASA's Land Processes Distributed Active Archive Center (LP DAAC), and can be downloaded through NASA's Earthdata search. For more information about the Surface Disturbance product suite, please refer to the DIST Product page: https://www.jpl.nasa.gov/go/opera/products/dist-product-suite/For more information about the GLAD project, visit https://glad.umd.edu/For more information about the Caltech-JPL ARIA project, visit https://aria.jpl.nasa.govFor more information about the JPL OPERA project, visit https://www.jpl.nasa.gov/go/opera/Suggested Use for DIST-HLS:VEG-DIST-STATUS:Layer values:0: No disturbance (suggested color: #121212)1: first <50% (suggested color: #005555)2: provisional <50% (suggested color: #897f4e)3: confirmed <50% (suggested color: #dee043)4: first >=50% (suggested color: #008888)5: provisional >=50% (suggested color: #e48727)6: confirmed >=50% (suggested color: #e01b07)7: confirmed <50%, finished (suggested color: #777777)8: confirmed >=50%, finished (suggested color: #dddddd)255: No data (suggested color: transparent)VEG-ANOM-MAXLayer values:0-100: Maximum loss of percent vegetation (suggested color: white to red gradient)255: No data (suggested color: transparent)Product POCs:Alexander L. Handwerger (alexander.handwerger@jpl.nasa.gov)Zhen Song (zhensong@umd.edu)Matt Hansen (mhansen@umd.edu)Steven Chan (steventsz.k.chan@jpl.nasa.gov)David Bekaert (david.bekaert@jpl.nasa.gov)Credits:The product contains modified Copernicus Sentinel data (2024) and is produced as part of the OPERA project, which is funded by NASA to address remote sensing needs identified by the Satellite Needs Working Group. Managed by NASA's Jet Propulsion Laboratory, OPERA funds and manages the DIST-ALERT-HLS product developed and produced by the Global Land Analysis and Discovery (GLAD) laboratory at the University of Maryland.NASA JPL-Caltech-UMD ARIA/OPERA Team==================Files:OPERA-DIST-ALERT-HLS-VEG-ANOM-MAX_20250112.tif: January 12, 2025 showing the maximum vegetation anomaly.OPERA-DIST-ALERT-HLS-VEG-DIST-STATUS_20250112.tif: January 12, 2025 showing the status anomaly.Last Update 14 January 2025REST Endpoint:See URL to the right.WMS Endpoint:https://maps.disasters.nasa.gov/arcgis/home/item.html?id=0d1c3d68a4094617904a2949a7620a43

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/

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 provides estimated percentages of public school students in grades 7, 9, 11, and non-traditional programs (community day schools or continuation education) who were bullied or harassed at school for any reason in the previous year. 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 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

Produced collectively by tsunami modelers, geologic hazard mapping specialists, and emergency planning scientists from CGS, Cal OES, and the Tsunami Research Center at the University of Southern California, the tsunami inundation maps for California cover most residentially and transient populated areas along the state’s coastline. Coordinated by Cal OES, these official maps are developed for all populated areas at risk to tsunamis in California and represent a combination of the maximum considered tsunamis for each area.For more information please visit: Official Tsunami Inundation ZonesMap Disclaimer: These maps were prepared to assist cities and counties in identifying their tsunami hazard. They are intended for local jurisdictional, coastal evacuation planning uses only. These maps are not a legal documents and do not meet disclosure requirements for real estate transactions nor for any other regulatory purpose. The California Emergency Management Agency (CalEMA), the University of Southern California (USC), and the California Geological Survey (CGS) make no representation or warranties regarding the accuracy of this inundation map nor the data from which the map was derived. Neither the State of California nor USC shall be liable under any circumstances for any direct, indirect, special, incidental or consequential damages with respect to any claim by any user or any third party on account of or arising from the use of this map.