AndreaBozzo/ceres-open-data-index huggingface dataset:
SF Bay Eelgrass 45m Buffer (BCDC 2020)

This layer is a 45-meter growth buffer surrounding the maximum extent of eelgrass (green layer called "SF Bay Eelgrass") surveyed in San Francisco Bay. Eelgrass beds are highly dynamic and the exact location and extent of eelgrass beds can change across seasons and years. Thus, the purpose of the 45-meter growth buffer, as described in the National Marine Fisheries Service's LTMS Programmatic Essential Fish Habitat consultation is to account for areas between eelgrass patches, temporal variation in bed extent, and potential bed expansion. In cases where a dredge project intersects with the 45-meter growth buffer direct impacts to eelgrass may occur and therefore assessment, minimization, and mitigation measures may be required on a project-by-project basis. A pre-dredge eelgrass area and density survey is required 30 days prior to the start of dredging and should be submitted to the LTMS permitting agencies. Methods for creating this layer are as follows: Downloaded Baywide Eelgrass Surveys for 2003, 2009, and 2014 by Merkel & Associates, Inc. (Merkel) from San Francisco Estuary Institute (SFEI) [website]. Obtained Richardson Bay 2019 eelgrass survey from Merkel. Loaded all layers into ArcGIS Pro © ESRI and re-projected all data to NAD 1983 UTM Zone 10N. Used Buffer tool to develop a single multipart shapefile with a 45-meter buffer of the input layers. Imported the Pacific Marine and Estuarine Fish Habitat Partnership (PMEP) Estuary Extent layer and clipped the 45-meter buffer over terrestrial areas based on the PEMP Estuary Extent. Some minor adjustments were made where the buffer layer resulted in fragments on land or behind levees.
SF Bay Eelgrass 250m Buffer (BCDC 2021)

This orange layer shows a 250-meter turbidity buffer of the blue 45-meter growth buffer (blue layer called "SF Bay Eelgrass 45m Buffer") adjacent to the maximum extent eelgrass survey in the San Francisco Bay. When a dredging project’s footprint overlaps with this 250-meter buffer, indirect impacts to eelgrass are assessed and best management practices are required per the National Marine Fisheries Service's LTMS Programmatic Essential Fish Habitat consultation. Methods for creating this layer are as follows: Downloaded Bay-wide Eelgrass Surveys for 2003, 2009, and 2014 by Merkel & Associates, Inc. (Merkel) from [SFEI]. Obtained Richardson Bay 2019 eelgrass survey from Merkel. Loaded all layers into ArcGIS Pro © ESRI and re-projected all data to NAD 1983 UTM Zone 10N. Used Buffer tool to develop a single multipart shapefile with a 45-meter buffer of the 2003, 2009, 2014, and 2019 survey data . Imported the Pacific Marine and Estuarine Fish Habitat Partnership (PMEP) Estuary Extent layer and clipped the 45-meter buffer over terrestrial areas based on the PEMP Estuary Extent (this represents the 45-meter eelgrass buffer layer also found in this Web Application). To create the 250-meter turbidity buffer from there, the same methods were used as follows. Used Buffer tool to develop a single multipart shapefile with a 250-meter buffer from the 45-meter buffer layer. Clipped the 250-meter turbidity buffer over terrestrial areas based on the PEMP Estuary Extent. Some minor adjustments were made where the 250-meter turbidity buffer layer resulted in fragments on land or behind levees.
Shoreline Vulnerability Index (BCDC, 2021)

This San Francisco Bay Shoreline Vulnerability Index (Index) is a measure of shoreline vulnerability to erosion and/or overtopping due to extreme tides, waves, storm surges, and sea level rise. The Index gives a comprehensive look at how different sections of the Bay respond to storm surge, erosion from waves, and sea level rise. It ranks each shoreline segment’s vulnerability to impacts such as erosion and overtopping relative to other types of shoreline by by scoring characteristics that affect shoreline vulnerability. The Shoreline Vulnerability Index (SVI) uses the following 6 characteristics to determine shoreline vulnerability for the primary shoreline protection, which is the first elevated shoreline from the Bay. These characteristics are weighted in their importance towards shoreline vulnerability to flooding. ───────────────────────────────────────────────────────────────── Shoreline Vulnerability Characteristics ───────────────────────────────────────────────────────────────── 1. Vulnerability of shoreline type to flooding and sea level rise ───────────────────────────────────────────────────────────────── 2. Adaptability to sea level rise by shoreline type ───────────────────────────────────────────────────────────────── 3. Presence of fortification ───────────────────────────────────────────────────────────────── 4. Presence of frontage and/or secondary shoreline protection ───────────────────────────────────────────────────────────────── 5. Elevation ───────────────────────────────────────────────────────────────── 6. Wave energy ───────────────────────────────────────────────────────────────── For more information visit the following links: ArcGIS Story Map: [https://bcdc.maps.arcgis.com/apps/Cascade/index.html?appid=a90eb7b4eb7249809505e8d940bb2419] Methodology Document: [https://www.adaptingtorisingtides.org/wp-content/uploads/2021/07/ShorelineVulnerabilityIndex_Methodology_2021.pdf] GitHub: [https://github.com/BCDC-GIS/shoreline-vulnerability-index] For more information, please contact [GIS@bcdc.ca.gov].
SF Bay Tidal Datums (2016)

This study is the first comprehensive publication of tidal datums and extreme tides for San Francisco Bay (Bay) since the United States Army Corps of Engineers (USACE) published itsSan Francisco Bay Tidal Stage vs. Frequency Study in 1984 (USACE 1984). The USACE study was groundbreaking at the time of publication, presenting tidal datums and the “100-year tide” elevation for 53 locations around the Bay. The purpose of this study is to update and expand on the USACE study and to present daily and extreme tidal information for more than 900 locations along the Bay shoreline. Tidal datums, described further in Section 2 , are standard elevations defined by a certain phase of the tide (e.g., mean high tide, mean low tide). A tidal datum is used as a reference to measure and define local water levels, and as such is specific to local hydrodynamic processes and is not easily extended from one area to another without substantiating measurements or analysis. Many industries and activities rely on tidal datums, including shipping and navigation, coastal flood management, coastal development, and wetland restoration. Extreme tidal elevations are estimated for less-frequent extreme tides (e.g., 2-year tides to 500-year tides [tides with a 50.0 percent to 0.2 percent annual chance of occurrence, respectively]). Knowledge of the 100-year tide, or the water elevation with a 1 percent annual chance of occurrence, is critical for shoreline planning, floodplain management, and sea level rise (SLR) adaptation efforts. This study presents detailed daily and extreme tide information for the entirety of the Bay shoreline. This data set will support floodplain management efforts; shoreline vulnerability and risk analyses; shoreline engineering, design, and permitting; ecological studies; and appropriate sea level rise adaptation planning. The goal of this study is to provide data that support a wide-range of planning efforts around the Bay, particularly as communities seek to understand—and begin to adapt to—rising sea levels. You can access the full report at: [http://www.adaptingtorisingtides.org/wp-content/uploads/2016/05/20160429.SFBay_Tidal-Datums_and_Extreme_Tides_Study.FINAL_.pdf].
2021 May Revise Locations (Anchor Build Fiber Highways)

Data provided from CPUC as shapefile. This layer represents initial proposed routes for a statewide open-access middle-mile broadband network (based on May 2021 selected highway segments). Filtered to STATUS = 'IN'. This network linework is based on the State Highway Network (SHN Lines). SHN Lines represents each route-alignment of the entire State Highway Network (SHN), including relinquishments, as single-record multi-part features based on extent of each route described in the TSN databases on October 14, 2019. The data used for this representation of the statewide open-access middle-mile network in comprised of a subset of these SHN Lines along the left route alignments.
Community Vulnerability (BCDC 2020)

The San Francisco Bay Conservation and Development Commission Adapting to Rising Tides Program developed a dataset to better understand community vulnerability to current and future flooding due to sea level rise and storm surges. This data has been used in the Adapting To Rising Tides Bay Area Sea Level Rise Vulnerability and Assessment project as well as helping inform the implementation of the BCDC Environmental Justice and Social Equity Bay Plan amendment. The community vulnerability dataset contains four categories of information: 1. Social Vulnerability Indicators: Certain socioeconomic characteristics may reduce ability to prepare for, respond to, or recover from a hazard event. Census block groups with high concentrations (relative to the nine county Bay Area) of these characteristics are flagged as socially vulnerable, with each block group assigned a rank of highest, high, moderate, and low. Data is currently from American Community Survey (ACS) 2018 5-year estimates but is anticipated to be updated as new ACS 5-year estimates become available. 2. Contamination Vulnerability Indicators: The presence of contaminated lands and water raises health and environmental justice concerns, which worsen with flooding and sea level rise. A rank of highest, high, moderate, and lower for the severity of contamination in each block group was calculated using data compiled by CalEPA Office of Environmental Health Hazard Assessment (OEHHA) for use in CalEnviroScreen 3.0. 3. Residential Exposure to Sea Level Rise: Calculated by joining Metropolitan Transportation Commission 2010 residential parcel data with 2017 ART Bay Area Sea Level Rise and Shoreline Analysis data, FEMA 100 and 500 year flood zone data, and San Francisco 100-year precipitation data to generate the number of residential units exposed at each water level summed by block group. This methodology assumes that once a parcel is exposed to any amount of flooding, the entire number of residential units within that parcel are considered impacted. 4. Complementary Community Vulnerability Screening Tools: Many screening approaches exist to characterize disadvantaged or vulnerable communities. Often in the Bay Area, different designations of disadvantaged/vulnerable communities are located in the same area. It is recommended to use the ART approach in combination with other complementary tools and designations. The following are included in this shapefile as fields for cross-referencing: CalEnviroScreen 3.0 total score, Metropolitan Transportation Commission Community of Concern designation, UC Berkeley Displacement and Gentrification Typologies. Data and resources can be accessed at [https://www.bcdc.ca.gov/data/community.html]. For information about data development and access please review the [Community Vulnerability User Guide] and [BCDC’s Github Repository]. For additional descriptions of GIS methods used in ART Bay Area, please see the [ART Bay Area Report Appendix: GIS Data and Methods]. For more information, please contact [GIS@bcdc.ca.gov].
Solar Resource Areas (2022)

Solar Resource Areas created were produced using a k-means method that groups solar generating plants into discrete regions based on their latitude, longitude, and distance to the coast. After facilities were grouped, the outermost facilities in each region were connected to create the boundaries for each region. To include all facilities inside each polygon, a 5 km buffer was used. Plants that are farther from a concentration of other plants are not included in a region and are instead displayed as outlying facilities. Areas outside California included in the the regions are a result of the mapping process and are not representing actual solar generating facilities. Based on the Quarterly Fuel and Energy Report dataset, this map focuses on data from plants of at least 1 MW capacity (commercial scale) and excludes smaller plants. For more information please contact John Hingtgen (916) 510-9747.
SWTG_Subgrids_3x3_forpublicuse

Statewide trapping sub grid layers: 3x3.  County codes included. 


Previous subgrid feature layer uploaded on 9/13/22 is deprecated. Do not use: https://cdfa.maps.arcgis.com/home/item.html?id=f23bc1ff791c45748e93d780b9ac9627
SWTG_Subgrids_Quint_forpublicuse

Statewide trapping sub grid layers: quints,   County codes included. 


Previous subgrid feature layer uploaded on 9/13/22 is deprecated. Do not use: https://cdfa.maps.arcgis.com/home/item.html?id=f23bc1ff791c45748e93d780b9ac9627
SWTG_Subgrids_4x4_forpublicuse

Statewide trapping sub grid layers: 4x4.  County codes included. 


Previous subgrid feature layer uploaded on 9/13/22 is deprecated. Do not use: https://cdfa.maps.arcgis.com/home/item.html?id=f23bc1ff791c45748e93d780b9ac9627
Federally Recognized Tribal Lands

This layer represents the geographic areas 4 below. Category 1-3, census tracts, are in the other layer. In this designation, CalEPA formally designated four categories of geographic areas as disadvantaged: 1. Census tracts receiving the highest 25 percent of overall scores in CalEnviroScreen 4.0 (1,984 tracts). 2. Census tracts lacking overall scores in CalEnviroScreen 4.0 due to data gaps, but receiving the highest 5 percent of CalEnviroScreen 4.0 cumulative pollution burden scores (19 tracts). 3. Census tracts identified in the 2017 DAC designation as disadvantaged, regardless of their scores in CalEnviroScreen 4.0 (305 tracts). 4. Lands under the control of federally recognized Tribes. For purposes of this designation, a Tribe may establish that a particular area of land is under its control even if not represented as such on CalEPA’s DAC map and therefore should be considered a DAC by requesting a consultation with the CalEPA Deputy Secretary for Environmental Justice, Tribal Affairs and Border Relations at [TribalAffairs@calepa.ca.gov]. This file contains legal AIANNH entities for which the Census Bureau publishes data. The legal entities consist of federally recognized American Indian Reservations (AIRs) and Off-Reservation Trust Lands (ORTL). Downloaded in 2022 from the US Census website here: https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-geodatabase-file.html
Regional Transportation Planning Agencies

The list of California Transportation Planning Agencies is current as of February, 2014, provided by Division of Transportation Planning, Office of Regional and Interagency Planning. With the exception of Tahoe Regional Planning Agency (TRPA*), all of the RTPA boundaries follow county boundaries, some RTPA are multi-county. Data downloaded in December 2022 from [https://gisdata-caltrans.opendata.arcgis.com/datasets/eade3fe45fa046418063d47846dd4c21_0/about].
Metropolitan Planning Organization Boundaries

The metropolitan planning organization (MPO) polygon feature class provides California MPO legislative boundaries, primarily for regional planning applications. Data downloaded in February 2023 from [https://gisdata-caltrans.opendata.arcgis.com/datasets/b3e0ef95520843ba8c1d3b9c0fa9a607_0/explore?location=36.748926%2C-119.524779%2C7.00].
2023TulareFloodingIncident_2023_DINS_Public_View

This database was designed in response to the Director Memorandum - "Effective January 1, 2019 all structure greater than 120 square feet in the State Responsibility Area (SRA) damaged by wildfire will be inspected and documented in the DINS Collector App." To document and structure damaged or destroyed by the 2023 Tulare Flooding Incident open the associated Field Map app. NOTE - this feature service is configured to not allow record deletion. If a record needs to be deleted contact the program manager below. This is the schema developed and used by the CAL FIRE Office of State Fire Marshal to assess and record structure damage on flooding incidents. The schema is designed to be configured in the Esri Collector/Field Maps app for data collection during or after an incident.
2023PineappleExpress_2023_DINS_Public_View

This database was designed in response to the Director Memorandum - "Effective January 1, 2019 all structure greater than 120 square feet in the State Responsibility Area (SRA) damaged by wildfire will be inspected and documented in the DINS Collector App." To document structures damaged or destroyed by the 2023 Pineapple Express flooding in Monterey County open the associated Field Map app. NOTE - this feature service is configured to not allow record deletion. If a record needs to be deleted contact the program manager below. This is the schema developed and used by the CAL FIRE Office of State Fire Marshal to assess and record structure damage on flooding incidents. The schema is designed to be configured in the Esri Collector/Field Maps app for data collection during or after an incident.
Recommended Broadband Strategic Priority Corridors

The regional consortia worked together to develop regional lists and a statewide map of "Strategic Broadband Corridors" that Caltrans and California Transportation Commission (CTC) could consider for inclusion in the CTC Guidelines, 2018 Comprehensive Multi-modal Corridor Plan Guidelines. The Roundtable, CETF, CAFWD and the California Broadband Council (CBC) requested the regional consortia to rank order the top three priority "strategic broadband corridors" in each region.
SWTG_Subgrids_5x5_forpublicuse

Statewide trapping sub grid layers:5x5, County codes included. 


Previous subgrid feature layer uploaded on 9/13/22 is deprecated. Do not use: https://cdfa.maps.arcgis.com/home/item.html?id=f23bc1ff791c45748e93d780b9ac9627
Wind Resource Areas (2023)

The method to create the Wind Resource Area datasets is to: Query Power Plant point locations from the California Energy Commission, [California Power Plants] data set by operational status and capacity greater than or equal to 2 MW at each facility from the [Quarterly Fuel and Energy Report, CEC-1304A]. Plants tracked include those of at least 1 MW, which are considered of commercial size. A polygon was generated around the resulting operational, commercial wind facilities using the Aggregate Points geoprocessing tool with an aggregation distance of 15 survey miles. A 5 mile spatial buffer was added to the resulting polygons. The buffer does not represent information regarding environmental analysis. It is used only to depict plant concentration regions.
Elk Home Range - American - 2005-2008, 2015-2017 [ds3111]

The project lead for the collection of this data was Bob Stafford. Elk (9 adult females, 5 adult males) from the American herd were captured and equipped with Lotek GPS collars (LifeCycle 800 GlobalStar/ GPS3300L, Lotek Wireless, Newmarket, Ontario, Canada) and ATS GPS collars (G2000, Advanced Telemetry Systems, Isanti, Minnesota, USA), transmitting data from 2005-2008 and 2015-2017. The study area was within the La Panza Elk Management Unit, northeast of State Highway 166 and largely residing within Carrizo Plain National Monument and Carrizo Plains Ecological Reserve. The American herd contains short distance, elevation-based movements likely due to seasonal habitat conditions, but this herd does not migrate between traditional summer and winter seasonal ranges. Instead, the herd displays a residential pattern, slowly moving up or down elevational gradients. Therefore, annual home ranges were modeled using year-round data to demarcate high use areas in lieu of modeling the specific winter ranges commonly seen in other ungulate analyses in California. GPS locations were fixed at 11-13 hour intervals in the dataset. To improve the quality of the data set as per Bjørneraas et al. (2010), the GPS data were filtered prior to analysis to remove locations which were: i) further from either the previous point or subsequent point than an individual elk is able to travel in the elapsed time, ii) forming spikes in the movement trajectory based on outgoing and incoming speeds and turning angles sharper than a predefined threshold , or iii) fixed in 2D space and visually assessed as a bad fix by the analyst. The methodology used for this analysis allowed for the mapping of the herd’s annual range based on a small sample. Brownian Bridge Movement Models (BBMMs; Sawyer et al. 2009) were constructed with GPS collar data from 11 elk in total, including 20 year-long sequences, location, date, time, and average location error as inputs in Migration Mapper to assess annual range. Annual range BBMMs were produced at a spatial resolution of 50 m using a sequential fix interval of less than 27 hours and a fixed motion variance of 1000. Population-level annual range designations for this herd may expand with a larger sample, filling in some of the gaps between high-use annual range polygons in the map. Annual range is visualized as the 50th percentile contour (high use) and the 99th percentile contour of the year-round utilization distribution.
Elk Home Range - California Valley - 2005-2006, 2015-2017 [ds3112]

The project lead for the collection of this data was Bob Stafford. Elk (7 adult females, 3 adult males) from the California Valley herd were captured and equipped with Lotek GPS collars (LifeCycle 800 GlobalStar/ GPS3300L, Lotek Wireless, Newmarket, Ontario, Canada) and ATS GPS collars (G2000, Advanced Telemetry Systems, Isanti, Minnesota, USA), transmitting data from 2005-2006 and 2015-2017. The study area was within the La Panza Elk Management Unit, north adjacent of State Highway 58 in the Carrizo Plain and largely residing on a patchwork of private lands and CDFW’s California Valley. The Cedar Canyon herd contains short distance, elevation-based movements likely due to seasonal habitat conditions, but this herd does not migrate between traditional summer and winter seasonal ranges. Instead, the herd displays a residential pattern, slowly moving up or down elevational gradients. Therefore, annual home ranges were modeled using year-round data to demarcate high use areas in lieu of modeling the specific winter ranges commonly seen in other ungulate analyses in California. GPS locations were fixed at 1-13 hour intervals in the dataset. To improve the quality of the data set as per Bjørneraas et al. (2010), the GPS data were filtered prior to analysis to remove locations which were: i) further from either the previous point or subsequent point than an individual elk is able to travel in the elapsed time, ii) forming spikes in the movement trajectory based on outgoing and incoming speeds and turning angles sharper than a predefined threshold , or iii) fixed in 2D space and visually assessed as a bad fix by the analyst. The methodology used for this analysis allowed for the mapping of the herd’s annual range based on a small sample. Brownian Bridge Movement Models (BBMMs; Sawyer et al. 2009) were constructed with GPS collar data from 9 elk in total, including 17 year-long sequences, location, date, time, and average location error as inputs in Migration Mapper to assess annual range. Annual range BBMMs were produced at a spatial resolution of 50 m using a sequential fix interval of less than 27 hours and a fixed motion variance of 1000. Population-level annual range designations for this herd may expand with a larger sample, filling in some of the gaps between high-use annual range polygons in the map. Annual range is visualized as the 50th percentile contour (high use) and the 99th percentile contour of the year-round utilization distribution.