A. SUMMARY This dataset contains San Francisco Board of Supervisor district boundaries approved by the San Francisco Redistricting Task Force in April 2022 following redistricting based on the 2020 Decennial Census.

B. HOW THE DATASET IS CREATED The dataset was created from the final map submitted by the San Francisco Redistricting Task Force. Boundaries in this map were decided using data from the 2020 Census on the number of people living in each census block in the City and County. This data includes the number of individuals incarcerated in facilities under the control of the Department of Corrections and Rehabilitation based on their last known residential address. This information is made available by the Statewide Database based on U.S. Census Bureau Census Redistricting Data (P.L. 94-171).

These map boundaries were trimmed to align with the city and county's physical boundaries for greater usability. This trimming mainly consisted of excluding the water around the City and County from the boundaries.

C. UPDATE PROCESS Supervisor District boundaries are updated every 10 years following the federal decennial census. The Supervisor District boundaries reflected in this dataset will remain unchanged. A new dataset will be created after the next decennial census and redistricting process are completed.

The dataset is manually updated as new members of the Board of Supervisors take office. The most recent manual update date is reflected in the 'data_as_of' field.

Once the redistricting process is completed after the next decennial census and a new dataset is published, this dataset will become static and will no longer be updated.

D. HOW TO USE THIS DATASET This dataset can be joined to other datasets for analysis and reporting at the Supervisor District level.

If you are building an automated reporting pipeline using Socrata API access, we recommend using this dataset if you'd like your boundaries to remain static. If you would like the boundaries to automatically update after each decennial census to reflect the most recent Supervisor District boundaries, see the Current Supervisor Districts dataset or the Current Supervisor Districts (trimmed to remove water and other non-populated City territories) dataset.

E. RELATED DATASETS Supervisor Districts (2012) Current Supervisor Districts Current Supervisor Districts (trimmed to remove water and non-populated areas)

The regional flooding and shoreline overtopping analysis maps provided in the ART Bay Shoreline Flood Explorer website capture permanent and temporary flooding impacts from sea level rise scenarios from 0- to 108-inches above MHHW (mean higher high water) and storm surge events from the 1-year to the 100-year storm surge. The process used to develop the maps included discussions with key stakeholders in each county, who reviewed the preliminary maps and provided on-the-ground verification and supplemental data to improve the accuracy of the maps. The maps and information produced through this effort can inform adaptation planning, assist in managing climate change risks, and help identify trigger points for implementing adaptation strategies to address sea level rise and flooding hazards, at both local and regional scales. The Flood Explorer maps were produced using the latest LiDAR topographic data sets, water level outputs from the FEMA San Francisco Bay Area Coastal Study (which relied in hydrodynamic modeling using MIKE21) and the San Francisco Tidal Datums Study. The 2010/2011 LIDAR applied (collected by USGS and NOAA at a 1-m resolution) was further refined through the stakeholder review process and integration of additional elevation data where available. The Flood Explorer also includes the regional shoreline delineation developed by the San Francisco Estuary Institute to represent coastal flooding and overtopping throughout the Bay Area. In sum, the maps include: 1) Flooding at ten total water levels that capture over 90 combinations of future sea level rise and storm surge scenarios; 2) Shoreline overtopping maps for all ten total water levels that depict where the Bay may overtop the shoreline and its depth of overtopping at that specific location. Coupled with the flood maps, the overtopping data can help identify vulnerable shoreline locations and their respective flow paths that could lead to inland flooding, and; 3) Hydraulically disconnected low-lying areas that represent areas that may be vulnerable to flooding due to their low elevation. These areas are not directly within flooding locations, but could be connected to flood waters through culverts and storm drains that are not captured in this analysis.

This data set contains features representing growth boundaries in the San Francisco Bay Region. The data set was developed by Greenbelt Alliance as part of their open space preservation mission. The Metropolitan Transportation Commission (MTC) updated the feature set in late 2019 as part of the jurisdiction review process for the BASIS data gathering project. Changes were made to the growth boundaries of the following jurisdictions based on their BASIS feedback: Antioch, Fremont, Livermore, Marin County, Pittsburg, Pleasanton, and San Ramon.The original features were created by referencing the local measures adopted by voters, city councils/boards of supervisors, or both and map images developed by local government. Web links to the measures and images are included in the attribute table. Greenbelt Alliance is responsible for updating the links to that information if their links change or the documents are taken offline. MTC's updates were made to a limited set of features using shapefiles downloaded from jurisdiction websites, hand editing features by referencing map images provided by jurisdictions, and erasing feature areas using base data features where one jurisdiction's growth boundary overlapped another jurisdiction's city limit. The MTC edits were limited, or related, to features receiving comments from jurisdiction reviewers.While commonly known as either Urban Growth Boundaries (UGBs) or Urban Limit Lines (ULLs), there are no standard designations so they are referred to by a number of designations in the attribute table (name). A couple of the alternative designations include Rural Urban Boundary and City Centered Corridor. Regardless of the designation used, growth boundaries are intended to control where development should be encouraged and discouraged. In this case, the polygons represent the areas where development should occur, and development outside the boundary is discouraged. Development proposed for areas outside the boundary is usually allowed based on special review and approval processes designated by the adopting jurisdiction.Jurisdictions are not required to adopt growth boundaries so the development restrictions imposed by them only apply to the cities and counties that create them. In the case of growth boundaries adopted by counties, they are usually developed with the consent and agreement of the cities adjacent to the county growth boundary.

In 2007, the California Ocean Protection Council initiated the California Seafloor Mapping Program (CSMP), designed to create a comprehensive seafloor map of high-resolution bathymetry, marine benthic habitats, and geology within California’s State Waters. The program supports a large number of coastal-zone- and ocean-management issues, including the California Marine Life Protection Act (MLPA) (California Department of Fish and Wildlife, 2008), which requires information about the distribution of ecosystems as part of the design and proposal process for the establishment of Marine Protected Areas. A focus of CSMP is to map California’s State Waters with consistent methods at a consistent scale. The CSMP approach is to create highly detailed seafloor maps through collection, integration, interpretation, and visualization of swath sonar data (the undersea equivalent of satellite remote-sensing data in terrestrial mapping), acoustic backscatter, seafloor video, seafloor photography, high-resolution seismic-reflection profiles, and bottom-sediment sampling data. The map products display seafloor morphology and character, identify potential marine benthic habitats, and illustrate both the surficial seafloor geology and shallow (to about 100 m) subsurface geology. It is emphasized that the more interpretive habitat and geology data rely on the integration of multiple, new high-resolution datasets and that mapping at small scales would not be possible without such data. This approach and CSMP planning is based in part on recommendations of the Marine Mapping Planning Workshop (Kvitek and others, 2006), attended by coastal and marine managers and scientists from around the state. That workshop established geographic priorities for a coastal mapping project and identified the need for coverage of “lands” from the shore strand line (defined as Mean Higher High Water; MHHW) out to the 3-nautical-mile (5.6-km) limit of California’s State Waters. Unfortunately, surveying the zone from MHHW out to 10-m water depth is not consistently possible using ship-based surveying methods, owing to sea state (for example, waves, wind, or currents), kelp coverage, and shallow rock outcrops. Accordingly, some of the data presented in this series commonly do not cover the zone from the shore out to 10-m depth. This data is part of a series of online U.S. Geological Survey (USGS) publications, each of which includes several map sheets, some explanatory text, and a descriptive pamphlet. Each map sheet is published as a PDF file. Geographic information system (GIS) files that contain both ESRI ArcGIS raster grids (for example, bathymetry, seafloor character) and geotiffs (for example, shaded relief) are also included for each publication. For those who do not own the full suite of ESRI GIS and mapping software, the data can be read using ESRI ArcReader, a free viewer that is available at http://www.esri.com/software/arcgis/arcreader/index.html (last accessed September 20, 2013). The California Seafloor Mapping Program is a collaborative venture between numerous different federal and state agencies, academia, and the private sector. CSMP partners include the California Coastal Conservancy, the California Ocean Protection Council, the California Department of Fish and Wildlife, the California Geological Survey, California State University at Monterey Bay’s Seafloor Mapping Lab, Moss Landing Marine Laboratories Center for Habitat Studies, Fugro Pelagos, Pacific Gas and Electric Company, National Oceanic and Atmospheric Administration (NOAA, including National Ocean Service–Office of Coast Surveys, National Marine Sanctuaries, and National Marine Fisheries Service), U.S. Army Corps of Engineers, the Bureau of Ocean Energy Management, the National Park Service, and the U.S. Geological Survey. These web services for the Offshore of San Francisco map area includes data layers that are associated to GIS and map sheets available from the USGS CSMP web page at https://res1walrusd-o-twrd-o-tusgsd-o-tgov.vcapture.xyz/mapping/csmp/index.html. Each published CSMP map area includes a data catalog of geographic information system (GIS) files; map sheets that contain explanatory text; and an associated descriptive pamphlet. This web service represents the available data layers for this map area. Data was combined from different sonar surveys to generate a comprehensive high-resolution bathymetry and acoustic-backscatter coverage of the map area. These data reveal a range of physiographic including exposed bedrock outcrops, large fields of sand waves, as well as many human impacts on the seafloor. To validate geological and biological interpretations of the sonar data, the U.S. Geological Survey towed a camera sled over specific offshore locations, collecting both video and photographic imagery; these “ground-truth” surveying data are available from the CSMP Video and Photograph Portal at https://res1doid-o-torg.vcapture.xyz/10.5066/F7J1015K. The “seafloor character” data layer shows classifications of the seafloor on the basis of depth, slope, rugosity (ruggedness), and backscatter intensity and which is further informed by the ground-truth-survey imagery. The “potential habitats” polygons are delineated on the basis of substrate type, geomorphology, seafloor process, or other attributes that may provide a habitat for a specific species or assemblage of organisms. Representative seismic-reflection profile data from the map area is also include and provides information on the subsurface stratigraphy and structure of the map area. The distribution and thickness of young sediment (deposited over the past about 21,000 years, during the most recent sea-level rise) is interpreted on the basis of the seismic-reflection data. The geologic polygons merge onshore geologic mapping (compiled from existing maps by the California Geological Survey) and new offshore geologic mapping that is based on integration of high-resolution bathymetry and backscatter imagery seafloor-sediment and rock samplesdigital camera and video imagery, and high-resolution seismic-reflection profiles. The information provided by the map sheets, pamphlet, and data catalog has a broad range of applications. High-resolution bathymetry, acoustic backscatter, ground-truth-surveying imagery, and habitat mapping all contribute to habitat characterization and ecosystem-based management by providing essential data for delineation of marine protected areas and ecosystem restoration. Many of the maps provide high-resolution baselines that will be critical for monitoring environmental change associated with climate change, coastal development, or other forcings. High-resolution bathymetry is a critical component for modeling coastal flooding caused by storms and tsunamis, as well as inundation associated with longer term sea-level rise. Seismic-reflection and bathymetric data help characterize earthquake and tsunami sources, critical for natural-hazard assessments of coastal zones. Information on sediment distribution and thickness is essential to the understanding of local and regional sediment transport, as well as the development of regional sediment-management plans. In addition, siting of any new offshore infrastructure (for example, pipelines, cables, or renewable-energy facilities) will depend on high-resolution mapping. Finally, this mapping will both stimulate and enable new scientific research and also raise public awareness of, and education about, coastal environments and issues. Web services were created using an ArcGIS service definition file. The ArcGIS REST service and OGC WMS service include all Offshore of San Francisco map area data layers. Data layers are symbolized as shown on the associated map sheets.

SUMMARY The Air Pollutant Exposure Zone (APEZ) map identifies areas in San Francisco where air modeling indicates higher levels of air pollution. This map is required to be updated every 5 years, as established in San Francisco Health Code article 38. The 2025 Air Pollutant Exposure Zone map is an update to the 2020 map. Additional information may be found at Air Quality Review | SF Planning.

HOW THE DATASET IS CREATED The 2025 APEZ update modeled areas of the city where: particulate matter (PM2.5) is greater than or equal to 9 µg/m3 or where the risk of cancer from air pollutants is greater than or equal to 100 in a million; in health vulnerable ZIP codes (94102, 94103, 94110, 94124, and 94134), where the risk of cancer from air pollutants is greater than or equal to 90 in a million; locations within 500 feet of freeways; or locations within 1,000 feet of roadways with a daily average of 100,000 vehicles. To learn more, visit San Francisco Citywide Health Risk Assessment: Technical Support Documentation, Air Pollutant Exposure Zone Handout and Air Pollutant Exposure Zone Story Map.

UPDATE PROCESS Updated every five years.

HOW TO USE THIS DATASET The City uses this dataset as follows. San Francisco Health Code article 38 requires new developments or major renovations within the APEZ with sensitive receptors, like housing or preschools, to include a ventilation system that sufficiently removes fine particulate matter (minimum efficiency reporting volume [MERV] 13 or equivalent filtration). In addition, Environment Code Chapter 25 requires public agencies implementing projects within the APEZ to use the cleanest construction equipment available. The City’s environmental review under the California Environmental Quality Act (CEQA) uses the APEZ in its analysis to mandate the use of clean construction equipment, when applicable. To learn more, visit Air Quality Review | SF Planning.

A. SUMMARY Geographic zones of the priority areas in the Tenderloin neighborhood used in the COVID-19 assessment and Tenderloin Neighborhood Plan. See more details on the plan here: https://sf.gov/news/san-francisco-releases-tenderloin-neighborhood-safety-assessment-and-plan-covid-19

B. HOW THE DATASET IS CREATED A team of representative City departments from the Healthy Streets Operation Center (Department of Emergency Management, Department of Public Health, Department of Homelessness and Supportive Housing, Human Rights Commission, San Francisco Police Department, San Francisco Fire Department, and Department of Public Works), SF Homeless Outreach Team, Felton Institute, and community groups and stakeholders was assembled to design and implement a robust Tenderloin Neighborhood Needs Assessment. This assessment was conducted on the morning of April 28, 2020 and consisted of multi-disciplinary teams walking each block of an area of the Tenderloin broken into six geographic zones. These zone locations are shown in the plan, and are mapped in this dataset.

C. UPDATE PROCESS This is a reference map that will not be updated.

D. HOW TO USE THIS DATASET These zones can be used with other datasets to track trends by zone. Note that these zones are the priority zones for the Tenderloin Plan and do not represent the entire Tenderloin Neighborhood boundary. For a boundary of the entire Tenderloin, use the analysis neighborhood boundary: https://data.sfgov.org/Geographic-Locations-and-Boundaries/Analysis-Neighborhoods/p5b7-5n3h

This report is a digital database package containing both plotfiles and Geographic Information Systems (GIS) databases of shaded relief maps of the San Francisco Bay Region. The data are provided for both the entire region and each county within the region, in two formats. The data is provided as ARC/INFO (Environmental Systems Research Institute, Redlands, CA) GRIDs for use in GIS packages, and as PostScript plotfiles of formatted maps similar to traditional U.S. Geological Survey map products.

[Summary provided by the USGS.]

In 2018, the Golden Gate National Parks Conservancy (Parks Conservancy) (https://parksconservancy.org), non-profit support partner to the National Park Service (NPS) Golden Gate National Recreation Area (GGNRA), initiated a fine scale vegetation mapping project in Marin County. The GGNRA includes lands in San Francisco and San Mateo counties, and NPS expressed interest in pursuing fine scale vegetation mapping for those lands as well. The Parks Conservancy facilitated multiple meetings with potential project stakeholders and was able to build a consortium of funders to map all of San Mateo County (and NPS lands in San Francisco). The consortium included the San Francisco Public Utilities Commission (SFPUC), Midpeninsula Regional Open Space District (MROSD), Peninsula Open Space Trust (POST), San Mateo City/County Association of Governments, and various County of San Mateo departments including Parks, Agricultural Weights and Measures, Public Works/Flood Control District, Office of Sustainability, and Planning and Building. Over a 3-year period, the project, collectively referred to as the “San Mateo Fine Scale Veg Map”, has produced numerous environmental GIS products including 1-foot contours, orthophotography, and other land cover maps. A 106-class fine-scale vegetation map was completed in April 2022 that details vegetation communities and agricultural land cover types, including forests, grasslands, riparian vegetation, wetlands, and croplands. The environmental data products from the San Mateo Fine Scale Veg Map are foundational and can be used by organizations and government departments for a wide range of purposes, including planning, conservation, and to track changes over time to San Mateo County’s habitats and natural resources.Development of the San Mateo fine-scale vegetation map was managed by the Golden Gate National Parks Conservancy and staffed by personnel from Tukman Geospatial (https://tukmangeospatial.com/), Aerial Information Systems (AIS; http://www.aisgis.com/), and Kass Green and Associates. The fine-scale vegetation map effort included field surveys by a team of trained botanists including Neal Kramer, Brett Hall, Lucy Ferneyhough, Brittany Burnett, Patrick Furtado, and Rosie Frederick. Data from these surveys, combined with older surveys from previous efforts, were analyzed by the California Native Plant Society (CNPS) Vegetation Program (https://www.cnps.org/vegetation), with support from the California Department of Fish and Wildlife Vegetation Classification and Mapping Program (VegCAMP; https://wildlife.ca.gov/Data/VegCAMP) and ecologists with NatureServe (https://www.natureserve.org/) to develop a San Mateo County-specific vegetation classification. For more information on the field sampling and vegetation classification work San Mateo County Fine Scale Vegetation Map Final Report refer to the final report (https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=212663) issued by CNPS and corresponding floristic descriptions (https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=212666 and https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=212667).Existing lidar data, collected in 2017 by San Mateo County was used to support the project. The lidar point cloud, and many of its derivatives, were used extensively during the process of developing the fine-scale vegetation and habitat map. The lidar data was used in conjunction with optical data. Optical data used throughout the project included 6-inch resolution airborne 4-band imagery collected in the summer of 2018, as well as various dates of National Agriculture Imagery Program (NAIP) imagery. Key data sets used in the lifeform and the enhanced lifeform mapping process include high resolution aerial imagery from 2018, the lidar-derived Canopy Height Model (CHM), and several other lidar-derived raster and vector datasets. In addition, a number of forest structure lidar derivatives are used in the machine learning portion of the enhanced lifeform workflow.In 2020, an enhanced lifeform map was produced which serves as the foundation for the much more floristically detailed fine-scale vegetation and habitat map. The lifeform map was developed using expert systems rulesets in Trimble Ecognition®, followed by manual editing.In 2020, Tukman Geospatial staff and partners conducted countywide reconnaissance field work to support fine-scale mapping. Field-collected data were used to train automated machine learning algorithms, which produced a fully automated countywide fine-scale vegetation and habitat map. Throughout 2021, AIS manually edited the fine-scale maps, and Tukman Geospatial and AIS went to the field for validation trips to inform and improve the manual editing process. In early January of 2022, draft maps were distributed and reviewed by San Mateo County’s community of land managers and by the funders of the project. Input from these groups was used to further refine the map. The countywide fine-scale vegetation map and related data products were made public in April 2022. In total, 106 vegetation classes were mapped. During the classification development phase, minimum mapping units (MMUs) were established for the vegetation mapping project. An MMU is the smallest area to be mapped on the ground. For this project, the mapping team chose to map different features at different MMUs. The MMU is 1/4 acre for agricultural, woody riparian, and wetland herbaceous classes; 1/2 acre for woody upland, upland herbaceous, and bare land classes; 1/5 acre for developed feature types; and 400 square feet for water.Accuracy assessment plot data were collected in 2021 and 2022. Accuracy assessment results were compiled and analyzed in the April of 2022. Overall accuracy of the lifeform map is 98 percent. Overall accuracy of the fine-scale vegetation map is 83.5 percent, with an overall ‘fuzzy’ accuracy of 90.8 percent.

This shapefile (polygon feature) contains the boundary of the July 1, 2022 100-Year Storm Flood Risk Zone, one of the layers of the July 1, 2022 100-Year Storm Flood Risk Map. Areas within this boundary are highly likely to experience “deep and contiguous” flooding during a 100-year storm. A 100-year storm is a storm that has a 1% chance of occurring in a given year. “Deep and contiguous flooding” means flooding at least 6-inches deep spanning an area at least the size of an average City block. The 100-Year Storm Flood Risk Zone does not provide the exact depth of flooding at a given location. It also does not show areas in the City that may experience shallower and/or more localized flooding in a 100-year storm. Finally, the 100-Year Storm Flood Risk Zone shows flood risk from storm runoff only. It does not consider flood risk in San Francisco from other causes such as shoreline overtopping and overland inundation from the San Francisco Bay or Pacific Ocean. In addition to the 100-Year Storm Flood Risk Zone, the 100-Year Storm Flood Risk Map shows: • “Areas not served by the Combined Sewer and Stormwater Collection System” - showing where data for rainfall driven storm runoff is not available, and where flood risk has not been analyzed. • “Historical Shoreline”, “Historical Creeks”, and “Historical Waterbodies” - historical hydrology layers to illustrate the general topography of low-lying areas in the City. The Horizontal Datum used for the GIS layers is “NAD_1983_2011_StatePlane_California_III_FIPS_0403_Ft_US.” Notes on Usage At a minimum, the 100-Year Storm Flood Risk Map is updated by the San Francisco Public Utilities Commission (SFPUC) on an annual basis on or before July 1 to account for any parcel review requests that remove properties from the Flood Zone. To confirm the latest version of the 100-Year Storm Flood Risk Map, check the SFPUC website at https://sfpuc.org/learning/emergency-preparedness/flood-maps to see if the map has been updated since the date of this shapefile or if there have been any parcel review determinations that identify parcels that are no longer part of the 100-Year Flood Risk Zone. The most recent official map, associated documentation, and list of parcels removed from the map from a parcel review process are available at https://sfpuc.org/learning/emergency-preparedness/flood-maps. Please be advised that the parcels listed are no longer considered to be within the 100-Year Flood Risk Zone as a result of the parcel review process. As of July 2022, this list is updated on an ongoing basis. Check the SFPUC website for any changes to this schedule. The boundaries of this zone align with San Francisco parcel boundaries. The user should confirm proper projection or use of the webmap at https://sfpuc.org/learning/emergency-preparedness/flood-maps to properly identify parcels within the flood zone.

A. SUMMARY The Department of Public Health and the Mayor’s Office of Housing and Community Development, with support from the Planning Department, created these 41 neighborhoods by grouping 2010 Census tracts, using common real estate and residents’ definitions for the purpose of providing consistency in the analysis and reporting of socio-economic, demographic, and environmental data, and data on City-funded programs and services. These neighborhoods are not codified in Planning Code nor Administrative Code, although this map is referenced in Planning Code Section 415 as the “American Community Survey Neighborhood Profile Boundaries Map. Note: These are NOT statistical boundaries as they are not controlled for population size. This is also NOT an official map of neighborhood boundaries in SF but an aggregation of Census tracts and should be used in conjunction with other spatial boundaries for decision making. B. HOW THE DATASET IS CREATED This dataset is produced by assigning Census tracts to neighborhoods based on existing neighborhood definitions used by Planning and MOHCD. A qualitative assessment is made to identify the appropriate neighborhood for a given tract based on understanding of population distribution and significant landmarks. Once all tracts have been assigned a neighborhood, the tracts are dissolved to produce this dataset, Analysis Neighborhoods. C. UPDATE PROCESS This dataset is static. Changes to the analysis neighborhood boundaries will be evaluated as needed by the Analysis Neighborhood working group led by DataSF and the Planning department and includes staff from various other city departments. Contact us for any questions. D. HOW TO USE THIS DATASET Downloading this dataset and opening it in Excel may cause some of the data values to be lost or not display properly (particularly the Analysis Neighborhood column). For a simple list of Analysis Neighborhoods without geographic coordinates, click here: https://data.sfgov.org/resource/xfcw-9evu.csv?$select=nhood E. RELATED DATASETS 2020 Census tracts assigned a neighborhood 2010 Census tracts assigned a neighborhood

A. SUMMARY This dataset includes COVID-19 tests by resident neighborhood and specimen collection date (the day the test was collected). Specifically, this dataset includes tests of San Francisco residents who listed a San Francisco home address at the time of testing. These resident addresses were then geo-located and mapped to neighborhoods. The resident address associated with each test is hand-entered and susceptible to errors, therefore neighborhood data should be interpreted as an approximation, not a precise nor comprehensive total.

In recent months, about 5% of tests are missing addresses and therefore cannot be included in any neighborhood totals. In earlier months, more tests were missing address data. Because of this high percentage of tests missing resident address data, this neighborhood testing data for March, April, and May should be interpreted with caution (see below)

Percentage of tests missing address information, by month in 2020 Mar - 33.6% Apr - 25.9% May - 11.1% Jun - 7.2% Jul - 5.8% Aug - 5.4% Sep - 5.1% Oct (Oct 1-12) - 5.1%

To protect the privacy of residents, the City does not disclose the number of tests in neighborhoods with resident populations of fewer than 1,000 people. These neighborhoods are omitted from the data (they include Golden Gate Park, John McLaren Park, and Lands End).

Tests for residents that listed a Skilled Nursing Facility as their home address are not included in this neighborhood-level testing data. Skilled Nursing Facilities have required and repeated testing of residents, which would change neighborhood trends and not reflect the broader neighborhood's testing data.

This data was de-duplicated by individual and date, so if a person gets tested multiple times on different dates, all tests will be included in this dataset (on the day each test was collected).

The total number of positive test results is not equal to the total number of COVID-19 cases in San Francisco. During this investigation, some test results are found to be for persons living outside of San Francisco and some people in San Francisco may be tested multiple times (which is common). To see the number of new confirmed cases by neighborhood, reference this map: https://sf.gov/data/covid-19-case-maps#new-cases-maps

B. HOW THE DATASET IS CREATED COVID-19 laboratory test data is based on electronic laboratory test reports. Deduplication, quality assurance measures and other data verification processes maximize accuracy of laboratory test information. All testing data is then geo-coded by resident address. Then data is aggregated by analysis neighborhood and specimen collection date.

Data are prepared by close of business Monday through Saturday for public display.

C. UPDATE PROCESS Updates automatically at 05:00 Pacific Time each day. Redundant runs are scheduled at 07:00 and 09:00 in case of pipeline failure.

D. HOW TO USE THIS DATASET San Francisco population estimates for geographic regions can be found in a view based on the San Francisco Population and Demographic Census dataset. These population estimates are from the 2016-2020 5-year American Community Survey (ACS).

Due to the high degree of variation in the time needed to complete tests by different labs there is a delay in this reporting. On March 24 the Health Officer ordered all labs in the City to report complete COVID-19 testing information to the local and state health departments.

In order to track trends over time, a data user can analyze this data by "specimen_collection_date".

Calculating Percent Positivity: The positivity rate is the percentage of tests that return a positive result for COVID-19 (positive tests divided by the sum of positive and negative tests). Indeterminate results, which could not conclusively determine whether COVID-19 virus was present, are not included in the calculation of pe

description: This part of DS 781 presents data for the geologic and geomorphic map of the Offshore of San Francisco map area, California. The polygon shapefile is included in "Geology_OffshoreSanFrancisco.zip," which is accessible from http://pubs.usgs.gov/ds/781/OffshoreSanFrancisco/data_catalog_OffshoreSanFrancisco.html. The Offshore of San Francisco map area includes the Golden Gate inlet which connects the Pacific Ocean and San Francisco Bay. San Francisco Bay, the largest estuary on the U.S. west coast, is located at the mouth of the Sacramento and San Joaquin rivers and drains over 40 percent of the state of California. The large surface area of the bay and diurnal tidal range of 1.78 m creates an enormous tidal prism (about 2 billion cu m) and strong tidal currents, commonly exceeding 2.5 m/s (Barnard and others, 2006a, 2006b, 2007). Acceleration of these currents through the constricted inlet has led to scouring of a bedrock channel that has a maximum depth of 113 m. Large fields of sand waves (Barnard and others, 2007) (unit Qmsw) have formed both west and east of this channel as flow expands and tidal currents decelerate. Active tidally influenced map units inside San Francisco Bay also include sand-dominated deposits (unit Qbs) and more coarse-grained sand, gravel, and pebble deposits (unit Qbsc). Sand wave fields resulting from tidal flow are also present in the nearshore along the Pacific Coast, both north and south of the Golden Gate inlet. The sand wave fields appear to be variably mobilized by both ebb and flood tides, but the presence of a large (~150 sq km) ebb-tidal delta at the mouth of the bay west of the inlet indicates net sediment transport has been to the west. The ebb-tidal delta west of the Golden Gate inlet is mapped as two units. The inner part of the delta (unit Qmst) comprises a semi-circular, inward-sloping (i.e., toward the Golden Gate inlet), sandy seafloor at water depths of about 12 to 24 m. This inner delta has a notably smooth surface, indicating sediment transport and deposition under different flow regimes (defined by tidal current strength and depth) than those in which the sand waves formed and are maintained. Further deceleration of tidal currents beyond the inner delta has led to development of a large, shoaling (about 8 to 12 m water depth), horse-shoe shaped, delta-mouth bar (unit Qmsb). This feature (the "San Francisco Bar") surrounds the inner delta, and its central crest is cut by a dredged shipping channel that separates the nothern and southern parts of the bar, the "North Bar" and "South Bar," respectively. The San Francisco Bar is shaped by both tidal currents and waves, which regularly exceed 6 m in height on the continental shelf during major winter storms (Barnard and others, 2007). This mix of tidal and wave influence results in a variably hummocky, mottled, and rilled seafloor, and this surface texture is used as a primary criteria for mapping the unit and defining its boundaries. Outside the San Francisco Bar to the limits of the map area, the notably flat shelf (less than 0.2 degrees) and the nearshore are wave-dominated and characterized by sandy marine sediment (unit Qms). Local zones of wave-winnowed (?) coarser sediment (unit Qmsc) indicated by high backscatter occur along the coast offshore Ocean Beach. Unit Qmsc is also mapped inside and at the mouth of the Golden Gate inlet where it presumably results from winnowing by strong tidal currents. Coarser sediment also occurs as winnowed lags in rippled scour depressions (unit Qmss), recognized on the basis of high-resolution bathymetry and backscatter. These depressions are typically a few tens of centimeters deep and are bounded by mobile sand sheets (for example, Cacchione and others, 1984). This unit occurs primarily in the nearshore south of the Golden Gate inlet offshore of Ocean Beach (water depth less than 13 m) and north of the inlet offshore Muir Beach (water depth less than 17 m). Artificial seafloor (unit af) has several distinct map occurrences: (1) sites of active sand mining inside San Francisco Bay; (2) the dredged shipping channel at the central crest of the San Francisco Bar; (3) the sewage outfall pipe, associated rip rap, and surrounding scour channel offshore Ocean Beach; and (4) the location of a former waste disposal site about 2.5 km offshore Point Lobos. Although the map shows the areas in which several active sedimentary units (Qmsw, Qmst, Qmsb, Qms, Qmsc, Qmss, Qbsm, Qbsc) presently occur, it is important to note that map units and contacts are dynamic and ephemeral, likely to change during large storms, and on seasonal to decadal scales based on changing external forces such as weather, climate, sea level, and sediment supply. Dallas and Barnard (2011) have noted, for example, that the ebb-tidal delta has dramatically shrunk since 1873 when the first bathymetric survey of the area was undertaken. They document an approximate 1 km landward migration of the crest of the San Francisco Bar, which they attribute to a reduction in the tidal prism of San Francisco Bay and a decrease in coastal sediment. Map unit polygons were digitized over underlying 2-meter base layers developed from multibeam bathymetry and backscatter data. The bathymetry and backscatter data were collected between 2006 and 2010. References Cited Barnard, P.L., Eshelman, J., Erikson, L., and Hanes, D.M., 2007, Coastal processes study at Ocean Beach, San Francisco, CA: Summary of data collection 2004-2006: U.S. Geological Survey Open-File Report 2007-1217, 165 p. Barnard, P.L., Hanes, D.M., Kvitek, R.G., and Iampietro, P.J., 2006a, Sand waves at the mouth of San Francisco Bay, California: U.S. Geological Survey Scientific Investigations Map 2944, 5 sheets. Barnard, P.L., Hanes, D.M., Rubin, D.M., and Kvitek, R.G., 2006b, Giant sand waves at the mouth of San Francisco Bay: EOS, V. 87, p. 285, 289. Cacchione, D.A., Drake, D.E., Grant, W.D., and Tate, G.B., 1984. Rippled scour depressions of the inner continental shelf off central California: Journal of Sedimentary Petrology, v 54, p. 1280-1291. Dallas, K.L., and Barnard, P.L., 2011, Anthropogenic influences on shoreline and nearshore evolution in the San Francisco coastal system: Estuarine Coastal and Shelf Science, v. 92, p. 195-204.; abstract: This part of DS 781 presents data for the geologic and geomorphic map of the Offshore of San Francisco map area, California. The polygon shapefile is included in "Geology_OffshoreSanFrancisco.zip," which is accessible from http://pubs.usgs.gov/ds/781/OffshoreSanFrancisco/data_catalog_OffshoreSanFrancisco.html. The Offshore of San Francisco map area includes the Golden Gate inlet which connects the Pacific Ocean and San Francisco Bay. San Francisco Bay, the largest estuary on the U.S. west coast, is located at the mouth of the Sacramento and San Joaquin rivers and drains over 40 percent of the state of California. The large surface area of the bay and diurnal tidal range of 1.78 m creates an enormous tidal prism (about 2 billion cu m) and strong tidal currents, commonly exceeding 2.5 m/s (Barnard and others, 2006a, 2006b, 2007). Acceleration of these currents through the constricted inlet has led to scouring of a bedrock channel that has a maximum depth of 113 m. Large fields of sand waves (Barnard and others, 2007) (unit Qmsw) have formed both west and east of this channel as flow expands and tidal currents decelerate. Active tidally influenced map units inside San Francisco Bay also include sand-dominated deposits (unit Qbs) and more coarse-grained sand, gravel, and pebble deposits (unit Qbsc). Sand wave fields resulting from tidal flow are also present in the nearshore along the Pacific Coast, both north and south of the Golden Gate inlet. The sand wave fields appear to be variably mobilized by both ebb and flood tides, but the presence of a large (~150 sq km) ebb-tidal delta at the mouth of the bay west of the inlet indicates net sediment transport has been to the west. The ebb-tidal delta west of the Golden Gate inlet is mapped as two units. The inner part of the delta (unit Qmst) comprises a semi-circular, inward-sloping (i.e., toward the Golden Gate inlet), sandy seafloor at water depths of about 12 to 24 m. This inner delta has a notably smooth surface, indicating sediment transport and deposition under different flow regimes (defined by tidal current strength and depth) than those in which the sand waves formed and are maintained. Further deceleration of tidal currents beyond the inner delta has led to development of a large, shoaling (about 8 to 12 m water depth), horse-shoe shaped, delta-mouth bar (unit Qmsb). This feature (the "San Francisco Bar") surrounds the inner delta, and its central crest is cut by a dredged shipping channel that separates the nothern and southern parts of the bar, the "North Bar" and "South Bar," respectively. The San Francisco Bar is shaped by both tidal currents and waves, which regularly exceed 6 m in height on the continental shelf during major winter storms (Barnard and others, 2007). This mix of tidal and wave influence results in a variably hummocky, mottled, and rilled seafloor, and this surface texture is used as a primary criteria for mapping the unit and defining its boundaries. Outside the San Francisco Bar to the limits of the map area, the notably flat shelf (less than 0.2 degrees) and the nearshore are wave-dominated and characterized by sandy marine sediment (unit Qms). Local zones of wave-winnowed (?) coarser sediment (unit Qmsc) indicated by high backscatter occur along the coast offshore Ocean Beach. Unit Qmsc is also mapped inside and at the mouth of the Golden Gate inlet where it presumably results from winnowing by strong tidal currents. Coarser sediment also occurs as winnowed lags in rippled scour depressions (unit Qmss), recognized on the basis of high-resolution bathymetry and backscatter. These depressions are typically a few tens of centimeters deep and

A. SUMMARY Census blocks, the smallest geographic area for which the Bureau of the Census collects and tabulates decennial census data, are formed by streets, roads, railroads, streams and other bodies of water, other visible physical and cultural features, and the legal boundaries shown on Census Bureau maps. More information on the census tracts can be found here.

B. HOW THE DATASET IS CREATED The boundaries are uploaded from TIGER/Line shapefiles provided by the U.S. Census Bureau.

C. UPDATE PROCESS This dataset is static. Changes to the census blocks are tracked in multiple datasets. See here for 2000 and 2010 census tract boundaries.

D. HOW TO USE THIS DATASET This boundary file can be joined to other census datasets on GEOID. Column descriptions can be found on in the technical documentation included on the census.gov website

E. RELATED DATASETS Census 2020: Census Tracts for San Francisco Analysis Neighborhoods - 2020 census tracts assigned to neighborhoods Census 2020: Blocks for San Francisco Clipped to SF Shoreline Census 2020: Blocks Groups for San Francisco Census 2020: Blocks Groups for San Francisco Clipped to SF Shoreline

This is a digital Seismic Hazard Zone Map presenting areas where liquefaction and landslides may occur during a strong earthquake. Three types of geological hazards, referred to as seismic hazard zones, may be featured on the map: 1) liquefaction, 2) earthquake-induced landslides, and 3) overlapping liquefaction and earthquake-induced landslides. Developers of properties falling within any of the three zones may be required to investigate the potential hazard and mitigate its threat during the local permitting process.

This project traces the history of urban planning in San Francisco, placing special emphasis on unrealized schemes. Rather than using visual material simply to illustrate outcomes, Imagined San Francisco uses historical plans, maps, architectural renderings, and photographs to show what might have been. By enabling users to layer a series of urban plans, the project presents the city not only as a sequence of material changes, but also as a contingent process and a battleground for political power. Savvy institutional actors--like banks, developers, and many public officials--understood that in some cases to clearly articulate their interests would be to invite challenges. That means that textual sources like newspapers and municipal reports are limited in what they can tell researchers about the shape of political power. Urban plans, however, often speak volumes about interests and dynamics upon which textual sources remain silent. Mortgage lenders, for example, apparently thought it unwise to state that they wished to see a poor neighborhood cleared, to be replaced with a freeway onramp. Yet visual analysis of planning proposals makes that interest plain. So in the process of showing how the city might have looked, Imagined San Francisco also shows how political power actually was negotiated and exercised.

Description:
This repository contains 90-meter resolution raster maps generated as part of the study titled “Soil Health in Latin America and the Caribbean”. These datasets provide geospatial information on soil health and its five primary functions across the Latin America and Caribbean (LAC) region. The data aim to support research, policy-making, and land management practices by offering insights into soil health conditions and functionality at a continental scale.

Data Included:

• Soil Health Index (SHI):

  • LAC_SHI: Comprehensive index integrating physical, chemical, and biological soil attributes to assess soil health across LAC (Size 3.19 Gb).

• Soil Functions (f):

  • LAC_fi: Storage and regulation of nutrient fluxes and availability (Size 2.12 Gb).

  • LAC_fii: Regulation of water fluxes, storage, and availability (Size 2.59 Gb).

  • LAC_fiii: Soil organic carbon sequestration and biodiversity support (Size 1.94 Gb).

  • LAC_fiv: Physical support for plant growth (Size 2.48 Gb).

  • LAC_fv: Resistance to erosion and degradation (Size 2.42 Gb).

Format:

• Raster maps in GeoTIFF format (*.tif).

• Spatial resolution: 90 meters.

• Coordinate reference system: EPSG:4326 (WGS 84).

• Scale factor: 0.01

Use and applications:

• Environmental research and modeling.

• Policy development for soil conservation and sustainable land management.

• Educational purposes in soil science and geospatial studies.

Visualization and other sources:
Additionally, the Soil Health Index (SHI) and soil functions (SF) maps can be visualized via the Earth Engine application at https://geocis.users.earthengine.app/view/lac-soil-health and downloaded from https://geocis.users.earthengine.app/view/lac-soil-health-download. For more information, access it on the GeoCiS website, available at https://esalqgeocis.wixsite.com/english/thematic-products.

Acknowledgments:
We thank the São Paulo Research Foundation (FAPESP, process 2014/22262-0; 2021/05129-8), the Center for Carbon Research in Tropical Agriculture (CCARBON/USP, process 2021/10573-4) and the Geotechnologies in Soil Science research group (GeoCiS, https://esalqgeocis.wixsite.com/english) for supporting this work.

This part of DS 781 presents the seafloor-character map (see sheet 5) Offshore of San Francisco, California (raster data file is included in "SFC_OffshoreSanFrancisco.zip," which is accessible from https://pubs.usgs.gov/ds/781/OffshoreSanFrancisco/data_catalog_OffshoreSanFrancisco.html). These data accompany the pamphlet and map sheets of Cochrane, G.R., Johnson, S.Y., Dartnell, P., Greene, H.G., Erdey, M.D., Golden, N.E., Hartwell, S.R., Endris, C.A., Manson, M.W., Sliter, R.W., Kvitek, R.G., Watt, J.T., Ross, S.L., and Bruns, T.R. (G.R. Cochrane and S.A. Cochran, eds.), 2015, California State Waters Map Series—Offshore of San Francisco, California (ver. 1.1, June 2015): U.S. Geological Survey Open-File Report 2015–1068, pamphlet 39 p., 10 sheets, scale 1:24,000, https://dx.doi.org/10.3133/ofr20151068. This raster-format seafloor-character map shows six substrate classes of Offshore of San Francisco, California. The substrate classes mapped in this area have been further divided into the following California Marine Life Protection Act depth zones and slope classes: Depth Zone 2 (intertidal to 30 m), Depth Zone 3 (30 to 100 m), Depth Zone 4 (100 to 200 m), Slope Class 1 (0 degrees - 5 degrees), and Slope Class 2 (5 degrees - 30 degrees). Depth Zone 1 (intertidal), Depth Zone 5 (greater than 200 m), and Slopes Classes 3-4 (greater than 30 degrees) are not present in the region covered by this block. The map is created using a supervised classification method described by Cochrane (2008). References Cited: California Department of Fish and Game, 2008, California Marine Life Protection Act master plan for marine protected areas; Revised draft: California Department of Fish and Game, accessed April 5 2011, at http://www.dfg.ca.gov/mlpa/masterplan.asp. Cochrane, G.R., 2008, Video-supervised classification of sonar data for mapping seafloor habitat, in Reynolds, J.R., and Greene, H.G., eds., Marine habitat mapping technology for Alaska: Fairbanks, University of Alaska, Alaska Sea Grant College Program, p. 185-194, accessed April 5, 2011, at http://doc.nprb.org/web/research/research%20pubs/615_habitat_mapping_workshop/Individual%20Chapters%20High-Res/Ch13%20Cochrane.pdf. Sappington, J.M., Longshore, K.M., and Thompson, D.B., 2007, Quantifying landscape ruggedness for animal habitat analysis--A case study using bighorn sheep in the Mojave Desert: Journal of Wildlife Management, v. 71, p. 1419-1426.

This project traces the history of urban planning in San Francisco, placing special emphasis on unrealized schemes. Rather than using visual material simply to illustrate outcomes, Imagined San Francisco uses historical plans, maps, architectural renderings, and photographs to show what might have been. By enabling users to layer a series of urban plans, the project presents the city not only as a sequence of material changes, but also as a contingent process and a battleground for political power. Savvy institutional actors--like banks, developers, and many public officials--understood that in some cases to clearly articulate their interests would be to invite challenges. That means that textual sources like newspapers and municipal reports are limited in what they can tell researchers about the shape of political power. Urban plans, however, often speak volumes about interests and dynamics upon which textual sources remain silent. Mortgage lenders, for example, apparently thought it unwise to state that they wished to see a poor neighborhood cleared, to be replaced with a freeway onramp. Yet visual analysis of planning proposals makes that interest plain. So in the process of showing how the city might have looked, Imagined San Francisco also shows how political power actually was negotiated and exercised.

This part of DS 781 presents data for the geologic and geomorphic map of the Offshore of Bolinas map area, California. The vector data file is included in "Geology_OffshoreBolinas.zip," which is accessible from http://pubs.usgs.gov/ds/781/OffshoreBolinas/data_catalog_OffshoreBolinas.html. The continental shelf within California's State waters in the Bolinas area is relatively flat (less than 0.3 degrees) and shallow (less than 30 m) in the entire area, however the seafloor of the "Marin shelf" east of the San Andreas Fault (see below) is smooth and covered with sediment, whereas the seafloor of the "Bolinas shelf" west of this fault has extensive bedrock outcrop from the nearshore to depths of about 25 m and much less sediment cover. The morphology and geology of this shelf result from the interplay between local tectonics, sea-level rise, sedimentary processes, and oceanography. Tectonic influences are related to local faulting, folding, uplift, and subsidence (see below). Sea level has risen about 125 to 130 m over about the last 21,000 years (for example, Lambeck and Chappel, 2001; Gornitz, 2009), leading to progressive eastward migration (a few tens of km) of the shoreline and wave-cut platform, and associated transgressive erosion and deposition (for example, Catuneanu, 2006). The Offshore of Bolinas map area is now subjected to full, and sometimes severe, Pacific Ocean wave energy and strong currents. Given their relatively shallow depths and exposure to high wave energy, modern shelf sediments are mostly sand (unit Qms). More coarse-grained sands and gravels (units Qmsc and Qmss) are primarily recognized on the basis of bathymetry and high backscatter (see Bathymetry--Offshore Bolinas, California and Backscattter A to E--Offshore Bolinas, California, DS 781, for more information). Unit Qmsc occurs in two areas, on the east flank of Bolinas shelf bedrock exposures, and as three mounds south of Bolinas near the outer boundary of California's State Waters at water depths of about 25 m. The largest of these mounds is about 450 m long and 70 m wide, and has 80 cm of positive relief above the seafloor. Unit Qmss is much more extensive and forms erosional lags in rippled scour depressions (for example, Cacchione and others, 1984) that are typically a few tens of centimeters deep and bounded by mobile sand sheets. The depressions occur in four distinct locations. (1) The first location lies adjacent to bedrock outcrops within 2 km of the shoreline south of Double Point (along the western edge of the map area) at water depths of 10 to 25 m. (2) The second unit Qmss location is about 2 to 6 km south of Bolinas Lagoon at similar water depths, along the eastern flank of the Bolinas shelf. (3) The third, more restricted location, occurs about 3 km southeast of Rocky Point at water depths of about 10 to 12 m along the eastern edge of the map area, adjacent to and offshore of small bedrock uplifts. (4) The fourth location, 2 km south of Stinson Beach, is notably different. The polygon on the map encloses a field that includes more than one hundred, much smaller (length less than 20 m) oval depressions and intervening sand flats, perhaps an originally much larger field that has been almost completely filled in by sediment. Similar unit Qmss rippled-scour depressions are common along this stretch of the California coast where offshore sandy sediment can be relatively thin (thus unable to fill the depressions) due to both lack of river input and to significant erosion and transport of sediment during large northwest winter swells. Although the general areas in which both unit Qmss scour depressions and surrounding mobile sand sheets occur are not likely to change substantially, the boundaries of the unit(s) are likely ephemeral, changing seasonally and during significant storm events. Areas where shelf sediments form thin (less than 2.5 m) veneers over low-relief Neogene bedrock (see below) occur in the western half of the map and are mapped as units Qms/Tsc (Santa Cruz Mudstone) and Qms/Tp? (Purisima Formation, queried). These hybrid units are recognized and delineated based on the combination of flat relief, continuity with moderate to high relief onshore or offshore bedrock outcrops, high-resolution seismic-reflection data (see field activities S-8-09-NC and L-1-06-SF), and in some cases moderate to high backscatter. The thin sediment layer is regarded as ephemeral and dynamic, and may or may not be present at a specific location based on storms, seasonal/annual patterns of sediment movement, or longer-term climate cycles. In a nearby, similarly high-energy setting, Storlazzi and others (2011) have described seasonal burial and exhumation of submerged bedrock in northern Monterey Bay. The southeastern corner of the map area includes a portion of the outer flank of the horseshoe-shaped "San Francisco Bar" (unit Qmsb), which has formed at the mouth of the San Francisco ebb-tidal delta (Barnard and others, 2007; Dallas and Barnard, 2011). This delta-mouth bar is shaped by both tidal currents and waves, resulting in a variably hummocky, mottled, and rilled seafloor, and this surface texture is used as a primary criteria for mapping the unit and defining its contacts. Map unit polygons were digitized over underlying 2-meter base layers developed from multibeam bathymetry and backscatter data (see Bathymetry--Offshore Bolinas, California and Backscattter A to E--Offshore Bolinas, California, DS 781, for more information). The bathymetry and backscatter data were collected between 2006 and 2010. References Cited Barnard, P.L., Eshelman, J., Erikson, L., and Hanes, D.M., 2007, Coastal processes study at Ocean Beach, San Francisco, CA: Summary of data collection 2004-2006: U.S. Geological Survey Open-File Report 2007-1217, 165 p. Cacchione, D.A., Drake, D.E., Grant, W.D., and Tate, G.B., 1984. Rippled scour depressions of the inner continental shelf off central California: Journal of Sedimentary Petrology, v 54, p. 1280-1291. Catuneanu, O., 2006, Principles of Sequence Stratigraphy: Amsterdam, Elsevier, 375 p. Dallas, K.L., and Barnard, P.L., 2011, Anthropogenic influences on shoreline and nearshore evolution in the San Francisco coastal system: Estuarine Coastal and Shelf Science, v. 92, p. 195-204. Gornitz, V., 2009, Sea level change, post-glacial, in Gornitz, V., ed., Encyclopedia of Paleoclimatology and Ancient Environments: Encyclopedia of Earth Sciences Series. Springer, pp. 887-893. Lambeck, K., and Chappell, J., 2001, Sea level change through the last glacial cycle: Science, v. 292, p. 679-686. Storlazzi, C.D., Fregoso, T.A., Golden, N.E., and Finlayson, D.P., 2011, Sediment dynamics and the burial and exhumation of bedrock reefs along on emergent coastline as elucidated by repretitive sonar surveys, northern Monterey Bay, CA: Marine Geology, v. 289, p. 46-59.

The current understanding of star formation (SF) contemplates that the regulation of this phenomenon in galaxy disks reflects a complex balance between processes that operate in molecular gas on local cloud-scales but also on global disk-scales. We analyse the influence of the dynamical environment on the SF relations of the dense molecular gas in the starburst (SB) ring of the Seyfert 2 galaxy NGC 1068. We used ALMA to image the emission of the 1-0 transitions of HCN and HCO^+^, which trace dense molecular gas in the r~1.3kpc SB ring of NGC 1068, with a resolution of 56pc. We also used ancillary data of CO(1-0), as well as CO(3-2) and its underlying continuum emission at the resolutions of 100pc and 40pc, respectively. These observations allow us to probe a wide range of molecular gas densities (n_H2_~10^3-5^cm^-3^). The SF rate (SFR) in the SB ring of NGC 1068 is derived from Pa{alpha} line emission imaged by HST/NICMOS. We analysed how different formulations of SF relations change depending on the adopted aperture sizes and on the choice of molecular gas tracer. The scatter in the Kennicutt-Schmidt relation, linking the SFR density (SFR) with the (dense) molecular gas surface density ({SIGMA}dense), is about a factor of two to three lower for the HCN and HCO^+^ lines compared to that derived from CO(1-0) for a common aperture. Correlations lose statistical significance below a critical spatial scale 300-400pc for all gas tracers. The efficiency of SF of the dense molecular gas, defined as SFE_dense_={SIGMA}SFR_/{SIGMA}dense, shows a scattered distribution as a function of the HCN luminosity (L0(HCN)) around a mean value of ~=0.01Myr^-1^. An alternative prescription for SF relations, which includes the dependence of SFE_dense_ on the combination of {SIGMA}dense and the velocity dispersion ({sigma}), resolves the degeneracy associated with the SFE_dense_-L'(HCN) plot. The SFE_dense_ values show a positive trend with the boundedness of the gas, measured by the parameter b={SIGMA}dense/{sigma}^2^.We identify two branches in the SFE_dense_-b plot that correspond to two dynamical environments within the SB ring, which are defined by their proximity to the region where the spiral structure is connected to the stellar bar. This region corresponds to the crossing of two overlapping density wave resonances, where an increased rate of cloud-cloud collisions would favour an enhanced compression of molecular gas. These results suggest that galactic dynamics plays a major role in the efficiency of the gas conversion into stars. Our work adds supporting evidence that density-threshold star formation models, which argue that the SFE_dense_ should be roughly constant, fail to account for spatially resolved SF relations of dense gas in the SB ring of NGC 1068.