Polygons of public and private school sites. Includes elementary, middle, and high schools. Intended use is mapping and general analysis purposes. Provided by SANGIS and hosted by H-Hub at UC San Diego. School site addresses maintained by California Department of Education (CDE) were geocoded to the SanGIS parcels and street centerlines. Using aerial imagery, online research, site visits, and/or over-the-phone verification, attributes were corrected and/or polygons were adjusted to best represent the portion of the parcel in active use for school-related activities. This layer includes active schools.

Existing (2023) SANDAG land use of San Diego County. Hosted by H-Hub at UC San Diego.SANDAG performs an annual land use and housing unit inventory in the interest of maintaining a robust and accurate catalog of the existing conditions for any given year. This catalog of snapshots are the base year inputs to SANDAG’s Regional Demographic, Economic, and Land Use Models. Prior to performing our annual inventory, the polygon geometry for the regionwide layer is updated with a snapshot of parcels from SanGIS, representative of January 1st of the given year. The land use information has been updated using aerial photography, the County Assessor Master Property Records file, and other ancillary information. To make the LANDUSE_2019 feature class, adjacent parcel polygons with the same land use have been aggregated (dissolved) into a single feature.

Data sourced from City of San Diego Get-It-Done / 3-1-1 service. The CSV file provided by 3-1-1 was filtered by service type ("Encampment") and geocoded by address if latitude/longitude are not provided. Some addresses and latitude/longitudes may be incorrect due to the nature of the Get-It-Done / 3-1-1 user input field.

Polygon file of Transit Priority Areas in the City of San Diego, California, USA. Provided by SANGIS and hosted by H-Hub at UC San Diego.Transit Priority Areas in the City of San Diego, California, USA. Provided by SANGIS and hosted by H-Hub at UC San Diego.Transit Priority Areas is based on the adopted SANDAG 2050 Regional Transportation Plan (RTP).In accordance with SB743,“Transit priority area” means “an area within one-half mile of a major transit stop that is existing or planned, if the planned stop is scheduled to be completed within the planning horizon included in a Transportation Improvement Program adopted pursuant to Section 450.216 or 450.322 of Title23 of the Code of Federal Regulations.”

Homelessness Hub is a research entity in the Department of Urban Studies and Planning at UC San Diego. Our mission is to engage in research and education that support impactful solutions to end homelessness. Research on reducing housing loss, including preventable displacement, is critical to this mission. This research was supported by a County of San Diego Board of Supervisors Community Enhancement Grant.

The Walkability Index is a quantitative tool used to measure the pedestrian environment within a geographic area. The Walkability Index is based on a model developed for King County, Washington and Baltimore, Maryland (see reference paper at http://sallis.ucsd.edu/Documents/Pubs_documents/NQLS_Frank%20et%20al%20published%20walkability%20paper.pdf). It includes four components: land use mix, residential density, retail density, and intersection density. Higher scores represent more walkable areas. This data shows the Walkability Index score for each census tract in Los Angeles. Tracts in the Central City, Westlake, Hollywood, and Venice Community Plan Areas have the highest relative Index scores.Use field "Walkabil_1" to project scores and correct features for legend. To learn more about this Index, please review the complete Health Atlas for the City of Los Angeles (pp. 85-86 and 91) or Chapter 7: Land Use of the Health Atlas, both available for download at http://healthyplan.la/the-health-atlas/.Produced by Raimi + Associates for the City of Los Angeles and Los Angeles County. Made possible with funding from the Centers for Disease Control and Prevention through the Los Angeles County Department of Public Health and The California Endowment, May 2013.

These layers are polar climatological and other summary environmental layers that may be useful for purposes such as general modelling, regionalisation, and exploratory analyses. All of the layers in this collection are provided on a consistent 0.1-degree grid, which covers -180 to 180E, 80S to 30S (Antarctic) and 45N to 90N (Arctic). As far as practicable, each layer is provided for both the Arctic and Antarctic regions. Where possible, these have been derived from the same source data; otherwise, source data have been chosen to be as compatible as possible between the two regions. Some layers are provided for only one of the two regions.

Each data layer is provided in netCDF and ArcInfo ASCII grid format. A png preview map of each is also provided.

Processing details for each layer:

Bathymetry File: bathymetry Measured and estimated seafloor topography from satellite altimetry and ship depth soundings. Antarctic: Source data: Smith and Sandwell V13.1 (Sep 4, 2010) Processing steps: Depth data subsampled from original 1-minute resolution to 0.05-degree resolution and interpolated to 0.1-degree grid using bilinear interpolation. Reference: Smith, W. H. F., and D. T. Sandwell (1997) Global seafloor topography from satellite altimetry and ship depth soundings. Science 277:1957-1962. http://topex.ucsd.edu/WWW_html/mar_topo.html Arctic: Source data: ETOPO1 Processing steps: Depth data subsampled to 0.05-degree resolution and interpolated to 0.1-degree grid using bilinear interpolation on polar stereographic projection. Reference: Amante, C. and B. W. Eakins, ETOPO1 1 Arc-Minute Global Relief Model: Procedures, Data Sources and Analysis. NOAA Technical Memorandum NESDIS NGDC-24, 19 pp, March 2009. http://www.ngdc.noaa.gov/mgg/global/global.html

Bathymetry slope File: bathymetry_slope Slope of sea floor, derived from Smith and Sandwell V13.1 and ETOPO1 bathymetry data (above). Processing steps: Slope calculated on 0.1-degree gridded depth data (above). Calculated using the equation given by Burrough, P. A. and McDonell, R.A. (1998) Principles of Geographical Information Systems (Oxford University Press, New York), p. 190 (see http://webhelp.esri.com/arcgisdesktop/9.2/index.cfm?TopicName=How%20Slope%20works)

CAISOM model-derived variables Variables derived from the CAISOM ocean model. This model has been developed by Ben Galton-Fenzi (AAD and ACE-CRC), and is based on the Regional Ocean Modelling System (ROMS). It has circum-Antarctic coverage out to 50S, with a spatial resolution of approximately 5km. The values here are averaged over 12 snapshots from the model, each separated by 2 months. These parameters should be treated as experimental.

Reference: Galton-Fenzi BK, Hunter JR, Coleman R, Marsland SJ, Warner RC (2012) Modeling the basal melting and marine ice accretion of the Amery Ice Shelf. Journal of Geophysical Research: Oceans, 117, C09031. http://dx.doi.org/10.1029/2012jc008214

Floor current speed File: caisom_floor_current_speed Current speed near the sea floor.

Floor temperature File: caisom_floor_temperature Potential temperature near the sea floor.

Floor vertical velocity File: caisom_floor_vertical_velocity Vertical water velocity near the sea floor.

Surface current speed File: caisom_surface_current_speed Near-surface current speed (at approximately 2.5m depth)

Chlorophyll summer File: chl_summer_climatology Source data: Near-surface chl-a summer climatology from MODIS Aqua Antarctic: Climatology spans the 2002/03 to 2009/10 austral summer seasons. Data interpolated from original 9km resolution to 0.1-degree grid using bilinear interpolation. Arctic: Climatology spans the 2002 to 2009 boreal summer seasons. Data interpolated from original 9km resolution to 0.1-degree grid using bilinear interpolation. Reference: Feldman GC, McClain CR (2010) Ocean Color Web, MODIS Aqua Reprocessing, NASA Goddard Space Flight Center. Eds. Kuring, N., Bailey, S.W. https://oceancolor.gsfc.nasa.gov/

Distance to Antarctica File: distance_antarctica Distance to nearest part of Antarctic continent (Antarctic only) Source data: A modified version of ESRI's world map shapefile Processing steps: Distances calculated in km using the Haversine formula on a spherical earth of radius 6378.137km.

Distance to nearest seabird breeding colony (Antarctic only) File: distance_colony Antarctic source data: Inventory of Antarctic seabird breeding sites, collated by Eric Woehler. http://data.aad.gov.au/aadc/biodiversity/display_collection.cfm?collection_id=61. Processing steps: The closest distance of each grid point to the colonies was calculated in km using the Haversine formula on a spherical earth of radius 6378.137km.

Distance to maximum winter sea ice extent File: distance_max_ice_edge Source data: SMMR-SSM/I passive microwave estimates of daily sea ice concentration from the National Snow and Ice Data Center (NSIDC). Processing steps: Antarctic: Mean maximum winter sea ice extent was derived from daily estimates of sea ice concentration as described at https://data.aad.gov.au/metadata/records/sea_ice_extent_winter. The closest distance of each grid point to this extent line was calculated in km using the Haversine formula on a spherical earth of radius 6378.137km. Arctic: The median March winter sea ice extent was obtained from the NSIDC at http://nsidc.org/data/g02135.html. The closest distance of each grid point to this extent line was calculated in km using the Haversine formula on a spherical earth of radius 6378.137km. Reference: Cavalieri, D., C. Parkinson, P. Gloersen, and H. J. Zwally. 1996, updated 2008. Sea ice concentrations from Nimbus-7 SMMR and DMSP SSM/I passive microwave data. Boulder, Colorado USA: National Snow and Ice Data Center. Digital media. tp://nsidc.org/data/nsidc-0051.html

Distance to shelf break File: distance_shelf Distance to nearest area of sea floor of depth 500m or less. Derived from Smith and Sandwell V13.1 and ETOPO1 bathymetry data (above). Processing steps: Distances calculated in km using the Haversine formula on a spherical earth of radius 6378.137km. Points in less than 500m of water (i.e. over the shelf) were assigned negative distances. See also distance to upper slope

Distance to subantarctic islands (Antarctic only) File: distance_subantarctic_islands Distance to nearest land mass north of 65S (includes land masses of e.g. South America, Africa, Australia, and New Zealand). Processing steps: Distances calculated in km using the Haversine formula on a spherical earth of radius 6378.137km.

Distance to canyon File: distance_to_canyon Distance to the axis of the nearest canyon (Antarctic only) Source data: O'Brien and Post (2010) seafloor geomorphic feature dataset, expanded from O'Brien et al. (2009). Mapping based on GEBCO contours, ETOPO2, seismic lines. Processing steps: Distances to nearest canyon axis calculated in km using the Haversine formula on a spherical earth of radius 6378.137km. NOTE: source data extend only as far north as 45S. Do not rely on this layer near or north of 45S. Reference: O'Brien, P.E., Post, A.L., and Romeyn, R. (2009) Antarctic-wide geomorphology as an aid to habitat mapping and locating vulnerable marine ecosystems. CCAMLR VME Workshop 2009. Document WS-VME-09/10

Distance to polynya File: distance_to_polynya Distance to the nearest polynya area (Antarctic only) Source data: AMSR-E satellite estimates of daily sea ice concentration at 6.25km resolution Processing steps: The seaice_gt_85 layer (see below) was used. Pixels which were (on average) covered by sea ice for less than 35% of the year were identified. The distance from each grid point on the 0.1-degree grid to the nearest such polynya pixel was calculated in km using the Haversine formula on a spherical earth of radius 6378.137km. (NB the threshold of 35% was chosen to give a good empirical match to the polynya locations identified by Arrigo and van Dijken (2003), although the results were not particularly sensitive to the choice of threshold. Reference: Arrigo KR, van Dijken GL (2003) Phytoplankton dynamics within 37 Antarctic coastal polynya systems. Journal of Geophysical Research, 108, 3271. http://dx.doi.org/10.1029/2002JC001739

Distance to upper slope (Antarctic only) File: distance_upper_slope Distance to the "upper slope" geomorphic feature from the Geoscience Australia geomorphology data set. This is probably a better indication of the distance to the Antarctic continental shelf break than the "distance to shelf break" data (above). Source data: O'Brien and Post (2010) seafloor geomorphic feature dataset, expanded from O'Brien et al. (2009). Mapping based on GEBCO contours, ETOPO2, seismic lines. Processing steps: Distances calculated in km using the Haversine formula on a spherical earth of radius 6378.137km. Points inside of an "upper slope" polygon were assigned negative distances. Reference: O'Brien, P.E., Post, A.L., and Romeyn, R. (2009) Antarctic-wide geomorphology as an aid to habitat mapping and locating vulnerable marine ecosystems. CCAMLR VME Workshop 2009. Document WS-VME-09/10

Fast ice File: fast_ice The average proportion of the year for which landfast sea ice is present in a location Source data: 20-day composite records of East Antarctic landfast sea-ice, derived from MODIS imagery (Fraser at al. 2012) Processing steps: The average proportion of the year for which each pixel was covered by landfast sea ice was calculated as an average across 2001--2008. Data were regridded to the 0.1-degree grid using bilinear interpolation.

Distance to fast ice File: distance_to_fast_ice Distance to the nearest location where fast ice is typically present. Source data: 20-day composite records of East Antarctic landfast sea ice, derived from MODIS imagery (Fraser at al. 2012) Processing steps: Pixels in the landfast sea ice data that were associated with fast ice presence for more than half of the

Public transit stops in San Diego County serviced by the San Diego County Metropolitan Transit System (MTS) and the North County Transit District (NCTD). Provided by SANGIS and hosted by H-Hub at UC San Diego. Current to 2024.Bus, commuter and light rail, and trolley stops developed from the General Transit Feed Specification (GTFS) downloaded from https://www.sdmts.com/google_transit_files/google_transit.zip and https://www.goncts.com/google_transit.zip. GTFS data is provided to the exchange by the transit agencies and processed by SANGIS to create a consolidated GIS layer containing stops for both MTS and NCTD systems. SanGIS uses built-in ArcGIS tools to develop the stops from the STOPS.txt data file. Stop layers for MTS and NCTD are created separately and combined into a single layer using ArcGIS tools.

This dataset is the NOAA NCEI SRTM15+ v2.0 (29 March 2019) clipped to the area containing and immediately surrounding the Gulf of Mexico.To provide an improved mapping of the seafloor fabric globally, NOAA NCEI have used available sounding data along with an improved global marine gravity model to develop at grid at 15 arcsecond resolution (~500 m). Land elevations are based on the best available data from SRTM, ASTER digital elevation models while the ice topography of Greenland and Antarctica is based on CryoSat-2 and IceSat. Ocean bathymetry is based on bathymetric predictions from the latest global gravity model from CryoSat-2 and Jason-1 along with 494 million carefully edited depth soundings at 15 arcsecond resolution.NOAA NCEI have used the bathymetry grid along with the improved gravity to construct a global map of abyssal hill amplitude and orientations and compare the orientations with predictions from seafloor age gradient analysis. Areas of disagreement reveal propagating rifts, microplates, and tectonic reorganizations. This SRTM15_PLUS provides the foundational bathymetry layer for Google Earth and is freely available at NOAA NCEI ftp site (topex.ucsd.edu).