UC Davis Building Footprints

UC Davis Building Footprints

UC Davis Campus Boundary

CDFW BIOS GIS Dataset, Contact: Fraser Shilling, Description: This data layer represents a gradient of connectivity for the San Joaquin Valley -- connectivity meaning an animal's ability to move across the landscape -- prepared in 2011 by the UC Davis Road Ecology Center and UC Davis Information Center for the Environment.

CDFW BIOS GIS Dataset, Contact: Amber Manfree, Description: Since 1979, UC Davis researchers have collected data monthly for the Suisun Marsh Fish and Invertebrate Study, a project initiated by Professor Peter Moyle. This long-term study of fishes and invertebrates has yielded an enormous amount of interrelated data.

UC Davis Tree Database

This vector tile layer provides a detailed vector basemap for the world symbolized with a light gray, neutral background style with minimal colors, labels, and features that is designed to draw attention to your thematic content. This layer is similar in content to the Light Gray Canvas raster basemap, which uses raster fused map cache tile layers. This vector tile layer provides unique capabilities for customization and high-resolution display.This layer includes highways, major roads, minor roads, railways, water features, cities, parks, landmarks, building footprints, and administrative boundaries. The map is built using the same data sources used for the Light Gray Canvas raster basemap and other Esri basemaps. Alignment of boundaries is a presentation of the feature provided by our data vendors and does not imply endorsement by Esri or any governing authority.Use this MapThis map is designed to be used as a basemap layer or reference layer in a web map. You can add this layer to a web map and save as your own map. If you would like to use this map as a basemap in a web map, you may use the vector basemap Light Gray Canvas web map.Customize this MapBecause this map is delivered as a vector tile layer, users can customize the map to change its content and symbology, including fonts. Users are able to turn on and off layers, change symbols for layers, switch to alternate local language (in some areas), and refine the treatment of disputed boundaries. See the Vector Basemap group for other vector tile layers. For details on how to customize this map, please refer to these articles on the ArcGIS Online Blog and view the Esri Vector Basemaps Reference Document.

This dataset includes one file for each of the 51 counties that were collected, as well as a CA_Merged file with the parcels merged into a single file.Note – this data does not include attributes beyond the parcel ID number (PARNO) – that will be provided when available, most likely by the state of California.DownloadA 1.6 GB zipped file geodatabase is available for download - click here.DescriptionA geodatabase with parcel boundaries for 51 (out of 58) counties in the State of California. The original target was to collect data for the close of the 2013 fiscal year. As the collection progressed, it became clear that holding to that time standard was not practical. Out of expediency, the date requirement was relaxed, and the currently available dataset was collected for a majority of the counties. Most of these were distributed with minimal metadata.The table “ParcelInfo” includes the data that the data came into our possession, and our best estimate of the last time the parcel dataset was updated by the original source. Data sets listed as “Downloaded from” were downloaded from a publicly accessible web or FTP site from the county. Other data sets were provided directly to us by the county, though many of them may also be available for direct download. Â These data have been reprojected to California Albers NAD84, but have not been checked for topology, or aligned to county boundaries in any way. Tulare County’s dataset arrived with an undefined projection and was identified as being California State Plane NAD83 (US Feet) and was assigned by ICE as that projection prior to reprojection. Kings County’s dataset was delivered as individual shapefiles for each of the 50 assessor’s books maintained at the county. These were merged to a single feature class prior to importing to the database.The attribute tables were standardized and truncated to include only a PARNO (APN). The format of these fields has been left identical to the original dataset. The Data Interoperablity Extension ETL tool used in this process is included in the zip file. Where provided by the original data sources, metadata for the original data has been maintained. Please note that the attribute table structure changes were made at ICE, UC Davis, not at the original data sources.Parcel Source InformationCountyDateCollecDateCurrenNotesAlameda4/8/20142/13/2014Download from Alamenda CountyAlpine4/22/20141/26/2012Alpine County PlanningAmador5/21/20145/14/2014Amador County Transportation CommissionButte2/24/20141/6/2014Butte County Association of GovernmentsCalaveras5/13/2014Download from Calaveras County, exact date unknown, labelled 2013Contra Costa4/4/20144/4/2014Contra Costa Assessor’s OfficeDel Norte5/13/20145/8/2014Download from Del Norte CountyEl Dorado4/4/20144/3/2014El Dorado County AssessorFresno4/4/20144/4/2014Fresno County AssessorGlenn4/4/201410/13/2013Glenn County Public WorksHumboldt6/3/20144/25/2014Humbodt County AssessorImperial8/4/20147/18/2014Imperial County AssessorKern3/26/20143/16/2014Kern County AssessorKings4/21/20144/14/2014Kings CountyLake7/15/20147/19/2013Lake CountyLassen7/24/20147/24/2014Lassen CountyLos Angeles10/22/201410/9/2014Los Angeles CountyMadera7/28/2014Madera County, Date Current unclear likely 7/2014Marin5/13/20145/1/2014Marin County AssessorMendocino4/21/20143/27/2014Mendocino CountyMerced7/15/20141/16/2014Merced CountyMono4/7/20144/7/2014Mono CountyMonterey5/13/201410/31/2013Download from Monterey CountyNapa4/22/20144/22/2014Napa CountyNevada10/29/201410/26/2014Download from Nevada CountyOrange3/18/20143/18/2014Download from Orange CountyPlacer7/2/20147/2/2014Placer CountyRiverside3/17/20141/6/2014Download from Riverside CountySacramento4/2/20143/12/2014Sacramento CountySan Benito5/12/20144/30/2014San Benito CountySan Bernardino2/12/20142/12/2014Download from San Bernardino CountySan Diego4/18/20144/18/2014San Diego CountySan Francisco5/23/20145/23/2014Download from San Francisco CountySan Joaquin10/13/20147/1/2013San Joaquin County Fiscal year close dataSan Mateo2/12/20142/12/2014San Mateo CountySanta Barbara4/22/20149/17/2013Santa Barbara CountySanta Clara9/5/20143/24/2014Santa Clara County, Required a PRA requestSanta Cruz2/13/201411/13/2014Download from Santa Cruz CountyShasta4/23/20141/6/2014Download from Shasta CountySierra7/15/20141/20/2014Sierra CountySolano4/24/2014Download from Solano Couty, Boundaries appear to be from 2013Sonoma5/19/20144/3/2014Download from Sonoma CountyStanislaus4/23/20141/22/2014Download from Stanislaus CountySutter11/5/201410/14/2014Download from Sutter CountyTehama1/16/201512/9/2014Tehama CountyTrinity12/8/20141/20/2010Download from Trinity County, Note age of data 2010Tulare7/1/20146/24/2014Tulare CountyTuolumne5/13/201410/9/2013Download from Tuolumne CountyVentura11/4/20146/18/2014Download from Ventura CountyYolo11/4/20149/10/2014Download from Yolo CountyYuba11/12/201412/17/2013Download from Yuba County

UC Davis Roads & Pathways

All well locations from all datasets standardized on the GAMA Program's Groundwater Information System (GAMA GIS). This is a replacement of previous versions, updated quarterly. Authoritative version. WGS 84.All groundwater wells on GAMA Groundwater Information System, accessed April 24, 2023. Sources of data include (as indicated in GM_DATA_SOURCE field):Geotracker: Wells sampled under regulated activities like cleanup and remediation. These are accessible through the California State Water Resources Control Board Geotracker web site.USGS: Wells sampled and analyzed by the U.S. Geological Survey (USGS) through the Groundwater Ambient Monitoring and Assessment (GAMA) Program Priority Basin Project.GAMA: Wells sampled by California State Water Resources Control Board staff for the GAMA Program Domestic Well Project.DDW: Division of Drinking Water (DDW) wells sampled and regulated for delivered water quality under DDW oversight.DPR: Wells sampled by the Department of Pesticide Regulation (DPR) groundwater program.WDL: Wells in the Department of Water Resources (DWR) water quality sampling network in their water data library.LLNL: Wells sampled for groundwater age, isotopes, or noble gas for the GAMA Program by Lawrence Livermore National Laboratory (LLNL).NWIS: Wells sampled by the USGS and accessible via the National Water Information System (NWIS).UC Davis: Location of wells gathered from multiple local entities for use in the UC Davis Nitrate Report, under agreement with the GAMA Program.LOCALGW: Wells sampled under various local groundwater projects. As of July 30, 2019, this only includes the domestic sampling completed by the Central Coast Regional Water Quality Control Board. ‘GAMA_LOCALGW: Wells sampled under local groundwater projects, generally sampled from private wells from various private and governmental organizations. Data was submitted through the GAMA Data Connection Portal.The field, GM_DATASET_NAME can also help explain the source of the dataset.The corresponding map image layer for these well locations can be found at the following link: All Wells on the GAMA Groundwater Information System - Overview (ca.gov)Direct any questions to: GAMA@waterboards.ca.gov.

The China County-Level Data on Population (Census) and Agriculture, Keyed To 1:1M GIS Map consists of census, agricultural economic, and boundary data for the administrative regions of China for 1990. The census data includes urban and rural residency, age and sex distribution, educational attainment, illiteracy, marital status, childbirth, mortality, immigration (since 1985), industrial/economic activity, occupation, and ethnicity. The agricultural economic data encompasses rural population, labor force, forestry, livestock and fishery, commodities, equipment, utilities, irrigation, and output value. The boundary data are at a scale of one to one million (1:1M) at the county level. This data set is produced in collaboration with the University of Washington as part of the China in Time and Space (CITAS) project, University of California-Davis China in Time and Space (CITAS) project, and the Center for International Earth Science Information Network (CIESIN).

SoilWeb AppsSoilWebSEE: Soil Series Extent ExplorerSDE: Soil Series Data ExplorerSoil PropertiesSoilWeb EarthSoilWeb products can be used to access USDA-NCSS detailed soil survey data (SSURGO) for most of the United States. This interactive map allows you to explore a variety of soil properties throughout the continental United States. The data shown here were obtained by aggregating current USDA-NCSS soil survey data (SSURGO back-filled with STATSGO where SSURGO is not available) within 800m grid cells. This data aggregation technique results in maps that may not match the original data at any given point, and is intended to depict regional trends in soil properties at the statewide scale.The source grids used to create the maps for each of these properties are available on the Download Page.Using the App:Select a property from one of the four categories on the Properties tab.Click the icon next to the currently active property to learn about the property and its data aggregation.Click the map to view specific values at that location.Adjust the map layer transparency by using the slider bar at the top left of the map pane.Use the Location tab to zoom to specific areas of interest.Right click (long press on mobile) the map to create a web link that bookmarks the current property and map location.This app was developed by the California Soil Resource Lab at UC Davis and UC-ANR in collaboration with the USDA Natural Resources Conservation Service. Please use the following citation for this website and gridded data products:Walkinshaw, Mike, A.T. O'Geen, D.E. Beaudette. "Soil Properties." California Soil Resource Lab, 1 Oct. 2022, casoilresource.lawr.ucdavis.edu/soil-properties/.

Bike parking

All well locations from all datasets standardized on the GAMA Program's Groundwater Information System (GAMA GIS). This is a replacement of previous versions, updated quarterly. Authoritative version. WGS 84.All groundwater wells on GAMA Groundwater Information System, accessed November 13, 2023. Sources of data include (as indicated in GM_DATA_SOURCE field):Geotracker: Wells sampled under regulated activities like cleanup and remediation. These are accessible through the California State Water Resources Control Board Geotracker web site.USGS: Wells sampled and analyzed by the U.S. Geological Survey (USGS) through the Groundwater Ambient Monitoring and Assessment (GAMA) Program Priority Basin Project.GAMA: Wells sampled by California State Water Resources Control Board staff for the GAMA Program Domestic Well Project.DDW: Division of Drinking Water (DDW) wells sampled and regulated for delivered water quality under DDW oversight.DPR: Wells sampled by the Department of Pesticide Regulation (DPR) groundwater program.WDL: Wells in the Department of Water Resources (DWR) water quality sampling network in their water data library.LLNL: Wells sampled for groundwater age, isotopes, or noble gas for the GAMA Program by Lawrence Livermore National Laboratory (LLNL).NWIS: Wells sampled by the USGS and accessible via the National Water Information System (NWIS). UC Davis: Location of wells gathered from multiple local entities for use in the UC Davis Nitrate Report, under agreement with the GAMA Program.LOCALGW: Wells sampled under various local groundwater projects. As of July 30, 2019, this only includes the domestic sampling completed by the Central Coast Regional Water Quality Control Board.GAMA_LOCALGW: Wells sampled under local groundwater projects, generally sampled from private wells from various private and governmental organizations. Data was submitted through the GAMA Data Connection Portal.The field, GM_DATASET_NAME can also help explain the source of the dataset.The corresponding point feature layer for these well locations can be found at the following link: GAMA GIS Wells - Overview (ca.gov)

Direct any questions to: GAMA@waterboards.ca.gov.

Napa County has used a 2004 edition vegetation map produced using the Manual of California Vegetation classification system (Thorne et al. 2004) as one of the input layers for land use decision and policy. The county decided to update the map because of its utility. A University of California, Davis (UCD) group was engaged to produce the map. The earlier map used black and white digital orthophoto quadrangles from 1993, with a pixel resolution of 3 meters. This image was delineated using a heads up digitization technique produced by ASI (Aerial Services Incorporated). The resulting polygons were the provided vegetation and landcover attributes following the classification system used by California State Department of Fish and Wildlife mappers in the Manual of California Vegetation. That effort included a brief field campaign in which surveyors drove accessible roads and verified or corrected the dominant vegetation of polygons adjacent to roadways or visible using binoculars. There were no field relevé or rapid assessment plots conducted. This update version uses a 2016 edition of 1 meter color aerial imagery taken by the National Agriculture Imagery Program (NAIP) as the base imagery. In consultation with the county we decided to use similar methods to the previous mapping effort, in order to preserve the capacity to assess change in the county over time. This meant forgoing recent data and innovations in remote sensing such as the use LiDAR and Ecognition’s segmentation of imagery to delineate stands, which have been recently used in a concurrent project mapping of Sonoma County. The use of such technologies would have made it more difficult to track changes in landcover, because differences between publication dates would not be definitively attributable to either actual land cover change or to change in methodology. The overall cost of updating the map in the way was approximately 20% of the cost of the Sonoma vegetation mapping program.Therefore, we started with the original map, and on-screen inspections of the 2004 polygons to determine if change had occurred. If so, the boundaries and attributes were modified in this new edition of the map. We also used the time series of imagery available on Google Earth, to further inspect many edited polygons. While funding was not available to do field assessments, we incorporated field expertise and other map data from four projects that overlap with parts of Napa Count: the Angwin Experimental Forest; a 2014 vegetation map of the Knoxville area; agricultural rock piles were identified by Amber Manfree; and parts of a Sonoma Vegetation Map that used 2013 imagery.The Angwin Experimental Forest was mapped by Peter Lecourt from Pacific Union College. He identified several polygons of redwoods in what are potentially the eastern-most extent of that species. We reviewed those polygons with him and incorporated some of the data from his area into this map.The 2014 Knoxville Vegetation map was developed by California Department of Fish and Wildlife. It was made public in February of 2019, close to the end of this project. We reviewed the map, which covers part of the northeast portion of Napa County. We incorporated polygons and vegetation types for 18 vegetation types including the rare ones, we reviewed and incorporated some data for another 6 types, and we noted in comments the presence of another 5 types. There is a separate report specifically addressing the incorporation of this map to our map.Dr Amber Manfree has been conducting research on fire return intervals for parts of Napa County. In her research she identified that large piles of rocks are created when vineyards are put in. These are mapable features. She shared the locations of rock piles she identified, which we incorporated into the map. The Sonoma Vegetation Map mapped some distance into the western side of Napa County. We reviewed that map’s polygons for coast redwood. We then examined our imagery and the Google imagery to see if we could discern the whorled pattern of tree branches. Where we could, we amended or expanded redwood polygons in our map.The Vegetation classification systems used here follows California’s Manual of California Vegetation and the National Vegetation Classification System (MCV and NVCS). We started with the vegetation types listed in the 2004 map. We predominantly use the same set of species names, with modifications/additions particularly from the Knoxville map. The NVCS uses Alliance and Association as the two most taxonomically detailed levels. This map uses those levels. It also refers to vegetation types that have not been sampled in the field and that has 3-6 species and a site descriptor as Groups, which is the next more general level in the NVCS classification. We conducted 3 rounds of quality assessment/quality control exercises.

There are estimated to be 120 native and non-native fish species that occur in California. The following list of fish species and their reported distributional ranges in California were compiled by Dr. Peter Moyle and his graduate student Paul Randall of the Department of Wildlife and Fisheries Conservation Biology at UC Davis for the Nature Conservancy as part of their Hexagon Project. Fish were mapped at the species level; subspecies were grouped into their respective species level. Present rather than historical distributions were mapped. As a source of data Dr. Moyle and Mr. Randall used a comprehensive fish database that consisted of about 10 separate fish databases from various agencies recording the presence and absence of a species. These databases varied from one another in structure and were queried separately for each species. Range maps were produced in accordance with the TNC's method of identifying species presence using three confidence ratings: confident, probable and possible. Information that came from existing databases (both digital and published reports) was categorized as confident.

The fish distribution data were graphically displayed on paper maps which were then used to create larger polygons showing species distributions at either the “probable” or “possible” levels of confidence. These polygons were initially drawn on paper maps (roughly 11 in. x 17 in.) which included locations of fish occurrences from all digital databases possessed, and GIS layers for hydrology (1:500,000 scale) and the state boundary (1:100,000 scale) . The boundaries of the fish range polygons were drawn on paper by Professor Peter Moyle and then digitized on the computer screen by Paul Randall using ARC/INFO GIS software. The resultant polygons are estimated to be accurate at a scale of roughly 1:1,000,000. This provided adequate resolution for the Hexagon Project which used a small geographic scale grid to compile species lists. A vast majority of the fish polygons were overlaid using the California 1:24,000 scale USGS quadrangle map grid lines to produce fish biodiversity (species richness) maps.

Layers and tables include: 500 Year Flood Zone, 100 Year Flood Zone, Lake Tahoe, Lake Tahoe at High Water 6229, Deepest Point Lake Tahoe, Watersheds, Hydrologic Resource Areas, Soil Survey - NRCS 1974, Soil Survey - NRCS 2003, Soil Survey - NRCS 2014, Land Capability - Bailey, Land Capability - NRCS 2007, Stream Environment Zone - Remotely Sensed, Stream Environment Zone - Assessment Unit, Meadow - UC Davis USFS, Fen Meadow - USFS, Aspen Stand - USFS, Aspen Stand - UNR, WBD HU12, WBD HU10, WBD HU08, NHD Point, NHD Flowline, NHD Area, NHD Waterbody, BMP Status.Spatial Reference: NAD83 / UTM zone 10N (26910)Area Covered: Tahoe Basin, Nevada, California

These methods describe the steps taken to calculate the attribute columns in the associated database. Compilations were done on publicly available data such as digital elevation models, climate data and others. For references to the public base data used, please see references in Table 1. There are two sections a. How we processed material into the hexagon framework b. The sequence of steps for each of the analyses presented in the results section of the main report a. How we processed material into the hexagon framework We created a geodatabase of 10 ha hexagons for the region in order to summarize the spatial data in this study into spatial units that are comparable across the region but that also represent an area size that is relevant for site-level plans such as landscape connectivity or forest conservation. The hexagon geodatabase covers 28,269 km2 in within the 5 watersheds in northern California, and 40,895 km2 in the 5 watersheds plus a 10 km buffer area. Integrating data into the hexes Data from a variety of grid scales, including 10, 30, 90, and 270m was added using the ArcGIS sample tool with the Hexagon centroids to sample the 270m resolution data, and the zonal statistics tool within Hexagon boundaries for raster data with smaller grid cell sizes. This study used four types of data (Table 1): Air temperature & topographic – Topographic data was used to calculate microrefugia buffering capacity for each hexagon. Temperature data was used to evaluate the effect of historical and projected future warming on the ability of local sites to retain baseline temperature conditions. Habitats / Dominant Vegetation Types – Habitat data was used to profile the presence and extent of microrefugia by habitat type for the region Landscape Connectivity Models – were used to find microrefugia in areas that are highly ranked for landscape connectivity Forest Structure data – was used to identify where large, late seral trees occupy microrefugia sites. Microrefugia – Air temperature & topographic National Elevation Dataset www.usgs.gov/core-science-systems/ngp/tnm-delivery Raster - 10m Solar Radiation Model Developed at UC Davis for this study from 25m DEM Raster - 25m Environmental Lapse Rate Model Developed at UC Davis for this study from 10m DEM Raster - 10m Linking Temperature to Hexagons Downscaled PRISM Tmax & Tmin – BCM – current & historical http://climate.calcommons.org/dataset/2014-CA-BCM Raster – 270 m Downscaled future climate projections MIROC & CNRM RCP8.5 http://climate.calcommons.org/dataset/2014-CA-BCM Raster – 270 m Habitats / Dominant Vegetation Types FVEG - CalFire (FRAP) https://frap.fire.ca.gov/mapping/gis-data/ Raster - 30m Vegetation and Climate Refugia Vegetative Climate Exposure (UCD Modeling) Raster - 270m Landscape Connectivity Models California Essential Connectivity https://wildlife.ca.gov/Conservation/Planning/Connectivity/CEHC Polygon Omniscape Climate Connectivity https://omniscape.codefornature.org/ 90 m Forest Structure Canopy Height - SALO Sciences https://forestobservatory.com/ Raster - 10m Table 1: Data sources b. The sequence of steps for each of the analyses presented in the results section of the main report Microrefugia – thermal buffering capacity Thermal buffering capacity combined two metrics that represent potential modifications to the air temperature in each 10-ha hexagon. First, a 10m digital elevation model was used to calculate the variation in air temperature within each hexagon due to variations in elevation, using a standard environmental lapse rate. Second, the influence of solar radiation on air temperature was calculated. These two metrics were combined. Elevational Effect on Air Temperature Column: ElevLR_NegEff2 Zonal Statistics was performed on a 10m DEM for each hex. The range of elevation was used with environmental lapse rate to calculate “buffering capacity” within each Hexagon. We used an environmental lapse rate of 0.00649606 C⁰/ meter (International Civil Aviation Organization, 1993) to calculate the range of temperatures within the hexagon. To calculate the effect of elevation on air temperature within each hexagon we used the following equation: (Average Elevation – Maximum Elevation) x 0.00649606 Solar Radiation Effect on Air Temperature: – Column: SRtemp_min We ran the analysis on a 25 m-resolution DEM. We calculated annualized solar radiation via the r.sun model available in GRASS 7.8 (https://grass.osgeo.org/grass70/manuals/r.sun.html) which calculates direct, diffuse, and reflected solar irradiation for a given day, location, topography, and atmospheric conditions. We assumed clear-sky conditions to run this model, and ran the model for 2 days in each month, from which we calculated solar radiation as a yearly total in watt-hours/m2. We converted the output to megajoules as follows. Convert yearly watt-hours to daily megajoules We used a regionally calibrated conversion...