This data layer gives values of summed wave fetch in 32 angular sectors around focal cells, using a model modified from that given in Burrows et al (2012 - see reference). Wave fetch is the distance to the nearest land in a defined direction. The model performs a three-scale search for land around each cell in the model, sparsely (every 10km) up to 200km, every 1km up to 20km away, and every 100m up to 1km distant.Values represent the log base 10 of the summed distance to the nearest land (as the number of 200m grid cell units) across all 32 11.5° sectors. The file is a GeoTIFF using the Ordnance Survey projection.

Oakland County's public-facing parcel viewer. Oakland County staff and CVTs can request free accounts by contacting the Oakland County Service Center (servicecenter@oakgov.com, 248-858-8812). More information about the products available in Property Gateway can be found here: https://www.oakgov.com/propertygateway/Pages/default.aspx.

This data layer gives values of summed wave fetch in 32 angular sectors around focal cells, using a model modified from that given in Burrows et al (2012 - see reference). Wave fetch is the distance to the nearest land in a defined direction. The model performs a three-scale search for land around each cell in the model, sparsely (every 10km) up to 200km, every 1km up to 20km away, and every 100m up to 1km distant.Values represent the summed number of grid cells to the nearest land across all 32 11.5° sectors. The file is a GeoTIFF using the WGS84 projection.

This data layer gives values of summed wave fetch in 32 angular sectors around focal cells, using a model modified from that given in Burrows et al (2012 - see reference). Wave fetch is the distance to the nearest land in a defined direction. The model performs a three-scale search for land around each cell in the model, sparsely (every 4.4km) up to 200km, every 440m up to 20km away, and every 22m up to 220m distant. Values represent the summed number of grid cells to the nearest land across all 32 11.5° sectors. The file is a GeoTIFF using the WGS84 UTM 30N projection.

This service (Nav_Rip_Statements) represents Riparian Statements of Diversion and Use for the Navarro watershed clipped from the Points of Diversion service published by wb_publish. Points of Diversion (PODs) are locations where water is being drawn from a surface water source such as a stream or river. Each water right registered with the California State Water Resources Control Board's Division of Water Rights includes an identified point of diversion. Ground water extraction points (such as water supply wells) are generally not included in this dataset. Last updated: 02/21/2020This service (Nav_Parcels) represents all parcels within the Navarro Watershed HUC 10 provided by parcels within the water49 geodatabase. The parcel boundaries should only be used for estimation purposes. The Water Board has a subscription for cadastral (parcel) GIS information with the California Department of Technology (CDT), who in turn receive the data through a contract with Digital Map Products (DMP). DMP collects parcel information from the 58 county assessors offices (the authoritative sources for this information), compiles it into a GIS dataset, and makes the data available via their LandVision web application. As part of their contract with DMP, CDT receives a quarterly snapshot of the parcel GIS information and redistributes this information to the subscriber state agencies. At the Water Boards, this information is uploaded to the water49 data library for staff use in ArcGIS. In order to facilitate the use of this data in desktop and web GIS applications, the GIS Unit has compiled the individual county layers and selected parcel attributes into a single statewide layer. For more information on the parcel attributes, please refer to the parcel data dictionary available at: http://wiki.waterboards.ca.gov/gis/lib/exe/fetch.php?media=dmp_datadictionary.pdf Please note that because there is no single standard for parcel information among the 58 county assessors, accuracy and attribution will vary across this dataset. Last updated: 02/21/2020

This data layer gives values of summed wave fetch in 32 angular sectors around focal cells, using a model modified from that given in Burrows et al (2012 - see reference). Wave fetch is the distance to the nearest land in a defined direction. The model performs a three-scale search for land around each cell in the model, sparsely (every 10km) up to 200km, every 1km up to 20km away, and every 100m up to 1km distant. Fetch is calculated up to 5km from the coastline. Values represent the summed number of grid cells to the nearest land across all 32 11.5° sectors. The file is a GeoTIFF using the WGS84 projection.

Statewide Property Inventory started in 1989 per legislation 11011.15, to begin a pro-active approach to managing the State’s Real Property assets in a computerized format. Having the information in an electronic format makes it available to top level decision-makers considering options for the best use of these assets. The Statewide Property Inventory is mandated to capture detailed information on the following: land owned and leased by the state, structures owned and leased by the state, property the state leases to the private sector. Statewide Property Inventory was established in 1988 by legislative mandate. Leases were added in 2004 by executive order. Data is updated annually by the agencies. Point of Contact: Any questions should be referred to the SPIWeb@dgs.ca.gov

Have you ever wanted to create your own maps, or integrate and visualize spatial datasets to examine changes in trends between locations and over time? Follow along with these training tutorials on QGIS, an open source geographic information system (GIS) and learn key concepts, procedures and skills for performing common GIS tasks – such as creating maps, as well as joining, overlaying and visualizing spatial datasets. These tutorials are geared towards new GIS users. We’ll start with foundational concepts, and build towards more advanced topics throughout – demonstrating how with a few relatively easy steps you can get quite a lot out of GIS. You can then extend these skills to datasets of thematic relevance to you in addressing tasks faced in your day-to-day work.

Point feature class includes cumulative scores and individual characteristic scores with class descriptions for symbology representing shoreline characteristics along the Maine coast that help determine suitability of that section of shoreline for potential living shoreline applications. This data in no way is meant to supersede site specific data, and is meant for guidance planning purposes only. The feature includes the following fields:FETCH_SCORE (Fetch Score) - determined by calculating the annualized fetch based on 10 years of wind data (2006-2016) from NDBC Buoy 44007 and the shoreline feature class. Data was input into the USGS fetch tool in order to determine the miles of potential fetch applicable to each shoreline segment. Scored as follows: Very Low (<=0.5 miles) = 8 pointsLow (0.5-1.0 miles) = 6 pointsModerate (1.0-3.0 miles) = 2 pointsHigh (3.0-5.0 miles) = 1 pointVery High (>5.0 miles) = 0 pointsBATHY_SCORE (Bathymetry Score) - determined by calculating the nearshore bathymetry using NOAA Portland 1/3 ArcSec DEM. If bathymetry within 100 feet of the MHW line was 1 meter or shallower, it was considered appropriate for living shorelines. Scored as follows:Shallow (<=1m within 30 m) = 6 pointsDeep (>1m within 30 m) = 0 pointsLAND_SCORE (Landward Shoreline Type) - determined using the landward shoreline type (landward of the MHW) from Environmental Vulnerability Index (EVI) mapping data by Woolpert for NOAA. Scored as follows:Wetlands, Swamps, Marshes = 6 pointsBeaches, Scarps, Banks = 5 pointsSheltered hard shorelines, rip-rap = 3 pointsExposed hard shorelines, rip-rap = 1 pointSEA_SCORE (Seaward Shoreline Type) - determined using the seaward shoreline type (seaward of the MHW) from Environmental Vulnerability Index (EVI) mapping data by Woolpert for NOAA, and as needed, MGS Coastal Marine Geological Environments (CMGE) maps. Scored as follows:Marshes and flats = 6 pointsBeaches, dunes, sand flats = 5 pointsLow-Moderate channels = 3 pointsHigh energy channels = 1 pointLedge/man-made land = 0 pointsRELIEF_SCORE (Relief Score) - determined by calculating the overall relief from the MHW to the elevation 50 feet inland. Note that this characteristic may lend itself to the stability of the shore, but may not be a key factor necessarily for whether or not a living shoreline may be suitable Scored as follows:0-5 feet = 6 points5-10 feet = 5 points10-20 feet = 3 points>20 feet = 1 pointSLOPE_SCORE (Slope Score) - determined by dividing the RELIEF_SCO by 50 feet to determine the slope of the shoreline (rise/run). Note that this characteristic may lend itself to the stability of the shore, but may not be a key factor necessarily for whether or not a living shoreline may be suitable Scored as follows:0-3% = 6 points4-9% = 5 points10-15% = 4 points15-30% = 2 points>30% = 1 pointASPECT_SCORE (Aspect Score) - determined using GIS to calculate the dominant apsect at each shoreline segment. This helps determine how well planted material may grow. Scored as follows:SE, SW = 6 pointsS, E, W = 4 pointsNE, NW = 2 pointsN = 0 pointsTOTAL_SCORE (Total Score) = determined by adding all of the factors. Scored as follows:0-15 (Probably Not Suitable)16-22 (LIkely Not Suitable)23-28 (Possibly Suitable)29-35 (Moderately Suitable)36-44 (Highly Suitable)38-44 (Highly Suitable)Additional Characteristics - determined by whether or not a special habitat type or structures are mapped within 100 feet of the shoreline (presence or absence). These factors are not included in the total living shoreline score, just provided as additional information. Factors include:TWWH_PA (Tidal Wading Bird and Waterfowl Habitat) - Present = 1, Absent = 0EEL_PA (Eelgrass Beds) - Present = 1, Absent = 0SHELL_PA (Shellfish) - Present = 1, Absent = 0 STRUCT_PA (Structures such as roads or buildings) - Present = 1, Absent = 0

This dataset includes five depth-attenuated relative wave exposure index layers in raster format. Relative Exposure Index (REI) values are calculated based on effective fetch (derived from fetch values) combined with modelled wind data. The output REI layers are attenuated by depth, resulting in greater values in shallow, nearshore areas (Bekkby et al. 2008). The cell values represent an estimate of wave exposure at bottom depth normalized between regions from 0 (protected) to 1 (exposed). The objective of this dataset is to provide an estimate of wave exposure at bottom depth, primarily for use in species distribution modelling. Each single-band raster corresponds to a marine region, which generally coincide with the following layers from the Species Distribution Modelling Boundaries (https://www.gis-hub.ca/dataset/sdm-boundaries) dataset: Nearshore_HG, Nearshore_NCC, Nearshore_QCS, Nearshore_QCS, and Shelf_SalishSea. These layers extend to 50 m depth and up to 5 km from shore. Tabular data (csv files) are also included as part of the data package. These data are the calculated Relative Exposure Index (REI) values with fields for position information. The fetch values from gridded nearshore fetch (https://gis-hub.ca/dataset/gridded-nearshore-fetch) are used as a source dataset and the locations in the REI are the same as the gridded fetch.

Wave exposure (m2/s) was modelled, with a spatial resolution of 25 m, as an index using data on fetch (distance to nearest shore, island or coast), averaged wind speed and wind frequency (estimated as the amount of time that the wind came from one of 16 direction). Data on wind speed and direction were delivered by the Norwegian Meteorological Institute and averaged over a 10-year period (i.e. 1995-2004). The model is run using the program WaveImpact based on the method ÔÇ£Simplified Wave ModelÔÇØ (SWM) developed and described by Is├ªus (2004). The method is a fetch model, where the fetch values are adjusted to simulate refraction and diffraction effects. The estimated fetch values for each of the 16 directions are multiplied with the average wind speed in the given direction.

The model has been run by NIVA for the whole Norwegian coast, and has been used as part of the habitat modelling of the National program for mapping biodiversity ÔÇô coast (Bekkby et al. 2013). The model has also been applied in several research projects in Norway (e.g. Bekkby et al. 2008, 2009, 2014, 2015, Bekkby & Moy 2011, Norderhaug et al. 2012, 2014, Pedersen et al. 2012, Rinde et al. 2014). The model has also been run for Sweden (e.g. Eriksson et al. 2004), Finland (Is├ªus & Rygg 2005), the Danish region of the Skagerrak coast and the Russian, Latvian, Estonian, Lithuanian and German territories of the Baltic Sea (Wijkmark & Is├ªus 2010). The wave exposure values range from Ultra sheltered to Extremely exposed (cf Wijkmark & Is├ªus 2010, similar to the EUNIS system of Davies & Moss 2004).

Vista points are informal pullouts where motorists can safely view scenery or park and relax. They do not include rest rooms. Vista points may have facilities including walkways, interpretive displays, railings, benches, interpretive information, trash receptacles, monuments and other pedestrian facilities that are accessible to all persons. Caltrans Division of Maintenance created this GIS layer by retrieving vista information from the Asset Management Inventory (AMI) database owned by Right of Way. The data was last updated on 08-04-2023, Verification Source: Office of Vegetation and Wildfire Management.

Dataset indicates the presence or absence of each ecosystems service at each coordinate Location. Also included are depth, fetch, and aquatic vegetation data. See supporting information for SAS code used to process data, sources of public spatial data, logic of GIS models used to generate presence absence assignments, GIS processing metadata, and KMZ maps (zipped file).

This dataset is associated with the following publication: Angradi , T., J. Launspach, D. Bolgrien , B. Bellinger, M. Starry, J. Hoffman , A. Trebitz , M. Sierszen , and T. Hollenhorst. Mapping ecosystem service indicators in a Great Lakes estuarine Area of Concern. JOURNAL OF GREAT LAKES RESEARCH. International Association for Great Lakes Research, Ann Arbor, MI, USA, 42(3): 717-727, (2016).

Multiple European city cases for the Urban Building (KUB) application. Each city is provided as a ZIP bundle containing all the data needed to run a KUB simulation end-to-end (GIS, mesh, weather, FMU building models, metadata, and configuration files). Note: The FMU files and associated metadata (description files) are downloaded or referenced automatically via Feel++ Remote-Data URIs when you run a simulation. You do not need to manually fetch or unpack FMUs—the containerized KUB workflow takes care of it. Overview Dataset name: ktirio-cases Number of city cases: 9 Purpose: Provide ready-to-use input bundles for urban-scale building energy and comfort simulations with Ktirio Urban Building (KUB). CKAN landing page: https://ckan.hidalgo2.eu/dataset/ktirio-cases Each ZIP archive includes: GIS metadata A JSON (or shapefile) mapping each building’s polygon to its OpenStreetMap (OSM) ID and other attributes. LoD-0 mesh An MSH file (mesh_0_Lod0.msh or similarly named) representing the terrain and building footprints at Level-of-Detail 0. Weather CSV A time-series file (e.g. weather.csv or weatherPoznan.csv) containing hourly (or sub-hourly) meteorological data. Typical columns include: time, temperature_2m, surface_pressure, relative_humidity_2m, wind_speed_10m, wind_direction_10m, direct_radiation, diffuse_radiation, cloud_cover, … FMU building models & descriptions A set of LoD-0 FMU files (e.g. Building_001.fmu, Building_002.fmu, …), each representing a single building’s thermal model. Corresponding metadata description files (XML or JSON) listing the outputs, variable names, and building element IDs. Configuration files A “simulator” config (e.g. idealHeater.cfg or BoilerHeaters.cfg) that lists the FMU URIs (remote or local) and FMU metadata URIs. A “city” config (e.g. strasbourg.cfg, poznan.cfg) that points to the mesh, GIS JSON, and weather CSV, and defines simulation parameters (cem.instance.*, postprocess.*, etc.). City Bundles Included athens.zip – Athens, Greece erlangen.zip – Erlangen, Germany gyor.zip – Győr, Hungary luxembourg.zip – Luxembourg, Luxembourg madrid.zip – Madrid, Spain

Data set of IS BK 5 soil map for forest site exploration of NRW 1 : 5.000. The dataset gives the contents of all digitally prepared large-scale ground maps, usually on a scale of 1: 5,000, again. For this purpose, the individual soil mapping projects ("procedures") were integrated into a largely unbroken overall package. Because the large-scale ground map was not created nationwide, the data set also shows white, uncharted areas. For these areas, soil information on a medium scale can be taken from the data set of the BK50. When retrieving the information from a GIS, each individual area is described with regard to soil unit, simplified soil type, soil type group of the topsoil, waterlogging, groundwater (former and current level), soils worthy of protection, rooting capacity, forestry site characteristics, need for soil protection limescale, optimum level of gradient, erosion of the topsoil, capillary rise of groundwater, usable field capacity, field capacity, air capacity, saturated water conductivity, infiltration suitability, cation exchange capacity and further evaluations.

Data set of the IS BK5 soil map for the agricultural site survey of NRW 1 : 5.000. The data set gives the contents of all digitally prepared large-scale soil maps of agricultural land, usually on a scale of 1: 5,000, again. For this purpose, the individual soil mapping projects ("procedures") were integrated into a largely unbroken overall package. Because the large-scale ground map was not created nationwide, the data set also shows white, uncharted areas. For these areas, soil information on a medium scale can be taken from the data set of the BK50. Soil science information of adjacent forest areas can be taken from the WMS of the BK5 for forestry site exploration. When retrieving the information from a GIS, each individual area is described by an information page with a plain text output with regard to soil unit, simplified soil type, soil type group of the topsoil, waterlogging, groundwater (former and current stage), soils worthy of protection, rooting capacity, leachate rate, optimal levelling, erosion of the topsoil, capillary rise of groundwater, usable field capacity, field capacity, air capacity, saturated water conductivity, infiltration suitability, cation exchange capacity and further evaluations.

A stand is a polygon representing a relatively homogenous area of similar cover type. Stands are classified as ‘forested’ (having a canopy of tree species greater than 3 feet tall covering at least 25% of the stand area) or ‘nonforested’ (all stands not meeting the definition of forested). In forested stands, the age class of trees, species composition, basal area stocking, and age structure will be consistent. Nonforested stands are areas of similar species composition.

The Vermont Fish & Wildlife Department maintains developed fishing access areas. These sites provide public access to waters in Vermont for shore fishing opportunities and launching of water craft. The department manages access areas with concrete or gravel ramps for launching and retrieving of boats. Additionally, there are access areas where non-motorized vessels can easily be launched. All access areas are open to hunting, trapping, fishing, and boating. Management and administration of all access areas is primarily funded through the sale of motorboat registration fees and the Sport Fish Restoration Program (SFR). The SFR Program was created to restore and better manage America's declining fishery resources and was modeled after the successful Wildlife Restoration Program. Excise taxes on fishing equipment, motorboat and small engine fuels, import duties, and interest are collected and appropriated from the Sport Fish Restoration and Boating Trust Fund. The Vermont Fish & Wildlife Department uses these monies for acquiring land as well as developing and maintaining boat and fishing access areas. These excise tax dollars, coupled with motorboat registration fees, have been the predominate source of funding for the access program over the last 20 years. Prior to the use of motorboat registration fees, angler license dollars were used in a similar fashion.

The dataset gives the contents of the ground map 1 : 50,000 from North Rhine-Westphalia until December 2016 cut-free, nationwide again. When retrieving the information from a GIS, each individual area is described with regard to soil unit, simplified soil type, soil type group of the topsoil, waterlogging, groundwater, soils worthy of protection, rootability, optimum distance between soils, erosion of the topsoil, capillary rise of groundwater, usable field capacity, field capacity, air capacity, saturated water conductivity, infiltration suitability, cation exchange capacity. ecological moisture level, overall filtration capability, digestion capability, suitability for geothermal collectors, denitrification potential and compaction sensitivity.

This layer contains information for locating past and present legal city boundaries within Los Angeles County. The Los Angeles County Department of Public Works provides the most current shapefiles representing city annexations and city boundaries on the Los Angeles County GIS Data Portal. True, legal boundaries are only determined on the ground by surveyors licensed in the State of California. Numerous records are freely available at the Land Records Information website, hosted by the Department of Public Works.Principal Attributes:NO: The row number in the attribute table of the PDF Annexation Maps. (See Below)

ANNEX_No: These values are only used for the City of Los Angeles and Long Beach.

NAME: The official annexation name.

TYPE: Indicates the legal action.

A - represents an Annexation to that city. D - represents a Detachment from that city. V - is used to indicate the annexation was void or withdrawn before an effective date could be declared. 33 - Some older city annexation maps indicate a city boundary declared 'as of February 8, 1933'.

ANNEX_AREA: is the land area annexed or detached, in square miles, per the recorded legal description.

TOTAL_AREA: is the cumulative total land area for each city, arranged chronologically.

SHADE: is used by some of our cartographers to store the color used on printed maps.

INDEXNO: is a matching field used for retrieving documents from our department's document management system.

STATE (Secretary of State): Date filed with the Secretary of State. These are not available for earlier annexations and are Null.

COUNTY (County Recorder): Date filed with the County Recorder. These are not available for earlier annexations and are Null.

EFFECTIVE (Effective Date): The effective date of the annexation or detachment.

CITY: The city to which the annexation or detachment took place.

URL: This text field contains hyperlinks for viewing city annexation documents. See the ArcGIS Help for using the Hyperlink Tool.

FEAT_TYPE: contains the type of feature each polygon represents:

Land - Use this value for your definition query if you want to see only land features on your map. Pier - This value is used for polygons representing piers along the coastline. One example is the Santa Monica Pier. Breakwater - This value is used for polygons representing man-made barriers that protect the harbors. Water - This value is used for polygons representing navigable waters inside the harbors and marinas. 3NM Buffer - Per the Submerged Lands Act, the seaward boundaries of coastal cities and unincorporated county areas are three nautical miles from the coastline. (A nautical mile is 1,852 meters, or about 6,076 feet.) Annexation Maps by City (PDF)Large format, high quality wall maps are available for each of the 88 cities in Los Angeles County in PDF format.Agoura HillsHermosa BeachNorwalkAlhambraHidden HillsPalmdaleArcadiaHuntington ParkPalos Verdes EstatesArtesiaIndustryParamountAvalonInglewoodPasadenaAzusaIrwindalePico RiveraBaldwin ParkLa Canada FlintridgePomonaBellLa Habra HeightsRancho Palos VerdesBell GardensLa MiradaRedondo BeachBellflowerLa PuenteRolling HillsBeverly HillsLa VerneRolling Hills EstatesBradburyLakewoodRosemeadBurbankLancasterSan DimasCalabasasLawndaleSan FernandoCarsonLomitaSan GabrielCerritosLong BeachSan MarinoClaremontLos Angeles IndexSanta ClaritaCommerceLos Angeles Map 1Santa Fe SpringsComptonLos Angeles Map 2Santa MonicaCovinaLos Angeles Map 3Sierra MadreCudahyLos Angeles Map 4Signal HillCulver CityLos Angeles Map 5South El MonteDiamond BarLos Angeles Map 6South GateDowneyLos Angeles Map 7South PasadenaDuarteLos Angeles Map 8Temple CityEl MonteLynwoodTorranceEl SegundoMalibuVernonGardenaManhattan BeachWalnutGlendaleMaywoodWest CovinaGlendoraMonroviaWest HollywoodHawaiian GardensMontebelloWestlake VillageHawthorneMonterey ParkWhittier