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 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.

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 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

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

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.

A web map used to visualize available digital parcel data for Organized Towns and Unorganized Territories throughout the state of Maine. Individual towns submit parcel data on a voluntary basis; the data are compiled by the Maine Office of GIS for dissemination by the Maine GeoLibrary, and where available, the web map also includes assessor data contained in the Parcels_ADB related table.This web map is intended for use within the Maine Geoparcel Viewer Application; it is not intended for use as a standalone web map.Within Maine, real property data is maintained by the government organization responsible for assessing and collecting property tax for a given location. Organized towns and townships maintain authoritative data for their communities and may voluntarily submit these data to the Maine GeoLibrary Parcel Project. Maine Parcels Organized Towns and Maine Parcels Organized Towns ADB are the product of these voluntary submissions. Communities provide updates to the Maine GeoLibrary on a non-regular basis, sometimes many years apart, which affects the currency of Maine GeoLibrary parcels data. Another resource for real property transaction data is the County Registry of Deeds, although organized town data should very closely match registry information, except in the case of in-process property conveyance transactions.

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.

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.

Hands-on presentation reviewing how to locate files on the DLI EFT and retrieving them.

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.

A comprehensive dataset of 1,513 Pakistani cities, towns, tehsils, districts and places with latitude/longitude, administrative region, population (when available) and Wikidata IDs — ideal for mapping, geospatial analysis, enrichment, and location-based ML.

Why this dataset is valuable:

• Full geocoordinates for every entry (100% coverage) — ready for mapping and spatial joins.
• Wide geographic coverage across all 7 major regions of Pakistan (provinces / administrative regions).
• Wikidata IDs included for reliable cross-referencing and automatic enrichment from external knowledge bases.
• Useful for data scientists, GIS engineers, civic tech projects, academic research, and startups building Pakistan-focused location services.

Highlights (fetched from the data):

• Total rows: 1,513
• Unique places (city field): 1,497
• Rows with population > 0: 526 (≈34.8%)
• Coordinate coverage: 1513 / 1513 (100%) — directly usable with mapping libraries.

Column definitions (short):

• id — Internal numeric row id (unique integer).
• wikiDataId — Wikidata QID (e.g., Q####) for the place; use to fetch rich metadata.
• type — Administrative/place type (e.g., ADM1, ADM2, city, district, tehsil).
• city — Common/local city/place name (short label).
• name — Full name / official name of the place (may include “District”, “Tehsil”, etc.).
• country — Country name (Pakistan).
• countryCode — ISO country code (e.g., PK).
• region — Primary administrative region / province (e.g., Punjab, Sindh).
• regionCode — Short code for region (e.g., PB, KP depending on your encoding).
• regionWdId — Wikidata QID for the region.
• latitude — Latitude in decimal degrees (float).
• longitude — Longitude in decimal degrees (float).
• population — Integer population (0 or NA where unknown).

Typical & high-value use cases:

• Mapping & visualization: choropleth maps, point overlays, heatmaps of population or density.
• Geospatial analysis: distance calculations, nearest-neighbor queries, clustering of urban centers.
• Data enrichment: join with other datasets (OpenStreetMap, Wikidata, census data) using wikiDataId and coordinates.
• Machine learning & NLP: training geolocation models, geoparsing, toponym resolution, place name disambiguation.
• Urban planning & research: analyze distribution of population-ready places vs administrative units.
• Mobile / location-based apps: lookup & reverse geocoding fallback, seeding POI databases for Pakistan.
• Humanitarian & disaster response: baseline location lists for logistics and situational awareness.

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.

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.

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

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

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).

Spawning habitat degradation has been linked to declines in naturally reproducing Muskellunge Esox masquinongy populations, and managers require efficient methods to identify and protect these habitats. We collected spawning habitat data from 28 lakes in northern Wisconsin to determine Muskellunge spawning habitat selection and to create a GIS-based model for predicting the locations of spawning sites. Spawning site selection by Muskellunge may be more complex than previously thought. Muskellunge showed selection for spawning in habitats with a sheltered effective fetch and east-facing shorelines. The strongest selection was for habitats with a combination of moderate slope, small flats, and concave bathymetric curvature. Muskellunge selected against steeply sloping shorelines; very large areas of shallow flats; developed shorelines; herbaceous wetlands; and complex-leafed submersed aquatic vegetation. Lake trophic status appears to interact with other habit variables to determine spawning site selection; sites without submersed aquatic vegetation were more strongly selected in eutrophic lakes than in other lake types. A GIS model of spawning site selection was created using the machine learning program MaxEnt (Maximum Entropy Modeling). The model predicted that Muskellunge would spawn in areas with moderately sheltered effective fetches, moderate to small areas of shallow flats, away from outflowing streams, and (to a lesser extent) along shorelines facing east or west. The model was tested on novel lakes using area-under-the-curve (AUC) analysis, in which values ranged from 0.5 (predictions no better than random) to 1.0 (perfect assignment). The mean AUCtest value (i.e., the expectation of model performance for a novel lake) was 0.637 (SD = 0.12). When the model was used to designate the best 20% of available spawning habitat area for Muskellunge in each lake (based on the relative probability of spawning), that area contained 32% of the spawning sites. The model provides an efficient method for management agencies and conservation groups to use in designating spawning habitat for conservation and in communicating with the public through spawning habitat maps.Received January 26, 2014; accepted October 7, 2014

Code snippet (URL shortened) defining the fetch function for the CHIRPS dataset module.

The purpose of this map is to assist in retrieving digitized PLSS notes and plats. Indiana has three to four sets of "original" PLSS notes and plats.The field survey set, which the field surveyor originally wrote, is preserved at the Indiana State Archive for approximately 30% of the counties in Indiana.The federal set, which the GLO transcribed, is preserved at the National Archive.The state set, which the GLO transcribed, is preserved at the Indiana State ArchiveThe county sets, transcribed later from the state set by the state auditor, are available from each county surveyor.The file name indicates the source and geographical location within the PLSS. O for the Original set F for the Federal set S for the State set C** for the County set PM0* for the 1st or 2nd Principal Meridian T**N or T**S for the Township (North & South) R**E or R**W for the Range (East & West)This project was made possible by Clayton J. Hogston, who donated over 11,000 hours to create the linked documents. Other contributors include Clayton J. Hogston – Sphere Surveying Co., Lorraine Wright – Rock Solid GIS, Rachel Savich Oser – Oser Surveying & Mapping LLC, and county surveyors with support from the Indiana State Archives, chapters of the Indiana Society of Professional Land Surveyors (ISPLS), the Indiana Geographic Information Council (IGIC), the Indiana Professional Land Surveyors Foundation (IPLSF), and others.Detailed metadata regarding the location of the physical documents within the holding institutions is available on our Internet Archive pages, where the digitized records can also be viewed or downloaded in bulk.