Polygon layer delineating the area covered by major lakes within Travis County Texas.

The Digital Geologic-GIS Map of the Clear Creek Mountain Quadrangle, Utah is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) an ESRI file geodatabase (clcm_geology.gdb), and a 2.) Open Geospatial Consortium (OGC) geopackage. The file geodatabase format is supported with a 1.) ArcGIS Pro 3.X map file (.mapx) file (clcm_geology.mapx) and individual Pro 3.X layer (.lyrx) files (for each GIS data layer). Upon request, the GIS data is also available in ESRI shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) a readme file (zion_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (zion_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (clcm_geology_metadata_faq.pdf). Please read the zion_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. QGIS software is available for free at: https://www.qgis.org/en/site/. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri.htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: Utah Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (clcm_geology_metadata.txt or clcm_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:24,000 and United States National Map Accuracy Standards features are within (horizontally) 12.2 meters or 40 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in ArcGIS Pro, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

CDFW BIOS GIS Dataset, Contact: Nick Santos, Description: Species range layer for Clear Lake roach, showing HUC12s with presence types for Extant Range - Expert Opinion

This dataset depicts the 1:24,000 scale land ownership status and areas of responsibility for the State of Utah. Revisions are posted weekly on the AGRC SGID.Maintenance of this data layer is performed by a cooperative federal and state effort. The Utah School and Institutional Trust Lands Administration (SITLA) revises this data regularly to reflect changes in State Trust Lands, other State Land and Private Land as needed. The BLM revises this data regularly to reflect changes in Federal Land as needed. Other information is edited and updated as needed but not on a regular schedule.

Clear Creek Data:

• Clear Creek DEM Hillshade Near IR U West - Near Infra-red (NIR) Lidar. Hillshade including canopy of western block in the watershed. QA/QC: By NCALM.

• Clear Creek DEM Hillshade Near IR U East - Near Infra-red (NIR) Lidar. Hillshade including canopy of eastern block in the watershed. QA/QC: By NCALM.

• Clear Creek DEM Hillshade Near IR F West - Near Infra-red (NIR) Lidar. Hillshade of topograpy without canopy of western block in the watershed. QA/QC: By NCALM.

• Clear Creek DEM Hillshade Near IR F East - Near Infra-red (NIR) Lidar. Hillshade of topograpy without canopy of eastern block in the watershed. QA/QC: By NCALM.

• Clear Creek DEM Hillshade Green Lidar F West - Green Lidar. Hillshade of topograpy without canopy of western block in the watershed. QA/QC: By NCALM.

• Clear Creek DEM Hillshade Green Lidar F East - Green Lidar. Hillshade of topograpy without canopy of eastern block in the watershed. QA/QC: By NCALM.

• Clear Creek DEM Near IR Lidar U West - Near Infra-red (NIR) Lidar. DEM including canopy of western block in the watershed. QA/QC: By NCALM.

• Clear Creek DEM Near IR Lidar U East - Near Infra-red (NIR) Lidar. DEM including canopy of eastern block in the watershed. QA/QC: By NCALM.

• Clear Creek DEM Near IR Lidar F West - Near Infra-red (NIR) Lidar. DEM of topography without canopy of western block in the watershed. QA/QC: By NCALM.

• Clear Creek DEM Near IR Lidar F East - Near Infra-red (NIR) Lidar. DEM of topography without canopy of eastern block in the watershed. QA/QC: By NCALM.

• Clear Creek DEM Green Lidar F West - Green Lidar. DEM of topography without canopy of western block in the watershed. QA/QC: By NCALM.

• Clear Creek DEM Green Lidar F East - Green Lidar. DEM of topography without canopy of eastern block in the watershed. QA/QC: By NCALM.

• Clear Creek CSD AQ 2015 - CZO Clear Creek IA - Waveform CSD Digitizer Data - CSD AQ 2015 Data.

• Clear Creek CSD AQ 2014 - Green Lidar. Raw Full Waveform Lidar. QA/QC: None.

• Clear Creek CSD NIR 2015 - CZO Clear Creek IA - Waveform CSD Digitizer Data - NIR 2015 Data.

• Clear Creek CSD NIR 2014 - Near Infra-red (NIR) Lidar. Raw Full Waveform Lidar. QA/QC: None.

• Clear Creek NIR - Near Infra-red (NIR) Lidar. Point Cloud data. QA/QC: By NCALM.

• Clear Creek AQ_532 - Green Lidar. Point Cloud data. QA/QC: By NCALM.

  GIS data in CCW - This dataset contains: * wss_gsmsoil_IA_[2006-07-06].zip = Soil data from SURRGO of the IA state * wss_SSA_IA095_soildb_IA_2003_[2016-09-22].zip = Soil data from SURRGO of watershed IA095. covers another half of CCW *. wss_SSA_IA103_soildb_IA_2003_[2016-09-22].zip = Soil data from SURRGO of watershed IA095. covers half of CCW * CCW_crop_cover_tif.zip = CCW crop cover in 2007 * ClearCreek_Streams.zip = Stream file for Clear Creek watershed in Iowa *. State_of_Iowa.zip = Shape file of the boundary of * ClearCreek_Border.zip = Shape file of the boundary of Iowa State QA/QC: Yes. * CCW 10 DEM - This dataset contains: * n42w093.zip = 10 meter resolution DEM at 42N 93W * n42w092.zip = 10 meter resolution DEM at 42N 92W * n42w091.zip = 10 meter resolution DEM at 42N 91W QA/QC: Yes. * CCW 1m lidar DEM - 1 meter resolution DEM for Clear Creek watershed QA/QC: Yes. * 2m Lidar DEM - 2 meter resolution DEM for Clear Creek watershed QA/QC: Yes.

Can your desktop computer crunch the large GIS datasets that are becoming increasingly common across the geosciences? Do you have access to or the know-how to take advantage of advanced high performance computing (HPC) capability? Web based cyberinfrastructure takes work off your desk or laptop computer and onto infrastructure or "cloud" based data and processing servers. This talk will describe the HydroShare collaborative environment and web based services being developed to support the sharing and processing of hydrologic data and models. HydroShare supports the upload, storage, and sharing of a broad class of hydrologic data including time series, geographic features and raster datasets, multidimensional space-time data, and other structured collections of data. Web service tools and a Python client library provide researchers with access to HPC resources without requiring them to become HPC experts. This reduces the time and effort spent in finding and organizing the data required to prepare the inputs for hydrologic models and facilitates the management of online data and execution of models on HPC systems. This presentation will illustrate the use of web based data and computation services from both the browser and desktop client software. These web-based services implement the Terrain Analysis Using Digital Elevation Model (TauDEM) tools for watershed delineation, generation of hydrology-based terrain information, and preparation of hydrologic model inputs. They allow users to develop scripts on their desktop computer that call analytical functions that are executed completely in the cloud, on HPC resources using input datasets stored in the cloud, without installing specialized software, learning how to use HPC, or transferring large datasets back to the user's desktop. These cases serve as examples for how this approach can be extended to other models to enhance the use of web and data services in the geosciences.

Slides for AGU 2015 presentation IN51C-03, December 18, 2015

This provides a polygon coastline and islands layer which is based on the Topo50 products. It is a combination of the following layers:

• NZ Coastlines (Topo 1:50k)
• NZ Island Polygons (Topo 1:50k)
• NZ Auckland Is Island Polygons (Topo, 1:50k)
• NZ Campbell Is / Motu Ihupuku Island Polygons (Topo, 1:50k)
• NZ Antipodes Is Island Polygons (Topo, 1:25k)
• NZ Kermadec Is Island Polygons (Topo, 1:25k)
• NZ Bounty Is Island Polygons (Topo, 1:25k)
• NZ Snares Is / Tini Heke Island Polygons (Topo, 1:25k)
• NZ Chatham Is island polygons (Topo, 1:50k)

This topographic coastline is the line forming the boundary between the land and sea, defined by mean high water.

Islands from the NZ Island Polygons layer that lie within the NZ Coastline and Chatham Islands areas (i.e. islands in lakes, rivers and estuaries) have been removed.

The GIS workflow to create the layer is:

1. NZ Coastlines were converted from a polyline to a polygon using a polyline to polygon tool.
2. The resulting coastal polygon was then used as an input into an erase tool and run against the NZ Island Polygon layer to remove all islands lying within the NZ Mainland and Stewart Island.
3. This was then merged with the NZ Chatham Is island polygons (Topo, 1:50k) that have had the islands within the main island polygon removed, NZ Auckland Is Island Polygons (Topo, 1:50k), NZ Campbell Is / Motu Ihupuku Island, NZ Antipodes Is Island Polygons (Topo, 1:25k), NZ Kermadec Is Island Polygons (Topo, 1:25k), NZ Bounty Is Island Polygons (Topo, 1:25k) and NZ Snares Is / Tini Heke Island Polygons (Topo, 1:25k) layers using a merge tool.

For more detailed description of each layer refer to the layer urls above.

APIs and web services This dataset is available via ArcGIS Online and ArcGIS REST services, as well as our standard APIs. LDS APIs and OGC web services ArcGIS Online map services ArcGIS REST API

Abstract

This dataset was derived by the Bioregional Assessment Programme. The parent datasets are identified in the Lineage field in this metadata statment. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

This is a cartographic dataset showing the area of the Great Artesian Basin that is outside the Cadna Owie - Hooray aquifer extent.

Dataset History

The polygon shapefile within this dataset was created by erasing the Cadna Owie - Hooray aquifer extent shapefile: CadnaOwie_Hooray_Aquifer_Extent.shp (GABATLAS - Cadna-owie-Hooray Aquifer and Equivalents - Thickness and Extent, GUID: bc55589c-1c6f-47ba-a1ac-f81b0151c630) from the Great Artesian Basin extent shapefile: GAB_Hydrological_Boundary.shp (Great Artesian Basin - Hydrogeology and Extent Boundary, GUID: 020957ea-4877-4009-872c-3cacfb6f8ded).

Using the Erase tool in ArcGIS (Analysis Tools > Overlay > Erase) with the following input parameters:

in_features: GAB_Hydrological_Boundary.shp

erase_features: CadnaOwie_Hooray_Aquifer_Extent.shp

Dataset Citation

Bioregional Assessment Programme (2016) Great Artesian Basin - Cadna-owie Hooray Aquifer Erase. Bioregional Assessment Derived Dataset. Viewed 05 July 2017, http://data.bioregionalassessments.gov.au/dataset/b2182f7c-2bc7-4101-8ab8-acecb157d931.

Dataset Ancestors

• Derived From GABATLAS - Cadna-owie-Hooray Aquifer and Equivalents - Thickness and Extent

• Derived From Great Artesian Basin - Hydrogeology and Extent Boundary

Feature layer generated from running the Overlay layers solution.

The project lead for the collection of this data was and Richard Shinn. Pronghorn (28 adult females) were captured and equipped with GPS collars (Sirtrack, Havelock North, NZ) transmitting data from 2015-2020. The Clear Lake herd contains migrants, but this herd does not migrate between traditional summer and winter seasonal ranges. Instead, much of the herd displays a somewhat nomadic migratory tendency, slowly migrating north, east, or south for the summer using various high use areas as they move. Therefore, annual home ranges were modeled using year-round data to demarcate high use areas in lieu of modeling the specific winter ranges commonly seen in other ungulate analyses in California. The areas adjacent to both east and west of Clear Lake Reservoir are highly used during winter by many of the collared animals. Additionally, a few individuals persist west of Highway 139 year-round, seemingly separated from the rest of the herd due to this highway barrier. However, other pronghorn cross this road near Cornell and join this subgroup. Summer ranges are spread out, with many individuals moving southeast through Modoc National Forest or as far north as Fremont National Forest in Oregon. A few outliers in the herd moved long distances south, crossing Rt 139 to Oak Ridge, or east into Likely Tables pronghorn herd areas. GPS locations were fixed between 1-6 hour intervals in the dataset. To improve the quality of the data set as per Bjørneraas et al. (2010), the GPS data were filtered prior to analysis to remove locations which were: i) further from either the previous point or subsequent point than an individual pronghorn is able to travel in the elapsed time, ii) forming spikes in the movement trajectory based on outgoing and incoming speeds and turning angles sharper than a predefined threshold , or iii) fixed in 2D space and visually assessed as a bad fix by the analyst. The methodology used for this migration analysis allowed for the mapping of the herd''s home range and the identification and prioritization of migration corridors. Brownian Bridge Movement Models (BBMMs; Sawyer et al. 2009) were constructed with GPS collar data from 23 migrating pronghorn, including 72 migration sequences, location, date, time, and average location error as inputs in Migration Mapper. The average migration time and average migration distance for pronghorn was 12.11 days and 34.18 km, respectively. Corridors and stopovers were prioritized based on the number of animals moving through a particular area. BBMMs were produced at a spatial resolution of 50 m using a sequential fix interval of less than 27 hours. Due to varying fix rates in the data, separate models using Brownian bridge movement models (BMMM), with an adaptable variance rate, and fixed motion variances of 1000 were produced per migration sequence and visually compared for the entire dataset, with best models being combined prior to population-level analyses (68% of sequences selected with BBMM). In general, fixed motion variances were used when BBMM variances exceeded 8000. Home range analyses were based on data from 24 pronghorn and 47 year-round sequences using a fixed motion variance of 1000. Home range designations for this herd may expand with a larger sample, filling in some of the gaps between home range polygons in the map. Large water bodies were clipped from the final outputs.Corridors are visualized based on pronghorn use per cell, with greater than or equal to 1 pronghorn, greater than or equal to 3 pronghorn (10% of the sample), and greater than or equal to 5 pronghorn (20% of the sample) representing migration corridors, medium use corridors, and high use corridors, respectively. Stopovers were calculated as the top 10 percent of the population level utilization distribution during migrations and can be interpreted as high use areas. Stopover polygon areas less than 20,000 m2 were removed, but remaining small stopovers may be interpreted as short-term resting sites, likely based on a small concentration of points from an individual animal. Home range is visualized as the 50th percentile contour of the home range utilization distribution.

The Georgetown Quadrangle is located in Clear Creek County. Includes cross section, map unit correlation, shaded-relief map with geology overlay, booklet with extended descriptions of map units, geologic hazards, structural geology, ore deposits and alteration products, economic geology, and selected references. 22 pages. 1 color plate (1:24,000). OF-01-05

This dataset provides locations and related information for CLEAR as of 12/10/2020 based on information provided by the ISDH HIV/STD Program. CLEAR: Choosing Life: Empowerment! Action! Results! is an evidence-based, health promotion intervention for males and females ages 16 and older living with HIV/AIDS. CLEAR is a client-centered program delivered one-on-one using cognitive behavioral techniques to change behavior. The intervention provides clients with the skills necessary to be able to make healthy choices for their lives. Visit https://www.in.gov/isdh/23728.htm for more information about this resource.

This feature service is available through CT ECO, a partnership between UConn CLEAR and CT DEEP. The tile grid service is as an index for accessing aerial imagery and lidar elevation data files for Connecticut and is used in the Download Tool.

Unincorporated Urban Growth Areas (UGA) as defined by the Growth Management Act (GMA). The annual update is conducted by collecting UGA polygons directly from each of Washington's 39 counties. As of 2025, there are 27 counties with UGAs.All UGA polygons are normalized against the Department of Revenue's (DOR) "City Boundaries" layer (shared to the Washington Geoportal a.k.a. the GIS Open Data site: geo.wa.gov). The City Boundaries layer was processed into this UGA layer such that any overlapping area of UGA polygons (from authoritative individual counties) was erased. Since DOR polygons and county-sourced UGA polygons do not have perfect topology, many slivers resulted after the erase operation. These are attempted to be irradicated by these processing steps. "Multipart To Singlepart" Esri tool; exploded all polygons to be individualSlivers were mathematically identified using a 4 acre area threshold and a 0.3 "thinness ratio" threshold as described by Esri's "Polygon Sliver" tool. These slivers are merged into the neighboring features using Esri's "Eliminate" tool.Polygons that are less than 5,000 sq. ft. and not part of a DOR city (CITY_NM = Null) were also merged via the "Eliminate" tool. (many very small slivers were manually found yet mathematically did not meet the thinness ratio threshold)The final 8 polygons less than 25 sq. ft. were manually deleted (also slivers but were not lined up against another feature and missed by the "Eliminate" tool runs)Dissolved all features back to multipart using all fieldsAll UGAs polygons remaining are unincorporated areas beyond the city limits. Any polygon with CITY_NM populated originated from the DOR "City Boundaries" layer. The DOR's City Boundaries are updated quarterly by DOR. For the purposes of this UGA layer, the city boundaries was downloaded one time (4/24/2025) and will not be updated quarterly. Therefore, if precise city limits are required by any user of UGA boundaries, please refer to the city boundaries layer and conduct any geoprocessing needed. The DOR's "City Boundaries" layer is available here:https://www.arcgis.com/home/item.html?id=69fcb668dc8d49ea8010b6e33e42a13aData is updated in conjunction with the annual statewide parcel layer update. Latest update completed April 2025.

SafeGraph Places provides baseline information for every record in the SafeGraph product suite via the Places schema and polygon information when applicable via the Geometry schema. The current scope of a place is defined as any location humans can visit with the exception of single-family homes. This definition encompasses a diverse set of places ranging from restaurants, grocery stores, and malls; to parks, hospitals, museums, offices, and industrial parks. Premium sets of Places include apartment buildings, Parking Lots, and Point POIs (such as ATMs or transit stations).

SafeGraph Places is a point of interest (POI) data offering with varying coverage depending on the country. Note that address conventions and formatting vary across countries. SafeGraph has coalesced these fields into the Places schema.

SafeGraph provides clean and accurate geospatial datasets on 51M+ physical places/points of interest (POI) globally. Hundreds of industry leaders like Mapbox, Verizon, Clear Channel, and Esri already rely on SafeGraph POI data to unlock business insights and drive innovation.

Complete accounting of all incorporated cities, including the boundary and name of each individual city. From 2009 to 2022 CAL FIRE maintained this dataset by processing and digitally capturing annexations sent by the state Board of Equalization (BOE). In 2022 CAL FIRE began sourcing data directly from BOE, in order to allow the authoritative department provide data directly. This data is then adjusted so it resembles the previous formats.Processing includes:• Clipping the dataset to traditional state boundaries• Erasing areas that span the Bay Area (derived from calw221.gdb)• Querying for incorporated areas only• Dissolving each incorporated polygon into a single feature• Calculating the COUNTY field to remove the word 'County'Version 24_1 is based on BOE_CityCounty_20240315, and includes all annexations present in BOE_CityAnx2023_20240315. Note: The Board of Equalization represents incorporated city boundaries as extending significantly into waterways, including beyond coastal boundaries. To see the representation in its original form please reference the datasets listed above.Note: The Board of Equalization represents incorporated city boundaries is extending significantly into waterways, including beyond coastal boundaries. To see the representation in its original form please reference the datasets listed above.

These data provide an accurate high-resolution shoreline compiled from imagery of Galveston Bay, Clear Lake to La Porte, TX . This vector shoreline data is based on an office interpretation of imagery that may be suitable as a geographic information system (GIS) data layer. This metadata describes information for both the line and point shapefiles. The NGS attribution scheme 'Coastal Cartographic Object Attribute Source Table (C-COAST)' was developed to conform the attribution of various sources of shoreline data into one attribution catalog. C-COAST is not a recognized standard, but was influenced by the International Hydrographic Organization's S-57 Object-Attribute standard so the data would be more accurately translated into S-57. This resource is a member of https://www.fisheries.noaa.gov/inport/item/39808

Sieve filters are lacking in ArcGIS. Therefore, I developed a simple model that will perform a sieve filter based on the Jeffrey Evans' comments in the following forum:http://gis.stackexchange.com/questions/91609/where-can-i-use-a-sieve-filterThe basic idea of the sieve filter is that you can remove small "specks" or "polygons" from a categorical raster replacing them with their neighoring values. Unlike a focal majority operation which generalizes your data the sieve filter preserves the basic shapes of the "polygons". the only parameter required is the minimum number of cells in "polygon" (region group in raster terminology).Alternatively there may be some instances where you wish to generalize your data using a focal majority operation. However, the focal majority will return No Data in the case of a tie. Usually these are single cells or very small clusters of cells. The focal sieve tool allows you to remove these "specks" from your data. Hence, you get the generalization of the focal majority but use the sieve operation to clean up the specks. The focal sieve tool requires both a neighborhood size like a typical focal statistic but also a minimum number of cells.