This digital dataset was created as part of a U.S. Geological Survey study, done in cooperation with the Monterey County Water Resource Agency, to conduct a hydrologic resource assessment and develop an integrated numerical hydrologic model of the hydrologic system of Salinas Valley, CA. As part of this larger study, the USGS developed this digital dataset of geologic data and three-dimensional hydrogeologic framework models, referred to here as the Salinas Valley Geological Framework (SVGF), that define the elevation, thickness, extent, and lithology-based texture variations of nine hydrogeologic units in Salinas Valley, CA. The digital dataset includes a geospatial database that contains two main elements as GIS feature datasets: (1) input data to the 3D framework and textural models, within a feature dataset called “ModelInput”; and (2) interpolated elevation, thicknesses, and textural variability of the hydrogeologic units stored as arrays of polygonal cells, within a feature dataset called “ModelGrids”. The model input data in this data release include stratigraphic and lithologic information from water, monitoring, and oil and gas wells, as well as data from selected published cross sections, point data derived from geologic maps and geophysical data, and data sampled from parts of previous framework models. Input surface and subsurface data have been reduced to points that define the elevation of the top of each hydrogeologic units at x,y locations; these point data, stored in a GIS feature class named “ModelInputData”, serve as digital input to the framework models. The location of wells used a sources of subsurface stratigraphic and lithologic information are stored within the GIS feature class “ModelInputData”, but are also provided as separate point feature classes in the geospatial database. Faults that offset hydrogeologic units are provided as a separate line feature class. Borehole data are also released as a set of tables, each of which may be joined or related to well location through a unique well identifier present in each table. Tables are in Excel and ascii comma-separated value (CSV) format and include separate but related tables for well location, stratigraphic information of the depths to top and base of hydrogeologic units intercepted downhole, downhole lithologic information reported at 10-foot intervals, and information on how lithologic descriptors were classed as sediment texture. Two types of geologic frameworks were constructed and released within a GIS feature dataset called “ModelGrids”: a hydrostratigraphic framework where the elevation, thickness, and spatial extent of the nine hydrogeologic units were defined based on interpolation of the input data, and (2) a textural model for each hydrogeologic unit based on interpolation of classed downhole lithologic data. Each framework is stored as an array of polygonal cells: essentially a “flattened”, two-dimensional representation of a digital 3D geologic framework. The elevation and thickness of the hydrogeologic units are contained within a single polygon feature class SVGF_3DHFM, which contains a mesh of polygons that represent model cells that have multiple attributes including XY location, elevation and thickness of each hydrogeologic unit. Textural information for each hydrogeologic unit are stored in a second array of polygonal cells called SVGF_TextureModel. The spatial data are accompanied by non-spatial tables that describe the sources of geologic information, a glossary of terms, a description of model units that describes the nine hydrogeologic units modeled in this study. A data dictionary defines the structure of the dataset, defines all fields in all spatial data attributer tables and all columns in all nonspatial tables, and duplicates the Entity and Attribute information contained in the metadata file. Spatial data are also presented as shapefiles. Downhole data from boreholes are released as a set of tables related by a unique well identifier, tables are in Excel and ascii comma-separated value (CSV) format.

Statistical analyses and maps representing mean, high, and low water-level conditions in the surface water and groundwater of Miami-Dade County were made by the U.S. Geological Survey, in cooperation with the Miami-Dade County Department of Regulatory and Economic Resources, to help inform decisions necessary for urban planning and development. Sixteen maps were created that show contours of (1) the mean of daily water levels at each site during October and May for the 2000-2009 water years; (2) the 25th, 50th, and 75th percentiles of the daily water levels at each site during October and May and for all months during 2000-2009; and (3) the differences between mean October and May water levels, as well as the differences in the percentiles of water levels for all months, between 1990-1999 and 2000-2009. The 80th, 90th, and 96th percentiles of the annual maximums of daily groundwater levels during 1974-2009 (a 35-year period) were computed to provide an indication of unusually high groundwater-level conditions. These maps and statistics provide a generalized understanding of the variations of water levels in the aquifer, rather than a survey of concurrent water levels. Water-level measurements from 473 sites in Miami-Dade County and surrounding counties were analyzed to generate statistical analyses. The monitored water levels included surface-water levels in canals and wetland areas and groundwater levels in the Biscayne aquifer. Maps were created by importing site coordinates, summary water-level statistics, and completeness of record statistics into a geographic information system, and by interpolating between water levels at monitoring sites in the canals and water levels along the coastline. Raster surfaces were created from these data by using the triangular irregular network interpolation method. The raster surfaces were contoured by using geographic information system software. These contours were imprecise in some areas because the software could not fully evaluate the hydrology given available information; therefore, contours were manually modified where necessary. The ability to evaluate differences in water levels between 1990-1999 and 2000-2009 is limited in some areas because most of the monitoring sites did not have 80 percent complete records for one or both of these periods. The quality of the analyses was limited by (1) deficiencies in spatial coverage; (2) the combination of pre- and post-construction water levels in areas where canals, levees, retention basins, detention basins, or water-control structures were installed or removed; (3) an inability to address the potential effects of the vertical hydraulic head gradient on water levels in wells of different depths; and (4) an inability to correct for the differences between daily water-level statistics. Contours are dashed in areas where the locations of contours have been approximated because of the uncertainty caused by these limitations. Although the ability of the maps to depict differences in water levels between 1990-1999 and 2000-2009 was limited by missing data, results indicate that near the coast water levels were generally higher in May during 2000-2009 than during 1990-1999; and that inland water levels were generally lower during 2000-2009 than during 1990-1999. Generally, the 25th, 50th, and 75th percentiles of water levels from all months were also higher near the coast and lower inland during 2000–2009 than during 1990-1999. Mean October water levels during 2000-2009 were generally higher than during 1990-1999 in much of western Miami-Dade County, but were lower in a large part of eastern Miami-Dade County.

Pursuant to the provisions of Minnesota Statutes, section 103G.223, this database contains points that represent calcareous fens as defined in Minnesota Rules, part 8420.0935, subpart 2. These calcareous fens have been identified by the commissioner by written order published in the State Register on June 2, 2008 (32 SR 2148-2154), August 31, 2009 (34 SR 278) and December 7, 2009 (34 SR 823-824). The current list of fens (DNR List of Known Calcareous Fens) is posted on the DNR's web site at: http://files.dnr.state.mn.us/eco/wetlands/calcareous_fen_list.pdf

This data set is a GIS point shapefile derived from the Natural Heritage "Biotics" Database. Data in the Biotics Database are maintained according to established Natural Heritage Methodology developed by NatureServe and The Nature Conservancy. The core of the Biotics Database is made up of Element Occurrence (EO) records of rare plant and animal species, animal aggregations, native plant communities, and geologic features. An Element is a unit of biological diversity, such as a species, subspecies, or a native plant community. An EO is an area of land and/or water in which an Element is, or was, present, and which has practical conservation value for the Element (e.g. species or community) as evidenced by potential continued (or historical) presence and/or regular recurrence at a given location. Source Features are the mapped representation of observations of rare features. Source Features are then evaluated using biological standards, and grouped into EOs as appropriate.

This data set contains a point for each Calcareous Fen (DNR List of Known Calcareous Fens) Source Feature in the Biotics database, and selected attributes from the Source Feature record and it’s linked Element Occurrence (EO) record.

Historical Points in the Geographic Names Information System (GNIS) This feature layer, utilizing National Geospatial Data Asset (NGDA) data from the U.S. Geological Survey, displays historical points from the Geographic Names Information System (GNIS). Per USGS, “The Geographic Names Information System (GNIS) is the Federal and national standard for geographic nomenclature. The U.S. Geological Survey's National Geospatial Program developed the GNIS in support of the U.S. Board on Geographic Names as the official repository of domestic geographic names data, the official vehicle for geographic names use by all departments of the Federal Government, and the source for applying geographic names to Federal electronic and printed products.” Washington D.C. Historical PointsData currency: This cached Esri federal service is checked weekly for updates from its enterprise federal source (Historical Points) and will support mapping, analysis, data exports and OGC API – Feature access.NGDAID: 34 (Geographic Names Information System (GNIS) - USGS National Map Downloadable Data Collection)OGC API Features Link: (Historical Points in the Geographic Names Information System (GNIS) - OGC Features) copy this link to embed it in OGC Compliant viewersFor more information, please visit: U.S. Board on Geographic NamesFor feedback please contact: Esri_US_Federal_Data@esri.comNGDA Data Set This data set is part of the NGDA Cultural Resources Theme Community. Per the Federal Geospatial Data Committee (FGDC), Cultural Resources are defined as "features and characteristics of a collection of places of significance in history, architecture, engineering, or society. Includes National Monuments and Icons."For other NGDA Content: Esri Federal Datasets

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.

These data show the sampling locations for the consumer datasets at Konza Prairie. Record type 1 defines the starting points for sweep samples of grasshoppers across Konza Prairie (GIS400). These data may be used in conjunction with the sweep sample datasets (CGR02). Record type 2 defines the starting points for sweep samples of grasshoppers across Konza Prairie (GIS401), focusing on grazing impact. These data may be used in conjunction with the sweep sample datasets (CGR02Z). Record type 6 defines the trap locations for small mammal sampling across Konza Prairie (GIS405). These data may be used in conjunction with CSM0X. Record type 11 defines the stream stretches for fish sampling across Konza Prairie (GIS410). These data may be used in conjunction with CFC01.

A major objective of plant ecology research is to determine the underlying processes responsible for the observed spatial distribution patterns of plant species. Plants can be approximated as points in space for this purpose, and thus, spatial point pattern analysis has become increasingly popular in ecological research. The basic piece of data for point pattern analysis is a point location of an ecological object in some study region. Therefore, point pattern analysis can only be performed if data can be collected. However, due to the lack of a convenient sampling method, a few previous studies have used point pattern analysis to examine the spatial patterns of grassland species. This is unfortunate because being able to explore point patterns in grassland systems has widespread implications for population dynamics, community-level patterns and ecological processes. In this study, we develop a new method to measure individual coordinates of species in grassland communities. This method records plant growing positions via digital picture samples that have been sub-blocked within a geographical information system (GIS). Here, we tested out the new method by measuring the individual coordinates of Stipa grandis in grazed and ungrazed S. grandis communities in a temperate steppe ecosystem in China. Furthermore, we analyzed the pattern of S. grandis by using the pair correlation function g(r) with both a homogeneous Poisson process and a heterogeneous Poisson process. Our results showed that individuals of S. grandis were overdispersed according to the homogeneous Poisson process at 0-0.16 m in the ungrazed community, while they were clustered at 0.19 m according to the homogeneous and heterogeneous Poisson processes in the grazed community. These results suggest that competitive interactions dominated the ungrazed community, while facilitative interactions dominated the grazed community. In sum, we successfully executed a new sampling method, using digital photography and a Geographical Information System, to collect experimental data on the spatial point patterns for the populations in this grassland community.

Methods 1. Data collection using digital photographs and GIS

A flat 5 m x 5 m sampling block was chosen in a study grassland community and divided with bamboo chopsticks into 100 sub-blocks of 50 cm x 50 cm (Fig. 1). A digital camera was then mounted to a telescoping stake and positioned in the center of each sub-block to photograph vegetation within a 0.25 m2 area. Pictures were taken 1.75 m above the ground at an approximate downward angle of 90° (Fig. 2). Automatic camera settings were used for focus, lighting and shutter speed. After photographing the plot as a whole, photographs were taken of each individual plant in each sub-block. In order to identify each individual plant from the digital images, each plant was uniquely marked before the pictures were taken (Fig. 2 B).

Digital images were imported into a computer as JPEG files, and the position of each plant in the pictures was determined using GIS. This involved four steps: 1) A reference frame (Fig. 3) was established using R2V software to designate control points, or the four vertexes of each sub-block (Appendix S1), so that all plants in each sub-block were within the same reference frame. The parallax and optical distortion in the raster images was then geometrically corrected based on these selected control points; 2) Maps, or layers in GIS terminology, were set up for each species as PROJECT files (Appendix S2), and all individuals in each sub-block were digitized using R2V software (Appendix S3). For accuracy, the digitization of plant individual locations was performed manually; 3) Each plant species layer was exported from a PROJECT file to a SHAPE file in R2V software (Appendix S4); 4) Finally each species layer was opened in Arc GIS software in the SHAPE file format, and attribute data from each species layer was exported into Arc GIS to obtain the precise coordinates for each species. This last phase involved four steps of its own, from adding the data (Appendix S5), to opening the attribute table (Appendix S6), to adding new x and y coordinate fields (Appendix S7) and to obtaining the x and y coordinates and filling in the new fields (Appendix S8).

1. Data reliability assessment

To determine the accuracy of our new method, we measured the individual locations of Leymus chinensis, a perennial rhizome grass, in representative community blocks 5 m x 5 m in size in typical steppe habitat in the Inner Mongolia Autonomous Region of China in July 2010 (Fig. 4 A). As our standard for comparison, we used a ruler to measure the individual coordinates of L. chinensis. We tested for significant differences between (1) the coordinates of L. chinensis, as measured with our new method and with the ruler, and (2) the pair correlation function g of L. chinensis, as measured with our new method and with the ruler (see section 3.2 Data Analysis). If (1) the coordinates of L. chinensis, as measured with our new method and with the ruler, and (2) the pair correlation function g of L. chinensis, as measured with our new method and with the ruler, did not differ significantly, then we could conclude that our new method of measuring the coordinates of L. chinensis was reliable.

We compared the results using a t-test (Table 1). We found no significant differences in either (1) the coordinates of L. chinensis or (2) the pair correlation function g of L. chinensis. Further, we compared the pattern characteristics of L. chinensis when measured by our new method against the ruler measurements using a null model. We found that the two pattern characteristics of L. chinensis did not differ significantly based on the homogenous Poisson process or complete spatial randomness (Fig. 4 B). Thus, we concluded that the data obtained using our new method was reliable enough to perform point pattern analysis with a null model in grassland communities.

This data release contains the analytical results and the evaluated source data files of a geospatial analysis for identifying areas in Alaska that may have potential for sediment-hosted Pb-Zn (lead-zinc) deposits. The spatial analysis is based on queries of statewide source datasets Alaska Geochemical Database (AGDB3), Alaska Resource Data File (ARDF), and Alaska Geologic Map (SIM3340) within areas defined by 12-digit HUCs (subwatersheds) from the National Watershed Boundary dataset. The packages of files available for download are: 1. The results in geodatabase format are in SedPbZn_Results_gdb.zip. The analytical results for sediment-hosted Pb-Zn deposits are in a polygon feature class which contains the points scored for each source data layer query, the accumulative score, and a designation for high, medium, or low potential and high, medium, or low certainty for sediment-hosted Pb-Zn deposits for each HUC. The data is described by FGDC metadata. An mxd file, layer file, and cartographic feature classes are provided for display of the results in ArcMap. Files sedPbZn_scoring_tables.pdf (list of the scoring parameters for the analysis) and sedPbZn_Results_gdb_README.txt (description of the files in this download package) are included. 2. The results in shapefile format are in SedPbZn_Results_shape.zip. The analytical results for sediment-hosted Pb-Zn deposits are in a polygon feature class which contains the points scored for each source data layer query, the accumulative score, and designation for high, medium, or low potential and high, medium, or low certainty for sediment-hosted Pb-Zn deposits for each HUC. The results are also provided as a CSV file. The data is described by FGDC metadata. Files sedPbZn_scoring_tables.pdf (list of the scoring parameters for the analysis) and sedPbZn_Results_shape_README.txt (description of the files in this download package) are included. 3. The source data in geodatabase format are in SedPbZn_SourceData_gdb.zip. Data layers include AGDB3, ARDF, lithology from SIM3340, and HUC subwatersheds, with FGDC metadata. An mxd file and cartographic feature classes are provided for display of the source data in ArcMap. Also included are two python scripts 1) to score the ARDF records based on the presence of certain keywords, and 2) to evaluate the ARDF, AGDB3, and lithology layers for the potential for sediment-hosted Pb-Zn deposits within subwatershed polygons. Users may modify the scripts to design their own analyses. Files sedPbZn_scoring_table.pdf (list of the scoring parameters for the analysis) and sedPbZn_sourcedata_gdb_README.txt (description of the files in this download package) are included. 4. The source data in shapefile and CSV format are in SedPbZn_SourceData_shape.zip. Data layers include ARDF and lithology from SIM3340, and HUC subwatersheds, with FGDC metadata. The ARDF keyword tables available in the geodatabase package are presented here as CSV files. All data files are described with the FGDC metadata. Files sedPb_Zn_scoring_table.pdf (list of the scoring parameters for the analysis) and sedPbZn_sourcedata_shapefile_README.txt (description of the files in this download package) are included. 5. Appendices 2, 3 and 4, which are cited by the larger work OFR2020-1147. Files are presented in XLSX and CSV formats.

This dataset accompanies Environment Agency Chief Scientist's Group research report on 'Exploring water recreation in England' (SC230022/R). The project outlines the extent of recreational use of environmental waters in England based on data provided by 17 organisations largely covering the period between 2017 and 2024. This provides a collated national overview and expands our understanding of where, when and how surface waters are used for recreation. The dataset was collated from from original data (“reports”) sourced from contributing organisations defined as: Water recreation report: A geolocated point where a form of water recreation was indicated either by physical infrastructure or a report of recreational activity. The open dataset is comprised of three feature layers: Water recreation locations: An aggregated geolocated point at the centroid of a water recreation zone where reports of water recreation were indicated either by physical infrastructure or recreational activity records. Water recreation zones: A polygonal area around each water recreation location constructed from the intersection of 500m radius buffer circles around individual geolocated water recreation reports. Waterbody catchments: Coastal, transitional and river waterbody catchments from the Water Framework Directive cycle 2. For each catchment water recreation information has been summarised and enhanced metadata columns are provided. Note that in some cases column names have been truncated and the metadata file should be checked for the correct alias names. There is also a layer file associated with this dataset. There is a layer file called 'Water_recreation_symbology.lyrx' which displays the column names correctly and sets symbology according to the number of data sources as shown in the research report. Limitations: Geocoordinates of water recreation locations in Easting and Northing values have been provided within the dataset for easier data handling, however these points should be treated with caution as they are located at the mathematical centroid of the recreation zone polygon and do not represent a precise water access point. Reports of water recreation locations have been determined from a desktop study of available existing data (provided as-is and not necessarily originally collected for this purpose). The dataset has been collated systematically but is not exhaustive as certain activities (such as bathing) do not require permission nor a licence and have a low barrier to participate they do not necessarily have a presence in the data. The scope of this study was mainly focused on immersive in-water recreation (swimming) with some limited capture of on-water activities (paddling, rowing, sailing, surfing). The data is primarily point-based water access locations and has limited coverage of the actual area (i.e. tracks) of where water recreation may take place within a waterbody once entered. Further, data has not been verified on the ground and some data is sourced from non-authoritative sources including community contributions. Inclusion is no guarantee of access (for example sites may require landowner permission, memberships or have time/season limitations). The time-based data primarily covers the 2017 to 2023 calendar years with limited assessment from the 2024 bathing season and events scheduled in the 2024-25 events season. As such, past indications do not necessarily mean locations are still active, permitted or safely accessible at the time of publication (for example, the data also includes now de-designated bathing points) and the inclusion of sites in this study does not imply sites will be monitored going forward. Attribution statement: © Environment Agency copyright and/or database right 2025. All rights reserved. Contains OS data © Crown copyright and database rights 2024, OS AC0000807064. Contains © data reproduced under licence from the Royal Yachting Association. Contains The Rivers Trust data licensed under the CC BY-NC-SA 4.0 license and included with permission. Contains Royal National Lifeboat Institution data licensed under the GIS Open Data licence. Contains Natural England, Marine Management Organisation, UK Centre for Ecology & Hydrology and Natural Resources Wales information licensed under the Open Government Licence v3.0. Contains data provided and/or sourced with permission © Channel Swimming & Piloting Federation, © British Rowing, © British Triathlon, © Paddle UK, © Royal Life Saving Society UK, © Surfers Against Sewage, © Surfing England, © Swim England, © TimeOutdoors, © Water Buoy Ltd 2024. Contains data sourced with permission from '2000 Wild Swims' by Rob Fryer (2022).

Vector-type georeferenced database containing information on the location and characteristics of places of particular geological and geomorphological interest, whose value is mainly scientific, landscape and cultural. These places together form a corpus that can be defined as Geosites of the Emilia-Romagna Region, and represent the geological heritage of the territory. The points identify either geosites of limited territorial extension, therefore effectively localizable with a point, or represent the centroid of geosites with the most important area extension (cartographable at 1:5000 scale).The identification of these areas took place with the preliminary analysis of the geological cartography, the study of terrain and the cataloguing of the geological object. The catalogue therefore collects data relating to outcrops, morphologies, caves and springs that during the surveys carried out by the Soil and Seismic Geology Area have returned particularly important and significant data from a scientific point of view. In accordance with Article 3 of Regional Law 09/2006, geosites of regional importance have been selected, for which it is particularly important to define forms of enhancement and protection.The data collected and structured through GIS provide information on the geographical location, geological characteristics and existing protections, in relation to all areas, points and observation routes surveyed. The database also contains photographs, in-depth texts, possible possibilities of use, bibliographic indications and is continuously updated in relation to the evolution of knowledge, environmental dynamics and changes induced by human actions. The database of Geosites is the basic tool for the identification of places that have aspects of rarity and uniqueness, where there are important testimonies of the geological and geomorphological history of the regional territory.

Description of columns in the ArcGIS point file "Points for Maps" which provides the final statistics used to make the maps of mean daily water levels and maps of the 25th, 50th, and 75th percentiles of daily water levels during 2000–2009 in Miami-Dade County; and maps showing the differences in the statistics of water levels between 1990–1999 and 2000–2009.

This dataset is a compilation of address point data from metro area address authorities, which are predominantly cities. The dataset is intended to contain a point location and the official address (as defined by the address authority) for all occupiable units and any other official addresses within the jurisdictional boundary of each address authority. A number of other attributes are available in the dataset, but may not be populated by some address authorities.

Metro area counties are playing a coordinative role to work with cities to create, maintain and aggregate address points. Currently this dataset contains points for all Seven Metropolitan Counties as well as participating bordering counties.

Some jurisdictions in this dataset contain parcel points and not complete address points. See Completeness in Section 2 of this metadata for more information.

The data used in this aggregated dataset are compliant with the Address Point Data Standard for Minnesota.
http://www.mngeo.state.mn.us/committee/standards/address/address_standard.html


For specific questions regarding centerline alignments or attributes, please contact the county below
Anoka: https://www.anokacounty.us/315/GIS
Carver: gis@co.carver.mn.us
Chisago: gisservices@chisagocountymn.gov
Dakota: gis@co.dakota.mn.us
Hennepin: gis.info@hennepin.us
Isanti: Nate.Kirkwold@co.isanti.mn.us
Ramsey: RCGISMetaData@co.ramsey.mn.us
Scott: gis@co.scott.mn.us
Sherburne: gis@co.sherburne.mn.us
Washington: gis@co.washington.mn.us

The NM Office of the State Engineer (OSE) "Point of Diversions" (POD) layer includes well locations, surface declarations, or surface permits updated on a monthly basis. These data were extracted from the OSE W.A.T.E.R.S. (Water Administration Technical Engineering Resource System) database and geo-located (mapped). These data have varying degrees of accuracy and have not been validated. Data included in this dataset only includes PODs that have coordinates located within the State of New Mexico. This message is to alert users of this data to various changes regarding how this POD data is generated and maintained by the NM Office of the State Engineer. In addition, all attribute fields are fully described in the metadata, including descriptions of field codes. Please read the metadata accompanying this GIS data layer for further information. Any questions regarding this GIS data should be directed NM OSE Information Technology Systems Bureau GIS at the contact information given below.

Large structures that formerly defined the end of a mainline such as maintenance holes, vaults, plugs, or other designated features or a location where such a structure was formerly located.

Township boundaries were generated from radian measurements of township corner coordinates, represented to the nearest 0.001 second, recorded on official protraction diagrams of the state from BLM and ADNR. ADNR used 1994 AEH coordinate files from BLM as the basis of its work. BLM provided information for 18,654 land-based townships, and ADNR added another 774 (prior to 1996) townships that cover marine areas. Based on ADNR research, corner coordinates were modified for approximately 600 townships to correct the east-west and/or north-south alignment of neighboring townships. ADNR research also ensured that townships match across meridian lines.

Out of a total 19,425 townships currently defined for the state, 52 were identified by BLM as being irregular, that is, they cannot be describe by four corner points. During ADNR processing, many other minor adjustments were made to resolve spatial anomalies. Irregular townships are outlined using as many corner points as necessary, which was typically six to represent L-shaped townships. Many complex townships, including those along the US/Canadian border and those where meridians join, are described by more than six corner points; a few by only 3 points.

Using a geographic projection, ADNR created a double-precision coverage for the entire state from a compilation of the regular and irregular townships. Several iterations using ARC/INFO were required to find and resolve discrepancies in the tabular database. Arcs were densified while the township outlines were still in a geographic projection, to maintain the proper curvature of boundary lines during subsequent projection to other coordinate systems. The final result is a set of statewide coverages, both single-precision and double-precision, in both the Albers projection and geographic coordinates. The final coverages maintain as closely as possible the original protracted coordinate values.

The National Wildfire Coordinating Group's Wildland Fire Event Point data standard defines the minimum attributes necessary for collection, storage and dissemination of incident based data on wildland fires (wildfires and prescribed fires). The standard is not intended for long term data storage, rather a standard to assist in the creation of incident based data management tools, minimum standards for data exchange, and to assist users in meeting GIS Standard Operating Procedures on Incidents (GSTOP) guidance.

This feature class will use a specific symbol set. The symbol set is defined by the GIS Standard Operating Procedures for Incidents (GSTOP). For additional information follow this link: http://gis.nwcg.gov/gstop_about.html

This feature class will be part of a Incident Geodatabase that will contain Fire Point, Fire Line, and Fire Polygon feature classes.

This standard is for incident based data collection, storage and exchange. The intent of this standard is to update existing data formats and provide a common set of attributes for use on wildland fires starting fire season 2016.

This standard should not be confused with the NWCG geospatial data standard for Wildland Fire Location Point, which is for the representation of the final spatial location of the fire occurrence ignition.

Township boundaries were generated from radian measurements of township corner coordinates, represented to the nearest 0.001 second, recorded on official protraction diagrams of the state from BLM and ADNR. ADNR used 1994 AEH coordinate files from BLM as the basis of its work. BLM provided information for 18,654 land-based townships, and ADNR added another 774 (prior to 1996) townships that cover marine areas. Based on ADNR research, corner coordinates were modified for approximately 600 townships to correct the east-west and/or north-south alignment of neighboring townships. ADNR research also ensured that townships match across meridian lines.

Out of a total 19,425 townships currently defined for the state, 52 were identified by BLM as being irregular, that is, they cannot be describe by four corner points. During ADNR processing, many other minor adjustments were made to resolve spatial anomalies. Irregular townships are outlined using as many corner points as necessary, which was typically six to represent L-shaped townships. Many complex townships, including those along the US/Canadian border and those where meridians join, are described by more than six corner points; a few by only 3 points.

Using a geographic projection, ADNR created a double-precision coverage for the entire state from a compilation of the regular and irregular townships. Several iterations using ARC/INFO were required to find and resolve discrepancies in the tabular database. Arcs were densified while the township outlines were still in a geographic projection, to maintain the proper curvature of boundary lines during subsequent projection to other coordinate systems. The final result is a set of statewide coverages, both single-precision and double-precision, in both the Albers projection and geographic coordinates. The final coverages maintain as closely as possible the original protracted coordinate values.

DownloadA daily extract of the NPDES Structures dataset is available for download as a zipped file geodatabase.BackgroundAs a government agency that owns and maintains separate storm sewer systems, the Maryland State Highway Administration (SHA) is mandated to file a National Pollutant Discharge Elimination System (NPDES) permit with the Maryland Department of the Environment (MDE). The permit requires the inventory, inspection, and maintenance of SHA stormwater infrastructure. SHA is responsible for maintaining storm drain infrastructure on more than 5,000 miles of roadway statewide. SHA has developed a program consisting of SHA personnel, data managers, and subject matter experts to support the permit requirements and maintain these roadways. The tasks involved in the SHA NPDES data collection program are often completed by engineering consultants for SHA. The data are organized into a series of drainage systems with stormwater management facilities that are interconnected, allowing for flow-tracing function through distinct systems. A drainage system is defined as a series of storm drain structures or point features (i.e., manholes, inlets, endwalls) that connect hydraulically through conveyance features such as pipes and / or ditches. Closed and open storm drain structures are connected by pipe and ditch conveyance to create the drainage system. Stormwater management facilities (SWMF), also known as stormwater best management practices (BMP) are inventoried with the storm drain system. A system can include both open and closed storm drain features. StructuresPhysical stormwater structures to be identified and inventoried include headwalls, endwalls, cross culverts, pumping stations, stormwater risers and weirs, inlets, pipe connections, and manholes. Storm drain structures are represented as point features in the database. Several database features are included that are not existing physical structures, but are employed to facilitate connection of drainage systems in the database. For detailed descriptions of each feature, refer to the SHA Book of Standard for Highway & Incidental Structures, Category 3 “Drainage.” Storm drain structures within SHA ROW are inventoried. Information on private storm drain structures will need to be collected if a private system ties into SHA-owned storm drain features. The only structures that are not inventoried within SHA ROW are single residential driveway culvert end structures (See below for more details), bridge inlets, under drains, roof drainage, or other private tie-ins with the exception of the first or last structure from a private storm drain system and curb opening. If an under-drain pipe has an end structure (such as an endwall), then the structure is inventoried. Curb openings are only inventoried when affecting the drainage area for a BMP or major outfalls. If it is deemed necessary to include a curb cut in the database, the curb cut is captured as an inlet feature with comments identifying the feature as a curb opening. A curb opening is not a COG or COS inlet with an open back, but simply a cut in the curb where sheet flow is exiting impervious. The following are brief discussions of the structures in the data. See Chapter 2 of the Maryland SHA Stormwater NPDES Program SOP for more information, figures, and descriptions of each field. End / Head StructuresAn end / head structure is any structure at the upstream or downstream end of a culvert or pipe. These can include headwalls, endwalls, end sections, and projection pipes. Often the end / head structure is designated on the contract sheets and field verified. When contract plans are not available for a roadway, the SHA Book of Standard for Highway & Incidental Structures should be referenced if structure types are unfamiliar with field teams. Outfall areas are not to be inventoried, but will be analyzed during the inspection process. Headwalls (HW) are structures that are placed at the upstream end of pipes and culverts to provide a stable or hydraulically desirable entrance to the conveyance. Headwalls are usually concrete but can be constructed of wood or masonry, such as brick or concrete block. Wall structures on the upstream side of a culvert or pipe are inventoried as headwalls. Plan sheets may designate the upstream end of a pipe or culvert as an endwall, but these structures should be inventoried as headwalls. All wall-end structures at the upstream end of a pipe or culvert should be inventoried as headwalls. Endwalls (EW) are structures that are placed at the downstream end of pipes and culverts to provide a stable or hydraulically desirable exit to the conveyance. Endwalls are usually concrete, but can be constructed of wood or masonry such as brick or concrete block. All wall structures on the downstream side of a culvert or pipe are inventoried as endwalls. Plan sheets may designate the downstream end of pipe or culvert as a headwall, but these structures should be inventoried as endwalls. All wall-end structures at the downstream end of a pipe or culvert should be inventoried as endwalls. End Sections (ES) are structures that transition the ends of pipes into slopes and provide stability to the pipe entrances and outflows. End sections do not affect the hydraulic capacity or efficiency of the pipes. End sections can be constructed of concrete, metal, or plastic (HDPE). End sections can either be inventoried at the upstream or downstream end of a pipe. Projection Pipes (PP) are not physical structures but represent the upstream and downstream end of a pipe if an end structure on a pipe does not exist. Projection pipes are captured spatially as a feature and represent the ends of pipes. Inlet StructuresInlets are structures that collect storm drain runoff. Inlets convey the runoff to closed storm drain systems, open conveyance, or outfalls. There are many different types of inlet structures, and all are discussed in the SHA Standard Design Manual and should be reviewed prior to conducting an inventory. Spring heads are also inventoried as inlets. Inlets (IN) are hydraulic structure chambers below surface grade that collect storm drain runoff. An inlet either has a grate or open sides / curb to allow runoff to enter the storm drain system. Inlets are often constructed of concrete, masonry brick, or concrete block. Spring Heads (SH) are inventoried as inlets. Spring heads are inventoried only where they emerge and are connected to a storm drain system. Spring heads are inventoried because they provide evidence for the presence of ground water for dry weather flows during illicit discharge field screening operation. Spring heads may be identified from contract drawings or identified during the field inventory. Spring heads are mostly found in rural areas. Connection StructuresA connection structure is a storm drain structure that connects conveyance (pipes and ditches) within a system and is not an inlet, riser, weir, or pumping station. These can include manholes, ditch intersections, junction boxes, pipe connections, wye connections, capped inlets, pipe bends, and pipe directions. Because field crews are not required to open manhole lids and enter closed storm drain structures, no designation type is necessary for connection structures. All of the attribute data for these structures will be collected from contract drawings, including connection material and top of manhole elevations. The existence of connection structures should be field verified for spatial accuracy, even though the attributed data will be collected from contract drawings. For structures that are buried or paved over, a GPS point is to be recorded at the best estimated location in the field based on contract plan sheets. The verification of attribute table data for structures that cannot be verified in the field will be completed based on plan sheet information. This also holds true for structures that are buried or cannot be accessed; the attribute data should be obtained from plan sheets. Manholes (MH) are hydraulic structures that connect pipes through a system. They are used as access points to a system, to change direction or invert elevations for pipes, as a junction to change pipe size and / or material, and as a junction of multiple pipes to a single pipe. Manholes are frequently paved over or buried, but are still inventoried. Unless it is certain that the manhole does not exist, the manhole is inventoried. Manholes with lids that have designed holes to allow runoff to enter are inventoried as manholes and not inlets. Ditch Intersections (ID) are geographic representations of where ditches meet, begin, or end a system and are captured as point features. These features are used to define the extents of ditches. Junction Boxes (JB) are underground hydraulic structures that connect pipes through a system. They are used to change direction or invert elevations for pipes, to change pipe size and / or material, and to connect multiple pipes to a single pipe. Identifying junction boxes in the field is difficult because these structures are usually buried with no part of the structure exposed to the surface. Junction boxes are only inventoried from contract drawings and should never be assumed in the field, unless the field crew is certain the structure is a junction box. If the field crew suspects that pipes are merging together and no contract plans are available to confirm this, the connection should be inventoried as a pipe connection and not a junction box. Pipe Connections (PC) are locations throughout the conveyance of a system where two or more pipes connect. A pipe connection is also captured at the location where a closed storm drain pipe connects to a culvert or stream crossing. Wye Connections (YC) are hydraulic structures that join two pipes together within a system’s conveyance. Wye connections will be identified from contract drawings and should not be assumed in the field. Instead of assuming a wye

In advance of design, permitting, and construction of a pipeline to deliver North Slope natural gas to out-of-state customers and Alaska communities, the Division of Geological & Geophysical Surveys (DGGS) has acquired lidar (Light Detection and Ranging) data along proposed pipeline routes, nearby areas of infrastructure, and regions where significant geologic hazards have been identified. Lidar data will serve multiple purposes, but have primarily been collected to (1) evaluate active faulting, slope instability, thaw settlement, erosion, and other engineering constraints along proposed pipeline routes, and (2) provide a base layer for the state-federal GIS database that will be used to evaluate permit applications and construction plans. This digital surface model (mean_DSM) represents mean above-ground height of vegetation returns.

In 2007, the California Ocean Protection Council initiated the California Seafloor Mapping Program (CSMP), designed to create a comprehensive seafloor map of high-resolution bathymetry, marine benthic habitats, and geology within California’s State Waters. The program supports a large number of coastal-zone- and ocean-management issues, including the California Marine Life Protection Act (MLPA) (California Department of Fish and Wildlife, 2008), which requires information about the distribution of ecosystems as part of the design and proposal process for the establishment of Marine Protected Areas. A focus of CSMP is to map California’s State Waters with consistent methods at a consistent scale. The CSMP approach is to create highly detailed seafloor maps through collection, integration, interpretation, and visualization of swath sonar data (the undersea equivalent of satellite remote-sensing data in terrestrial mapping), acoustic backscatter, seafloor video, seafloor photography, high-resolution seismic-reflection profiles, and bottom-sediment sampling data. The map products display seafloor morphology and character, identify potential marine benthic habitats, and illustrate both the surficial seafloor geology and shallow (to about 100 m) subsurface geology. It is emphasized that the more interpretive habitat and geology data rely on the integration of multiple, new high-resolution datasets and that mapping at small scales would not be possible without such data. This approach and CSMP planning is based in part on recommendations of the Marine Mapping Planning Workshop (Kvitek and others, 2006), attended by coastal and marine managers and scientists from around the state. That workshop established geographic priorities for a coastal mapping project and identified the need for coverage of “lands” from the shore strand line (defined as Mean Higher High Water; MHHW) out to the 3-nautical-mile (5.6-km) limit of California’s State Waters. Unfortunately, surveying the zone from MHHW out to 10-m water depth is not consistently possible using ship-based surveying methods, owing to sea state (for example, waves, wind, or currents), kelp coverage, and shallow rock outcrops. Accordingly, some of the data presented in this series commonly do not cover the zone from the shore out to 10-m depth. This data is part of a series of online U.S. Geological Survey (USGS) publications, each of which includes several map sheets, some explanatory text, and a descriptive pamphlet. Each map sheet is published as a PDF file. Geographic information system (GIS) files that contain both ESRI ArcGIS raster grids (for example, bathymetry, seafloor character) and geotiffs (for example, shaded relief) are also included for each publication. For those who do not own the full suite of ESRI GIS and mapping software, the data can be read using ESRI ArcReader, a free viewer that is available at http://www.esri.com/software/arcgis/arcreader/index.html (last accessed September 20, 2013). The California Seafloor Mapping Program is a collaborative venture between numerous different federal and state agencies, academia, and the private sector. CSMP partners include the California Coastal Conservancy, the California Ocean Protection Council, the California Department of Fish and Wildlife, the California Geological Survey, California State University at Monterey Bay’s Seafloor Mapping Lab, Moss Landing Marine Laboratories Center for Habitat Studies, Fugro Pelagos, Pacific Gas and Electric Company, National Oceanic and Atmospheric Administration (NOAA, including National Ocean Service–Office of Coast Surveys, National Marine Sanctuaries, and National Marine Fisheries Service), U.S. Army Corps of Engineers, the Bureau of Ocean Energy Management, the National Park Service, and the U.S. Geological Survey. These web services for the Offshore of Coal Oil Point map area includes data layers that are associated to GIS and map sheets available from the USGS CSMP web page at https://walrus.wr.usgs.gov/mapping/csmp/index.html. Each published CSMP map area includes a data catalog of geographic information system (GIS) files; map sheets that contain explanatory text; and an associated descriptive pamphlet. This web service represents the available data layers for this map area. Data was combined from different sonar surveys to generate a comprehensive high-resolution bathymetry and acoustic-backscatter coverage of the map area. These data reveal a range of physiographic including exposed bedrock outcrops, large fields of sand waves, as well as many human impacts on the seafloor. To validate geological and biological interpretations of the sonar data, the U.S. Geological Survey towed a camera sled over specific offshore locations, collecting both video and photographic imagery; these “ground-truth” surveying data are available from the CSMP Video and Photograph Portal at https://doi.org/10.5066/F7J1015K. The “seafloor character” data layer shows classifications of the seafloor on the basis of depth, slope, rugosity (ruggedness), and backscatter intensity and which is further informed by the ground-truth-survey imagery. The “potential habitats” polygons are delineated on the basis of substrate type, geomorphology, seafloor process, or other attributes that may provide a habitat for a specific species or assemblage of organisms. Representative seismic-reflection profile data from the map area is also include and provides information on the subsurface stratigraphy and structure of the map area. The distribution and thickness of young sediment (deposited over the past about 21,000 years, during the most recent sea-level rise) is interpreted on the basis of the seismic-reflection data. The geologic polygons merge onshore geologic mapping (compiled from existing maps by the California Geological Survey) and new offshore geologic mapping that is based on integration of high-resolution bathymetry and backscatter imagery seafloor-sediment and rock samplesdigital camera and video imagery, and high-resolution seismic-reflection profiles. The information provided by the map sheets, pamphlet, and data catalog has a broad range of applications. High-resolution bathymetry, acoustic backscatter, ground-truth-surveying imagery, and habitat mapping all contribute to habitat characterization and ecosystem-based management by providing essential data for delineation of marine protected areas and ecosystem restoration. Many of the maps provide high-resolution baselines that will be critical for monitoring environmental change associated with climate change, coastal development, or other forcings. High-resolution bathymetry is a critical component for modeling coastal flooding caused by storms and tsunamis, as well as inundation associated with longer term sea-level rise. Seismic-reflection and bathymetric data help characterize earthquake and tsunami sources, critical for natural-hazard assessments of coastal zones. Information on sediment distribution and thickness is essential to the understanding of local and regional sediment transport, as well as the development of regional sediment-management plans. In addition, siting of any new offshore infrastructure (for example, pipelines, cables, or renewable-energy facilities) will depend on high-resolution mapping. Finally, this mapping will both stimulate and enable new scientific research and also raise public awareness of, and education about, coastal environments and issues. Web services were created using an ArcGIS service definition file. The ArcGIS REST service and OGC WMS service include all Offshore Coal Oil Point map area data layers. Data layers are symbolized as shown on the associated map sheets.