Succeeds and combines earlier versions of the tools - Topography Toolbox for ArcGIS 9.x - http://arcscripts.esri.com/details.asp?dbid=15996Riparian Topography Toolbox for calculating Height Above River and Height Above Nearest Drainage - http://arcscripts.esri.com/details.asp?dbid=16792PRISM Data Helper - http://arcscripts.esri.com/details.asp?dbid=15976Tools:UplandBeer’s AspectMcCune and Keon Heat Load IndexLandform ClassifcationPRISM Data HelperSlope Position ClassificationSolar Illumination IndexTopographic Convergence/Wetness IndexTopographic Position IndexRiparianDerive Stream Raster using Cost DistanceHeight Above Nearest DrainageHeight Above RiverMiscellaneousMoving Window Correlation

ArcGIS for Utilities and Telecommunications provides a standard set of templates that include maps, apps, and tools to support water, electric, gas, and telecommunication industry workflows. In this seminar, you will learn how to configure and deploy these templates to support common asset inspection workflows. The presenters also show how to quickly configure the templates to feature your GIS content.This seminar was developed to support the following:ArcGIS Desktop 10.3 (Standard Or Advanced)ArcGIS OnlineCollector for ArcGIS (iOS) 10.3Operations Dashboard for ArcGIS 10.3

Data collected between 3/29/17 and 5/3/17. Contours created on 12/13/18. The 1-foot elevation contours were presented as a feature class in an Esri ArcGIS 10.3 File Geodatabase. The contours were derived from Bare-Earth (DEMs) created from QL2 LiDAR data. Source Data Description: MI 2016 LiDAR project called for the Planning, Acquisition, processing and derivative products of LIDAR data to be collected at a nominal pulse spacing (NPS) of 0.7 meter. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base LIDAR Specification, Version 1.2. The data was developed based on a horizontal projection/datum of Michigan State Plane South, NAD83(2011), international feet and vertical datum of NAVD1988 (GEOID12A), international feet. Deliverables included classified LAS, Raw LAS, Bare-Earth hydro-flattened DEMs, Breaklines and Intensity images. Ground Conditions: LiDAR was collected in spring or fall, while no snow was on the ground and rivers were at or below normal levels.

Data created for use in planning, design, assessment, research, general mapping and hydrologic modeling. These 2' contours were created by Oakland County from the 1' contours provided by the State of Michigan in December, 2018.

Description	Potential fens within a 500-m buffer of Colorado highways were identified in ArcGIS 10.3/10.4 using true color aerial photography taken by the National Agricultural Imagery Program (NAIP) in 2004, 2009, 2011, 2013, and 2015, as well as color-infrared imagery from 2013 and 2015. High (but variable) resolution World Imagery from Environmental Systems Research Institute (ESRI) was also used. Using all available imagery and data, potential fen polygons were hand-drawn based on the best estimation of fen boundaries. Each potential fen polygon was attributed with a confidence value of 1 (low confidence), 3 (possible fen), or 5 (likely fen). In addition to the confidence rating, any justifications of the rating or interesting observations were noted, including iron fens, beaver influence, floating mats, and springs. Once all potential fens were mapped, each polygon was assigned a code for tracking throughout the verification process. The code was a combination of the closest highway segment and a running sequential four-digit number (e.g., 082A-0278).After field verification, additional confidence values of 7 (confirmed fen) and 0 (confirmed non-fen) were added.
Last Update	2018
Update Frequency	As needed
Data Owner	Division of Transportation Development
Data Contact	Wetland Program Manager
Collection Method
Projection	NAD83 / UTM zone 13N
Coverage Area	Statewide
Temporal
Disclaimer/Limitations	There are no restrictions and legal prerequisites for using the data set. The State of Colorado assumes no liability relating to the completeness, correctness, or fitness for use of this data.

People use mobile apps for a lot of their daily activities, and many organizations want to take advantage of this convenience. However, it can be a challenge to create targeted apps if that organization does not have a team of developers.AppStudio for ArcGIS allows you to quickly create native apps that use GIS content, such as web maps, Map Tours, and editable map layers, without writing a single line of code. One app that you create can then be deployed on multiple platforms—including iOS and Android—and this course will quickly show you how to do just that.After completing this course, you will be able to perform the following tasks:List template features for creating apps with AppStudio for ArcGIS.Choose a template based on your app requirements.Use a template to configure GIS resources, branding, and metadata for your app.Use AppStudio Player for ArcGIS to preview and test your app.

Aerial light detection and ranging (lidar) data were collected over the study site between April 12 – 14, 2012 as part of the Fauquier, Fairfax, Frederick (MD), and Jefferson County acquisition for FEMA Region 3 FY12 VA lidar (Dewberry 2012). Lidar points classified as ground and water were used to create a 3-m digital elevation model (DEM) clipped to the Difficult Run watershed with a 500-m buffer in ArcGIS 10.3.1 (ESRI, Redlands, CA).

Abstract The dataset was derived by the Bioregional Assessment Programme. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. This dataset contains the hand-contoured potentiometric surfaces of the various aquifers within the Galilee subregion. Contour intervals are 20m. Purpose To depict the various thickness contours for the Aquifers which …Show full descriptionAbstract The dataset was derived by the Bioregional Assessment Programme. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. This dataset contains the hand-contoured potentiometric surfaces of the various aquifers within the Galilee subregion. Contour intervals are 20m. Purpose To depict the various thickness contours for the Aquifers which lie within the Galilee subregion. Dataset History Created using the QLD DNRM bores waterlevel dataset [ff44f450-8fec-486b-b60d-0d53333a478d]. Contours were interpreted and drawn by hand based on the above source data well lithology information. These contours were then scanned and digitized into ArcGIS shape files. Data was then refined using the ArcGIS spatial analyst tool set - 'smooth contour tools' to smooth the contours. All edits and geoprocessing were performed using ESRI ArcGIS 10.3 software. QAQC: Data sets were searched for errors such as negative thickness, missing data, incorrectly calculated thickness, aquifers/aquitards with missing formations, and false XY data. Data has undergone a QAQC verification process in order to capture and repair attribute and geometric errors. Dataset Citation Bioregional Assessment Programme (2015) GAL Group 2 contour data. Bioregional Assessment Derived Dataset. Viewed 07 December 2018, http://data.bioregionalassessments.gov.au/dataset/803b0f5c-acb8-4adf-9ae1-3cb385ed4061. Dataset Ancestors Derived From QLD DNRM Galilee Mine Groundwater Bores - Water Levels

The bathymetry raster with a resolution of 5 m x 5 m was processed from unpublished single beam data from the Argentine Antarctica Institute (IAA, 2010) and multibeam data from the United Kingdom Hydrographic Office (UKHO, 2012) with a cell size of 5 m x 5 m. A coastline digitized from a satellite image (DigitalGlobe, 2014) supplemented the interpolation process. The 'Topo to Raster' tool in ArcMap 10.3 was used to merge the three data sets, while the coastline represented the 0-m-contour to the interpolation process ('contour type option'). Subproject: JE 680/1-1

RAV Network information periodically changes with additions or removal of data and users should confirm that information is current and accurate. The RAV Network Road Tables and RAV Mapping Tool can be found on the Main Roads Western Australia website, refer Hyperlink below.https://www.mainroads.wa.gov.au/heavy-vehicles/Main Roads Open Data: Restricted Access Networkshttps://portal-mainroads.opendata.arcgis.com/pages/hvs-networksUpdate Frequency: WeeklySpatial Coverage: Western AustraliaLegalYou are accessing this data pursuant to a Creative Commons (Attribution) Licence which has a disclaimer of warranties and limitation of liability. You accept that the data provided pursuant to the Licence is subject to changes. Main Roads WA website is the official and current source of RAV Network data.Pursuant to section 3 of the Licence you are provided with the following notice to be included when you Share the Licenced Material and when you Share your Adapted Material: The Commissioner of Main Roads is the creator and owner of the data and Licenced Material, which is accessed pursuant to a Creative Commons (Attribution) Licence, which has a disclaimer of warranties and limitation of liability. Main Roads WA website is the official and current source of RAV Network data.Licensinghttps://creativecommons.org/licenses/by/4.0/legalcode

These are the main layers that were used in the mapping and analysis for the Santa Monica Mountains Local Coastal Plan, which was adopted by the Board of Supervisors on August 26, 2014, and certified by the California Coastal Commission on October 10, 2014. Below are some links to important documents and web mapping applications, as well as a link to the actual GIS data:

Plan Website – This has links to the actual plan, maps, and a link to our online web mapping application known as SMMLCP-NET. Click here for website. Online Web Mapping Application – This is the online web mapping application that shows all the layers associated with the plan. These are the same layers that are available for download below. Click here for the web mapping application. GIS Layers – This is a link to the GIS layers in the form of an ArcGIS Map Package, click here (LINK TO FOLLOW SOON) for ArcGIS Map Package (version 10.3). Also, included are layers in shapefile format. Those are included below.

Below is a list of the GIS Layers provided (shapefile format):

Recreation (Zipped - 5 MB - click here)

Coastal Zone Campground Trails (2012 National Park Service) Backbone Trail Class III Bike Route – Existing Class III Bike Route – Proposed

Scenic Resources (Zipped - 3 MB - click here)

Significant Ridgeline State-Designated Scenic Highway State-Designated Scenic Highway 200-foot buffer Scenic Route Scenic Route 200-foot buffer Scenic Element

Biological Resources (Zipped - 45 MB - click here)

National Hydrography Dataset – Streams H2 Habitat (High Scrutiny) H1 Habitat H1 Habitat 100-foot buffer H1 Habitat Quiet Zone H2 Habitat H3 Habitat

Hazards (Zipped - 8 MB - click here)

FEMA Flood Zone (100-year flood plain) Liquefaction Zone (Earthquake-Induced Liquefaction Potential) Landslide Area (Earthquake-Induced Landslide Potential) Fire Hazard and Responsibility Area

Zoning and Land Use (Zipped - 13 MB - click here)

Malibu LCP – LUP (1986) Malibu LCP – Zoning (1986) Land Use Policy Zoning

Other Layers (Zipped - 38 MB - click here)

Coastal Commission Appeal Jurisdiction Community Names Santa Monica Mountains (SMM) Coastal Zone Boundary Pepperdine University Long Range Development Plan (LRDP) Rural Village

Contact the L.A. County Dept. of Regional Planning's GIS Section if you have questions. Send to our email.

RAV Network information periodically changes with additions or removal of data and users should confirm that information is current and accurate. The RAV Network Road Tables and RAV Mapping Tool can be found on the Main Roads Western Australia website, refer Hyperlink below.https://www.mainroads.wa.gov.au/heavy-vehicles/Main Roads Open Data: Restricted Access Networkshttps://portal-mainroads.opendata.arcgis.com/pages/hvs-networksUpdate Frequency: WeeklySpatial Coverage: Western AustraliaLegalYou are accessing this data pursuant to a Creative Commons (Attribution) Licence which has a disclaimer of warranties and limitation of liability. You accept that the data provided pursuant to the Licence is subject to changes. Main Roads WA website is the official and current source of RAV Network data.Pursuant to section 3 of the Licence you are provided with the following notice to be included when you Share the Licenced Material and when you Share your Adapted Material: The Commissioner of Main Roads is the creator and owner of the data and Licenced Material, which is accessed pursuant to a Creative Commons (Attribution) Licence, which has a disclaimer of warranties and limitation of liability. Main Roads WA website is the official and current source of RAV Network data.Licensinghttps://creativecommons.org/licenses/by/4.0/legalcode

In this seminar, the presenter introduces essential concepts of ArcGIS Data Reviewer and highlights automated and semi-automated methods to streamline and expedite data validation.This seminar was developed to support the following:ArcGIS Desktop 10.3 (Basic, Standard, or Advanced)ArcGIS Server 10.3 Workgroup (Standard Or Advanced)ArcGIS Data Reviewer for DesktopArcGIS Data Reviewer for Server

Historical shoreline surveys were conducted by the National Ocean Service (NOS), dating back to the early 1800s. The maps resulting from these surveys, often called t-sheets, provide a reference of historical shoreline position that can be compared to modern data to identify shoreline change. The t-sheets are stored at the National Archives and many have been scanned by the National Oceanic and Atmospheric Administration (NOAA) and are available on the NOAA Shoreline Web site (http://www.shoreline.noaa.gov/data/datasheets/t-sheets.html). While some scanned t-sheets were georeferenced and digitized by NOAA, still others remain as non-georeferenced raster files (http://nosimagery.noaa.gov/images/shoreline_surveys/survey_scans/NOAA_Shoreline_Survey_Scans.html). New_Jersey_1839_75_t-sheets.zip features 8 georeferenced raster t-sheets for the New Jersey coastline from 1839 to 1875. The data were scanned by NOAA, but were not georeferenced. The t-sheets included in this data release are: T-121 (1839), T-119 Part 1 (1841), T-1084 (1868), T-1166 (1870), T-1333 (1871), T-1315a (1872), T-1371 (1874), T-1407 (1875). Digital files were georeferenced, corrected to a modern datum, and shorelines digitized to provide a vector polyline depicting the historical shoreline position using ArcGIS 10.3.1. GEoreferenced t-sheets were used to delineate and shorelines for use in long-term shoreline and wetland analyses for Hurricane Sandy wetland physical change assessment.

Hydrologically conditioned digital elevation model (DEM) generated from lidar data clipped to the Difficult Run watershed with a 500-m buffer in ArcGIS 10.3.1 (ESRI, Redlands, CA). The DEM was hydrologically corrected by breaching through pits with no downslope neighboring cells to force surface flow to continuously move downslope using Whitebox Geospatial Analysis Tools (Lindsay and Dhun 2015, Lindsay 2016). Pits that were not properly breached were manually adjusted using elevation information from the DEM and aerial imagery to locate culverts under roadways.

pockmarks are defined as depressions on the seabed and are usually formed by fluid expulsions. recently discovered, pockmarks along the aquitaine slope within the french eez, were manually mapped although two semi-automated methods were tested without convincing results. in order to potentially highlight different groups and possibly discriminate the nature of the fluids involved in their formation and evolution, a morphological study was conducted, mainly based on multibeam data and in particular bathymetry from the marine expedition gazcogne1, 2013. bathymetry and seafloor backscatter data, covering more than 3200 km², were acquired with the kongsberg em302 ship-borne multibeam echosounder of the r/v le suroît at a speed of ~8 knots, operated at a frequency of 30 khz and calibrated with ©sippican shots. precision of seafloor backscatter amplitude is +/- 1 db. multibeam data, processed using caraibes (©ifremer), were gridded at 15x15 m and down to 10x10 m cells, for bathymetry and seafloor backscatter, respectively. the present table includes 11 morphological attributes extracted from a geographical information system project (mercator 44°n conserved latitude in wgs84 datum) and additional parameters related to seafloor backscatter amplitudes. pockmark occurrence with regards to the different morphological domains is derived from a morphological analysis manually performed and based on gazcogne1 and bobgeo2 bathymetric datasets.the pockmark area and its perimeter were calculated with the “calculate geometry” tool of arcmap 10.2 (©esri) (https://desktop.arcgis.com/en/arcmap/10.3/manage-data/tables/calculating-area-length-and-other-geometric-properties.htm). a first method to calculate pockmark internal depth developed by gafeira et al. was tested (gafeira j, long d, diaz-doce d (2012) semi-automated characterisation of seabed pockmarks in the central north sea. near surface geophysics 10 (4):303-315, doi:10.3997/1873-0604.2012018). this method is based on the “fill” function from the hydrology toolset in spatial analyst toolbox arcmap 10.2 (©esri), (https://pro.arcgis.com/en/pro-app/tool-reference/spatial-analyst/fill.htm) which fills the closed depressions. the difference between filled bathymetry and initial bathymetry produces a raster grid only highlighting filled depressions. thus, only the maximum filling values which correspond to the internal depths at the apex of the pockmark were extracted. for the second method, the internal pockmark depth was calculated with the difference between minimum and maximum bathymetry within the pockmark.latitude and longitude of the pockmark centroid, minor and major axis lengths and major axis direction of the pockmarks were calculated inside each depression with the “zonal geometry as table” tool from spatial analyst toolbox in arcgis 10.2 (©esri) (https://pro.arcgis.com/en/pro-app/tool-reference/spatial-analyst/zonal-statistics.htm). pockmark elongation was calculated as the ratio between the major and minor axis length.cell count is the number of cells used inside each pockmark to calculate statistics (https://pro.arcgis.com/en/pro-app/tool-reference/spatial-analyst/zonal-geometry.htm). cell count and minimum, maximum and mean bathymetry, slope and seafloor backscatter values were calculated within each pockmark with “zonal statistics as table” tool from spatial analyst toolbox in arcgis 10.2 (©esri). slope was calculated from bathymetry with “slope” function from spatial analyst toolbox in arcgis 10.2 (©esri) and preserves its 15 m grid size (https://pro.arcgis.com/en/pro-app/tool-reference/spatial-analyst/slope.htm). seafloor backscatter amplitudes (minimum, maximum and mean values) of the surrounding sediments were calculated within a 100 m buffer around the pockmark rim.

This dataset provides locations and values of water quality parameters from a four-day survey conducted between August 23, 2016 and August 26, 2016 using an Autonomous Underwater Vehicle (AUV) in Great South Bay, New York. Measured parameters include bottom dissolved oxygen (DO), salinity, specific conductance, water temperature, and pH. During the four day period, data was collected along 15 transects of the Great South Bay, totaling 60,480 observation points. From these point data, rasters showing the spatial distribution of bottom dissolved oxygen were generated using an interpolator in a GIS. A unique raster is provided for each day of the survey. All data files for download are available within 'Child Items' below. Observation point data are available as shapefiles while DO rasters are available as TIFFs. Both point data and DO rasters are made available as web mapping services. This allows for use in ArcGIS for Desktop, ArcGIS Online, and other web applications. For additional information on how to use web mapping services please visit http://server.arcgis.com/en/server/10.3/publish-services/linux/what-is-a-map-service.htm. Please note that the .sd files included are not meant to be open as standalone files, but rather were uploaded to generate the online web mapping service links provided.

APP is synthesising the wealth of new and existing information and knowledge within GNS Science and other open-file sources, to produce a nationally-significant baseline reference dataset that summarises the current understanding of the petroleum prospectivity of New Zealand's offshore sedimentary basins. Polygon, point, and polyline features are organised thematically into Feature Datasets, each of which contains multiple Feature Classes. Raster datasets are included at the highest hierarchical level of the database. The co-ordinate system used for the database is New Zealand Transverse Mercator, based on the NZGD2000 datum. Database compilation was undertaken using ArcGIS 10.3.1 for Desktop (version 10.3.1.4959). The atlas is subdivided into a number of provinces, each with common geological setting.

This map is designated as Final.Land-Use Data Quality ControlEvery published digital survey is designated as either ‘Final’, or ‘Provisional’, depending upon its status in a peer review process.Final surveys are peer reviewed with extensive quality control methods to confirm that field attributes reflect the most detailed and specific land-use classification available, following the standard DWR Land Use Legendspecific to the survey year. Data sets are considered ‘final’ following the reconciliation of peer review comments and confirmation by the originating Regional Office. During final review, individual polygons are evaluated using a combination of aerial photointerpretation, satellite image multi-spectral data and time series analysis, comparison with other sources of land use data, and general knowledge of land use patterns at the local level.Provisional data sets have been reviewed for conformance with DWR’s published data record format, and for general agreement with other sources of land use trends. Comments based on peer review findings may not be reconciled, and no significant edits or changes are made to the original survey data.The 2015 Sacramento County land use survey data was developed by the State of California, Department of Water Resources (DWR) through its Division of Integrated Regional Water Management (DIRWM) and Division of Statewide Integrated Water Management (DSIWM). Land use boundaries were digitized and land use data were gathered by staff of DWR’s North Central Region using extensive field visits and aerial photography. Land use polygons in agricultural areas were mapped in greater detail than areas of urban or native vegetation. Quality control procedures were performed jointly by staff at DWR’s DSIWM headquarters, under the leadership of Jean Woods, and North Central Region, under the supervision of Kim Rosmaier. This data was developed to aid DWR’s ongoing efforts to monitor land use for the main purpose of determining current and projected water uses. The associated data are considered DWR enterprise GIS data, which meet all appropriate requirements of the DWR Spatial Data Standards, specifically the DWR Spatial Data Standards version 2.1, dated March 9, 2016. DWR makes no warranties or guarantees - either expressed or implied - as to the completeness, accuracy, or correctness of the data. DWR neither accepts nor assumes liability arising from or for any incorrect, incomplete, or misleading subject data. Comments, problems, improvements, updates, or suggestions should be forwarded to gis@water.ca.gov. This data represents a land use survey of Sacramento County conducted by the California Department of Water Resources, North Central Regional Office staff. Land use field boundaries were digitized with ArcGIS 10.3 using 2014 U.S.D.A National Agriculture Imagery Program (NAIP) one-meter imagery as the base. Agricultural fields were delineated by following actual field boundaries instead of using the centerlines of roads to represent the field borders. Field boundaries were reviewed and updated using 2015 Landsat 8 imagery. Field boundaries were not drawn to represent legal parcel (ownership) boundaries, and are not meant to be used as parcel boundaries. The field work for this survey was conducted from July 2015 through August 2015. Images, land use boundaries and ESRI ArcMap software were loaded onto laptop computers that were used as the field data collection tools. Staff took these laptops into the field and virtually all agricultural fields were visited to identify the land use. Global positioning System (GPS) units connected to the laptops were used to confirm the surveyor's location with respect to the fields. Land use codes were digitized in the field using dropdown selections from defined domains. Agricultural fields the staff were unable to access were designated 'E' in the Class field for Entry Denied in accordance with the 2009 Landuse Legend. Upon completion of the survey, a Python script was used to convert the data table into the standard land use format. ArcGIS geoprocessing tools and topology rules were used to locate errors for quality control. The primary focus of this land use survey is mapping agricultural fields. Urban residences and other urban areas were delineated using aerial photo interpretation. Some urban areas may have been missed. Rural residential land use was delineated by drawing polygons to surround houses and other buildings along with some of the surrounding land. These footprint areas do not represent the entire footprint of urban land. Sources of irrigation water were identified for general areas and occasionally supplemented by information obtained from landowners. Water source information was not collected for each field in the survey, so the water source listed for a specific agricultural field may not be accurate. Before final processing, standard quality control procedures were performed jointly by staff at DWR’s North Central Region, and at DSIWM headquarters under the leadership of Jean Woods. Senior Land and Water Use Supervisor. After quality control procedures were completed, the data was finalized. The positional accuracy of the digital line work, which is based upon the orthorectified NAIP imagery, is approximately 6 meters. The land use attribute accuracy for agricultural fields is high, because almost every delineated field was visited by a surveyor. The accuracy is 95 percent because some errors may have occurred. Possible sources of attribute errors are: a) Human error in the identification of crop types, b) Data entry errors.

This ArcGIS 10.3 point feature class contains identification, location, and outfall attributes including outfall size and receiving water body, and class information for New York City Municipal Separate Storm Sewer System Outfall (MS4). The information was collected by New York City in the Long Term Control Plan (LTCP) portion of the SPDES draft permit submission to NYSDEC. This information layer, and all R2 GIS layers, are maintained in a SQLServer 2012 geodatabase. The National Pollutant Discharge Elimination System (NPDES) program is implemented by NYSDEC via the compliance and enforcement elements of the State Pollutant Discharge Elimination System (SPDES) permit program. The National Pollutant Discharge Elimination System (NPDES) permit program is authorized by the Clean Water Act. The Integrated Compliance Information System (ICIS) for NPDES data exchange allows Partners to provide ICIS-NPDES data to EPA in an XML format and provides processing results to assist Partners with correcting common errors that may occur with their submissions.