To view and download tax plat maps, click on the DATA tab above. To find a map, select a column heading to sort the table by zone or section number. Or refine your search by the clicking the filter icon at the top of each column. To view or download the tax plat map, click on the URL under ViewMap next to the ZSP map number. Alternatively, use the online Parcel and Zoning Map to search for tax plat maps by address, tax map key (TMK), or using a map interface.

Web App. Use the tabs provided to discover information about map features and capabilities. Link to Metadata. A variety of searches can be performed to find the parcel of interest. Use the Query Tool to build searches. Click Apply button at the bottom of the tool.Query by Name (Last First) (e.g. Bond James)Query by Address (e.g. 41 S Central)Query by Locator number (e.g. 21J411046)Search results will be listed under the Results tab. Click on a parcel in the list to zoom to that parcel. Click on the parcel in the map and scroll through the pop-up to see more information about the parcel. Click the ellipse in the Results tab or in the pop-up to view information in a table. Attribute information can be exported to CSV file. Build a custom Filter to select and map properties by opening the Parcels attribute table:1. Click the arrow tab at the bottom middle of the map to expand the attribute table window2. Click on the Parcels tab3. Check off Filter by map extent4. Open Options>Filter5. Build expressions as needed to filter by owner name or other variables6. Select the needed records from the returned list7. Click Zoom to which will zoom to the selected recordsPlease note that as the map zooms out detailed layers, such as the parcel boundaries will not display.In addition to Search capabilities, the following tools are provided:MeasureThe measure tool provides the capabilities to draw a point, line, or polygon on the map and specify the unit of measurement.DrawThe draw tool provides the capabilities to draw a point, line, or polygon on the map as graphics. PrintThe print tool exports the map to either a PDF or image file. Click Settings button to configure map or remove legend.Map navigation using mouse and keyboard:Drag to panSHIFT + CTRL + Drag to zoom outMouse Scroll Forward to zoom inMouse Scroll Backward to zoom outUse Arrow keys to pan+ key to zoom in a level- key to zoom out a levelDouble Click to Zoom inFAQsHow to select a parcel: Click on a parcel in the map, or use Query Tool to search for parcel by owner, address or parcel id.How to select more than one parcel: Go to Select Tool and choose options on Select button.How to clear selected parcel(s): Go to Select Tool and click Clear.

Use the Zone Lookup template to allow users to search for an address or use their current location to identify locations that are within a zone or region. With apps created with this template, users can learn more about a location and features of interest in the surrounding area. Grouping results by layer provides an organized view of search results. You can also include the export tool to capture images of the map with the search results. Examples: Facilitate finding hurricane evacuation zones by address in an emergency. Build an app where users can identify schools within a school district, based on a searched address or location. Provide city planning information by zone or area. Data requirements The Zone Lookup template requires a feature layer to use all of its capabilities. Key app capabilities Results - Customize result panel location information with feature attributes from a configured pop-up. Show selected result only - Display the selected result feature in the map while hiding the other features. Attribute filter - Configure map filter options that are available to or added by app users. Sketch a zone - Enable app users to draw a search zone with sketch tools, including buffer capabilities. Export - Print or export the search results or selected features as a .pdf, .jpg, or .png file that includes the pop-up content of returned features and an option to include the map. Additionally, download the search results as a .csv file. Language switcher - Provide translations for custom text and create a multilingual app. Home, Zoom controls, Legend, Layer List, Search Supportability This web app is designed responsively to be used in browsers on desktops, mobile phones, and tablets. We are committed to ongoing efforts towards making our apps as accessible as possible. Please feel free to leave a comment on how we can improve the accessibility of our apps for those who use assistive technologies.

Surface albedo determines radiative forcing and is a key parameter for driving Earth’s climate. Better characterization of surface albedo for individual land cover types can reduce the uncertainty in estimating changes to Earth’s radiation balance due to land cover change. The dataset includes albedo look-up maps (LUMs) using a multiscale hierarchical approach based on moderate resolution imaging spectroradiometer (MODIS) bidirectional reflectance distribution function (BRDF)/albedo products and Landsat imagery. Ten years (2001 to 2011) of MODIS BRDF/albedo products were used to generate global albedo climatology. Albedo LUMs of land cover classes defined by the International Geosphere-Biosphere Programme (IGBP) at multiple spatial resolutions were generated. The albedo LUMs included monthly statistics of white-sky (diffuse) and black-sky (direct) albedo for each IGBP class for visible, near-infrared, and shortwave broadband under both snow-free and snow-covered conditions. The LUMs provide high temporal and spatial resolution global albedo statistics without gaps for investigating albedo variations under different land cover scenarios and could be used for land surface modeling.Resources in this dataset:Resource Title: Multiscale climatological albedo look-up maps (LUMS).File Name: Web Page, url: https://app.globus.org/file-manager?origin_id=904c2108-90cf-11e8-9672-0a6d4e044368&origin_path=/LTS/ADCdatastorage/NAL/published/node35339/The albedo look-up maps (LUMs) were built on moderate resolution imaging spectroradiometer (MODIS) bidirectional reflectance distribution function (BRDF)/albedo products and Landsat imagery. Ten years (2001 to 2011) of MODIS BRDF/albedo products were used to generate global albedo climatology. Albedo LUMs of land cover classes defined by the International Geosphere-Biosphere Programme (IGBP) at multiple spatial resolutions were generated. The albedo LUMs included monthly statistics of white-sky (diffuse) and black-sky (direct) albedo for each IGBP class for visible, near-infrared, and shortwave broadband under both snow-free and snow-covered conditions.The zipped file includes: readme.docx snow_covered_hierarchical.v3.tar snow_covered_LUM.v3.tar snow_free_hierarchical.v3.tar snow_free_LUM.v3.tarSCINet users: The .zip file can be accessed/retrieved with valid SCINet account at this location: /LTS/ADCdatastorage/NAL/published/node35339/See the SCINet File Transfer guide for more information on moving large files: https://scinet.usda.gov/guides/data/datatransferGlobus users: The files can also be accessed through Globus by following this data link. The user will need to log in to Globus in order to retrieve this data. User accounts are free of charge with several options for signing on. Instructions for creating an account are on the login page.

This dataset is called the Gridded SSURGO (gSSURGO) Database and is derived from the Soil Survey Geographic (SSURGO) Database. SSURGO is generally the most detailed level of soil geographic data developed by the National Cooperative Soil Survey (NCSS) in accordance with NCSS mapping standards. The tabular data represent the soil attributes, and are derived from properties and characteristics stored in the National Soil Information System (NASIS). The gSSURGO data were prepared by merging traditional SSURGO digital vector map and tabular data into a Conterminous US-wide extent, and adding a Conterminous US-wide gridded map layer derived from the vector, plus a new value added look up (valu) table containing "ready to map" attributes. The gridded map layer is offered in an ArcGIS file geodatabase raster format. The raster and vector map data have a Conterminous US-wide extent. The raster map data have a 30 meter cell size. Each cell (and polygon) is linked to a map unit identifier called the map unit key. A unique map unit key is used to link to raster cells and polygons to attribute tables, including the new value added look up (valu) table that contains additional derived data. The value added look up (valu) table contains attribute data summarized to the map unit level using best practice generalization methods intended to meet the needs of most users. The generalization methods include map unit component weighted averages and percent of the map unit meeting a given criteria. The Gridded SSURGO dataset was created for use in national, regional, and state-wide resource planning and analysis of soils data. The raster map layer data can be readily combined with other national, regional, and local raster layers, e.g., National Land Cover Database (NLCD), the National Agricultural Statistics Service (NASS) Crop Data Layer, or the National Elevation Dataset (NED).

The gSSURGO dataset provides detailed soil survey mapping in raster format with ready-to-map attributes organized in statewide tiles for desktop GIS. gSSURGO is derived from the official Soil Survey Geographic (SSURGO) Database. SSURGO generally has the most detailed level of soil geographic data developed by the National Cooperative Soil Survey (NCSS) in accordance with NCSS mapping standards. The tabular data represent the soil attributes and are derived from properties and characteristics stored in the National Soil Information System (NASIS).

The gSSURGO data were prepared by merging the traditional vector-based SSURGO digital map data and tabular data into statewide extents, adding a statewide gridded map layer derived from the vector layer, and adding a new value-added look up table (valu) containing ready-to-map attributes. The gridded map layer is in an ArcGIS file geodatabase in raster format, thus it has the capacity to store significantly more data and greater spatial extents than the traditional SSURGO product. The raster map data have a 10-meter cell size that approximates the vector polygons in an Albers Equal Area projection. Each cell (and polygon) is linked to a map unit identifier called the map unit key. A unique map unit key is used to link the raster cells and polygons to attribute tables.

For more information, see the gSSURGO webpage: https://www.nrcs.usda.gov/resources/data-and-reports/description-of-gridded-soil-survey-geographic-gssurgo-database

The objective of this research was to collect new bathymetry for all of Florida Bay, digitize the historical shoreline and bathymetric data, compare previous data to modern data, and produce maps and digital grids of historical and modern bathymetry.

Detailed, high-resolution maps of Florida Bay mudbank elevations are needed to understand sediment dynamics and provide input into water quality and circulation models. The bathymetry of Florida Bay had not been systematically mapped in nearly 100 years, and some shallow areas of the bay have never been mapped. An accurate, modern bathymetric survey provides a baseline for assessing future sedimentation rates in the Bay, and a foundation for developing a sediment budget. Due to the complexity of the Bay and age of existing data, a current bathymetric grid (digitally derived from the survey) is critical for numerical models. Numerical circulation and sediment transport models being developed for the South Florida Ecosystem Restoration Program are being used to address water quality issues in Florida Bay. Application of these models is complicated due to the complex seafloor topography (basin/mudbank morphology) of the Bay. The only complete topography data set of the Bay is 100 years old. Consequently, an accurate, modern seafloor bathymetry map of the Bay is critical for numerical modeling research. A modern bathymetry data set will also permit a comparison to historical data in order to help access sedimentation rates within the Bay.

Contents: This is an ArcGIS Pro zip file that you can download and use for creating map books based on United States National Grid (USNG). It contains a geodatabase, layouts, and tasks designed to teach you how to create a basic map book.Version 1.0.0 Uploaded on May 24th and created with ArcGIS Pro 2.1.3 - Please see the README below before getting started!Updated to 1.1.0 on August 20thUpdated to 1.2.0 on September 7thUpdated to 2.0.0 on October 12thUpdate to 2.1.0 on December 29thBack to 1.2.0 due to breaking changes in the templateBack to 1.0.0 due to breaking changes in the template as of June 11th 2019Updated to 2.1.1 on October 8th 2019Audience: GIS Professionals and new users of ArcGIS Pro who support Public Safety agencies with map books. If you are looking for apps that can be used by any public safety professional, see the USNG Lookup Viewer.Purpose: To teach you how to make a map book with critical infrastructure and a basemap, based on USNG. You NEED to follow the steps in the task and not try to take shortcuts the first time you use this task in order to receive the full benefits. Background: This ArcGIS Pro template is meant to be a starting point for your map book projects and is based on best practices by the USNG National Implementation Center (TUNIC) at Delta State University and is hosted by the NAPSG Foundation. This does not replace previous templates created in ArcMap, but is a new experimental approach to making map books. We will continue to refine this template and work with other organizations to make improvements over time. So please send us your feedback admin@publicsafetygis.org and comments below. Instructions: Download the zip file by clicking on the thumbnail or the Download button.Unzip the file to an appropriate location on your computer (C:\Users\YourUsername\Documents\ArcGIS\Projects is a common location for ArcGIS Pro Projects).Open the USNG Map book Project File (APRX).If the Task is not already open by default, navigate to Catalog > Tasks > and open 'Create a US National Grid Map Book' Follow the instructions! This task will have some automated processes and models that run in the background but you should pay close attention to the instructions so you also learn all of the steps. This will allow you to innovate and customize the template for your own use.FAQsWhat is US National Grid? The US National Grid (USNG) is a point and area reference system that provides for actionable location information in a uniform format. Its use helps achieve consistent situational awareness across all levels of government, disciplines, and threats & hazards – regardless of your role in an incident.One of the key resources NAPSG makes available to support emergency responders is a basic USNG situational awareness application. See the NAPSG Foundation and USNG Center websites for more information.What is an ArcGIS Pro Task? A task is a set of preconfigured steps that guide you and others through a workflow or business process. A task can be used to implement a best-practice workflow, improve the efficiency of a workflow, or create a series of interactive tutorial steps. See "What is a Task?" for more information.Do I need to be proficient in ArcGIS Pro to use this template? We feel that this is a good starting point if you have already taken the ArcGIS Pro QuickStart Tutorials. While the task will automate many steps, you will want to get comfortable with the map layouts and other new features in ArcGIS Pro.Is this template free? This resources is provided at no-cost, but also with no guarantees of quality assurance or support at this time. Can't I just use ArcMap? Ok - here you go. USNG 1:24K Map Template for ArcMapKnown Limitations and BugsZoom To: It appears there may be a bug or limitation with automatically zooming the map to the proper extent, so get comfortable with navigation or zoom to feature via the attribute table.FGDC Compliance: We are seeking feedback from experts in the field to make sure that this meets minimum requirements. At this point in time we do not claim to have any official endorsement of standardization. File Size: Highly detailed basemaps can really add up and contribute to your overall file size, especially over a large area / many pages. Consider making a simple "Basemap" of street centerlines and building footprints.We will do the best we can to address limitations and are very open to feedback!

The ArcGIS Online US Geological Survey (USGS) topographic map collection now contains over 177,000 historical quadrangle maps dating from 1882 to 2006. The USGS Historical Topographic Map Explorer app brings these maps to life through an interface that guides users through the steps for exploring the map collection:

Finding the maps of interest is simple. Users can see a footprint of the map in the map view before they decide to add it to the display, and thumbnails of the maps are shown in pop-ups on the timeline. The timeline also helps users find maps because they can zoom and pan, and maps at select scales can be turned on or off by using the legend boxes to the left of the timeline. Once maps have been added to the display, users can reorder them by dragging them. Users can also download maps as zipped GeoTIFF images. Users can also share the current state of the app through a hyperlink or social media. This ArcWatch article guides you through each of these steps: https://www.esri.com/esri-news/arcwatch/1014/envisioning-the-past.

This dataset is called the Gridded SSURGO (gSSURGO) Database and is derived from the Soil Survey Geographic (SSURGO) Database. SSURGO is generally the most detailed level of soil geographic data developed by the National Cooperative Soil Survey (NCSS) in accordance with NCSS mapping standards. The tabular data represent the soil attributes, and are derived from properties and characteristics stored in the National Soil Information System (NASIS). The gSSURGO data were prepared by merging traditional SSURGO digital vector map and tabular data into a Conterminous US-wide extent, and adding a Conterminous US-wide gridded map layer derived from the vector, plus a new value added look up (valu) table containing "ready to map" attributes. The gridded map layer is offered in an ArcGIS file geodatabase raster format.

The raster and vector map data have a Conterminous US-wide extent. The raster map data have a 10 meter cell size that approximates the vector polygons in an Albers Equal Area projection. Each cell (and polygon) is linked to a map unit identifier called the map unit key. A unique map unit key is used to link to raster cells and polygons to attribute tables, including the new value added look up (valu) table that contains additional derived data.

The value added look up (valu) table contains attribute data summarized to the map unit level using best practice generalization methods intended to meet the needs of most users. The generalization methods include map unit component weighted averages and percent of the map unit meeting a given criteria.

The Gridded SSURGO dataset was created for use in national, regional, and state-wide resource planning and analysis of soils data. The raster map layer data can be readily combined with other national, regional, and local raster layers, e.g., National Land Cover Database (NLCD), the National Agricultural Statistics Service (NASS) Crop Data Layer, or the National Elevation Dataset (NED).

During a search and rescue (SAR) operation, officials don't have time to wait until a GIS specialist is on scene. They need maps immediately. Preconfigured and ready-to-use GIS tools must be available to SAR teams before an incident occurs.

In this lesson, you'll create a web map to prepare data for search operations. Your map will contain static base data showing regional boundaries and key features, as well as editable layers that can be changed as an incident develops. Then, you'll use the map to create a web app that even non-GIS professionals can use. Finally, you'll use the app to track a fictional SAR mission.

In this lesson you will build skills in the these areas:

• Mapping base and incident data
• Configure widgets in a web app
• Adding Situational Awareness and Smart Editor widgets
• Plotting Initial Planning Point and adding trail blocks
• Creating search assignments
• Mapping incident data

Learn ArcGIS is a hands-on, problem-based learning website using real-world scenarios. Our mission is to encourage critical thinking, and to develop resources that support STEM education.

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

Resource contains an ArcGIS file geodatabase raster for the National Vegetation Information System (NVIS) Major Vegetation Groups - Australia-wide, present extent (FGDB_NVIS4_1_AUST_MVG_EXT).

Related datasets are also included: FGDB_NVIS4_1_KEY_LAYERS_EXT - ArcGIS File Geodatabase Feature Class of the Key Datasets that make up NVIS Version 4.1 - Australia wide; and FGDB_NVIS4_1_LUT_KEY_LAYERS - Lookup table for Dataset Key Layers.

This raster dataset provides the latest summary information (November 2012) on Australia's present (extant) native vegetation. It is in Albers Equal Area projection with a 100 m x 100 m (1 Ha) cell size. A comparable Estimated Pre-1750 (pre-european, pre-clearing) raster dataset is available: - NVIS4_1_AUST_MVG_PRE_ALB. State and Territory vegetation mapping agencies supplied a new version of the National Vegetation Information System (NVIS) in 2009-2011. Some agencies did not supply new data for this version but approved re-use of Version 3.1 data. Summaries were derived from the best available data in the NVIS extant theme as at June 2012. This product is derived from a compilation of data collected at different scales on different dates by different organisations. Please refer to the separate key map showing scales of the input datasets. Gaps in the NVIS database were filled by non-NVIS data, notably parts of South Australia and small areas of New South Wales such as the Curlewis area. The data represent on-ground dates of up to 2006 in Queensland, 2001 to 2005 in South Australia (depending on the region) and 2004/5 in other jurisdictions, except NSW. NVIS data was partially updated in NSW with 2001-09 data, with extensive areas of 1997 data remaining from the earlier version of NVIS. Major Vegetation Groups were identified to summarise the type and distribution of Australia's native vegetation. The classification contains different mixes of plant species within the canopy, shrub or ground layers, but are structurally similar and are often dominated by a single genus. In a mapping sense, the groups reflect the dominant vegetation occurring in a map unit where there are a mix of several vegetation types. Subdominant vegetation groups which may also be present in the map unit are not shown. For example, the dominant vegetation in an area may be mapped as dominated by eucalypt open forest, although it contains pockets of rainforest, shrubland and grassland vegetation as subdominants. The (related) Major Vegetation Subgroups represent more detail about the understorey and floristics of the Major Vegetation Groups and are available as separate raster datasets: - NVIS4_1_AUST_MVS_EXT_ALB - NVIS4_1_AUST_MVS_PRE_ALB A number of other non-vegetation and non-native vegetation land cover types are also represented as Major Vegetation Groups. These are provided for cartographic purposes, but should not be used for analyses. For further background and other NVIS products, please see the links on http://www.environment.gov.au/erin/nvis/index.html.

The current NVIS data products are available from http://www.environment.gov.au/land/native-vegetation/national-vegetation-information-system.

Purpose

For use in Bioregional Assessment land classification analyses

Dataset History

NVIS Version 4.1

The input vegetation data were provided from over 100 individual projects representing the majority of Australia's regional vegetation mapping over the last 50 years. State and Territory custodians translated the vegetation descriptions from these datasets into a common attribute framework, the National Vegetation Information System (ESCAVI, 2003). Scales of input mapping ranged from 1:25,000 to 1:5,000,000. These were combined into an Australia-wide set of vector data. Non-terrestrial areas were mostly removed by the State and Territory custodians before supplying the data to the Environmental Resources Information Network (ERIN), Department of Sustainability Environment Water Population and Communities (DSEWPaC).

Each NVIS vegetation description was written to the NVIS XML format file by the custodian, transferred to ERIN and loaded into the NVIS database at ERIN. A considerable number of quality checks were performed automatically by this system to ensure conformity to the NVIS attribute standards (ESCAVI, 2003) and consistency between levels of the NVIS Information Hierarchy within each description. Descriptions for non-vegetation and non-native vegetation mapping codes were transferred via CSV files.

The NVIS vector (polygon) data for Australia comprised a series of jig-saw pieces, eachup to approx 500,000 polygons - the maximum tractable size for routine geoprocesssing. The spatial data was processed to conform to the NVIS spatial format (ESCAVI, 2003; other papers). Spatial processing and attribute additions were done mostly in ESRI File Geodatabases. Topology and minor geometric corrections were also performed at this stage. These datasets were then loaded into ESRI Spatial Database Engine as per the ERIN standard. NVIS attributes were then populated using Oracle database tables provided by custodians, mostly using PL/SQL Developer or in ArcGIS using the field calculator (where simple).

Each spatial dataset was joined to and checked against a lookup table for the relevant State/Territory to ensure that all mapping codes in the dominant vegetation type of each polygon (NVISDSC1) had a valid lookup description, including an allocated MVG. Minor vegetation components of each map unit (NVISDSC2-6) were not checked, but could be considered mostly complete.

Each NVIS vegetation description was allocated to a Major Vegetation Group (MVG) by manual interpretation at ERIN. The Australian Natural Resources Atlas (http://www.anra.gov.au/topics/vegetation/pubs/native_vegetation/vegfsheet.html) provides detailed descriptions of most Major Vegetation Groups. Three new MVGs were created for version 4.1 to better represent open woodland formations and forests (in the NT) with no further data available. NVIS vegetation descriptions were reallocated into these classes, if appropriate:

• Unclassified Forest

• Other Open Woodlands

• Mallee Open Woodlands and Sparse Mallee Shublands

(Thus there are a total of 33 MVGs existing as at June 2012). Data values defined as cleared or non-native by data custodians were attributed specific MVG values such as 25 - Cleared or non native, 27 - naturally bare, 28 - seas & estuaries, and 99 - Unknown.

As part of the process to fill gaps in NVIS, the descriptive data from non-NVIS sources was also referenced in the NVIS database, but with blank vegetation descriptions. In general. the gap-fill data comprised (a) fine scale (1:250K or better) State/Territory vegetation maps for which NVIS descriptions were unavailable and (b) coarse-scale (1:1M) maps from Commonwealth and other sources. MVGs were then allocated to each description from the available desciptions in accompanying publications and other sources.

Parts of New South Wales, South Australia, QLD and the ACT have extensive areas of vector "NoData", thus appearing as an inland sea. The No Data areas were dealt with differently by state. In the ACT and SA, the vector data was 'gap-filled' and attributed using satellite imagery as a guide prior to rasterising. Most of these areas comprised a mixture of MVG 24 (inland water) and 25 (cleared), and in some case 99 (Unknown). The NSW & QLD 'No Data' areas were filled using a raster mask to fill the 'holes'. These areas were attributed with MVG 24, 26 (water & unclassified veg), MVG 25 (cleared); or MVG 99 Unknown/no data, where these areas were a mixture of unknown proportions.

Each spatial dataset with joined lookup table (including MVG_NUMBER linked to NVISDSC1) was exported to a File Geodatabase as a feature class. These were reprojected into Albers Equal Area projection (Central_Meridian: 132.000000, Standard_Parallel_1: -18.000000, Standard_Parallel_2: -36.000000, Linear Unit: Meter (1.000000), Datum GDA94, other parameters 0).

Each feature class was then rasterised to a 100m raster with extents to a multiple of 1000 m, to ensure alignment. In some instances, areas of 'NoData' had to be modelled in raster. For example, in NSW where non-native areas (cleared, water bodies etc) have not been mapped. The rasters were then merged into a 'state wide' raster. State rasters were then merged into this 'Australia wide' raster dataset.

November 2012 Corrections

Closer inspection of the original 4.1 MVG Extant raster dataset highlighted some issues with the raster creation process which meant that raster pixels in some areas did not align as intended. These were corrected, and the new properly aligned rasters released in November 2012.

Dataset Citation

Department of the Environment (2012) Australia - Present Major Vegetation Groups - NVIS Version 4.1 (Albers 100m analysis product). Bioregional Assessment Source Dataset. Viewed 10 July 2017, http://data.bioregionalassessments.gov.au/dataset/57c8ee5c-43e5-4e9c-9e41-fd5012536374.

Use the Nearby template to guides your app users to places of interest close to an address. This template helps users find focused types of locations (such as schools) within a search distance of an address, their current location, or other place they specify. They can adjust distance values to change the search radius and get directions to locations they select. For users who are searching, you can set a range for the distance slider so users can define their search buffer or pan the map to see results from the map view. Include directions to help users navigate to locations within a defined search radius. Include the export tool to allow users to capture images of the map along with results from the search. Examples: Create a store locator app that allows customers to input a location, find a nearby store, and navigate to it. Create an app for finding health care facilities within a specified distance of a searched address. Provide users with directions and information for election polling locations. Build an app where users can find nearby trails and view an elevation profile of each result. Data requirements The Nearby template requires a feature layer to take full advantage of its capabilities. Key app capabilities Distance slider - Set a minimum and maximum search radius for finding results. Map extent result - Show all the results in the map view. Panel options - Customize result panel location information with feature attributes from a configured pop-up. Results-focused layout - Keep the map out of the app to maintain focus on the search and results. Attribute filter - Configure map filter options that are available to app users. Export - Print or export the search results or selected features as a .pdf, .jpg, or .png file that includes the pop-up content of returned features and an option to include the map. Alternatively, download the search results as a .csv file. Directions - Provide directions from a searched location to a result location. Elevation profile - Generate an elevation profile graph across an input line feature that can be selected in the scene or from drawing a single or multisegment line using the tool. Language switcher - Provide translations for custom text and create a multilingual app. Home, Zoom controls, Legend, Layer List, Search Supportability This web app is designed responsively to be used in browsers on desktops, mobile phones, and tablets. We are committed to ongoing efforts towards making our apps as accessible as possible. Please feel free to leave a comment on how we can improve the accessibility of our apps for those who use assistive technologies.

Summary data of fixed broadband coverage by geographic area. License and Attribution: Broadband data from FCC Form 477, and data from the U.S. Census Bureau that are presented on this site are offered free and not subject to copyright restriction. Data and content created by government employees within the scope of their employment are not subject to domestic copyright protection under 17 U.S.C. § 105. See, e.g., U.S. Government Works. While not required, when using content, data, documentation, code and related materials from fcc.gov or broadbandmap.fcc.gov in your own work, we ask that proper credit be given. Examples include: • Source data: FCC Form 477 • Map layer based on FCC Form 477 • Code data based on broadbandmap.fcc.gov The geography look ups are created from the US census shapefiles, which are in Global Coordinate System North American Datum of 1983 (GCS NAD83). The coordinates do not get reprojected during processing. The "centroid_lng", "centroid_lat" columns in the lookup table are the exact values from the US census shapefile (INTPTLON, INTPTLAT). The "bbox_arr" column is calculated from the bounding box/extent of the original geometry in the shapefile; no reprojection or transformations are done to the geometry.

This application provides users with detailed information on hunting opportunities available on Department of Conservation and Recreation (DCR), Division of Water Supply Protection (DWSP) land in central Massachusetts. This is comprised of multiple pages that provide information (as text) and two interactive maps. The first interactive map is the Hunting Map, which provides all of the information a hunter might need to plan a successful hunt. The information in this map provides a comprehensive look at where hunting is allowed, the permits required, and the locations of features like fields and stone walls (and more!). The second interactive map is the Harvest Map, which provides hunters with information on deer harvested as part of the Quabbin Controlled Deer Hunts since 2010; this map is included for interest and informational purposes only. Learn more about each map below. For accessible PDF versions of this information, please select from one of the following options:Full Public Access Plan (PDF)Public Access Summary (PDF)Hunting Map InformationThis interactive map highlights hunting opportunities on DCR-DWSP land in central Massachusetts. The key data layers to this map are:Waterfowl Hunting Opportunities - a collection of points highlighting various waterbodies within the DWSP watersheds where waterfowl hunting is or is not allowed. For waterbodies that allow hunting, detailed information on exactly what is or is not allowed is provided in the feature pop-up, or in the attribute table record. Each feature pop-up also tells the user if trapping is allowed at a waterbody.Gates for Parking and Access - this layer shows the point locations of gates that can be used for parking or access onto DWSP lands. Depending on the type of point selected, the user will be provided with information on the number of parking spots available, a word of caution and (when available) photo(s) of the location. DCR-DWSP Lands by Permit Type - this polygon layer shows DWSP property boundaries and indicates if (and what type) of hunting is allowed in a certain area. These serve as a backdrop to all other data presented within the map. Click on a feature for more information, or review the attribute table to see what activities are allowed. Many layers in this map have a visibility constraint placed on them, and will only appear in the map as the user zooms in closer to an area. This helps ensure the map does not become cluttered when viewed at the full map extent. This map also contains numerous reference layers which provide important contextual information, such as the locations of marked intersections with labels for intersection number, locations of road and trails (with name and basic information on trail markings), locations of portable toilets, locations of Wildlife Management Areas (WMAs), hydrography, such as streams with names labeled, wetlands and lakes/ponds with names labeled and town boundaries. A group layer provides a collection of layers specifically relevant to the Quabbin Controlled Deer Hunts and includes: road barriers, marked intersections, hunt check-in locations, moose survey stations, deer check stations, and special road access.Together, this information provides a comprehensive map detailing where hunting and trapping is allowed within the DWSP watersheds and what specific hunting and trapping activities are allowed at each location. This map can help users plan a successful visit to DWSP property. When accessed through the ArcGIS Online Map Viewer, each layer's attribute table can be accessed, providing data in an alternative format. This map is also compatible with ArcGIS Field Maps and can be downloaded to "go offline", enabling a user access to the downloaded portion of the map when there is no cell service. Harvest Map InformationDeer Harvested by Weight - a collection of points showing where deer have been harvested during the Quabbin Controlled Deer Hunt within the Quabbin Reservoir Watershed. These points are symbolized to differentiate between male (dark blue circles with a light blue outline) and female (light red diamonds with a dark maroon outline) deer. DCR-DWSP Lands by Permit Type - this polygon layer shows DWSP property boundaries and indicates the type of permit required to hunt in any given area. This layer is configured to display four types of features; areas where a 5-Year Access Permit is required is shown in dark teal, areas where a Two-Day Deer Controlled Hunt Permit is required are shown in light orange, areas where a Deer Shotgun Season Controlled Hunt Permit is required are shown in bright yellow with dark yellow dots, and No Hunting Allowed areas are shown in a dark red. Click on a feature for more information, or review the attribute table to see what activities are allowed.Many layers in this map have a visibility constraint placed on them, and will only appear in the map as the user zooms in closer to an area. This helps ensure the map does not become cluttered when viewed at the full map extent. As you zoom in, these references features will appear. They provide important contextual information, such as the locations of marked intersections with labels for intersection number, locations of road and trails, hydrography, such as streams with names labeled, wetlands and lakes/ponds with names labeled.Together, this information provides a comprehensive map detailing where deer have been harvested within the Quabbin Reservoir Watershed. This map is designed for deer hunters and may help them plan where to hunt in the watershed. When accessed through the ArcGIS Online Map Viewer, each layer's attribute table can be accessed, providing data in an alternative format. This map is best used through the DCR-DWSP Hunting Map's Harvest Map page.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. October 29, 1996: Aerial photography for Rock Creek Park is flown February 25, 1997: Initial meeting at Rock Creek Park headquarters - acquisition of aerial photography and ancillary data including existing vegetation maps March - April 1997: First cut delineations onto aerial photography of the photo signatures (see Figure 3 - Rock Creek Park Photo Index). May 12 - 16, 1997: Photointerpretation field reconnaissance trip, TNC training of park biologists in field sampling methodology May - June 1997: Develop initial list of photo signature types, Revise initial delineations based on field reconnaissance findings, Label existing polygons with photo signature types, Deliver copies of overlays to park biologists for plot selection and feedback June - September 1997: Park vegetation sampling effort February 19, 1998: Received draft TNC report of the vegetation classification for Rock Creek March 1998: Received final TNC report on the vegetation classification for Rock Creek, Received plot data and locations for vegetation sampling effort, Received TNC key for communities, Development of PI signature / TNC community lookup table, Polygons attributized with initial communities April 1998: Received DOQQ files (April 1989) May 11-12, 1998: Photointerpretation field verification trip May 1998: Revise photo signature / TNC community lookup table, Update and correct PI community calls and PI linework June - October 1998: Data rectification and conversion, Interim files and plots delivered to NPS-ROCR, Final documentation

Publication Date: April 2025 2024 Parcel Data. Updated annually, or as needed. The data can be downloaded here: https://gis.ny.gov/parcels#data-download. This feature service has two layers: 1) NYS Tax Parcels Public, and 2) NYS Tax Parcels Public Footprint which contains polygons representing counties for which tax parcel polygons are available in the NYS Tax Parcels Public layer. County footprint polygons display when zoomed out beyond 1:37,050-scale. Tax parcel polygons display when zoomed in below 1:37,051-scale. The NYS Tax Parcels Public layer contains 2024 parcel data only for NY State counties which gave NYS ITS Geospatial Services permission to share this data with the public. Work to obtain parcel data from additional counties, as well as permission to share the data, is ongoing. To date, 36 counties have provided Geospatial Services permission to share their parcel data with the public. Parcel data for counties which do not allow Geospatial Services to redistribute their data must be obtained directly from those counties. Geospatial Services' goal is to eventually include parcel data for all counties in New York State. Parcel geometry was incorporated as received from County Real Property Departments. No attempt was made to edge-match parcels along adjacent counties. County attribute values were populated using 2024 Assessment Roll tabular data the NYS ITS Geospatial Services obtained from the NYS Department of Tax and Finance’s Office of Real Property Tax Services (ORPTS). Tabular assessment data was joined to the county provided parcel geometry using the SWIS & SBL or SWIS & PRINT KEY unique identifier for each parcel. Detailed information about assessment attributes can be found in the ORPTS Assessor’s Manuals available here: https://www.tax.ny.gov/research/property/assess/manuals/assersmanual.htm. New York City data comes from NYC MapPluto which can be found here: https://www1.nyc.gov/site/planning/data-maps/open-data/dwn-pluto-mappluto.page. Thanks to the following counties that specifically authorized Geospatial Services to share their GIS tax parcel data with the public: Albany, Cayuga, Chautauqua, Cortland, Erie, Genesee, Greene, Hamilton, Lewis, Livingston, Montgomery, NYC- Bronx, NYC- Kings (Brooklyn), NYC- New York (Manhattan), NYC- Queens, NYC- Richmond (Staten Island), Oneida, Onondaga, Ontario, Orange, Oswego, Otsego, Putnam, Rensselaer, Rockland, Schuyler, St Lawrence, Steuben, Suffolk, Sullivan, Tioga, Tompkins, Ulster, Warren, Wayne, and Westchester. Geometry accuracy varies by contributing county. This map service is available to the public. The State of New York, acting through the New York State Office of Information Technology Services, makes no representations or warranties, express or implied, with respect to the use of or reliance on the Data provided. The User accepts the Data provided “as is” with no guarantees that it is error free, complete, accurate, current or fit for any particular purpose and assumes all risks associated with its use. The State disclaims any responsibility or legal liability to Users for damages of any kind, relating to the providing of the Data or the use of it. Users should be aware that temporal changes may have occurred since this Data was created.

The size of the Simultaneous Localization and Mapping Industry market was valued at USD XX Million in 2023 and is projected to reach USD XXX Million by 2032, with an expected CAGR of 26.78% during the forecast period.Simultaneous Localization and Mapping is the process through which robots and self-driving cars map out a space they do not know. They are doing it while trying to find out where they are in this space. This process relies on sensors, such as cameras, lidar, and radar, that take pictures of the surrounding environment. SLAM algorithms perform this data processing from sensors to look for landmarks, infer distances and movements of the robot and map the environment and the localizations. SLAM applies in different fields of application. Most applications by robots rely heavily on SLAM to navigate, explore areas, and map out several areas. Self-driving cars rely on SLAM to establish real-time maps of surroundings, which allows them to navigate safely and efficiently. SLAM is also applied in other areas: warehouse automation for inventory management and order fulfillment, drone delivery for autonomous flight and package drop-off, and in medical robotics for precise surgical procedures. With the continuous development of technology, SLAM will play a much larger role so that autonomous systems can be operated safely and effectively in complex and dynamic environments. Recent developments include: November 2022 - Singapore based autonomous navigation solutions provider dConstruct introduced Ouster digital lidar to create highly accurate SLAMs and point cloud maps. Dconstruct creates these maps virtually and then studies the deployment of autonomous robots and the inspection and reconstruction of working environments. For instance - A map of a smart office building, The Galen, in Singapore was created on the cloud and was used to facilitate the deployment of autonomous robots ranging from cleaning robots to last-mile delivery robots., February 2023 - KUKA, the German manufacturer of industrial robots, launched Intralogistics Robot, with wheel sensors and laser scanners that let it safely move through its surroundings. The company claims this product is compatible to meets the highest safety requirements. It the specification such as 3D object detection, laser scanners, a payload of 1,322 pounds, and an automated guided vehicle system. The robot or the collision-free worker has been developed to work with logistics workers without the need for safety fencing. It employs eight safety zones in the front and back that can be adjusted for vehicle speeds and particular applications., July 2022 - Polymath Robotics, a start-up, developed an SDK-integrated plug-and-play software platform that enables businesses to quickly and affordably automate industrial vehicles. The start-up is developing fundamentally generalizable autonomy intending to automate the roughly 50 million industrial vehicles currently used in enclosed spaces.. Key drivers for this market are: Growing Penetration of Mapping Technologies in Domestic Robots and UAV, Advancements in Visual SLAM Algorithm; Increasing Application of SLAM in Augmented Reality. Potential restraints include: , The Risk of Interference from Other Wireless Device. Notable trends are: UAVs and Robots Will Experience Significant Growth in the Market.

This is Version 2 of the Depth of Regolith product of the Soil and Landscape Grid of Australia (produced 2015-06-01).

The Soil and Landscape Grid of Australia has produced a range of digital soil attribute products. The digital soil attribute maps are in raster format at a resolution of 3 arc sec (~90 x 90 m pixels).

Attribute Definition: The regolith is the in situ and transported material overlying unweathered bedrock; Units: metres; Spatial prediction method: data mining using piecewise linear regression; Period (temporal coverage; approximately): 1900-2013; Spatial resolution: 3 arc seconds (approx 90m); Total number of gridded maps for this attribute:3; Number of pixels with coverage per layer: 2007M (49200 * 40800); Total size before compression: about 8GB; Total size after compression: about 4GB; Data license : Creative Commons Attribution 4.0 (CC BY); Variance explained (cross-validation): R^2 = 0.38; Target data standard: GlobalSoilMap specifications; Format: GeoTIFF. Lineage: The methodology consisted of the following steps: (i) drillhole data preparation, (ii) compilation and selection of the environmental covariate raster layers and (iii) model implementation and evaluation.

Drillhole data preparation: Drillhole data was sourced from the National Groundwater Information System (NGIS) database. This spatial database holds nationally consistent information about bores that were drilled as part of the Bore Construction Licensing Framework (http://www.bom.gov.au/water/groundwater/ngis/). The database contains 357,834 bore locations with associated lithology, bore construction and hydrostratigraphy records. This information was loaded into a relational database to facilitate analysis.

Regolith depth extraction: The first step was to recognise and extract the boundary between the regolith and bedrock within each drillhole record. This was done using a key word look-up table of bedrock or lithology related words from the record descriptions. 1,910 unique descriptors were discovered. Using this list of new standardised terms analysis of the drillholes was conducted, and the depth value associated with the word in the description that was unequivocally pointing to reaching fresh bedrock material was extracted from each record using a tool developed in C# code.

The second step of regolith depth extraction involved removal of drillhole bedrock depth records deemed necessary because of the “noisiness” in depth records resulting from inconsistencies we found in drilling and description standards indentified in the legacy database.

On completion of the filtering and removal of outliers the drillhole database used in the model comprised of 128,033 depth sites.

Selection and preparation of environmental covariates The environmental correlations style of DSM applies environmental covariate datasets to predict target variables, here regolith depth. Strongly performing environmental covariates operate as proxies for the factors that control regolith formation including climate, relief, parent material organisms and time.

Depth modelling was implemented using the PC-based R-statistical software (R Core Team, 2014), and relied on the R-Cubist package (Kuhn et al. 2013). To generate modelling uncertainty estimates, the following procedures were followed: (i) the random withholding of a subset comprising 20% of the whole depth record dataset for external validation; (ii) Bootstrap sampling 100 times of the remaining dataset to produce repeated model training datasets, each time. The Cubist model was then run repeated times to produce a unique rule set for each of these training sets. Repeated model runs using different training sets, a procedure referred to as bagging or bootstrap aggregating, is a machine learning ensemble procedure designed to improve the stability and accuracy of the model. The Cubist rule sets generated were then evaluated and applied spatially calculating a mean predicted value (i.e. the final map). The 5% and 95% confidence intervals were estimated for each grid cell (pixel) in the prediction dataset by combining the variance from the bootstrapping process and the variance of the model residuals. Version 2 differs from version 1, in that the modelling of depths was performed on the log scale to better conform to assumptions of normality used in calculating the confidence intervals. The method to estimate the confidence intervals was improved to better represent the full range of variability in the modelling process. (Wilford et al, in press)