We proposed a new methodology for reducing multiple types of rasterization errors to simultaneously preserve the spatial properties of area, shape, and topology in polygon-to-raster conversion. By reassigning cells of the rasterized outcome, the method first compensates for the loss in shape properties. Topological changes are then corrected by comparing the topological relations of raster regions and their corresponding polygons. Finally, the areas between pairs of neighboring regions are coordinated to maintain area properties.

The One Tree Point 1964 to NZVD2016 Conversion Raster provides users with a two arc-minute (approximately 3.6 kilometres) raster image of the conversion of normal-orthometric heights from the One Tree Point 1964 local vertical datum to the New Zealand Vertical Datum 2016 (NZVD2016).

The conversion value is represented by the attribute “O”, in metres. This conversion and NZVD2016 are formally defined in the LINZ standard LINZS25009.

The height conversion grid models the difference between the One Tree Point 1964 vertical datum and NZVD2016 using the LINZ GPS-levelling marks. From the GPS-levelling marks the expected accuracy is better than 2 centimetres (95% Confidence interval).

More information on converting heights between vertical datums can be found on the LINZ website.

Warning: This raster is a grid of a floating-point values; not a surface. To derive an accurate height transformation value, this raster grid must be downloaded in terms of NZGD2000 and then converted into a surface using bilinear interpolation.

The Napier 1962 to NZVD2016 Conversion Raster provides users with a two arc-minute (approximately 3.6 kilometres) raster image of the conversion of normal-orthometric heights from the Napier 1962 local vertical datum to the New Zealand Vertical Datum 2016 (NZVD2016).

The conversion value is represented by the attribute “O”, in metres. This conversion and NZVD2016 are formally defined in the LINZ standard LINZS25009.

The height conversion grid models the difference between the Napier 1962 vertical datum and NZVD2016 using the LINZ GPS-levelling marks. From the GPS-levelling marks the expected accuracy is better than 2 centimetres (95% Confidence interval).

More information on converting heights between vertical datums can be found on the LINZ website.

Important Note: This item is in mature support as of June 2024 and will be retired in December 2026. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. Areas protected from conversion include areas that are permanently protected and managed for biodiversity such as Wilderness Areas and National Parks. In addition to protected lands, portions of areas protected from conversion includes multiple-use lands that are subject to extractive uses such as mining, logging, and off-highway vehicle use. These areas are managed to maintain a mostly undeveloped landscape including many areas managed by the Bureau of Land Management and US Forest Service.The Protected Areas Database of the United States classifies lands into four GAP Status classes. This layer displays lands managed for biodiversity conservation (GAP Status 1 and 2) and multiple-use lands (GAP Status 3). Dataset SummaryPhenomenon Mapped: Protected and multiple-use lands (GAP Status 1, 2, and 3)Units: MetersCell Size: 30.92208102 metersSource Type: ThematicPixel Type: 8-bit unsigned integerData Coordinate System: WGS 1984Mosaic Projection: Web Mercator Auxiliary SphereExtent: 50 United States plus Puerto Rico, the US Virgin Islands, Guam, Northern Mariana Islands and American Samoa.Source: USGS National Gap Analysis Program PAD-US version 3.0Publication Date: July 2022ArcGIS Server URL: https://landscape10.arcgis.com/arcgis/This layer displays protected areas from the Protected Areas Database of the United States version 3.0 created by the USGS National Gap Analysis Program. This layer displays areas managed for biodiversity where natural disturbances are allowed to proceed or are mimicked by management (GAP Status 1), areas managed for biodiversity where natural disturbance is suppressed (GAP Status 2), and multiple-use lands where extract activities are allowed (GAP Status 3). The source data for this layer are available here. A feature layer published from this dataset is also available.The polygon vector layer was converted to raster layers using the Polygon to Raster Tool using the National Elevation Dataset 1 arc second product as a snap raster.The service behind this layer was published with 8 functions allowing the user to select different views of the service. Other layers created from this service using functions include:USA Protected AreasUSA Unprotected AreasUSA Protected Areas - Gap Status 1-4USA Protected Areas - Gap Status 1USA Protected Areas - Gap Status 2USA Protected Areas - Gap Status 3USA Protected Areas - Gap Status 4What can you do with this layer? This layer is suitable for both visualization and analysis across the ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application.Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map:In ArcGIS Online, you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "Protected from Land Cover Conversion" in the search box and browse to the layer. Select the layer then click Add to Map.In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "Protected from Land Cover Conversion" in the search box, browse to the layer then click OK.In ArcGIS Pro you can use the built-in raster functions to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro.The ArcGIS Living Atlas of the World provides an easy way to explore many other beautiful and authoritative maps on hundreds of topics like this one.

The Napier 1962 to NZVD2016 Conversion Raster provides users with a two arc-minute (approximately 3.6 kilometres) raster image of the conversion of normal-orthometric heights from the Napier 1962 local vertical datum to the New Zealand Vertical Datum 2016 (NZVD2016). The conversion value is represented by the attribute “O”, in metres. This conversion and NZVD2016 are formally defined in the LINZ standard LINZS25009. The height conversion grid models the difference between the Napier 1962 vertical datum and NZVD2016 using the LINZ GPS-levelling marks. From the GPS-levelling marks the expected accuracy is better than 2 centimetres (95% Confidence interval). More information on converting heights between vertical datums can be found on the LINZ website.

Warning: This raster is a grid of a floating-point values; not a surface. To derive an accurate height transformation value, this raster grid must be downloaded in terms of NZGD2000 and then converted into a surface using bilinear interpolation.

The Dunedin 1958 to NZVD2016 Conversion Raster provides users with a two arc-minute (approximately 3.6 kilometres) raster image of the conversion of normal-orthometric heights from the Dunedin 1958 local vertical datum to the New Zealand Vertical Datum 2016 (NZVD2016).

The conversion value is represented by the attribute “O”, in metres. This conversion and NZVD2016 are formally defined in the LINZ standard LINZS25009.

The height conversion grid models the difference between the Dunedin 1958 vertical datum and NZVD2016 using the LINZ GPS-levelling marks. From the GPS-levelling marks the expected accuracy is better than 2 centimetres (95% Confidence interval).

More information on converting heights between vertical datums can be found on the LINZ website.

Warning: This raster is a grid of a floating-point values; not a surface. To derive an accurate height transformation value, this raster grid must be downloaded in terms of NZGD2000 and then converted into a surface using bilinear interpolation.

The Stewart Island 1977 to NZVD2016 Conversion Raster provides users with a two arc-minute (approximately 3.6 kilometres) raster image of the conversion of normal-orthometric heights from the Stewart Island 1977 local vertical datum to the New Zealand Vertical Datum 2016 (NZVD2016).

The conversion value is represented by the attribute “O”, in metres. This conversion and NZVD2016 are formally defined in the LINZ standard LINZS25009.

The height conversion grid models the difference between the Stewart Island 1977 vertical datum and NZVD2016 using the LINZ GPS-levelling marks. From the GPS-levelling marks the expected accuracy is better than 2 centimetres (95% Confidence interval).

More information on converting heights between vertical datums can be found on the LINZ website.

The Moturiki 1953 to NZVD2016 Conversion Raster provides users with a two arc-minute (approximately 3.6 kilometres) raster image of the conversion of normal-orthometric heights from the Moturiki 1953 local vertical datum to the New Zealand Vertical Datum 2016 (NZVD2016). The conversion value is represented by the attribute “O”, in metres. This conversion and NZVD2016 are formally defined in the LINZ standard LINZS25009. The height conversion grid models the difference between the Moturiki 1953 vertical datum and NZVD2016 using the LINZ GPS-levelling marks. From the GPS-levelling marks the expected accuracy is better than 2 centimetres (95% Confidence interval). More information on converting heights between vertical datums can be found on the LINZ website.

Warning: This raster is a grid of a floating-point values; not a surface. To derive an accurate height transformation value, this raster grid must be downloaded in terms of NZGD2000 and then converted into a surface using bilinear interpolation.

The Bluff 1955 to NZVD2016 Conversion Raster provides users with a two arc-minute (approximately 3.6 kilometres) raster image of the conversion of normal-orthometric heights from the Bluff 1955 local vertical datum to the New Zealand Vertical Datum 2016 (NZVD2016).

The conversion value is represented by the attribute “O”, in metres. This conversion and NZVD2016 are formally defined in the LINZ standard LINZS25009.

The height conversion grid models the difference between the Bluff 1955 vertical datum and NZVD2016 using the LINZ GPS-levelling marks. From the GPS-levelling marks the expected accuracy is better than 2 centimetres (95% Confidence interval).

More information on converting heights between vertical datums can be found on the LINZ website.

The Dunedin 1958 to NZVD2016 Conversion Raster provides users with a two arc-minute (approximately 3.6 kilometres) raster image of the conversion of normal-orthometric heights from the Dunedin 1958 local vertical datum to the New Zealand Vertical Datum 2016 (NZVD2016). The conversion value is represented by the attribute “O”, in metres. This conversion and NZVD2016 are formally defined in the LINZ standard LINZS25009. The height conversion grid models the difference between the Dunedin 1958 vertical datum and NZVD2016 using the LINZ GPS-levelling marks. From the GPS-levelling marks the expected accuracy is better than 2 centimetres (95% Confidence interval). More information on converting heights between vertical datums can be found on the LINZ website.

Warning: This raster is a grid of a floating-point values; not a surface. To derive an accurate height transformation value, this raster grid must be downloaded in terms of NZGD2000 and then converted into a surface using bilinear interpolation. The One Tree Point 1964 to NZVD2016 Conversion Raster provides users with a two arc-minute (approximately 3.6 kilometres) raster image of the conversion of normal-orthometric heights from the One Tree Point 1964 local vertical datum to the New Zealand Vertical Datum 2016 (NZVD2016). The conversion value is represented by the attribute “O”, in metres. This conversion and NZVD2016 are formally defined in the LINZ standard LINZS25009. The height conversion grid models the difference between the One Tree Point 1964 vertical datum and NZVD2016 using the LINZ GPS-levelling marks. From the GPS-levelling marks the expected accuracy is better than 2 centimetres (95% Confidence interval). More information on converting heights between vertical datums can be found on the LINZ website.

Warning: This raster is a grid of a floating-point values; not a surface. To derive an accurate height transformation value, this raster grid must be downloaded in terms of NZGD2000 and then converted into a surface using bilinear interpolation.

The Moturiki 1953 to NZVD2016 Conversion Raster provides users with a two arc-minute (approximately 3.6 kilometres) raster image of the conversion of normal-orthometric heights from the Moturiki 1953 local vertical datum to the New Zealand Vertical Datum 2016 (NZVD2016).

The conversion value is represented by the attribute “O”, in metres. This conversion and NZVD2016 are formally defined in the LINZ standard LINZS25009.

The height conversion grid models the difference between the Moturiki 1953 vertical datum and NZVD2016 using the LINZ GPS-levelling marks. From the GPS-levelling marks the expected accuracy is better than 2 centimetres (95% Confidence interval).

More information on converting heights between vertical datums can be found on the LINZ website.

The Raster Based GIS Coverage of Mexican Population is a gridded coverage (1 x 1 km) of Mexican population. The data were converted from vector into raster. The population figures were derived based on available point data (the population of known localities - 30,000 in all). Cell values were derived using a weighted moving average function (Burrough, 1986), and then calculated based on known population by state. The result from this conversion is a coverage whose population data is based on square grid cells rather than a series of vectors. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the Instituto Nacional de Estadistica Geografia e Informatica (INEGI).

This raster maps average atmospheric methane concentrations in 2016. The data used to create it is from the NASA Aqua satellite (specifically the AIRS instrument) that records monthly average atmospheric methane concentrations. AIRS collects methane data at different pressure levels. The raster depicts data at the 400 hPa level because that is where the instrument is most sensitive to methane concentration. The monthly data was consolidated using the NASA tool, Giovanni https://giovanni.gsfc.nasa.gov/giovanni/, to create a raster with annual average methane concentrations. Giovanni output two rasters: one for daytime averaged data and one for nighttime averaged data. ArcGIS was then used to combine the two rasters to create a single annual raster. A conversion factor of 1.0e+9 was multiplied to convert the final raster from mole fractions to parts per billion. The results are attached. The Carnegie Endowment for International Peace would like to eventually incorporate a methane raster as a new layer in the Oil Climate Index (OCI) web tool http://oci.carnegieendowment.org/. Carnegie is currently updating the OCI, adding greenhouse gas comparisons of global gas fields and visualizing their methane emissions. Carnegie is planning to work with our OCI partners at Stanford to further analyze the methane concentration raster to separate out signal from noise and to identify potential methane concentration hot spots associated with oil and gas operations. This raster is useful when studying short term climate risks, especially when it comes to Arctic oil and gas resources.

This file collection contains data with conversion length from forests to agriculture in the Brazilian Amazon, from 1985 to 2021. Calculations were based in the MapBiomas thematic maps. More information can be found in the repository website (https://github.com/hugotseixas/forest-agri-conversion/tree/3.0.0).

The collection is composed of six sets of data:

c_raster_mosaic: raster files of the Amazon biome with values of conversions;

c_raster_tiles: raster files of smaller raster tiles, with values of conversions;

c_tabular_dataset: a group of tables that contains data about conversions;

figures: data to create figures of the manuscript;

raw_raster_tiles: raster files of smaller raster tiles with land use and land cover classification data from MapBiomas;

validation: data used to performed validation of the conversion results.

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

The Stewart Island 1977 to NZVD2016 Conversion Raster provides users with a two arc-minute (approximately 3.6 kilometres) raster image of the conversion of normal-orthometric heights from the Stewart Island 1977 local vertical datum to the New Zealand Vertical Datum 2016 (NZVD2016). The conversion value is represented by the attribute “O”, in metres. This conversion and NZVD2016 are formally defined in the LINZ standard LINZS25009. The height conversion grid models the difference between the Stewart Island 1977 vertical datum and NZVD2016 using the LINZ GPS-levelling marks. From the GPS-levelling marks the expected accuracy is better than 2 centimetres (95% Confidence interval). More information on converting heights between vertical datums can be found on the LINZ website.

This Jupyter Notebook used Python to convert a binary file containing a one-dimensional data array into geo-referenced raster data. The link to the dataset used for developing this code is provided within the "Related Resources" section below.

This is a collection of all GPS- and computer-generated geospatial data specific to the Alpine Treeline Warming Experiment (ATWE), located on Niwot Ridge, Colorado, USA. The experiment ran between 2008 and 2016, and consisted of three sites spread across an elevation gradient. Geospatial data for all three experimental sites and cone/seed collection locations are included in this package. ––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Geospatial files include cone collection, experimental site, seed trap, and other GPS location/terrain data. File types include ESRI shapefiles, ESRI grid files or Arc/Info binary grids, TIFFs (.tif), and keyhole markup language (.kml) files. Trimble-imported data include plain text files (.txt), Trimble COR (CorelDRAW) files, and Trimble SSF (Standard Storage Format) files. Microsoft Excel (.xlsx) and comma-separated values (.csv) files corresponding to the attribute tables of many files within this package are also included. A complete list of files can be found in this document in the “Data File Organization” section in the included Data User's Guide. Maps are also included in this data package for reference and use. These maps are separated into two categories, 2021 maps and legacy maps, which were made in 2010. Each 2021 map has one copy in portable network graphics (.png) format, and the other in .pdf format. All legacy maps are in .pdf format. .png image files can be opened with any compatible programs, such as Preview (Mac OS) and Photos (Windows). All GIS files were imported into geopackages (.gpkg) using QGIS, and double-checked for compatibility and data/attribute integrity using ESRI ArcGIS Pro. Note that files packaged within geopackages will open in ArcGIS Pro with “main.” preceding each file name, and an extra column named “geom” defining geometry type in the attribute table. The contents of each geospatial file remain intact, unless otherwise stated in “niwot_geospatial_data_list_07012021.pdf/.xlsx”. This list of files can be found as an .xlsx and a .pdf in this archive. As an open-source file format, files within gpkgs (TIFF, shapefiles, ESRI grid or “Arc/Info Binary”) can be read using both QGIS and ArcGIS Pro, and any other geospatial softwares. Text and .csv files can be read using TextEdit/Notepad/any simple text-editing software; .csv’s can also be opened using Microsoft Excel and R. .kml files can be opened using Google Maps or Google Earth, and Trimble files are most compatible with Trimble’s GPS Pathfinder Office software. .xlsx files can be opened using Microsoft Excel. PDFs can be opened using Adobe Acrobat Reader, and any other compatible programs. A selection of original shapefiles within this archive were generated using ArcMap with associated FGDC-standardized metadata (xml file format). We are including these original files because they contain metadata only accessible using ESRI programs at this time, and so that the relationship between shapefiles and xml files is maintained. Individual xml files can be opened (without a GIS-specific program) using TextEdit or Notepad. Since ESRI’s compatibility with FGDC metadata has changed since the generation of these files, many shapefiles will require upgrading to be compatible with ESRI’s latest versions of geospatial software. These details are also noted in the “niwot_geospatial_data_list_07012021” file.

Important Note: This item is in mature support as of June 2024 and will be retired in December 2026. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version.The Protected Areas Database of the United States provides a comprehensive map of lands protected by government agencies and private land owners. This database combines federal lands with information on state and local government lands and conservation easements on private lands to create a powerful resource for land-use planning.Dataset SummaryPhenomenon Mapped: Areas mapped in the Protected Areas Data base of the United States (GAP Status 1-4)Units: MetersCell Size: 30.92208102 metersSource Type: ThematicPixel Type: 8-bit unsigned integerData Coordinate System: WGS 1984Mosaic Projection: Web Mercator Auxiliary SphereExtent: 50 United States plus Puerto Rico, the US Virgin Islands, Guam, Northern Mariana Islands and American Samoa.Source: USGS National Gap Analysis Program PAD-US version 3.0Publication Date: July 2022ArcGIS Server URL: https://landscape10.arcgis.com/arcgis/This layer displays lands mapped in Protected Areas Database of the United States version 3.0 created by the USGS National Gap Analysis Program. This layer displays all four GAP Status classes: GAP Status 1 - Areas managed for biodiversity where natural disturbances are allowed to proceedGAP Status 2 - Areas managed for biodiversity where natural disturbance is suppressedGAP Status 3 - Areas protected from land cover conversion but subject to extractive uses such as logging and miningGAP Status 4 - Areas with no known mandate for protectionThe source data for this layer are available here. A feature layer published from this dataset is also available. The polygon vector layer was converted to raster layers using the Polygon to Raster Tool using the National Elevation Dataset 1 arc second product as a snap raster.The service behind this layer was published with 8 functions allowing the user to select different views of the service. Other layers created from this service using functions include:USA Protected AreasUSA Protected from Land Cover ConversionUSA Unprotected AreasUSA Protected Areas - Gap Status 1USA Protected Areas - Gap Status 2USA Protected Areas - Gap Status 3USA Protected Areas - Gap Status 4What can you do with this Layer? This layer is suitable for both visualization and analysis across the ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application.Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map:In ArcGIS Online, you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "Protected Areas" in the search box and browse to the layer. Select the layer then click Add to Map.In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "Protected Areas" in the search box, browse to the layer then click OK.In ArcGIS Pro you can use the built-in raster functions to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro.The ArcGIS Living Atlas of the World provides an easy way to explore many other beautiful and authoritative maps on hundreds of topics like this one.