The National Hydrography Dataset Plus High Resolution (NHDplus High Resolution) maps the lakes, ponds, streams, rivers and other surface waters of the United States. Created by the US Geological Survey, NHDPlus High Resolution provides mean annual flow and velocity estimates for rivers and streams. Additional attributes provide connections between features facilitating complicated analyses.For more information on the NHDPlus High Resolution dataset see the User’s Guide for the National Hydrography Dataset Plus (NHDPlus) High Resolution.Dataset SummaryPhenomenon Mapped: Surface waters and related features of the United States and associated territoriesGeographic Extent: The Contiguous United States, Hawaii, portions of Alaska, Puerto Rico, Guam, US Virgin Islands, Northern Marianas Islands, and American SamoaProjection: Web Mercator Auxiliary Sphere Visible Scale: Visible at all scales but layer draws best at scales larger than 1:1,000,000Source: USGSUpdate Frequency: AnnualPublication Date: July 2022This layer was symbolized in the ArcGIS Map Viewer and while the features will draw in the Classic Map Viewer the advanced symbology will not. Prior to publication, the network and non-network flowline feature classes were combined into a single flowline layer. Similarly, the Area and Waterbody feature classes were merged under a single schema.Attribute fields were added to the flowline and waterbody layers to simplify symbology and enhance the layer's pop-ups. Fields added include Pop-up Title, Pop-up Subtitle, Esri Symbology (waterbodies only), and Feature Code Description. All other attributes are from the original dataset. No data values -9999 and -9998 were converted to Null values.What can you do with this layer?Feature layers work throughout the ArcGIS system. Generally your work flow with feature layers will begin in ArcGIS Online or ArcGIS Pro. Below are just a few of the things you can do with a feature service in Online and Pro.ArcGIS OnlineAdd this layer to a map in the map viewer. The layer or a map containing it can be used in an application. Change the layer’s transparency and set its visibility rangeOpen the layer’s attribute table and make selections. Selections made in the map or table are reflected in the other. Center on selection allows you to zoom to features selected in the map or table and show selected records allows you to view the selected records in the table.Apply filters. For example you can set a filter to show larger streams and rivers using the mean annual flow attribute or the stream order attribute.Change the layer’s style and symbologyAdd labels and set their propertiesCustomize the pop-upUse as an input to the ArcGIS Online analysis tools. This layer works well as a reference layer with the trace downstream and watershed tools. The buffer tool can be used to draw protective boundaries around streams and the extract data tool can be used to create copies of portions of the data.ArcGIS ProAdd this layer to a 2d or 3d map.Use as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class.Change the symbology and the attribute field used to symbolize the dataOpen table and make interactive selections with the mapModify the pop-upsApply Definition Queries to create sub-sets of the layerThis layer is part of the ArcGIS Living Atlas of the World that provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Questions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.

The Quick Attribute Calculator v.1.0 is a toolbar developed for use with ArcGIS 9.3 on Windows XP. It enables a user to select an attribute from a drop-down list and change the value of a sub-set for bulk updates.

This statistic shows the results of an online survey conducted in 2015. Consumers were asked which convenience attributes they consider highly influential in their decision to shop at a particular retailer. During the survey, 56 percent of the respondents cited convenient store location as an important attribute.

This statistic shows the results of an online survey conducted in 2015. Consumers were asked which price/value attributes they consider highly influential in their decision to shop at a particular retailer. During the survey, 52 percent of the respondents cited "good value for money" as an important attribute.

This metadata record describes a series of data sets of natural and anthropogenic landscape features linked to NHDPlus Version 2.1’s (NHDPlusV2) approximately 2.7 million stream segments, their associated catchments, and their upstream watersheds within the conterminous United States. The data were linked to four spatial components of NHDPlusV2: individual reach catchments, riparian buffer zones around individual reaches, reach catchments accumulated downstream through the river network, and riparian buffer zones accumulated downstream through the river network. All data can be linked to NHDPlus using the COMID field in these tables and the ComID in the flowline shapefiles or FEATUREID in the catchment ones in the NHDPlus data suite. The datasets were derived using a topologically reconditioned version of the NHDPlusv2 routing network (Schwarz and Wieczorek, 2018). This database is used for the routing of upstream watersheds only. No cartographic changes were made to the original NHDPlusv2 in either the flowline or reach catchment line work. These data are listed under 13 themes which include: 1) Best Management Practices, characteristics such as agricultural management practices and land in conservation practices. 2) Chemical, characteristics such as nitrogen application or toxicity weighted use. 3) Climate and Water Balance Model, characteristics such as model outputs of runoff, actual evapotranspiration or ground water storage. 4) Climate, characteristics such as mean precipitation, temperature, relative humidity, or evapotranspiration. 5) Geology, characteristics such as Hunt or Soller surficial geologies. 6) Hydrologic, characteristics such as base flow or infiltration excess overland flow.Hydrologic Modifications, characteristics such as dam storage or tile drains. 7) Hydrologic Modifications, characteristics such as dam storage or tile drains. 8) Landscape, characteristics such as NLCD, CDL or NWALT. 9) Population Infrastructure, characteristics such as population, housing, and road densities. 10) Regions, characteristics such as EcoRegions, Physiography or Hydrologic Landscapes. 11) Soils, characteristics such as STATSGO, soil salinity, and soil restrictive layer. 12) Topographic Characteristics, characteristics such as basin area, slope and elevation. 13) Water use, characteristics such as estimated freshwater withdrawls and estimated freshwater consumption by thermo-electric power plants These data allow researchers and managers to acquire landscape information for both catchments (for example, the nearby landscape flowing directly into streams) and full upstream watersheds of specific stream reaches anywhere in the within the conterminous United States without having to perform specialized geospatial processing. Aside from comma separated text files, parquet files with the same file structure were also added to each data file under each child item theme. This format will allow researchers to acquire all the information from this data release in an efficient and consistent manner by utilizing and thereby adhering to the FAIR guidelines outlined in Lightsom and others (USGS, 2022).

Geoform is a configurable app template for form based data editing of a Feature Service. This application allows users to enter data through a form instead of a map's pop-up while leveraging the power of the Web Map and editable Feature Services. This app geo-enables data and workflows by lowering the barrier of entry for completing simple tasks. Use CasesProvides a form-based experience for entering data through a form instead of a map pop-up. This is a good choice for users who find forms a more intuitive format than pop-ups for entering data.Useful to collect new point data from a large audience of non technical staff or members of the community.Configurable OptionsGeoform has an interactive builder used to configure the app in a step-by-step process. Use Geoform to collect new point data and configure it using the following options:Choose a web map and the editable layer(s) to be used for collection.Provide a title, logo image, and form instructions/details.Control and choose what attribute fields will be present in the form. Customize how they appear in the form, the order they appear in, and add hint text.Select from over 15 different layout themes.Choose the display field that will be used for sorting when viewing submitted entries.Enable offline support, social media sharing, default map extent, locate on load, and a basemap toggle button.Choose which locate methods are available in the form, including: current location, search, latitude and longitude, USNG coordinates, MGRS coordinates, and UTM coordinates.Supported DevicesThis application is responsively designed to support use in browsers on desktops, mobile phones, and tablets.Data RequirementsThis web app includes the capability to edit a hosted feature service or an ArcGIS Server feature service. Creating hosted feature services requires an ArcGIS Online organizational subscription or an ArcGIS Developer account. Get Started This application can be created in the following ways:Click the Create a Web App button on this pageShare a map and choose to Create a Web AppOn the Content page, click Create - App - From Template Click the Download button to access the source code. Do this if you want to host the app on your own server and optionally customize it to add features or change styling.

The statistic shows which food attributes are important to U.S. survey respondents when selecting a healthy meal in 2016. During the survey, 41 percent of respondents cited low-calorie options as important when selecting a healthy meal.

Overview:This document describes the 2021 accessibility data released by the Accessibility Observatory at the University of Minnesota. The data are included in the National Accessibility Evaluation Project for 2021, and this information can be accessed for each state in the U.S. at https://access.umn.edu/research/america. The following sections describe the format, naming, and content of the data files.Data Formats: The data files are provided in a Geopackage format. Geopackage (.gpkg) files are an open-source, geospatial filetype that can contain multiple layers of data in a single file, and can be opened with most GIS software, including both ArcGIS and QGIS.Within this zipfile, there are six geopackage files (.gpkg) structured as follows. Each of them contains the blocks shapes layer, results at the block level for all LEHD variables (jobs and workers), with a layer of results for each travel time (5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60 minutes). {MPO ID}_tr_2021_0700-0859-avg.gpkg = Average Transit Access Departing Every Minute 7am-9am{MPO ID}_au_2021_08.gpkg = Average Auto Access Departing 8am{MPO ID}_bi_2021_1200_lts1.gpkg = Average Bike Access on LTS1 Network{MPO ID}_bi_2021_1200_lts2.gpkg = Average Bike Access on LTS2 Network{MPO ID}_bi_2021_1200_lts3.gpkg = Average Bike Access on LTS3 Network{MPO ID}_bi_2021_1200_lts4.gpkg = Average Bike Access on LTS4 NetworkFor mapping and geospatial analysis, the blocks shape layer within each geopackage can be joined to the blockid of the access attribute data. Opening and Using Geopackages in ArcGIS:Unzip the zip archiveUse the "Add Data" function in Arc to select the .gpkg fileSelect which layer(s) are needed — always select "main.blocks" as this layer contains the Census block shapes; select any other attribute data layers as well.There are three types of layers in the geopackage file — the "main.blocks" layer is the spatial features layer, and all other layers are either numerical attribute data tables, or the "fieldname_descriptions" metadata layer. The numerical attribute layers are named with the following format:[mode]_[threshold]_minutes[mode] is a two-character code indicating the transport mode used[threshold] is an integer indicating the travel time threshold used for this data layerTo use the data spatially, perform a join between the "main.blocks" layer and the desired numerical data layer, using either the numerical "id" fields, or 15-digit "blockid" fields as join fields.

Image Mask is a configurable app template for identifying areas of an image that have changed over time or that meet user-set thresholds for calculated spectral indexes. The template also includes tools for measurement, recording locations, and more.App users can zoom to bookmarked areas of interest (or search for their own), select any of the imagery layers from the associated web map to analyze, use a time slider or dropdown menu to select images, then choose between the Change Detection or Mask tools to produce results.Image Mask users can do the following:Zoom to bookmarked areas of interest (or bookmark their own)Select specific images from a layer to visualize (search by date or another attribute)Use the Change Detection tool to compare two images in a layer (see options, below)Use the Mask tool to highlight areas that meet a user-set threshold for common spectral indexes (NDVI, SAVI, a burn index, and a water index). For example, highlight all the areas in an image with NDVI values above 0.25 to find vegetation.Annotate imagery using editable feature layersPerform image measurement on imagery layers that have mensuration capabilitiesExport an imagery layer to the user's local machine, or as a layer in the user’s ArcGIS accountUse CasesA student investigating urban expansion over time using Esri’s Multispectral Landsat image serviceA farmer using NAIP imagery to examine changes in crop healthAn image analyst recording burn scar extents using satellite imageryAn aid worker identifying regions with extreme drought to focus assistanceChange detection methodsFor each imagery layer, give app users one or more of the following change detection options:Image Brightness (calculates the change in overall brightness)Vegetation Index (NDVI) (requires red and infrared bands)Soil-Adjusted Vegetation Index (SAVI) (requires red and infrared bands)Water Index (requires green and short-wave infrared bands)Burn Index (requires infrared and short-wave infrared bands)For each of the indexes, users also have a choice between three modes:Difference Image: calculates increases and decreases for the full extent Difference Mask: users can focus on significant change by setting the minimum increase or decrease to be masked—for example, a user could mask only areas where NDVI increased by at least 0.2Threshold Mask: The user sets a threshold and magnitude for what is masked as change. The app will only identify change that’s above the user-set lower threshold and bigger than the user-set minimum magnitude.Supported DevicesThis application is responsively designed to support use in browsers on desktops, mobile phones, and tablets.Data RequirementsCreating an app with this template requires a web map with at least one imagery layer.Get Started This application can be created in the following ways:Click the Create a Web App button on this pageShare a map and choose to Create a Web AppOn the Content page, click Create - App - From Template Click the Download button to access the source code. Do this if you want to host the app on your own server and optionally customize it to add features or change styling.

According to a 2021 survey in China, 39 percent of respondents stated that they felt they were able to disconnect from work outside of working hours. This includes time in the evening, at weekends, on vacation, and during study times. 25 percent of respondents noted that their employers help them to manage stress and encourage them to create mental and emotional wellbeing.

This polygon vector dataset represents over 18,000 derived watersheds associated with meadows listed in the Sierra Nevada Meadows Compilation (version 2.0). The watershed polygons are attributed with geology, landform, and fire recurrence interval departure information thought to potentially influence the hydrology of meadows.

This dynamic World Elevation TopoBathy service combines topography (land elevation) and bathymetry (water depths) around the world. Heights are based on multiple sources and are orthometric (sea level = 0, and bathymetric values are negative downward from sea level). The source data of land elevation in this service is same as in the Terrain layer. When possible, the water areas are represented by the best available bathymetry. What can you do with this layer?Use for Visualization: This layer is generally not optimal for direct visualization. By default, 32 bit floating point values are returned, resulting in higher bandwidth requirements. Therefore, usage should be limited to applications requiring elevation data values. Alternatively, client applications can select additional functions, applied on the server, that return rendered data. For visualizations such as hillshade or elevation tinted hillshade, consider using the appropriate server-side function defined on this service. Use for Analysis: Yes. This layer provides data as floating point elevation values suitable for use in analysis. NOTE: This image services combine data from different sources and resample the data dynamically to the requested projection, extent and pixel size. For analyses using ArcGIS Desktop, you can filter a dataset, specify the projection, extent and cell size using the Make Image Server Layer geoprocessing tool. Server Functions: This layer has server functions defined for the following elevation derivatives. In ArcGIS desktop, server function can be invoked from Layer Properties - Processing Templates.

Slope Degrees Slope Percentage Hillshade Multi-Directional Hillshade Elevation Tinted HillshadeSlope MapData Sources and Coverage: This layer is compiled from a variety of best available sources from several data providers. To see the coverage and extents of various datasets comprising this service in an interactive map, see Elevation Coverage Map.Mosaic Method: This image service uses a default mosaic method of "By Attribute”, using Field 'Best' and target of 0. Each of the rasters has been attributed with ‘Best’ field value that is generally a function of the pixel size such that higher resolution datasets are displayed at higher priority. Other mosaic methods can be set, but care should be taken as the order of the rasters may change. Where required, queries can also be set to display only specific datasets such as only NED or the lock raster mosaic rule used to lock to a specific dataset.Accuracy: The accuracy of these services will vary as a function of location and data source. Please refer to the metadata available in the services, and follow the links to the original sources for further details. An estimate of CE90 and LE90 is included as attributes, where available.This layer allows query, identify, and export image requests. The layer is restricted to a 5,000 x 5,000 pixel limit in a single request. This layer is part of a larger collection of elevation layers that you can use to perform a variety of mapping analysis tasks.Disclaimer: Bathymetry data sources are not to be used for navigation/safety at sea.

The effectiveness of skincare was the leading factor female consumers in Japan considered when buying cosmetics. In a survey conducted between December 2024 and January 2025, over 62 percent of respondents named products' effectiveness an important factor in skincare purchases.

During a 2020 survey, the main attribute that Chileans said they considered when looking for a product online was the price, selected by 70 percent of respondents. The product description came in second, with 68 percent of participants, followed by the product image, considered by 59 percent.

In a survey conducted in 2020, it was found that the main attribute taken into account by Argentineans when looking for a product online was the price, selected by 76 percent of respondents. This was followed by the product description and image, considered by 67 percent and 52 percent of the participants, respectively.

PLEASE NOTE: If choosing the Download option of "Spreadsheet" the field PIN is reformatted to a number - you will need to format it as a 10 character text string with leading zeros to join this data with data from King County.King County Assessor data has been summarized to the tax parcel identification number (PIN) and City of Seattle spatial overlay data has been assigned through geographic overlay processes. This data is updated periodically and is used to support the analytical and reporting functions of the City of Seattle long-range and policy planning office.The table includes attribute data from the King County Assessor as well as spatial overlay data for various City of Seattle reporting geographies. These geographic attributes are assigned as "majority rules" by land area in cases where multiple geographies span a single tax parcel.KCA tax parcels are created by King County for property tax assessment and collection and may not match development sites as defined by the City of Seattle (single buildings may span multiple tax parcels), may be stacked on top of each other to represent undivided interest and vertical parcels, or may be made up of several sites that are not contiguous. Every effort is made to accurately summarize key tax parcel attributes to a single PIN. Attributes include parcel centroid locations in latitude/longitude and Washington State Plane X,Y. To get polygon representation of the data please see King County's open data page for parcels and join this table through the PIN field. Please be aware that the King County Assessor site address is not a postal address and may not match other address sources for the same property such as postal, utility billing, and permitting.See the detailed data dictionary for more information.

NOTE: This layer is a subset of the corresponding seasonal range layer for this species. All of the same metadata is used for this subset. The citation title is modified to replace "Seasonal" with "Crucial" and only the following seasonal ranges are included: anything with a "crucial" (CRU) designation in the RANGE attribute field (Select By Attributes... > "RANGE" LIKE '%CRU%').This data set represents the 2016 pronghorn antelope seasonal range boundaries for Wyoming. Seasonal range delineations depict lands that are important in each season for certain biological processes within a herd unit. Seasonal range boundaries are based on long-term observation data, specific research projects, and professional judgement. Ranges are digitized at a scale of 1:100,000 using USGS 1:100,000 DRGs as a backdrop for heads up digitizing, and are revised as needed by the Wyoming Game and Fish Department. Current seasonal range definitions are based on a 1990 document drafted by the Wyoming Chapter of The Wildlife Society in cooperation with the Wyoming Game and Fish Department and federal land agencies.

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.

According to a 2020 survey, the main attribute consulted by Colombians before purchasing a product over the internet was its price, listed by about 72 percent of respondents. The product description came in second place, with 68 percent of participants, while images of the product ranked third, with 54 percent.

Overview:This document describes the 2021 accessibility data released by the Accessibility Observatory at the University of Minnesota. The data are included in the National Accessibility Evaluation Project for 2021, and this information can be accessed for each state in the U.S. at https://access.umn.edu/research/america. The following sections describe the format, naming, and content of the data files.Data Formats: The data files are provided in a Geopackage format. Geopackage (.gpkg) files are an open-source, geospatial filetype that can contain multiple layers of data in a single file, and can be opened with most GIS software, including both ArcGIS and QGIS.Within this zipfile, there are six geopackage files (.gpkg) structured as follows. Each of them contains the blocks shapes layer, results at the block level for all LEHD variables (jobs and workers), with a layer of results for each travel time (5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60 minutes). {MPO ID}_tr_2021_0700-0859-avg.gpkg = Average Transit Access Departing Every Minute 7am-9am{MPO ID}_au_2021_08.gpkg = Average Auto Access Departing 8am{MPO ID}_bi_2021_1200_lts1.gpkg = Average Bike Access on LTS1 Network{MPO ID}_bi_2021_1200_lts2.gpkg = Average Bike Access on LTS2 Network{MPO ID}_bi_2021_1200_lts3.gpkg = Average Bike Access on LTS3 Network{MPO ID}_bi_2021_1200_lts4.gpkg = Average Bike Access on LTS4 NetworkFor mapping and geospatial analysis, the blocks shape layer within each geopackage can be joined to the blockid of the access attribute data. Opening and Using Geopackages in ArcGIS:Unzip the zip archiveUse the "Add Data" function in Arc to select the .gpkg fileSelect which layer(s) are needed — always select "main.blocks" as this layer contains the Census block shapes; select any other attribute data layers as well.There are three types of layers in the geopackage file — the "main.blocks" layer is the spatial features layer, and all other layers are either numerical attribute data tables, or the "fieldname_descriptions" metadata layer. The numerical attribute layers are named with the following format:[mode]_[threshold]_minutes[mode] is a two-character code indicating the transport mode used[threshold] is an integer indicating the travel time threshold used for this data layerTo use the data spatially, perform a join between the "main.blocks" layer and the desired numerical data layer, using either the numerical "id" fields, or 15-digit "blockid" fields as join fields.