Learn the building blocks of a query expression and how to select features that meet one or more attribute criteria.

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.

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.

The data in this map service is updated every weekend.Note: This data includes all activities regardless of whether there is a spatial feature attached.Note: This is a large dataset. Metadata and Downloads are available at: https://data.fs.usda.gov/geodata/edw/datasets.php?xmlKeyword=FACTS+common+attributesTo download FACTS activities layers, search for the activity types you want, such as timber harvest or hazardous fuels treatments. The Forest Service's Natural Resource Manager (NRM) Forest Activity Tracking System (FACTS) is the agency standard for managing information about activities related to fire/fuels, silviculture, and invasive species. This feature class contains the FACTS attributes most commonly needed to describe FACTS activities.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService CSV Shapefile GeoJSON KML https://apps.fs.usda.gov/arcx/rest/services/EDW/EDW_ActivityFactsCommonAttributes_01/MapServer/0 Geodatabase Download Shapefile Download For complete information, please visit https://data.gov.

This story map explains how to use two attributes to make a map using both color and size using the smart mapping capability within ArcGIS Online and ArcGIS Enterprise. You can easily select two attributes, and one will be shown in your map using color, while the other will be used to represent size. This mapping technique can help to show relationships you might not have known existed. This method can also help turn multiple maps into a single map to share with others. This story map walks you through multiple examples, which can help get you started with smart mapping color and size.

This layer shows demographic context for senior well-being work. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. The layer is symbolized to show the percentage of population aged 65 and up (senior population). To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B01001, B09021, B17020, B18101, B23027, B25072, B25093, B27010, B28005, C27001B-IData downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

This layer shows demographic context for emergency response efforts. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percentage of households who do not have access to internet. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B01001, B08201, B09021, B16003, B16004, B17020, B18101, B25040, B25117, B27010, B28001, B28002 Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

This data set depicts lands selected by ANCSA village and regional corporations. Data attributes are a snapshot of the BLM-AK Land Information System Database and are only accurate as that database. Alaska, being a state within the Public Land Survey System (PLSS), describes land to the aliquot part (subsections of larger land plots) where ever possible. Where data is not able to be described with an aliquot part, the data is generalized to the nearest PLSS section (640 acres). In some instances lands have been mapped based on survey or digitized using a Master Title Plat (MTP).

This layer shows workers' place of residence by commute length. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percentage of commuters whose commute is 90 minutes or more. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2015-2019ACS Table(s): B08303Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 10, 2020National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

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.

This layer shows health insurance coverage by type and by age group. This is shown by tract, county, and state centroids. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the count and percent uninsured. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B27010 (Not all lines of this ACS table are available in this feature layer.)Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

Water supply lakes are the primary source of water for many communities in northern and western Missouri. Therefore, accurate and up-to-date estimates of lake capacity are important for managing and predicting adequate water supply. Many of the water supply lakes in Missouri were previously surveyed by the U.S. Geological Survey (USGS) in the early 2000s (Richards, 2013) and in 2013 (Huizinga, 2014); however, years of potential sedimentation may have resulted in reduced water storage capacity. Periodic bathymetric surveys are useful to update the area/capacity table and to determine changes in the bathymetric surface. In April and May 2022, the USGS, in cooperation with the Missouri Department of Natural Resources (MoDNR) and in collaboration with the cities of Cameron, Springfield, and Unionville, Missouri, completed bathymetric surveys of seven (7) lakes using a marine-based mobile mapping unit, which consists of a multibeam echosounder (MBES) and an inertial navigation system (INS) mounted on a marine survey vessel. Bathymetric data were collected as the vessel traversed longitudinal transects to provide nearly complete coverage of the lake. The MBES was electronically tilted in some areas to improve data collection along the shoreline, in coves, and in areas that are shallower than about 2.0 meters deep (the practical limit of reasonable and safe data collection with the MBES). At some lakes, supplemental data were collected in shallow areas using an acoustic Doppler current profiler (ADCP) mounted on a remote-controlled vessel equipped with a differential global positioning system (DGPS). Bathymetric quality-assurance data also were collected at each lake to evaluate the vertical accuracy of the gridded bathymetric point data from the MBES. As part of the survey at each of these lakes, one or more reference marks or temporary bench marks were established to provide a point of known location and elevation from which the water surface could be measured or another survey could be referenced at a later date. In addition, the elevation of a primary spillway or intake was surveyed, when present. These points were surveyed using a real-time kinematic (RTK) Global Navigation Satellite System (GNSS) receiver connected to the Missouri Department of Transportation real-time network (RTN), which provided real-time survey-grade horizontal and vertical positioning, using field procedures as described in Rydlund and Densmore (2012) for a Level II real-time positioning survey. The MBES data can be combined with light detection and ranging (lidar) data to prepare a bathymetric map and a surface area and capacity table for each lake. These data also can be used to compare the current bathymetric surface with any previous bathymetric surface. Data from each of the surveys are provided in ESRI Shapefile format (ESRI, 2023). Each of the seven lakes surveyed in 2022 has a child page containing the metadata and two zip files, one for the bathymetric data, and the other for the bathymetric quality-assurance data. The zip files follow the format of "####2022_bathy_pts.zip" or ####2022_QA_raw.zip," where "####" is the lake name. Each of these zip files contains a shapefile with an attribute table. Attribute/column labels of each table are described in the "Entity and attribute" section of the metadata file. The various reference marks and additional points from all the lake surveys are provided in ESRI Shapefile format (ESRI, 2023) with an attribute table on the main landing page. Attribute/column labels of this table are described in the "Entity and attribute" section of the metadata file. References Cited: Environmental Systems Research Institute, 2023, ArcGIS: accessed July 12, 2023, at https://www.esri.com/en-us/arcgis/about-arcgis/overview Huizinga, R.J., 2014, Bathymetric surveys and area/capacity tables of water-supply reservoirs for the city of Cameron, Missouri, July 2013: U.S. Geological Survey Open-File Report 2014–1005, 15 p., https://doi.org/10.3133/ofr20141005. Richards, J.M., 2013, Bathymetric surveys of selected lakes in Missouri—2000–2008: U.S. Geological Survey Open-File Report 2013–1101, 9 p. with appendix, https://doi.org/10.3133/ofr20131101. Rydlund, P.H., Jr., and Densmore, B.K., 2012, Methods of practice and guidelines for using survey-grade global navigation satellite systems (GNSS) to establish vertical datum in the United States Geological Survey: U.S. Geological Survey Techniques and Methods, book 11, chap. D1, 102 p. with appendixes, https://doi.org/10.3133/tm11D1. First posted November 29, 2023 Revised July 31, 2024, ver. 1.1

This layer shows computer ownership and internet access by age and race. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percent of population age 18 to 64 in households with no computer. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B28005, B28003, B28009B, B28009C, B28009D, B28009E, B28009F, B28009G, B28009H, B28009I Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

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.

Important Note: This item is in mature support as of February 2023 and will be retired in December 2025. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. This layer displays change in pixels of the Sentinel-2 10m Land Use/Land Cover product developed by Esri, Impact Observatory, and Microsoft. Available years to compare with 2021 are 2018, 2019 and 2020. By default, the layer shows all comparisons together, in effect showing what changed 2018-2021. But the layer may be changed to show one of three specific pairs of years, 2018-2021, 2019-2021, or 2020-2021.Showing just one pair of years in ArcGIS Online Map ViewerTo show just one pair of years in ArcGIS Online Map viewer, create a filter. 1. Click the filter button. 2. Next, click add expression. 3. In the expression dialogue, specify a pair of years with the ProductName attribute. Use the following example in your expression dialogue to show only places that changed between 2020 and 2021:ProductNameis2020-2021By default, places that do not change appear as a transparent symbol in ArcGIS Pro. But in ArcGIS Online Map Viewer, a transparent symbol may need to be set for these places after a filter is chosen. To do this:4. Click the styles button. 5. Under unique values click style options. 6. Click the symbol next to No Change at the bottom of the legend. 7. Click the slider next to "enable fill" to turn the symbol off.Showing just one pair of years in ArcGIS ProTo show just one pair of years in ArcGIS Pro, choose one of the layer's processing templates to single out a particular pair of years. The processing template applies a definition query that works in ArcGIS Pro. 1. To choose a processing template, right click the layer in the table of contents for ArcGIS Pro and choose properties. 2. In the dialogue that comes up, choose the tab that says processing templates. 3. On the right where it says processing template, choose the pair of years you would like to display. The processing template will stay applied for any analysis you may want to perform as well.How the change layer was created, combining LULC classes from two yearsImpact Observatory, Esri, and Microsoft used artificial intelligence to classify the world in 10 Land Use/Land Cover (LULC) classes for the years 2017-2021. Mosaics serve the following sets of change rasters in a single global layer: Change between 2018 and 2021Change between 2019 and 2021Change between 2020 and 2021To make this change layer, Esri used an arithmetic operation combining the cells from a source year and 2021 to make a change index value. ((from year * 16) + to year) In the example of the change between 2020 and 2021, the from year (2020) was multiplied by 16, then added to the to year (2021). Then the combined number is served as an index in an 8 bit unsigned mosaic with an attribute table which describes what changed or did not change in that timeframe. Variable mapped: Change in land cover between 2018, 2019, or 2020 and 2021 Data Projection: Universal Transverse Mercator (UTM)Mosaic Projection: WGS84Extent: GlobalSource imagery: Sentinel-2Cell Size: 10m (0.00008983152098239751 degrees)Type: ThematicSource: Esri Inc.Publication date: January 2022What can you do with this layer?Global LULC maps provide information on conservation planning, food security, and hydrologic modeling, among other things. This dataset can be used to visualize land cover anywhere on Earth. This layer can also be used in analyses that require land cover input. For example, the Zonal Statistics tools allow a user to understand the composition of a specified area by reporting the total estimates for each of the classes. Land Cover processingThis map was produced by a deep learning model trained using over 5 billion hand-labeled Sentinel-2 pixels, sampled from over 20,000 sites distributed across all major biomes of the world. The underlying deep learning model uses 6 bands of Sentinel-2 surface reflectance data: visible blue, green, red, near infrared, and two shortwave infrared bands. To create the final map, the model is run on multiple dates of imagery throughout the year, and the outputs are composited into a final representative map. Processing platformSentinel-2 L2A/B data was accessed via Microsoft’s Planetary Computer and scaled using Microsoft Azure Batch.Class definitions1. WaterAreas where water was predominantly present throughout the year; may not cover areas with sporadic or ephemeral water; contains little to no sparse vegetation, no rock outcrop nor built up features like docks; examples: rivers, ponds, lakes, oceans, flooded salt plains.2. TreesAny significant clustering of tall (~15-m or higher) dense vegetation, typically with a closed or dense canopy; examples: wooded vegetation,
clusters of dense tall vegetation within savannas, plantations, swamp or mangroves (dense/tall vegetation with ephemeral water or canopy too thick to detect water underneath).4. Flooded vegetationAreas of any type of vegetation with obvious intermixing of water throughout a majority of the year; seasonally flooded area that is a mix of grass/shrub/trees/bare ground; examples: flooded mangroves, emergent vegetation, rice paddies and other heavily irrigated and inundated agriculture.5. CropsHuman planted/plotted cereals, grasses, and crops not at tree height; examples: corn, wheat, soy, fallow plots of structured land.7. Built AreaHuman made structures; major road and rail networks; large homogenous impervious surfaces including parking structures, office buildings and residential housing; examples: houses, dense villages / towns / cities, paved roads, asphalt.8. Bare groundAreas of rock or soil with very sparse to no vegetation for the entire year; large areas of sand and deserts with no to little vegetation; examples: exposed rock or soil, desert and sand dunes, dry salt flats/pans, dried lake beds, mines.9. Snow/IceLarge homogenous areas of permanent snow or ice, typically only in mountain areas or highest latitudes; examples: glaciers, permanent snowpack, snow fields. 10. CloudsNo land cover information due to persistent cloud cover.11. Rangeland Open areas covered in homogenous grasses with little to no taller vegetation; wild cereals and grasses with no obvious human plotting (i.e., not a plotted field); examples: natural meadows and fields with sparse to no tree cover, open savanna with few to no trees, parks/golf courses/lawns, pastures. Mix of small clusters of plants or single plants dispersed on a landscape that shows exposed soil or rock; scrub-filled clearings within dense forests that are clearly not taller than trees; examples: moderate to sparse cover of bushes, shrubs and tufts of grass, savannas with very sparse grasses, trees or other plants.CitationKarra, Kontgis, et al. “Global land use/land cover with Sentinel-2 and deep learning.” IGARSS 2021-2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021.AcknowledgementsTraining data for this project makes use of the National Geographic Society Dynamic World training dataset, produced for the Dynamic World Project by National Geographic Society in partnership with Google and the World Resources Institute.For questions please email environment@esri.com

Wetlands are areas where water is present at or near the surface of the soil during at least part of the year. Wetlands provide habitat for many species of plants and animals that are adapted to living in wet habitats. Wetlands form characteristic soils, absorb pollutants and excess nutrients from aquatic systems, help buffer the effects of high flows, and recharge groundwater. Data on the distribution and type of wetland play an important role in land use planning and several federal and state laws require that wetlands be considered during the planning process.The National Wetlands Inventory (NWI) was designed to assist land managers in wetland conservation efforts. The NWI is managed by the US Fish and Wildlife Service.Dataset SummaryPhenomenon Mapped: WetlandsCoordinate System: Web Mercator Auxiliary SphereExtent: 50 United States plus Puerto Rico, the US Virgin Islands and the Northern Mariana IslandsVisible Scale: The data is visible at scales from 1:144,000 to 1:1,000Resolution/Tolerance: 0.0001 meters/0.001 metersNumber of Features: 34,954,623 diced, after applying a 50,000 vertex limit to an original set of 34,950,653 featuresFeature Limit: 10,000Source: U.S. Fish and Wildlife ServicePublication Date: September 29, 2020ArcGIS Server URL: https://landscape11.arcgis.com/arcgis/This layer was created from the September 29, 2020 version of the NWI. This layer includes attributes from the original dataset as well as attributes added by Esri for use in the default pop-up and to allow the user to query and filter the data.NWI derived attributes:Wetland Code - a code that identifies specific attributes of the wetlandWetland Type - one of 8 wetland typesArea - area of the wetland in acresEsri created attributes:System - code indicating the system and subsystem of the wetlandClass - code indicating the class and subclass of the wetlandModifier 1, Modifier 2, Modifier 3, Modifier 4 - these four fields contain letter codes for modifiers applied to the wetland descriptionSystem Name - the name of the system (Marine, Estuarine, Riverine, Lacustrine, or Palustrine)Subsystem Name - the name of the subsystemClass Name - the name of the classSubclass Name - the name of the subclassModifier 1 Name, Modifier 2 Name, Modifier 3 Name , Modifier 4 Name - these four fields contain names for modifiers applied to the wetland descriptionPopup Header - this field contains a text string that is used to create the header in the default pop-up System Text - this field contains a text string that is used to create the system description text in the default pop-upClass Text - this field contains a text string that is used to create the class description text in the default pop-upModifier Text - this field contains a text string that is used to create the modifier description text in the default pop-upSpecies Text - this field contains a text string that is used to create the species description text in the default pop-upCodes, names, and text fields were derived from the publication Classification of Wetlands and Deepwater Habitats of the United States.What can you do with this Feature 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 is limited to scales of approximately 1:144,000 or larger but an imagery layer created from the same data can be used at smaller scales to produce a webmap that displays across the full scale range. 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 and apply filters. 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.Change the layer’s style and filter the data. For example, you could set a filter for System Text = 'Palustrine' to create a map of palustrine wetlands only.Add labels and set their propertiesCustomize the pop-upArcGIS ProAdd this layer to a 2d or 3d map. The same scale limit as Online applies in ProUse 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 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.

This layer displays change in pixels of the Sentinel-2 10m Land Use/Land Cover product developed by Esri, Impact Observatory, and Microsoft. Available years to compare with 2021 are 2018, 2019 and 2020.By default, the layer shows all comparisons together, in effect showing what changed 2018-2021. But the layer may be changed to show one of three specific pairs of years, 2018-2021, 2019-2021, or 2020-2021.Showing just one pair of years in ArcGIS Online Map ViewerTo show just one pair of years in ArcGIS Online Map viewer, create a filter.1. Click the filter button.2. Next, click add expression.3. In the expression dialogue, specify a pair of years with the ProductName attribute. Use the following example in your expression dialogue to show only places that changed between 2020 and 2021:ProductNameis2020-2021By default, places that do not change appear as a transparent symbol in ArcGIS Pro. But in ArcGIS Online Map Viewer, a transparent symbol may need to be set for these places after a filter is chosen. To do this:4. Click the styles button.5. Under unique values click style options.6. Click the symbol next to No Change at the bottom of the legend.7. Click the slider next to "enable fill" to turn the symbol off.Showing just one pair of years in ArcGIS ProTo show just one pair of years in ArcGIS Pro, choose one of the layer's processing templates to single out a particular pair of years. The processing template applies a definition query that works in ArcGIS Pro.1. To choose a processing template, right click the layer in the table of contents for ArcGIS Pro and choose properties.2. In the dialogue that comes up, choose the tab that says processing templates.3. On the right where it says processing template, choose the pair of years you would like to display.The processing template will stay applied for any analysis you may want to perform as well.How the change layer was created, combining LULC classes from two yearsImpact Observatory, Esri, and Microsoft used artificial intelligence to classify the world in 10 Land Use/Land Cover (LULC) classes for the years 2017-2021. Mosaics serve the following sets of change rasters in a single global layer:Change between 2018 and 2021Change between 2019 and 2021Change between 2020 and 2021To make this change layer, Esri used an arithmetic operation combining the cells from a source year and 2021 to make a change index value. ((from year * 16) + to year) In the example of the change between 2020 and 2021, the from year (2020) was multiplied by 16, then added to the to year (2021). Then the combined number is served as an index in an 8 bit unsigned mosaic with an attribute table which describes what changed or did not change in that timeframe.Variable mapped: Change in land cover between 2018, 2019, or 2020 and 2021Data Projection: Universal Transverse Mercator (UTM)Mosaic Projection: WGS84Extent: GlobalSource imagery: Sentinel-2Cell Size: 10m (0.00008983152098239751 degrees)Type: ThematicSource: Esri Inc.Publication date: January 2022What can you do with this layer?Global LULC maps provide information on conservation planning, food security, and hydrologic modeling, among other things. This dataset can be used to visualize land cover anywhere on Earth. This layer can also be used in analyses that require land cover input. For example, the Zonal Statistics tools allow a user to understand the composition of a specified area by reporting the total estimates for each of the classes.Land Cover processingThis map was produced by a deep learning model trained using over 5 billion hand-labeled Sentinel-2 pixels, sampled from over 20,000 sites distributed across all major biomes of the world. The underlying deep learning model uses 6 bands of Sentinel-2 surface reflectance data: visible blue, green, red, near infrared, and two shortwave infrared bands. To create the final map, the model is run on multiple dates of imagery throughout the year, and the outputs are composited into a final representative map.Processing platformSentinel-2 L2A/B data was accessed via Microsoft’s Planetary Computer and scaled using Microsoft Azure Batch.Class definitions1. WaterAreas where water was predominantly present throughout the year; may not cover areas with sporadic or ephemeral water; contains little to no sparse vegetation, no rock outcrop nor built up features like docks; examples: rivers, ponds, lakes, oceans, flooded salt plains.2. TreesAny significant clustering of tall (~15-m or higher) dense vegetation, typically with a closed or dense canopy; examples: wooded vegetation, clusters of dense tall vegetation within savannas, plantations, swamp or mangroves (dense/tall vegetation with ephemeral water or canopy too thick to detect water underneath).4. Flooded vegetationAreas of any type of vegetation with obvious intermixing of water throughout a majority of the year; seasonally flooded area that is a mix of grass/shrub/trees/bare ground; examples: flooded mangroves, emergent vegetation, rice paddies and other heavily irrigated and inundated agriculture.5. CropsHuman planted/plotted cereals, grasses, and crops not at tree height; examples: corn, wheat, soy, fallow plots of structured land.7. Built AreaHuman made structures; major road and rail networks; large homogenous impervious surfaces including parking structures, office buildings and residential housing; examples: houses, dense villages / towns / cities, paved roads, asphalt.8. Bare groundAreas of rock or soil with very sparse to no vegetation for the entire year; large areas of sand and deserts with no to little vegetation; examples: exposed rock or soil, desert and sand dunes, dry salt flats/pans, dried lake beds, mines.9. Snow/IceLarge homogenous areas of permanent snow or ice, typically only in mountain areas or highest latitudes; examples: glaciers, permanent snowpack, snow fields. 10. CloudsNo land cover information due to persistent cloud cover.11. RangelandOpen areas covered in homogenous grasses with little to no taller vegetation; wild cereals and grasses with no obvious human plotting (i.e., not a plotted field); examples: natural meadows and fields with sparse to no tree cover, open savanna with few to no trees, parks/golf courses/lawns, pastures. Mix of small clusters of plants or single plants dispersed on a landscape that shows exposed soil or rock; scrub-filled clearings within dense forests that are clearly not taller than trees; examples: moderate to sparse cover of bushes, shrubs and tufts of grass, savannas with very sparse grasses, trees or other plants.CitationKarra, Kontgis, et al. “Global land use/land cover with Sentinel-2 and deep learning.” IGARSS 2021-2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021.AcknowledgementsTraining data for this project makes use of the National Geographic Society Dynamic World training dataset, produced for the Dynamic World Project by National Geographic Society in partnership with Google and the World Resources Institute.For questions please email environment@esri.com

This is a collection of maps, layers, apps and dashboards that show population access to essential retail locations, such as grocery stores. Data sourcesPopulation data is from the 2010 U.S. Census blocks. Each census block has a count of stores within a 10 minute walk, and a count of stores within a ten minute drive. Census blocks known to be unpopulated are given a score of 0. The layer is available as a hosted feature layer.Grocery store locations are from SafeGraph, reflecting what was in the data as of October 2020. Access to the layer was obtained from the SafeGraph offering in ArcGIS Marketplace. For this project, ArcGIS StreetMap Premium was used for the street network in the origin-destination analysis work, because it already has the necessary attributes on each street segment to identify which streets are considered walkable, and supports a wide variety of driving parameters.The walkable access layer and drivable access layers are rasters, whose colors were chosen to allow the drivable access layer to serve as backdrop to the walkable access layer. Alternative versions of these layers are available. These pairs use different colors but are otherwise identical in content.Data PreparationArcGIS Network Analyst was used to set up a network street layer for analysis. ArcGIS StreetMap Premium was installed to a local hard drive and selected in the Origin-Destination workflow as the network data source. This allows the origins (Census block centroids) and destinations (SafeGraph grocery stores) to be connected to that network, to allow origin-destination analysis.The Census blocks layer contains the centroid of each Census block. The data allows a simple popup to be created. This layer's block figures can be summarized further, to tract, county and state levels.The SafeGraph grocery store locations were created by querying the SafeGraph source layer based on primary NAICS code. After connecting to the layer in ArcGIS Pro, a definition query was set to only show records with NAICS code 445110 as an initial screening. The layer was exported to a local disk drive for further definition query refinement, to eliminate any records that were obviously not grocery stores. The final layer used in the analysis had approximately 53,600 records. In this map, this layer is included as a vector tile layer.MethodologyEvery census block in the U.S. was assigned two access scores, whose numbers are simply how many grocery stores are within a 10 minute walk and a 10 minute drive of that census block. Every census block has a score of 0 (no stores), 1, 2 or more stores. The count of accessible stores was determined using Origin-Destination Analysis in ArcGIS Network Analyst, in ArcGIS Pro. A set of Tools in this ArcGIS Pro package allow a similar analysis to be conducted for any city or other area. The Tools step through the data prep and analysis steps. Download the Pro package, open it and substitute your own layers for Origins and Destinations. Parcel centroids are a suggested option for Origins, for example. Origin-Destination analysis was configured, using ArcGIS StreetMap Premium as the network data source. Census block centroids with population greater than zero were used as the Origins, and grocery store locations were used as the Destinations. A cutoff of 10 minutes was used with the Walk Time option. Only one restriction was applied to the street network: Walkable, which means Interstates and other non-walkable street segments were treated appropriately. You see the results in the map: wherever freeway overpasses and underpasses are present near a grocery store, the walkable area extends across/through that pass, but not along the freeway.A cutoff of 10 minutes was used with the Drive Time option. The default restrictions were applied to the street network, which means a typical vehicle's access to all types of roads was factored in.The results for each analysis were captured in the Lines layer, which shows which origins are within the cutoff of each destination over the street network, given the assumptions about that network (walking, or driving a vehicle).The Lines layer was then summarized by census block ID to capture the Maximum value of the Destination_Rank field. A census block within 10 minutes of 3 stores would have 3 records in the Lines layer, but only one value in the summarized table, with a MAX_Destination_Rank field value of 3. This is the number of stores accessible to that census block in the 10 minutes measured, for walking and driving. These data were joined to the block centroids layer and given unique names. At this point, all blocks with zero population or null values in the MAX_Destination_Rank fields were given a store count of 0, to help the next step.Walkable and Drivable areas are calculated into a raster layer, using Nearest Neighbor geoprocessing tool on the count of stores within a 10 minute walk, and a count of stores within a ten minute drive, respectively. This tool uses a 200 meter grid and interpolates the values between each census block. A census tracts layer containing all water polygons "erased" from the census tract boundaries was used as an environment setting, to help constrain interpolation into/across bodies of water. The same layer use used to "shoreline" the Nearest Neighbor results, to eliminate any interpolation into the ocean or Great Lakes. This helped but was not perfect.Notes and LimitationsThe map provides a baseline for discussing access to grocery stores in a city. It does not presume local population has the desire or means to walk or drive to obtain groceries. It does not take elevation gain or loss into account. It does not factor time of day nor weather, seasons, or other variables that affect a person's commute choices. Walking and driving are just two ways people get to a grocery store. Some people ride a bike, others take public transit, have groceries delivered, or rely on a friend with a vehicle. Thank you to Melinda Morang on the Network Analyst team for guidance and suggestions at key moments along the way; to Emily Meriam for reviewing the previous version of this map and creating new color palettes and marker symbols specific to this project. Additional ReadingThe methods by which access to food is measured and reported have improved in the past decade or so, as has the uses of such measurements. Some relevant papers and articles are provided below as a starting point.Measuring Food Insecurity Using the Food Abundance Index: Implications for Economic, Health and Social Well-BeingHow to Identify Food Deserts: Measuring Physical and Economic Access to Supermarkets in King County, WashingtonAccess to Affordable and Nutritious Food: Measuring and Understanding Food Deserts and Their ConsequencesDifferent Measures of Food Access Inform Different SolutionsThe time cost of access to food – Distance to the grocery store as measured in minutes

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.