The U.S. Geological Survey has conducted geologic mapping to characterize the sea floor offshore of Massachusetts. The mapping was carried out using a Simrad Subsea EM 1000 Multibeam Echo Sounder on the Frederick G. Creed on four cruises conducted between 1994 and 1998. The mapping was conducted in cooperation with the National Oceanic and Atmospheric Administration (NOAA) and with support from the Canadian Hydrographic Service and the University of New Brunswick. The long-term goal of this mapping effort is to produce high-resolution geologic maps and a Geographic Information System (GIS) project that presents images and grids of bathymetry, shaded relief bathymetry, and backscatter intensity data from these surveys that will serve the needs of research, management and the public.

This dataset is designed to be used as a "graticule layer", allowing a graticule to be drawn on maps when using software packages that don't support their generation in other ways. It consists of lines spaced at 1km intervals, running north-south (attributed with Easting) and east-west (attributed with Northing). It is applicable for use where an MGA graticule is required. Can be projected to provide AMG graticules over non-MGA data (eg Geographic or AMG).

This dataset forms part of a series of graticule layers, one for each common projection.

This resource is a compilation of Michigan Borehole Lithology Interval data from oil and gas wells, provided by Western Michigan University Geosciences Department. The data are available in the following formats: web feature service, web map service, ESRI service endpoint, and an Excel workbook for download. The workbook contains 4 worksheets, including information about the template with notes related to revisions of the template, resource provider information, the data, a field list (data mapping view). This data was provided by the Michigan Geological Survey at Western Michigan University and made available for distribution through the National Geothermal Data System.

This digital data release contains previously published contours of thickness values of 23 named geological horizons, ranging in age from Cambrian to Tertiary. In alphabetical order, these horizons are the Atokan, Chesterian, Cretaceous, Desmoinesian, Guadalupian, Jurassic, Kinderhookian, Leonardian, Lower Hunton, Meramecian, Missourian, Morrowan, Ochoan, Osagean, Simpson-Viola, Sylvan-Cason, Tertiary, Timbered Hills-Arbuckle, Triassic, Upper Hunton, Virgillian, Wolfcampian, and Woodford-Chatanooga. The thicknesses were published in plates in the back matter of the Sedimentary Cover – North American Craton: U.S. volume of the larger Geological Society of America’s Decade of North American Geology effort. This volume, edited by L.L. Sloss, contains efforts from many geologists. In particular, the Southern Midcontinent plates were generated by K.S. Johnson, T.W. Amsden, R.E. Denison, S.P. Dutton, A.G. Goldstein, B. Rascoe, Jr., P.K Sutherland, and D.M. Thompson. In these thickness c ...

Abstract The objectives of this study were to map genomic regions associated with QTL for aluminum (Al) tolerance in maize and determine the phenotypic effects of tolerance loci. QTL analysis for Al tolerance was carried out in a population of F2:3 progenies resulting from a cross between the contrasting lines LT 99-4 and LS 04-2. SSR (Simple Sequence Repeat) loci and AFLP (Amplified Fragment Length Polymorphism) were used to construct the linkage map and to detect QTL mapped by composite interval mapping analysis. Nine tolerance QTL among eight linkage groups (chromosomes 2, 4, 5, 6, 7, 8, 9, and 10) were mapped, which explained 70.3% of the phenotypic variance. The results confirmed three major QTL (bins 6.00, 8.05, and 10.01) that are described in the literature for Al tolerance, which accounted for most of the phenotypic variance (40.3%).

The percent area of a landscape analysis unit identified as having a fire deficit by comparing modern fire occurrence (MTBS: 1984-2017) with historical fire rotations (LANDFIRE Mean Fire Return Intervals).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 For complete information, please visit https://data.gov.

This report contains a contour map showing net sand in interval 50'above G seam.

The U.S. Geological Survey has conducted geologic mapping to characterize the sea floor offshore of Massachusetts. The mapping was carried out using a Simrad Subsea EM 1000 Multibeam Echo Sounder on the Frederick G. Creed on four cruises conducted between 1994 and 1998. The mapping was conducted in cooperation with the National Oceanic and Atmospheric Administration (NOAA) and with support from the Canadian Hydrographic Service and the University of New Brunswick. The long-term goal of this mapping effort is to produce high-resolution geologic maps and a Geographic Information System (GIS) project that presents images and grids of bathymetry, shaded relief bathymetry, and backscatter intensity data from these surveys that will serve the needs of research, management and the public.

Live Maps is a configurable app template that provides the ability to consume a live data feeds from a variety of sources.Use CasesProvide a map that shows locations of health care facilities and the reported cases of the influenza.Present the locations of political campaign events with related tweets.Configurable OptionsLive Maps is used to combine social media feeds with your operational content, it can be configured using the following options:Map: Choose the web map used in your application.Title: The application name displayed in the header.Subitle: The application subtitle displayed in the header.Color: Choose the color scheme for the application.Feed: The live feed to use in the application, currently supports: Twitter, Flickr, SickWeather.Keyword: Optional search keyword for feeds like Twitter and Flickr.Interval: The interval in minutes to switch between records.Refresh interval: The interval in minutes to refresh the feed.Supported DevicesThis application is responsively designed to support use in browsers on desktops, mobile phones, and tablets.Data RequirementsThis application has no data requirements.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.

From the site: "Late in 1994, the Pennsylvania Bureau of Topographic and Geologic Survey was asked to develop a digital physiographic provinces map at 1:100,000 scale. The then-available physiographic provinces map was compiled by the Survey at 1:500,000 scale and published at 1:2,000,000 scale in 1989. A new physiographic provinces map was recompiled on county 1:50,000-scale topographic maps having 20-foot contour intervals. Boundaries based primarily on geology were positioned using published geological maps. Most boundaries were positioned by topographic interpretation. The use of a 20-foot contour interval (a 200-foot interval was used in 1989) resulted in the repositioning of some boundaries. New scale-enhanced understanding of topographic/geologic patterns in the Appalachian Plateaus province resulted in the creation of three new sections and the revision of other section boundaries. The new compilation was reduced 50 percent and transferred to 1:100,000-scale mylar base maps. The province and section boundaries and the late Wisconsinan glacial border were digitized from the mylars, edgematched, assembled into a single dataset, and attributed with physiographic province and section names using UNIX-based Arc/Info. The late Wisconsinan glacial border, which coincides with province and section boundaries in some places, was copied to a separate dataset and removed from the dataset containing the province and section boundaries. There are two datasets for the late Wisconsinan glacial border and the physiographic province and section boundaries. The original datasets are accurate at 1:100,000 scale. The other datasets have been generalized to 1:500,000-scale accuracy for more regional work. A companion dataset consisting of the state and county boundaries of Pennsylvania was compiled from the U.S. Geological Survey (USGS) 1:100,000-scale digital-line-graph (DLG) files for boundaries. The dataset has been attributed with the county names."

(See USGS Digital Data Series DDS-69-E) A geographic information system focusing on the Jurassic-Cretaceous Cotton Valley Group was developed for the U.S. Geological Survey's (USGS) 2002 assessment of undiscovered, technically recoverable oil and natural gas resources of the Gulf Coast Region. The USGS Energy Resources Science Center has developed map and metadata services to deliver the 2002 assessment results GIS data and services online. The Gulf Coast assessment is based on geologic elements of a total petroleum system (TPS) as described in Dyman and Condon (2005). The estimates of undiscovered oil and gas resources are within assessment units (AUs). The hydrocarbon assessment units include the assessment results as attributes within the AU polygon feature class (in geodatabase and shapefile format). Quarter-mile cells of the land surface that include single or multiple wells were created by the USGS to illustrate the degree of exploration and the type and distribution of production for each assessment unit. Other data that are available in the map documents and services include the TPS and USGS province boundaries. To easily distribute the Gulf Coast maps and GIS data, a web mapping application has been developed by the USGS, and customized ArcMap (by ESRI) projects are available for download at the Energy Resources Science Center Gulf Coast website. ArcGIS Publisher (by ESRI) was used to create a published map file (pmf) from each ArcMap document (.mxd). The basemap services being used in the GC map applications are from ArcGIS Online Services (by ESRI), and include the following layers: -- Satellite imagery -- Shaded relief -- Transportation -- States -- Counties -- Cities -- National Forests With the ESRI_StreetMap_World_2D service, detailed data, such as railroads and airports, appear as the user zooms in at larger scales. This map service shows the structural configuration on the top of the Cotton Valley Group in feet below sea level. The map was produced by calculating the difference between a datum at the land surface (either the kelly bushing elevation or the ground surface elevation) and the reported depth of the Cotton Valley Group. This map service also shows the thickness of the interval from the top of the Cotton Valley Group to the top of the Smackover Formation.

Preston, R. and Mills, P., 2002. Generation of a Hydrologically Corrected Digital Elevation Model for the Republic of Ireland. Unpublished report submitted to EPA by Compass Informatics as part of the 2000-LS-2.2.2 Fourth Progress Report.Ordnance Survey Ireland (OSi) 1:50,000 data was used to create it. Individual DEMs were generated for hydrometric areas for the Republic of Ireland, including the coastal islands of Ireland at grid resolution of 20m. The project required significant pre-processing of source data to facilitate the generation of DEMs but the end result is a hydrologically corrected digital representation of terrain for the whole of the country, suitable for numerous environmental applications. Contours at 10m intervals were generated from the hDTM.Digital Terrain Models (DTM) are bare earth models (no trees or buildings) of the Earth’s surface.The map is a vector dataset. The contours are shown as lines. Each line has info on the contour interval and an ID.

This is the data set behind the Wind Generation Interactive Query Tool created by the CEC. The visualization tool interactively displays wind generation over different time intervals in three-dimensional space. The viewer can look across the state to understand generation patterns of regions with concentrations of wind power plants. The tool aids in understanding high and low periods of generation. Operation of the electric grid requires that generation and demand are balanced in each period. The height and color of columns at wind generation areas are scaled and shaded to represent capacity factors (CFs) of the areas in a specific time interval. Capacity factor is the ratio of the energy produced to the amount of energy that could ideally have been produced in the same period using the rated nameplate capacity. Due to natural variations in wind speeds, higher factors tend to be seen over short time periods, with lower factors over longer periods. The capacity used is the reported nameplate capacity from the Quarterly Fuel and Energy Report, CEC-1304A. CFs are based on wind plants in service in the wind generation areas.Renewable energy resources like wind facilities vary in size and geographic distribution within each state. Resource planning, land use constraints, climate zones, and weather patterns limit availability of these resources and where they can be developed. National, state, and local policies also set limits on energy generation and use. An example of resource planning in California is the Desert Renewable Energy Conservation Plan.

By exploring the visualization, a viewer can gain a three-dimensional understanding of temporal variation in generation CFs, along with how the wind generation areas compare to one another. The viewer can observe that areas peak in generation in different periods. The large range in CFs is also visible.

These data were collected under a cooperative agreement between the Massachusetts Office of Coastal Zone Management (CZM) and the U.S. Geological Survey (USGS), Coastal and Marine Geology Program, Woods Hole Coastal and Marine Science Center (WHCMSC). Initiated in 2003, the primary objective of this program is to develop regional geologic framework information for the management of coastal and marine resources. Accurate data and maps of sea floor geology are important first steps toward protecting fish habitat, delineating marine resources, and assessing environmental changes due to natural or human impacts. The project is focused on the inshore waters of coastal Massachusetts, primarily in water depths of 5 to 30 meters (m) deep. Data collected for the mapping cooperative have been released in a series of USGS Open-File Reports (http://woodshole.er.usgs.gov/project-pages/coastal_mass/). The geophysical data were collected during a survey in 2013 during USGS Field Activity 2013-003-FA (http://cmgds.marine.usgs.gov/fan_info.php?fa=2013-003-FA) and cover approximately 185 square kilometers of the inner continental shelf.

This part of DS 781 presents data for the bathymetric contours for several seafloor maps of the Offshore of Tomales Point map area, California. The vector data file is included in "Contours_OffshoreTomalesPoint.zip," which is accessible from https://pubs.usgs.gov/ds/781/OffshoreTomalesPoint/data_catalog_OffshoreTomalesPoint.html. These data accompany the pamphlet and map sheets of Johnson, S.Y., Dartnell, P., Golden, N.E., Hartwell, S.R., Greene, H.G., Erdey, M.D., Cochrane, G.R., Watt, J.T., Kvitek, R.G., Manson, M.W., Endris, C.A., Dieter, B.E., Krigsman, L.M., Sliter, R.W., Lowe, E.N., and Chin, J.L. (S.Y. Johnson and S.A. Cochran, eds.), 2015, California State Waters Map Series—Offshore of Tomales Point, California: U.S. Geological Survey Open-File Report 2015–1088, pamphlet 38 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20151088. 10-m interval contours of the Offshore of Tomales Point map area, California, were generated from bathymetry data collected by California State University, Monterey Bay (CSUMB), by Fugro Pelagos, and by the U.S. Geological Survey. Mapping was completed between 2004 and 2010, using a combination of 200-kHz and 400-kHz Reson 7125, and 244-kHz Reson 8101 multibeam echosounders, as well as 234-kHz and 468-kHz SEA SWATHPlus phase-differencing sidescan sonars. These mapping missions combined to collect bathymetry from about the 10-m isobath to beyond the 3-nautical-mile limit of California's State Waters. Bathymetric contours at 10-m intervals were generated from a bathymetric surface model. The most continuous contour segments were preserved while smaller segments and isolated island polygons were excluded from the final output. Contours were smoothed via a polynomial approximation with exponential kernel (PAEK) algorithm using a tolerance value of 60 m. The contours were then clipped to the boundary of the map area. These data are not intended for navigational purposes.

This Kentucky-specific quadrangle index grid was developed for the KyTopo Map Series. The 60,000' x 40,000' grid tiles are landscape oriented, fit on a standard Arch-D sized sheet, and have newly generated contours based on a KyFromAbove LiDAR-derived DEM. The 60k x 40k grid is a superset of the Kentucky Single Zone based 5k grid that is utilized for organizing and distributing most all of the Commonwealth's raster data holdings. Quadrangle names were developed utilizing a USGS methodology that focuses on the most prominent map features. Clicking on a grid tile shows the names, contour interval, contour index interval, and provides links to download currently available versions of that map.

This layer shows workers' place of residence by mode of commute. 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 by the percentage of workers who drove alone. 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): B08301 (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.

Soil Landscapes of the United States (SOLUS)metadataDescriptionSoil Landscapes of the United States, or SOLUS, is a national map product developed by the National Cooperative Soil Survey that is focused on providing a consistent set of spatially continuous soil property maps to support large scope soil investigations and land use decisions. SOLUS maps use a digital soil mapping framework that combines multiple sources of soil survey data with environmental covariate data and machine learning. Digital soil mapping is the production of georeferenced soil databases based on the quantitative relationships between soil measurements made in the field or laboratory and environmental data. Numerical models use the quantitative relationships to predict the spatial distribution of either discrete soil classes, such as map units, or continuous soil properties, such as clay content. SOLUS maps use continuous property mapping, which predicts soil physical or chemical properties in horizontal and vertical dimensions. The soil properties are represented across a continuous range of values. Raster datasets of select soil properties can be predicted at specified depths or depth intervals. Continuous soil property maps such as SOLUS provide critical natural resource information to support environmental researchers and modelers, conservationists, and others making land management decisions. SOLUS will be updated annually with improved data and methodology. SOLUS100The first version of SOLUS, called SOLUS100, is 100 m spatial resolution. Each 100 m raster cell represents a 100 m by 100 m square on the ground with soil property values estimated at seven depths: 0, 5, 15, 30, 60, 100, and 150 cm. The next version will be 30 m spatial resolution and called SOLUS30. SOLUS100 predicts 20 soil properties (listed below with units) at seven depths for the continental United States for a total of 512 maps.Very fine sand (%)Fine sand (%)Medium sand (%)Coarse sand (%)Very coarse sand (%)Total sand (%)Silt (%)Clay (%)pHSoil organic carbon (%)Calcium carbonate equivalent (%)Gypsum content (% by weight)Electrical conductivity (mmhos/cm)Sodium adsorption ratioCation exchange capacity (meq/100g)Effective cation exchange capacity (meq/100g)Oven dry bulk density (g/cm3)Depth to bedrock (cm)Depth to restriction (cm)Rock fragment volume (%)Property Prediction and Uncertainty LayersEach property-depth prediction is accompanied by estimates of uncertainty expressed as prediction interval low and high and relative prediction interval (RPI). Prediction interval low and high define the range within which future predictions may occur. The relative prediction interval ranges from 0 to 1 and is a relative measure of uncertainty with high values being more uncertain. It is computed as the ratio of the 95% prediction interval width to the training set 95% quantile width (97.5% quantile value – 2.5% quantile value). Values closer to 0 indicate lower uncertainty and values closer to 1 indicate higher uncertainty. Values greater than 1 indicate that the prediction at that location is outside the range of the training data used for that property at that depth. The Soil and Plant Science Division delivers each property-depth combination through Google Cloud Platform as four raster data layers: the property prediction, the prediction interval low and high, and the RPI. Property prediction and uncertainty layers follow the naming convention: propertyname_depth_cm_p (predicted property values)propertyname_depth_cm_rpi (relative prediction interval)propertyname_depth_cm_l (prediction interval low)propertyname_depth_cm_h (prediction interval high)SOLUS100 map of clay content predicted at the 0 cm depth for the continental U.S.AccessSOLUS100 maps are available for download or use within scripting or GIS software environments: SOLUS100 Cloud Storage BucketDetails on background, methodology, accuracy, uncertainty, and other results and discussion of SOLUS100 maps are available at SOLUS100 Ag Data Commons Repository and in the following publication:Nauman, T. W., Kienast-Brown, S., Roecker, S. M., Brungard, C., White, D., Philippe, J., & Thompson, J. A. (2024). Soil landscapes of the United States (SOLUS): developing predictive soil property maps of the conterminous United States using hybrid training sets. Soil Science Society of America Journal, 1–20. https://acsess.onlinelibrary.wiley.com/doi/10.1002/saj2.20769Data CitationsSoil Survey Staff. Soil Landscapes of the United States. United States Department of Agriculture, Natural Resources Conservation Service. Available online at storage.googleapis.com/solus100pub/index.html. Month, day, year accessed (year of official release).Citation ExampleThe following example is for the 2024 SOLUS maps. Such citations should appear in the reference section of your document.Soil Survey Staff. Soil Landscapes of the United States. United States Department of Agriculture, Natural Resources Conservation Service. Available online at storage.googleapis.com/solus100pub/index.html. May 22, 2024 (2024 official release).

This layer shows population broken down by race and Hispanic origin. 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 predominant race living within an area. 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): B03002Data 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.