The arrival of ArcGIS Pro has brought a challenge to ArcMap users. The new software is sufficiently different in architecture and layout that switching from the old to the new is not a simple process. In some ways, Pro is harder to learn for ArcMap users than for new GIS users, because some workflows have to be unlearned, or at least heavily modified. Current ArcMap users are pressed for time, trying to learn the new software while still completing their daily tasks, so a book that teaches Pro from the start is not an efficient method.Switching to ArcGIS Pro from ArcMap aims to quickly transition ArcMap users to ArcGIS Pro. Rather than teaching Pro from the start, as for a novice user, this book focuses on how Pro is different from ArcMap. Covering the most common and important workflows required for most GIS work, it leverages the user’s prior experience to enable a more rapid adjustment to Pro.AUDIENCEProfessional and scholarly; College/higher education; General/trade.AUTHOR BIOMaribeth H. Price, PhD, South Dakota School of Mines and Technology, has been using Esri products since 1991, teaching college GIS since 1995 and writing textbooks utilizing Esri’s software since 2001. She has extensive familiarity with both ArcMap/ArcCatalog and Pro, both as a user and in the classroom, as well as long experience writing about GIS concepts and developing software tutorials. She teaches GIS workshops, having offered more than 100 workshops to over 1,200 participants since 2000.Pub Date: Print: 2/14/2019 Digital: 1/28/2019 Format: PaperbackISBN: Print: 9781589485440 Digital: 9781589485457 Trim: 8 x 10 in.Price: Print: $49.99 USD Digital: $49.99 USD Pages: 172Table of ContentsPreface1 Contemplating the switch to ArcGIS ProBackgroundSystem requirementsLicensingCapabilities of ArcGIS ProWhen should I switch?Time to exploreObjective 1.1: Downloading the data for these exercisesObjective 1.2: Starting ArcGIS Pro, signing in, creating a project, and exploring the interfaceObjective 1.3: Accessing maps and data from ArcGIS OnlineObjective 1.4: Arranging the windows and panesObjective 1.5: Accessing the helpObjective 1.6: Importing a map document2 Unpacking the GUIBackgroundThe ribbon and tabsPanesViewsTime to exploreObjective 2.1: Getting familiar with the Contents paneObjective 2.2: Learning to work with objects and tabsObjective 2.3: Exploring the Catalog pane3 The projectBackgroundWhat is a project?Items stored in a projectPaths in projectsRenaming projectsTime to exploreObjective 3.1: Exploring different elements of a projectObjective 3.2: Accessing properties of projects, maps, and other items4 Navigating and exploring mapsBackgroundExploring maps2D and 3D navigationTime to exploreObjective 4.1: Learning to use the Map toolsObjective 4.2: Exploring 3D scenes and linking views5 Symbolizing mapsBackgroundAccessing the symbol settings for layersAccessing the labeling propertiesSymbolizing rastersTime to exploreObjective 5.1: Modifying single symbolsObjective 5.2: Creating maps from attributesObjective 5.3: Creating labelsObjective 5.4: Managing labelsObjective 5.5: Symbolizing rasters6 GeoprocessingBackgroundWhat’s differentAnalysis buttons and toolsTool licensingTime to exploreObjective 6.1: Getting familiar with the geoprocessing interfaceObjective 6.2: Performing interactive selectionsObjective 6.3: Performing selections based on attributesObjective 6.4: Performing selections based on locationObjective 6.5: Practicing geoprocessing7 TablesBackgroundGeneral table characteristicsJoining and relating tablesMaking chartsTime to exploreObjective 7.1: Managing table viewsObjective 7.2: Creating and managing properties of a chartObjective 7.3: Calculating statistics for tablesObjective 7.4: Calculating and editing in tables8 LayoutsBackgroundLayouts and map framesLayout editing proceduresImporting map documents and templatesTime to exploreObjective 8.1: Creating the maps for the layoutObjective 8.2: Setting up a layout page with map framesObjective 8.3: Setting map frame extent and scaleObjective 8.4: Formatting the map frameObjective 8.5: Creating and formatting map elementsObjective 8.6: Fine-tuning the legendObjective 8.7: Accessing and copying layouts9 Managing dataBackgroundData modelsManaging the geodatabase schemaCreating domainsManaging data from diverse sourcesProject longevityManaging shared data for work groupsTime to exploreObjective 9.1: Creating a project and exporting data to itObjective 9.2: Creating feature classesObjective 9.3: Creating and managing metadataObjective 9.4: Creating fields and domainsObjective 9.5: Modifying the table schemaObjective 9.6: Sharing data using ArcGIS Online10 EditingBackgroundBasic editing functionsCreating featuresModifying existing featuresCreating and editing annotationTime to exploreObjective 10.1: Understanding the editing tools in ArcGIS ProObjective 10.2: Creating pointsObjective 10.3: Creating linesObjective 10.4: Creating polygonsObjective 10.5: Modifying existing featuresObjective 10.6: Creating an annotation feature classObjective 10.7: Editing annotationObjective 10.8: Creating annotation features11 Moving forwardData sourcesIndex

Continuing the tradition of the best-selling Getting to Know series, Getting to Know ArcGIS Pro 2.6 teaches new and existing GIS users how to get started solving problems using ArcGIS Pro. Using ArcGIS Pro for these tasks allows you to understand complex data with the leading GIS software that many businesses and organizations use every day.Getting to Know ArcGIS Pro 2.6 introduces the basic tools and capabilities of ArcGIS Pro through practical project workflows that demonstrate best practices for productivity. Explore spatial relationships, building a geodatabase, 3D GIS, project presentation, and more. Learn how to navigate ArcGIS Pro and ArcGIS Online by visualizing, querying, creating, editing, analyzing, and presenting geospatial data in both 2D and 3D environments. Using figures to show each step, Getting to Know ArcGIS Pro 2.6 demystifies complicated process like developing a geoprocessing model, using Python to write a script tool, and the creation of space-time cubes. Cartographic techniques for both web and physical maps are included.Each chapter begins with a prompt using a real-world scenario in a different industry to help you explore how ArcGIS Pro can be applied for operational efficiency, analysis, and problem solving. A summary and glossary terms at the end of every chapter help reinforce the lessons and skills learned.Ideal for students, self-learners, and seasoned professionals looking to learn a new GIS product, Getting to Know ArcGIS Pro 2.6 is a broad textbook and desk reference designed to leave users feeling confident in using ArcGIS Pro on their own.AUDIENCEProfessional and scholarly. Higher education.AUTHOR BIOMichael Law is a cartographer and GIS professional with more than a decade of experience. He was a cartographer for Esri, where he developed cartography for books, edited and tested GIS workbooks, and was the editor of the Esri Map Book. He continues to work with GIS software, writing technical documentation, teaching training courses, and designing and optimizing user interfaces.Amy Collins is a writer and editor who has worked with GIS for over 16 years. She was a technical editor for Esri, where she honed her GIS skills and cultivated an interest in designing effective instructional materials. She continues to develop books on GIS education, among other projects.Pub Date: Print: 10/6/2020 Digital: 8/18/2020 ISBN: Print: 9781589486355 Digital: 9781589486362 Price: Print: $84.99 USD Digital: $84.99 USD Pages: 420 Trim: 7.5 x 9.25 in.Table of ContentsPrefaceChapter 1 Introducing GISExercise 1a: Explore ArcGIS OnlineChapter 2 A first look at ArcGIS Pro Exercise 2a: Learn some basics Exercise 2b: Go beyond the basics Exercise 2c: Experience 3D GISChapter 3 Exploring geospatial relationshipsExercise 3a: Extract part of a dataset Exercise 3b: Incorporate tabular data Exercise 3c: Calculate data statistics Exercise 3d: Connect spatial datasetsChapter 4 Creating and editing spatial data Exercise 4a: Build a geodatabase Exercise 4b: Create features Exercise 4c: Modify featuresChapter 5 Facilitating workflows Exercise 5a: Manage a repeatable workflow using tasks Exercise 5b: Create a geoprocessing model Exercise 5c: Run a Python command and script toolChapter 6 Collaborative mapping Exercise 6a: Prepare a database for data collection Exercise 6b: Prepare a map for data collection Exercise 6c: Collect data using ArcGIS CollectorChapter 7 Geoenabling your projectExercise 7a: Prepare project data Exercise 7b: Geocode location data Exercise 7c: Use geoprocessing tools to analyze vector dataChapter 8 Analyzing spatial and temporal patternsExercise 8a: Create a kernel density map Exercise 8b: Perform a hot spot analysis Exercise 8c: Explore the results in 3D Exercise 8d: Animate the dataChapter 9 Determining suitability Exercise 9a: Prepare project data Exercise 9b: Derive new surfaces Exercise 9c: Create a weighted suitability modelChapter 10 Presenting your project Exercise 10a: Apply detailed symbology Exercise 10b: Label features Exercise 10c: Create a page layout Exercise 10d: Share your projectAppendix Image and data source credits Data license agreement GlossaryGetting to Know ArcGIS Pro 2.6 | Official Trailer | 2020-08-10 | 00:57

The GIS Controller market is growing tremendously with the surging adoption rates of smart grids, automation, and advanced electrical systems for distribution purposes. GIS controls are an inevitable part of high-voltage gis systems along with monitoring needs to ensure well-transmitted electric power and distributive needs around utilities, industrial manufacturing, as well as any other energy distribution sectors.Fueled by the requirement for enhancements in reliability, minimization of maintenance cost, and better performance in the power systems, demand in GIS controllers space is presently being fueled. Integration along with smart grids in line with the renewable sources further helps to propel the market growth-for real-time monitoring and control of electrical networks.Some of the main market trends include automation, digitalization, and energy efficiency. Significant adoption is happening in North America, Europe, and Asia-Pacific. High installation costs and the complexity in maintenance continue to cause hurdles in the growth of the market. Innovations that can be helpful in such issues are expected from the manufacturer.Concentration & Characteristics Key drivers for this market are: Increasing demand for geospatial data in decision-making. Technological advancements enhancing data processing capabilities. Government initiatives promoting GIS adoption. Growing investment in infrastructure and development projects.. Potential restraints include: Data interoperability and standardization issues. Limited technical expertise in some sectors. Security breaches and data privacy concerns.. Notable trends are: GIS controllers enable real-time data acquisition and visualization, enhancing decision-making. Cloud computing provides scalability, flexibility, and cost-effectiveness. GIS controllers integrate with IoT devices and analyze large datasets to provide actionable insights. Increased focus on protecting sensitive geospatial data due to privacy concerns..

Basemap natural color RGB image.Produced from ESA’s Sentinel-2 A/B imagery, 10 meter resolution Satellite Derive Bathymetry (SDB) is a highly accurate, extremely cost effective bathymetry product that can be produced in clear shallow water regions. The surface in this web scene was calibrated and validated using nautical charts as a survey planning surface to demonstrate shoal points and "no-go" areas.TCarta is a leading global provider of innovative hydrospatial products and Earth observation analysis services. TCarta GIS professionals, hydrographers, and developers provide solutions for onshore and offshore geospatial applications from engineering to environmental monitoring and beyond.TCarta’s primary focus is on providing affordability and accessibility of data and analytics utilizing cutting edge technology and approaches to best serve our clients where traditional methods fail with proven integrity of services and professional practices in a changing and dynamic world.USES: Satellite Derived Bathymetry (SDB) is a lower cost alternative to marine surveys and much higher resolution than ETOPO and GEBCO datasets. Coastal Engineering: Floating Solar Facilities: Suitability Analysis - Location siting using modern and accurate bathymetryWave modeling for construction planningMooring design & Cable routing to shore Offshore Wind Farms:Planning and AppraisalEnvironmental Impact assessmentsMooring design & Cable routingSite characterization Fiber Optic Cable Route Planning:Protecting marine life sanctuariesDecrease distance Aquaculture:Site selectionMonitoringFlow prediction Dredging:Measuring materialMonitoring Water Quality Monitoring:Chlorophyll IndexSediment flowNatural Disasters:Inundation modellingEnvironmental Compliance monitoring.TOOLS: ArcGIS PRO add-in and toolboxDELIVERABLES: GIS ready raster and vector formats, typically as GeoTiff, ASCII data with xyzu(where u represents Uncertainty of Z value) files in map projection coordinates (WGS84) with metadata. Other formats are available upon request like geodatabases, KML/KMZ, HDF, NetCDF

This layer was created as part of Esri’s Green Infrastructure Initiative and is one of five newly generated companion datasets that can be used for Green Infrastructure (GI) planning at national, regional, and more local scales. If used together, these layers should have corresponding date-based suffixes (YYYYMMDD). The corresponding layer names are: Intact Habitat Cores, Habitat Connectors, Habitat Fragments, Habitat Cost Surface, and Intact Habitat Cores by Betweeness. These Esri derived data, and additional data central to GI planning from other authoritative sources, are also available as Map Packages for each U.S. State and can be downloaded from the Green Infrastructure Data Gallery.This layer represents a cost surface for use in landscape connectivity modeling. It reflects the relative ease of movement for terrestrial species taking into account several factors including: NLCD landcover classes, slope, proximity to water, and habitat core score. Generally speaking, natural land cover classes and areas proximal to water are parametrized to exhibit lower costs to species movement while developed areas and areas proximal to built infrastructure are parameterized to exhibit higher costs to species movement. Esri created this layer by following a methodology outlined by the Green Infrastructure Center, Inc. The cost surface was generated using a raster overlay process to create a composite comprised of several landscape variables. Characteristics within each variable were scored based on their perceived impact on species movement whereas reduced movement is reflected as high cost. Landscape variables were categorized into three themed classes based on their expected influence on the cost surface. The first class, impedance, represents the expected cost of species movement as it relates to land cover. The second class, bonuses, represents reductions in cost resulting from being within an existing core, fragment, or proximal to surface water; these conditions are assumed to enhance movement. The third class, penalties, represents increases in cost resulting from steeply sloping terrain and road infrastructure; these conditions are assumed to discourage movement.

This cost surface was used to generate a comprehensive network of Least Cost Paths (LCPs) using the Cost Connectivity tool which was introduced in ArcGIS 10.4 and ArcGISPro in 1.3. The resulting network was also utilized to compute Betweenness Centrality attribution for the Intact Habitat Cores by Betweenness layer, denoting a measure of the Core’s connectivity importance compared to all others in the network. This cost surface may be applicable for use in additional structurally focused landscape connectivity assessments and the generation of landscape corridors.Data Coordinate System: NAD_1983_Albers

Table from the American Community Survey (ACS) 5-year series on housing tenure and cost related topics for City of Seattle Council Districts, Comprehensive Plan Growth Areas and Community Reporting Areas. Table includes B25003 Tenure of Occupied Housing Units, B25070 Gross Rent as a Percentage of Household Income in the Past 12 Months, B25063 Gross Rent, B25091 Mortgage Status by Selected Monthly Owner Costs as a Percentage of Household Income in the Past 12 Months, B25087 Mortgage Stauts and Selected Monthly Owner Costs, B25064 Median Gross Rent, B25088 Median Selected Monthly Owner Costs by Mortgage Status. Data is pulled from block group tables for the most recent ACS vintage and summarized to the neighborhoods based on block group assignment.Table created for and used in the Neighborhood Profiles application.Vintages: 2023ACS Table(s): B25003, B25070, B25063, B25091, B25087, B25064, B25088Data downloaded from: Census Bureau's Explore Census Data The 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. 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: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 2020 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 Census of Agriculture, produced by the USDA National Agricultural Statistics Service (USDA), provides a complete count of America's farms, ranches and the people who grow our food. The census is conducted every five years, most recently in 2017, and provides an in-depth look at the agricultural industry.This layer summarizes payments made to producers by the Federal government from the 2017 Census of Agriculture at the county level. This layer was produced from data downloaded using the USDA's QuickStats Application. The data was transformed using the Pivot Table tool in ArcGIS Pro and joined to the county boundary file provided by the USDA. The layer was published as feature layer in ArcGIS Online.Dataset SummaryPhenomenon Mapped: Payments made to producers by the Federal government Coordinate System: Web Mercator Auxiliary SphereExtent: United States including Hawaii and AlaskaVisible Scale: All ScalesSource: USDA National Agricultural Statistics Service QuickStats ApplicationPublication Date: 2017AttributesThis layer provides values for the following attributes. Note that some values are not disclosed (coded as -1 in the layer) to protect the privacy of producers in areas with limited production.Federal Payments - Operations with ReceiptsFederal Payments - Receipts in US DollarsFederal Payments - Receipts in US Dollars per OperationFederal Payments not Including Conservation and Wetland Programs - Operations with ReceiptsFederal Payments not Including Conservation and Wetland Programs - Receipts in US DollarsFederal Payments not Including Conservation and Wetland Programs - Receipts in US Dollars per OperationFederal Payments for Conservation and Wetland Programs - Operations with ReceiptsFederal Payments for Conservation and Wetland Programs - Receipts in US DollarsFederal Payments for Conservation and Wetland Programs - Receipts in US Dollars per OperationConservation and wetland programs include:Conservation Reserve Program (CRP)Wetlands Reserve Program (WRP)Farmable Wetlands Program (FWP)Conservation Reserve Enhancement Program (CREP)Other programs with payments to producers include:2014 Agricultural Act (Farm Bill)Agriculture Risk Coverage (ARC)Price Loss Coverage (PLC)Commodity Credit Corporation (CCC)Loan Deficiency PaymentsDisaster Assistance ProgramsState and local government agricultural program payments and Federal crop insurance payments are not included.Additionally, attributes of State Name, State Code, County Name and County Code are included to facilitate cartography and use with other layers.What can you do with this layer?This layer can be used throughout the ArcGIS system. Feature layers can be used just like any other vector layer. You can use feature layers as an input to geoprocessing tools in ArcGIS Pro or in Analysis in ArcGIS Online. Combine the layer with others in a map and set custom symbology or create a pop-up tailored for your users. For the details of working with feature layers the help documentation for ArcGIS Pro or the help documentation for ArcGIS Online are great places to start. The ArcGIS Blog is a great source of ideas for things you can do with feature layers. This layer is part of ArcGIS Living Atlas of the World that provides an easy way to find and explore many other beautiful and authoritative layers, maps, and applications on hundreds of topics.

This layer was created as part of Esri’s Green Infrastructure Initiative and is one of five newly generated companion datasets that can be used for Green Infrastructure (GI) planning at national, regional, and more local scales. If used together, these layers should have corresponding date-based suffixes (YYYYMMDD). The corresponding layer names are: Intact Habitat Cores, Habitat Connectors, Habitat Fragments, Habitat Cost Surface, and Intact Habitat Cores by Betweeness. These Esri derived data, and additional data central to GI planning from other authoritative sources, are also available as Map Packages for each U.S. State and can be downloaded from the Green Infrastructure Data Gallery.

This layer represents Intact Habitat Cores, symbolized based on their connectivity importance (Betweenness Centrality (BC). BC provides a metric depicting each core’s importance in a connectivity network, thus illuminating each core’s relative contribution to facilitating landscape connectivity and potential species movement. BC represents the number of paths that flow through a given habitat core. It is normalized by the total number of shortest paths between all pairs of nodes, except those paths connecting immediate neighbors. High BC values reflect greater use of that core in traversing the network, thus elevating the core’s importance in facilitating connectivity when compared to cores of lower value. Every time you change your viewing extent, the layer dynamically recalculates to depict the most important cores, based on its betweenness centrality value.

The Habitat Cost Surface layer was used to generate a comprehensive network of Least Cost Paths (LCPs) amongst the Intact Habitat Cores layer. This was accomplished using the Cost Connectivity tool which was introduced in ArcGIS 10.4 and ArcGISPro in 1.3. The resulting Habitat Connectors layer was then utilized as the network upon which BC was calculated. The Betweenness_Centrality function in the NetworkX Python library was used to first calculate the number of cost based optimal paths passing through each core and then dividing this number by the range of values in the LCP Routes field. This work was based on the NetworkX Parallel Betweenness example. Data Coordinate System: NAD_1983_Albers

This layer contains 2010-2014 American Community Survey (ACS) 5-year data, and contains estimates and margins of error. The layer shows fertility in past 12 months by age of mother. This is shown by tract, county, and state boundaries. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. The calculated percentages are slightly different from traditional age-specific fertility rates in that the total number of live births (due to twins or higher-order multiple births) is not available in this table. Note: Data are not available for all geographies within this layer due to data suppression done by the American Community Survey. Since there was a data collection error in the 2011 and 2012 surveys, this impacts the 2010-2014 5-year estimates. To learn more and to see a list of the affected geographies, visit this page about Errata 119.This layer is symbolized to show the percent of women age 15 to 50 who had a birth in the past 12 months. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Vintage: 2010-2014ACS Table(s): B13016 Data downloaded from: Census Bureau's API for American Community Survey Date of API call: November 11, 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 has associated layers containing the most recent ACS data available by the U.S. Census Bureau. Click here to learn more about ACS data releases and click here for the associated boundaries layer. The reason this data is 5+ years different from the most recent vintage is due to the overlapping of survey years. It is recommended by the U.S. Census Bureau to compare non-overlapping datasets.Boundaries come from the US Census TIGER geodatabases. Boundary vintage (2014) 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.