Depicted on this map is British North America less than one hundred years after the fall of New France. It also shows the emergence of British influence prior to Confederation. British North America circa 1823 was comprised of Lower Canada, Upper Canada, New Brunswick, Nova Scotia, Prince Edward Island, and Newfoundland (including the Labrador Coast). The Northwest Territories were considered British possessions, while the Hudson’s Bay Company controlled Rupert’s Land. The United States and Britain jointly administered the Oregon Territory. This map along with New France circa 1740 shows the settlement and population in Canada for two important periods in Canadian history prior to Confederation.

Important Note: This item is in mature support as of July 2021. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version.This map is designed to be used as a general reference map for informational and educational purposes as well as a basemap by GIS professionals and other users for creating web maps and web mapping applications.The map was developed by National Geographic and Esri and reflects the distinctive National Geographic cartographic style in a multi-scale reference map of the world. The map was authored using data from a variety of leading data providers, including Garmin, HERE, UNEP-WCMC, NASA, ESA, USGS, and others.This reference map includes administrative boundaries, cities, protected areas, highways, roads, railways, water features, buildings and landmarks, overlaid on shaded relief and land cover imagery for added context. The map includes global coverage down to ~1:144k scale and more detailed coverage for North America down to ~1:9k scale.Map Note: Although small-scale boundaries, place names and map notes were provided and edited by National Geographic, boundaries and names shown do not necessarily reflect the map policy of the National Geographic Society, particularly at larger scales where content has not been thoroughly reviewed or edited by National Geographic.Data Notes: The credits below include a list of data providers used to develop the map. Below are a few additional notes:Reference Data: National Geographic, Esri, Garmin, HERE, iPC, NRCAN, METILand Cover Imagery: NASA Blue Marble, ESA GlobCover 2009 (Copyright notice: © ESA 2010 and UCLouvain)Protected Areas: IUCN and UNEP-WCMC (2011), The World Database on Protected Areas (WDPA) Annual Release. Cambridge, UK: UNEP-WCMC. Available at:www.protectedplanet.net.Ocean Data: GEBCO, NOAA

At Driver Technologies, we specialize in collecting high-quality, highly-anonymized, driving data crowdsourced using our dash cam app. Our Traffic Light Map Video Data is built from the millions of miles of driving data captured and is optimized to be trained for whatever computer vision models you need and enhancing various applications in transportation and safety.

What Makes Our Data Unique? What sets our Traffic Light Map Video Data apart is its comprehensive approach to road object detection. By leveraging advanced computer vision models, we analyze the captured video to identify and classify various road objects encountered during an end user's trip. This includes road signs, pedestrians, vehicles, traffic signs, and road conditions, resulting in rich, annotated datasets that can be used for a range of applications.

How Is the Data Generally Sourced? Our data is sourced directly from users who utilize our dash cam app, which harnesses the smartphone’s camera and sensors to record during a trip. This direct sourcing method ensures that our data is unbiased and represents a wide variety of conditions and environments. The data is not only authentic and reflective of current road conditions but is also abundant in volume, offering millions of miles of recorded trips that cover diverse scenarios.

Primary Use-Cases and Verticals The Traffic Light Map Video Data is tailored for various sectors, particularly those involved in transportation, urban planning, and autonomous vehicle development. Key use cases include:

Training Computer Vision Models: Clients can utilize our annotated data to develop and refine their own computer vision models for applications in autonomous vehicles, ensuring better object detection and decision-making capabilities in complex road environments.

Urban Planning and Infrastructure Development: Our data helps municipalities understand road usage patterns, enabling them to make informed decisions regarding infrastructure improvements, safety measures, and traffic light placement. Our data can also aid in making sure municipalities have an accurate count of signs in their area.

Integration with Our Broader Data Offering The Traffic Light Map Video Data is a crucial component of our broader data offerings at Driver Technologies. It complements our extensive library of driving data collected from various vehicles and road users, creating a comprehensive data ecosystem that supports multiple verticals, including insurance, automotive technology, and computer vision models.

In summary, Driver Technologies' Traffic Light Map Video Data provides a unique opportunity for data buyers to access high-quality, actionable insights that drive innovation across mobility. By integrating our Traffic Light Map Video Data with other datasets, clients can gain a holistic view of transportation dynamics, enhancing their analytical capabilities and decision-making processes.

Depicted on this map is the extent of New France at its territorial height circa 1740 prior to its great territorial losses to British North America. Also shown on the map are the territorial claims, administrative divisions, and the distribution of population and settlement (including fur trading posts) circa 1740. This map along with British North America circa 1823 shows the settlement and population in Canada for two important periods in Canadian history prior to Confederation.

The size and share of the market is categorized based on Type (Hardware, Software, Services;) and Application (Autonomous Vehicles, Advanced Driver Assistance Systems (ADAS), Mapping & Surveying;) and geographical regions (North America, Europe, Asia-Pacific, South America, and Middle-East and Africa).

In the century between Napoleon's defeat and the outbreak of the First World War (known as the "Pax Britannica"), the British Empire grew to become the largest and most powerful empire in the world. At its peak in the 1910s and 1920s, it encompassed almost one quarter of both the world's population and its land surface, and was known as "the empire on which the sun never sets". The empire's influence could be felt across the globe, as Britain could use its position to affect trade and economies in all areas of the world, including many regions that were not part of the formal empire (for example, Britain was able to affect trading policy in China for over a century, due to its control of Hong Kong and the neighboring colonies of India and Burma). Some historians argue that because of its economic, military, political and cultural influence, nineteenth century Britain was the closest thing to a hegemonic superpower that the world ever had, and possibly ever will have. "Rule Britannia" Due to the technological and logistical restrictions of the past, we will never know the exact borders of the British Empire each year, nor the full extent of its power. However, by using historical sources in conjunction with modern political borders, we can gain new perspectives and insights on just how large and influential the British Empire actually was. If we transpose a map of all former British colonies, dominions, mandates, protectorates and territories, as well as secure territories of the East India Trading Company (EIC) (who acted as the precursor to the British Empire) onto a current map of the world, we can see that Britain had a significant presence in at least 94 present-day countries (approximately 48 percent). This included large territories such as Australia, the Indian subcontinent, most of North America and roughly one third of the African continent, as well as a strategic network of small enclaves (such as Gibraltar and Hong Kong) and islands around the globe that helped Britain to maintain and protect its trade routes. The sun sets... Although the data in this graph does not show the annual population or size of the British Empire, it does give some context to how Britain has impacted and controlled the development of the world over the past four centuries. From 1600 until 1920, Britain's Empire expanded from a small colony in Newfoundland, a failing conquest in Ireland, and early ventures by the EIC in India, to Britain having some level of formal control in almost half of all present-day countries. The English language is an official language in all inhabited continents, its political and bureaucratic systems are used all over the globe, and empirical expansion helped Christianity to become the most practiced major religion worldwide. In the second half of the twentieth century, imperial and colonial empires were eventually replaced by global enterprises. The United States and Soviet Union emerged from the Second World War as the new global superpowers, and the independence movements in longstanding colonies, particularly Britain, France and Portugal, gradually succeeded. The British Empire finally ended in 1997 when it seceded control of Hong Kong to China, after more than 150 years in charge. Today, the United Kingdom consists of four constituent countries, and it is responsible for three crown dependencies and fourteen overseas territories, although the legacy of the British Empire can still be seen, and it's impact will be felt for centuries to come.

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

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Layered GeoPDF 7.5 Minute Quadrangle Map. Layers of geospatial data include orthoimagery, roads, grids, geographic names, elevation contours, hydrography, and other selected map features.

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Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

National Library of Scotland Historic Maps APIHistorical Maps of Great Britain for use in mashups and ArcGIS Onlinehttps://nls.tileserver.com/https://maps.nls.uk/projects/api/index.htmlThis seamless historic map can be:embedded in your own websiteused for research purposesused as a backdrop for your own markers or geographic dataused to create derivative work (such as OpenStreetMap) from it.The mapping is based on out-of-copyright Ordnance Survey maps, dating from the 1920s to the 1940s.The map can be directly opened in a web browser by opening the Internet address: https://nls.tileserver.com/The map is ready for natural zooming and panning with finger pinching and dragging.How to embed the historic map in your websiteThe easiest way of embedding the historical map in your website is to copy < paste this HTML code into your website page. Simple embedding (try: hello.html):You can automatically position the historic map to open at a particular place or postal address by appending the name as a "q" parameter - for example: ?q=edinburgh Embedding with a zoom to a place (try: placename.html):You can automatically position the historic map to open at particular latitude and longitude coordinates: ?lat=51.5&lng=0&zoom=11. There are many ways of obtaining geographic coordinates. Embedding with a zoom to coordinates (try: coordinates.html):The map can also automatically detect the geographic location of the visitor to display the place where you are right now, with ?q=auto Embedding with a zoom to coordinates (try: auto.html):How to use the map in a mashupThe historic map can be used as a background map for your own data. You can place markers on top of it, or implement any functionality you want. We have prepared a simple to use JavaScript API to access to map from the popular APIs like Google Maps API, Microsoft Bing SDK or open-source OpenLayers or KHTML. To use our map in your mashups based on these tools you should include our API in your webpage: ... ...

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Geographic Information System Analytics Market Size 2024-2028

The geographic information system analytics market size is forecast to increase by USD 12 billion at a CAGR of 12.41% between 2023 and 2028.

The GIS Analytics Market analysis is experiencing significant growth, driven by the increasing need for efficient land management and emerging methods in data collection and generation. The defense industry's reliance on geospatial technology for situational awareness and real-time location monitoring is a major factor fueling market expansion. Additionally, the oil and gas industry's adoption of GIS for resource exploration and management is a key trend. Building Information Modeling (BIM) and smart city initiatives are also contributing to market growth, as they require multiple layered maps for effective planning and implementation. The Internet of Things (IoT) and Software as a Service (SaaS) are transforming GIS analytics by enabling real-time data processing and analysis.
Augmented reality is another emerging trend, as it enhances the user experience and provides valuable insights through visual overlays. Overall, heavy investments are required for setting up GIS stations and accessing data sources, making this a promising market for technology innovators and investors alike.

What will be the Size of the GIS Analytics Market during the forecast period?

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The geographic information system analytics market encompasses various industries, including government sectors, agriculture, and infrastructure development. Smart city projects, building information modeling, and infrastructure development are key areas driving market growth. Spatial data plays a crucial role in sectors such as transportation, mining, and oil and gas. Cloud technology is transforming GIS analytics by enabling real-time data access and analysis. Startups are disrupting traditional GIS markets with innovative location-based services and smart city planning solutions. Infrastructure development in sectors like construction and green buildings relies on modern GIS solutions for efficient planning and management. Smart utilities and telematics navigation are also leveraging GIS analytics for improved operational efficiency.
GIS technology is essential for zoning and land use management, enabling data-driven decision-making. Smart public works and urban planning projects utilize mapping and geospatial technology for effective implementation. Surveying is another sector that benefits from advanced GIS solutions. Overall, the GIS analytics market is evolving, with a focus on providing actionable insights to businesses and organizations.

How is this Geographic Information System Analytics Industry segmented?

The geographic information system analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

End-user

  Retail and Real Estate
  Government
  Utilities
  Telecom
  Manufacturing and Automotive
  Agriculture
  Construction
  Mining
  Transportation
  Healthcare
  Defense and Intelligence
  Energy
  Education and Research
  BFSI


Components

  Software
  Services


Deployment Modes

  On-Premises
  Cloud-Based


Applications

  Urban and Regional Planning
  Disaster Management
  Environmental Monitoring Asset Management
  Surveying and Mapping
  Location-Based Services
  Geospatial Business Intelligence
  Natural Resource Management


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    South Korea


  Middle East and Africa

    UAE


  South America

    Brazil


  Rest of World

By End-user Insights

The retail and real estate segment is estimated to witness significant growth during the forecast period.

The GIS analytics market analysis is witnessing significant growth due to the increasing demand for advanced technologies in various industries. In the retail sector, for instance, retailers are utilizing GIS analytics to gain a competitive edge by analyzing customer demographics and buying patterns through real-time location monitoring and multiple layered maps. The retail industry's success relies heavily on these insights for effective marketing strategies. Moreover, the defense industries are integrating GIS analytics into their operations for infrastructure development, permitting, and public safety. Building Information Modeling (BIM) and 4D GIS software are increasingly being adopted for construction project workflows, while urban planning and designing require geospatial data for smart city planning and site selection.

The oil and gas industry is leveraging satellite imaging and IoT devices for land acquisition and mining operations. In the public sector,

Explore the interactive maps showing the average delay and average speed on the Strategic Road Network and local ‘A’ roads in England, in 2022.

On the Strategic Road Network (SRN) for 2022, the average delay is estimated to be 9.3 seconds per vehicle per mile (spvpm), compared to free flow, a 9.4% increase on 2021 and a 2.1% decrease on 2019.

The average speed is estimated to be 58.1 mph, down 1.4% from 2021 and up 0.2% from 2019.

On local ‘A’ roads for 2022, the average delay was estimated to be 45.5 seconds per vehicle per mile compared to free flow, up 2.5% from 2021 and down 2.8% from 2019 (pre-coronavirus)

The average speed is estimated to be 23.7 mph, down 1.7% from 2021 and up 2.2% from 2019 (pre-coronavirus).

Average speeds in 2022 have stabilised towards similar trends observed before the effects of the coronavirus pandemic.

The outbreak of coronavirus (COVID-19) has had a marked impact on everyday life, including on congestion on the road network. As some of these data are affected by the coronavirus pandemic in the UK, caution should be taken when interpreting these statistics and comparing them with other time periods. Additional http://bit.ly/COVID_Congestion_Analysis" class="govuk-link">analysis on the impact of the coronavirus pandemic on road journeys in 2020 is also available. This Storymap contains charts and interactive maps for road journeys in England in 2020.

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Environmental Sensitivity Index (ESI) maps are an integral component in oil-spill contingency planning and assessment. They serve as a source of information in the event of an oil spill incident. ESI maps contain three types of information: shoreline habitats (classified according to their sensitivity to oiling), sensitive biological resources, and human-use resources. Most often, this information is plotted on 7.5 minute USGS quadrangles, although in the Alaska ESI maps, USGS topographic maps at scales of 1:63,360 and 1:250,000 are used, and in other ESI maps, NOAA charts have been used as the base map. Collections of these maps, grouped by state or a logical geographic area, are published as ESI atlases. Digital data have been published for most of the U.S. shoreline, including Alaska, Hawaii, and Puerto Rico.

Paper abstract: This paper compares the associative system of early-learned verbs and body parts in Hebrew with previously published data on American English (Maouene, Josita, Shohei Hidaka & Linda B. Smith. 2008. Body parts and early-learned verbs. Cognitive Science 32(7). 1200–1216). Following the methodology of the former study, 51 Hebrew-speaking college students gave the first body part that came to mind for each of 103 early-learned Hebrew verbs, 81 of which were translational equivalents. Rate of convergence and divergence and underlying patterns were used to make inferences about the constraints at work. Overall convergence (92.3% of the Hebrew data and 93.7% of the English data) reveal similar entropy levels, comparable semantic field shapes of verbs organized by body parts and similar general cluster patterns of verbs by body parts. Most divergence lies in the infrequent responses (offered fewer than 1% of the time) which arise around body parts that are internal, very detailed, very general categorically, used in figurative language, uniquely provided and tend to be subject to cultural taboos. This is a new contribution, as previous work has not quantified the relative proportion of convergent to divergent associations. We discuss how these findings support neural and developmental continuity and stability in the verbal system with respect to the categorization of verbs by body parts cross-culturally. Dataset description: The dataset consists of two matrices: the first matrix is composed of 101 early-learned American English verbs by 61 body parts as provided by 50 American English speakers. The second matrix displays 103 early-learned Hebrew verbs by 73 body parts provided by 51 Hebrew speakers. The English data collections was done at Indiana University Bloomington, IN, in 2004. A partial analysis of the data set was published between 2006 and 2008. The data collection in Hebrew was done at Ono Academic College (in Hebrew: הקריה האקדמית אונו) located in Kiryat Ono, Tel Aviv region, Israel, in 2012. This is the first publication for this data set.

The 2020 TIGER/Line Shapefiles contain current geographic extent and boundaries of both legal and statistical entities (which have no governmental standing) for the United States, the District of Columbia, Puerto Rico, and the Island areas. This vintage includes boundaries of governmental units that match the data from the surveys that use 2020 geography (e.g., 2020 Population Estimates and the 2020 American Community Survey). In addition to geographic boundaries, the 2020 TIGER/Line Shapefiles also include geographic feature shapefiles and relationship files. Feature shapefiles represent the point, line and polygon features in the MTDB (e.g., roads and rivers). Relationship files contain additional attribute information users can join to the shapefiles. Both the feature shapefiles and relationship files reflect updates made in the database through September 2020. To see how the geographic entities, relate to one another, please see our geographic hierarchy diagrams here.Census Urbanized Areashttps://www2.census.gov/geo/tiger/TIGER2020/UACCensus Urban/Rural Census Block Shapefileshttps://www.census.gov/cgi-bin/geo/shapefiles/index.php2020 TIGER/Line and Redistricting shapefiles:https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.2020.htmlTechnical documentation:https://www2.census.gov/geo/pdfs/maps-data/data/tiger/tgrshp2020/TGRSHP2020_TechDoc.pdfTIGERweb REST Services:https://tigerweb.geo.census.gov/tigerwebmain/TIGERweb_restmapservice.htmlTIGERweb WMS Services:https://tigerweb.geo.census.gov/tigerwebmain/TIGERweb_wms.htmlThe legal entities included in these shapefiles are:American Indian Off-Reservation Trust LandsAmerican Indian Reservations – FederalAmerican Indian Reservations – StateAmerican Indian Tribal Subdivisions (within legal American Indian areas)Alaska Native Regional CorporationsCongressional Districts – 116th CongressConsolidated CitiesCounties and Equivalent Entities (except census areas in Alaska)Estates (US Virgin Islands only)Hawaiian Home LandsIncorporated PlacesMinor Civil DivisionsSchool Districts – ElementarySchool Districts – SecondarySchool Districts – UnifiedStates and Equivalent EntitiesState Legislative Districts – UpperState Legislative Districts – LowerSubminor Civil Divisions (Subbarrios in Puerto Rico)The statistical entities included in these shapefiles are:Alaska Native Village Statistical AreasAmerican Indian/Alaska Native Statistical AreasAmerican Indian Tribal Subdivisions (within Oklahoma Tribal Statistical Areas)Block Groups3-5Census AreasCensus BlocksCensus County Divisions (Census Subareas in Alaska)Unorganized Territories (statistical county subdivisions)Census Designated Places (CDPs)Census TractsCombined New England City and Town AreasCombined Statistical AreasMetropolitan and Micropolitan Statistical Areas and related statistical areasMetropolitan DivisionsNew England City and Town AreasNew England City and Town Area DivisionsOklahoma Tribal Statistical AreasPublic Use Microdata Areas (PUMAs)State Designated Tribal Statistical AreasTribal Designated Statistical AreasUrban AreasZIP Code Tabulation Areas (ZCTAs)Shapefiles - Features:Address Range-FeatureAll Lines (called Edges)All RoadsArea HydrographyArea LandmarkCoastlineLinear HydrographyMilitary InstallationPoint LandmarkPrimary RoadsPrimary and Secondary RoadsTopological Faces (polygons with all geocodes)Relationship Files:Address Range-Feature NameAddress RangesFeature NamesTopological Faces – Area LandmarkTopological Faces – Area HydrographyTopological Faces – Military Installations

This data was compiled by the Washington Military Department on June 27, 2019. The Limited English Proficiency (LEP) information is summarized at the county level as well as at the census track/ county subdivision layer level. County level data was derived from the 2016 Office of Financial Management (OFM) study which provided an estimate of population with limited English proficiency at the state and county levels. The census tract data are derived from the 2015 census update and indicates language spoken at home and ability to speak English for those over five years old. All data displayed indicate a population of at least 1,000 or 5% of the population.LEPCountyv2 = Limited English Proficiency County version 2 – from OFM census dataLEPCSDv2 = Limited English Proficiency County Subdivision version 2 – data drawn from US Census 2010

LEPtractsv2 = Limited English Proficiency Census Tracts version 2 – data drawn from US Census 2010Attribute DescriptionCounty - County nameLanguage - Limited English Proficiency Language(s) spoken for the corresponding polygon - each Language is followed by a number to indicate a sequence number for each data fieldSym - Symbology field used to symbolize the polygons - holds the total count of LEP languages spoken for that polygonAFFGEOID - American Fact Finder Geospatial ID used to link tabular data to the polygons - consists of the -- Census block identifier; a concatenation of 2010 Census state FIPS code, 2010 Census county FIPS code, 2010 Census tract code, and 2010 Census block numberName - County Subdivision name from American Fact Finder (AFF) dataLanguage - Limited English Proficiency Language(s) spoken for the corresponding polygon - each Language is followed by a number to indicate a sequence number for each data fieldSym - Symbology field used to symbolize the polygons - holds the total count of LEP languages spoken for that polygonNAMELSAD10 2010 Census translated legal/statistical area description and the block group numberAFFGEOID - American Fact Finder Geospatial ID used to link tabular data to the polygons - consists of the -- Census block identifier; a concatenation of 2010 Census state FIPS code, 2010 Census county FIPS code, 2010 Census tract code, and 2010 Census block numberDisplay Label - Geographic name for each polygon from AFFSym - Symbology field used to symbolize the polygons - holds the total count of LEP languages spoken for that polygonLanguage - Limited English Proficiency Language(s) spoken for the corresponding polygon - each Language is followed by a number to indicate a sequence number for each data field

This layer shows English ability and linguistic isolation by age group. 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. Linguistically isolated households are households in which no one 14 and over speak English only or speaks a language other than English at home and speaks English very well. This layer is symbolized to show the percent of adult (18+) population who have limited English ability. 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): B16003, B16004 (Not all lines of ACS table B16004 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.

description: This data set comprises an update of the Environmental Sensitivity Index (ESI) data for the Virgin Islands. ESI data characterize estuarine environments and wildlife by their sensitivity to spilled oil. The ESI data include information for three main components: shoreline habitats, sensitive biological resources, and human-use resources. The ESI data were collected, mapped, and digitized to provide environmental data for oil spill planning and response. The Clean Water Act with amendments by the Oil Pollution Act of 1990 requires response plans for immediate and effective protection of sensitive resources. This digital data re-release includes shoreline, hydrological, and biological data in formats for GIS systems and as Adobe Acrobat maps with extensive documentation. These data were originally distributed as a DVD product.; abstract: This data set comprises an update of the Environmental Sensitivity Index (ESI) data for the Virgin Islands. ESI data characterize estuarine environments and wildlife by their sensitivity to spilled oil. The ESI data include information for three main components: shoreline habitats, sensitive biological resources, and human-use resources. The ESI data were collected, mapped, and digitized to provide environmental data for oil spill planning and response. The Clean Water Act with amendments by the Oil Pollution Act of 1990 requires response plans for immediate and effective protection of sensitive resources. This digital data re-release includes shoreline, hydrological, and biological data in formats for GIS systems and as Adobe Acrobat maps with extensive documentation. These data were originally distributed as a DVD product.