Description: The Railroad Bridges dataset was compiled on October 14, 2022 from the Federal Railroad Administration (FRA) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). Data subset to the Thrive region in November 2025. A railroad bridge is defined as “Railroad bridge means any structure with a deck, regardless of length, which supports one or more railroad tracks, or any other undergrade structure with an individual span length of 10 feet or more located at such a depth that it is affected by live loads.” based on the Code of Federal Regulations (49 CFR Part 237). The FRA does not have a mandate to inspect railroad bridges: these inspections are required by the owner of the track. The FRA will use this railroad bridge dataset to determine the number of bridges per railroad, state, etc. and will assist in determining priority field activities.
Copyright Text: Acknowledgement of the Federal Railroad Adminsitration, FRA National American Rail Network (NARN), Census Topologically Integrated Geographic encoding and Referencing (TIGER), National Hydrography Dataset (NHD) Waterway , FHWA’s National Bridge Inventory (NBI), FRA’s Automated Track Inspection Program (ATIP) Bridges, USDOT Grade Crossing Inventory, HIFLD Bridge data, and the Bureau of Transportation Statistics (BTS) [distributor].
Description: Point locations of freight rail stations subset to the Thrive region. Data obtained from transportation.gov. For more information, click here
Description: A trespasser is a person or persons who are on the part of railroad property used in railroad operations and whose presence is prohibited, forbidden, or unlawful. This data was obtained from Transportation.gov. For more information, click here
Copyright Text: USDOT Federal Railroad Administration.
Description: The National Tunnel Inventory dataset was compiled on September 02, 2025 and published on August 26, 2025 from the Federal Highway Administration (FHWA) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The National Tunnel Inventory (NTI) is a collection of information (database) describing the more than 500 of the Nation's tunnels located on public roads, including Interstate Highways, U.S. highways, State and county roads, as well as publicly-accessible tunnels on Federal lands. The inventory data present a complete picture of the location, description, and classification data for each tunnel, as well as any load rating and inspection information. The Specifications for the National Tunnel Inventory (SNTI) contains a detailed description of each data element including coding instructions and attribute definitions. The Coding Manual is published for each year of data collection; the manual is available at: https://doi.org/10.21949/1519104. For additional questions regarding regulations for the National Tunnel Inventory or the Coding Guide please contact the National Bridge and Tunnel Inventory team at NBTIS_Support@dot.gov. For questions on the geospatial component of the dataset, contact the NTAD team at NTAD@dot.gov. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529051Data subset to TN on 11/5/2025
Copyright Text: Acknowledgment of the Federal Highway Administration (FHWA) and the Bureau of Transportation Statistics (BTS) [distributor].
Description: The dataset provides users with 2023 air cargo information about Chattanooga (Lovell Field) and Huntsville International airports. Data was obtained from the Bureau of Transportation Statistics. Data includes Total air cargo in pounds for domestic and international and both departing and arriving from each airport on US carriers. Data taken from the Air Carrier Statistics (Form 41 Traffic)- U.S. Carriers database.
Copyright Text: U.S. Department of Transportation ArcGIS Online
USDOT BTS
Description: Data compiled manually through internet search in early 2024 and updated in 2025. Geocoded in ArcGIS Pro. Some features contain website links.FMCSA DOT Unified Carriers:https://ai.fmcsa.dot.gov/hhg/SearchResults.asp?lan=EN&search=5&ads=a&state=TNSAFER System:https://safer.fmcsa.dot.gov/query.asp?searchtype=ANY&query_type=queryCarrierSnapshot&query_param=USDOT&query_string=282659
Description: The BETA - Hazard Exposure: National Highway System Bridges dataset was updated on October 16, 2025 from the Bureau of Transportation Statistics (BTS) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). Datat subset to the Thrive Study Region on 10/30/2025. The BETA - Hazard Exposure: National Highway System Bridges dataset shows the exposure of bridges on the National Highway System to select natural hazards. The dataset combines hazard data from FEMA, USGS, NOAA, and USDA with information for relevant bridges in the National Bridge Inventory dataset from the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). Hazard data include estimates of annualized frequency of Coastal Flooding, Cold Wave, Drought, Hail, Hurricane, Heat Wave, Ice Storm, Lightning, Strong Wind, Tornado, and Winter Weather from the FEMA National Risk Index. Landslide Index and Sinkhole Susceptibility derive from USGS models. USDA Burn Probability data underlie estimates of wildfire exposure. NOAA SLOSH models estimate storm surge inundation from hurricanes. The data set covers all 50 states of the United States, the District of Columbia, and Puerto Rico. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/bsd7-6743
Description: Monthly summary statistics are based on data from the Lock Performance Monitoring System (LPMS). The LPMS was developed to collect a 100% sample of data on the locks that are owned and/or operated by the US Army Corps of Engineers. Each record contains data summarized monthly by lock chamber, and direction (upbound and number and types of vessels and lockages (recreation, commercial, tows, other), cuts, hardware operations, delay and processing times, number of tows and all vessels delayed, total tons, commodity tonnages, and number of barges. The data are by waterway and by calendar year. The waterway files contain 5 years of data for one waterway. The calendar year files contain 1 year of data for all waterways.The Waterway Locks dataset is periodically updated by the United States Army Corp of Engineers (USACE) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The data included in these files are based upon the annual summary of lock statistics published by the USACE Institute for Water Resources. The data are collected at each Corps owned and/or operated Lock by Corps personnel and towing industry vessel operators. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529089.Locks summary - Based on USACE Lock Performance Monitoring System Reports. Aggregated estimated Monthly Tonnage Reports (for tonnage) and Annual Usage Report (vessel data) https://ndc.ops.usace.army.mil/ords/f?p=108:1::::::
Description: This feature class represents truck parking locations obtained from TDOT, USDOT, and FHWA. The primary data source is a result of work between TDOT and CDM Smith. Data subset to greater Chattanooga study region for Thrive freight hub project. Data sources listed below. https://tdot-lrp-mm.maps.arcgis.com/home/item.html?id=ea2a0d4886e64f12bd06d60d5e79b22dhttps://data-usdot.opendata.arcgis.com/datasets/truck-stop-parking/explore?location=35.921976%2C-95.686250%2C5.28https://ops.fhwa.dot.gov/freight/infrastructure/truck_parking/index.htmData compiled in 2022
Description: Data for Georgia obtained from Georgia DOT. Tennessee data obtained from TN DOT. Alabama data obtained from AL DOT. All data subset to the Thrive study area. Data obtained from GDOT in May 2022 and updated in late 2023. Data attributes include AADT (average annual daily traffic), single-unit truck AADT, combo-unit truck AADT, truck percentage).
Description: This layer contains 2019-2023 fatal motor vehicle accidents data from the National Highway Traffic Safety Administration (NHTSA)'s Fatality Analysis Reporting System (FARS). This point data contains fatal motor vehicle traffic crashes within the Georgia, Tennessee, and Alabama portions of the Thrive study area. Data currency: 1/1/2023Data source: NHTSA File DownloadsFor more information about FARS: Fatality Analysis Reporting System, Fatality Analysis Reporting System (FARS) 2023 - Accidents, Support documentation for field definitions & domains: Fatality Analysis Reporting System Analytical User’s Manual, 1975-2023, Fatality Analysis Reporting System (FARS) Auxiliary Datasets Analytical User’s Manual 1982-2019
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VE_TOTAL (type: esriFieldTypeInteger, alias: Number of Vehicles in Crash, SQL Type: sqlTypeOther, nullable: true, editable: true)
VE_FORMS (type: esriFieldTypeInteger, alias: Number of Vehicle Forms, SQL Type: sqlTypeOther, nullable: true, editable: true)
PVH_INVL (type: esriFieldTypeInteger, alias: Number of Parked/Working Vehicles in the Crash, SQL Type: sqlTypeOther, nullable: true, editable: true)
PEDS (type: esriFieldTypeInteger, alias: Number of Persons Not in Motor Vehicles, SQL Type: sqlTypeOther, nullable: true, editable: true)
PERSONS (type: esriFieldTypeInteger, alias: Number of Person Forms, SQL Type: sqlTypeOther, nullable: true, editable: true)
PERMVIT (type: esriFieldTypeInteger, alias: Number of Persons in Motor Vehicles In-Transport, SQL Type: sqlTypeOther, nullable: true, editable: true)
PERNOTMVIT (type: esriFieldTypeInteger, alias: Number of Persons Not in Motor Vehicles In-Transport, SQL Type: sqlTypeOther, nullable: true, editable: true)
COUNTY (type: esriFieldTypeInteger, alias: County, SQL Type: sqlTypeOther, nullable: true, editable: true)
MAN_COLL (type: esriFieldTypeInteger, alias: Manner of Collision of the First Harmful Event (Code), SQL Type: sqlTypeOther, nullable: true, editable: true)
MAN_COLLNAME (type: esriFieldTypeString, alias: Manner of Collision of the First Harmful Event, SQL Type: sqlTypeOther, length: 8000, nullable: true, editable: true)
RELJCT1 (type: esriFieldTypeInteger, alias: Relation to Junction Within Interchange Area (Code), SQL Type: sqlTypeOther, nullable: true, editable: true)
RELJCT1NAME (type: esriFieldTypeString, alias: Relation to Junction Within Interchange Area, SQL Type: sqlTypeOther, length: 8000, nullable: true, editable: true)
RELJCT2 (type: esriFieldTypeInteger, alias: Relation to Junction Specific Location (Code), SQL Type: sqlTypeOther, nullable: true, editable: true)
RELJCT2NAME (type: esriFieldTypeString, alias: Relation to Junction Specific Location, SQL Type: sqlTypeOther, length: 8000, nullable: true, editable: true)
TYP_INT (type: esriFieldTypeInteger, alias: Type of Intersection (Code), SQL Type: sqlTypeOther, nullable: true, editable: true)
TYP_INTNAME (type: esriFieldTypeString, alias: Type of Intersection, SQL Type: sqlTypeOther, length: 8000, nullable: true, editable: true)
WRK_ZONE (type: esriFieldTypeInteger, alias: Work Zone (Code), SQL Type: sqlTypeOther, nullable: true, editable: true)
BIA (type: esriFieldTypeString, alias: Tribal lands based on geographic location and spatial data, SQL Type: sqlTypeOther, length: 255, nullable: true, editable: true)
SPJ_INDIAN (type: esriFieldTypeString, alias: Special Jurisdiction Indian Reservation, SQL Type: sqlTypeOther, length: 255, nullable: true, editable: true)
INDIAN_RES (type: esriFieldTypeString, alias: Indian Reservation based on special jurisdiction and geographic location data, SQL Type: sqlTypeOther, length: 255, nullable: true, editable: true)
Description: Large truck crash data obtained from TN DOT in 2023. Data subset to 2017-2022. https://www.tn.gov/tdot/long-range-planning-home/longrange-data-visualization/gis-mapping-and-support.html
Description: Large truck accident locations in the greater Chattanooga Georgia counties between 2020-2025. Obtained from GDOT in late 2025 from https://www.dot.ga.gov/GDOT/Pages/CrashReporting.aspx
Description: The Navigation Data Center had several objectives in developing the U.S. Waterway Data. These objectives support the concept of a National Spatial Data Provide public access to national waterway data. Foster interagency and intra-agency cooperation through data sharing. Provide a mechanism to integrate waterway data (U.S. Army Corps of Engineers Port/Facility and U.S. Coast Guard Accident Data, for example) Provide a basis for intermodal analysis. Assist standardization of waterway entity definitions (Ports/Facilities, Locks, etc.). Provide public access to the National Waterway Network, which can be used as a basemap to support graphical overlays and analysis with other spatial data (waterway and modal network/facility databases, for example). Provide reliable data to support future waterway and intermodal applications.The Ports dataset as of December 17, 2019 is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics's (BTS') National Transportation Atlas Database (NTAD). Contains physical information on commercial facilities at U.S. Coastal, Great Lakes and Inland Ports. The data consists of location description, street address, city, county name, congressional district FIPS code, type of construction, cargo-handling equipment, water depth alongside the facility, facility type ( dock, fleeting area, lock and/or dam) berthing space, latitude, longitude, current operators and owner's information, list of commodities handled at facility, road/railway connections, equipment available at facility, storage facilities, cranes, transit sheds, grain elevators, marine repair plants, fleeting areas, and docking, and facility start/stop date.
Copyright Text: USDOT BTS
U.S. Department of Transportation ArcGIS Online
Description: TN Statewide Transportation Improvement Program project locationsData extracted from here in December 2025 and subset to the Thrive region.For more information on TN STIP, please visit https://www.tn.gov/tdot/program-development-and-administration-home/program-development-and-administration-state-programs.html
Description: The Navigation Facilities files provide data for nearly 12,000 ports-and-waterway facilities and other navigation points of interest, such as docks, anchorages, etc., that describe the physical and inter-modal (infrastructure) characteristics of sites at the coastal, Great Lakes, and inland ports of the United States; with additional data for facilities in Alaska, Hawaii, Puerto Rico, the U.S. Virgin Islands, and the trust territories of the Pacific. This data was subset to the Thrive Study Region on 10/30/2025The Navgiational Facilities dataset is periodically updated by the United States Army Corp of Engineers (USACE) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The data includes location (latitude, longitude, waterway, mile, and bank); operations (name, owner, operator, purpose, handling equipment, rates, and details of open-and-covered storage facilities); and type of construction (length of berthing space for vessels and/or barges, depth, deck elevation, and details of rail-and-highway access). Additional attributes included in the data are the unique navigation-unit identifier, official name, facility type, United Nations Location Code, a location description, stress address, city, state, zip code, county, congressional district, TOWS link location identifiers, port name, waterway name, mile, bank, and service-initiated dates. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529017
Description: This dataset is modified to include information on manufacturing facilities (NAICS Codes 31, 32, 33) in the Thrive Study Area. This builds off the data provided from HIFLD's general manufacturing facilities dataset (https://hifld-geoplatform.opendata.arcgis.com/datasets/geoplatform::general-manufacturing-facilities/about).
Copyright Text: U.S. Department of Energy, National Energy Technology Laboratory, DHS, HILFD, EPA, U.S. Census Bureau.
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Description: This is a public dataset created in 2007 for the Department of Transportation, Research and Innovative Technology Administration's Bureau of Transportation Statistics. The public database consists of four tables. One of the tables is a spatial table: INTERMODAL_FACILITY. The three other tables consist of attribute data for the database: INTERMODAL_CARGO, INTERMODAL_COMMODITY and INTERMODAL_DIRECTIONALITY. This database was based on the requirements from the Commodity Flow Survey and with the different modes of DOT, supervised by RITA/BTS. The database will extend its design to support all of the modes within the DOT and in reference to modes involved with Intermodal transfer.The 2015 version of this data was updated by Georgia Tech in 2023 and verified through a combination of air photo interpretation, google maps, and parcel data review.
Description: This dataset represents private non-retail shipping facilities. The Private Non-Retail Shipping layer contains motor carrier freight terminals for some of largest trucking companies in the U.S. (as measured by revenue reported by the Bureau of Transportation Statistics). Entities from the following companies are included: United Parcel Service (UPS), Yellow Freight System, Schneider, Roadway Express , FedEx , Con-Way Transportation , ABF Freight System, DHL, Corporate headquarters and other locations that do not actually handle freight are not included. No data could be obtained for Consolidated Freightways, Ryder Integrated Logistics, RPS and J.B. Hunt Transport Services. Facilities associated with these companies are not included in this dataset even though these companies have more annual revenue that some of the companies that are represented in this dataset. Many smaller, but still significant, companies have been excluded due to resource constraints. Examples include United Van Lines, Overnite Transportation, and American Freightways. Rail, air, sea, pipeline and inter-modal terminals are not included unless they are operated by one of the companies represented in this dataset. Facilities associated with the United States Postal Service (USPS) are not included. This dataset does not include retail shipping stores, drop boxes, and transportation operations that are not operated for hire. No entities located in American Samoa, The Northern Mariana Islands or the Virgin Islands are included in this dataset. The companies represented in this dataset are involved in the parcel delivery / courier service business or the freight service provider business. Parcel delivery / courier services deliver parcels, packages and other small shipments that typically weight less than 100 pounds according to the Bureau of Transportation Statistics. A freight service provider can include motor carriers, for-hire carriers and freight forwarders. According to the United Shippers Corporation, a motor carrier is defined as a company that provides truck transportation. A for-hire carrier is defined as a company that provides truck transportation of cargo belonging to others and is paid for doing so. A freight forwarder is defined as a company that arranges for truck transportation of cargo belonging to others, utilizing for-hire carriers to provide the actual truck transportation. The forwarder does assume the responsibility for the cargo from origin to destination and usually does take possession of the cargo at some point during the transportation. Forwarders typically assemble and consolidate less-than-truckload shipments into truckload shipments at origin and disassemble and deliver less-than-truckload shipments at destination. The intention of TGS is to include only those entities that meet the above definition. TGS was not able to contact all of the entities in this layer to verify that they met this definition, therefore some entities may be included in this layer that do not meet the definition. Text fields in this dataset have been set to all upper case to facilitate consistent database engine search results. All diacritics (e.g. the German umlaut or the Spanish tilde) have been replaced with their closest equivalent English character to facilitate use with database systems that may not support diacritics. The currentness of this dataset is indicated by the [CONTDATE] attribute. Based upon this attribute the oldest record dates from 09/08/2006 and the newest record dates from 10/03/2006.
Copyright Text: Private Member
GeoPlatform ArcGIS Online
Description: The Airports dataset includes all official and operational aerodromes (public only) as of July 16, 2020 and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The Airports database is a geographic point database of official operational aerodromes in the United States and U.S. Territories. Attribute data is provided on the physical and operational characteristics of the aerodrome, current usage including enplanements and aircraft operations, congestion levels and usage categories. This geospatial data is derived from the FAA's National Airspace System Resource Aeronautical Data Product.
Copyright Text: U.S. Department of Transportation ArcGIS Online
USDOT BTS
Description: The National Bridge Inventory dataset is as of June 20, 2025 from the Federal Highway Administration (FHWA) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The data describes more than 624,000 of the Nation's bridges located on public roads, including Interstate Highways, U.S. highways, State and local roads, as well as publicly-accessible bridges on Federal and Tribal lands. The inventory data presented includes information on the location, description, classification, and general condition for each bridge. The Recording and Coding Guide for the Structure Inventory and Appraisal of the Nation's Bridges (Coding Guide) contains a detailed description of each data element including coding instructions and attribute definitions. The Coding Guide is available at: https://doi.org/10.21949/1519105. For additional questions regarding regulations for the National Bridge Inventory or the Coding Guide please contact the National Bridge and Tunnel Inventory team at NBTIS_Support@dot.gov. For questions on the geospatial component of the dataset, contact the NTAD team at NTAD@dot.gov. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1519105
Copyright Text: Acknowledgment of the Federal Highway Administration (FHWA) and the Bureau of Transportation Statistics (BTS) [distributor].
Description: The Railroad Grade Crossings dataset was updated on July 13, 2025 and was created on July 15, 2025 by the Federal Railroad Administration (FRA), and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The Railroad Grade Crossings is a spatial file that originates from the National Highway-Rail Crossing Inventory Program. The program is to provide information to the public, Federal, State, and Local governments, as well as the railroad industry for information and the improvements of safety at highway-rail crossings. Some railroad grade crossings were located outside the US or their respective states, and relocated to latitude longitude 0,0. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529075
Type_Of_Train_Service_IDs (type: esriFieldTypeString, alias: Type Of Train Service IDs, SQL Type: sqlTypeOther, length: 4000, nullable: true, editable: true)
Type_Of_Train_Service_ID_1 (type: esriFieldTypeString, alias: Type Of Train Service ID 1, SQL Type: sqlTypeOther, length: 4000, nullable: true, editable: true)
Type_Of_Train_Service_1 (type: esriFieldTypeString, alias: Type Of Train Service 1, SQL Type: sqlTypeOther, length: 4000, nullable: true, editable: true)
Type_Of_Train_Service_ID_2 (type: esriFieldTypeString, alias: Type Of Train Service ID 2, SQL Type: sqlTypeOther, length: 4000, nullable: true, editable: true)
Type_Of_Train_Service_2 (type: esriFieldTypeString, alias: Type Of Train Service 2, SQL Type: sqlTypeOther, length: 4000, nullable: true, editable: true)
Type_Of_Train_Service_ID_3 (type: esriFieldTypeString, alias: Type Of Train Service ID 3, SQL Type: sqlTypeOther, length: 4000, nullable: true, editable: true)
Type_Of_Train_Service_3 (type: esriFieldTypeString, alias: Type Of Train Service 3, SQL Type: sqlTypeOther, length: 4000, nullable: true, editable: true)
Type_Of_Train_Service_ID_4 (type: esriFieldTypeString, alias: Type Of Train Service ID 4, SQL Type: sqlTypeOther, length: 4000, nullable: true, editable: true)
Type_Of_Train_Service_4 (type: esriFieldTypeString, alias: Type Of Train Service 4, SQL Type: sqlTypeOther, length: 4000, nullable: true, editable: true)
Type_Of_Train_Service_ID_5 (type: esriFieldTypeString, alias: Type Of Train Service ID 5, SQL Type: sqlTypeOther, length: 4000, nullable: true, editable: true)
Type_Of_Train_Service_5 (type: esriFieldTypeString, alias: Type Of Train Service 5, SQL Type: sqlTypeOther, length: 4000, nullable: true, editable: true)
Type_Of_Train_Service_ID_6 (type: esriFieldTypeString, alias: Type Of Train Service ID 6, SQL Type: sqlTypeOther, length: 4000, nullable: true, editable: true)
Type_Of_Train_Service_6 (type: esriFieldTypeString, alias: Type Of Train Service 6, SQL Type: sqlTypeOther, length: 4000, nullable: true, editable: true)
Less_Than_One_Passenger_Train_P (type: esriFieldTypeString, alias: Less Than One Passenger Train Per Day, SQL Type: sqlTypeOther, length: 4000, nullable: true, editable: true)
Number_Passenger_Train_Per_Day (type: esriFieldTypeInteger, alias: Number Passenger Train Per Day, SQL Type: sqlTypeOther, nullable: true, editable: true)
Development_Type_Code (type: esriFieldTypeInteger, alias: Development Type Code, SQL Type: sqlTypeOther, nullable: true, editable: true)
Description: This feature class/shapefile represents electric power plants. Power plants are all the land and land rights, structures and improvements, boiler or reactor vessel equipment, engines and engine-driven generators, turbo generator units, accessory electric equipment, and miscellaneous power plant equipment are grouped together for each individual facility. Included are the following plant types: hydroelectric dams, fossil fuel (coal, natural gas, or oil), nuclear, solar, wind, geothermal, and biomass. Updated in 2024
Copyright Text: Oak Ridge National Laboratory (ORNL), Los Alamos National Laboratory (LANL), Idaho National Laboratory (INL), National Geospatial-Intelligence Agency (NGA) Homeland Security Infrastructure Program (HSIP) Team
Description: This feature class/shapefile is for the Homeland Infrastructure Foundation Level Database (HIFLD) (https://gii.dhs.gov/HIFLD) as well as the Energy modelling and simulation community.Data source: Data source:Oak Ridge National Laboratory (ORNL) Data and details are hosted by HIFLD as received from the data provider. For questions /feedback, please email hifld@hq.dhs.gov Data and details are hosted by HIFLD as received from the data provider. For questions /feedback, please email hifld@hq.dhs.govThis feature class/shapefile represents electric power substations primarily associated with electric power transmission subset to the Greater Chattanooga area. In this layer, substations are considered facilities and equipment that switch, transform, or regulate electric power at voltages equal to, or greater than, 69 kilovolts. Substations with a maximum operating voltage less than 69 kilovolts may be included, depending on the availability of authoritative sources, but coverage of these features should not be considered complete. The Substations feature class/shapefile includes taps, a location where power on a transmission line is tapped by another transmission line. The following updates have been made since the previous release: 2,630 features added.
Description: This feature class/shapefile contains Petroleum Terminals for the Homeland Infrastructure Foundation-Level (HIFLD) Database (https://hifld-dhs-gii.gov/HIFLD) as well as the Energy modeling and simulation community.Data source: Federal Communications Commission Data and details are hosted by HIFLD as received from the data provider. For questions /feedback, please email hifld@hq.dhs.govThis feature class/shapefile represents Petroleum Terminals. Petroleum Terminals are used to provide storage of both crude oil and refined petroleum products. Data contains locational and other attribute information for operable bulk petroleum product terminals with a total bulk shell storage capacity of 50,000 barrels or more, and/or ability to receive volumes from tanker, barge, or pipeline. Geographical coverage includes the United States, U.S. Virgin Islands, Puerto Rico, and Guam. This update includes an increase of 62 records for a total of 2,341 terminals. Two terminals were removed because it was confirmed they no longer exist. 66 new terminals were added; 57 of these were located at international airports. 3 terminals were changed to a STATUS of "DISMANTLED" because they no longer exist but still appear in some imagery sources. A new STATUS of "DISMANTLED" was added. Several NAICS codes and descriptions were corrected.Data updated in 2023
Description: Original description - This feature class/shapefile contains points showing locations of Biodiesel Plants in the United States and parts of Canada for the Homeland Infrastructure Foundation-Level (HIFLD) Database (https://hifld-dhs-gii.gov/HIFLD) as well as the Energy modeling and simulation community.Data source:Oak Ridge National Laboratory (ORNL) Data and details are hosted by HIFLD as received from the data provider. For questions /feedback, please email hifld@hq.dhs.govThis point feature class/shapefile contains locational and other attribute information for Biodiesel Plants located within the United States and parts of Canada. Biodiesel Plants are those plants which manufacture diesel fuel from vegetable oils, animal fats, or recycled greases.
Description: This feature class/shapefile contains POL Pumping Stations for the Homeland Infrastructure Foundation-Level (HIFLD) Database (https://gii.dhs.gov/HIFLD) as well as the Energy modeling and simulation community.Data source: Oak Ridge National Laboratory. Data and details are hosted by HIFLD as received from the data provider. For questions /feedback, please email hifld@hq.dhs.govThis feature class/shapefile represents POL Pumping Stations. A POL Pumping Station is a facility that supports the transportation of petroleum products from one location to another via a transmission pipeline. In addition, these facilities allow for the pumping of petroleum-based products along pipelines, the monitoring and maintaining of pressure and flow, and the ability to provide information about the transmission of the petroleum product. Geographical coverage includes the United States. This update adds 367 new POL Pumping Stations and deletes 14 POL Pumping Stations. Geolocations were updated for 67 POL Pumping Stations. The contents of the field ADDRESS2 were concatenated with the ADDRESS field and the ADDRESS2 field was removed. The NAICS codes were reviewed and updated to meet the 2017 NAICS Code Standard. NOTE: In some cases a NAICS Code may have several approved descriptions. When two different descriptions were available the more appropriate one would be used. This results in a single NAICS Code having different descriptions in this release.Data updated in 2023
Description: The Alternative Fueling Station dataset helps fleet managers and drivers of alternative fuel vehicles find stations that offer biodiesel (B20 and above), electric vehicle charging, ethanol (E85), hydrogen, natural gas (compressed and liquefied), and propane (LPG).The Alternative Fueling Stations dataset is updated daily from the National Renewable Energy Laboratory (NREL) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). This dataset was downloaded on 10/30/2025 and subset to the Thrive Study Region. For more information about the update cycle and data collection methods, please refer to https://afdc.energy.gov/stations/#/find/nearest?show_about=true. This dataset shows all station access types (public and private) and statuses (available, planned, and temporarily unavailable) by default. To view only publicly available stations, use the access and status filters. The U.S. Department of Energy collects these data in partnership with Clean Cities coalitions and their stakeholders to help fleets and consumers find alternative fueling stations. Clean Cities coalitions foster the nation's economic, environmental, and energy security by working locally to advance affordable, efficient, and clean transportation fuels and technologies. This data can be found on the Alternative Fuels Data Center: https://doi.org/10.21949/1519144. For more information about the data schema and data dictionary, please see https://developer.nrel.gov/docs/transportation/alt-fuel-stations-v1/all/#response-fields. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529008
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Description: This layer shows workers' place of residence by mode of commute. This is shown by tract, county, and state centroids. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized by the count of total workers and 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., -555555...) have been set to null. 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. NOTE: any calculated percentages or counts that contain estimates that have null margins of error yield null margins of error for the calculated fields.
County (type: esriFieldTypeString, alias: County, SQL Type: sqlTypeOther, length: 255, nullable: true, editable: true)
B08301_001E (type: esriFieldTypeInteger, alias: Total Workers 16 Years and Over, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_001M (type: esriFieldTypeDouble, alias: Total Workers 16 Years and Over - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_002E (type: esriFieldTypeInteger, alias: All Workers who commuted by car, truck, or van, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_002M (type: esriFieldTypeDouble, alias: All Workers who commuted by car, truck, or van - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_004M (type: esriFieldTypeDouble, alias: Workers who carpooled - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_010E (type: esriFieldTypeInteger, alias: All Workers who commuted by public transportation (excluding taxicab), SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_010M (type: esriFieldTypeDouble, alias: All Workers who commuted by public transportation (excluding taxicab) - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_011E (type: esriFieldTypeInteger, alias: Workers who commuted by bus, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_011M (type: esriFieldTypeDouble, alias: Workers who commuted by bus - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_012E (type: esriFieldTypeInteger, alias: Workers who commuted by subway or elevated rail, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_012M (type: esriFieldTypeDouble, alias: Workers who commuted by subway or elevated rail - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_013E (type: esriFieldTypeInteger, alias: Workers who commuted by long-distance train or commuter rail, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_013M (type: esriFieldTypeDouble, alias: Workers who commuted by long-distance train or commuter rail - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_014E (type: esriFieldTypeInteger, alias: Workers who commuted by light rail, streetcar or trolley (carro público in Puerto Rico), SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_014M (type: esriFieldTypeDouble, alias: Workers who commuted by light rail, streetcar or trolley (carro público in Puerto Rico) - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_015E (type: esriFieldTypeInteger, alias: Workers who commuted by ferryboat, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_015M (type: esriFieldTypeDouble, alias: Workers who commuted by ferryboat - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_016E (type: esriFieldTypeInteger, alias: Workers who commuted by taxicab, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_016M (type: esriFieldTypeDouble, alias: Workers who commuted by taxicab - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_017E (type: esriFieldTypeInteger, alias: Workers who commuted by motorcycle, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_017M (type: esriFieldTypeDouble, alias: Workers who commuted by motorcycle - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_018E (type: esriFieldTypeInteger, alias: Workers who commuted by bicycle, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_018M (type: esriFieldTypeDouble, alias: Workers who commuted by bicycle - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_019E (type: esriFieldTypeInteger, alias: Workers who commuted by walking, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_019M (type: esriFieldTypeDouble, alias: Workers who commuted by walking - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_020E (type: esriFieldTypeInteger, alias: Workers who commuted by other means, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_020M (type: esriFieldTypeDouble, alias: Workers who commuted by other means - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_021E (type: esriFieldTypeInteger, alias: Workers who worked from home, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_021M (type: esriFieldTypeDouble, alias: Workers who worked from home - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_calc_pctDroveAloneE (type: esriFieldTypeDouble, alias: Percent of workers who commuted by driving alone, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_calc_pctDroveAloneM (type: esriFieldTypeDouble, alias: Percent of workers who commuted by driving alone - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_calc_pctCarpooledE (type: esriFieldTypeDouble, alias: Percent of workers who commuted by carpooling, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_calc_pctCarpooledM (type: esriFieldTypeDouble, alias: Percent of workers who commuted by carpooling - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_calc_pctPublicE (type: esriFieldTypeDouble, alias: Percent of workers who commuted by public transportation, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_calc_pctPublicM (type: esriFieldTypeDouble, alias: Percent of workers who commuted by public transportation - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_calc_pctBusE (type: esriFieldTypeDouble, alias: Percent of workers who commuted by bus, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_calc_pctBusM (type: esriFieldTypeDouble, alias: Percent of workers who commuted by bus - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_calc_pctStreetcarE (type: esriFieldTypeDouble, alias: Percent of workers who commuted by light rail, streetcar or trolley, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_calc_pctStreetcarM (type: esriFieldTypeDouble, alias: Percent of workers who commuted by light rail, streetcar or trolley - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_calc_pctSubwayE (type: esriFieldTypeDouble, alias: Percent of workers who commuted by subway or elevated rail, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_calc_pctSubwayM (type: esriFieldTypeDouble, alias: Percent of workers who commuted by subway or elevated rail - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_calc_pctRailE (type: esriFieldTypeDouble, alias: Percent of workers who commuted by long-distance train or commuter rail, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_calc_pctRailM (type: esriFieldTypeDouble, alias: Percent of workers who commuted by long-distance train or commuter rail - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_calc_pctFerryE (type: esriFieldTypeDouble, alias: Percent of workers who commuted by ferryboat, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_calc_pctFerryM (type: esriFieldTypeDouble, alias: Percent of workers who commuted by ferryboat - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_calc_pctTaxiE (type: esriFieldTypeDouble, alias: Percent of workers who commuted by taxicab, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_calc_pctTaxiM (type: esriFieldTypeDouble, alias: Percent of workers who commuted by taxicab - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_calc_pctMotorcycleE (type: esriFieldTypeDouble, alias: Percent of workers who commuted by motorcycle, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_calc_pctMotorcycleM (type: esriFieldTypeDouble, alias: Percent of workers who commuted by motorcycle - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_calc_pctBicycleE (type: esriFieldTypeDouble, alias: Percent of workers who commuted by bicycle, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_calc_pctBicycleM (type: esriFieldTypeDouble, alias: Percent of workers who commuted by bicycle - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_calc_pctWalkE (type: esriFieldTypeDouble, alias: Percent of workers who commuted by walking, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_calc_pctWalkM (type: esriFieldTypeDouble, alias: Percent of workers who commuted by walking - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_calc_pctOtherE (type: esriFieldTypeDouble, alias: Percent of workers who commuted by other means, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_calc_pctOtherM (type: esriFieldTypeDouble, alias: Percent of workers who commuted by other means - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_calc_pctHomeE (type: esriFieldTypeDouble, alias: Percent of workers who worked at home, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_calc_pctHomeM (type: esriFieldTypeDouble, alias: Percent of workers who worked at home - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
Description: The BETA - Hazard Exposure: Principal Ports dataset was updated on October 16, 2025 from the Bureau of Transportation Statistics (BTS) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). This dataset was subset to the Thrive Study Region on 10/30/2025. The BETA - Hazard Exposure: Principal Ports dataset shows the exposure of Principal Ports to select natural hazards. The dataset combines hazard data from FEMA, USGS, NOAA, and USDA with information for the top 150 Principal Ports as determined by the United States Army Corp of Engineers (USACE). Data as of CY 2023. Hazard data include estimates of annualized frequency of Coastal Flooding, Cold Wave, Drought, Hail, Hurricane, Heat Wave, Ice Storm, Lightning, Strong Wind, Tornado, and Winter Weather from the FEMA National Risk Index. Landslide Index and Sinkhole Susceptibility derive from USGS models. USDA Burn Probability data underlie estimates of wildfire exposure. NOAA SLOSH models estimate storm surge inundation from hurricanes. The data set covers all 50 states of the United States, the District of Columbia, the US Virgin Islands, and Puerto Rico. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/gc9g-2396
Description: The BETA - Hazard Exposure: Select North American Rail Network Lines dataset was updated on October 16, 2025 from the Bureau of Transportation Statistics (BTS) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). Dataset subset to the Thrive Study Region on 10/30/2025. The BETA - Hazard Exposure: Select North American Rail Network Lines dataset shows the exposure of North America's railway system to select natural hazards. The dataset combines hazard data from FEMA, USGS, NOAA, and USDA with information for all mainline or operational rail lines that are represented in the North American Rail Network (NARN) Rail Lines dataset from the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). Hazard data include estimates of annualized frequency of Coastal Flooding, Cold Wave, Drought, Hail, Hurricane, Heat Wave, Ice Storm, Lightning, Strong Wind, Tornado, and Winter Weather from the FEMA National Risk Index. Landslide Index and Sinkhole Susceptibility derive from USGS models. USDA Burn Probability data underlie estimates of wildfire exposure. NOAA SLOSH models estimate storm surge inundation from hurricanes. Presence in the floodplain is calculated using data from the FEMA National Flood Hazard Layer. The data set covers the 50 states of the United States and the District of Columbia. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/mrpb-7x82
Description: The BETA - Hazard Exposure: National Highway System dataset was updated on October 16, 2025 from the Bureau of Transportation Statistics (BTS) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). Dataset subset to Thrive Study Region on 10/30/2025. The BETA - Hazard Exposure: National Highway System dataset shows the exposure of North America's roadway system to select natural hazards. The dataset combines hazard data from FEMA, USGS, NOAA, and USDA with information for the National Highway System (NHS) dataset -- comprising roadways important to the nation's economy, defense, and mobility -- from the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). Hazard data include estimates of annualized frequency of Coastal Flooding, Cold Wave, Drought, Hail, Hurricane, Heat Wave, Ice Storm, Lightning, Strong Wind, Tornado, and Winter Weather from the FEMA National Risk Index. Landslide Index and Sinkhole Susceptibility derive from USGS models. USDA Burn Probability data underlie estimates of wildfire exposure. NOAA SLOSH models estimate storm surge inundation from hurricanes. The data set covers all 50 states of the United States, the District of Columbia, and Puerto Rico. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/3psn-b298
Description: It is intended for use in understanding and managing the National Highway System, which includes roadways crucial to the nation's economy, defense, and mobility. Also, the dataset is being used by the US DOT to reflect the FHWA-approved technical correction for NHS and STRAHNET. The National Highway System (NHS) dataset and its geometries was updated on August 08, 2025 from the Federal Highway Administration (FHWA) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). This dataset was downloaded from BTS on 10/30/2025 and subset to the Thrive Study Region. The National Highway System consists of roadways important to the nation's economy, defense, and mobility. The National Highway System (NHS) includes the following subsystems of roadways: Interstate - The Eisenhower Interstate System of highways, Other Principal Arterials - highways in rural and urban areas which provide access between an arterial and a major port, airport, public transportation facility, or other intermodal transportation facility, Strategic Highway Network (STRAHNET) - a network of highways which are important to the United States' strategic defense policy and which provide defense access, continuity and emergency capabilities for defense purposes, Major Strategic Highway Network Connectors - highways which provide access between major military installations and highways which are part of the Strategic Highway Network, Intermodal Connectors - highways providing access between major intermodal facilities and the other four subsystems making up the National Highway System. A specific highway route may be on more than one subsystem. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529838
Description: The Link Tonnages dataset was sourced on December 31, 2023 and was updated on August 22, 2025 by the United States Army Corp of Engineers (USACE) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The tonnage summarized for each link by commodity and direction (upbound and down bound). The commodities include Coal, Petroleum Products, Chemicals, Crude Materials, Manufactured Goods, Farm Products, Machinery, Waste, and Unknown. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/xm0g-sr86
Description: This Bureau of Transportation Statistics (BTS) feature layer, utilizing National Geospatial Data Asset (NGDA) data, displays the North American Rail Network (NARN) subset to the Thrive Study Region. Per BTS, "NARN Rail Lines dataset was created...from the Federal Railroad Administration (FRA) and is part of the...National Transportation Atlas Database (NTAD). It is a database that provides ownership, trackage rights, type, passenger, STRACNET, and geographic reference for North America's railway system...within the United States.It can be used within the public, the federal government, rail industry, state DOTs, and academia for routing, mapping and analysis."Data currency: Current Federal service (NTAD North American Rail Network Lines)NGDAID: 145 (North American Rail Network Lines)For more information: North American Rail Network Lines; Rail Network DevelopmentSupport documentation: North American Rail Network LinesFor feedback please contact: Esri_US_Federal_Data@esri.com
STFIPS (type: esriFieldTypeString, alias: Federal Information Processing Standard (FIPS) State Code, SQL Type: sqlTypeOther, length: 2, nullable: true, editable: true)
Description: This map shows the traffic volume in an area measured by the annual vehicle miles travelled (AVMT). Data is from the Bureau of Transportation Statistics, and depicts the Federal Highway Administration's data that reflects the extent, use, condition, and performance of the public roads in the United States. Roadways in yellow indicate lower AVMT, while areas in dark green indicate areas with higher AVMT.Annual Vehicle miles traveled (AVMT) is a measure used in transportation planning for a variety of purposes. It measures the amount of travel for all vehicles in a roadway segment over one year period. AVMT is calculated by multiplying the average annual day traffic (AADT) by the length of the roadway segment.According to the Texas A&M's Transportation Policy Research Center, Vehicle Miles Traveled has many use cases, such as: Assess the differences in travel demand and impact between regions and other states.Project future revenue streams from fuel taxes and proposed VMT fees.Compare personal travel and freight/commercial vehicle travel. Project future congestion levels.Estimate the amount of travel resulting from local residence and freight activity versus external travel.Assess the impact of various population forecasts.Support many more measures of interest for transportation planning
Description: The Alternative Fuel Corridors dataset was created in 2016 and was updated on January 16, 2025 with new Round 8 designations from the Federal Highway Administration (FHWA) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The dataset is a highway layer of corridors, primarily along the NHS, that are designated as Corridor Ready or Corridor Pending. It includes designations of five types of alternative fuels, Electric Vehicle Charging (EV), Compressed Natural Gas (CNG), Liquefied Natural Gas (LNG), Propane (LPG), and Hydrogen. Corridor-ready segments currently contain a sufficient number of fueling facilities to allow for corridor travel with the designated alternative fuel, and to qualify for highway signage. Corridors that do not have sufficient alternative fuel facilities to support alternative fuel vehicle travel are designated as corridor pending. A data dictionary, or other source of attribute information, is accessible at https://www.transportation.gov/gis/nad/nad-schema
Copyright Text: Acknowledgment of the Federal Highway Administration (FHWA), State DOTs, Trucker's Friend database, and the Bureau of Transportation Statistics (BTS) [distributor].
Description: SA Detailed Water Bodies represents the major water features in the United States. USA Detailed Water Bodies represents the major lakes, reservoirs, large rivers, lagoons, and estuaries in the United States. Data updated in 2023 and subset to the greater Chattanooga area.
Description: This file contains existing and proposed routes for those highways in he Thrive Regional Infrastructure Portal study area that are part of the Appalachian Development Highway System, aka the "Corridor" system. This includes Corridors, D, E, G, H, L, and Q. Information provided in this layer includes Corridor Name, Section Left, Section Number, Section, State and County FIPS, Congressional District, HPMS Inventory and Sign, Classification Code, Urban Code Strip Map, National Highways Segment, Speed Limit, ADT Base, ROW Width, Median Width, Access, Design Year, Truck Year, DD Factors, Number of Lanes , Cost for Improvements, Construction Costs, Overlooks, Links, Additional Information. Data is in the AD_1983_UTM_Zone_17N coordinate system.
Name: Highway Performance Monitoring System (HPMS)
Display Field: ROUTE_NAME
Type: Feature Layer
Geometry Type: esriGeometryPolyline
Description: This feature layer, utilizing data from the Bureau of Transportation Statistics (BTS), depicts the Federal Highway Administration's (FHWA) data that reflects the extent, use, condition, and performance of the public roads in the United States. According to BTS, the data "supports and informs highway planning, policy making, and decision making at the national, state, and local levels." This Highway Performance Monitoring System (HPMS) layer was compiled by combining 52 datasets including the 50 U.S. States, Puerto Rico and the District of Columbia. This data was subset to the Thrive study area. Data currency: 1/1/2020Data source: Highway Performance Monitoring System (HPMS) 2020Data modification: NoneFor more information: Highway Performance Monitoring System (HPMS)Support documentation: Highway Performance Monitoring System - Field ManualFor feedback, please contact: ArcGIScomNationalMaps@esri.com
Description: The Freight Analysis Framework (FAF), produced through a partnership between BTS and FHWA, integrates data from various sources to create a comprehensive picture of freight movement among states and major metropolitan areas by all modes of transportation. The 2017 Commodity Flow Survey (CFS) and international trade data from the Census Bureau serve as the backbone of FAF and are integrated with ancillary data sources that capture goods movement in agriculture, resource extraction, utility, construction, retail, services, and other sectors.The current version of FAF5 (FAF5.3) provides estimates for tonnage (unit: thousand tons), value (unit: million dollars), and ton-miles (unit: million ton-miles) by origin-destination pair of FAF regions, commodity type, and mode for the base year (2017), the recent years (2018 - 2019), the forecast year estimates (2020 - 2050), and the state level historical trend estimates (1997-2012). The information may be accessed through the Data Tabulation Tool and downloaded as either a complete database or in summary files.This dataset represents the FAF5 network and has been subset to the greater Chattanooga region.
Description: The Freight Analysis Framework (FAF), produced through a partnership between BTS and FHWA, integrates data from various sources to create a comprehensive picture of freight movement among states and major metropolitan areas by all modes of transportation. The 2017 Commodity Flow Survey (CFS) and international trade data from the Census Bureau serve as the backbone of FAF and are integrated with ancillary data sources that capture goods movement in agriculture, resource extraction, utility, construction, retail, services, and other sectors.The current version of FAF5 (FAF5.3) provides estimates for tonnage (unit: thousand tons), value (unit: million dollars), and ton-miles (unit: million ton-miles) by origin-destination pair of FAF regions, commodity type, and mode for the base year (2017), the recent years (2018 - 2019), the forecast year estimates (2020 - 2050), and the state level historical trend estimates (1997-2012). The information may be accessed through the Data Tabulation Tool and downloaded as either a complete database or in summary files.This dataset represents the FAF5 network and has been subset to the greater Chattanooga region.
Description: This dataset contains geospatial line data showing Annual Average Daily Traffic (AADT), Vehicle Miles Traveled (VMT), calculation methods for all AADTs, Truck Percentages, and the TN-TIMES link for the station. The dataset also references some features of the roadway, such as functional class, state ownership, urban boundary, and more, as they were assigned to the linear network for the given reporting year.The Calculation Method for the AADT indicates whether the Annual Average Daily Traffic was created directly from a raw count or if it was calculated based on an alternate methodology:Actual – The AADT is based on an actual raw count from the given year.Grown – The AADT was grown from the previous year AADT based on the average growth rate of the county. AADTs calculated using this methodology have a superscript value 3 in TN-TIMES.Combined – The AADT was combined from two separate directional AADTs. Please refer to TN-TIMES for the directional AADT methodology. AADTs calculated using this methodology have a superscript value 7 in TN-TIMES.Doubled – The AADT was doubled from only one directional AADT. Please refer to TN-TIMES for the directional AADT methodology. AADTs calculated using this methodology have a superscript value 12 in TN-TIMES.Balanced – The AADT was calculated from corridor balancing. Corridor balancing is a method that relies on a start node on an interstate and adds and subtracts subsequent ramp volumes to calculate additional AADTs along the mainline. AADTs calculated using this methodology have a superscript value 8 in TN-TIMES.Virtual – The AADT was calculated based on a combination of related station AADTs. Please refer to TN-TIMES for the specific stations involved. AADTs calculated using this methodology have a superscript value 19 in TN-TIMES.Scaled – The AADT was derived from GPS based vehicle movements, sourced from Bentley's OpenPaths Patterns.The Calculation Method for the Class AADTs similarly indicates the source data for the value:Actual – The Class AADT is based on a raw class count collected at the station in the given year.Distributed – The Class AADTs are based on the average class distribution of the base stations that are in the same Class Distribution Group. The group assignment for the given station can be found in TN-TIMES.Derived – The Class AADTs are based on a combination of length data from the given station and the class distribution. Actual Class AADTs and Distributed Class AADTs are based on axle class counts rather than length counts. This dataset is updated annually.
Copyright Text: Tennessee Department of Transportation, Planning Bureau, Planning Division, Roadway Data Office, Planning Data Team
Name: GA Road Network Truck 2024: % Peak Multi-Unit Trucks
Display Field: ROUTE_ID
Type: Feature Layer
Geometry Type: esriGeometryPolyline
Description: 2024 Average Annual Daily Traffic for selected routes in Georgia obtained from GDOT in 2025. https://www.dot.ga.gov/GDOT/Pages/RoadTrafficData.aspx
Description: The Navigable Waterways dataset is as of June 26, 2019, and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics's (BTS's) National Transportation Atlas Database (NTAD). The National Waterway Network is a comprehensive network database of the nation's navigable waterways. The data set covers parts of TN, AL, and GA. The nominal scale of the dataset varies with the source material. The majority of the information is at 1:100,000 with larger scales used in harbor/bay/port areas and smaller scales used in open waters. These data could be used for analytical studies of waterway performance, for compiling commodity flow statistics, and for mapping purposes.
Description: Layer includes interstate trunk lines and selected intrastate lines. Based on publicly available data from a variety of sources with varying scales and levels of accuracy.
Description: This feature class/shapefile is for the Homeland Infrastructure Foundation Level Database (HIFLD) (https://gii.dhs.gov/HIFLD) as well as the Energy modelling and simulation community.Data source:Oak Ridge National Laboratory (ORNL) Data and details are hosted by HIFLD as received from the data provider. For questions /feedback, please email hifld@hq.dhs.gov. This feature class/shapefile represents electric power transmission lines. Transmission Lines are the system of structures, wires, insulators and associated hardware that carry electric energy from one point to another in an electric power system. Lines are operated at relatively high voltages varying from 69 kV up to 765 kV, and are capable of transmitting large quantities of electricity over long distances. Underground transmission lines are included where sources were available.
Description: USA Detailed Water Bodies represents the major lakes, reservoirs, large rivers, lagoons, and estuaries in the United States. Data updated in 2023
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Description: The U.S. Census Bureau, together with the Bureau of Transportation Statistics (BTS), has released the 2022 Commodity Flow Survey (CFS) Subarea Estimates experimental data product providing additional geographic granularity to existing CFS estimates. These Subarea estimates divide the existing 134 CFS Areas into 329 Subareas such that each Subarea consists of at least one county and – in general – at least 10,000 CFS shipments.This is the second time this experimental data has been made available publicly on the Census Bureau’s experimental data product webpage. No changes, except for the addition of two subareas to reflect changes to the standard CFS Area geographic areas, have been made to the methodology or to the tables of estimates being published when compared with the 2017 CFS Subareas experimental product.Data are available for origin by destination by commodity group at one mode of transportation – truck and ground parcel combined. Finer geographic detail is provided for origins and destinations that are geographically near each other. For rarer, long-distance shipments, paired geographies are aggregated at either the origin or the destination.Note that these estimates of shipment activity flowing into and out of CFS subareas are contained in a single table; whereas the 2017 estimates were provided in two separate tables. The CFS Subarea experimental data product represents a step toward providing more granular and relevant products that meet data users’ needs, while minimizing burden on respondents. With the CFS Subarea estimates, additional geographic details are provided to supplement our main CFS products.For more information on this product, see the 2022 Commodity Flow Survey Subarea Estimates Methodology.This dataset shows commodity flow out of the Thrive region
Number_of_Shipments_W (type: esriFieldTypeInteger, alias: Number of Shipments W, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Value__Millions___Ag_Products_and_Fish__Grains__Alcohol_and_Tobacco (type: esriFieldTypeInteger, alias: Total Value (Millions) Ag Products and Fish, Grains, Alcohol and Tobacco, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Weight__Thousand_Tons___Ag_Products_and_Fish__Grains__Alcohol_and_Tobacco (type: esriFieldTypeInteger, alias: Total Weight (Thousand Tons) Ag Products and Fish, Grains, Alcohol and Tobacco, SQL Type: sqlTypeOther, nullable: true, editable: true)
Avg_Mile_Per_Shipment__Ag_Products_and_Fish__Grains__Alcohol_and_Tobacco (type: esriFieldTypeInteger, alias: Avg Mile Per Shipment Ag Products and Fish, Grains, Alcohol and Tobacco, SQL Type: sqlTypeOther, nullable: true, editable: true)
Number_of_Shipments__Ag_Products_and_Fish__Grains__Alcohol_and_Tobacco (type: esriFieldTypeInteger, alias: Number of Shipments Ag Products and Fish, Grains, Alcohol and Tobacco, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Value__Millions__USA_Ag_Products_and_Fish__Grains__Alcohol_and_Tobacco (type: esriFieldTypeInteger, alias: Total Value (Millions) USA Ag Products and Fish, Grains, Alcohol and Tobacco, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Weight__Thousand_Tons__USA_Ag_Products_and_Fish__Grains__Alcohol_and_Tobacco (type: esriFieldTypeInteger, alias: Total Weight (Thousand Tons) USA Ag Products and Fish, Grains, Alcohol and Tobacco, SQL Type: sqlTypeOther, nullable: true, editable: true)
Avg_Mile_Per_Shipment_USA_Ag_Products_and_Fish__Grains__Alcohol_and_Tobacco (type: esriFieldTypeInteger, alias: Avg Mile Per Shipment USA Ag Products and Fish, Grains, Alcohol and Tobacco, SQL Type: sqlTypeOther, nullable: true, editable: true)
Number_of_Shipments_USA_Ag_Products_and_Fish__Grains__Alcohol_and_Tobacco (type: esriFieldTypeInteger, alias: Number of Shipments USA Ag Products and Fish, Grains, Alcohol and Tobacco, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Value__Millions__N_Ag_Products_and_Fish__Grains__Alcohol_and_Tobacco (type: esriFieldTypeInteger, alias: Total Value (Millions) N Ag Products and Fish, Grains, Alcohol and Tobacco, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Weight__Thousand_Tons__N_Ag_Products_and_Fish__Grains__Alcohol_and_Tobacco (type: esriFieldTypeInteger, alias: Total Weight (Thousand Tons) N Ag Products and Fish, Grains, Alcohol and Tobacco, SQL Type: sqlTypeOther, nullable: true, editable: true)
Avg_Mile_Per_Shipment_N_Ag_Products_and_Fish__Grains__Alcohol_and_Tobacco (type: esriFieldTypeInteger, alias: Avg Mile Per Shipment N Ag Products and Fish, Grains, Alcohol and Tobacco, SQL Type: sqlTypeOther, nullable: true, editable: true)
Number_of_Shipments_N_Ag_Products_and_Fish__Grains__Alcohol_and_Tobacco (type: esriFieldTypeInteger, alias: Number of Shipments N Ag Products and Fish, Grains, Alcohol and Tobacco, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Value__Millions__M_Ag_Products_and_Fish__Grains__Alcohol_and_Tobacco (type: esriFieldTypeInteger, alias: Total Value (Millions) M Ag Products and Fish, Grains, Alcohol and Tobacco, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Weight__Thousand_Tons__M_Ag_Products_and_Fish__Grains__Alcohol_and_Tobacco (type: esriFieldTypeInteger, alias: Total Weight (Thousand Tons) M Ag Products and Fish, Grains, Alcohol and Tobacco, SQL Type: sqlTypeOther, nullable: true, editable: true)
Avg_Mile_Per_Shipment_M_Ag_Products_and_Fish__Grains__Alcohol_and_Tobacco (type: esriFieldTypeInteger, alias: Avg Mile Per Shipment M Ag Products and Fish, Grains, Alcohol and Tobacco, SQL Type: sqlTypeOther, nullable: true, editable: true)
Number_of_Shipments_M_Ag_Products_and_Fish__Grains__Alcohol_and_Tobacco (type: esriFieldTypeInteger, alias: Number of Shipments M Ag Products and Fish, Grains, Alcohol and Tobacco, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Value__Millions__S_Ag_Products_and_Fish__Grains__Alcohol_and_Tobacco (type: esriFieldTypeInteger, alias: Total Value (Millions) S Ag Products and Fish, Grains, Alcohol and Tobacco, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Weight__Thousand_Tons__S_Ag_Products_and_Fish__Grains__Alcohol_and_Tobacco (type: esriFieldTypeInteger, alias: Total Weight (Thousand Tons) S Ag Products and Fish, Grains, Alcohol and Tobacco, SQL Type: sqlTypeOther, nullable: true, editable: true)
Avg_Mile_Per_Shipment_S_Ag_Products_and_Fish__Grains__Alcohol_and_Tobacco (type: esriFieldTypeInteger, alias: Avg Mile Per Shipment S Ag Products and Fish, Grains, Alcohol and Tobacco, SQL Type: sqlTypeOther, nullable: true, editable: true)
Number_of_Shipments_S_Ag_Products_and_Fish__Grains__Alcohol_and_Tobacco (type: esriFieldTypeInteger, alias: Number of Shipments S Ag Products and Fish, Grains, Alcohol and Tobacco, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Value__Millions__W_Ag_Products_and_Fish__Grains__Alcohol_and_Tobacco (type: esriFieldTypeInteger, alias: Total Value (Millions) W Ag Products and Fish, Grains, Alcohol and Tobacco, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Weight__Thousand_Tons__W_Ag_Products_and_Fish__Grains__Alcohol_and_Tobacco (type: esriFieldTypeInteger, alias: Total Weight (Thousand Tons) W Ag Products and Fish, Grains, Alcohol and Tobacco, SQL Type: sqlTypeOther, nullable: true, editable: true)
Avg_Mile_Per_Shipment_W_Ag_Products_and_Fish__Grains__Alcohol_and_Tobacco (type: esriFieldTypeInteger, alias: Avg Mile Per Shipment W Ag Products and Fish, Grains, Alcohol and Tobacco, SQL Type: sqlTypeOther, nullable: true, editable: true)
Number_of_Shipments_W_Ag_Products_and_Fish__Grains__Alcohol_and_Tobacco (type: esriFieldTypeInteger, alias: Number of Shipments W Ag Products and Fish, Grains, Alcohol and Tobacco, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Value__Millions___Stones__minerals_and_ores__coal_and_petroleum (type: esriFieldTypeInteger, alias: Total Value (Millions) Stones, minerals and ores, coal and petroleum, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Weight__Thousand_Tons___Stones__minerals_and_ores__coal_and_petroleum (type: esriFieldTypeInteger, alias: Total Weight (Thousand Tons) Stones, minerals and ores, coal and petroleum, SQL Type: sqlTypeOther, nullable: true, editable: true)
Avg_Mile_Per_Shipment__Stones__minerals_and_ores__coal_and_petroleum (type: esriFieldTypeInteger, alias: Avg Mile Per Shipment Stones, minerals and ores, coal and petroleum, SQL Type: sqlTypeOther, nullable: true, editable: true)
Number_of_Shipments__Stones__minerals_and_ores__coal_and_petroleum (type: esriFieldTypeInteger, alias: Number of Shipments Stones, minerals and ores, coal and petroleum, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Value__Millions__USA_Stones__minerals_and_ores__coal_and_petroleum (type: esriFieldTypeInteger, alias: Total Value (Millions) USA Stones, minerals and ores, coal and petroleum, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Weight__Thousand_Tons__USA_Stones__minerals_and_ores__coal_and_petroleum (type: esriFieldTypeInteger, alias: Total Weight (Thousand Tons) USA Stones, minerals and ores, coal and petroleum, SQL Type: sqlTypeOther, nullable: true, editable: true)
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Total_Weight__Thousand_Tons__N_Stones__minerals_and_ores__coal_and_petroleum (type: esriFieldTypeInteger, alias: Total Weight (Thousand Tons) N Stones, minerals and ores, coal and petroleum, SQL Type: sqlTypeOther, nullable: true, editable: true)
Avg_Mile_Per_Shipment_N_Stones__minerals_and_ores__coal_and_petroleum (type: esriFieldTypeInteger, alias: Avg Mile Per Shipment N Stones, minerals and ores, coal and petroleum, SQL Type: sqlTypeOther, nullable: true, editable: true)
Number_of_Shipments_N_Stones__minerals_and_ores__coal_and_petroleum (type: esriFieldTypeInteger, alias: Number of Shipments N Stones, minerals and ores, coal and petroleum, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Value__Millions__M_Stones__minerals_and_ores__coal_and_petroleum (type: esriFieldTypeInteger, alias: Total Value (Millions) M Stones, minerals and ores, coal and petroleum, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Weight__Thousand_Tons__M_Stones__minerals_and_ores__coal_and_petroleum (type: esriFieldTypeInteger, alias: Total Weight (Thousand Tons) M Stones, minerals and ores, coal and petroleum, SQL Type: sqlTypeOther, nullable: true, editable: true)
Avg_Mile_Per_Shipment_M_Stones__minerals_and_ores__coal_and_petroleum (type: esriFieldTypeInteger, alias: Avg Mile Per Shipment M Stones, minerals and ores, coal and petroleum, SQL Type: sqlTypeOther, nullable: true, editable: true)
Number_of_Shipments_M_Stones__minerals_and_ores__coal_and_petroleum (type: esriFieldTypeInteger, alias: Number of Shipments M Stones, minerals and ores, coal and petroleum, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Value__Millions__S_Stones__minerals_and_ores__coal_and_petroleum (type: esriFieldTypeInteger, alias: Total Value (Millions) S Stones, minerals and ores, coal and petroleum, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Weight__Thousand_Tons__S_Stones__minerals_and_ores__coal_and_petroleum (type: esriFieldTypeInteger, alias: Total Weight (Thousand Tons) S Stones, minerals and ores, coal and petroleum, SQL Type: sqlTypeOther, nullable: true, editable: true)
Avg_Mile_Per_Shipment_S_Stones__minerals_and_ores__coal_and_petroleum (type: esriFieldTypeInteger, alias: Avg Mile Per Shipment S Stones, minerals and ores, coal and petroleum, SQL Type: sqlTypeOther, nullable: true, editable: true)
Number_of_Shipments_S_Stones__minerals_and_ores__coal_and_petroleum (type: esriFieldTypeInteger, alias: Number of Shipments S Stones, minerals and ores, coal and petroleum, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Value__Millions__W_Stones__minerals_and_ores__coal_and_petroleum (type: esriFieldTypeInteger, alias: Total Value (Millions) W Stones, minerals and ores, coal and petroleum, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Weight__Thousand_Tons__W_Stones__minerals_and_ores__coal_and_petroleum (type: esriFieldTypeInteger, alias: Total Weight (Thousand Tons) W Stones, minerals and ores, coal and petroleum, SQL Type: sqlTypeOther, nullable: true, editable: true)
Avg_Mile_Per_Shipment_W_Stones__minerals_and_ores__coal_and_petroleum (type: esriFieldTypeInteger, alias: Avg Mile Per Shipment W Stones, minerals and ores, coal and petroleum, SQL Type: sqlTypeOther, nullable: true, editable: true)
Number_of_Shipments_W_Stones__minerals_and_ores__coal_and_petroleum (type: esriFieldTypeInteger, alias: Number of Shipments W Stones, minerals and ores, coal and petroleum, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Value__Millions___chemicals__pharmaceuticals__logs__wood_products__textiles__and_leather__metal_and_machinery (type: esriFieldTypeInteger, alias: Total Value (Millions) chemicals, pharmaceuticals, logs, wood products, textiles, and leather, metal and machinery, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Weight__Thousand_Tons___chemicals__pharmaceuticals__logs__wood_products__textiles__and_leather__metal_and_machinery (type: esriFieldTypeInteger, alias: Total Weight (Thousand Tons) chemicals, pharmaceuticals, logs, wood products, textiles, and leather, metal and machinery, SQL Type: sqlTypeOther, nullable: true, editable: true)
Avg_Mile_Per_Shipment__chemicals__pharmaceuticals__logs__wood_products__textiles__and_leather__metal_and_machinery (type: esriFieldTypeInteger, alias: Avg Mile Per Shipment chemicals, pharmaceuticals, logs, wood products, textiles, and leather, metal and machinery, SQL Type: sqlTypeOther, nullable: true, editable: true)
Number_of_Shipments__chemicals__pharmaceuticals__logs__wood_products__textiles__and_leather__metal_and_machinery (type: esriFieldTypeInteger, alias: Number of Shipments chemicals, pharmaceuticals, logs, wood products, textiles, and leather, metal and machinery, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Value__Millions__USA_chemicals__pharmaceuticals__logs__wood_products__textiles__and_leather__metal_and_machinery (type: esriFieldTypeInteger, alias: Total Value (Millions) USA chemicals, pharmaceuticals, logs, wood products, textiles, and leather, metal and machinery, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Weight__Thousand_Tons__USA_chemicals__pharmaceuticals__logs__wood_products__textiles__and_leather__metal_and_machinery (type: esriFieldTypeInteger, alias: Total Weight (Thousand Tons) USA chemicals, pharmaceuticals, logs, wood products, textiles, and leather, metal and machinery, SQL Type: sqlTypeOther, nullable: true, editable: true)
Avg_Mile_Per_Shipment_USA_chemicals__pharmaceuticals__logs__wood_products__textiles__and_leather__metal_and_machinery (type: esriFieldTypeInteger, alias: Avg Mile Per Shipment USA chemicals, pharmaceuticals, logs, wood products, textiles, and leather, metal and machinery, SQL Type: sqlTypeOther, nullable: true, editable: true)
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Total_Value__Millions__N_chemicals__pharmaceuticals__logs__wood_products__textiles__and_leather__metal_and_machinery (type: esriFieldTypeInteger, alias: Total Value (Millions) N chemicals, pharmaceuticals, logs, wood products, textiles, and leather, metal and machinery, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Weight__Thousand_Tons__N_chemicals__pharmaceuticals__logs__wood_products__textiles__and_leather__metal_and_machinery (type: esriFieldTypeInteger, alias: Total Weight (Thousand Tons) N chemicals, pharmaceuticals, logs, wood products, textiles, and leather, metal and machinery, SQL Type: sqlTypeOther, nullable: true, editable: true)
Avg_Mile_Per_Shipment_N_chemicals__pharmaceuticals__logs__wood_products__textiles__and_leather__metal_and_machinery (type: esriFieldTypeInteger, alias: Avg Mile Per Shipment N chemicals, pharmaceuticals, logs, wood products, textiles, and leather, metal and machinery, SQL Type: sqlTypeOther, nullable: true, editable: true)
Number_of_Shipments_N_chemicals__pharmaceuticals__logs__wood_products__textiles__and_leather__metal_and_machinery (type: esriFieldTypeInteger, alias: Number of Shipments N chemicals, pharmaceuticals, logs, wood products, textiles, and leather, metal and machinery, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Value__Millions__M_chemicals__pharmaceuticals__logs__wood_products__textiles__and_leather__metal_and_machinery (type: esriFieldTypeInteger, alias: Total Value (Millions) M chemicals, pharmaceuticals, logs, wood products, textiles, and leather, metal and machinery, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Weight__Thousand_Tons__M_chemicals__pharmaceuticals__logs__wood_products__textiles__and_leather__metal_and_machinery (type: esriFieldTypeInteger, alias: Total Weight (Thousand Tons) M chemicals, pharmaceuticals, logs, wood products, textiles, and leather, metal and machinery, SQL Type: sqlTypeOther, nullable: true, editable: true)
Avg_Mile_Per_Shipment_M_chemicals__pharmaceuticals__logs__wood_products__textiles__and_leather__metal_and_machinery (type: esriFieldTypeInteger, alias: Avg Mile Per Shipment M chemicals, pharmaceuticals, logs, wood products, textiles, and leather, metal and machinery, SQL Type: sqlTypeOther, nullable: true, editable: true)
Number_of_Shipments_M_chemicals__pharmaceuticals__logs__wood_products__textiles__and_leather__metal_and_machinery (type: esriFieldTypeInteger, alias: Number of Shipments M chemicals, pharmaceuticals, logs, wood products, textiles, and leather, metal and machinery, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Value__Millions__S_chemicals__pharmaceuticals__logs__wood_products__textiles__and_leather__metal_and_machinery (type: esriFieldTypeInteger, alias: Total Value (Millions) S chemicals, pharmaceuticals, logs, wood products, textiles, and leather, metal and machinery, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Weight__Thousand_Tons__S_chemicals__pharmaceuticals__logs__wood_products__textiles__and_leather__metal_and_machinery (type: esriFieldTypeInteger, alias: Total Weight (Thousand Tons) S chemicals, pharmaceuticals, logs, wood products, textiles, and leather, metal and machinery, SQL Type: sqlTypeOther, nullable: true, editable: true)
Avg_Mile_Per_Shipment_S_chemicals__pharmaceuticals__logs__wood_products__textiles__and_leather__metal_and_machinery (type: esriFieldTypeInteger, alias: Avg Mile Per Shipment S chemicals, pharmaceuticals, logs, wood products, textiles, and leather, metal and machinery, SQL Type: sqlTypeOther, nullable: true, editable: true)
Number_of_Shipments_S_chemicals__pharmaceuticals__logs__wood_products__textiles__and_leather__metal_and_machinery (type: esriFieldTypeInteger, alias: Number of Shipments S chemicals, pharmaceuticals, logs, wood products, textiles, and leather, metal and machinery, SQL Type: sqlTypeOther, nullable: true, editable: true)
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Total_Value__Millions___electronic__motorized_vehicles__and_precision_instruments__furniture__mixed_freight__and_misc__manufactu (type: esriFieldTypeInteger, alias: Total Value (Millions) electronic, motorized vehicles, and precision instruments, furniture, mixed freight, and misc. manufactured products, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Weight__Thousand_Tons___electronic__motorized_vehicles__and_precision_instruments__furniture__mixed_freight__and_misc__man (type: esriFieldTypeInteger, alias: Total Weight (Thousand Tons) electronic, motorized vehicles, and precision instruments, furniture, mixed freight, and misc. manufactured products, SQL Type: sqlTypeOther, nullable: true, editable: true)
Avg_Mile_Per_Shipment__electronic__motorized_vehicles__and_precision_instruments__furniture__mixed_freight__and_misc__manufactur (type: esriFieldTypeInteger, alias: Avg Mile Per Shipment electronic, motorized vehicles, and precision instruments, furniture, mixed freight, and misc. manufactured products, SQL Type: sqlTypeOther, nullable: true, editable: true)
Number_of_Shipments__electronic__motorized_vehicles__and_precision_instruments__furniture__mixed_freight__and_misc__manufactured (type: esriFieldTypeInteger, alias: Number of Shipments electronic, motorized vehicles, and precision instruments, furniture, mixed freight, and misc. manufactured products, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Value__Millions__USA_electronic__motorized_vehicles__and_precision_instruments__furniture__mixed_freight__and_misc__manufa (type: esriFieldTypeInteger, alias: Total Value (Millions) USA electronic, motorized vehicles, and precision instruments, furniture, mixed freight, and misc. manufactured products, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Weight__Thousand_Tons__USA_electronic__motorized_vehicles__and_precision_instruments__furniture__mixed_freight__and_misc_ (type: esriFieldTypeInteger, alias: Total Weight (Thousand Tons) USA electronic, motorized vehicles, and precision instruments, furniture, mixed freight, and misc. manufactured products, SQL Type: sqlTypeOther, nullable: true, editable: true)
Avg_Mile_Per_Shipment_USA_electronic__motorized_vehicles__and_precision_instruments__furniture__mixed_freight__and_misc__manufac (type: esriFieldTypeInteger, alias: Avg Mile Per Shipment USA electronic, motorized vehicles, and precision instruments, furniture, mixed freight, and misc. manufactured products, SQL Type: sqlTypeOther, nullable: true, editable: true)
Number_of_Shipments_USA_electronic__motorized_vehicles__and_precision_instruments__furniture__mixed_freight__and_misc__manufactu (type: esriFieldTypeInteger, alias: Number of Shipments USA electronic, motorized vehicles, and precision instruments, furniture, mixed freight, and misc. manufactured products, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Value__Millions__N_electronic__motorized_vehicles__and_precision_instruments__furniture__mixed_freight__and_misc__manufact (type: esriFieldTypeInteger, alias: Total Value (Millions) N electronic, motorized vehicles, and precision instruments, furniture, mixed freight, and misc. manufactured products, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Weight__Thousand_Tons__N_electronic__motorized_vehicles__and_precision_instruments__furniture__mixed_freight__and_misc__ma (type: esriFieldTypeInteger, alias: Total Weight (Thousand Tons) N electronic, motorized vehicles, and precision instruments, furniture, mixed freight, and misc. manufactured products, SQL Type: sqlTypeOther, nullable: true, editable: true)
Avg_Mile_Per_Shipment_N_electronic__motorized_vehicles__and_precision_instruments__furniture__mixed_freight__and_misc__manufactu (type: esriFieldTypeInteger, alias: Avg Mile Per Shipment N electronic, motorized vehicles, and precision instruments, furniture, mixed freight, and misc. manufactured products, SQL Type: sqlTypeOther, nullable: true, editable: true)
Number_of_Shipments_N_electronic__motorized_vehicles__and_precision_instruments__furniture__mixed_freight__and_misc__manufacture (type: esriFieldTypeInteger, alias: Number of Shipments N electronic, motorized vehicles, and precision instruments, furniture, mixed freight, and misc. manufactured products, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Value__Millions__M_electronic__motorized_vehicles__and_precision_instruments__furniture__mixed_freight__and_misc__manufact (type: esriFieldTypeInteger, alias: Total Value (Millions) M electronic, motorized vehicles, and precision instruments, furniture, mixed freight, and misc. manufactured products, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Weight__Thousand_Tons__M_electronic__motorized_vehicles__and_precision_instruments__furniture__mixed_freight__and_misc__ma (type: esriFieldTypeInteger, alias: Total Weight (Thousand Tons) M electronic, motorized vehicles, and precision instruments, furniture, mixed freight, and misc. manufactured products, SQL Type: sqlTypeOther, nullable: true, editable: true)
Avg_Mile_Per_Shipment_M_electronic__motorized_vehicles__and_precision_instruments__furniture__mixed_freight__and_misc__manufactu (type: esriFieldTypeInteger, alias: Avg Mile Per Shipment M electronic, motorized vehicles, and precision instruments, furniture, mixed freight, and misc. manufactured products, SQL Type: sqlTypeOther, nullable: true, editable: true)
Number_of_Shipments_M_electronic__motorized_vehicles__and_precision_instruments__furniture__mixed_freight__and_misc__manufacture (type: esriFieldTypeInteger, alias: Number of Shipments M electronic, motorized vehicles, and precision instruments, furniture, mixed freight, and misc. manufactured products, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Value__Millions__S_electronic__motorized_vehicles__and_precision_instruments__furniture__mixed_freight__and_misc__manufact (type: esriFieldTypeInteger, alias: Total Value (Millions) S electronic, motorized vehicles, and precision instruments, furniture, mixed freight, and misc. manufactured products, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Weight__Thousand_Tons__S_electronic__motorized_vehicles__and_precision_instruments__furniture__mixed_freight__and_misc__ma (type: esriFieldTypeInteger, alias: Total Weight (Thousand Tons) S electronic, motorized vehicles, and precision instruments, furniture, mixed freight, and misc. manufactured products, SQL Type: sqlTypeOther, nullable: true, editable: true)
Avg_Mile_Per_Shipment_S_electronic__motorized_vehicles__and_precision_instruments__furniture__mixed_freight__and_misc__manufactu (type: esriFieldTypeInteger, alias: Avg Mile Per Shipment S electronic, motorized vehicles, and precision instruments, furniture, mixed freight, and misc. manufactured products, SQL Type: sqlTypeOther, nullable: true, editable: true)
Number_of_Shipments_S_electronic__motorized_vehicles__and_precision_instruments__furniture__mixed_freight__and_misc__manufacture (type: esriFieldTypeInteger, alias: Number of Shipments S electronic, motorized vehicles, and precision instruments, furniture, mixed freight, and misc. manufactured products, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Value__Millions__W_electronic__motorized_vehicles__and_precision_instruments__furniture__mixed_freight__and_misc__manufact (type: esriFieldTypeInteger, alias: Total Value (Millions) W electronic, motorized vehicles, and precision instruments, furniture, mixed freight, and misc. manufactured products, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Weight__Thousand_Tons__W_electronic__motorized_vehicles__and_precision_instruments__furniture__mixed_freight__and_misc__ma (type: esriFieldTypeInteger, alias: Total Weight (Thousand Tons) W electronic, motorized vehicles, and precision instruments, furniture, mixed freight, and misc. manufactured products, SQL Type: sqlTypeOther, nullable: true, editable: true)
Avg_Mile_Per_Shipment_W_electronic__motorized_vehicles__and_precision_instruments__furniture__mixed_freight__and_misc__manufactu (type: esriFieldTypeInteger, alias: Avg Mile Per Shipment W electronic, motorized vehicles, and precision instruments, furniture, mixed freight, and misc. manufactured products, SQL Type: sqlTypeOther, nullable: true, editable: true)
Number_of_Shipments_W_electronic__motorized_vehicles__and_precision_instruments__furniture__mixed_freight__and_misc__manufacture (type: esriFieldTypeInteger, alias: Number of Shipments W electronic, motorized vehicles, and precision instruments, furniture, mixed freight, and misc. manufactured products, SQL Type: sqlTypeOther, nullable: true, editable: true)
Description: The U.S. Census Bureau, together with the Bureau of Transportation Statistics (BTS), has released the 2022 Commodity Flow Survey (CFS) Subarea Estimates experimental data product providing additional geographic granularity to existing CFS estimates. These Subarea estimates divide the existing 134 CFS Areas into 329 Subareas such that each Subarea consists of at least one county and – in general – at least 10,000 CFS shipments.This is the second time this experimental data has been made available publicly on the Census Bureau’s experimental data product webpage. No changes, except for the addition of two subareas to reflect changes to the standard CFS Area geographic areas, have been made to the methodology or to the tables of estimates being published when compared with the 2017 CFS Subareas experimental product.Data are available for origin by destination by commodity group at one mode of transportation – truck and ground parcel combined. Finer geographic detail is provided for origins and destinations that are geographically near each other. For rarer, long-distance shipments, paired geographies are aggregated at either the origin or the destination.Note that these estimates of shipment activity flowing into and out of CFS subareas are contained in a single table; whereas the 2017 estimates were provided in two separate tables. The CFS Subarea experimental data product represents a step toward providing more granular and relevant products that meet data users’ needs, while minimizing burden on respondents. With the CFS Subarea estimates, additional geographic details are provided to supplement our main CFS products.For more information on this product, see the 2022 Commodity Flow Survey Subarea Estimates Methodology.This dataset shows commodity flow into the Thrive region
Number_of_Shipments_W (type: esriFieldTypeInteger, alias: Number of Shipments W, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Value__Millions__Ag_Products_and_Fish__Grains__Alcohol_and_Tobacco (type: esriFieldTypeInteger, alias: Total Value (Millions) Ag Products and Fish, Grains, Alcohol and Tobacco, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Weight__Thousand_Tons__Ag_Products_and_Fish__Grains__Alcohol_and_Tobacco (type: esriFieldTypeInteger, alias: Total Weight (Thousand Tons) Ag Products and Fish, Grains, Alcohol and Tobacco, SQL Type: sqlTypeOther, nullable: true, editable: true)
Avg_Mile_Per_Shipment_Ag_Products_and_Fish__Grains__Alcohol_and_Tobacco (type: esriFieldTypeInteger, alias: Avg Mile Per Shipment Ag Products and Fish, Grains, Alcohol and Tobacco, SQL Type: sqlTypeOther, nullable: true, editable: true)
Number_of_Shipments_Ag_Products_and_Fish__Grains__Alcohol_and_Tobacco (type: esriFieldTypeInteger, alias: Number of Shipments Ag Products and Fish, Grains, Alcohol and Tobacco, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Value__Millions__USA_Ag_Products_and_Fish__Grains__Alcohol_and_Tobacco (type: esriFieldTypeInteger, alias: Total Value (Millions) USA Ag Products and Fish, Grains, Alcohol and Tobacco, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Weight__Thousand_Tons__USA_Ag_Products_and_Fish__Grains__Alcohol_and_Tobacco (type: esriFieldTypeInteger, alias: Total Weight (Thousand Tons) USA Ag Products and Fish, Grains, Alcohol and Tobacco, SQL Type: sqlTypeOther, nullable: true, editable: true)
Avg_Mile_Per_Shipment_USA_Ag_Products_and_Fish__Grains__Alcohol_and_Tobacco (type: esriFieldTypeInteger, alias: Avg Mile Per Shipment USA Ag Products and Fish, Grains, Alcohol and Tobacco, SQL Type: sqlTypeOther, nullable: true, editable: true)
Number_of_Shipments_USA_Ag_Products_and_Fish__Grains__Alcohol_and_Tobacco (type: esriFieldTypeInteger, alias: Number of Shipments USA Ag Products and Fish, Grains, Alcohol and Tobacco, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Value__Millions__N_Ag_Products_and_Fish__Grains__Alcohol_and_Tobacco (type: esriFieldTypeInteger, alias: Total Value (Millions) N Ag Products and Fish, Grains, Alcohol and Tobacco, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Weight__Thousand_Tons__N_Ag_Products_and_Fish__Grains__Alcohol_and_Tobacco (type: esriFieldTypeInteger, alias: Total Weight (Thousand Tons) N Ag Products and Fish, Grains, Alcohol and Tobacco, SQL Type: sqlTypeOther, nullable: true, editable: true)
Avg_Mile_Per_Shipment_N_Ag_Products_and_Fish__Grains__Alcohol_and_Tobacco (type: esriFieldTypeInteger, alias: Avg Mile Per Shipment N Ag Products and Fish, Grains, Alcohol and Tobacco, SQL Type: sqlTypeOther, nullable: true, editable: true)
Number_of_Shipments_N_Ag_Products_and_Fish__Grains__Alcohol_and_Tobacco (type: esriFieldTypeInteger, alias: Number of Shipments N Ag Products and Fish, Grains, Alcohol and Tobacco, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Value__Millions__M_Ag_Products_and_Fish__Grains__Alcohol_and_Tobacco (type: esriFieldTypeInteger, alias: Total Value (Millions) M Ag Products and Fish, Grains, Alcohol and Tobacco, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Weight__Thousand_Tons__M_Ag_Products_and_Fish__Grains__Alcohol_and_Tobacco (type: esriFieldTypeInteger, alias: Total Weight (Thousand Tons) M Ag Products and Fish, Grains, Alcohol and Tobacco, SQL Type: sqlTypeOther, nullable: true, editable: true)
Avg_Mile_Per_Shipment_M_Ag_Products_and_Fish__Grains__Alcohol_and_Tobacco (type: esriFieldTypeInteger, alias: Avg Mile Per Shipment M Ag Products and Fish, Grains, Alcohol and Tobacco, SQL Type: sqlTypeOther, nullable: true, editable: true)
Number_of_Shipments_M_Ag_Products_and_Fish__Grains__Alcohol_and_Tobacco (type: esriFieldTypeInteger, alias: Number of Shipments M Ag Products and Fish, Grains, Alcohol and Tobacco, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Value__Millions__S_Ag_Products_and_Fish__Grains__Alcohol_and_Tobacco (type: esriFieldTypeInteger, alias: Total Value (Millions) S Ag Products and Fish, Grains, Alcohol and Tobacco, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Weight__Thousand_Tons__S_Ag_Products_and_Fish__Grains__Alcohol_and_Tobacco (type: esriFieldTypeInteger, alias: Total Weight (Thousand Tons) S Ag Products and Fish, Grains, Alcohol and Tobacco, SQL Type: sqlTypeOther, nullable: true, editable: true)
Avg_Mile_Per_Shipment_S_Ag_Products_and_Fish__Grains__Alcohol_and_Tobacco (type: esriFieldTypeInteger, alias: Avg Mile Per Shipment S Ag Products and Fish, Grains, Alcohol and Tobacco, SQL Type: sqlTypeOther, nullable: true, editable: true)
Number_of_Shipments_S_Ag_Products_and_Fish__Grains__Alcohol_and_Tobacco (type: esriFieldTypeInteger, alias: Number of Shipments S Ag Products and Fish, Grains, Alcohol and Tobacco, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Value__Millions__W_Ag_Products_and_Fish__Grains__Alcohol_and_Tobacco (type: esriFieldTypeInteger, alias: Total Value (Millions) W Ag Products and Fish, Grains, Alcohol and Tobacco, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Weight__Thousand_Tons__W_Ag_Products_and_Fish__Grains__Alcohol_and_Tobacco (type: esriFieldTypeInteger, alias: Total Weight (Thousand Tons) W Ag Products and Fish, Grains, Alcohol and Tobacco, SQL Type: sqlTypeOther, nullable: true, editable: true)
Avg_Mile_Per_Shipment_W_Ag_Products_and_Fish__Grains__Alcohol_and_Tobacco (type: esriFieldTypeInteger, alias: Avg Mile Per Shipment W Ag Products and Fish, Grains, Alcohol and Tobacco, SQL Type: sqlTypeOther, nullable: true, editable: true)
Number_of_Shipments_W_Ag_Products_and_Fish__Grains__Alcohol_and_Tobacco (type: esriFieldTypeInteger, alias: Number of Shipments W Ag Products and Fish, Grains, Alcohol and Tobacco, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Value__Millions__Stones__minerals_and_ores__coal_and_petroleum (type: esriFieldTypeInteger, alias: Total Value (Millions) Stones, minerals and ores, coal and petroleum, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Weight__Thousand_Tons__Stones__minerals_and_ores__coal_and_petroleum (type: esriFieldTypeInteger, alias: Total Weight (Thousand Tons) Stones, minerals and ores, coal and petroleum, SQL Type: sqlTypeOther, nullable: true, editable: true)
Avg_Mile_Per_Shipment_Stones__minerals_and_ores__coal_and_petroleum (type: esriFieldTypeInteger, alias: Avg Mile Per Shipment Stones, minerals and ores, coal and petroleum, SQL Type: sqlTypeOther, nullable: true, editable: true)
Number_of_Shipments_Stones__minerals_and_ores__coal_and_petroleum (type: esriFieldTypeInteger, alias: Number of Shipments Stones, minerals and ores, coal and petroleum, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Value__Millions__USA_Stones__minerals_and_ores__coal_and_petroleum (type: esriFieldTypeInteger, alias: Total Value (Millions) USA Stones, minerals and ores, coal and petroleum, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Weight__Thousand_Tons__USA_Stones__minerals_and_ores__coal_and_petroleum (type: esriFieldTypeInteger, alias: Total Weight (Thousand Tons) USA Stones, minerals and ores, coal and petroleum, SQL Type: sqlTypeOther, nullable: true, editable: true)
Avg_Mile_Per_Shipment_USA_Stones__minerals_and_ores__coal_and_petroleum (type: esriFieldTypeInteger, alias: Avg Mile Per Shipment USA Stones, minerals and ores, coal and petroleum, SQL Type: sqlTypeOther, nullable: true, editable: true)
Number_of_Shipments_USA_Stones__minerals_and_ores__coal_and_petroleum (type: esriFieldTypeInteger, alias: Number of Shipments USA Stones, minerals and ores, coal and petroleum, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Value__Millions__N_Stones__minerals_and_ores__coal_and_petroleum (type: esriFieldTypeInteger, alias: Total Value (Millions) N Stones, minerals and ores, coal and petroleum, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Weight__Thousand_Tons__N_Stones__minerals_and_ores__coal_and_petroleum (type: esriFieldTypeInteger, alias: Total Weight (Thousand Tons) N Stones, minerals and ores, coal and petroleum, SQL Type: sqlTypeOther, nullable: true, editable: true)
Avg_Mile_Per_Shipment_N_Stones__minerals_and_ores__coal_and_petroleum (type: esriFieldTypeInteger, alias: Avg Mile Per Shipment N Stones, minerals and ores, coal and petroleum, SQL Type: sqlTypeOther, nullable: true, editable: true)
Number_of_Shipments_N_Stones__minerals_and_ores__coal_and_petroleum (type: esriFieldTypeInteger, alias: Number of Shipments N Stones, minerals and ores, coal and petroleum, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Value__Millions__M_Stones__minerals_and_ores__coal_and_petroleum (type: esriFieldTypeInteger, alias: Total Value (Millions) M Stones, minerals and ores, coal and petroleum, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Weight__Thousand_Tons__M_Stones__minerals_and_ores__coal_and_petroleum (type: esriFieldTypeInteger, alias: Total Weight (Thousand Tons) M Stones, minerals and ores, coal and petroleum, SQL Type: sqlTypeOther, nullable: true, editable: true)
Avg_Mile_Per_Shipment_M_Stones__minerals_and_ores__coal_and_petroleum (type: esriFieldTypeInteger, alias: Avg Mile Per Shipment M Stones, minerals and ores, coal and petroleum, SQL Type: sqlTypeOther, nullable: true, editable: true)
Number_of_Shipments_M_Stones__minerals_and_ores__coal_and_petroleum (type: esriFieldTypeInteger, alias: Number of Shipments M Stones, minerals and ores, coal and petroleum, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Value__Millions__S_Stones__minerals_and_ores__coal_and_petroleum (type: esriFieldTypeInteger, alias: Total Value (Millions) S Stones, minerals and ores, coal and petroleum, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Weight__Thousand_Tons__S_Stones__minerals_and_ores__coal_and_petroleum (type: esriFieldTypeInteger, alias: Total Weight (Thousand Tons) S Stones, minerals and ores, coal and petroleum, SQL Type: sqlTypeOther, nullable: true, editable: true)
Avg_Mile_Per_Shipment_S_Stones__minerals_and_ores__coal_and_petroleum (type: esriFieldTypeInteger, alias: Avg Mile Per Shipment S Stones, minerals and ores, coal and petroleum, SQL Type: sqlTypeOther, nullable: true, editable: true)
Number_of_Shipments_S_Stones__minerals_and_ores__coal_and_petroleum (type: esriFieldTypeInteger, alias: Number of Shipments S Stones, minerals and ores, coal and petroleum, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Value__Millions__W_Stones__minerals_and_ores__coal_and_petroleum (type: esriFieldTypeInteger, alias: Total Value (Millions) W Stones, minerals and ores, coal and petroleum, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Weight__Thousand_Tons__W_Stones__minerals_and_ores__coal_and_petroleum (type: esriFieldTypeInteger, alias: Total Weight (Thousand Tons) W Stones, minerals and ores, coal and petroleum, SQL Type: sqlTypeOther, nullable: true, editable: true)
Avg_Mile_Per_Shipment_W_Stones__minerals_and_ores__coal_and_petroleum (type: esriFieldTypeInteger, alias: Avg Mile Per Shipment W Stones, minerals and ores, coal and petroleum, SQL Type: sqlTypeOther, nullable: true, editable: true)
Number_of_Shipments_W_Stones__minerals_and_ores__coal_and_petroleum (type: esriFieldTypeInteger, alias: Number of Shipments W Stones, minerals and ores, coal and petroleum, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Value__Millions__chemicals__pharmaceuticals__logs__wood_products__textiles__and_leather__metal_and_machinery (type: esriFieldTypeString, alias: Total Value (Millions) chemicals, pharmaceuticals, logs, wood products, textiles, and leather, metal and machinery, SQL Type: sqlTypeOther, length: 8000, nullable: true, editable: true)
Total_Weight__Thousand_Tons__chemicals__pharmaceuticals__logs__wood_products__textiles__and_leather__metal_and_machinery (type: esriFieldTypeInteger, alias: Total Weight (Thousand Tons) chemicals, pharmaceuticals, logs, wood products, textiles, and leather, metal and machinery, SQL Type: sqlTypeOther, nullable: true, editable: true)
Avg_Mile_Per_Shipment_chemicals__pharmaceuticals__logs__wood_products__textiles__and_leather__metal_and_machinery (type: esriFieldTypeInteger, alias: Avg Mile Per Shipment chemicals, pharmaceuticals, logs, wood products, textiles, and leather, metal and machinery, SQL Type: sqlTypeOther, nullable: true, editable: true)
Number_of_Shipments_chemicals__pharmaceuticals__logs__wood_products__textiles__and_leather__metal_and_machinery (type: esriFieldTypeInteger, alias: Number of Shipments chemicals, pharmaceuticals, logs, wood products, textiles, and leather, metal and machinery, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Value__Millions__USA_chemicals__pharmaceuticals__logs__wood_products__textiles__and_leather__metal_and_machinery (type: esriFieldTypeInteger, alias: Total Value (Millions) USA chemicals, pharmaceuticals, logs, wood products, textiles, and leather, metal and machinery, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Weight__Thousand_Tons__USA_chemicals__pharmaceuticals__logs__wood_products__textiles__and_leather__metal_and_machinery (type: esriFieldTypeInteger, alias: Total Weight (Thousand Tons) USA chemicals, pharmaceuticals, logs, wood products, textiles, and leather, metal and machinery, SQL Type: sqlTypeOther, nullable: true, editable: true)
Avg_Mile_Per_Shipment_USA_chemicals__pharmaceuticals__logs__wood_products__textiles__and_leather__metal_and_machinery (type: esriFieldTypeInteger, alias: Avg Mile Per Shipment USA chemicals, pharmaceuticals, logs, wood products, textiles, and leather, metal and machinery, SQL Type: sqlTypeOther, nullable: true, editable: true)
Number_of_Shipments_USA_chemicals__pharmaceuticals__logs__wood_products__textiles__and_leather__metal_and_machinery (type: esriFieldTypeInteger, alias: Number of Shipments USA chemicals, pharmaceuticals, logs, wood products, textiles, and leather, metal and machinery, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Value__Millions__N_chemicals__pharmaceuticals__logs__wood_products__textiles__and_leather__metal_and_machinery (type: esriFieldTypeInteger, alias: Total Value (Millions) N chemicals, pharmaceuticals, logs, wood products, textiles, and leather, metal and machinery, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Weight__Thousand_Tons__N_chemicals__pharmaceuticals__logs__wood_products__textiles__and_leather__metal_and_machinery (type: esriFieldTypeInteger, alias: Total Weight (Thousand Tons) N chemicals, pharmaceuticals, logs, wood products, textiles, and leather, metal and machinery, SQL Type: sqlTypeOther, nullable: true, editable: true)
Avg_Mile_Per_Shipment_N_chemicals__pharmaceuticals__logs__wood_products__textiles__and_leather__metal_and_machinery (type: esriFieldTypeInteger, alias: Avg Mile Per Shipment N chemicals, pharmaceuticals, logs, wood products, textiles, and leather, metal and machinery, SQL Type: sqlTypeOther, nullable: true, editable: true)
Number_of_Shipments_N_chemicals__pharmaceuticals__logs__wood_products__textiles__and_leather__metal_and_machinery (type: esriFieldTypeInteger, alias: Number of Shipments N chemicals, pharmaceuticals, logs, wood products, textiles, and leather, metal and machinery, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Value__Millions__M_chemicals__pharmaceuticals__logs__wood_products__textiles__and_leather__metal_and_machinery (type: esriFieldTypeInteger, alias: Total Value (Millions) M chemicals, pharmaceuticals, logs, wood products, textiles, and leather, metal and machinery, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Weight__Thousand_Tons__M_chemicals__pharmaceuticals__logs__wood_products__textiles__and_leather__metal_and_machinery (type: esriFieldTypeInteger, alias: Total Weight (Thousand Tons) M chemicals, pharmaceuticals, logs, wood products, textiles, and leather, metal and machinery, SQL Type: sqlTypeOther, nullable: true, editable: true)
Avg_Mile_Per_Shipment_M_chemicals__pharmaceuticals__logs__wood_products__textiles__and_leather__metal_and_machinery (type: esriFieldTypeInteger, alias: Avg Mile Per Shipment M chemicals, pharmaceuticals, logs, wood products, textiles, and leather, metal and machinery, SQL Type: sqlTypeOther, nullable: true, editable: true)
Number_of_Shipments_M_chemicals__pharmaceuticals__logs__wood_products__textiles__and_leather__metal_and_machinery (type: esriFieldTypeInteger, alias: Number of Shipments M chemicals, pharmaceuticals, logs, wood products, textiles, and leather, metal and machinery, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Value__Millions__S_chemicals__pharmaceuticals__logs__wood_products__textiles__and_leather__metal_and_machinery (type: esriFieldTypeInteger, alias: Total Value (Millions) S chemicals, pharmaceuticals, logs, wood products, textiles, and leather, metal and machinery, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Weight__Thousand_Tons__S_chemicals__pharmaceuticals__logs__wood_products__textiles__and_leather__metal_and_machinery (type: esriFieldTypeInteger, alias: Total Weight (Thousand Tons) S chemicals, pharmaceuticals, logs, wood products, textiles, and leather, metal and machinery, SQL Type: sqlTypeOther, nullable: true, editable: true)
Avg_Mile_Per_Shipment_S_chemicals__pharmaceuticals__logs__wood_products__textiles__and_leather__metal_and_machinery (type: esriFieldTypeInteger, alias: Avg Mile Per Shipment S chemicals, pharmaceuticals, logs, wood products, textiles, and leather, metal and machinery, SQL Type: sqlTypeOther, nullable: true, editable: true)
Number_of_Shipments_S_chemicals__pharmaceuticals__logs__wood_products__textiles__and_leather__metal_and_machinery (type: esriFieldTypeInteger, alias: Number of Shipments S chemicals, pharmaceuticals, logs, wood products, textiles, and leather, metal and machinery, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Value__Millions__W_chemicals__pharmaceuticals__logs__wood_products__textiles__and_leather__metal_and_machinery (type: esriFieldTypeInteger, alias: Total Value (Millions) W chemicals, pharmaceuticals, logs, wood products, textiles, and leather, metal and machinery, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Weight__Thousand_Tons__W_chemicals__pharmaceuticals__logs__wood_products__textiles__and_leather__metal_and_machinery (type: esriFieldTypeInteger, alias: Total Weight (Thousand Tons) W chemicals, pharmaceuticals, logs, wood products, textiles, and leather, metal and machinery, SQL Type: sqlTypeOther, nullable: true, editable: true)
Avg_Mile_Per_Shipment_W_chemicals__pharmaceuticals__logs__wood_products__textiles__and_leather__metal_and_machinery (type: esriFieldTypeInteger, alias: Avg Mile Per Shipment W chemicals, pharmaceuticals, logs, wood products, textiles, and leather, metal and machinery, SQL Type: sqlTypeOther, nullable: true, editable: true)
Number_of_Shipments_W_chemicals__pharmaceuticals__logs__wood_products__textiles__and_leather__metal_and_machinery (type: esriFieldTypeInteger, alias: Number of Shipments W chemicals, pharmaceuticals, logs, wood products, textiles, and leather, metal and machinery, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Value__Millions__electronic__motorized_vehicles__and_precision_instruments__furniture__mixed_freight__and_misc__manufactur (type: esriFieldTypeInteger, alias: Total Value (Millions) electronic, motorized vehicles, and precision instruments, furniture, mixed freight, and misc. manufactured products, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Weight__Thousand_Tons__electronic__motorized_vehicles__and_precision_instruments__furniture__mixed_freight__and_misc__manu (type: esriFieldTypeInteger, alias: Total Weight (Thousand Tons) electronic, motorized vehicles, and precision instruments, furniture, mixed freight, and misc. manufactured products, SQL Type: sqlTypeOther, nullable: true, editable: true)
Avg_Mile_Per_Shipment_electronic__motorized_vehicles__and_precision_instruments__furniture__mixed_freight__and_misc__manufacture (type: esriFieldTypeInteger, alias: Avg Mile Per Shipment electronic, motorized vehicles, and precision instruments, furniture, mixed freight, and misc. manufactured products, SQL Type: sqlTypeOther, nullable: true, editable: true)
Number_of_Shipments_electronic__motorized_vehicles__and_precision_instruments__furniture__mixed_freight__and_misc__manufactured (type: esriFieldTypeInteger, alias: Number of Shipments electronic, motorized vehicles, and precision instruments, furniture, mixed freight, and misc. manufactured products, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Value__Millions__USA_electronic__motorized_vehicles__and_precision_instruments__furniture__mixed_freight__and_misc__manufa (type: esriFieldTypeInteger, alias: Total Value (Millions) USA electronic, motorized vehicles, and precision instruments, furniture, mixed freight, and misc. manufactured products, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Weight__Thousand_Tons__USA_electronic__motorized_vehicles__and_precision_instruments__furniture__mixed_freight__and_misc_ (type: esriFieldTypeInteger, alias: Total Weight (Thousand Tons) USA electronic, motorized vehicles, and precision instruments, furniture, mixed freight, and misc. manufactured products, SQL Type: sqlTypeOther, nullable: true, editable: true)
Avg_Mile_Per_Shipment_USA_electronic__motorized_vehicles__and_precision_instruments__furniture__mixed_freight__and_misc__manufac (type: esriFieldTypeInteger, alias: Avg Mile Per Shipment USA electronic, motorized vehicles, and precision instruments, furniture, mixed freight, and misc. manufactured products, SQL Type: sqlTypeOther, nullable: true, editable: true)
Number_of_Shipments_USA_electronic__motorized_vehicles__and_precision_instruments__furniture__mixed_freight__and_misc__manufactu (type: esriFieldTypeInteger, alias: Number of Shipments USA electronic, motorized vehicles, and precision instruments, furniture, mixed freight, and misc. manufactured products, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Value__Millions__N_electronic__motorized_vehicles__and_precision_instruments__furniture__mixed_freight__and_misc__manufact (type: esriFieldTypeInteger, alias: Total Value (Millions) N electronic, motorized vehicles, and precision instruments, furniture, mixed freight, and misc. manufactured products, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Weight__Thousand_Tons__N_electronic__motorized_vehicles__and_precision_instruments__furniture__mixed_freight__and_misc__ma (type: esriFieldTypeInteger, alias: Total Weight (Thousand Tons) N electronic, motorized vehicles, and precision instruments, furniture, mixed freight, and misc. manufactured products, SQL Type: sqlTypeOther, nullable: true, editable: true)
Avg_Mile_Per_Shipment_N_electronic__motorized_vehicles__and_precision_instruments__furniture__mixed_freight__and_misc__manufactu (type: esriFieldTypeInteger, alias: Avg Mile Per Shipment N electronic, motorized vehicles, and precision instruments, furniture, mixed freight, and misc. manufactured products, SQL Type: sqlTypeOther, nullable: true, editable: true)
Number_of_Shipments_N_electronic__motorized_vehicles__and_precision_instruments__furniture__mixed_freight__and_misc__manufacture (type: esriFieldTypeInteger, alias: Number of Shipments N electronic, motorized vehicles, and precision instruments, furniture, mixed freight, and misc. manufactured products, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Value__Millions__M_electronic__motorized_vehicles__and_precision_instruments__furniture__mixed_freight__and_misc__manufact (type: esriFieldTypeInteger, alias: Total Value (Millions) M electronic, motorized vehicles, and precision instruments, furniture, mixed freight, and misc. manufactured products, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Weight__Thousand_Tons__M_electronic__motorized_vehicles__and_precision_instruments__furniture__mixed_freight__and_misc__ma (type: esriFieldTypeInteger, alias: Total Weight (Thousand Tons) M electronic, motorized vehicles, and precision instruments, furniture, mixed freight, and misc. manufactured products, SQL Type: sqlTypeOther, nullable: true, editable: true)
Avg_Mile_Per_Shipment_M_electronic__motorized_vehicles__and_precision_instruments__furniture__mixed_freight__and_misc__manufactu (type: esriFieldTypeInteger, alias: Avg Mile Per Shipment M electronic, motorized vehicles, and precision instruments, furniture, mixed freight, and misc. manufactured products, SQL Type: sqlTypeOther, nullable: true, editable: true)
Number_of_Shipments_M_electronic__motorized_vehicles__and_precision_instruments__furniture__mixed_freight__and_misc__manufacture (type: esriFieldTypeInteger, alias: Number of Shipments M electronic, motorized vehicles, and precision instruments, furniture, mixed freight, and misc. manufactured products, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Value__Millions__S_electronic__motorized_vehicles__and_precision_instruments__furniture__mixed_freight__and_misc__manufact (type: esriFieldTypeInteger, alias: Total Value (Millions) S electronic, motorized vehicles, and precision instruments, furniture, mixed freight, and misc. manufactured products, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Weight__Thousand_Tons__S_electronic__motorized_vehicles__and_precision_instruments__furniture__mixed_freight__and_misc__ma (type: esriFieldTypeInteger, alias: Total Weight (Thousand Tons) S electronic, motorized vehicles, and precision instruments, furniture, mixed freight, and misc. manufactured products, SQL Type: sqlTypeOther, nullable: true, editable: true)
Avg_Mile_Per_Shipment_S_electronic__motorized_vehicles__and_precision_instruments__furniture__mixed_freight__and_misc__manufactu (type: esriFieldTypeInteger, alias: Avg Mile Per Shipment S electronic, motorized vehicles, and precision instruments, furniture, mixed freight, and misc. manufactured products, SQL Type: sqlTypeOther, nullable: true, editable: true)
Number_of_Shipments_S_electronic__motorized_vehicles__and_precision_instruments__furniture__mixed_freight__and_misc__manufacture (type: esriFieldTypeInteger, alias: Number of Shipments S electronic, motorized vehicles, and precision instruments, furniture, mixed freight, and misc. manufactured products, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Value__Millions__W_electronic__motorized_vehicles__and_precision_instruments__furniture__mixed_freight__and_misc__manufact (type: esriFieldTypeInteger, alias: Total Value (Millions) W electronic, motorized vehicles, and precision instruments, furniture, mixed freight, and misc. manufactured products, SQL Type: sqlTypeOther, nullable: true, editable: true)
Total_Weight__Thousand_Tons__W_electronic__motorized_vehicles__and_precision_instruments__furniture__mixed_freight__and_misc__ma (type: esriFieldTypeInteger, alias: Total Weight (Thousand Tons) W electronic, motorized vehicles, and precision instruments, furniture, mixed freight, and misc. manufactured products, SQL Type: sqlTypeOther, nullable: true, editable: true)
Avg_Mile_Per_Shipment_W_electronic__motorized_vehicles__and_precision_instruments__furniture__mixed_freight__and_misc__manufactu (type: esriFieldTypeInteger, alias: Avg Mile Per Shipment W electronic, motorized vehicles, and precision instruments, furniture, mixed freight, and misc. manufactured products, SQL Type: sqlTypeOther, nullable: true, editable: true)
Number_of_Shipments_W_electronic__motorized_vehicles__and_precision_instruments__furniture__mixed_freight__and_misc__manufacture (type: esriFieldTypeInteger, alias: Number of Shipments W electronic, motorized vehicles, and precision instruments, furniture, mixed freight, and misc. manufactured products, SQL Type: sqlTypeOther, nullable: true, editable: true)
Description: The Commodity Flow Survey (CFS) is the primary source of national and state-level data on domestic freight shipments by American establishments. Data are provided on the types of commodities being moved, along with their origins and destinations, values, weights, modes of transportation, distance shipped, and ton-miles of commodities shipped. The CFS is a component of the Census Bureau’s economic census and is conducted every five years. The CFS regions are a hybrid of Core-Based Statistical Areas (defined by the Office of Management and Budget) and state boundaries (any single CFS region is limited to only one State). BTS and Census selected these major regional economic centers with the goal of balancing the need for wide geographic representation with the need for data of good quality. The result is a unique region delineation that includes Metropolitan Areas (including partial), state remainders, and entire states.
Description: This layer shows workers' place of residence by commute length. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percentage of commuters whose commute is 90 minutes or more. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B08303Data 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.
County (type: esriFieldTypeString, alias: County, SQL Type: sqlTypeOther, length: 255, nullable: true, editable: true)
B08303_001E (type: esriFieldTypeInteger, alias: Total Commuters (Workers 16 Years and Over Who Did Not Work from Home), SQL Type: sqlTypeOther, nullable: true, editable: true)
B08303_001M (type: esriFieldTypeDouble, alias: Total Commuters (Workers 16 Years and Over Who Did Not Work from Home) - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08303_002E (type: esriFieldTypeInteger, alias: Workers whose commute was less than 5 minutes, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08303_002M (type: esriFieldTypeDouble, alias: Workers whose commute was less than 5 minutes - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08303_003E (type: esriFieldTypeInteger, alias: Workers whose commute was 5 to 9 minutes, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08303_003M (type: esriFieldTypeDouble, alias: Workers whose commute was 5 to 9 minutes - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08303_004E (type: esriFieldTypeInteger, alias: Workers whose commute was 10 to 14 minutes, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08303_004M (type: esriFieldTypeDouble, alias: Workers whose commute was 10 to 14 minutes - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08303_005E (type: esriFieldTypeInteger, alias: Workers whose commute was 15 to 19 minutes, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08303_005M (type: esriFieldTypeDouble, alias: Workers whose commute was 15 to 19 minutes - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08303_006E (type: esriFieldTypeInteger, alias: Workers whose commute was 20 to 24 minutes, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08303_006M (type: esriFieldTypeDouble, alias: Workers whose commute was 20 to 24 minutes - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08303_007E (type: esriFieldTypeInteger, alias: Workers whose commute was 25 to 29 minutes, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08303_007M (type: esriFieldTypeDouble, alias: Workers whose commute was 25 to 29 minutes - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08303_008E (type: esriFieldTypeInteger, alias: Workers whose commute was 30 to 34 minutes, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08303_008M (type: esriFieldTypeDouble, alias: Workers whose commute was 30 to 34 minutes - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08303_009E (type: esriFieldTypeInteger, alias: Workers whose commute was 35 to 39 minutes, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08303_009M (type: esriFieldTypeDouble, alias: Workers whose commute was 35 to 39 minutes - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08303_010E (type: esriFieldTypeInteger, alias: Workers whose commute was 40 to 44 minutes, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08303_010M (type: esriFieldTypeDouble, alias: Workers whose commute was 40 to 44 minutes - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08303_011E (type: esriFieldTypeInteger, alias: Workers whose commute was 45 to 59 minutes, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08303_011M (type: esriFieldTypeDouble, alias: Workers whose commute was 45 to 59 minutes - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08303_012E (type: esriFieldTypeInteger, alias: Workers whose commute was 60 to 89 minutes, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08303_012M (type: esriFieldTypeDouble, alias: Workers whose commute was 60 to 89 minutes - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08303_013E (type: esriFieldTypeInteger, alias: Workers whose commute was 90 minutes or more, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08303_013M (type: esriFieldTypeDouble, alias: Workers whose commute was 90 minutes or more - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08303_calc_pctLE5E (type: esriFieldTypeDouble, alias: Percent of workers whose commute was less than 5 minutes, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08303_calc_pctLE5M (type: esriFieldTypeDouble, alias: Percent of workers whose commute was less than 5 minutes - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08303_calc_pct5to9E (type: esriFieldTypeDouble, alias: Percent of workers whose commute was 5 to 9 minutes, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08303_calc_pct5to9M (type: esriFieldTypeDouble, alias: Percent of workers whose commute was 5 to 9 minutes - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08303_calc_pct10to14E (type: esriFieldTypeDouble, alias: Percent of workers whose commute was 10 to 14 minutes, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08303_calc_pct10to14M (type: esriFieldTypeDouble, alias: Percent of workers whose commute was 10 to 14 minutes - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08303_calc_pct15to19E (type: esriFieldTypeDouble, alias: Percent of workers whose commute was 15 to 19 minutes, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08303_calc_pct15to19M (type: esriFieldTypeDouble, alias: Percent of workers whose commute was 15 to 19 minutes - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08303_calc_pct20to24E (type: esriFieldTypeDouble, alias: Percent of workers whose commute was 20 to 24 minutes, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08303_calc_pct20to24M (type: esriFieldTypeDouble, alias: Percent of workers whose commute was 20 to 24 minutes - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08303_calc_pct25to29E (type: esriFieldTypeDouble, alias: Percent of workers whose commute was 25 to 29 minutes, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08303_calc_pct25to29M (type: esriFieldTypeDouble, alias: Percent of workers whose commute was 25 to 29 minutes - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08303_calc_pct30to34E (type: esriFieldTypeDouble, alias: Percent of workers whose commute was 30 to 34 minutes, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08303_calc_pct30to34M (type: esriFieldTypeDouble, alias: Percent of workers whose commute was 30 to 34 minutes - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08303_calc_pct35to39E (type: esriFieldTypeDouble, alias: Percent of workers whose commute was 35 to 39 minutes, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08303_calc_pct35to39M (type: esriFieldTypeDouble, alias: Percent of workers whose commute was 35 to 39 minutes - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08303_calc_pct40to44E (type: esriFieldTypeDouble, alias: Percent of workers whose commute was 40 to 44 minutes, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08303_calc_pct40to44M (type: esriFieldTypeDouble, alias: Percent of workers whose commute was 40 to 44 minutes - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08303_calc_pct45to59E (type: esriFieldTypeDouble, alias: Percent of workers whose commute was 45 to 59 minutes, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08303_calc_pct45to59M (type: esriFieldTypeDouble, alias: Percent of workers whose commute was 45 to 59 minutes - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08303_calc_pct60to89E (type: esriFieldTypeDouble, alias: Percent of workers whose commute was 60 to 89 minutes, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08303_calc_pct60to89M (type: esriFieldTypeDouble, alias: Percent of workers whose commute was 60 to 89 minutes - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08303_calc_pctGE90E (type: esriFieldTypeDouble, alias: Percent of workers whose commute was 90 minutes or more, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08303_calc_pctGE90M (type: esriFieldTypeDouble, alias: Percent of workers whose commute was 90 minutes or more - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
Description: This layer shows workers' place of residence by mode of commute. This is shown by tract, county, and state centroids. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized by the count of total workers and 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., -555555...) have been set to null. 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. NOTE: any calculated percentages or counts that contain estimates that have null margins of error yield null margins of error for the calculated fields.
County (type: esriFieldTypeString, alias: County, SQL Type: sqlTypeOther, length: 255, nullable: true, editable: true)
B08301_001E (type: esriFieldTypeInteger, alias: Total Workers 16 Years and Over, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_001M (type: esriFieldTypeDouble, alias: Total Workers 16 Years and Over - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_002E (type: esriFieldTypeInteger, alias: All Workers who commuted by car, truck, or van, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_002M (type: esriFieldTypeDouble, alias: All Workers who commuted by car, truck, or van - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_004M (type: esriFieldTypeDouble, alias: Workers who carpooled - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_010E (type: esriFieldTypeInteger, alias: All Workers who commuted by public transportation (excluding taxicab), SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_010M (type: esriFieldTypeDouble, alias: All Workers who commuted by public transportation (excluding taxicab) - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_011E (type: esriFieldTypeInteger, alias: Workers who commuted by bus, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_011M (type: esriFieldTypeDouble, alias: Workers who commuted by bus - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_012E (type: esriFieldTypeInteger, alias: Workers who commuted by subway or elevated rail, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_012M (type: esriFieldTypeDouble, alias: Workers who commuted by subway or elevated rail - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_013E (type: esriFieldTypeInteger, alias: Workers who commuted by long-distance train or commuter rail, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_013M (type: esriFieldTypeDouble, alias: Workers who commuted by long-distance train or commuter rail - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_014E (type: esriFieldTypeInteger, alias: Workers who commuted by light rail, streetcar or trolley (carro público in Puerto Rico), SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_014M (type: esriFieldTypeDouble, alias: Workers who commuted by light rail, streetcar or trolley (carro público in Puerto Rico) - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_015E (type: esriFieldTypeInteger, alias: Workers who commuted by ferryboat, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_015M (type: esriFieldTypeDouble, alias: Workers who commuted by ferryboat - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_016E (type: esriFieldTypeInteger, alias: Workers who commuted by taxicab, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_016M (type: esriFieldTypeDouble, alias: Workers who commuted by taxicab - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_017E (type: esriFieldTypeInteger, alias: Workers who commuted by motorcycle, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_017M (type: esriFieldTypeDouble, alias: Workers who commuted by motorcycle - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_018E (type: esriFieldTypeInteger, alias: Workers who commuted by bicycle, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_018M (type: esriFieldTypeDouble, alias: Workers who commuted by bicycle - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_019E (type: esriFieldTypeInteger, alias: Workers who commuted by walking, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_019M (type: esriFieldTypeDouble, alias: Workers who commuted by walking - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_020E (type: esriFieldTypeInteger, alias: Workers who commuted by other means, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_020M (type: esriFieldTypeDouble, alias: Workers who commuted by other means - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_021E (type: esriFieldTypeInteger, alias: Workers who worked from home, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_021M (type: esriFieldTypeDouble, alias: Workers who worked from home - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_calc_pctDroveAloneE (type: esriFieldTypeDouble, alias: Percent of workers who commuted by driving alone, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_calc_pctDroveAloneM (type: esriFieldTypeDouble, alias: Percent of workers who commuted by driving alone - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_calc_pctCarpooledE (type: esriFieldTypeDouble, alias: Percent of workers who commuted by carpooling, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_calc_pctCarpooledM (type: esriFieldTypeDouble, alias: Percent of workers who commuted by carpooling - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_calc_pctPublicE (type: esriFieldTypeDouble, alias: Percent of workers who commuted by public transportation, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_calc_pctPublicM (type: esriFieldTypeDouble, alias: Percent of workers who commuted by public transportation - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_calc_pctBusE (type: esriFieldTypeDouble, alias: Percent of workers who commuted by bus, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_calc_pctBusM (type: esriFieldTypeDouble, alias: Percent of workers who commuted by bus - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_calc_pctStreetcarE (type: esriFieldTypeDouble, alias: Percent of workers who commuted by light rail, streetcar or trolley, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_calc_pctStreetcarM (type: esriFieldTypeDouble, alias: Percent of workers who commuted by light rail, streetcar or trolley - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_calc_pctSubwayE (type: esriFieldTypeDouble, alias: Percent of workers who commuted by subway or elevated rail, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_calc_pctSubwayM (type: esriFieldTypeDouble, alias: Percent of workers who commuted by subway or elevated rail - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_calc_pctRailE (type: esriFieldTypeDouble, alias: Percent of workers who commuted by long-distance train or commuter rail, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_calc_pctRailM (type: esriFieldTypeDouble, alias: Percent of workers who commuted by long-distance train or commuter rail - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_calc_pctFerryE (type: esriFieldTypeDouble, alias: Percent of workers who commuted by ferryboat, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_calc_pctFerryM (type: esriFieldTypeDouble, alias: Percent of workers who commuted by ferryboat - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_calc_pctTaxiE (type: esriFieldTypeDouble, alias: Percent of workers who commuted by taxicab, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_calc_pctTaxiM (type: esriFieldTypeDouble, alias: Percent of workers who commuted by taxicab - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_calc_pctMotorcycleE (type: esriFieldTypeDouble, alias: Percent of workers who commuted by motorcycle, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_calc_pctMotorcycleM (type: esriFieldTypeDouble, alias: Percent of workers who commuted by motorcycle - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_calc_pctBicycleE (type: esriFieldTypeDouble, alias: Percent of workers who commuted by bicycle, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_calc_pctBicycleM (type: esriFieldTypeDouble, alias: Percent of workers who commuted by bicycle - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_calc_pctWalkE (type: esriFieldTypeDouble, alias: Percent of workers who commuted by walking, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_calc_pctWalkM (type: esriFieldTypeDouble, alias: Percent of workers who commuted by walking - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_calc_pctOtherE (type: esriFieldTypeDouble, alias: Percent of workers who commuted by other means, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_calc_pctOtherM (type: esriFieldTypeDouble, alias: Percent of workers who commuted by other means - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_calc_pctHomeE (type: esriFieldTypeDouble, alias: Percent of workers who worked at home, SQL Type: sqlTypeOther, nullable: true, editable: true)
B08301_calc_pctHomeM (type: esriFieldTypeDouble, alias: Percent of workers who worked at home - Margin of Error, SQL Type: sqlTypeOther, nullable: true, editable: true)
Description: USA Census Urban Areas provides the boundaries, and 2020 U.S. Census names, codes, populations, and housing information for the Thrive region of Georgia, Alabama and Tennessee. For the 2020 U.S. Census, an urban area comprises a densely settled core of census blocks that meet minimum housing unit density or population density requirements. This includes adjacent territory containing non-residential urban land uses. To qualify as an urban area, the territory identified according to criteria must encompass at least 2,000 housing units or have a population of at least 5,000. Urban areas represent densely developed territory, and encompass residential, commercial, and other non-residential urban land uses.The sources for this layer are the U.S. Census Bureau's 2020 Census Urban Areas TIGER/Line data and the corresponding List of 2020 Census Population attribute fields.
NECTAPCI (type: esriFieldTypeString, alias: New England City and
Town Area Principal City Indicator, SQL Type: sqlTypeOther, length: 1, nullable: true, editable: true)
Description: This map layer provides the local development districts (LDDs) — also known as Area Development Districts (ADDs), Council of Governments (COGs), or Regional Planning and Development Commissions — in the Thrive Study Region. LDDs are multi-county planning organizations facilitating community-based, regionally driven, economic development. This layer was derived from the U.S. Census Bureau's 2021 version of their 1:500,000 Cartographic Boundary Files.
Description: The Metropolitan Planning Organizations (MPO) dataset was compiled on August 27, 2025 from the Federal Highway Administration (FHWA) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). This dataset contains the geographic boundaries of Metropolitan Planning Organizations subset to the Thrive Study Region. It provides users with transportation planning locations, sizes and names and is intended for metropolitan area multimodal transportation planning and programming. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529038
Description: The Truck Crashes by County dataset was created by aggregating crash data obtained from the FHWA Motor Carrier Management System (MCMIS). The Motor Carrier Management Information System (MCMIS) is a database of crashes involving motor carriers with USDOT numbers. The database includes trucks, buses, passenger cars, and light trucks with hazardous materials placard. Data are available for only those crashes resulting in either a tow-away, injury, or fatality. Data are collected from all 50 states, the District of Columbia, and Puerto Rico. The data are transmitted electronically from the States, but are maintained and operated by the Federal Motor Carrier Safety Administration (FMCSA). The crash data may contain multiple records for a crash, but due to privacy restrictions, driver data are not included in any crash files released to the public. MCMIS data is valid from 2019 - mid 2025. FARS data, which is included in this dataset, is valid from 2019 - 2023
Description: U.S. 119th Congressional Districts provides the congressional district boundaries and the district representatives for the United States, primarily for national planning applications. The boundaries are consistent with the state, county, and Census block group and tract datasets.
U.S. 119th Congressional Districts represents the political boundaries for the United States 119th congressional districts which begins in January 2025.
Copyright Text: Esri; U.S. Department of Commerce, Census Bureau; Clerk of the U.S. House of Representatives; U.S. Department of Commerce (DOC), National Oceanic and Atmospheric Administration (NOAA), National Ocean Service (NOS), National Geodetic Survey (NGS)
Description: The Appalachian Regional Commission's (ARC) economic status designation for counties and census tracts in the Appalachian Region for fiscal year 2026 subset to the Thrive regionFor more information, please visit https://www.arc.gov/about-the-appalachian-region/county-economic-status-and-distressed-areas-by-state-fy-2025/ and Economic Condition Definitions
Description: The Appalachian Regional Commission's (ARC) economic status designation for counties and census tracts in the Appalachian Region for fiscal year 2026 subset to the Thrive regionFor more information, please visit https://www.arc.gov/about-the-appalachian-region/county-economic-status-and-distressed-areas-by-state-fy-2026/ and Economic Condition Definitions
Description: Thrive counties were extracted from the Tiger/Line shapefiles. The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation.
Description: Thrive Large Study Area Counties were extracted from the Tiger/Line shapefiles. The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. County transportation profile information comes from BTS and can be found here.
OFMTT25 (type: esriFieldTypeSingle, alias: Mean Freight Mobility Travel Time in minutes from this County (25th Percentile) , SQL Type: sqlTypeOther, nullable: true, editable: true)
OFMTT50 (type: esriFieldTypeSingle, alias: Mean Freight Mobility Travel Time in minutes from this County (50th Percentile) , SQL Type: sqlTypeOther, nullable: true, editable: true)
PFMTT75 (type: esriFieldTypeSingle, alias: Mean Freight Mobility Travel Time in minutes from this County (75th Percentile) , SQL Type: sqlTypeOther, nullable: true, editable: true)
DFMTT25 (type: esriFieldTypeSingle, alias: Mean Freight Mobility Travel Time in minutes To this County (25th Percentile) , SQL Type: sqlTypeOther, nullable: true, editable: true)
DFMTT50 (type: esriFieldTypeSingle, alias: Mean Freight Mobility Travel Time in minutes To this County (50th Percentile) , SQL Type: sqlTypeOther, nullable: true, editable: true)
DFMTT75 (type: esriFieldTypeSingle, alias: Mean Freight Mobility Travel Time in minutes To this County (75th Percentile) , SQL Type: sqlTypeOther, nullable: true, editable: true)