This is the CFS area file. It describes geographic movements of all commodities and includes breakdowns by mode, NAICS, commodity, shipping distance, shipment weight. It includes estimates for tonnage, value, average mile per shipment (Geomiler estimate (2012 and 2017) or GCD (2022))

Release Date: 2020-07-16.Release Schedule:.The data in this file was released in July 2020. The data in this file was modified January 28, 2021...Source:.Suggested Citation: U.S. Department of Transportation, Bureau of Transportation Statistics; and, U.S. Department of Commerce, U.S. Census Bureau. (2021-01-28). Geographic Area Series: Shipment Characteristics by Mode of Transportation - Truck: 2017 [dataset]. 2017 Commodity Flow Survey. Accessed [enter date you accessed/downloaded this table here] from https://data.census.gov/cedsci/table?q=cf1700a03&hidePreview=true&tid=CFSAREA2017.CF1700A03...Key Table Information:.The estimates presented are based on data from the 2017 Commodity Flow Surveys (CFS) and supersede data previously released in the 2017 CFS Preliminary Report. These estimates only cover businesses with paid employees. All dollar values are expressed in current dollars, i.e., they are based on price levels in effect at the time of each sample. Estimates may not be additive due to rounding...Due to definitional and processing changes made each survey year, any data comparisons between one CFS survey and another should be made with caution. See the Comparability of Estimates section of the Survey Methodology for more details...Table CF1700A03, new for the 2017 CFS, details the breakdown by truck mode of transportation. Please note that the estimates in this table may differ from CF1700A01. Table CF1700A03 displays estimates where truck was used for any part of the distance...Note: For this table only, the following exceptions apply:."All modes" refers to the total of all Truck shipments, single-mode and multi-mode.. "Truck" refers to single-mode truck shipments only.. "Multiple modes" refers to only those multi-mode shipments which include a truck segment.. "Air" refers to only those Air shipments which include truck to/from airport.. "Other multiple modes" refers to only those Other multiple mode shipments which include a truck segment....Data Items and Other Identifying Records:.This file contains data on:.Value ($ Millions). Tons (Thousands). Ton-miles (Millions). Average miles per shipment (Number). Coefficient of variation or standard error for all above data items...Geography Coverage:.The data are shown at the U.S. only. For information on Commodity Flow Survey geographies, including changes for 2017, see Census Geographies...Industry Coverage:.N/A..Footnotes:.N/A..FTP Download:.Download the entire table at: https://www2.census.gov/programs-surveys/cfs/data/2017/CF1700A03.zip...API Information:.Commodity Flow Survey data are housed in the Census Bureau API. For more information, see https://api.census.gov/data/2017/cfsarea.html...Methodology:.The noise infusion data protection method has been applied to prevent data disclosure, and to protect respondent's confidentiality. Estimates are based on a sample of establishments and are subject to both sampling and nonsampling error. Estimated measures of sampling variability are provided in the tables. For information on confidentiality protection, sampling error, and nonsampling error see Survey Methodology...Symbols:. S - Estimate does not meet publication standards because of high sampling variability, poor response quality, or other concerns about the estimate quality. Unpublished estimates derived from this table by subtraction are subject to these same limitations and should not be attributed to the U.S. Census Bureau. For a description of publication standards and the total quantity response rate, see link to program methodology page.. Z - Rounds to Zero.. X - Not Applicable..For a complete list of all economic programs symbols, see the Symbols Glossary...Contact Information:.U.S. Census Bureau.Commodity Flow Survey.Tel: (301) 763 - 2108.Email: erd.cfs@census.gov

Subarea: The U.S. Census Bureau, together with the Bureau of Transportation Statistics (BTS), has released an experimental data product providing additional geographic granularity to existing Commodity Flow Survey (CFS) estimates. These Subarea estimates divide the existing 132 CFS Areas into 329 Subareas such that each Subarea consists of at least one county and – in general – at least 10,000 CFS shipments. Data are available for origin by destination by commodity group at one mode of transportation – combined truck and ground parcel. 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.

This is the CFS area file. It describes geographic movements of hazardous commodities and includes breakdowns by mode, NAICS, commodity, shipping distance, shipment weight. It includes estimates for tonnage, value, average mile per shipment (Geomiler estimate (2012 and 2017) or GCD (2022))

The Freight Analysis Framework (FAF5) - Regions dataset was created from 2017 base year data and was published on April 11, 2022 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). The 2017 Commodity Flow Survey (CFS) contains 132 zones for U.S. domestic regions, which are directly carried over to the geography definitions for the FAF (Version 5) Regions. These geographic areas can be classified as one of the following three types: (1) Metropolitan Area (MA): The state part of a selected metropolitan statistical area (MSA) or combined statistical area (CSA). (2) The Remainder of State (ROS): The portion of a state containing the counties that are not included in the MA type CFS Areas defined above. (3) Whole State: An entire state where no MA type CFS Areas are defined within the state. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529028

Release Date: 2020-07-16.Release Schedule:.The data in this file was released in July 2020...Source:.Suggested Citation: U.S. Department of Transportation, Bureau of Transportation Statistics; and, U.S. Department of Commerce, U.S. Census Bureau. (2020-07-16). Geographic Area Series: Shipment Characteristics by Origin Geography by Shipment Weight: 2017 and 2012 [dataset]. 2017 Commodity Flow Survey. Accessed [enter date you accessed/downloaded this table here] from https://data.census.gov/cedsci/table?q=cf1700a07&hidePreview=true&tid=CFSAREA2017.CF1700A07...Key Table Information:.The estimates presented are based on data from the 2017 and 2012 Commodity Flow Surveys (CFS) and supersede data previously released in the 2017 CFS Preliminary Report. These estimates only cover businesses with paid employees. All dollar values are expressed in current dollars relative to each sample year (2017 and 2012), i.e., they are based on price levels in effect at the time of each sample. Estimates may not be additive due to rounding...Due to definitional and processing changes made each survey year, any data comparisons between one CFS survey and another should be made with caution. See the Comparability of Estimates section of the Survey Methodology for more details...Data Items and Other Identifying Records:.This file contains data on:.Value ($ Millions). Tons (Thousands). Ton-miles (Millions). Average miles per shipment (Number). Percent change from 2012 and coefficient of variation or standard error for all above data items...Geography Coverage:.The data are shown at the U.S., region, division, state, and CFS metropolitan area levels. For information on Commodity Flow Survey geographies, including changes for 2017, see Census Geographies...Industry Coverage:.N/A..Footnotes:.N/A..FTP Download:.Download the entire table at: https://www2.census.gov/programs-surveys/cfs/data/2017/CF1700A07.zip...API Information:.Commodity Flow Survey data are housed in the Census Bureau API. For more information, see https://api.census.gov/data/2017/cfsarea.html...Methodology:.The noise infusion data protection method has been applied to prevent data disclosure, and to protect respondent's confidentiality. Estimates are based on a sample of establishments and are subject to both sampling and nonsampling error. Estimated measures of sampling variability are provided in the tables. For information on confidentiality protection, sampling error, and nonsampling error see Survey Methodology...Symbols:. S - Estimate does not meet publication standards because of high sampling variability, poor response quality, or other concerns about the estimate quality. Unpublished estimates derived from this table by subtraction are subject to these same limitations and should not be attributed to the U.S. Census Bureau. For a description of publication standards and the total quantity response rate, see link to program methodology page.. Z - Rounds to Zero.. X - Not Applicable..For a complete list of all economic programs symbols, see the Symbols Glossary...Contact Information:.U.S. Census Bureau.Commodity Flow Survey.Tel: (301) 763 - 2108.Email: erd.cfs@census.gov

The USGS Protected Areas Database of the United States (PAD-US) is the nation's inventory of protected areas, including public land and voluntarily provided private protected areas, identified as an A-16 National Geospatial Data Asset in the Cadastre Theme ( https://communities.geoplatform.gov/ngda-cadastre/ ). The PAD-US is an ongoing project with several published versions of a spatial database including areas dedicated to the preservation of biological diversity, and other natural (including extraction), recreational, or cultural uses, managed for these purposes through legal or other effective means. The database was originally designed to support biodiversity assessments; however, its scope expanded in recent years to include all open space public and nonprofit lands and waters. Most are public lands owned in fee (the owner of the property has full and irrevocable ownership of the land); however, permanent and long-term easements, leases, agreements, Congressional (e.g. 'Wilderness Area'), Executive (e.g. 'National Monument'), and administrative designations (e.g. 'Area of Critical Environmental Concern') documented in agency management plans are also included. The PAD-US strives to be a complete inventory of U.S. public land and other protected areas, compiling “best available” data provided by managing agencies and organizations. The PAD-US geodatabase maps and describes areas using thirty-six attributes and five separate feature classes representing the U.S. protected areas network: Fee (ownership parcels), Designation, Easement, Marine, Proclamation and Other Planning Boundaries. An additional Combined feature class includes the full PAD-US inventory to support data management, queries, web mapping services, and analyses. The Feature Class (FeatClass) field in the Combined layer allows users to extract data types as needed. A Federal Data Reference file geodatabase lookup table (PADUS3_0Combined_Federal_Data_References) facilitates the extraction of authoritative federal data provided or recommended by managing agencies from the Combined PAD-US inventory. This PAD-US Version 3.0 dataset includes a variety of updates from the previous Version 2.1 dataset (USGS, 2020, https://doi.org/10.5066/P92QM3NT ), achieving goals to: 1) Annually update and improve spatial data representing the federal estate for PAD-US applications; 2) Update state and local lands data as state data-steward and PAD-US Team resources allow; and 3) Automate data translation efforts to increase PAD-US update efficiency. The following list summarizes the integration of "best available" spatial data to ensure public lands and other protected areas from all jurisdictions are represented in the PAD-US (other data were transferred from PAD-US 2.1). Federal updates - The USGS remains committed to updating federal fee owned lands data and major designation changes in annual PAD-US updates, where authoritative data provided directly by managing agencies are available or alternative data sources are recommended. The following is a list of updates or revisions associated with the federal estate: 1) Major update of the Federal estate (fee ownership parcels, easement interest, and management designations where available), including authoritative data from 8 agencies: Bureau of Land Management (BLM), U.S. Census Bureau (Census Bureau), Department of Defense (DOD), U.S. Fish and Wildlife Service (FWS), National Park Service (NPS), Natural Resources Conservation Service (NRCS), U.S. Forest Service (USFS), and National Oceanic and Atmospheric Administration (NOAA). The federal theme in PAD-US is developed in close collaboration with the Federal Geographic Data Committee (FGDC) Federal Lands Working Group (FLWG, https://communities.geoplatform.gov/ngda-govunits/federal-lands-workgroup/ ). 2) Improved the representation (boundaries and attributes) of the National Park Service, U.S. Forest Service, Bureau of Land Management, and U.S. Fish and Wildlife Service lands, in collaboration with agency data-stewards, in response to feedback from the PAD-US Team and stakeholders. 3) Added a Federal Data Reference file geodatabase lookup table (PADUS3_0Combined_Federal_Data_References) to the PAD-US 3.0 geodatabase to facilitate the extraction (by Data Provider, Dataset Name, and/or Aggregator Source) of authoritative data provided directly (or recommended) by federal managing agencies from the full PAD-US inventory. A summary of the number of records (Frequency) and calculated GIS Acres (vs Documented Acres) associated with features provided by each Aggregator Source is included; however, the number of records may vary from source data as the "State Name" standard is applied to national files. The Feature Class (FeatClass) field in the table and geodatabase describe the data type to highlight overlapping features in the full inventory (e.g. Designation features often overlap Fee features) and to assist users in building queries for applications as needed. 4) Scripted the translation of the Department of Defense, Census Bureau, and Natural Resource Conservation Service source data into the PAD-US format to increase update efficiency. 5) Revised conservation measures (GAP Status Code, IUCN Category) to more accurately represent protected and conserved areas. For example, Fish and Wildlife Service (FWS) Waterfowl Production Area Wetland Easements changed from GAP Status Code 2 to 4 as spatial data currently represents the complete parcel (about 10.54 million acres primarily in North Dakota and South Dakota). Only aliquot parts of these parcels are documented under wetland easement (1.64 million acres). These acreages are provided by the U.S. Fish and Wildlife Service and are referenced in the PAD-US geodatabase Easement feature class 'Comments' field. State updates - The USGS is committed to building capacity in the state data-steward network and the PAD-US Team to increase the frequency of state land updates, as resources allow. The USGS supported efforts to significantly increase state inventory completeness with the integration of local parks data in the PAD-US 2.1, and developed a state-to-PAD-US data translation script during PAD-US 3.0 development to pilot in future updates. Additional efforts are in progress to support the technical and organizational strategies needed to increase the frequency of state updates. The PAD-US 3.0 included major updates to the following three states: 1) California - added or updated state, regional, local, and nonprofit lands data from the California Protected Areas Database (CPAD), managed by GreenInfo Network, and integrated conservation and recreation measure changes following review coordinated by the data-steward with state managing agencies. Developed a data translation Python script (see Process Step 2 Source Data Documentation) in collaboration with the data-steward to increase the accuracy and efficiency of future PAD-US updates from CPAD. 2) Virginia - added or updated state, local, and nonprofit protected areas data (and removed legacy data) from the Virginia Conservation Lands Database, provided by the Virginia Department of Conservation and Recreation's Natural Heritage Program, and integrated conservation and recreation measure changes following review by the data-steward. 3) West Virginia - added or updated state, local, and nonprofit protected areas data provided by the West Virginia University, GIS Technical Center. For more information regarding the PAD-US dataset please visit, https://www.usgs.gov/gapanalysis/PAD-US/. For more information about data aggregation please review the PAD-US Data Manual available at https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/pad-us-data-manual . A version history of PAD-US updates is summarized below (See https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/pad-us-data-history for more information): 1) First posted - April 2009 (Version 1.0 - available from the PAD-US: Team pad-us@usgs.gov). 2) Revised - May 2010 (Version 1.1 - available from the PAD-US: Team pad-us@usgs.gov). 3) Revised - April 2011 (Version 1.2 - available from the PAD-US: Team pad-us@usgs.gov). 4) Revised - November 2012 (Version 1.3) https://doi.org/10.5066/F79Z92XD 5) Revised - May 2016 (Version 1.4) https://doi.org/10.5066/F7G73BSZ 6) Revised - September 2018 (Version 2.0) https://doi.org/10.5066/P955KPLE 7) Revised - September 2020 (Version 2.1) https://doi.org/10.5066/P92QM3NT 8) Revised - January 2022 (Version 3.0) https://doi.org/10.5066/P9Q9LQ4B Comparing protected area trends between PAD-US versions is not recommended without consultation with USGS as many changes reflect improvements to agency and organization GIS systems, or conservation and recreation measure classification, rather than actual changes in protected area acquisition on the ground.

Release Date: 2020-07-16.Release Schedule:.The data in this file was released in July 2020...Source:.Suggested Citation: U.S. Department of Transportation, Bureau of Transportation Statistics; and, U.S. Department of Commerce, U.S. Census Bureau. (2020-07-16). Geographic Area Series: Shipment Characteristics by Origin Geography by Commodity by Shipment Weight: 2017 [dataset]. 2017 Commodity Flow Survey. Accessed [enter date you accessed/downloaded this table here] from https://data.census.gov/cedsci/table?q=cf1700a11&hidePreview=true&tid=CFSAREA2017.CF1700A11...Key Table Information:.The estimates presented are based on data from the 2017 Commodity Flow Survey (CFS) and supersede data previously released in the 2017 CFS Preliminary Report. These estimates only cover businesses with paid employees. All dollar values are expressed in current dollars, i.e., they are based on price levels in effect at the time of the sample. Estimates may not be additive due to rounding...Due to definitional and processing changes made each survey year, any data comparisons between one CFS survey and another should be made with caution. See the Comparability of Estimates section of the Survey Methodology for more details...Data Items and Other Identifying Records:.This file contains data on:.Value ($ Millions). Tons (Thousands). Ton-miles (Millions). Average miles per shipment (Number). Coefficient of variation or standard error for all above data items...Geography Coverage:.The data are shown at the U.S., region, division, state, and CFS metropolitan area levels. For information on Commodity Flow Survey geographies, including changes for 2017, see Census Geographies...Industry Coverage:.N/A..Footnotes:.N/A..FTP Download:.Download the entire table at: https://www2.census.gov/programs-surveys/cfs/data/2017/CF1700A11.zip...API Information:.Commodity Flow Survey data are housed in the Census Bureau API. For more information, see https://api.census.gov/data/2017/cfsarea.html...Methodology:.The noise infusion data protection method has been applied to prevent data disclosure, and to protect respondent's confidentiality. Estimates are based on a sample of establishments and are subject to both sampling and nonsampling error. Estimated measures of sampling variability are provided in the tables. For information on confidentiality protection, sampling error, and nonsampling error see Survey Methodology...Symbols:. S - Estimate does not meet publication standards because of high sampling variability, poor response quality, or other concerns about the estimate quality. Unpublished estimates derived from this table by subtraction are subject to these same limitations and should not be attributed to the U.S. Census Bureau. For a description of publication standards and the total quantity response rate, see link to program methodology page.. Z - Rounds to Zero.. X - Not Applicable..For a complete list of all economic programs symbols, see the Symbols Glossary...Contact Information:.U.S. Census Bureau.Commodity Flow Survey.Tel: (301) 763 - 2108.Email: erd.cfs@census.gov

The Commodity Flow Survey (CFS) is undertaken through a partnership between the U.S. Census Bureau, U.S. Department of Commerce, and the Research and Innovation Technology Administration, Bureau of Transportation Statistics (BTS), U.S. Department of Transportation. This survey produces data on the movement of goods in the United States. It provides information on commodities shipped, their value, weight, and mode of transportation, as well as the origin and destination of shipments of manufacturing, mining, wholesale, and select retail and services establishments. The data from the CFS are used by public policy analysts and for transportation planning and decision making to access the demand for transportation facilities and services, energy use, and safety risk and environmental concerns. This dataset provides data for the Geographic Area Series.

This dataset consists of polygon features representing forest lands defined as Community Forests (Frey et al 2024). The polygons were sourced from several areas, including the USGS Protected Areas Database of the United States (PAD-US) and from the individual governing organization of some of the Community Forests (CFs, hereafter). In some cases, the boundaries were heads-up digitized using state tax lot data and maps of the Community Forest. There is not a formal definition of CFs, rather they are loosely defined as forest lands that are under the local control of the adjacent communities. They may be governed by the local government (city, county), non-government organizations. There are an estimated 136 CFs in the US (Hajjar et al 2024). This data set is a subset of 18 of those CFs which are the subject of on-going research by USDA Forest Service, Oregon State University, and North Carolina State University scientists. Frey, Gregory E.; Hajjar, Reem; Charnley, Susan; McGinley, Kathleen; Schelhas, John; Tarr, Nathan A.; McCaskill, Lauren; Cubbage, Frederick W. 2024. “Community Forests” in the United States – How Do we Know One When we See One?. Society & Natural Resources. 37(8): 1240-1252. https://doi.org/10.1080/08941920.2024.2361413.Reem Hajjar, Kathleen McGinley, Susan Charnley, Gregory E Frey, Meredith Hovis, Frederick W Cubbage, John Schelhas, Kailey Kornhauser, Characterizing Community Forests in the United States, Journal of Forestry, Volume 122, Issue 3, May 2024, Pages 273–284, https://doi.org/10.1093/jofore/fvad054

The Tidal Restriction network represents network and connectivity impacts of tidal restrictions in the Puget Sound Large River Deltas created using regional data, remotely sensed aerial imagery, and oblique shoreline imagery depicted as polylines. The CFS Tidal Restriction network is represented by polylines digitized at a 1:1000 scale using regional data, remote sensed aerial imagery (Hexagon Imagery Program 0.3 m 4-band aerial imagery collected during summer leaf on conditions from August to September in 2017), and oblique shoreline imagery (DOE). Features were classified in a two-tiered nested structure that included a primary structure type and water crossing structure type (where applicable). Where features were not present in the regional data, aerial imagery was used to determine the feature type for primary and water crossing structures, or the feature was called Unknown. This database also contains details on feature type (e.g., road, dike/levee, culvert, bridge), status (e.g., present, removed, abandoned, breached), feature existence certainty, tidal connectivity impacts and certainty, fish passage and fishways, physical feature attributes (e.g., lengths, widths, elevations), feature installation and modifications, and review status, as well as whether the feature was copied from a regional data layer or identified using remotely sensed aerial imagery. These classifications were assigned using the regional data and aerial imagery and rely on CFS staff interpretation and pre-determined classification rules for connectivity and feature type assignments where regional data were lacking.The Tidal Wetland network represents the current and potential tidal wetland habitat network as polygons and their tidal connectivity determined using the Tidal Restriction network, remotely sensed aerial imagery, SHSTMP tidal wetland habitat extent and feature polygons (NOAA 2011; 2016), and Pacific Marine and Estuarine Fish Habitat Partnership (PMEP) tidal exceedance polygons (PMEP 2018). The maps of current and potential tidal wetland habitat were updated using the spatial database of the tidal restriction network developed in this project, SHSTMP tidal wetland habitat extent, feature polygons, and overwater structures (NOAA 2011; 2016; 2019), and Pacific Marine and Estuarine Fish Habitat Partnership (PMEP) tidal exceedance polygons (PMEP 2018). Tidal restrictions were buffered and used to segment PMEP wetland polygons into individual polygons, to associate tidal restriction features, feature types, and tidal connectivity ratings to polygons of tidal wetland habitat. Tidal wetland connectivity ratings were determined using the Tidal Restriction network and the SHSTMP tidal wetland habitat features to assess pathways of tidal movement and were assigned as Completely Restricted, Significantly Restricted, Partially Restricted, Unrestricted, or Unknown. Tidal wetlands were classified by both their tidal feature connectivity and landscape connectivity with certainty ratings. The feature tidal connectivity classified the tidal connectivity of the feature based on the tidal connectivity to that feature given the immediately downstream tidal restriction features. Feature tidal connectivity was determined by the downstream feature allowing tidal connectivity regardless of upstream or landward tidal restrictions connectivity restrictions. The landscape tidal connectivity classified the tidal connectivity of the wetland feature based on its connectivity to the delta as a whole. The landscape tidal connectivity of a feature was determined by the connectivity rating of the landscape wetlands and the downstream tidal restriction features.CFS digitized polygons representing built land, including residential and commercial land, and filled areas, as observed in the Hexagon Imagery Program 0.3 m 4-band aerial imagery. Developed polygons impact tidal connectivity as they contain dense networks of roads and buildings that would act as obstructions, either partial or significant to hydrological, biological, and physical tidal movement. These polygons are preliminary and may over or under-estimate developed areas in a given delta. These were solely used to isolate the impacts of developed areas on the Tidal Wetland network.

The Container Freight Station Market Report is Segmented by Service (Consolidation Services and Deconsolidation Services) and Geography (North America, Europe, Asia-Pacific, Middle East and Africa, and Latin America). The Report Offers Market Sizes, Forecasts, and Volumes in Value (USD) for all the Above Segments.

The Container Freight Station (CFS) market is experiencing robust growth, fueled by the expansion of global trade and e-commerce. The market, estimated at $XX million in 2025, is projected to maintain a Compound Annual Growth Rate (CAGR) exceeding 5% through 2033. This growth is driven by several key factors. Increased reliance on containerized shipping for efficient logistics, particularly in the Asia-Pacific region, significantly boosts demand for CFS services. The rise of e-commerce necessitates efficient handling of smaller shipments, a strength of CFS operations. Furthermore, the ongoing trend of supply chain diversification and the need for improved inventory management are increasing the utilization of CFS facilities for consolidation and deconsolidation services. Value-added services offered by CFS providers, such as warehousing, packaging, and labeling, are also enhancing market attractiveness. While challenges like port congestion and fluctuating fuel prices pose restraints, the overall market outlook remains positive. The competitive landscape is highly fragmented, with numerous global and regional players vying for market share. Major players like Omni Logistics, MSA Global Group, MSC, Maersk, CMA CGM, and DHL offer a wide spectrum of CFS services, catering to diverse customer needs across different regions. The North American and Asia-Pacific regions are currently the largest markets, driven by high import/export volumes and robust infrastructure. However, developing economies in regions like Latin America and Africa present significant growth opportunities. The continued expansion of e-commerce, coupled with advancements in technology like improved tracking and automation within CFS operations, will further propel market expansion in the coming years. The market segmentation by service type (consolidation, deconsolidation, value-added) allows for a tailored approach to customer needs, further enhancing market growth potential. Recent developments include: April 2024: Major global container lines like MSC, Maersk, and CMA CGM, alongside terminal giants PSA International and DP World, competed for a tender issued by the state-run Container Corporation of India Ltd (CONCOR). The tender aims to establish three container freight stations in collaboration with private firms. These stations will be located at CONCOR's dedicated freight corridor-linked multimodal logistics park in Kathuwas, Rajasthan.March 2024: DHL Express, the global leader in international express services, inaugurated its inaugural automatic shipment sorting hub in New Delhi. Spanning 34,256 sq. ft, this hub is equipped with cutting-edge automatic sorting machines capable of processing 2,000 pieces per hour, marking a significant 30% boost in productivity.. Key drivers for this market are: Increasing Trade Growth, Increasing E-commerce Growth. Potential restraints include: Increasing Trade Growth, Increasing E-commerce Growth. Notable trends are: Asia-Pacific is Expected to Dominate the Market in the Coming Years.

Release Date: 2020-07-16.Release Schedule:.The data in this file was released in July 2020...Source:.Suggested Citation: U.S. Department of Transportation, Bureau of Transportation Statistics; and, U.S. Department of Commerce, U.S. Census Bureau. (2020-07-16). Geographic Area Series: Shipment Characteristics by Origin Geography by Destination Geography: 2017 [dataset]. 2017 Commodity Flow Survey. Accessed [enter date you accessed/downloaded this table here] from https://data.census.gov/cedsci/table?q=cf1700a20&hidePreview=true&tid=CFSAREA2017.CF1700A20...Key Table Information:.The estimates presented are based on data from the 2017 Commodity Flow Survey (CFS) and supersede data previously released in the 2017 CFS Preliminary Report. These estimates only cover businesses with paid employees. All dollar values are expressed in current dollars, i.e., they are based on price levels in effect at the time of each sample. Estimates may not be additive due to rounding...Due to definitional and processing changes made each survey year, any data comparisons between one CFS survey and another should be made with caution. See the Comparability of Estimates section of the Survey Methodology for more details...Data Items and Other Identifying Records:.This file contains data on:.Value ($ Millions). Tons (Thousands). Ton-miles (Millions). Average miles per shipment (Number). Coefficient of variation or standard error for all above data items...Geography Coverage:.The data are shown at the U.S., region, division, state, and CFS metropolitan area levels. For information on Commodity Flow Survey geographies, including changes for 2017, see Census Geographies...Industry Coverage:.N/A..Footnotes:.N/A..FTP Download:.Download the entire table at: https://www2.census.gov/programs-surveys/cfs/data/2017/CF1700A20.zip...API Information:.Commodity Flow Survey data are housed in the Census Bureau API. For more information, see https://api.census.gov/data/2017/cfsarea.html...Methodology:.The noise infusion data protection method has been applied to prevent data disclosure, and to protect respondent's confidentiality. Estimates are based on a sample of establishments and are subject to both sampling and nonsampling error. Estimated measures of sampling variability are provided in the tables. For information on confidentiality protection, sampling error, and nonsampling error see Survey Methodology...Symbols:. S - Estimate does not meet publication standards because of high sampling variability, poor response quality, or other concerns about the estimate quality. Unpublished estimates derived from this table by subtraction are subject to these same limitations and should not be attributed to the U.S. Census Bureau. For a description of publication standards and the total quantity response rate, see link to program methodology page.. Z - Rounds to Zero.. X - Not Applicable..For a complete list of all economic programs symbols, see the Symbols Glossary...Contact Information:.U.S. Census Bureau.Commodity Flow Survey.Tel: (301) 763 - 2108.Email: erd.cfs@census.gov

Calls For Service are the events captured in an agency’s Computer-Aided Dispatch (CAD) system used to facilitate incident response.

This dataset includes both proactive and reactive police incident data.

The source of this data is the City of Cincinnati's computer-aided dispatch (CAD) database.

This data is updated daily.

DISCLAIMER: In compliance with privacy laws, all Public Safety datasets are anonymized and appropriately redacted prior to publication on the City of Cincinnati’s Open Data Portal. This means that for all public safety datasets: (1) the last two digits of all addresses have been replaced with “XX,” and in cases where there is a single digit street address, the entire address number is replaced with "X"; and (2) Latitude and Longitude have been randomly skewed to represent values within the same block area (but not the exact location) of the incident.

Downloadable Tables by Origin or Destination. Filterable by Year, Commodity and Mode.

Number of CFS Volunteers in each Region.

An aggregation of historical CFS data at a state level. This dataset comprises all historical CFS data from 1997-2017.Data is presented at its most granular level. The sum of sublevels may not equal the totals due to individual data items being suppressed due to preserve confidentiality

The _domain of the model is as follows: Row River from Dorena dam to the confluence with the Coast Fork; Coast Fork from Cottage Grove dam to the confluence with the Middle Fork; Silk Creek from River Mile 1.7 to the confluence with the Coast Fork. The basis for these features is the Willamette Flood Insurance Study – Phase One (2013). The hydraulics and hydrology for the FIS were reused in the production of these polygons; the reports and information associated with the FIS are applicable to this product. The Digital Elevation Model (DEM) utilized for the Willamette FIS submittal was produced by combining multiple overlapping topographic surveys for the Middle Fork and Coast Fork of the Willamette River. This DEM was created from four sources: LiDAR of the Springfield area that was flown in 2008, LiDAR of Silk Creek that was flown in 2011, LiDAR of Fall Creek that was flown in 2012, and photogrammetry of the Middle Fork and Coast Fork of the Willamette River that was flown in 2004. In areas where no high-resolution elevation data were available, USGS National Elevation Dataset (NED) data were used to supplement the DEM. The shapefiles Hi_Res_Extents.shp and Low_Res_Extents.shp define the limits of these areas. The horizontal datum of the DEM is NAD 1983 State Plane-Oregon South HARN with units of International Feet (NAD83). The vertical datum of the elevation model is NAVD 1988 with units of international feet (NAVD-88). In addition, some areas show surveyed bathymetry within the channel. These can be noted by the sharp increase in apparent depth, creating a stripe across the depth grid when compared to the LiDAR data, which represents the water surface elevation at the time of the aerial data collection. Bridge decks are generally removed from DEMs as standard practice. Therefore, these features may be shown as inundated when they are not. An effort to clip flood extents on bridge decks was made, but judgement should be used when estimating the usefulness of a bridge during flood flow. Comparing the bridge to the surrounding ground can be more informative in this respect than simply looking at the bridge itself. The features and depth grids stop as the Coast Fork approaches the Middle Fork on the northern end of the reach. See cfwgoshOR_breach.shp for information regarding this file. This represents the depth grid for the 12,000 cfs profile.

A Description of data fields, data definitions, and some context on changes between the different Commodity Flow Surveys