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A crosswalk table between NHDPlus version 2.1 flowlines (using the unique field COMID) and the Watershed Boundary Dataset (WBD) 12-digit-hydrologic units (HU-12) is provided for the 48 contiguous United States. The crosswalk table provides a WBD HU-12 assignment for every networked flowline in the NHDPlus. In this way, the network developed for navigation and modeling, NHDPlus, is aligned with accounting units of the WBD HU-12s to the extent possible given the assumptions that were made in creating each. A crosswalk table for NHDPlus isolated sinks was produced by a simple overlay process where the sinks were assigned HU-12 values based on their position relative to the WBD snapshot HU-12s. This table was integrated with the flowline associations into one crosswalk table for both feature types. There is good alignment between aggregated NHDPlus catchments and WBD HU-12 units in many locations. These are areas where the flows and chemical or nutrient loads that are accumulated leav ...
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## Overview
Comic Panels is a dataset for object detection tasks - it contains Panels annotations for 356 images.
## Getting Started
You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.
## License
This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).
The comid field of these data can be used to join to the National Hydrography Dataset Plus V2.1 (NHDPlusV2) flowline comid or catchment featureid attributes. The included attributes follow the same data model as the NHDPlusV2 but include numerous updates and improvements to network connectivity. All attributes that depend on network connectivity have been recalculated. These attributes are based on the NHDPlusV2 network geometry and modifications retrieved from the National Water Model V2.1 (NWMv2.1) and "E2NHDPlusV2_us: Database of Ancillary Hydrologic Attributes and Modified Routing for NHDPlus Version 2.1 Flowlines" (E2NHDPlusV2) datasets. The attributes included are: comid, tocomid, fcode, nameID, lengthkm, reachcode, frommeas, tomeas, arbolate_sum, terminalpa ,hydroseq, levelpathi, pathlength, dnhydroseq, areasqkm, totdasqkm, terminalfl, dnlevelpat. These attributes are available in three formats: csv, fst, and parquet. "fst" is a high-performance format for use with the R programming language "fst" package. "parquet" is a high-performance format for use with multiple programming languages (including python) that support the Apache Arrow Parquet format.
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This tabular data set represents the presence of six National Hydrography Dataset (NHD) high resolution waterbody types compiled for two spatial components of the NHDPlus version 2 data suite (NHDPlusv2) for the conterminous United States; 1) individual reach catchments and 2) reach catchments accumulated upstream through the river network. The six types of waterbodies presented here are: playa, ice mass, lake/pond, reservoir, swamp/marsh, and estuary. This dataset can be linked to the NHDPlus version 2 data suite by the unique identifier COMID. The source data is the NHDPlus high resolution waterbodies produced by USGS , 2015. Units are percent. Reach catchment information characterizes data at the local scale. Reach catchments accumulated upstream through the river network characterizes cumulative upstream conditions. Network-accumulated values are computed using two methods, 1) divergence-routed and 2) total cumulative drainage area. Both approaches use a modified routing data ...
These attributes are based on the NHDPlusV2 network geometry and modifications retrieved from the National Water Model V2.1 (NWMv2.1) and "E2NHDPlusV2_us: Database of Ancillary Hydrologic Attributes and Modified Routing for NHDPlus Version 2.1 Flowlines" (E2NHDPlusV2) datasets. The attributes included are: comid, tocomid, ftype, fcode, gnis_name, gnis_id, fromnode, tonode, divergence, streamleve, streamorde, streamcalc, reachcode, frommeas, tomeas, lengthkm, arbolatesu, pathlength, areasqkm, totdasqkm, hwnodesqkm, hydroseq, dnhydroseq, levelpathi, terminalpa, dnlevelpat, terminalfl, terminalfl, wbareatype, wbareacomi, slope, slopelenkm, roughness, vpuin, vpuout, rpuid, vpuid. These attributes are available in four formats: csv, fst, parquet, and geopackage. "fst" is a high-performance format for use with the R programming language "fst" package. "parquet" is a high-performance format for use with multiple programming languages (including python) that support the Apache Arrow Parquet format. Geopackage is an open standard geodatabase format and includes both the attributes and geometry included in this data release. The comid field of these data can be used to join to the National Hydrography Dataset Plus V2.1 (NHDPlusV2) flowline comid or catchment featureid attributes. The included attributes follow the same data model as the NHDPlusV2 but include numerous updates and improvements to network connectivity. All attributes that depend on network connectivity have been recalculated. Version 3.0 includes updated flowline geometry in a geopackage geodatabase file with node geometry digitized in agreement with the network topology and all derived attributes compatible with non-dendritic network assumptions. Version 3.0 also now includes specific records of edits applied to the NHDPlusV2 from the NWMV2.1 and E2NHDPlusV2 in seperate geopackage geodatabases. See the entity and attributes section of this metadata record for more information.
This metadata record describes a series of data sets of climate and land-use/land-cover features (provided in parquet file format) linked to NHDPlus Version 2.1’s (NHDPlusV2) stream segments, their associated catchments and their upstream watersheds within selected regions in the conterminous United States. All data can be linked to NHDPlusV2 using the COMID field in these tables and the ComID in the flowline shapefiles or FEATUREID in the catchment shapefiles in the NHDPlusV2 data suite.
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This metadata record describes a series of data sets of natural and anthropogenic landscape features linked to NHDPlus Version 2.1’s (NHDPlusV2) approximately 2.7 million stream segments, their associated catchments, and their upstream watersheds within the conterminous United States. The data were linked to four spatial components of NHDPlusV2: individual reach catchments, riparian buffer zones around individual reaches, reach catchments accumulated downstream through the river network, and riparian buffer zones accumulated downstream through the river network. All data can be linked to NHDPlus using the COMID field in these tables and the ComID in the flowline shapefiles or FEATUREID in the catchment ones in the NHDPlus data suite. The datasets were derived using a topologically reconditioned version of the NHDPlusv2 routing network (Schwarz and Wieczorek, 2018). This database is used for the routing of upstream watersheds only. No cartographic changes were made to the original NHDPlusv2 in either the flowline or reach catchment line work. These data are listed under 13 themes which include: 1) Best Management Practices, characteristics such as agricultural management practices and land in conservation practices. 2) Chemical, characteristics such as nitrogen application or toxicity weighted use. 3) Climate and Water Balance Model, characteristics such as model outputs of runoff, actual evapotranspiration or ground water storage. 4) Climate, characteristics such as mean precipitation, temperature, relative humidity, or evapotranspiration. 5) Geology, characteristics such as Hunt or Soller surficial geologies. 6) Hydrologic, characteristics such as base flow or infiltration excess overland flow.Hydrologic Modifications, characteristics such as dam storage or tile drains. 7) Hydrologic Modifications, characteristics such as dam storage or tile drains. 8) Landscape, characteristics such as NLCD, CDL or NWALT. 9) Population Infrastructure, characteristics such as population, housing, and road densities. 10) Regions, characteristics such as EcoRegions, Physiography or Hydrologic Landscapes. 11) Soils, characteristics such as STATSGO, soil salinity, and soil restrictive layer. 12) Topographic Characteristics, characteristics such as basin area, slope and elevation. 13) Water use, characteristics such as estimated freshwater withdrawls and estimated freshwater consumption by thermo-electric power plants These data allow researchers and managers to acquire landscape information for both catchments (for example, the nearby landscape flowing directly into streams) and full upstream watersheds of specific stream reaches anywhere in the within the conterminous United States without having to perform specialized geospatial processing. Aside from comma separated text files, parquet files with the same file structure were also added to each data file under each child item theme. This format will allow researchers to acquire all the information from this data release in an efficient and consistent manner by utilizing and thereby adhering to the FAIR guidelines outlined in Lightsom and others (USGS, 2022).
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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## Overview
Dataset Comida Chilena is a dataset for object detection tasks - it contains Ensalada A La Chilena annotations for 2,015 images.
## Getting Started
You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.
## License
This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Corresponding peer-reviewed publication
This dataset corresponds to all the RAPID input and output files that were used in the study reported in:
David, Cédric H., David R. Maidment, Guo-Yue Niu, Zong-Liang Yang, Florence Habets and Victor Eijkhout (2011), River Network Routing on the NHDPlus Dataset, Journal of Hydrometeorology, 12(5), 913-934. DOI: 10.1175/2011JHM1345.1.
When making use of any of the files in this dataset, please cite both the aforementioned article and the dataset herein.
Time format
The times reported in this description all follow the ISO 8601 format. For example 2000-01-01T16:00-06:00 represents 4:00 PM (16:00) on Jan 1st 2000 (2000-01-01), Central Standard Time (-06:00). Additionally, when time ranges with inner time steps are reported, the first time corresponds to the beginning of the first time step, and the second time corresponds to the end of the last time step. For example, the 3-hourly time range from 2000-01-01T03:00+00:00 to 2000-01-01T09:00+00:00 contains two 3-hourly time steps. The first one starts at 3:00 AM and finishes at 6:00AM on Jan 1st 2000, Universal Time; the second one starts at 6:00 AM and finishes at 9:00AM on Jan 1st 2000, Universal Time.
Data sources
The following sources were used to produce files in this dataset:
The National Hydrography Dataset Plus (NHDPlus) Version 1, obtained from http://www.horizon-systems.com/nhdplus.
The National Water Information System (NWIS), obtained from http://waterdata.usgs.gov/nwis.
Outputs from a simulation using the community Noah land surface model with multiparameterization options (Noah-MP, Niu et al. 2011, http://www.jsg.utexas.edu/noah-mp). The simulation was run by Guo-Yue Niu, and produced 3-hourly time steps from 2004-01-01T00:00+00:00 to 2008-01-01T00:00+00:00. Further details on the inputs and options used for this simulation are provided in David et al. (2011).
Software
The following software were used to produce files in this dataset:
The Routing Application for Parallel computation of Discharge (RAPID, David et al. 2011, http://rapid-hub.org), Version 1.0.0. Further details on the inputs and options used for this series of simulations are provided below and in David et al. (2011).
ESRI ArcGIS (http://www.arcgis.com).
Microsoft Excel (https://products.office.com/en-us/excel).
CUAHSI HydroGET (http://his.cuahsi.org/hydroget.html).
The GNU Compiler Collection (https://gcc.gnu.org) and the Intel compilers (https://software.intel.com/en-us/intel-compilers).
Study domain
The files in this dataset correspond to two study domains:
The combination of the San Antonio and Guadalupe River Basins, TX. RAPID can only use the river reaches of NHDPlus that have a known flow direction and focus is made on these reaches here (a total of 5,175). The temporal range corresponding to this domain is from 2004-01-01T00:00-06:00 to 2007-12-31 T00:00-06:00.
The Upper Mississippi River Basin. RAPID can only use the river reaches of NHDPlus that have a known flow direction and focus is made on these reaches here (a total of 182,240). The temporal range corresponding to this domain spans 100 fictitious days.
Description of files for the San Antonio and Guadalupe River Basins
All files below were prepared by Cédric H. David, using the data sources and software mentioned above.
rapid_connect_San_Guad.csv. This CSV file contains the river network connectivity information and is based on the unique IDs of NHDPlus reaches (the COMIDs). For each river reach, this file specifies: the COMID of the reach, the COMID of the unique downstream reach, the number of upstream reaches with a maximum of four reaches, and the COMIDs of all upstream reaches. A value of zero is used in place of NoData. The river reaches are sorted in increasing value of COMID. The values were computed using a combination of the following NHDPlus fields: COMID, DIVERGENCE, FROMNODE and TONODE. This file was prepared using ArcGIS and Excel.
m3_riv_San_Guad_2004_2007_cst.nc. This netCDF file contains the 3-hourly accumulated inflows of water (in cubic meters) from surface and subsurface runoff into the upstream point of each river reach. The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv. The time range for this file is from 2004-01-01T00:00-06:00 to 2007/12/31T18:00-06:00. The values were computed by superimposing a 900-m gridded map of NHDPlus catchments to the outputs of Noah-MP. This file was prepared using ArcGIS and a Fortran program.
kfac_San_Guad_1km_hour.csv. This CSV file contains a first guess of Muskingum k values (in seconds) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv. The values were computed based on the following NHDPlus fields: COMID, LENGTHKM, Equation (13) in David et al. (2011), and using a wave celerity of 1 km/h. This file was prepared using a Fortran program.
kfac_San_Guad_celerity.csv. This CSV file contains a first guess of Muskingum k values (in seconds) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv. The values were computed based on the following NHDPlus fields: COMID, LENGTHKM, Equation (13) in David et al. (2011), and using the wave celerity numbers of Table 2 in David et al. (2011). This file was prepared using a Fortran program.
k_San_Guad_2004_1.csv. This CSV file contains Muskingum k values (in seconds) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv. The values were computed based on the following NHDPlus fields: COMID, LENGTHKM, and using Equation (17) in David et al. (2011). This file was prepared using a Fortran program.
k_San_Guad_2004_2.csv. This CSV file contains Muskingum k values (in seconds) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv. The values were computed based on the following NHDPlus fields: COMID, LENGTHKM, and using Equation (18) in David et al. (2011). This file was prepared using a Fortran program.
k_San_Guad_2004_3.csv. This CSV file contains Muskingum k values (in seconds) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv. The values were computed based on the following NHDPlus fields: COMID, LENGTHKM, and using Equation (19) in David et al. (2011). This file was prepared using a Fortran program.
k_San_Guad_2004_4.csv. This CSV file contains Muskingum k values (in seconds) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv. The values were computed based on the following NHDPlus fields: COMID, LENGTHKM, and using Equation (21) in David et al. (2011). This file was prepared using a Fortran program.
x_San_Guad_2004_1.csv. This CSV file contains Muskingum x values (dimensionless) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv. The values were computed based on Equation (17) in David et al. (2011). This file was prepared using a Fortran program.
x_San_Guad_2004_2.csv. This CSV file contains Muskingum x values (dimensionless) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv. The values were computed based on Equation (18) in David et al. (2011). This file was prepared using a Fortran program.
x_San_Guad_2004_3.csv. This CSV file contains Muskingum x values (dimensionless) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv. The values were computed based on Equation (19) in David et al. (2011). This file was prepared using a Fortran program.
x_San_Guad_2004_4.csv. This CSV file contains Muskingum x values (dimensionless) for all river reaches. The river reaches have the same COMIDs and are sorted similarly to rapid_connect_San_Guad.csv. The values were computed based on Equation (21) in David et al. (2011). This file was prepared using a Fortran program.
basin_id_San_Guad_hydroseq.csv. This CSV file contains the list of unique IDs of NHDPlus river reaches (COMID) in the San Antonio and Guadalupe River Basins. The river reaches are sorted from upstream to downstream. The values were computed using the following NHDPlus fields: COMID and HYDROSEQ. This file was prepared using Excel.
Qout_San_Guad_1460days_p1_dtR=900s.nc. This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach. The river reaches have the same COMIDs and are sorted similarly to basin_id_San_Guad_hydroseq.csv. The time range for this file is from 2004-01-01T00:00-06:00 to 2007-12-31-00:00-06:00. The values were computed using the Muskingum method with parameters of Equation (17) in David et al. (2011). This file was prepared using RAPID v1.0.0 running with the preonly ILU solver on one core.
Qout_San_Guad_1460days_p2_dtR=900s.nc. This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) from RAPID corresponding to the downstream point of each reach. The river reaches have the same COMIDs and are sorted similarly to basin_id_San_Guad_hydroseq.csv. The time range for this file is from 2004-01-01T00:00-06:00 to 2007-12-31-00:00-06:00. The values were computed using the Muskingum method with parameters of Equation (18) in David et al. (2011). This file was prepared using RAPID v1.0.0 running with the preonly ILU solver on one core.
Qout_San_Guad_1460days_p3_dtR=900s.nc. This netCDF file contains the 3-hourly averaged outputs (in cubic meters per second) from RAPID corresponding to the downstream
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This data release provides the reanalysis streamflow data from versions 1.2, 2.0, and 2.1 of the National Water Model structured for timeseries extraction. The impact of this is that user can query time series for a given NHDPlusV2 COMID without downloading the hourly CONUS files and extracting the sample of relevant values.
The data is hosted on the RENCI THREDDS Data Server and is accessible via OPeNDAP at the follwoing URLs:
Version 1.2 (https://thredds.hydroshare.org/thredds/catalog/nwm/retrospective/catalog.html?dataset=NWM_Retrospective/nwm_retro_full.ncml) - Spans 1993-01-01 00:00:00 to 2017-12-31 23:00:00 - Contains 219,144 hourly time steps for - 2,729,077 NHD reaches
Version 2.0 (https://thredds.hydroshare.org/thredds/catalog/nwm/retrospective/catalog.html?dataset=NWM_Retrospective/nwm_v2_retro_full.ncml) - Spans 1993-01-01 00:00:00 to 2018-12-31 00:00:00 - Contains 227,903 hourly time steps for - 2,729,076 NHD reaches
Version 2.1 (https://cida.usgs.gov/thredds/catalog/demo/morethredds/nwm/nwm_v21_retro_full.ncml) - Spans 1979-02-02 18:00:00 to 2020-12-31 00:00:00 - Contains 227,903 hourly time steps for - 2,729,076 NHD reaches
Raw Data (https://registry.opendata.aws/nwm-archive/) - 227,000+ hourly netCDF files (depending on version)
The data description structure (DDS) can be viewed at the NcML page for each respective resource (linked above). More broadly each resource includes:
The nwmTools
R package provides easier interaction with the OPeNDAP resources. Package documentation can be found here and the GitHub repository here.
This effort is supported by the Consortium of Universities for the Advancement of Hydrologic Science, Inc. under the HydroInformatics Fellowship. See program here
J.M. Johnson, David L. Blodgett, Keith C. Clarke, Jon Pollack. (2020). "Restructuring and serving web-accessible streamflow data from the NOAA National Water Model historic simulations". Nature Scienfic Data. (In Review)
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
## Overview
Proyecto Comida Peruana is a dataset for classification tasks - it contains Platos De Comida annotations for 2,902 images.
## Getting Started
You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.
## License
This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
## Overview
Comic Book Panel Detection is a dataset for instance segmentation tasks - it contains Panels annotations for 966 images.
## Getting Started
You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.
## License
This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).
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Learn more about Market Research Intellect's Comic Books Reading Apps Market Report, valued at USD 1.2 billion in 2024, and set to grow to USD 2.4 billion by 2033 with a CAGR of 8.7% (2026-2033).
Output generated from running the Join Features analysis tool.
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North America Comic Book Market was valued at USD 1.36 billion in 2024 and is anticipated to grow to USD 2.39 billion by 2030 with a CAGR of 9.91% during forecast period.
Pages | 131 |
Market Size | 2024: USD 1.36 Billion |
Forecast Market Size | 2030: USD 2.39 Billion |
CAGR | 2025-2030: 9.91% |
Fastest Growing Segment | E-Book |
Largest Market | United States |
Key Players | 1. Marvel Entertainment, LLC 2. Warner Bros. Discovery, Inc. 3. Image Comics, Inc. 4. Dark Horse Comics LLC 5. Idea and Design Works LLC (IDW Publishing) 6. Valiant Entertainment, LLC 7. Archie Comics Publications, Inc. 8. TOKYOPOP Inc. 9. VIZ Media, LLC 10. Titan Publishing Group Ltd |
The graph presents data on the format preference among comic book readers worldwide as of June 2013. During a survey carried out by the Comic Book Resources website, more than 75 percent of respondents stated they preferred reading comic books in print.
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The global market size for the Online Comic Platform Market is anticipated to grow from USD 3.2 billion in 2023 to USD 7.5 billion by 2032, reflecting a compound annual growth rate (CAGR) of 9.7%. This growth is driven by increasing digitalization and the rising popularity of online content consumption. The market is witnessing significant traction due to the shift from traditional print comics to digital platforms, fueled by technological advancements and the increasing proliferation of smartphones and tablets.
The first significant growth factor for the online comic platform market is the increasing penetration of the internet and the widespread use of smart devices such as smartphones and tablets. With more individuals having access to high-speed internet and affordable smart devices, the consumption of digital content, including comics, has surged. This shift is particularly pronounced among younger demographics who prefer the convenience and accessibility of digital platforms over traditional print media. The ease of access to a plethora of comic genres and the availability of interactive features further enhance the user experience, driving market growth.
Another critical growth factor is the evolving business models within the online comic platform market. Subscription-based models, ad-supported content, and pay-per-view options offer flexibility to consumers, catering to different preferences and budgets. Subscription-based models provide users with unlimited access to a vast library of content for a monthly fee, which is attractive to avid readers. Ad-supported models, on the other hand, allow free access to content with advertisements, making it accessible to a broader audience. Pay-per-view models cater to those who prefer to purchase specific titles. These diverse business models ensure a steady revenue stream for platform providers and content creators, fostering market expansion.
The growing popularity of webtoons, a form of digital comics optimized for mobile devices, is another significant driver for the online comic platform market. Originating from South Korea, webtoons have gained immense popularity worldwide due to their unique vertical scrolling format, which is ideal for reading on smartphones. The success of webtoons has spurred the adoption of similar formats in other regions, contributing to the global expansion of the online comic platform market. Furthermore, the integration of multimedia elements such as animations and sound effects in webtoons enhances the reading experience, attracting more users to the digital comic realm.
The world of online comics is not just limited to traditional formats; it has expanded into various forms of Comic Derivatives. These derivatives include adaptations and spin-offs that leverage popular comic characters and storylines, creating new content that appeals to diverse audiences. For instance, many digital platforms are now exploring the potential of animated series and interactive games based on popular comic franchises. This trend not only broadens the scope of storytelling but also enhances user engagement by offering a multi-dimensional experience. As a result, the market for comic derivatives is becoming an integral part of the online comic ecosystem, driving further innovation and growth.
Regionally, North America is expected to hold a significant share of the online comic platform market, driven by the high adoption rate of digital media and the presence of major market players. Asia Pacific is projected to exhibit the highest growth rate, fueled by the increasing popularity of webtoons and the rising number of internet users. Europe is also anticipated to witness substantial growth due to the growing demand for digital content and the increasing preference for online comic platforms among younger audiences. Latin America and the Middle East & Africa are expected to experience moderate growth, supported by improving internet infrastructure and growing digital literacy.
The Online Comic Platform Market can be segmented by type into Subscription-Based, Ad-Supported, and Pay-Per-View. Subscription-based models have gained significant traction, offering users unlimited access to a vast library of comics for a monthly or annual fee. This model provides a steady revenue stream for platform providers and content creators, making it a sustainable business model. Consumers are drawn to the convenience and cost-effectiveness of subs
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North America Comic Book Market held 40% of the global revenue with a market size of USD 6085.68 million in 2024 and will grow at a compound annual growth rate (CAGR) of 3.4% from 2024 to 2031.
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Market Overview The global comic platform market size is expected to grow from USD XXX million in 2025 to USD XXX million by 2033, at a CAGR of XX% over the forecast period. The market growth is attributed to the increasing popularity of comics and graphic novels, the growing adoption of digital reading platforms, and the expanding user base of smartphones and tablets. The market is segmented based on type (restricted level and unrestricted), application (adult and child), and region (North America, South America, Europe, Middle East & Africa, and Asia Pacific). Key Market Drivers and Restraints Key drivers of the comic platform market include the growing popularity of comics and graphic novels, the increasing adoption of digital reading platforms, the expanding user base of smartphones and tablets, and the rising disposable income in developing countries. However, the market is also subject to certain restraints, such as piracy, competition from traditional publishing houses, and the lack of awareness about digital comics in some regions. To overcome these restraints, key market players are focusing on developing innovative content, partnering with traditional publishers, and expanding their distribution channels.
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License information was derived automatically
A crosswalk table between NHDPlus version 2.1 flowlines (using the unique field COMID) and the Watershed Boundary Dataset (WBD) 12-digit-hydrologic units (HU-12) is provided for the 48 contiguous United States. The crosswalk table provides a WBD HU-12 assignment for every networked flowline in the NHDPlus. In this way, the network developed for navigation and modeling, NHDPlus, is aligned with accounting units of the WBD HU-12s to the extent possible given the assumptions that were made in creating each. A crosswalk table for NHDPlus isolated sinks was produced by a simple overlay process where the sinks were assigned HU-12 values based on their position relative to the WBD snapshot HU-12s. This table was integrated with the flowline associations into one crosswalk table for both feature types. There is good alignment between aggregated NHDPlus catchments and WBD HU-12 units in many locations. These are areas where the flows and chemical or nutrient loads that are accumulated leav ...