FlowMapper.org is a web-based framework for automated production and design of origin-destination flow maps. FlowMapper has four major features that contribute to the advancement of existing flow mapping systems. First, users can upload and process their own data to design and share customized flow maps. The ability to save data, cartographic design and map elements in a project file allows users to easily share their data and/or cartographic design with others. Second, users can generate customized flow symbols to support different flow map reading tasks such as comparing flow magnitudes and directions and identifying flow and location clusters that are strongly connected with each other. Third, FlowMapper supports supplementary layers such as node symbols, choropleth, and base maps to contextualize flow patterns with location references and characteristics. Finally, the web-based architecture of FlowMapper supports server-side computational capabilities to process and normalize large flow data and reveal natural patterns of flows.

This map service represents the percent change in modeled streamflow metrics between the historical (1977-2006) and mid-century (2030-2059) time periods in the United States. In addition to standard NHD attributes, the streamflow datasets include metrics on mean daily flow (annual and seasonal), flood levels associated with 1.5-year, 10-year, and 25-year floods; annual and decadal minimum weekly flows and date of minimum weekly flow, center of flow mass date; baseflow index, and average number of winter floods.�These files and additional information are available on the project website,�https://www.fs.usda.gov/rm/boise/AWAE/projects/modeled_stream_flow_metrics.shtml. Streams without flow metrics (null values) were removed from this dataset to improve display speed; to see all stream lines, use an NHD flowline dataset.Hydro flow metrics data can be downloaded from�here.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService Geodatabase Download Shapefile Download For complete information, please visit https://data.gov.

This repository contains the example datasets used in the following article by Caglar Koylu, Geng Tian and Mary Windsor: FlowMapper.org: A web-based framework for designing origin-destination flow maps

This map service represents modeled streamflow metrics from the end-of-century time period (2070-2099) in the United States. In addition to standard NHD attributes, the streamflow datasets include metrics on mean daily flow (annual and seasonal), flood levels associated with 1.5-year, 10-year, and 25-year floods; annual and decadal minimum weekly flows and date of minimum weekly flow, center of flow mass date; baseflow index, and average number of winter floods. These files and additional information are available on the project website, https://www.fs.usda.gov/rm/boise/AWAE/projects/modeled_stream_flow_metrics.shtml. Streams without flow metrics (null values) were removed from this dataset to improve display speed; to see all stream lines, use an NHD flowline dataset.Hydro flow metrics data can be downloaded from here.

ABSTRACT Objective: To identify and eliminate steps that do not add value for customers in the disinfection center. Method: We applied the Lean tool: Value Flow Map, using the concepts of gemba and kaizen in the work process of the disinfection unit for ventilatory care materials, aiming at improving such process. After performing a training with the team on the Lean concepts described above, applying the Value Flow Map in the gemba, analyzing the opportunities for improvement, and approving the changes, the Value Flow Map of the future state was devised and changes were implemented. Result: The time of the disinfection process was reduced in 2h37 and the financial resources required also decreased, in R$ 809.08/month. Conclusion: The application of Lean concepts presented positive results for the elimination of wastages in the disinfection center.

This dataset contains the interviews conducted at four universities. The objective of the research was to understand how Internet of Things applications could support strategic decision-making processes at universities. Therefore, interviews were conducted to map a decision-making process at each university (i.e. the design or adjustment of the campus strategy). The outcomes of the first set of interviews were used to conduct process and information analysis. The process analysis was validated in a second round of interviews. The information analysis is based on the process analysis and connects process activities to information needs (which can be delivered by the Internet of Things).

CONTRIBUTORS

This map service represents the percent change in modeled streamflow metrics between the historical (1977-2006) and end-of-century (2070-2099) time periods in the western United States. In addition to standard NHD attributes, the streamflow datasets include metrics on mean daily flow (annual and seasonal), flood levels associated with 1.5-year, 10-year, and 25-year floods; annual and decadal minimum weekly flows and date of minimum weekly flow, center of flow mass date; baseflow index, and average number of winter floods.�These files and additional information are available on the project website,�https://www.fs.usda.gov/rm/boise/AWAE/projects/modeled_stream_flow_metrics.shtml. Streams without flow metrics (null values) were removed from this dataset to improve display speed; to see all stream lines, use an NHD flowline dataset.Hydro flow metrics data can be downloaded from�here.

This dataset contains all data and code necessary to reproduce the analysis presented in the manuscript: Winzeler, H.E., Owens, P.R., Read Q.D.., Libohova, Z., Ashworth, A., Sauer, T. 2022. 2022. Topographic wetness index as a proxy for soil moisture in a hillslope catena: flow algorithms and map generalization. Land 11:2018. DOI: 10.3390/land11112018. There are several steps to this analysis. The relevant scripts for each are listed below. The first step is to use the raw digital elevation data (DEM) to produce different versions of the topographic wetness index (TWI) for the study region (Calculating TWI). Then, these TWI output files are processed, along with soil moisture (volumetric water content or VWC) time series data from a number of sensors located within the study region, to create analysis-ready data objects (Processing TWI and VWC). Next, models are fit relating TWI to soil moisture (Model fitting) and results are plotted (Visualizing main results). A number of additional analyses were also done (Additional analyses). Input data The DEM of the study region is archived in this dataset as SourceDem.zip. This contains the DEM of the study region (DEM1.sgrd) and associated auxiliary files all called DEM1.* with different extensions. In addition, the DEM is provided as a .tif file called USGS_one_meter_x39y400_AR_R6_WashingtonCO_2015.tif. The remaining data and code files are archived in the repository created with a GitHub release on 2022-10-11, twi-moisture-0.1.zip. The data are found in a subfolder called data.

2017_LoggerData_HEW.csv through 2021_HEW.csv: Soil moisture (VWC) logger data for each year 2017-2021 (5 files total). 2882174.csv: weather data from a nearby station. DryPeriods2017-2021.csv: starting and ending days for dry periods 2017-2021. LoggerLocations.csv: Geographic locations and metadata for each VWC logger. Logger_Locations_TWI_2017-2021.xlsx: 546 topographic wetness indexes calculated at each VWC logger location. note: This is intermediate input created in the first step of the pipeline.

Code pipeline To reproduce the analysis in the manuscript run these scripts in the following order. The scripts are all found in the root directory of the repository. See the manuscript for more details on the methods. Calculating TWI

TerrainAnalysis.R: Taking the DEM file as input, calculates 546 different topgraphic wetness indexes using a variety of different algorithms. Each algorithm is run multiple times with different input parameters, as described in more detail in the manuscript. After performing this step, it is necessary to use the SAGA-GIS GUI to extract the TWI values for each of the sensor locations. The output generated in this way is included in this repository as Logger_Locations_TWI_2017-2021.xlsx. Therefore it is not necessary to rerun this step of the analysis but the code is provided for completeness.

Processing TWI and VWC

read_process_data.R: Takes raw TWI and moisture data files and processes them into analysis-ready format, saving the results as CSV. qc_avg_moisture.R: Does additional quality control on the moisture data and averages it across different time periods.

Model fitting Models were fit regressing soil moisture (average VWC for a certain time period) against a TWI index, with and without soil depth as a covariate. In each case, for both the model without depth and the model with depth, prediction performance was calculated with and without spatially-blocked cross-validation. Where cross validation wasn't used, we simply used the predictions from the model fit to all the data.

fit_combos.R: Models were fit to each combination of soil moisture averaged over 57 months (all months from April 2017-December 2021) and 546 TWI indexes. In addition models were fit to soil moisture averaged over years, and to the grand mean across the full study period. fit_dryperiods.R: Models were fit to soil moisture averaged over previously identified dry periods within the study period (each 1 or 2 weeks in length), again for each of the 546 indexes. fit_summer.R: Models were fit to the soil moisture average for the months of June-September for each of the five years, again for each of the 546 indexes.

Visualizing main results Preliminary visualization of results was done in a series of RMarkdown notebooks. All the notebooks follow the same general format, plotting model performance (observed-predicted correlation) across different combinations of time period and characteristics of the TWI indexes being compared. The indexes are grouped by SWI versus TWI, DEM filter used, flow algorithm, and any other parameters that varied. The notebooks show the model performance metrics with and without the soil depth covariate, and with and without spatially-blocked cross-validation. Crossing those two factors, there are four values for model performance for each combination of time period and TWI index presented.

performance_plots_bymonth.Rmd: Using the results from the models fit to each month of data separately, prediction performance was averaged by month across the five years of data to show within-year trends. performance_plots_byyear.Rmd: Using the results from the models fit to each month of data separately, prediction performance was averaged by year to show trends across multiple years. performance_plots_dry_periods.Rmd: Prediction performance was presented for the models fit to the previously identified dry periods. performance_plots_summer.Rmd: Prediction performance was presented for the models fit to the June-September moisture averages.

Additional analyses Some additional analyses were done that may not be published in the final manuscript but which are included here for completeness.

2019dryperiod.Rmd: analysis, done separately for each day, of a specific dry period in 2019. alldryperiodsbyday.Rmd: analysis, done separately for each day, of the same dry periods discussed above. best_indices.R: after fitting models, this script was used to quickly identify some of the best-performing indexes for closer scrutiny. wateryearfigs.R: exploratory figures showing median and quantile interval of VWC for sensors in low and high TWI locations for each water year. Resources in this dataset:Resource Title: Digital elevation model of study region. File Name: SourceDEM.zipResource Description: .zip archive containing digital elevation model files for the study region. See dataset description for more details.Resource Title: twi-moisture-0.1: Archived git repository containing all other necessary data and code . File Name: twi-moisture-0.1.zipResource Description: .zip archive containing all data and code, other than the digital elevation model archived as a separate file. This file was generated by a GitHub release made on 2022-10-11 of the git repository hosted at https://github.com/qdread/twi-moisture (private repository). See dataset description and README file contained within this archive for more details.

Mean (± standard deviation) voxel-wise within and between-session: (A) wsCV and (B) ICC value for aCBF and rCBF images in select ROIs.

As part of the Government’s instruction of 3 June 2015 on the development of river mapping, DDT developed, in conjunction with the National Water and Aquatic Environments Office, a map of rivers in the Essonne department. The purpose of this map is to enable all users to share the knowledge available to date to differentiate rivers from other flows. The objective of this map is to allow the application of Articles L.214-1 to L.2014-6 of the Environmental Code. on the mapping, identification and maintenance of rivers. The recommended zoom threshold is 25 000th for a good reading of the data.

Mapping of rivers and non-stream water points in Puy-de-Dôme prepared in accordance with the Government Instruction of 3 June 2015 on the mapping and identification of rivers and their maintenance and the Ministerial Orders of 04/05/2017 and Prefectural of 05/07/2017 on untreated areas.

Based on the definition of the watercourse (constitutes a stream, a flow of running water in a natural bed originally fed by a source and having a sufficient flow of much of the year) and the definition of water points (spray, beef and water body), a mapping project is proposed in the interactive map classifying the hydrographic sections and water surfaces of the IGN TOPO BD into four categories: — watercourses for the application of Articles L214-1 to L214-6 of the Environmental Code — the sections that need to be examined to determine whether they meet the definition of watercourse — non-stream water points for which an untreated area is to be set up — non-stream sections that need to be examined to determine whether they meet the definition of a water point within the meaning of the untreated area

Based on the definition of the watercourse (constitutes a stream, a flow of running water in a natural bed originally fed by a source and having a sufficient flow of much of the year) and the definition of water points (spray, beef and water body), a mapping project is proposed in the interactive map classifying the hydrographic sections and water surfaces of the IGN TOPO BD into four categories: — watercourses for the application of Articles L214-1 to L214-6 of the Environmental Code — the sections that need to be examined to determine whether they meet the definition of watercourse — non-stream water points for which an untreated area is to be set up — non-stream sections that need to be examined to determine whether they meet the definition of a water point within the meaning of the untreated area

Fig 2D upper panel: The flow velocities from individual arterioles and venules as the function of the normalized signal intensities of each vessel in the A–V map. The raw data was acquired from 11 animals. Fig 2D lower panel: Mean velocity of arterioles and venules for the bar graph from 11 animals. MATLAB code and mat file: The MATLAB code and raw data for the histogram of the blood velocity distribution across arterioles and venules from the animals. A–V, arteriole–venule. (RAR)

Ice velocity constitutes a key parameter for quantifying ice-sheet discharge rates and is thus crucial for improving the coupled models of the Antarctic ice sheet towards accurately predict its contribution to future global sea-level rise. However, in Antarctica, high-resolution and continuous ice velocity estimates remain elusive, which is key to unravel Antarctica’s present-day ice mass balance processes. Here, we present a suite of newly estimated Antarctic-wide, annually-sampled ice velocity products at 105-m grid-spacing observed by Landsat 8 optical images data. We first describe a procedure that can automatically calibrate and integrate ice displacement maps to generate Antarctic-wide seamless ice velocity products. The annual ice velocity mosaics are assembled using a total of 250,000 displacement maps inferred from more than 80,000 Landsat 8 images acquired between December 2013 and April 2019. The new annual Antarctic ice velocity data product exhibits an improved quantification of near-decadal Antarctic-wide ice flow, and an opportunity to investigate ice dynamics at a higher spatial resolution and annual sampling, as compared to existing data products. Validation studies confirmed improved accuracy and consistency of this new data product, when compared with independently estimated optical and radar ice velocity data products, as well as in situ data.

Additional file 2. Lists of articles excluded on the basis of full-text assessment with reasons for exclusion and unobtainable articles. Separate lists of articles excluded on the basis of full-text assessment and articles that were unobtainable.

The Watershed Boundary Dataset (WBD) from The National Map (TNM) defines the perimeter of drainage areas formed by the terrain and other landscape characteristics. The drainage areas are nested within each other so that a large drainage area, such as the Upper Mississippi River, is composed of multiple smaller drainage areas, such as the Wisconsin River. Each of these smaller areas can further be subdivided into smaller and smaller drainage areas. The WBD uses six different levels in this hierarchy, with the smallest averaging about 30,000 acres. The WBD is made up of polygons nested into six levels of data respectively defined by Regions, Subregions, Basins, Subbasins, Watersheds, and Subwatersheds. For additional information on the WBD, go to https://nhd.usgs.gov/wbd.html. The USGS National Hydrography Dataset (NHD) service is a companion dataset to the WBD. The NHD is a comprehensive set of digital spatial data that encodes information about naturally occurring and constructed bodies of surface water (lakes, ponds, and reservoirs), paths through which water flows (canals, ditches, streams, and rivers), and related entities such as point features (springs, wells, stream gages, and dams). The information encoded about these features includes classification and other characteristics, delineation, geographic name, position and related measures, a "reach code" through which other information can be related to the NHD, and the direction of water flow. The network of reach codes delineating water and transported material flow allows users to trace movement in upstream and downstream directions. In addition to this geographic information, the dataset contains metadata that supports the exchange of future updates and improvements to the data. The NHD is available nationwide in two seamless datasets, one based on 1:24,000-scale maps and referred to as high resolution NHD, and the other based on 1:100,000-scale maps and referred to as medium resolution NHD. Additional selected areas in the United States are available based on larger scales, such as 1:5,000-scale or greater, and referred to as local resolution NHD. For more information on the NHD, go to https://nhd.usgs.gov/index.html. Hydrography data from The National Map supports many applications, such as making maps, geocoding observations, flow modeling, data maintenance, and stewardship. Hydrography data is commonly combined with other data themes, such as boundaries, elevation, structures, and transportation, to produce general reference base maps. The National Map viewer allows free downloads of public domain WBD and NHD data in either Esri File or Personal Geodatabase, or Shapefile formats. The Watershed Boundary Dataset is being developed under the leadership of the Subcommittee on Spatial Water Data, which is part of the Advisory Committee on Water Information (ACWI) and the Federal Geographic Data Committee (FGDC). The USDA Natural Resources Conservation Service (NRCS), along with many other federal agencies and national associations, have representatives on the Subcommittee on Spatial Water Data. As watershed boundary geographic information systems (GIS) coverages are completed, statewide and national data layers will be made available via the Geospatial Data Gateway to everyone, including federal, state, local government agencies, researchers, private companies, utilities, environmental groups, and concerned citizens. The database will assist in planning and describing water use and related land use activities. Resources in this dataset:Resource Title: Watershed Boundary Dataset (WBD). File Name: Web Page, url: https://www.nrcs.usda.gov/wps/portal/nrcs/detail/national/water/watersheds/dataset/?cid=nrcs143_021630 Web site for the Watershed Boundary Dataset (WBD), including links to:

Review Data Availability (Status Maps) Obtain Data by State, County, or Other Area Obtain Seamless National Data offsite link image
Geospatial Data Tools National Technical and State Coordinators Information about WBD dataset

Available water supply varies greatly across the United States depending on topography, climate, elevation and geology. Forested and mountainous locations, such as national forests, tend to receive more precipitation than adjacent non-forested or low-lying areas. However, contributions of national forest lands to regional streamflow volumes is largely unknown. Using outputs from the Variable Infiltration Capacity hydrologic model, we calculated mean annual and mean summer (July and August) streamflow metrics based on total flow and flow from national forest lands for each 1:100,000 scale National Hydrography Dataset stream reach in the contiguous United States. Specifically, this data publication contains twenty-one comma-delimited ASCII text files (for different drainage areas and processing units across the United States) containing 1915-2011 mean annual flow and mean summer flow.Data can be downloaded here: Geodatabase or ShapefileThese files also contain the mean annual and mean summer flows from National Forest System (NFS) lands as well as the portion of total mean annual and summer flow contributed by flow from NFS lands.These data provide insight into 1915-2011 hydrologic regimes and national forest contributions to total water yield. These non-spatial files were then merged and joined to the September 2012 snapshot of the National Hydrography Dataset (NHD), version 2.Note: 'Forest Service lands' are here defined as those lands within the Forest Service administrative boundaries; these include some inholdings and other non-USFS lands enclosed within these boundaries.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

The NRCS National Water and Climate Center's Interactive Map displays both current and historic hydrometeorological data in an easy-to-use, visual interface. The information on the map comes from many sources. Natural Resources Conservation Service snowpack and precipitation data are derived from manually-collected snow courses and automated Snow Telemetry (SNOTEL) and Soil Climate Analysis Network (SCAN) stations. Other data sources include precipitation, streamflow, and reservoir data from the U.S. Bureau of Reclamation (BoR), the Applied Climate Information System (ACIS), the U.S. Geological Survey (USGS), and other hydrometeorological monitoring entities. The Interactive Map has two regions: the map display itself, and the map controls which determine both the display mode and the types of data and stations to show on the map: Display Modes; Map Components; Station Conditions Controls; Basin Conditions Controls; Station Inventory Controls. Resources in this dataset:Resource Title: Interactive Map home. File Name: Web Page, url: https://www.nrcs.usda.gov/wps/portal/wcc/home/quicklinks/predefinedMaps/ The Interactive Map provides spatial visualization of current and historic hydrometeorological data collected by the Natural Resources Conservation Service and other monitoring agencies. The map also provides station inventories based on sensor and geographic filters. This page has links to pre-defined maps organized by data type. After opening a map, users can zoom to area of interest, customize the map, and then bookmark the URL to save the settings.

Maps are currently experiencing a paradigm shift from static representations to dynamic platforms that capture, visualize and analyse new data, bringing different possibilities for exploration and research. The first objective of this paper is to present a map that illustrates, for the first time, the real flow of casual cyclists and bike messengers in the city of Madrid. The second objective is to describe the development and results of the Madrid Cycle Track initiative, an online platform launched with the aim of collecting cycling routes and other information from volunteers. In the framework of this initiative, different online maps are presented and their functionalities described. Finally, a supplemental video visualizes the cyclist flow over the course of a day.

HazMatMapper is an online and interactive geographic visualization tool designed to facilitate exploration of transnational flows of hazardous waste in North America (http://geography.wisc.edu/hazardouswaste/map/). While conventional narratives suggest that wealthier countries such as Canada and the United States (US) export waste to poorer countries like Mexico, little is known about how waste trading may affect specific sites within any of the three countries. To move beyond anecdotal discussions and national aggregates, we assembled a novel geographic dataset describing transnational hazardous waste shipments from 2007 to 2012 through two Freedom of Information Act requests for documents held by the US Environmental Protection Agency. While not yet detailing all of the transnational hazardous waste trade in North America, HazMatMapper supports multiscale and site-specific visual exploration of US imports of hazardous waste from Canada and Mexico. It thus enables academic researchers, waste regulators, and the general public to generate hypotheses on regional clustering, transnational corporate structuring, and environmental justice concerns, as well as to understand the limitations of existing regulatory data collection itself. Here, we discuss the dataset and design process behind HazMatMapper and demonstrate its utility for understanding the transnational hazardous waste trade.

ObjectiveTo explore the development context, research hotspots and frontiers in the glymphatic system (GS) field from 2012 to 2022 by bibliometric analysis.MethodsThe Web of Science Core Collection (WoSCC) database was searched for articles published between 2012 and 2022. Microsoft Excel was used to manage the data. VOSviewer, CiteSpace, GraphPad Prism, the Web of Science, and an online analysis platform for bibliometrics (http://bibliometric.com/) were used to analyze the countries, institutions, journals, and collaboration networks among authors and the types of articles, developmental directions, references, and top keywords of published articles.ResultsA total of 412 articles were retrieved, including 39 countries/regions, 223 research institutes and 171 academic journals. The subject classifications related to the GS were Neuroscience, Clinical Neuroscience and Radiology/Nuclear Medicine/Medical Imaging. The United States has maintained its dominant and most influential position in GS research. Among research institutions and journals, the Univ Rochester and Journal of Cerebral Blood Flow and Metabolism had the highest number of academic articles, respectively. Nedergaard M had the most published article, and Iliff JJ had the most co-citations. The top two keywords with the highest frequency were “glymphatic system” and “cerebrospinal fluid.”ConclusionThis research provides valuable information for the study of the GS. The bibliometric analysis of this area will encourage potential collaborations among researchers, defining its frontiers and directions for development.