Class I and II surface water classification. The Clean Water Act requires that the surface waters of each state be classified according to designated uses. Florida has six classes with associated designated uses, which are arranged in order of degree of protection required: Class I - Potable Water Supplies Fourteen general areas throughout the state including: impoundments and associated tributaries, certain lakes, rivers, or portions of rivers, used as a drinking water supply. Class II - Shellfish Propagation or Harvesting Generally coastal waters where shellfish harvesting occurs. For a more detailed description of classes and specific waterbody designations, see 62-302.400.

Yearly citation counts for the publication titled "The Low/High BCS Permeability Class Boundary: Physicochemical Comparison of Metoprolol and Labetalol".

The same 20 features were measured for the in-graphs (representing the cellular structure associated with each boundary) and for the out-graphs (representing the corresponding areas in the background), giving 40 metrics in total.

The 2014 update of the U.S. Geological Survey (USGS) National Seismic Hazard Model (NSHM) for the conterminous United States (2014 NSHM; Petersen and others, 2014; https://pubs.usgs.gov/of/2008/1128/) included probabilistic ground motion maps for 2 percent and 10 percent probabilities of exceedance in 50 years, derived from seismic hazard curves for peak ground acceleration (PGA) and 0.2 and 1.0 second spectral accelerations (SAs) with 5 percent damping for the National Earthquake Hazards Reduction Program (NEHRP) site class boundary B/C (time-averaged shear wave velocity in the upper 30 meters [VS30]=760 meters per second [m/s]). This data release provides 0.1 degree by 0.1 degree gridded seismic hazard curves, 0.1 degree by 0.1 degree gridded probabilistic ground motions, and seismic hazard maps calculated for additional periods and additional uniform NEHRP site classes using the 2014 NSHM. For both the central and eastern U.S. (CEUS) and western U.S. (WUS), data and maps are provided for PGA, 0.1, 0.2, 0.3, 0.5, 1.0, and 2.0 second SAs with 5% damping for the NEHRP site class boundary B/C for 2, 5, and 10% probabilities of exceedance in 50 years. The WUS additionally includes data and maps for 0.75, 3.0, 4.0, and 5.0 SAs. The use of region-specific suites of weighted ground motion models (GMMs) in the 2014 NSHM precluded the calculation of ground motions for a uniform set of periods and site classes for the conterminous U.S. At the time of development of the 2014 NSHM, there was no consensus in the CEUS on an appropriate site-amplification model to use, therefore, we calculated hazard curves and maps for NEHRP Site Class A (VS30 = 2000 m/s), for which most stable continental GMMs were original developed, based on simulations for hard rock conditions. In the WUS, however, the GMMs allow amplification based on site class (defined by VS30), so we calculated hazard curves and maps for NEHRP site classes B (VS30 = 1080 m/s), C (VS30 = 530 m/s), D (VS30 = 260 m/s), and E (VS30 = 150 m/s) and site class boundaries A/B (VS30 = 1500 m/s), B/C (VS30 = 760 m/s), C/D (VS30 = 365 m/s), and D/E (VS30 = 185 m/s). Further explanation about how the data and maps were generated can be found in the accompanying U.S. Geological Survey Open-File Report 2018-1111 (https://doi.org/10.3133/ofr20181111). First Posted - July 18, 2018 Revised - February 20, 2019 (ver. 1.1)

As required by Dallas City Charter, redistricting of elected official districts is required every ten years based on updated Census information. This feature layer represents the archived city council district boundaries as approved by the Dallas City Council on October 5, 2011. This feature class is useful for representing Council District borders as patterned lines in web map applications. This feature class is not authoritative; it is derived from the Enterprise GIS Council District polygon feature class. Boundaries are based upon 2010 Census Block geography and may not conform with other data. Where street boundaries serve as district borders, the entire right of way is assigned to the northern or eastern boundary. Utilizing this file with non-census shapefiles may result in discrepancies between boundaries. To identify appropriate boundaries, please use in conjunction with the 2010 Census edges and block files. Note: External boundaries conflated to match base City Limits GIS data.

Firm age class limits.

GDB Version: ArcGIS Pro 3.3Additional Resources:Shapefile DownloadShapefile Download (Clipped to VIMS shoreline)Administrative Boundary Data Standard REST Endpoint (Unclipped) - REST Endpoint (Clipped)The Administrative Boundary feature classes represent the best available boundary information in Virginia. VGIN initially sought to develop an improved city, county, and town boundary dataset in late 2013, spurred by response of the Virginia Administrative Boundaries Workgroup community. The feature class initially started from an extraction of features from the Census TIGER dataset for Virginia. VGIN solicited input from localities in Virginia through the Road Centerlines data submission process as well as through public forums such as the Virginia Administrative Boundaries Workgroup and VGIN listservs. Data received were analyzed and incorporated into the appropriate feature classes where locality data were a superior representation of boundaries. Administrative Boundary geodatabase and shapefiles are unclipped to hydrography features by default. The clipped to hydro dataset is included as a separate shapefile download below.

IntroductionDrug-induced liver injury (DILI) has been investigated at the patient level. Analysis of gene perturbation at the cellular level can help better characterize biological mechanisms of hepatotoxicity. Despite accumulating drug-induced transcriptome data such as LINCS, analyzing such transcriptome data upon drug treatment is a challenging task because the perturbation of expression is dose and time dependent. In addition, the mechanisms of drug toxicity are known only as literature information, not in a computable form.MethodsTo address these challenges, we propose a Multi-Dimensional Transcriptomic Ruler (MDTR) that quantifies the degree of DILI at the transcriptome level. To translate transcriptome data to toxicity-related mechanisms, MDTR incorporates KEGG pathways as representatives of mechanisms, mapping transcriptome data to biological pathways and subsequently aggregating them for each of the five hepatotoxicity mechanisms. Given that a single mechanism involves multiple pathways, MDTR measures pathway-level perturbation by constructing a radial basis kernel-based toxicity space and measuring the Mahalanobis distance in the transcriptomic kernel space. Representing each mechanism as a dimension, MDTR is visualized in a radar chart, enabling an effective visual presentation of hepatotoxicity at transcriptomic level.Results and DiscussionIn experiments with the LINCS dataset, we show that MDTR outperforms existing methods for measuring the distance of transcriptome data when describing for dose-dependent drug perturbations. In addition, MDTR shows interpretability at the level of DILI mechanisms in terms of the distance, i.e., in a metric space. Furthermore, we provided a user-friendly and freely accessible website (http://biohealth.snu.ac.kr/software/MDTR), enabling users to easily measure DILI in drug-induced transcriptome data.

This polygon files contains 2015-2016 school-year data delineating school attendance boundaries. These data were collected and processed as part of the School Attendance Boundary Survey (SABS) project which was funded by NCES to create geography delineating school attendance boundaries. Original source information that was used to create these boundary files were collected were collected over a web-based self-reporting system, through e-mail, and mailed paper maps. The web application provided instructions and assistance to users via a user guide, a frequently asked questions document, and instructional videos. Boundaries supplied outside of the online reporting system typically fell into one of six categories: a digital geographic file, such as a shapefile or KML file; digital image files, such as jpegs and pdfs; narrative descriptions; an interactive web map; Excel or pdf address lists; and paper maps. 2015 TIGER/line features (that consist of streets, hydrography, railways, etc.) were used to digitize school attendance boundaries and was the primary source of information used to digitize analog information. This practice works well as most school attendance boundaries align with streets, railways, water bodies and similar line features included in the 2015 TIGER/line "edges" files. In those few cases in which a portion of a school attendance boundary serves both sides of a street contractor staff used Esri’s Imagery base map to estimate the property lines of parcels. The data digitized from analog maps and verbal descriptions do not conform to cadastral data (and many of the original GIS files created by school districts do not conform with cadastral or parcel data).The SABS 2015-2016 file uses the WGS 1984 Web Mercator Auxiliary Sphere coordinate system.Additional information about SABS can be found on the EDGE website.The SABS dataset is intended for research purposes only and reflects a single snapshot in time. School boundaries frequently change from year to year. To verify legal descriptions of boundaries, users must contact the school district directly.All information contained in this file is in the public domain. Data users are advised to review NCES program documentation and feature class metadata to understand the limitations and appropriate use of these data.

Accuracy assessment is one of the most important components of both applied and research-oriented remote sensing projects. For mapped classes that have sharp and easily identified boundaries, a broad array of accuracy assessment methods has been developed. However, accuracy assessment is in many cases complicated by classes that have fuzzy, indeterminate, or gradational boundaries, a condition which is common in real landscapes; for example, the boundaries of wetlands, many soil map units, and tree crowns. In such circumstances, the conventional approach of treating all reference pixels as equally important, whether located on the map close to the boundary of a class, or in the class center, can lead to misleading results. We therefore propose an accuracy assessment approach that relies on center-weighting map segment area to calculate a variety of common classification metrics including overall accuracy, class user’s and producer’s accuracy, precision, recall, specificity, and the F1 score. This method offers an augmentation of traditional assessment methods, can be used for both binary and multiclass assessment, allows for the calculation of count- and area-based measures, and permits the user to define the impact of distance from map segment edges based on a distance weighting exponent and a saturation threshold distance, after which the weighting ceases to grow. The method is demonstrated using synthetic and real examples, highlighting its use when the accuracy of maps with inherently uncertain class boundaries is evaluated.

This digital, geographically referenced data set was developed to identify the city boundaries of the Des Moines 9 County Regional GIS community. This feature class is one many feature classes developed for and maintained by the Des Moines Area Regional GIS for the purpose of performing internal and external functions of the local government it cover.

These data are a polygon feature class that represents the administrative boundaries of the US Forest Service Research and Development Stations. These territories consist of a collection of states' geographic areas, within which all research and development facilities and lands are managed by a station headquarters.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. Spatial data from field observation points and quantitative plots were used to edit the formation-level maps of Petersburg National Battlefield to better reflect vegetation classes. Using ArcView 3.3, polygon boundaries were revised onscreen over leaf-off photography. Units used to label polygons on the map (i.e. map classes) are equivalent to one or more vegetation classes from the regional vegetation classification, or to a land-use class from the Anderson (Anderson et al. 1976) Level II classification system. Each polygon on the Petersburg National Battlefield map was assigned to one of twenty map classes based on plot data, field observations, aerial photography signatures, and topographic maps. The mapping boundary was based on park boundary data obtained from Petersburg National Battlefield in May 2006. Spatial data depicting the locations of earthworks was obtained from the park and used to identify polygons of the cultural map classes Open Earthworks and Forested Earthworks. One map class used to attribute polygons combines two similar associations that, in some circumstances, are difficult to distinguish in the field. The vegetation map was clipped at the park boundary because areas outside the park were not surveyed or included in the accuracy assessment. Twenty map classes were used in the vegetation map for Petersburg National Battlefield. Map classes are equivalent to one or more vegetation classes from the regional vegetation classification, or to a land-use class from the Anderson (Anderson et al. 1976) Level II classification system.

Classification algorithms face difficulties when one or more classes have limited training data. We are particularly interested in classification trees, due to their interpretability and flexibility. When data are limited in one or more of the classes, the estimated decision boundaries are often irregularly shaped due to the limited sample size, leading to poor generalization error. We propose a novel approach that penalizes the Surface-to-Volume Ratio (SVR) of the decision set, obtaining a new class of SVR-Tree algorithms. We develop a simple and computationally efficient implementation while proving estimation consistency for SVR-Tree and rate of convergence for an idealized empirical risk minimizer of SVR-Tree. SVR-Tree is compared with multiple algorithms that are designed to deal with imbalance through real data applications. Supplementary materials for this article are available online.

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

In late 1996, the Dept of Conservation (DOC) surveyed state and federal agencies about the county boundary coverage they used. As a result, DOC adopted the 1:24,000 (24K) scale U.S. Bureau of Reclamation (USBR) data set (USGS source) for their Farmland Mapping and Monitoring Program (FMMP) but with several modifications. Detailed documentation of these changes is provided by FMMP and included in the Process Step section of the Feature Class metadata. A data set named cnty24k97_1 was made available (approximately 2004) through the California Department of Forestry and Fire Protection - Fire and Resource Assessment Program (CDF - FRAP) and the California Spatial Information Library (CaSIL). In late 2006, the Department of Fish and Game (DFG) reviewed cnty24k97_1. Comparisons were made to a high-quality 100K dataset (co100a/county100k from the former Teale Data Center GIS Solutions Group) and legal boundary descriptions from ( http://www.leginfo.ca.gov ). The cnty24k97_1 data set was missing Anacapa and Santa Barbara islands. DFG added the missing islands using previously-digitized coastline data (coastn27 of State Lands Commission origin), corrected a few county boundaries, built region topology, added additional attributes, and renamed the data set to county24k. In 2007, the California Mapping Coordinating Committee (CMCC) requested that the California Department of Forestry and Fire Protection (CAL FIRE) resume stewardship of the statewide county boundaries data. CAL FIRE adopted the changes made by DFG and collected additional suggestions for the county data from DFG, DOC, and local government agencies. CAL FIRE incorporated these suggestions into the latest revision, which has been renamed cnty24k09_1. Detailed documentation of changes is included in the Process Step section of the Feature Class metadata. This Geo database contains 3 feature classes representing California county boundaries (arc, polygon, and multipart-polygon feature classes) and also contains a polygon feature class representing the state boundary: 1. Line - can be useful for cartographic purposes, especially when different line symbology is needed for different boundaries (e.g. Coastline, Mexico, Nevada, etc). 2. Multipart - features from a common county are combined into a single record (equivalent to a region feature class in a coverage). May be useful for selections and overlays when all parts of a county are needed. 3. Poly - all county features are represented as individual polygons. 4. State Poly - state boundary polygon to be used for cartography or overlay analysis that requires a state polygon.

The number of cross-boundary students (CBS) using various land-based boundary control points, with a breakdown by class level

One of the largest hydraulic mines (1.6 km2) is located in California’s Sierra Nevada within the Humbug Creek watershed and Malakoff Diggins State Historic Park (MDSHP). MDSHP’s denuded and dissected landscape is composed of weathered Eocene auriferous sediments susceptible to chronic rill and gully erosion whereas block failures and debris flows occur in more cohesive terrain. This data release includes a 2014 digital elevation model (DEM), a study area boundary, and a geomorphic map. The 2014 DEM was derived from an available aerial LiDAR dataset collected in 2014 by the California Department of Conservation. The geomorphic map was derived for the study area from using a multi-scale spatial analysis. A topographic position index (TPI) was created using focal statistics to compare the elevations across the study area. We calculated a fine-scale TPI using a circular neighborhood with a radius of 25-meters and large-scale TPI using a circular neighborhood with a radius of 100-meters. In the resulting raster positive TPI values are assigned to cells with elevations higher than the surrounding area and negative TPI values are assigned to cells with elevations lower than the surrounding area. The geomorphic map was then created using a nested conditional statement to apply classification thresholds on the basis the fine and large-scale TPI rasters and a slope raster. Ten geomorphic feature classes were defined and the map can be symbolized by feature class. The geomorphic map includes both channel and hillslope features and can be used to assess erosional and depositional processes at the landscape scale.

AdminVector is the vector data set of Belgian administrative and statistical units. It includes various classes. First class: Belgian statistic sectors as defined by the FPS Economy. Second class: municipal sections, with no unanimous definition. The five following classes correspond to official administrative units as managed by the FPS Finance. Other classes are added to these classes, like border markers or the Belgian maritime zone. The boundaries of the seven first classes are consolidated together in order to keep the topological cohrence of the objects. This data set can be freely downloaded.

The BNDHASH dataset depicts Vermont villages, towns, counties, Regional Planning Commissions (RPC), and LEPC (Local Emergency Planning Committee) boundaries. It is a composite of generally 'best available' boundaries from various data sources (refer to ARC_SRC and SRC_NOTES attributes). However, this dataset DOES NOT attempt to provide a legally definitive boundary. The layer was originally developed from TBHASH, which was the master VGIS town boundary layer prior to the development and release of BNDHASH. By integrating village, town, county, RPC, and state boundaries into a single layer, VCGI has assured vertical integration of these boundaries and simplified maintenance. BNDHASH also includes annotation text for town, county, and RPC names. BNDHASH includes the following feature classes: 1) VILLAGES = Vermont villages 2) TOWNS = Vermont towns 3) COUNTIES = Vermont counties 4) RPCS = Vermont's Regional Planning Commissions 5) LEPC = Local Emergency Planning Committee boundaries 6) VTBND = Vermont's state boundary The master BNDHASH layer is managed as ESRI geodatabase feature dataset by VCGI. The dataset stores villages, towns, counties, and RPC boundaries as seperate feature classes with a set of topology rules which binds the features. This arrangement assures vertical integration of the various boundaries. VCGI will update this layer on an annual basis by reviewing records housed in the VT State Archives - Secretary of State's Office. VCGI also welcomes documented information from VGIS users which identify boundary errors. NOTE - VCGI has NOT attempted to create a legally definitive boundary layer. Instead the idea is to maintain an integrated village/town/county/rpc boundary layer which provides for a reasonably accurate representation of these boundaries (refer to ARC_SRC and SRC_NOTES). BNDHASH includes all counties, towns, and villages listed in "Population and Local Government - State of Vermont - 2000" published by the Secretary of State. BNDHASH may include changes endorsed by the Legislature since the publication of this document in 2000 (eg: villages merged with towns). Utlimately the Vermont Secratary of State's Office and the VT Legislature are responsible for maintaining information which accurately describes the location of these boundaries. BNDHASH should be used for general mapping purposes only. * Users who wish to determine which boundaries are different from the original TBHASH boundaries should refer to the ORIG_ARC field in the BOUNDARY_BNDHASH_LINE (line featue with attributes). Also, updates to BNDHASH are tracked by version number (ex: 2003A). The UPDACT field is used to track changes between versions. The UPDACT field is flushed between versions.