NLCD 2019 - reclassification to suitable/unsuitable for alligator gar spawning - LouisianaSuitable: any low open vegetation classes: emergent herbaceous, agriculture, grassland, shrub/scrub Unsuitable: all other classesUsed in conjunction with other layers to evaluate the accuracy of a statewide (Louisiana) assessment of habitat suitable for alligator gar spawning using the techniques described in Allen et. al 2020. Allen, Y., K. Kimmel, and G. Constant. 2020. Using Remote Sensing to Assess Alligator Gar Spawning Habitat Suitability in the Lower Mississippi River. North American Journal of Fisheries Management 40:580–594.

To download this dataset, click below:Zipped TIFF File: LC_FCD_RECLASS_2016.zip (2GB)The reclassified landcover dataset was derived from the 2016 landcover, one of the products available as part of the the LARIAC program.NOTE: The extent of the derived dataset only covers the area located within the County's flood control district. This raster dataset was combined with the County's parcel layer to produce a file geodatabase of impermeable and permeable areas by parcel for use by the County's Safe Clean Water program.Attributes0 = Permeable1 = ImpermeableThe 2016 landcover dataset was reclassified as follows:Tree Canopy - PermeableGrass/Shrubs - PermeableBare Soil - PermeableWater - PermeableBuildings - ImpermeableRoads/Railroads - ImpermeableOther Paved - ImpermeableTall Shrubs - PermeableFor more information, please contact Bowen Liang (bliang@dpw.lacounty.gov)

The Vermont Water Quality Standards (VTWQS) are rules intended to achieve the goals of the Vermont Surface Water Strategy, as well as the objective of the federal Clean Water Act which is to restore and maintain the chemical, physical, and biological integrity of the Nation's water. The classification of waters is in included in the VTWQS. The classification of all waters has been established by a combination of legislative acts and by classification or reclassification decisions issued by the Water Resources Board or Secretary pursuant to 10 V.S.A. � 1253. Those waters reclassified by the Secretary to Class A(1), A(2), or B(1) for any use shall include all waters within the entire watershed of the reclassified waters unless expressly provided otherwise in the rule. All waters above 2,500 feet altitude, National Geodetic Vertical Datum, are designated Class A(1) for all uses, unless specifically designated Class A(2) for use as a public water source. All waters at or below 2,500 feet altitude, National Geodetic Vertical Datum, are designated Class B(2) for all uses, unless specifically designated as Class A(1), A(2), or B(1) for any use.

Click to downloadClick for metadataService URL: https://gis.dnr.wa.gov/site2/rest/services/Public_Forest_Practices/WADNR_PUBLIC_FP_Water_Type/MapServer/4For large areas, like Washington State, download as a file geodatabase. Large data sets like this one, for the State of Washington, may exceed the limits for downloading as shape files, excel files, or KML files. For areas less than a county, you may use the map to zoom to your area and download as shape file, excel or KML, if that format is desired.The DNR Forest Practices Wetlands Geographic Information System (GIS) Layer is based on the National Wetlands Inventory (NWI). In cooperation with the Washington State Department of Ecology, DNR Forest Practices developed a systematic reclassification of the original USFWS wetlands codes into WAC 222-16-035 types. The reclassification was done in 1995 according to the Forest Practice Rules in place at the time. The WAC's for defining wetlands are 222-16-035 and 222-16-050.The DNR Forest Practices Wetlands Geographic Information System (GIS) Layer is based on the National Wetlands Inventory (NWI). In cooperation with the Washington State Department of Ecology, DNR Forest Practices developed a systematic reclassification of the original USFWS wetlands codes into WAC 222-16-035 types. The reclassification was done in 1995 according to the Forest Practice Rules in place at the time. The WAC's for defining wetlands are 222-16-035 and 222-16-050.It is intended that these data be only a first step in determining whether or not wetland issues have been or need to be addressed in an area. The DNR Forest Practices Division and the Department of Ecology strongly supports the additional use of hydric soils (from the GIS soils layer) to add weight to the call of 'wetland'. Reports from the Department of Ecology indicate that these data may substantially underestimate the extent of forested wetlands. Various studies show the NWI data is 25-80% accurate in forested areas. Most of these data were collected from stereopaired aerial photos at a scale of 1:58,000. The stated accuracy is that of a 1:24,000 map, or plus or minus 40 feet. In addition, some parts of the state have data that are 30 years old and only a small percentage have been field checked. Thus, for regulatory purposes, the user should not rely solely on these data. On-the-ground checking must accompany any regulatory call based on these data.The reclassification is based on the USFWS FWS_CODE. The FWS_CODE is a concatenation of three subcomponents: Wetland system, class, and water regime. Forest Practices further divided the components into system, subsystem, class, subclass, water regime, special modifiers, xclass, subxclass, and xsystem. The last three items (xsomething) are for wetland areas which do not easily lend themselves to one class alone. The resulting classification system uses two fields: WLND_CLASS and WLND_TYPE. WLND_CLASS indicates whether the polygon is a forested wetland (F), open water (O), or a vegetated wetland (W). WLND_TYPE, indicates whether the wetland is a type A (1), type B (2), or a generic wetland (3) that doesn't fit the categories for A or B type wetlands. WLND_TYPE = 0 (zero) is used where WLND_CLASS = O (letter "O").

The wetland polygon is classified as F, forested wetland; O, open water; or W, vegetated wetland depending on the following FWS_CODE categories: F O W --------------------------------------------------- Forested Open Vegetated Wetland Water Wetland --------------------------------------------PFO* POW PUB5 E2FO PRB* PML2 PUB1-4 PEM* PAB* L2US5 PUS1-4 L2EM2 PFL* PSS* L1RB* PML1 L1UB*
L1AB* L1OW L2RB* L2UB* L2AB* L2RS* L2US1-4 L2OW

• indicates inclusion of the subcategory (ie. PEM* includes PEM1F, PEM1FB, etc.).

DNR FOREST PRACTICES WETLANDS DATASET ON FPARS Internet Mapping Website: The FPARS Resource Map and Water Type Map display Forested, Type A, Type B, and "other" wetlands. Open water polygons are not displayed on the FPARS Resource Map and Water Type Map in an attempt to minimize clutter. The following code combinations are found in the DNR Forest Practices wetlands dataset:

WLND_CLASS WLND_TYPE wetland polygon classification F 3 Forested wetland as defined in WAC 222-16-035 O 0 *NWI open water (not displayed on FPARS Resource or Water Type Maps) W 1 Type A Wetland as defined in WAC 222-16-035 W 2 Type B Wetland as defined in WAC 222-16-035 W 3 other wetland

• NWI open water polygons are indicated by WLND_CLASS = O and WLND_TYPE = 0. Open water is used in the USFWS and WAC 222-16-035 classification system. These open water polygons are not included in the FPARS Resource Map and Water Type Map views of this dataset in an attempt to minimize clutter on the FPARS maps.

Attribute reclassification for fixed Cmin and varying amplitude.

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. We followed methods in Anderson and Merrill (1998) for combining gradient layers into an “ecological land units” map (also referred to as a “biophysical units” map). Our goal was to use this information to create sampling strata that capture the range of environments observed. The Anderson and Merrill (1998) method (implemented as a set of GIS scripts by F. Biasi (2001)) builds an ecological units map by classifying and combining individual environmental gradient maps in a GIS. Maps of aspect, moisture, slope, and slope shape are reclassified and assembled to produce maps of landform units. These landform units are then combined with reclassified elevation and geologic maps to produce a final ecological land units or “ELU” map. We used these methods as a guide to building an ecological land units map for Shenandoah National Park, adapting the procedures for local conditions. Individual steps in the process and maps resulting from intermediate and final stages are described in the report.

This data layer is a compilation of the MUPOLYGON feature class, muaggatt table and component table of the Gridded Soil Survey Geographic (gSSURGO) Database for Maryland. United States Department of Agriculture, Natural Resources Conservation Service. Under the direction of the Watershed Resources Registry (WRR) Technical Advisory Committee (TAC) this data has been altered from its original state. A reclassification of the hydric classification field was performed which classifies all soil map units consisting of less that 40% total hydric soils as not hydric, all soil map units from 41% - 79% as partially hydric and all soil map units 80% and greater as hydric. This reclassification was performed to provide a more refined input for modeling purposes. A full version of this database is available at: http://datagateway.nrcs.usda.gov/.This is a MD iMAP hosted service layer. Find more information at https://imap.maryland.gov.Feature Service Layer Link:https://mdgeodata.md.gov/imap/rest/services/Geoscientific/MD_Geology/MapServer/2**Please note, due to the size of this dataset, you may receive an error message when trying to download the dataset. You can download this dataset directly from MD iMAP Services at: https://mdgeodata.md.gov/imap/rest/services/Geoscientific/MD_Geology/MapServer/exts/MDiMAPDataDownload/customLayers/2**

Background: Malaria continues to pose a major public health challenge in tropical regions. Despite significant efforts to control malaria in Tanzania, there are still residual transmission cases. Unfortunately, little is known about where these residual malaria transmission cases occur and how they spread. In Tanzania, for example, the transmission is heterogeneously distributed. In order to effectively control and prevent the spread of malaria, it is essential to understand the spatial distribution and transmission patterns of the disease. This study seeks to predict areas that are at high risk of malaria transmission so that intervention measures can be developed to accelerate malaria elimination efforts.

Methods: This study employs a geospatial-based model to predict and map out malaria risk area in Kilombero Valley. Environmental factors related to malaria transmission were considered and assigned valuable weights in the Analytic Hierarchy Process (AHP), an online system using a pairwise comparison technique. The malaria hazard map was generated by a weighted overlay of the altitude, slope, curvature, aspect, rainfall distribution, and distance to streams in Geographic Information Systems (GIS). Finally, the risk map was created by overlaying components of malaria risk including hazards, elements at risk, and vulnerability. Results: The study demonstrates that the majority of the study area falls under the moderate-risk level (61%), followed by the low-risk level (31%), while the high-malaria risk area covers a small area, which occupies only 8% of the total area. Conclusion: The findings of this study are crucial for developing spatially targeted interventions against malaria transmission in residual transmission settings. Predicted areas prone to malaria risk provide information that will inform decision-makers and policymakers for proper planning, monitoring, and deployment of interventions. Methods Data acquisition and description The study employed both primary and secondary data, which were collected from numerous sources based on the input required for the implementation of the predictive model. Data collected includes the locations of all public and private health centers that were downloaded free from the health portal of the United Republic of Tanzania, Ministry of Health, Community Development, Gender, Elderly, and Children, through the universal resource locator (URL) (http://moh.go.tz/hfrportal/). Human population data was collected from the 2012 population housing census (PHC) for the United Republic of Tanzania report. Rainfall data were obtained from two local offices; Kilombero Agricultural Training and Research Institute (KATRIN) and Kilombero Valley Teak Company (KVTC). These offices collect meteorological data for agricultural purposes. Monthly data from 2012 to 2017 provided from thirteen (13) weather stations. Road and stream network shapefiles were downloaded free from the MapCruzin website via URL (https://mapcruzin.com/free-tanzania-arcgis-maps-shapefiles.htm). With respect to the size of the study area, five neighboring scenes of the Landsat 8 OLI/TIRS images (path/row: 167/65, 167/66, 167/67, 168/66 and 168/67) were downloaded freely from the United States Geological Survey (USGS) website via URL: http://earthexplorer.usgs.gov. From July to November 2017, the images were selected and downloaded from the USGS Earth Explorer archive based on the lowest amount of cloud cover coverage as viewed from the archive before downloading. Finally, the digital elevation data with a spatial resolution of three arc-seconds (90m by 90m) using WGS 84 datum and the Geographic Coordinate System were downloaded free from the Shuttle Radar Topography Mission (SRTM) via URL (https://dds.cr.usgs.gov/srtm/version2_1/SRTM3/Africa/). Only six tiles that fall in the study area were downloaded, coded tiles as S08E035, S09E035, S10E035, S08E036, S09E036, S10E036, S08E037, S09E037 and S10E037. Preparation and Creation of Model Factor Parameters Creation of Elevation Factor All six coded tiles were imported into the GIS environment for further analysis. Data management tools, with raster/raster data set/mosaic to new raster feature, were used to join the tiles and form an elevation map layer. Using the spatial analyst tool/reclassify feature, the generated elevation map was then classified into five classes as 109–358, 359–530, 531–747, 748–1017 and >1018 m.a.s.l. and new values were assigned for each class as 1, 2, 3, 4 and 5, respectively, with regards to the relationship with mosquito distribution and malaria risk. Finally, the elevation map based on malaria risk level is levelled as very high, high, moderate, low and very low respectively. Creation of Slope Factor A slope map was created from the generated elevation map layer, using a spatial analysis tool/surface/slope feature. Also, the slope raster layer was further reclassified into five subgroups based on predefined slope classes using standard classification schemes, namely quantiles as 0–0.58, 0.59–2.90, 2.91–6.40, 6.41–14.54 and >14.54. This classification scheme divides the range of attribute values into equal-sized sub-ranges, which allow specifying the number of the intervals while the system determines where the breaks should be. The reclassified slope raster layer subgroups were ranked 1, 2, 3, 4 and 5 according to the degree of suitability for malaria incidence in the locality. To elaborate, the steeper slope values are related to lesser malaria hazards, and the gentler slopes are highly susceptible to malaria incidences. Finally, the slope map based on malaria risk level is leveled as very high, high, moderate, low and very low respectively. Creation of Curvature Factor Curvature is another topographical factor that was created from the generated elevation map using the spatial analysis tool/surface/curvature feature. The curvature raster layer was further reclassified into five subgroups based on predefined curvature class. The reclassified curvature raster layer subgroups were ranked to 1, 2, 3, 4 and 5 according to their degree of suitability for malaria occurrence. To explain, this affects the acceleration and deceleration of flow across the surface. A negative value indicates that the surface is upwardly convex, and flow will be decelerated, which is related to being highly susceptible to malaria incidences. A positive profile indicates that the surface is upwardly concave and the flow will be accelerated which is related to a lesser malaria hazard, while a value of zero indicates that the surface is linear and related to a moderate malaria hazard. Lastly, the curvature map based on malaria risk level is leveled as very high, high, moderate, low, and very low respectively.
Creation of Aspect Factor As a topographic factor associated with mosquito larval habitat formation, aspect determines the amount of sunlight an area receives. The more sunlight received the stronger the influence on temperature, which may affect mosquito larval survival. The aspect of the study area also was generated from the elevation map using spatial analyst tools/ raster /surface /aspect feature. The aspect raster layer was further reclassified into five subgroups based on predefined aspect class. The reclassified aspect raster layer subgroups were ranked as 1, 2, 3, 4 and 5 according to the degree of suitability for malaria incidence, and new values were re-assigned in order of malaria hazard rating. Finally, the aspect map based on malaria risk level is leveled as very high, high, moderate, low, and very low, respectively. Creation of Human Population Distribution Factor Human population data was used to generate a population distribution map related to malaria occurrence. Kilombero Valley has a total of 42 wards, the data was organized in Ms excel 2016 and imported into the GIS environment for the analysis, Inverse Distance Weighted (IDW) interpolation in the spatial analyst tool was applied to interpolate the population distribution map. The population distribution map was further reclassified into five subgroups based on potential to malaria risk. The reclassified map layer subgroups were ranked according to the vulnerability to malaria incidence in the locality such as areas having high population having the highest vulnerability and the less population having less vulnerable, and the new value was assigned as 1, 2, 3, 4 and 5, and then leveled as very high, high, moderate, low and very low malaria risk level, respectively. Creation of Proximity to Health Facilities Factor The distribution of health facilities has a significant impact on the malaria vulnerability of the population dwellings in the Kilombero Valley. The health facility layer was created by computing distance analysis using proximity multiple ring buffer features in spatial analyst tool/multiple ring buffer. Then the map layer was reclassified into five sub-layers such as within (0–5) km, (5.1–10) km, (10.1–20) km, (20.1–50) km and >50km. According to a WHO report, it is indicated that the human population who live nearby or easily accessible to health facilities is less vulnerable to malaria incidence than the ones who are very far from the health facilities due to the distance limitation for the health services. Later on, the new values were assigned as 1, 2, 3, 4 and 5, and then reclassified as very high, high, moderate, low and very low malaria risk levels, respectively. Creation of Proximity to Road Network Factor The distance to the road network is also a significant factor, as it can be used as an estimation of the access to present healthcare facilities in the area. Buffer zones were calculated on the path of the road to determine the effect of the road on malaria prevalence. The road shapefile of the study area was inputted into GIS environment and spatial analyst tools / multiple ring buffer feature were used to generate five buffer zones with the

Please note that this dataset is not an official City of Toronto land use dataset. It was created for personal and academic use using City of Toronto Land Use Maps (2019) found on the City of Toronto Official Plan website at https://www.toronto.ca/city-government/planning-development/official-plan-guidelines/official-plan/official-plan-maps-copy, along with the City of Toronto parcel fabric (Property Boundaries) found at https://open.toronto.ca/dataset/property-boundaries/ and Statistics Canada Census Dissemination Blocks level boundary files (2016). The property boundaries used were dated November 11, 2021. Further detail about the City of Toronto's Official Plan, consolidation of the information presented in its online form, and considerations for its interpretation can be found at https://www.toronto.ca/city-government/planning-development/official-plan-guidelines/official-plan/ Data Creation Documentation and Procedures Software Used The spatial vector data were created using ArcGIS Pro 2.9.0 in December 2021. PDF File Conversions Using Adobe Acrobat Pro DC software, the following downloaded PDF map images were converted to TIF format. 9028-cp-official-plan-Map-14_LandUse_AODA.pdf 9042-cp-official-plan-Map-22_LandUse_AODA.pdf 9070-cp-official-plan-Map-20_LandUse_AODA.pdf 908a-cp-official-plan-Map-13_LandUse_AODA.pdf 978e-cp-official-plan-Map-17_LandUse_AODA.pdf 97cc-cp-official-plan-Map-15_LandUse_AODA.pdf 97d4-cp-official-plan-Map-23_LandUse_AODA.pdf 97f2-cp-official-plan-Map-19_LandUse_AODA.pdf 97fe-cp-official-plan-Map-18_LandUse_AODA.pdf 9811-cp-official-plan-Map-16_LandUse_AODA.pdf 982d-cp-official-plan-Map-21_LandUse_AODA.pdf Georeferencing and Reprojecting Data Files The original projection of the PDF maps is unknown but were most likely published using MTM Zone 10 EPSG 2019 as per many of the City of Toronto's many datasets. They could also have possibly been published in UTM Zone 17 EPSG 26917 The TIF images were georeferenced in ArcGIS Pro using this projection with very good results. The images were matched against the City of Toronto's Centreline dataset found here The resulting TIF files and their supporting spatial files include: TOLandUseMap13.tfwx TOLandUseMap13.tif TOLandUseMap13.tif.aux.xml TOLandUseMap13.tif.ovr TOLandUseMap14.tfwx TOLandUseMap14.tif TOLandUseMap14.tif.aux.xml TOLandUseMap14.tif.ovr TOLandUseMap15.tfwx TOLandUseMap15.tif TOLandUseMap15.tif.aux.xml TOLandUseMap15.tif.ovr TOLandUseMap16.tfwx TOLandUseMap16.tif TOLandUseMap16.tif.aux.xml TOLandUseMap16.tif.ovr TOLandUseMap17.tfwx TOLandUseMap17.tif TOLandUseMap17.tif.aux.xml TOLandUseMap17.tif.ovr TOLandUseMap18.tfwx TOLandUseMap18.tif TOLandUseMap18.tif.aux.xml TOLandUseMap18.tif.ovr TOLandUseMap19.tif TOLandUseMap19.tif.aux.xml TOLandUseMap19.tif.ovr TOLandUseMap20.tfwx TOLandUseMap20.tif TOLandUseMap20.tif.aux.xml TOLandUseMap20.tif.ovr TOLandUseMap21.tfwx TOLandUseMap21.tif TOLandUseMap21.tif.aux.xml TOLandUseMap21.tif.ovr TOLandUseMap22.tfwx TOLandUseMap22.tif TOLandUseMap22.tif.aux.xml TOLandUseMap22.tif.ovr TOLandUseMap23.tfwx TOLandUseMap23.tif TOLandUseMap23.tif.aux.xml TOLandUseMap23.tif.ov Ground control points were saved for all georeferenced images. The files are the following: map13.txt map14.txt map15.txt map16.txt map17.txt map18.txt map19.txt map21.txt map22.txt map23.txt The City of Toronto's Property Boundaries shapefile, "property_bnds_gcc_wgs84.zip" were unzipped and also reprojected to EPSG 26917 (UTM Zone 17) into a new shapefile, "Property_Boundaries_UTM.shp" Mosaicing Images Once georeferenced, all images were then mosaiced into one image file, "LandUseMosaic20211220v01", within the project-generated Geodatabase, "Landuse.gdb" and exported TIF, "LandUseMosaic20211220.tif" Reclassifying Images Because the original images were of low quality and the conversion to TIF made the image colours even more inconsistent, a method was required to reclassify the images so that different land use classes could be identified. Using Deep learning Objects, the images were re-classified into useful consistent colours. Deep Learning Objects and Training The resulting mosaic was then prepared for reclassification using the Label Objects for Deep Learning tool in ArcGIS Pro. A training sample, "LandUseTrainingSamples20211220", was created in the geodatabase for all land use types as follows: Neighbourhoods Insitutional Natural Areas Core Employment Areas Mixed Use Areas Apartment Neighbourhoods Parks Roads Utility Corridors Other Open Spaces General Employment Areas Regeneration Areas Lettering (not a land use type, but an image colour (black), used to label streets). By identifying the letters, it then made the reclassification and vectorization results easier to clean up of unnecessary clutter caused by the labels of streets. Reclassification Once the training samples were created and saved, the raster was then reclassified using the Image Classification Wizard tool in ArcGIS Pro, using the Support...

This GIS dataset represents a reclassification of existing surficial map information for the purpose of portraying the distribution of sand and gravel deposits in Alberta. The surficial geology of Alberta ungeneralised digital mosaic (Alberta Geological Survey DIG2013-0001) represents the primary source of information used in this reclassification. This dataset was updated with more recently published 1:100,000 scale surficial geology maps, and where appropriate new polygon features that were digitized from line features in the Glacial Landforms of Alberta (Alberta Geological Survey Map 604 and DIG2014-0022). The updated surficial geology mosaic was then reclassified using a thematically-based attribute table which categorizes the original surficial geology features based on their sand and gravel component. Attributes within this table comprise: (1) an approximation of the material type (MATERIAL). (2) the aerial proportion that this material represents of the polygon, as a percentage (PROPORTION). (3) an indication of whether the sand and gravel unit is mapped at the land surface or is buried (SRF_BURIED). (4) the depositional environment relating to the sand and gravel unit (GENESIS). (5) the reference source to the original data (SOURCE_MAP). (6) the GIS dataset from which the features were derived (DATASET). and (7) the mapping scale (SCALE). The MATERIAL honours the original surficial geology polygons when sufficiently precise texture/material information was provided. Otherwise MATERIAL is based on the typical range of materials that are associated with each surficial geology unit on a litho-genetic basis, using the standard Alberta Geological Survey surficial geology legend. When multiple surficial geological units that contain sand and gravel are present within a single polygon (i.e. 60% eolian deposits and 40% fluvial deposits), MATERIAL reflects the unit with the greatest proportion. For geological units whose material properties are of marginal significance as a sand and gravel deposit, particularly those that contain a mixture of silt and sand, a hierarchy was used to determine whether they are included as sand and gravel deposits. Fluvial deposits, littoral and nearshore deposits, and eolian deposits with a silt textural modifier in the original mapping data were included as potential sand and/or gravel deposits because these units are often interspersed with sand and/or gravel materials. Glaciolacustrine deposits with a silt textural modifier were not included because this environment generally does not result in the deposition of extensive sand and gravel sediments. After all of the attributes had been updated, all polygons that may contain some component of sand or gravel were extracted from this dataset to create the sand and gravel potential for Alberta digital mosaic. With this dataset, users can view the extent of surficial sand and gravel deposits in the province in a single GIS layer without the need to interpret this information from a variety of legends in the original surficial geology datasets. Users can further highlight polygons that may represent more suitable targets for sand and gravel based on the estimated material type (i.e. by eliminating polygons that typically contain large amounts of silt and fine sand), the estimated proportion of sand and gravel within the polygon, and depositional environment. This dataset best portrays sand and gravel potential that occurs at the land surface or in the very near surface, and does not attempt evaluate the sub-surface distribution of sand and gravel units. This dataset also does not provide any direct assessment of aggregate quality or thickness, and the material information is mostly inferred from the general association between certain surficial material types and their geological, depositional environment. Furthermore, the sand and gravel potential dataset is based on surficial geology maps produced at different scales and using different legends, therefore the detail and amount of information provided by these polygons will exhibit regional variations. The mapping scale for each polygon is provided in the SCALE attribute.

The African surficial lithology dataset is a map of parent materials - a mix of bedrock geology and unconsolidated surficial materials classes. The goal was to produce a map that reflected the key geological parent materials which act as primary determinants in the distribution of African vegetation /ecosystems. It is a compilation and reclassification of twelve digital geology, soil and lithology databases. Nineteen surficial lithology classes were delineated in Africa based on geology, soil and landform. Whenever available, multiple sources of ancillary digital data, hard copy maps and literature were reviewed to assist in the reclassification of the source data to the African surficial lithology classification. Of particular note, due to the varying spatial and classification resolutions of the geologic source data, the African surficial lithology map varies in spatial complexity and classification detail across Africa. Purpose: The African surficial lithology data was developed as a primary input dataset for an African Ecological Footprint mapping project undertaken by the U.S. Geological Survey and The Nature Conservancy. The project used a biophysical stratification approach which was based on mapping the major structural components of ecosystems (land surface forms, lithology, isobioclimates and biogeographic regions). These unique physical components, which are considered as the fundamental building blocks of ecosystems, were reviewed by regional vegetation and landscape ecology experts and used in a classification and regression tree (CART) inductive model to map intermediate scale African ecosystems.

Buildings_BACI File Geodatabase Feature Class Thumbnail Not Available Tags Buildings, structures, ruins, storage tanks, silos, water towers, Baltimore City Planimetric, Biophysical Resources, Land, Socio-Economic Resources, Capital Summary This data was created as a landbase feature as part of the planimetric data. Description This dataset represents photogrammetrically captured Building footprints => 100sq. ft. including storage tanks, silos, water towers, power plants, substations, and structures under construction and ruins. Feature capture rules: Buildings - Outline edge of roofline. All buildings shall be captured as polygons. In commercial areas especially, it is important that the plotted building represent the face of the building where it meets the sidewalk. Polygons shall be created for the outer boundary of the building when a partywall exists. Does not include sheds and small temporary structures. Attached garages shall be represented as part of the building structure. Large structures such as stadiums shall also be represented. Structures under construction or demolition - Delineate the rooflines of all buildings under construction as interpreted from aerial photography. If roofline is not visible compile visible foundation or walls Ruins - Delineate old overgrown areas of old structures that have been demolished or are in disrepair. Original data will be reclassified to define as separate subtype. Storage tanks, silos, and water towers - Outlines of all storage tanks, silos and water towers. . Original data will be reclassified to define as separate subtype. Power plants and substations - Outline of power plant and substation structure. . Original data will be reclassified to define as separate subtype. Credits There are no credits for this item. Use limitations Every reasonable effort has been made to ensure the accuracy of these data. The City of Baltimore, Maryland makes no representations nor warranties, either express or implied, regarding the accuracy of this information or its suitability for any particular purpose whatsoever. The data is licensed "as is" and the City of Baltimore will not be liable for its use or misuse by any party. Reliance of these data is at the risk of the user. Extent West -76.714715 East -76.525355 North 39.375162 South 39.193953 Scale Range There is no scale range for this item.

Geospatial data about Reclass light pollution. Export to CAD, GIS, PDF, CSV and access via API.

Audience: PublicExtent: UKUpdate Frequency: MonthlyReclassified routes, formerly National Cycle Network, are considered suitable for people who are confident and experienced at cycling. Sustrans continues to promote reclassified routes, including some long distance challenge routes, on our website and maps. Sustrans has reclassified 18.6% (3090 miles) of the National Cycle Network (July 2020). The routes will continue to be signed on the ground but will not have the red National Cycle Network number patch on the signs. However, the branded signage or logo for the named route that the reclassified section runs on will be retained for navigation purposes. A large majority of these are on-road sections.You can read more about the methodology we used in our Removal and Reclassification Methodology

Data Source: KYEM 2018 Hazard Mitigation Plan - LandslidesA statewide landslide susceptibility model was developed in ArcGIS using two map layers: geology and slope. The geology and slope maps (raster images) were reclassified based on a matrix of weighted scores that were assigned to particular geologic formations and ranges of slope values (Table 2-5). The weighted score for slope doubled with each increasing slope range. The weighted score for the geology ranged from 10 to 40 depending on the rock type. Using the ArcGIS Weighted Sum tool, the newly reclassified values of both raster map layers were multiplied by an assigned weight and then values for both layers were added together (Eq. 2-1). In order to have slope be a greater influence on the susceptibility model, a 70 percent weight was assigned for slope and a 30 percent weight was assigned for geology.

Eq. 2-1 (geology reclass value × 0.30) + (slope reclass value × 0.70) = landslide susceptibility value

Using the summed cell values from the two layers, landslide susceptibility was manually classified into low, moderate, and high categories (Fig. 2-8). Classification was made by visually inspecting the map and by determining the distribution of existing landslides cataloged in the Kentucky Geological Survey inventory.

Historic flowways in Lee County, Florida represent historic paths of surface water conveyance. This dataset was developed from reclassification of the soils layer and aerial photo interpretation.A flowway for the purpose of this analysis is generally described as the lowest "pathway" allowing for the successive conveyance of surface waters. These areas were delineated by visual interpretation of the spatial differences between that shown on the 2005 aerials and that determined from the categorical reclassification of the soils layer.See associated layers, FlowwayArrows and FlowwaysHistoricConnections

This tile layer displays percent slope in the Dismal Creek area of the Eastern Divide Insect & Disease Control management project being undertaken by the USFS in the Jefferson National Forest. It uses the hillshade function.Purpose:The data was included to provide additional environmental context for the user’s understanding of the project’s likely environmental impactsSource & Date:Downloaded from the Virginia Geographic Information Network's (VGIN's) Virginia GIS Clearinghouse on 3/31/2021. Data was collected in 2016.Processing:The slope was calculated from the 1-meter LIDAR-derived digital elevation model mosaic. The slope raster was reclassified, as shown below. ABRA published the reclassified raster to ArcGIS Online as a tile layer.Symbology:EDID Dismal Creek Slope0 - 5%: Gray5 - 10%: Dark Green10 - 15%: Med Green15 - 20%: Light Green20 - 25%: Yellow-Green25 - 30%: Yellow30 - 40%: Light Orange40 -50%: Dark Orange50 - 60%: Orange-Red> 60%: Dark Red

Pursuant to the Groundwater Protection Act (RSA 485-C:5), all groundwater in New Hampshire is classified into one of four categories: GAA, GA1, GA2, and GB. Groundwater reclassification is typically used to provide higher levels of protection in areas that contribute to public water systems or have high-value groundwater resources for present or future water supply.This spatial dataset represents Groundwater Classification Areas (GA1) as designated under RSA, including those with enhanced state and local protections. Classifications do not necessarily reflect existing water quality. Reclassification to GA1 is initiated by a local entity—such as a municipality or public water supplier—or by the New Hampshire Department of Environmental Services (NHDES).Entities seeking GA1 reclassification must develop a Potential Contaminant Source (PCS) management program. This program includes maintaining an inventory of PCSs that store, use, or handle regulated substances above household quantities. Additionally, it requires regular distribution of best management practices (BMP) materials, on-site inspections of PCS facilities at least once every three years, and compliance with BMP regulations outlined in Administrative Rule Env-Wq 401.Reclassification provides added protections by restricting certain high-risk land uses, such as hazardous waste disposal facilities, landfills, and junkyards, within designated areas. Existing high-risk facilities within GA1 areas must obtain a Groundwater Release Detection permit and comply with monitoring requirements. While NHDES provides guidance and oversight, enforcement of BMP compliance is primarily the responsibility of the local entity managing the reclassified area.For further information, contact the NHDES Drinking Water and Groundwater Bureau at (603) 271-2513 or visit www.des.nh.gov.

This map shows the global vulnerability with respect to natural hazards. To derive the vulnerability using the above described resilience the population density is used as the main impact factor for the vulnerability. So the more people live at a certain place, the higher the vulnerability.To calculate the vulnerability the value gained from the population reclassification is divided by the value from the global resilience map.The population density is evaluated by the following reclass table:• < 0.1 person/km²: not being used• 0.1 - 10 person/km²: 1• 10 - 100 person/km²: 2• 100 - 1 000 person/km²: 3• 1 000 - 10 000 person/km²: 4• > 10 000 person/km²: 5The resulting raster values have a range between 0 (low vulnerability) and 4 (high vulnerability).DataPopulation density © Socioeconomic Data and Applications Center (SEDAC)Airports © ourairports.comWorld Port Index © msi.nga.milTerrain Data (250 grid resolution) © SRTMGlobal Needs Assessment - Vulnerability Index and Crisis Index 2013 © European Commission ECHOMedical Care © www.laenderdaten.deIncome © World BankHuman rights and state stability © The Global Slavery Index 2013Education © World BankBackgroundBasemap © Esri, DeLorme, GEBCO, NOAA NGDC, and other contributors