This geodatabase includes spatial datasets that represent the Basin and Range basin-fill aquifers in the States of Arizona, California, Idaho, Nevada, New Mexico, Oregon, and Utah. Included are: (1) polygon extents; datasets that represent the aquifer system extent, the entire extent subdivided into subareas or subunits, and any polygon extents of special interest (outcrop areas, no data available, areas underlying other aquifers, anomalies, for example), (2) contours: thickness contours used to generate the surface rasters in subarea 4 (Arizona), (3) modified source raster datasets for subareas 1 and 3, (4) corrected altitudes of top and bottom surface rasters of the entire aquifer. The thickness contours and modified surface rasters are supplied for reference. The extent of the Basin and Range basin-fill aquifer is from the linework of the Basin and Range aquifer extent maps in U.S. Geological Survey Hydrologic Atlas 730 Chapters B and C, and a digital version of the aquifer extent presented in the Groundwater Atlas of the United States (the U.S. Geological Survey Hydrologic Atlas. The Basin and Range basin-fill aquifer has no aquifer subunits, but is defined by five subareas: 1. Subarea 1 is the area that overlies the Basin and Range Carbonate aquifer, which was the subject of U.S. Geological Survey Scientific Investigations Report 2010-5193 (USGS SIR 2010-5193). 2. Subarea 2 is the area of a different aquifer system, which is set to null for use within the Basin and Range basin-fill aquifer from U.S. Geological Survey Principal Aquifers, 2003 (USGS Circular 1323, Figure 2) 3. Subarea 3 is the area of the Basin and Range basin-fill aquifer that was the subject of U.S. Geological Survey Geophysical Map 1012 (USGS GP-1012) and not covered by USGS SIR 2010-5193 or the Basin and Range basin-fill aquifer in Arizona, Arizona Geological Survey, Digital Geological Map 52 (AZGS DGM-52). Top of aquifer is land surface. USGS GP-1012 dataset is depth from land surface to basin bottom. 4. Subarea 4 is the area of the 01BSNRGB aquifer in Arizona, (AZGS DGM-52) 5. Subarea 5 areas are in the Basin and Range basin-fill extent areas that do not have top/bot defined. The resultant top and bottom surface rasters for each subarea were merged into surface rasters of the top and bottom of the entire Basin and Range basin-fill aquifer within a GIS using tools that create hydrologically correct surfaces from contour data, deriving the altitude from the thickness (depth from the land surface), and merging the subareas into a single surface. The primary tools were a version of "Topo to Raster", and "Mosaic to New Raster" used in ArcGIS, ArcMap, Esri 2014.

Saturated thickness map of the Rush Springs aquifer in central Oklahoma. Map displays the thickness of the water level (potentiometric) surface in the Rush Springs aquifer to the base of the unit, which is defined as the base of the Marlow Formation by the OWRB for their study released in 2018. Saturated thickness ranges between 0-432 feet, with an average saturated thickness of 181 feet. In areas where the potentiometric surface rises above the top of the Rush Springs Formation into the Cloud Chief Formation, the thickness was capped at the Rush Springs/Cloud Chief contact. This calculation was done in ArcGIS 10.2.2 using a raster calculator subtracting the saturated thickness within the Cloud Chief from the total saturated thickness. Also used in the process was the Mosaic to New Raster tool to create a raster that included values from both the smaller extent Cloud Chief and the larger extent Rush Springs in one output raster with the extent of the entire Rush Springs aquifer.

This is a dataset of georeferenced 1917 Sanborn Fire Insurance maps of Knoxville TN, including individual sheets, a sheet index, a seamless mosaic, and a map key. Digital images of the data sheets were downloaded from the University of Tennessee Library https://digital.lib.utk.edu/collections/sanbornmapcollection. Multi-part sheets were clipped into pieces for georeferencing. Chris DeRolph georeferenced each sheet and piece, where possible. There were a few outlying images that were unable to be georeferenced due to lack of recognizable common features between the sheets and reference maps/imagery in the sheet vicinity. The sheet index shapefile includes a field with a hyperlink to the UTK library download page for the sheet. The seamless mosaic was created using the Mosaic to New Raster tool in ArcGIS Pro with all georeferenced sheets/pieces as inputs and the Minimum Mosaic Operator. No attempt was made prior to the mosaicking process to remove sheet numbers, scale bars, north arrows, overlapping labels/annotation, etc. Viewing individual sheets will provide the cleanest look at an area, while the seamless mosaic provides the most comprehensive view of the city at the time the maps were created.

Last Revised: April 2016

Map Information

This nowCOAST™ time-enabled map service provides maps of NOAA/National Weather Service (NWS) and Office of Oceanic and Atmospheric Research (OAR) Multi-Radar/Multi-Sensor (MRMS) mosaics of quality-corrected base reflectivity images across the Contiguous United States (CONUS) as well as Puerto Rico, Hawaii, Guam and Alaska with a 1 kilometer (0.62 mile) horizontal resolution. The mosaics are compiled by combining regional base reflectivity radar data obtained from Weather Surveillance Radar 1988 Doppler (WSR-88D) also known as NEXt-generation RADar (NEXRAD) sites across the country operated by the NWS and the Dept. of Defense and also from data from Terminal Doppler Weather Radars (TDWR) at major airports. The combined data is then adjusted using a quality-control algorithm developed by the NOAA National Severe Storms Laboratory (NSSL), and published in both GRIB2 and RGB GeoTIFF formats. nowCOAST processes and displays the data from the GRIB2 files. The colors on the map represent the strength of the energy reflected back toward the radar. The reflected intensities (echoes) are measured in dBZ (decibels of z). The color scale is the same as used in the NWS RIDGE2 map viewer, however dBZ values are rounded down to the integer during processing in order to improve display performance. The radar data itself is updated by the NWS every 10 minutes during non-precipitation mode, but every 2-6 minutes during precipitation mode. nowCOAST™ downloads, processes, and displays the latest mosaics every 4 minutes. For more detailed information about layer update frequency and timing, please reference the nowCOAST™ Dataset Update Schedule.

Background Information

Reflectivity is related to the power, or intensity, of the reflected radiation that is sensed by the radar antenna. Reflectivity is expressed on a logarithmic scale in units called dBZ. The "dB" in the dBZ scale is logarithmic and is unitless, and is used only to express a ratio. The "Z" is the ratio of the density of water drops (measured in millimeters raised to the 6th power) in each cubic meter (mm^6/m^3). When the "Z" is large (many drops in a cubic meter), the reflected power is large. A small "Z" means little returned energy. In fact, "Z" can be less than 1 mm^6/m^3 and since it is logarithmic, dBZ values will become negative, as is often the case when the radar is in clear air mode and indicated by earth tone colors. dBZ values are related to the intensity of rainfall. The higher the dBZ, the stronger the rain rate. A value of 20 dBZ is typically the point at which light rain begins. The values of 60 to 65 dBZ is about the level where 3/4 inch hail can occur. However, a value of 60 to 65 dBZ does not mean that severe weather is occurring at that location. The base reflectivity is the lowest (1/2 degree elevation angle) reflectivity scan from the radar. The source of the base reflectivity mosaics is the NOAA Multi-Radar/Multi-Sensor (MRMS) System, which is developed by the NOAA National Severe Storms Laboratory (NSSL) and operated by NWS/National Centers for Environmental Prediction (NCEP) Central Operations (NCO).

Time Information

This map service is time-enabled, meaning that each individual layer contains time-varying data and can be utilized by clients capable of making map requests that include a time component.

In addition to ArcGIS Server REST access, time-enabled OGC WMS 1.3.0 access is also provided by this service.

This particular service can be queried with or without the use of a time component. If the time parameter is specified in a request, the data or imagery most relevant to the provided time value, if any, will be returned. If the time parameter is not specified in a request, the latest data or imagery valid for the present system time will be returned to the client. If the time parameter is not specified and no data or imagery is available for the present time, no data will be returned.

This service is configured with time coverage support, meaning that the service will always return the most relevant available data, if any, to the specified time value. For example, if the service contains data valid today at 12:00 and 12:10 UTC, but a map request specifies a time value of today at 12:07 UTC, the data valid at 12:10 UTC will be returned to the user. This behavior allows more flexibility for users, especially when displaying multiple time-enabled layers together despite slight differences in temporal resolution or update frequency.

When interacting with this time-enabled service, only a single instantaneous time value should be specified in each request. If instead a time range is specified in a request (i.e. separate start time and end time values are given), the data returned may be different than what was intended.

Care must be taken to ensure the time value specified in each request falls within the current time coverage of the service. Because this service is frequently updated as new data becomes available, the user must periodically determine the service's time extent. However, due to software limitations, the time extent of the service and map layers as advertised by ArcGIS Server does not always provide the most up-to-date start and end times of available data. Instead, users have three options for determining the latest time extent of the service:

  Issue a returnUpdates=true request (ArcGIS REST protocol only)
  for an individual layer or for the service itself, which will return
  the current start and end times of available data, in epoch time format
  (milliseconds since 00:00 January 1, 1970). To see an example, click on
  the "Return Updates" link at the bottom of the REST Service page under
  "Supported Operations". Refer to the
  ArcGIS REST API Map Service Documentation
  for more information.


  Issue an Identify (ArcGIS REST) or GetFeatureInfo (WMS) request against
  the proper layer corresponding with the target dataset. For raster
  data, this would be the "Image Footprints with Time Attributes" layer
  in the same group as the target "Image" layer being displayed. For
  vector (point, line, or polygon) data, the target layer can be queried
  directly. In either case, the attributes returned for the matching
  raster(s) or vector feature(s) will include the following:


      validtime: Valid timestamp.


      starttime: Display start time.


      endtime: Display end time.


      reftime: Reference time (sometimes referred to as
      issuance time, cycle time, or initialization time).


      projmins: Number of minutes from reference time to valid
      time.


      desigreftime: Designated reference time; used as a
      common reference time for all items when individual reference
      times do not match.


      desigprojmins: Number of minutes from designated
      reference time to valid time.




  Query the nowCOAST™ LayerInfo web service, which has been created to
  provide additional information about each data layer in a service,
  including a list of all available "time stops" (i.e. "valid times"),
  individual timestamps, or the valid time of a layer's latest available
  data (i.e. "Product Time"). For more information about the LayerInfo
  web service, including examples of various types of requests, refer to
  the 
  nowCOAST™ LayerInfo Help Documentation

References

Lin Tang, Jian Zhang, Carrie Langston and John Krause, Kenneth Howard,
Valliappa Lakshmanan, 2014: A Physically Based Precipitation–Nonprecipitation
Radar Echo Classifier Using Polarimetric and Environmental Data in a Real-Time
National System. Weather and Forecasting, 29, 1106–1119, doi: 10.1175/WAF-D-13-00072.1.
(Available at http://journals.ametsoc.org/doi/full/10.1175/WAF-D-13-00072.1).



NWS, 2013: Radar Images for GIS Software
(http://www.srh.noaa.gov/jetstream/doppler/gis.htm).

This data set represents a 0.76-meter resolution LiDAR-derived bare earth Digital Elevation Model (DEM) layer for New Hampshire. It was generated from a statewide Esri Mosaic Dataset which comprised 7 separate LiDAR collections that covered the state as of, July 2022. The Mosaic Dataset was converted to this img raster data set.

This data set represents a 5-meter resolution LiDAR-derived degree slope layer for New Hampshire. It was generated from a statewide Esri Mosaic Dataset which comprised 8 separate LiDAR collections that covered the state as of January, 2020. The Mosaic Dataset was used as input to the ArcGIS Spatial Analyst "Slope" geoprocessing tool which calculates the degree slope for each cell of the input raster, in this case, the statewide mosaic dataset.

We created a mosaic of the U.S. NLCD 01 (2001 edition) and the Canadian 2000 PLO and the SOLRIS v1.2. We selected these datasets since they were created from imagery collected in approximately the same time frames, included similar categories, and covered the full extent of the Great Lakes Basin. To process the data, we first clipped each input data layer by the extent of the Great Lakes states and the Province of Ontario. Next, we reclassified the raster values using the Reclassify tool, changing the original values to crosswalked values. Next, we projected the data into the standardized projection used by the Great Lakes Aquatic Habitat Framework (GLAHF) USA Contiguous Albers Equal Area Conic USGS projection and resampled the pixel size to 30 meters, the standard projection and pixel size for the Great Lakes Aquatic Habitat Framework (GLAHF). We then created a mosaic using Mosaic to New Raster, incorporating the waters of the Great Lakes using the shoreline of the watersheds in the GLAHF Hydrology Data Package V1 as a mask. Land cover/land use codes: 1 = Great Lakes Waters; 11 = Water; 2 = Developed; 31 = Barren Land; 41 = Deciduous Forest; 42 = Evergreen Forest; 43 = Evergreen Forest; 43 = Mixed Forest; 52 = Scrub/Shrub; 71 = Grassland/Herbaceous; 8 = Agriculture; 90 = Forested Wetland; 95 = Emergent Wetland; 98 = Other/Undefined.

Crown fire potential was modeled using FlamMap, an interagency fire behavior mapping and analysis program that computes potential fire behavior characteristics. The tool uses eight spatial input data layers to represent biophysical conditions and weather parameters to simulate wind and fuel moisture conditions. The spatial input layers were created by Landscape Fire and Resource Management Planning Tools Project (LANDFIRE) and include elevation, slope, aspect, canopy closure, fuel model 40, canopy base height, and canopy bulk density. The weather parameters were collected from the RAWS weather stations in New Mexico. Crown fire potential was modeled by individual fire zones, created by the Southwest Coordination Center (SWCC) then combined using the mosaic to new raster function in ArcGIS. The weather data for the northern and western fire zones (101, 102, 103, 109, 110, and 113) represents the average early summer (May and June ) conditions; the weather data for the eastern fire zones (104, 108, 114, 115) represents the average early spring (March and April ) conditions; and the weather data for the southern and central fire zones (105, 106, 107, 111, 112) represents the average spring (April and May) conditions. The Flam Map model result classifies crown fire potential into three categories: surface fire, passive crown fire, and active crown fire. The technical team recommended that the result be grouped into two categories: 1. areas with no crown fire potential and 2. areas with crown fire potential.

For this crosswalk, we selected the NLCD (2011 version) and the Canadian SOLRIS 2.0 because they were created from imagery collected in approximately the same time framesand because they were comparable to the 2001 NLCD and the 2000 SOLRIS v1.2. However, a comparable layer from the same time period was not available for Northern Ontario. While the 2005-2011 Far North Land Cover 1.4 was available, it did not cover the entire extent of the 2000 PLO, and the classification scheme was very different from the 2000 PLO. This made it difficult to compare the two datasets as there were large differences in the distribution of classes due to classification scheme and mapping differences, resulting in artificially high land cover change statistics (see Far North Land Cover Data Specifications Version 1.4). As a result, we have incorporated the 2000 PLO for Northern Ontario, but will reevaluate the crosswalk as new data becomes available. To process these data, we first clipped each input data layer by the extent of the Great Lakes states and the Province of Ontario. Next, we reclassified the raster values using the Reclassify tool, changing the original values to crosswalked values. Next we projected the data into the standardized projection used by the Great Lakes Aquatic Habitat Framework (GLAHF) USA Contiguous Albers Equal Area Conic USGS projection and resampled the pixel size to 30 meters, the standard projection and pixel size for the Great Lakes Aquatic Habitat Framework (GLAHF). We then created a mosaic using Mosaic to New Raster, incorporating the waters of the Great Lakes as the value “1†, using the shoreline of the watersheds in the GLAHF Hydrology Data Package V1 as a mask. Land cover/land use codes: 1 = Great Lakes Waters; 11 = Water; 2 = Developed; 31 = Barren Land; 41 = Deciduous Forest; 42 = Evergreen Forest; 43 = Evergreen Forest; 43 = Mixed Forest; 52 = Scrub/Shrub; 71 = Grassland/Herbaceous; 8 = Agriculture; 90 = Forested Wetland; 95 = Emergent Wetland; 98 = Other/Undefined.

The Belgian soil organic carbon (SOC) stock map for topsoils (0-30 cm) was composed of 2 regional SOC stock maps. For the regional maps a different approach was used for agricultural land as compared to forest. The maps are based on digital soil mapping approaches using empirical models calibrated to predict the SOC stock and using covariates that are available at a sufficient resolution at the regional scale. All maps are strongly dependent on the Belgian Soil Map (texture and drainage parameters). The regional maps were compiled at a finer resolution (10m x 10m and 40m x 40m grid cells). Next they were joined (40m x 40m grid cells) and finally scaled up to the required 1 by 1 km grid cells. This was done using the following tools: block statistics (mean), mosaic to new raster (mean), project to raster, block statistics (mean), resample (nearest neighbour) and project raster. Given the different origin of the individual maps, the uncertainty varies between maps. For instance, a map of the 90% confidence interval of the SOC stocks was produced for agricultural soils in Wallonia based on a Monte Carlo Approach taking into account both the measurement and the model uncertainties. For Flemish forest soils, spatial and analytical uncertainties were taken into account using bootstrapping techniques. For Flemish agricultural soils, the uncertainty reported is the model uncertainty on point estimates for each data point, in which the estimated model parameters are simulated 1000 times as being independent normal distributed variables using their model estimation and standard error as distribution parameters. No additional uncertainty is taken into account for the conversion functions that use the stochastic variables "bulk density". The SOC stock maps are the first comprehensive map for Belgium integrating grasslands, croplands and forests. There are two versions of the SOC stock maps for Belgium: 1) resolution of 40m x 40m in the coordinate reference system Lambert72 and 2) resolution of 1km x 1km in the coordinate reference system WGS84. The metadata are available and allow assessing the uncertainties of the stock estimates in the different component maps.

This data set represents a 5-meter resolution LiDAR-derived degree slope layer for New Hampshire. It was generated from a statewide Esri Mosaic Dataset which comprised 8 separate LiDAR collections that covered the state as of January, 2020. The Mosaic Dataset was used as input to the ArcGIS Spatial Analyst "Slope" geoprocessing tool which calculates the degree slope for each cell of the input raster, in this case, the statewide mosaic dataset.

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

The GDAL/OGR libraries are open-source, geo-spatial libraries that work with a wide range of raster and vector data sources. One of many impressive features of the GDAL/OGR libraries is the ViRTual (VRT) format. It is an XML format description of how to transform raster or vector data sources on the fly into a new dataset. The transformations include: mosaicking, re-projection, look-up table (raster), change data type (raster), and SQL SELECT command (vector). VRTs can be used by GDAL/OGR functions and utilities as if they were an original source, even allowing for chaining of functionality, for example: have a VRT mosaic hundreds of VRTs that use look-up tables to transform original GeoTiff files. We used the VRT format for the presentation of hydrologic model results, allowing for thousands of small VRT files representing all components of the monthly water balance to be transformations of a single land cover GeoTiff file.

Presentation at 2018 AWRA Spring Specialty Conference: Geographic Information Systems (GIS) and Water Resources X, Orlando, Florida, April 23-25, http://awra.org/meetings/Orlando2018/

This dataset contains maps of a sample of large Martian fluvial systems. These systems have been mapped as vector-based polygons within the QGIS software, using the more recent and, to date, the best resolution THEMIS (Thermal Emission Imaging Spectrometer) daytime IR mosaic (100 m/pixel). In addition, we used, where necessary (for small-scale systems and valleys with high erosion), CTX (Contex Camera) data, with a resolution up to 6 m/pixel. The imagery data were coupled with the MOLA (Mars Orbiter Laser Altimeter Mosaic) mosaic which has a spatial resolution of 463 m/pixel. At low latitudes, we used an equidistant cylindrical projection, while at high latitudes, we used sinusoidal and polar stereographic projections to represent and analyze the data. Topographic information and data of higher image quality (new THEMIS mosaic plus CTX data) than those of previous manual maps, allowed us to identify new structures and more tributaries for a large number of systems. A MOLA-based raster file is also associated to each valley mapped.

Attribution:

If you use this data set in your own work, please cite this DOI:
10.5281/zenodo.4591003

Please also cite these works:

Alemanno et al.: 2018, Global Map of Martian Fluvial Systems: Age and Total Eroded Volume Estimations, Earth and Space Science Journal, 5, 560-577, doi:https://doi.org/10.1029/2018EA000362
Orofino et al.: 2018, Estimate of the water flow duration in large Martian fluvial systems. Planetary and Space Science Journal, 163, 83-96. doi: 10.1016/j.pss.2018.06.001
Alemanno G.: 2018, Study of the fluvial activity on Mars through mapping, sediment transport modelling and spectroscopic analyses. PhD dissertation thesis, arXiv:1805.02208 [astro-ph.EP]

This data set is a digital soil survey and generally is the most detailed level of soil geographic data developed by the National Cooperative Soil Survey. The information was prepared by digitizing maps, by compiling information onto a planimetric correct base and digitizing, or by revising digitized maps using remotely sensed and other information. This data set consists of georeferenced digital map data and computerized attribute data. The map data are in a soil survey area extent format and include a detailed, field verified inventory of soils and miscellaneous areas that normally occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped. A special soil features layer (point and line features) is optional. This layer displays the location of features too small to delineate at the mapping scale, but they are large enough and contrasting enough to significantly influence use and management. The soil map units are linked to attributes in the National Soil Information System relational database, which gives the proportionate extent of the component soils and their properties.

Geoscience Australia is distributing Landsat MSS, TM and ETM+ data for 19 epochs or timeframes ranging from 1972 to 2010 covering Australia. This data has been provided bythe Department of Climate Change and Energy Efficiency formerly known as the Australian Greenhouse Office (AGO).This data is only available through Geoscience Australia and not through the Department of Climate Change and Energy Efficiency.Epoch formatsEpochs are available as 1:1M tiles or as a Continental mosaic in the following formats:TilesEpoch Projection Bands File Format All MGA94 All bands except Band 6 (thermal) ERS/BIL 2004, 2005 & 2006 Geographic All bands except Band 6 (thermal) ERS/BIL Pre 2004 Geographic Bands 543 for TM & ETM+ and all bands for MSS ERS/BIL 2002 MGA94 Panchromatic only ERS/BIL 2002 Geographic Panchromatic only ERS/BIL Continental mosaicsEpoch Projection Bands File Format All Geographic 543/RGB bands for TM, ETM+ and MSS ECW 2002 Panchromatic Sharpened Geographic Bands 543 plus Panchromatic band ECW File sizes and mediaFile sizes of the data are significantEpoch Minimum Date Maximum Date Tile data - ERS/BIL Continental mosaic - ECW Total size Gb - MGA Total size Gb - Geographic Total size Gb - Geographic 2006 4/11/2005 7/10/2006 162.1* 145.0* 4.6 2005 5/12/2004 9/10/2005 96.1 85.3 4.2 2004 1/09/2003 25/09/2004 96.1 85.3 4.6 2002 (No Pan) 9/11/2001 27/11/2002 96.1 43.4 4.6 2002 Pan 9/11/2001 27/11/2002 64.1 57.8 N/A 2002 Pan sharpened 9/11/2001 27/11/2002 N/A N/A 4.6 2000 (No Pan) 14/07/1999 21/09/2000 96.1 43.4 4.6 1998 20/05/1997 31/08/1998 96.1 43.4 3.9 1995 11/06/1994 29/08/1995 96.1 43.4 3.9 1992 1/01/1992 11/03/1993 96.1 43.4 3.9 1991 27/09/1990 9/07/1991 96.1 43.4 3.9 1989 1/07/1989 3/04/1990 112.1 43.4 4.0 1988 12/07/1987 7/09/1988 16.1 14.5 3.7 1985 18/06/1984 20/07/1985 16.1 14.5 3.6 1980 21/09/1979 24/01/1981 16.1 14.5 3.7 1977 25/02/1975 20/12/1978 16.1 14.5 2.7 1972 28/07/1972 29/10/1976 16.1 14.5 3.5 Includes date and boundary (datebound) data.Sensor/Epoch Landsat TM and ETM+ SLC-Off: 2006;Landsat MSS: 1972, 1977, 1980, 1985, 1988;Landsat TM: 1989, 1991, 1992, 1995, 1998, 2004 and 2005; andLandsat ETM+: 2000 and 2002.*Includes simulated MSS from Landsat TM (MGA projection only). Projection Either MGA94 OR Geographic. Bands All bands except thermal bands - MGA coordinates; All bands except thermal bands - Geographical coordinates, 2004, 2005 & 2006 epochs;5,4,3 for TM & ETM+ and all bands for MSS - Geographical coordinates, Pre 2004 epochs only;Panchromatic band available separately only for 2002 epoch - MGA & Geographical coordinates. Processing Ortho-corrected, radiometrically corrected and mosaiced into tiles. All data is calibrated to a common geographic and spectral base (AGO year 2000 base).Note: Single scene boundaries can be quite obvious within a tile due mainly to the seasonal changes associated with different acquisition dates.Tiling system Tiles approximate 1:1 million map sheets covering Australia. Most tiles contain overlap beyond the quoted extents. Coverage View a detailed map of the 1972 to 2005 epoch extents. Download an ESRI shapefile of the date and boundary (datebound) of each Landsat scene used to produce the epochs from 1972 to 2005.The introduction of SLC-Off and bumper mode Landsat products for the 2006 epoch has meant that a new method has been required for creating datebound information. The new 2006 date bounds are currently in raster form as opposed to the traditional vector. The rasters are in ER Mapper Storage format (ERS) format as Geodetic or MGA projections - file size 61 or 66 Gb. Pixel size 25 metres TM and ETM+ and 50 metres MSS. Format Generic BIL files with ER Mapper ASCII header. File size Variable -11:45 AM 1/04/2010:This data is available under Creative Commons Licence 3.0:http://creativecommons.org/licenses/by/3.0/au/

This data set represents a 5-meter resolution LiDAR-derived percent slope layer for New Hampshire. It was generated from a statewide Esri Mosaic Dataset which comprised 8 separate LiDAR collections that covered the state as of January, 2020. The Mosaic Dataset was used as input to the ArcGIS Spatial Analyst "Slope" geoprocessing tool which calculates the percent slope for each cell of the input raster, in this case, the statewide mosaic dataset.

Map Information

This nowCOAST time-enabled map service provides maps depicting the NWS Multi-Radar Multi-Sensor (MRMS) quantitative precipitation estimate mosaics for 1-, 3-, 6-, 12-, 24-, 48-, and 72-hr time periods at a 1 km (0.6 miles) horizontal resolution for CONUS and southern part of Canada. The precipitation estimates are based only on radar data. The total precipitation amount is indicated by different colors at 0.01, 0.10, 0.25 and then at 1/4 inch intervals up to 4.0 inches (e.g. 0.50, 0.75, 1.00, 1.25, etc.), at 1-inch intervals from 4 to 10 inches and then at 2-inch intervals up to 14 inches. The increments from 0.01 to 1.00 or 2.00 inches are similar to what are used on NCEP's Weather Prediction Center QPF products and the NWS River Forecast Center (RFC) daily precipitation analysis. The 1-hr mosaic is updated every 4 minutes with a latency on nowCOAST of about 6-7 minutes from valid time. The 3-, 6-, 12-, and 24-hr QPEs are updated on nowCOAST every hour for the period ending at the top of the hour.
The 48- and 72-hr QPEs are generated daily for the period ending at 12 UTC (i.e. 7AM EST) and available on nowCOAST shortly afterwards. For more detailed information about the update schedule, please see: http://new.nowcoast.noaa.gov/help/#section=updateschedule

Background Information

The NWS Multi-Radar Multi-Sensor System (MRMS)/Q3 QPEs are radar-only based quantitative precipitation analyses. The 1-h precipitation accumulation is obtained by aggregating 12 instantaneous rate fields. Missing rate fields are filled with the neighboring rate fields if the data gap is not significantly large (e.g.<=15 minutes). The instantaneous rate is computed from the hybrid scan reflectivity and the precipitation flag fields. (Both are 2-D derivative products from the National 3-D Reflectivity Mosaic grid which has a 1-km horizontal resolution, 31 vertical levels and a 5-minute update cycle). The instantaneous rate currently uses four Z-R relationships (i.e. tropical, convective, stratiform, or snow). The particular ZR relationship used in any grid cell depends on precipitation type which is indicated by the precipitation flag. The other accumulation products are derived by aggregating the hourly accumulations. The 1-hr QPE are generated every 4 minutes, while the 3-,6-,12-, and 24-hr accumulations are generated every hour at the top of the hour. The 48- and 72-hr QPEs are updated daily at approximately 12 UTC. MRMS was developed by NOAA/OAR/National Severe Storms Laboratory and migrated into NWS operations at NOAA Integrated Dissemination Program.

Time Information

This map is time-enabled, meaning that each individual layer contains time-varying data and can be utilized by clients capable of making map requests that include a time component.

This particular service can be queried with or without the use of a time component. If the time parameter is specified in a request, the data or imagery most relevant to the provided time value, if any, will be returned. If the time parameter is not specified in a request, the latest data or imagery valid for the present system time will be returned to the client. If the time parameter is not specified and no data or imagery is available for the present time, no data will be returned.

In addition to ArcGIS Server REST access, time-enabled OGC WMS 1.3.0 access is also provided by this service.

Due to software limitations, the time extent of the service and map layers displayed below does not provide the most up-to-date start and end times of available data. Instead, users have three options for determining the latest time information about the service:

Issue a returnUpdates=true request for an individual layer or for
the service itself, which will return the current start and end times of
available data, in epoch time format (milliseconds since 00:00 January 1,
1970). To see an example, click on the "Return Updates" link at the bottom of
this page under "Supported Operations". Refer to the
ArcGIS REST API Map Service Documentation
for more information.


  Issue an Identify (ArcGIS REST) or GetFeatureInfo (WMS) request against
  the proper layer corresponding with the target dataset. For raster
  data, this would be the "Image Footprints with Time Attributes" layer
  in the same group as the target "Image" layer being displayed. For
  vector (point, line, or polygon) data, the target layer can be queried
  directly. In either case, the attributes returned for the matching
  raster(s) or vector feature(s) will include the following:


      validtime: Valid timestamp.


      starttime: Display start time.


      endtime: Display end time.


      reftime: Reference time (sometimes reffered to as
      issuance time, cycle time, or initialization time).


      projmins: Number of minutes from reference time to valid
      time.


      desigreftime: Designated reference time; used as a
      common reference time for all items when individual reference
      times do not match.


      desigprojmins: Number of minutes from designated
      reference time to valid time.




  Query the nowCOAST LayerInfo web service, which has been created to
  provide additional information about each data layer in a service,
  including a list of all available "time stops" (i.e. "valid times"),
  individual timestamps, or the valid time of a layer's latest available
  data (i.e. "Product Time"). For more information about the LayerInfo
  web service, including examples of various types of requests, refer to
  the nowCOAST help documentation at:
  http://new.nowcoast.noaa.gov/help/#section=layerinfo

References

For more information about the MRMS/Q3 system, please see http://nmq.ou.edu and http://www.nssl.noaa.gov/projects/mrms.

Reason for Selection Impervious cover is easy to monitor and model and is widely used and understood by diverse partners. It is also strongly linked to water quality, estuary condition, eutrophication, and freshwater inflow. Impervious surface affects not only aquatic habitats and biodiversity, but also human communities. High levels of impervious surface cause more frequent flooding by increasing the volume of stormwater runoff, reduce the amount of available drinking water by preventing groundwater recharge, and pollute waterways where people swim and fish (Chesapeake 2023, USGS 2018, EPA 2018).

The 90% permeable surface threshold (i.e., 10% impervious) is a well-documented signal of major, negative changes to aquatic ecosystems (Schueler et al. 2009). The 95% permeable surface threshold (i.e., 5% impervious) has been documented to impact Piedmont fish tricolor shiner (Cyprinella trichroistia), bronze darter (Percina palmaris), Etowah darter (Etheostoma etowahae) and estuarine species blue crab (Callinectes sapidus), white perch (Morone americana), striped bass (M. Saxatilis) and spot (Leiostomus xanthurus).

While most of these species do not occur in Puerto Rico and the U.S. Virgin Islands, we kept these thresholds in the Caribbean for consistency with the continental version of the indicator. Input Data

Southeast Blueprint 2023 subregions: Caribbean 
Southeast Blueprint 2023 extent
2012 National Oceanic and Atmospheric Administration (NOAA) Coastal Change Analysis Program (C-CAP) land cover files for the U.S. Virgin Islands (St. Thomas, St. John, and St. Croix are provided as separate rasters) accessed 11-10-2022; learn more about C-CAP high resolution land cover and change products
2010 NOAA C-CAP land cover files for Puerto Rico, accessed 11-10-2022; learn more about C-CAP high resolution land cover and change products
National Hydrography Dataset Plus High Resolution (NHDPlus HR) National Release catchments, accessed 11-30-2022; download the data

CatchmentsA catchment is the local drainage area of a specific stream segment based on the surrounding elevation. Catchments are defined based on surface water features, watershed boundaries, and elevation data. It can be difficult to conceptualize the size of a catchment because they vary significantly in size based on the length of a particular stream segment and its surrounding topography—as well as the level of detail used to map those characteristics.

To learn more about catchments and how they’re defined, check out these resources:

An article from USGS explaining the differences between various NHD products
The glossary at the bottom of this tutorial for an EPA water resources viewer, which defines some key terms 


  NOAA Continuously Updated Shoreline Product (CUSP), accessed 1-11-2023; read a 1-page factsheet about CUSP; view and download CUSP data in the NOAA Shoreline Data Explorer (to download, select “Download CUSP by Region” and select Southeast Caribbean)

Mapping Steps

NHDPlus HR catchments are currently only available for the islands of Puerto Rico, Vieques, Culebra, St. Croix, St. John, and St. Thomas. Because the catchments don’t cover many of the smaller islands, use CUSP to add islands larger than 900 sq m (the area of a 30 m pixel). Start by converting CUSP shoreline lines to polygons.
Dissolve interior waterbodies on islands to represent each island with only one polygon.
To eliminate alignment issues between the CUSP and catchment polygons, remove most island areas that overlap with or are near (<10 m from) the NHDPlus HR catchments, ensuring that all of Culebra is retained.
The original NHDPlus HR catchment data was missing coverage of a small area on the west coast of Puerto Rico (just east of Parcelas Aguas Claras). Create an additional catchment polygon for this missing area so that the indicator covers the entire island of Puerto Rico.The missing area is essentially outlined by extremely thin catchment polygons. To fill the gap, make a new rectangular feature class covering the missing area, then union it together with the original NHDPlus HR catchments. From that output, select the newly created polygon that fills in the hole. 
    The resulting polygon is a multipart feature, so use the explode tool to separate out just the missing catchment. Export it as a shapefile.
    Union together the missing catchment with the other NHDPlus HR catchments and use that combined output as the catchment layer for the rest of the mapping steps.


Remove islands created from the CUSP dataset that are less than 900 sq m.
Merge the remaining CUSP islands with the NHDPlus catchments to create a single set of polygons in which to calculate average permeable surface.
Convert the C-CAP land cover rasters for Puerto Rico (2 m resolution) and the U.S. Virgin Islands (separate downloads for St. Thomas, St. John, and St. Croix with 2.4 m resolution) from .img format to .tif using the Copy Raster function.
For each individual C-CAP layer, use the ArcPy Conditional function to make a binary raster assigning the impervious class a value of 100 (representing fully impervious) and all other classes a value of 0 (representing fully permeable). This mimics the data format of the 2019 National Land Cover Database used in the continental Southeast permeable surface indicator, which provides a continuous impervious surface value ranging from 0 to 100.
Using the ArcPy Mosaic to New Raster function, mosaic all 4 rasters into 1 raster. Reproject to match the Blueprint projection and the 2 m cell size of the original Puerto Rico C-CAP data.
Calculate the average percent of impervious surface for each NHDPlus catchment or CUSP island using the ArcPy Spatial Analyst Zonal Statistics “MEAN” function, assigning the average impervious surface value to each catchment or island.
Convert percent impervious to percent permeable using the formula [percent permeable = 100 - percent impervious] to maintain consistent scoring across Southeast Blueprint indicators (where high values indicate better ecological condition).
Reclassify the above raster into 4 classes, seen in the final indicator values below.
Clip to the Caribbean Blueprint 2023 subregion.
As a final step, clip to the spatial extent of Southeast Blueprint 2023. 

Note: For more details on the mapping steps, code used to create this layer is available in the Southeast Blueprint Data Download under > 6_Code. Final indicator values Indicator values are assigned as follows: 4 = >95% of catchment or small island permeable (likely high water quality and supporting most sensitive aquatic species) 3 = >90-95% of catchment or small island permeable (likely declining water quality and supporting most aquatic species) 2 = >70-90% of catchment or small island permeable (likely degraded water quality and not supporting many aquatic species) 1 = ≤70% of catchment or small island permeable (likely degraded instream flow, water quality, and aquatic species communities) Known Issues

This indicator may not account for differences in permeability between different types of soils and land uses.
The C-CAP impervious layer used in this indicator contains classification inaccuracies that may cause this indicator to overestimate or underestimate the amount of permeable surface in some catchments.
C-CAP dates from 2010 for Puerto Rico and 2012 for the U.S. Virgin Islands. As a result, this indicator likely overestimates permeable surface values in areas that have been developed since the data was collected. 
C-CAP landcover is not available for some islands over 900 sq m. While these islands exceeded the size threshold for inclusion in this indicator, they are therefore scored as NoData. This indicator only covers areas where C-CAP landcover is present, and either NHDPlus HR catchments or islands over 900 sq m that were generated using CUSP data are also present. 
NHDPlus HR contains multiple catchments that are very small. The reduced size of these catchments may result in exaggerating their values in the indicator. 

Other Things to Keep in Mind

The impervious surface in the C-CAP data has impervious surface as one class in the landcover, which differs from the 2019 NLCD percent developed impervious layer used in the continental Southeast version of the permeable surface indicator. NLCD 2019 is served up as a continuous raster ranging from 0-100% impervious.
We used the Caribbean island size and extent layer for this indicator and not others because landcover data was available for small islands that were not covered by catchments, which otherwise would have been excluded. This was not the case for other indicators. For example, while we use catchments in natural landcover in floodplains, the floodplains and flowlines did not occur on small islands, anyway, so we did not leave any data out by using the catchments only and not supplementing with the islands layer.

Disclaimer: Comparing with Older Indicator Versions There are numerous problems with using Southeast Blueprint indicators for change analysis. Please consult Blueprint staff if you would like to do this (email hilary_morris@fws.gov). Literature Cited Chesapeake Bay Program. 2023. Stormwater Runoff. Accessed September 7, 2023. [https://www.chesapeakebay.net/issues/threats-to-the-bay/stormwater-runoff].

Environmental Protection Agency. EnviroAtlas. Data Fact Sheet. January 2018. Percent of Stream and Shoreline with 15% or More Impervious Cover within 30 Meters. Accessed September 7, 2023. [https://enviroatlas.epa.gov/enviroatlas/DataFactSheets/pdf/ESN/Percstreamw15percentimperviousin30meters.pdf].

Moore, R.B., McKay, L.D., Rea, A.H., Bondelid, T.R., Price, C.V., Dewald, T.G., and Johnston, C.M., 2019, User’s guide for the national hydrography