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City Of Sacramento's Survey Division has developed a high accuracy GPS control point grid. This file currently contains data points for the entire City of Sacramento. The latitude and longitude values have an accuracy level of +/- .05 feet. Elevation data has accuracy of +/- .24 feet.

Field: GPSNUMBER Alias: Survey reference number Field Description: Reference to latitude/longitude minute

Field: NORTHINGFT Alias: False Northing, California State Plane, Zone II, Feet

Field: EASTINGFT Alias: False Easting, California State Plane, Zone II, Feet

Field: ELVORTHOFT Alias: Elevation Ortho (ft)- a preliminary ground elevation to which the orthometric leveling correction has been applied

Field: DFNGVD29FT Alias: Differential NGVD 29- elevation obtained by spirit leveling based on the national geodetic vertical datum of 1929

Field: STREET Alias: Street location of control point

Field: XSTREET Alias: Cross street or reference information

Field: MONTYPE Alias: Control point or monument type

Field: LAT_DMS Alias: Latitude values in Degrees, Minutes, Seconds

Field: LONG_DMS Alias: Latitude values in Degrees, Minutes, Seconds

Field: ELLIPSHT Alias: Ellipsoid Height- the distance, measured along the mormal, from the surface of the ellipsoid to a point

Field: CNVERGENCE Alias: The angle difference at a given location between grid north and astronomic north

Field: GRDSCLFCTR Alias: Grid Scale Factor- a multiplier for reducing a sea level lengths to grid lengths

Field: COMBNDFCTR Alias: Combined Factor- multiplier obtained from the product of the sea level and grid scale factor and applied to ground distance to obtain grid distance

Field: GEOIDHT Alias: Distance of the geoid above (positive) or below (negative) the mathematical reference spheroid

Field: ARCHIVELOC Alias: Use To be Determined Field Description: Associated with crossed out GPS No.-Point ID

Annual average wind resource potential of North and South Dakota at a 50 meter height.

This data set was produced and validated by NREL using their WRAM model. This shapefile was generated from a raster dataset with a 1000 m resolution, in a Lambert Azimuthal projection with the following parameters:

• Projection LAMBERT_AZIMUTHAL Datum NONE Zunits NO Units METERS Xshift 0.0000000000 Yshift 0.0000000000 Parameters 6370997.0000000000 0.0000000000 6370997.00000
• radius of the sphere of reference -100 15 0.000
• longitude of center of projection 46 3 0.000
• latitude of center of projection 0.00000
• false easting (meters) 0.00000
• false northing (meters)

Geographic analysis of impervious cover and demographic attributes by 2010 Census block group. Demographic attributes and Census block groups were downloaded from the EJScreen web page (www.epa.gov/ejscreen). EJScreen technical documentation is available at website; click on the technical information link on the landing page; the link of the documentation is in the center of the web page. NLCD2019 data for impervious cover were downloaded from www.mrlc.gov. The impervious cover for 2001 and 2019 in the NLCD2019 database were used in the analysis. The Census block groups downloaded from EJScreen were projected into Albers Conic Equal Area to match the NLCD2019 projection (Albers Conic Equal Area: central meridian=96.0 W; origin latitude=23.0 N; false easting=0.0; false northing=0.0; standard parallel 1=29.5 N; standard parallel 2=49.5 N; Geographic coordinate system=WGS84; WKID=4326) .

Continuous Wavelet Transform was applied to bed elevation profiles (BEP) and used in the study in order to recognise the spatial distribution of bedforms and discriminate between their hierarchical scales. In particular, the spatial distribution of the hierarchical scales is highlighted by averaging wavelet power spectra over different bands, and displayed as the wavelet variance of the BEP (see map). Four dune classes were defined, following Ashley (1990): small dunes (1-5 m), medium dunes (5-10 m), large dunes (10-100 m), and very large dunes (>100 m).

The daily cloudless MODIS Snow area ratio data set (2000-2015) of the Qinghai Tibet Plateau is based on MODIS daily snow product - mod10a1, which is obtained by using a cloud removal algorithm based on cubic spline interpolation. The data set is projected by UTM with spatial resolution of 500m, providing daily snow cover FSC results in the Tibetan Plateau. The data set is a day-to-day document, from 24 February 2000 to 31 December 2015. Each file is the result of snow area proportion on that day, the value is 0-100%, which is envi standard file, the naming rule is: yyyddd_fsc_0.5km.img, where yyyy represents the year, DDD represents Julian day (001-365 / 366). Files can be opened and viewed directly with envi or ArcMap. The original MODIS Snow data product for cloud removal comes from the mod10a1 product processed by the National Snow and Ice Data Center (NSIDC). This data set is in the format of HDF and uses the sinusional projection. The attributes of the daily cloudless MODIS Snow area ratio data set (2000-2015) on the Qinghai Tibet Plateau consist of the spatial-temporal resolution, projection information and data format of the data set. Temporal and spatial resolution: the temporal resolution is day by day, the spatial resolution is 500m, the longitude range is 72.8 ° ~ 106.3 ° e, and the latitude is 25.0 ° ~ 40.9 ° n. Projection information: UTM projection. Data format: envi standard format. File naming rules: "yyyyddd" + ". Img", where yyyy stands for year, DDD stands for Julian day (001-365 / 366), and ". Img" is the file suffix added for easy viewing in ArcMap and other software. For example, 2000055 ﹐ FSC ﹐ 0.5km.img represents the result on the 55th day of 2000. The envi file of this data set is composed of header file and body content. The header file includes row number, column number, band number, file type, data type, data record format, projection information, etc.; take 2000055 ﹣ FSC ﹣ 0.5km.img file as an example, the header file information is as follows: ENVI Description = {envi file, created [sat APR 27 18:40:03 2013]} Samples = 5760 Lines = 3300 Bands = 1 Header offset = 0 File type = envi standard Data type = 1: represents byte type Interleave = BSQ: data record format is BSQ Sensor type = unknown Byte order = 0 Map Info = {UTM, 1.500, 1.500, - 711320.359, 4526650.881, 5.0000000000e + 002, 5.0000000000e + 002, 45, north, WGS-84, units = meters} Coordinate system string = {projcs ["UTM [u zone [45N], geocs [" GCS [WGS [1984], data ["d [WGS [1984", organization ID ["WGS [1984", 6378137.0298.257223563]], prime ["Greenwich", 0.0], unit ["degree", 0.01745532925199433]]] project ["transfer [Mercator"]] parameter ["false [easting", 500000.0], parameter ["false [easting", 500000.0], parameter [500000.0], parameter [500000.0], parameter [false [false [easting ", 500000.0], parameter], parameter [500000.0], parameter [500000.0], parameter [500000.0], parameter [false [easting", 500000.0], parameter [500000.0], parameter [500000.0], parameter [500000.0], parameter ["false_northing", 0.0], parameter ["central_meridian", 87.0], parameter ["scale" _Factor ", 0.9996], parameter [" latitude ﹣ of ﹣ origin ", 0.0], unit [" meter ", 1.0]]} Wavelength units = unknown, band names = {2000055}

An Open Context "predicates" dataset item. Open Context publishes structured data as granular, URL identified Web resources. This "Variables" record is part of the "The Eastern Korinthia Archaeological Survey" data publication.

Map Projection - A Lambert Conformal Conic projection (with two standard parallels) is used for the ArcticNet Basemap series of mapsheets. In order to minimize distortion in the areas of most interest, the standard parallels were specifically chosen to follow the two major east-west channels through the Northwest Passage (Parry Channel and Queen Maud/Coronation Gulf). The projection parameters are as follows: ¿ 1st Standard parallel: 70° ¿ 2nd Standard parallel: 73° ¿ Origin latitude: 70° ¿ Origin longitude: -105° ¿ False easting: 2,000,000 metres ¿ False northing: 2,000,000 metres Datums - Horizontal datum: NAD83; Vertical datum: soundings are reduced to mean sea level (MSL) using the WebTide tidal prediction models.

Misc. Processing Details Since time is at a premium while underway, there are often cases where long transits are undertaken with little or no sound speed profiles collected. On the other hand, some oceanographic sections sampled by the Amundsen provide very dense sound speed information throughout the watercolumn. In sections where the watercolumn is poorly sampled, the 1/4 ° World Ocean Atlas (2001) climatology is used as a source of sound speed.

This is the dataset for oil and gas potential as described in Copeland et al. (2009) Mapping Oil and Gas Development Potential in the US Intermountain West and Estimating Impacts to Species, PLoSOne. (http://www.plosone.org/article/info:doi%2F10.1371%2Fjournal.pone.0007400) Please see paper in PLoSOne for more detailed methods. The dataset should be cited as: Copeland, H., K. Doherty, D. Naugle, A. Pocewicz, J. Kiesecker (2010) Mapping Oil and Gas Development Potential in the US Intermountain West and Estimating Impacts to Species. The projection of this dataset is: US NAD83 Lambert Conformal Conic False easting 0 False northing 0 Central meridian -107.0 Standard parallel 1 33.0 Standard parallel 2 45.0 Latitude of origin 41.0

TIF files: ---> DEM, ---> Lat, Lon, ---> Mask, ---> DayMET precipitation Daymet (annual) precipitation SVR processing example Daymet data set provides annual and summary climate data for minimum and maximum temperature, precipitation, and vapor pressure (Thornton et al. 2014). Daymet data consider the total accumulated precipitation over the annual period of the daily total precipitation. Precipitation is the sum of all forms of precipitation converted to water equivalent (mm/yr) (Thornton et al. 2014). The Daymet data layers are produced on a 1-km x 1-km gridded surface over the conterminous United States in Lambert Conformal Conic projection (units are meters) with the following parameters, a) horizontal datum: WGS 84, b) 1st standard parallel= 25o, 2nd standard parallel= 60o, c) Central meridian= -100o, and Latitude of origin= 42.5o, d) false easting= 0, false northing= 0. X is in the range -1949774 to -725054 m, and Y is in the range -1144097 to 222385 m. So the data is projected to a rectangular grid instead of a geographic grid. Thus, the 3 independent variables will be h, X (Easting) and Y (Northing) instead of h, ö and ë. The 12 Daymet gridded annual precipitation (P) images for the period 2003 to 2014 are used. Thornton, P.E., Thornton, M.M., Mayer, B.W., Wilhelmi, N., Wei, Y., Devarakonda, R., & Cook, R.B. (2014), Daymet: Daily Surface Weather Data on a 1-km Grid for North America, Version 2. ORNL DAAC, Oak Ridge, Tennessee, USA. http://dx.doi.org/10.3334/ORNLDAAC/1277.

Israeli_butterfly_move_dataPrimary data for estimating movement rates. UniqueID combines the site name, track number and flight step number. Species are identified in Supplemental Information, Table S3. Field types are Wheat and Olives. Locations are Wheat, Olive, Nature and (Field) Margin. See Figure 1 for visual of field types vs locations. Projected coordinate system is GRS_1980_Transverse_Mercator with False Easting = 219529.584 and False Northing = 626907.390. Each move consisted of a move length, measured as the distance between turning or stopping points i and i+1, and a turning angle θi , measured as the angle between move i-1 and i. The file contains Time (seconds), Length (m) and Turn (Cosine of turning angle). Note that steps starting in edge regions, defined as 10m outside the Margin towards Nature or Fields, were omitted from the analysis (see text for details). However, if a move step starts in one of the included Locations and ends in edge region, the step and associat...

USGS is assessing the feasibility of map projections and grid systems for lunar surface operations. We propose developing a new Lunar Transverse Mercator (LTM), the Lunar Polar Stereographic (LPS), and the Lunar Grid Reference Systems (LGRS). We have also designed additional grids designed to NASA requirements for astronaut navigation, referred to as LGRS in Artemis Condensed Coordinates (ACC), but this is not released here. LTM, LPS, and LGRS are similar in design and use to the Universal Transverse Mercator (UTM), Universal Polar Stereographic (LPS), and Military Grid Reference System (MGRS), but adhere to NASA requirements. LGRS ACC format is similar in design and structure to historic Army Mapping Service Apollo orthotopophoto charts for navigation. The Lunar Transverse Mercator (LTM) projection system is a globalized set of lunar map projections that divides the Moon into zones to provide a uniform coordinate system for accurate spatial representation. It uses a transverse Mercator projection, which maps the Moon into 45 transverse Mercator strips, each 8°, longitude, wide. These transverse Mercator strips are subdivided at the lunar equator for a total of 90 zones. Forty-five in the northern hemisphere and forty-five in the south. LTM specifies a topocentric, rectangular, coordinate system (easting and northing coordinates) for spatial referencing. This projection is commonly used in GIS and surveying for its ability to represent large areas with high positional accuracy while maintaining consistent scale. The Lunar Polar Stereographic (LPS) projection system contains projection specifications for the Moon’s polar regions. It uses a polar stereographic projection, which maps the polar regions onto an azimuthal plane. The LPS system contains 2 zones, each zone is located at the northern and southern poles and is referred to as the LPS northern or LPS southern zone. LPS, like is equatorial counterpart LTM, specifies a topocentric, rectangular, coordinate system (easting and northing coordinates) for spatial referencing. This projection is commonly used in GIS and surveying for its ability to represent large polar areas with high positional accuracy, while maintaining consistent scale across the map region. LGRS is a globalized grid system for lunar navigation supported by the LTM and LPS projections. LGRS provides an alphanumeric grid coordinate structure for both the LTM and LPS systems. This labeling structure is utilized in a similar manner to MGRS. LGRS defines a global area grid based on latitude and longitude and a 25×25 km grid based on LTM and LPS coordinate values. Two implementations of LGRS are used as polar areas require a LPS projection and equatorial areas a transverse Mercator. We describe the difference in the techniques and methods report associated with this data release. Request McClernan et. al. (in-press) for more information. ACC is a method of simplifying LGRS coordinates and is similar in use to the Army Mapping Service Apollo orthotopophoto charts for navigation. These data will be released at a later date. Two versions of the shape files are provided in this data release, PCRS and Display only. See LTM_LPS_LGRS_Shapefiles.zip file. PCRS are limited to a single zone and are projected in either LTM or LPS with topocentric coordinates formatted in Eastings and Northings. Display only shapefiles are formatted in lunar planetocentric latitude and longitude, a Mercator or Equirectangular projection is best for these grids. A description of each grid is provided below: Equatorial (Display Only) Grids: Lunar Transverse Mercator (LTM) Grids: LTM zone borders for each LTM zone Merged LTM zone borders Lunar Polar Stereographic (LPS) Grids: North LPS zone border South LPS zone border Lunar Grid Reference System (LGRS) Grids: Global Areas for North and South LPS zones Merged Global Areas (8°×8° and 8°×10° extended area) for all LTM zones Merged 25km grid for all LTM zones PCRS Shapefiles:` Lunar Transverse Mercator (LTM) Grids: LTM zone borders for each LTM zone Lunar Polar Stereographic (LPS) Grids: North LPS zone border South LPS zone border Lunar Grid Reference System (LGRS) Grids: Global Areas for North and South LPS zones 25km Gird for North and South LPS zones Global Areas (8°×8° and 8°×10° extended area) for each LTM zone 25km grid for each LTM zone The rasters in this data release detail the linear distortions associated with the LTM and LPS system projections. For these products, we utilize the same definitions of distortion as the U.S. State Plane Coordinate System. Scale Factor, k - The scale factor is a ratio that communicates the difference in distances when measured on a map and the distance reported on the reference surface. Symbolically this is the ratio between the maps grid distance and distance on the lunar reference sphere. This value can be precisely calculated and is provided in their defining publication. See Snyder (1987) for derivation of the LPS scale factor. This scale factor is unitless and typically increases from the central scale factor k_0, a projection-defining parameter. For each LPS projection. Request McClernan et. al., (in-press) for more information. Scale Error, (k-1) - Scale-Error, is simply the scale factor differenced from 1. Is a unitless positive or negative value from 0 that is used to express the scale factor’s impact on position values on a map. Distance on the reference surface are expended when (k-1) is positive and contracted when (k-1) is negative. Height Factor, h_F - The Height Factor is used to correct for the difference in distance caused between the lunar surface curvature expressed at different elevations. It is expressed as a ratio between the radius of the lunar reference sphere and elevations measured from the center of the reference sphere. For this work, we utilized a radial distance of 1,737,400 m as recommended by the IAU working group of Rotational Elements (Archinal et. al., 2008). For this calculation, height factor values were derived from a LOLA DEM 118 m v1, Digital Elevation Model (LOLA Science Team, 2021). Combined Factor, C_F – The combined factor is utilized to “Scale-To-Ground” and is used to adjust the distance expressed on the map surface and convert to the position on the actual ground surface. This value is the product of the map scale factor and the height factor, ensuring the positioning measurements can be correctly placed on a map and on the ground. The combined factor is similar to linear distortion in that it is evaluated at the ground, but, as discussed in the next section, differs numerically. Often C_F is scrutinized for map projection optimization. Linear distortion, δ - In keeping with the design definitions of SPCS2022 (Dennis 2023), we refer to scale error when discussing the lunar reference sphere and linear distortion, δ, when discussing the topographic surface. Linear distortion is calculated using C_F simply by subtracting 1. Distances are expended on the topographic surface when δ is positive and compressed when δ is negative. The relevant files associated with the expressed LTM distortion are as follows. The scale factor for the 90 LTM projections: LUNAR_LTM_GLOBAL_PLOT_HEMISPHERES_distortion_K_grid_scale_factor.tif Height Factor for the LTM portion of the Moon: LUNAR_LTM_GLOBAL_PLOT_HEMISPHERES_distortion_EF_elevation_factor.tif Combined Factor in LTM portion of the Moon LUNAR_LTM_GLOBAL_PLOT_HEMISPHERES_distortion_CF_combined_factor.tif The relevant files associated with the expressed LPS distortion are as follows. Lunar North Pole The scale factor for the northern LPS zone: LUNAR_LGRS_NP_PLOT_LPS_K_grid_scale_factor.tif Height Factor for the north pole of the Moon: LUNAR_LGRS_NP_PLOT_LPS_EF_elevation_factor.tif Combined Factor for northern LPS zone: LUNAR_LGRS_NP_PLOT_LPS_CF_combined_factor.tif Lunar South Pole Scale factor for the northern LPS zone: LUNAR_LGRS_SP_PLOT_LPS_K_grid_scale_factor.tif Height Factor for the south pole of the Moon: LUNAR_LGRS_SP_PLOT_LPS_EF_elevation_factor.tif Combined Factor for northern LPS zone: LUNAR_LGRS_SP_PLOT_LPS_CF_combined_factor.tif For GIS utilization of grid shapefiles projected in Lunar Latitude and Longitude, referred to as “Display Only”, please utilize a registered lunar geographic coordinate system (GCS) such as IAU_2015:30100 or ESRI:104903. LTM, LPS, and LGRS PCRS shapefiles utilize either a custom transverse Mercator or polar Stereographic projection. For PCRS grids the LTM and LPS projections are recommended for all LTM, LPS, and LGRS grid sizes. See McClernan et. al. (in-press) for such projections. Raster data was calculated using planetocentric latitude and longitude. A LTM and LPS projection or a registered lunar GCS may be utilized to display this data. Note: All data, shapefiles and rasters, require a specific projection and datum. The projection is recommended as LTM and LPS or, when needed, IAU_2015:30100 or ESRI:104903. The datum utilized must be the Jet Propulsion Laboratory (JPL) Development Ephemeris (DE) 421 in the Mean Earth (ME) Principal Axis Orientation as recommended by the International Astronomy Union (IAU) (Archinal et. al., 2008).

Please refer to the article: Paleogeography of a lake and river system in north-eastern
Mecklenburg Bay, southern Baltic Sea, ..., .... doi:.... for further information on the datasets.

Seismic Lines (.sgy files)

Seismic Lines in segy format in the area SW of the Darss Sill.

Data were recorded during research survey 176 of Elisabeth Mann Borgese between the 16. and 18. of February 2018.

Data were recorded with an Innomar SES Medium parametric echo sounder and subsequently converted to segy file format using the native INNOMAR converter software.

Side Scan Sonar backscatter mosaics (.tif and .png files)

Various mosaics from the archive of the Leibniz Institute for Baltic Sea Research (IOW).

Files starting with "u" were recorded by vessels of the Bundesamt für Seeschiffahrt und Hydrographie (BSH) and processed at IOW. The coordinate system of the files is defined by:

PROJCRS["WGS_1984_Mercator",BASEGEOGCRS["WGS 84",DATUM["World Geodetic System 1984",ELLIPSOID["WGS 84",6378137,298.257223563,LENGTHUNIT["metre",1],ID["EPSG",7030]]],PRIMEM["Greenwich",0,ANGLEUNIT["Degree",0.0174532925199433]]],CONVERSION["unnamed",METHOD["Mercator (variant B)",ID["EPSG",9805]],PARAMETER["False easting",0,LENGTHUNIT["metre",1],ID["EPSG",8806]],PARAMETER["False northing",0,LENGTHUNIT["metre",1],ID["EPSG",8807]],PARAMETER["Longitude of natural origin",0,ANGLEUNIT["Degree",0.0174532925199433],ID["EPSG",8802]],PARAMETER["Latitude of 1st standard parallel",54,ANGLEUNIT["Degree",0.0174532925199433],ID["EPSG",8823]]],CS[Cartesian,2],AXIS["easting",east,ORDER[1],LENGTHUNIT["metre",1,ID["EPSG",9001]]],AXIS["northing",north,ORDER[2],LENGTHUNIT["metre",1,ID["EPSG",9001]]]]

For all other files, projection files are provided.

This glacier inventory is supported by the International Centre for Integrated Mountain Development (ICIMOD) and the United Nation Environment Programme/Regional Resources Centre, Asia and The Pacific (UNEP/RRC-AP)。

1、The glacier inventory uses the remote sensing data of Landsat，reflecting the current status of glaciers in Nepal in 2000. 2、The spatial coverage of the glacier inventory: Nepal 3、Contents of the glacier inventory: glacier location, glacier code, glacier name, glacier area, glacier length, glacier thickness, glacier stocks, glacier type, glacier orientation, etc. 4、Data Projection： Grid Zone IIA

Projection: Lambert conformal conic

Ellipsoid: Everest (India 1956)

Datum: India (India, Nepal)

False easting: 2743196.40

False northing: 914398.80

Central meridian: 90°00'00"E

Central parallel: 26°00'00"N

Scale factor: 0.998786

Standard parallel 1: 23°09'28.17"N

Standard parallel 2: 28°49'8.18"N

Minimum X Value: 1920240

Maximum X Value: 2651760

Minimum Y Value: 914398

Maximum Y Value: 1188720

Grid Zone IIB

Projection: Lambert conformal conic

Ellipsoid: Everest (India 1956)

Datum: India (India, Nepal)

False easting: 2743196.40

False northing: 914398.80

Central meridian: 90°00'00"E

Central parallel: 26°00'00"N

Scale factor: 0.998786

Standard parallel 1: 21°30'00"N

Standard parallel 2: 30°00'00"N

Minimum X Value: 1823188

Maximum X Value: 2000644

Minimum Y Value: 1306643

Maximum Y Value: 1433476 For a detailed data description, please refer to the data file and report.

This study investigates the distribution and evolution of seafloor seepage in the vicinity of the salt front, i.e., the seaward boundary of salt-induced deformation in the Lower Congo Basin (LCB). Seafloor topography, backscatter data and TV-sled observations indicate active fluid seepage from the seafloor directly at the salt front, whereas suspected seepage sites appear to be inactive at a distance of >10 km landward of the deformation front. High resolution multichannel seismic data give detailed information on the structural development of the area and its influence on the activity of individual seeps during the geologic evolution of the salt front region. The unimpeded migration of gas from fan deposits along sedimentary strata towards the base of the gas hydrate stability zone within topographic ridges associated with relatively young salt-tectonic deformation facilitates seafloor seepage at the salt front. Bright and flat spots within sedimentary successions suggest geological trapping of gas on the flanks of mature salt structures in the eastern part of the study area. Onlap structures associated with fan deposits which were formed after the onset of salt-tectonic deformation represent potential traps for gas, which may hinder gas migration towards seafloor seeps. Faults related to the thrusting of salt bodies seawards also disrupt along-strata gas migration pathways. Additionally, the development of an effective gas hydrate seal after the cessation of active salt-induced uplift and the near-surface location of salt bodies may hamper or prohibit seafloor seepage in areas of advanced salt-tectonic deformation. This process of seaward shifting active seafloor seepage may propagate as seaward migrating deformation affects Congo Fan deposits on the abyssal plain. These observations of the influence of the geologic evolution of the salt front area on seafloor seepage allows for a characterization of the large variety of hydrocarbon seepage activity throughout this compressional tectonic setting.

This metadata refers to the EEA marine assessment grid, to which all data and assessment results have been spatially mapped in order to ensure that data can be compared in a uniform way across the European regional seas.

The marine assessment grid is based on the EEA reference grid system. The EEA reference grid is based on ERTS89 Lambert Azimuthal Equal Area projection with parameters: latitude of origin 52° N, longitude of origin 10° E, false northing 3 210 000.0 m, false easting 4 321 000.0 m. All grid cells are named with a unique identifier containing information on grid cell size and the distance from origin in meters (easting and northing). An important attribute of the EEA reference grid system is that by using an equal area projection all grid cells are having the same area for the same grid size.

In this marine assessment grid, two grid sizes are used: * 100 x 100 km in offshore areas (> 20 km from the coastline) * 20 x 20 km in coastal areas (<= 20 km from the coastline) The grid sizes were choosen after an evaluation of data availability versus the need for sufficient detail in the resulting assessment. The resulting assessment grid is a combination of two grid sizes using the EEA reference grid system.

The overall area of interest used in the grid is based on the marine regions and subregions under the Marine Strategy Framework Directive (MSFD). Additionally, Norwegian (Barent Sea and Norwegian Sea) and Icelandic waters (’Iceland Sea’) have been added (see Surrounding seas of Europe). Note that, within the North East Atlantic region, only the subregions within EEZ boundaries (~200 nm) have been included.

The data set "Population density Wuppertal" describes the population density in the city of Wuppertal on the basis of a kilometre quadratraster, which is defined by the kilometre coordinate lines of the official situation reference system ETRS89/UTM32. The Wuppertal urban area lies in 220 of the resulting square grid cells with an area of 1 square kilometer each. The dataset contains the geometries of these raster cells as closed polygons, for which the name of the raster cell (attribute NAME), the number of persons registered with the main residence in this cell (attribute EW_KM2), if applicable, and a classification of the population density in four density classes (attribute KAT) is given (class 1: 0 to 300 people, class 2: 301 to 749 persons, class 3: 750 to 1249 persons, class 4: from 1250 people). The grid cells are designated by the kilometre coordinates of the respective south-west corner point (false Easting without UTM zone digits 1 to 3 followed by Northing digits 3 and 4). The anonymised data on the registered persons are taken from the VOIS / MESO specialist procedure of the Population Registration Office. Available in Shape, KML, and GeoJSON formats under an Open Data license (CC BY 4.0), the open data record is automatically updated on a fixed weekly basis.

TIF files: ---> DEM, ---> Lat, Lon, ---> Mask, ---> DayMET precipitation Daymet (annual) precipitation SVR processing example Daymet data set provides annual and summary climate data for minimum and maximum temperature, precipitation, and vapor pressure (Thornton et al. 2014). Daymet data consider the total accumulated precipitation over the annual period of the daily total precipitation. Precipitation is the sum of all forms of precipitation converted to water equivalent (mm/yr) (Thornton et al. 2014). The Daymet data layers are produced on a 1-km x 1-km gridded surface over the conterminous United States in Lambert Conformal Conic projection (units are meters) with the following parameters, a) horizontal datum: WGS 84, b) 1st standard parallel= 25o, 2nd standard parallel= 60o, c) Central meridian= -100o, and Latitude of origin= 42.5o, d) false easting= 0, false northing= 0. X is in the range -1949774 to -725054 m, and Y is in the range -1144097 to 222385 m. So the data is projected to a rectangular grid instead of a geographic grid. Thus, the 3 independent variables will be h, X (Easting) and Y (Northing) instead of h, ö and ë. The 12 Daymet gridded annual precipitation (P) images for the period 2003 to 2014 are used. Thornton, P.E., Thornton, M.M., Mayer, B.W., Wilhelmi, N., Wei, Y., Devarakonda, R., & Cook, R.B. (2014), Daymet: Daily Surface Weather Data on a 1-km Grid for North America, Version 2. ORNL DAAC, Oak Ridge, Tennessee, USA. http://dx.doi.org/10.3334/ORNLDAAC/1277.

Survey control points in the Susquehanna Shale Hills CZO.

In the recent past, there has been continuing growth in using GIS and related technologies by many organizations engaged in planning and management of the Kathmandu Valley. As a result, the demand for accurate and homogenous spatial data of the Valley has been realized by government as well as research and development organizations.

    This study attempts to build a comprehensive GIS Database of the Kathmandu
    Valley with an aim to bridge the important data gaps in the Valley. The study
    employs a fresh approach in constructing a GIS database with the available maps
    and integrates many different kinds of satellite imageries. The maps presented
    in this publication visualize the different scenarios and raise the awareness
    of exiting digital database. The application presented in this publication
    shall increase awareness about the usefulness of digital database and
    demonstrate what can be achieved with the GIS and related technologies. The
    database thus developed shall improve the availability of information of the
    Kathmandu Valley and assist different stakeholders engaged in planning and
    management of the Valley.

    Furthermore, the study advocates a building block approach to development,
    management and revision of database in a complementary way and it hopes to
    avoid duplication of efforts in costly production of digital data. The study
    hopes to sensitise senior executives and decision-makers about the need for a
    sound policy on database sharing, development and standards. Such a policy, at
    the national level known as National Spatial Database Infrastructure (NSDI)
    should evolve in order to benefit from the prevailing GIS technology. In using
    GIS and related technologies, the study facilitated the establishment of
    Spatial Data Infrastructure of the Kathmandu Valley in a concrete manner.


    Members informations:
    Attached Vector(s):
     MemberID: 1
    Vector Name: Contours
    Source Map Name: topo sheets
    Source Map Scale: 25000
    Source Map Date: 1905-06-17
    Projection: transverse mercator
    Projection_desc: origin 87E/ 0N, false easting=900000, scale=0.9999
    Projection_meas: Meter
    Feature_type: lines
    Vector 
    Contours digitized from topo sheets

    Members informations:
    Attached Vector(s):
     MemberID: 2
    Vector Name: Roads
    Source Map Name: topo sheet
    Source Map Scale: 25000
    Source Map Date: 1905-06-17
    Projection: see member1
    Feature_type: lines
    Vector 
    Road Network

    Members informations:
    Attached Vector(s):
     MemberID: 3
    Vector Name: Drainage
    Source Map Name: topo sheets
    Source Map Scale: 25000
    Source Map Date: 1905-06-17
    Projection: see member 1
    Feature_type: lines
    Vector 
    Drainage Network

    Members informations:
    Attached Vector(s):
     MemberID: 4
    Vector Name: Land use 78
    Source Map Name: LRMP
    Source Map Scale: 50000
    Source Map Date: 1905-05-31
    Feature_type: polygon
    Vector 
    Land use

    Members informations:
    Attached Vector(s):
     MemberID: 5
    Vector Name: Land use 1995
    Source Map Name: topo sheet
    Source Map Scale: 25000
    Source Map Date: 1905-06-17
    Feature_type: polygon
    Vector 
    Land cover


    Members informations:
    Attached Vector(s):
     MemberID: 6
    Vector Name: Administrative boundaries
    Source Map Name: topo sheet
    Source Map Scale: 25000
    Source Map Date: 1905-06-17
    Feature_type: polygon
    Vector 
    District and VDC boundaries and various socio-economic data

    Attached Report(s)
    Member ID: 7
    Report Name: Kathmandu Valley GIS database
    Report Authors: B. Shrestha & S. Pradhan
    Report Publisher: ICIMOD
    Report Date: 2000-02-01
    Report 
    Report