LiDAR (Light Detection and Ranging) is a remote sensing technology, i.e. the technology is not in direct contact with what is being measured. From satellite, aeroplane or helicopter, a LiDAR system sends a light pulse to the ground. This pulse hits the ground and returns back to a sensor on the system. The time is recorded to measure how long it takes for this light to return. Knowing this time measurement scientists are able to create topography maps.LiDAR data are collected as points (X,Y,Z (x & y coordinates) and z (height)). The data is then converted into gridded (GeoTIFF) data to create a Digital Terrain Model and Digital Surface Model of the earth. This LiDAR data was collected on 25th March 2015.An ordnance datum (OD) is a vertical datum used as the basis for deriving heights on maps. This data is referenced to the Malin Head Vertical Datum which is the mean sea level of the tide gauge at Malin Head, County Donegal. It was adopted as the national datum in 1970 from readings taken between 1960 and 1969 and all heights on national grid maps are measured above this datum. Digital Terrain Models (DTM) are bare earth models (no trees or buildings) of the Earth’s surface.Digital Surface Models (DSM) are earth models in its current state. For example, a DSM includes elevations from buildings, tree canopy, electrical power lines and other features. Hillshading is a method which gives a 3D appearance to the terrain. It shows the shape of hills and mountains using shading (levels of grey) on a map, by the use of graded shadows that would be cast by high ground if light was shining from a chosen direction.This data shows the hillshade of the DSM.This data was collected by New York University. All data formats are provided as GeoTIFF rasters. Raster data is another name for gridded data. Raster data stores information in pixels (grid cells). Each raster grid makes up a matrix of cells (or pixels) organised into rows and columns. NYU data has a grid cell size of 1meter by 1meter. This means that each cell (pixel) represents an area of 1meter squared.

This is a collection of all GPS- and computer-generated geospatial data specific to the Alpine Treeline Warming Experiment (ATWE), located on Niwot Ridge, Colorado, USA. The experiment ran between 2008 and 2016, and consisted of three sites spread across an elevation gradient. Geospatial data for all three experimental sites and cone/seed collection locations are included in this package. ––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Geospatial files include cone collection, experimental site, seed trap, and other GPS location/terrain data. File types include ESRI shapefiles, ESRI grid files or Arc/Info binary grids, TIFFs (.tif), and keyhole markup language (.kml) files. Trimble-imported data include plain text files (.txt), Trimble COR (CorelDRAW) files, and Trimble SSF (Standard Storage Format) files. Microsoft Excel (.xlsx) and comma-separated values (.csv) files corresponding to the attribute tables of many files within this package are also included. A complete list of files can be found in this document in the “Data File Organization” section in the included Data User's Guide. Maps are also included in this data package for reference and use. These maps are separated into two categories, 2021 maps and legacy maps, which were made in 2010. Each 2021 map has one copy in portable network graphics (.png) format, and the other in .pdf format. All legacy maps are in .pdf format. .png image files can be opened with any compatible programs, such as Preview (Mac OS) and Photos (Windows). All GIS files were imported into geopackages (.gpkg) using QGIS, and double-checked for compatibility and data/attribute integrity using ESRI ArcGIS Pro. Note that files packaged within geopackages will open in ArcGIS Pro with “main.” preceding each file name, and an extra column named “geom” defining geometry type in the attribute table. The contents of each geospatial file remain intact, unless otherwise stated in “niwot_geospatial_data_list_07012021.pdf/.xlsx”. This list of files can be found as an .xlsx and a .pdf in this archive. As an open-source file format, files within gpkgs (TIFF, shapefiles, ESRI grid or “Arc/Info Binary”) can be read using both QGIS and ArcGIS Pro, and any other geospatial softwares. Text and .csv files can be read using TextEdit/Notepad/any simple text-editing software; .csv’s can also be opened using Microsoft Excel and R. .kml files can be opened using Google Maps or Google Earth, and Trimble files are most compatible with Trimble’s GPS Pathfinder Office software. .xlsx files can be opened using Microsoft Excel. PDFs can be opened using Adobe Acrobat Reader, and any other compatible programs. A selection of original shapefiles within this archive were generated using ArcMap with associated FGDC-standardized metadata (xml file format). We are including these original files because they contain metadata only accessible using ESRI programs at this time, and so that the relationship between shapefiles and xml files is maintained. Individual xml files can be opened (without a GIS-specific program) using TextEdit or Notepad. Since ESRI’s compatibility with FGDC metadata has changed since the generation of these files, many shapefiles will require upgrading to be compatible with ESRI’s latest versions of geospatial software. These details are also noted in the “niwot_geospatial_data_list_07012021” file.

This dataset contains a unified backscatter GeoTiff with 1x1 meter cell size representing the 2014 Long Island Sound Benthic Habitat Priority Area of Interest off of Port Jefferson, NY. This backscatter dataset is a mosaic of surveys from the NOAA Ship S-222 Thomas Jefferson and its two inshore launch vessels, the NOAA Ship S-590 Rude, as well as surveys conducted by the University of Rhode Island and Stony Brook University in coordination with the NOAA Biogeography Branch and the Integrated OCean and Coastal Mapping Branch between 2001 and 2013. Backscatter data was collected using multibeam sonars and side scan sonars and mosaiced into a raster using ArcGIS 10.1 and Envi 5.0 software at the Biogeography Branch by a NOAA contractor.

These data show how different types of rocks resist the flow of electrical currents across Ireland. The rock types can then be mapped. The data were collected between 2005 and 2021.Several surveys were merged to create this dataset. (1) Tellus Northern Ireland 2005-2006(2) Cavan-Monaghan, 2006(3) Tellus Border, 2011-2012(4) Tellus North Midlands, 2014-2015(5) Block A1, 2015(6) Block A2, 2016(7) Waterford, 2016(8) Block A3, 2017(9) Block A4, 2017(10) Block A5, 2018-2019(11) Block A6, 2018-2019(12) Block A7, 2019(13) Block A8 2020-2021(14) Block A9 2021The data were collected using an airplane. The airplane flies at 60 m flight height along lines that are 200 m apart. Electromagnetic data are recorded at around 6 m intervals along the flight lines. The electromagnetic system mounted on the airplane sends an electromagnetic signal (at different frequencies) into the ground and records the response of the ground returning to the system receiver. The response changes depending on the type of rock or soil that the electromagnetic signal meets. For example, graphite has a high response value (meaning it is a low resistivity rock) while limestone has a low response value (it is a high resistivity rock).The data are collected as points in XYZ format. X and Y are the airplane coordinates. Z is the different recorded data, which include electromagnetic responses and aircraft flight height. The XYZ data for each line contains thousands of points. The data from separate lines are merged to create a resistivity grid for each survey block. All the survey blocks are then merged to create a final resistivity grid for Ireland.Colours are used to show resistivity ranges. Resistivity values are defined in ohm-metre units. Pinks and reds show the highest values. Greens and blues show lower values.This is a raster dataset. Raster data stores information in a cell-based manner and consists of a matrix of cells (or pixels) arranged into rows and columns. The format of the raster is a grid. The grid cell size is 50 m by 50 m. This means that each cell (pixel) represents an area on the ground of 50 metres squared. Each cell has a colour showing the resistivity value of the rocks.The Tellus project is a national survey which collects geochemical and geophysical data across Ireland. It allows us to study the chemical and physical properties of our soil, rocks and water. It is managed by the Geological Survey Ireland.

2021 Orthophoto - 3 inch resolution: This document describes the processes used to create the orthoimagery data produced for the District of Columbia from 2021 digital aerial photography. It was flown on March 11, 2021. The aerial imagery acquisition was flown to support the creation of 4-band digital orthophotography with a 3 inch/0.08 meter pixel resolution over the full project area covering the District of Columbia which is approximately 69 square miles. The contractor received waivers to fly in the Flight Restricted Zone (FRZ) and P-56 areas. The ortho imagery was submitted to DC OCTO in GeoTiff/TFW format tiles following the tile scheme provided by OCTO. MrSID and JPEG2000 compressed mosaics were delivered as well using a 50:1 compression ratio. This dataset provided as an ArcGIS Image service. Please note, the download feature for this image service in Open Data DC provides a compressed PNG, JPEG or TIFF. The compressed MrSID and JPEG2000 mosaic raster datasets are available under additional options when viewing downloads. Requests for the individual GeoTIFF set of images should be sent to open.data@dc.gov.

Wind Direction, LV Watershed, raster, 1/2000 to 12/2015 Reference Information and Units: GCS: EPSG:4326 (http://spatialreference.org/). Projection: Data has not been projected. Pixel Size: 0.125 degrees, approx. 14km at the equator. Units: degrees from north 0. At surface. Data values are monthly means of daily means. Note: 0 is wind from north, 90 is wind from east, and so on. Surface level winds File Naming Convention: Wind_dir_YYYY_MM Data Origin: ERA Interim, Monthly Means of Daily Means, and was developed by the European Centre for Medium-Range Weather Forecasts (ECMWF). http://apps.ecmwf.int/datasets/data/interim-full-moda/levtype=sfc/ Sensor: Various, "Reanalysis (as well as analysis) is a process by which model information and observations of many different sorts are combined in an optimal way to produce a consistent, global best estimate of the various atmospheric, wave and oceanographic parameters." Code: for %A in ("C:\temp*.nc") do gdal_translate -of GTiff -ot FLOAT32 -a_srs "+init=epsg:4326" -unscale -co "COMPRESS=PACKBITS" "%A" "%A.tif Data Development/Processing: Converted TIFF data was validated against the parent NetCDF file for correct cell size and pixel value. The GCS was batch defined in ArcGIS as SR-ORG:14. Processed data was then batch clipped to Lake Victoria and the surrounding lakes and statistics were calculated. Notes: Data was downloaded in two components; 10 meter V, and 10 meter U, where V represents wind direction from West to East (Westerly) and U represents wind direction from South to North (Southerly).

Composite ecological change as a function of three metrics (the potential degree of ecological change and of disappearing and novel ecological environments) shows where change might be greatest and different types of vulnerability using 30-year climate averages between the present (1990:1976- 2005) and projected future (2050:2036-2065) under the CanESM2 global climate model (RCP 8.5), based on a Generalised Dissimilarity Modelling (GDM) of compositional turnover for reptiles (REP_R3_V2).

Wherever the Potential degree of ecological change is scored low, ecological environments can neither be novel nor disappearing and minimal change is expected. But when the Potential degree of ecological change is scored high, a variety of possible types of change can occur depending on whether scores for Novel and/or Disappearing ecological environments are also high.

To create a composite view, we assigned each of the three component measures to a colour band in a composite-band raster: local similarity as shades of green (inverted, 1-0 rescaled 0-255); novel as shades of blue (0-1 rescaled 0-255); and disappearing as shades of red (0-1 rescaled 0-255). The three layers can then be mapped simultaneously (red: band 3; green: band 1; blue: band 2) each scaled 0-255 to show the varying degrees of similar, novel and disappearing ecological environments and their combinations.

This metric was developed along with others for use in an assessment of the efficacy of the protected area system for biodiversity under climate change at continental and global scales, presented at the IUCN World Parks Congress 2014. It is described in the AdaptNRM Guide “Implications of Climate Change for Biodiversity: a community-level modelling approach”, available online at: www.adaptnrm.org.

Data are provided as zipped ESRI tiff grids containing: raster image (.tif) with associated header (.tfw) and projection (*.xml) files. After extracting from the zip archive, these files can be imported into most GIS software packages. A readme file describes how to correctly reproduce the colour legend. In ArcGIS, the symbology statistics file can be used: "SND_display.stat.XML".

Reproducing RGB composite colours for 3-band raster in ArcGIS: 1. In file properties in ARCGIS, Symbology tab, Load XML "SND_display.stat.XML" 2. RED = BAND_3 (Disappearing) 3. GREEN = BAND_1 (Similarity ) 4. BLUE = BAND_2 (Novel) 5. Always use min-max legend 6. Set each band in the custom range 0-255, mean = 126, std = 0

Layers in this 9s series use a consistent naming convention: BIOLOGICAL GROUP _ FROM BASE TO SCENARIO _ ANALYSIS e.g. A_90CAN85_SND or R_90MIR85_SND where BIOLOGICAL GROUP is A: amphibians, M: mammals, R: reptiles and V: vascular plants and scenario is CAN: CanESM2; MIR: MIROC5 analysis, SND refers to – similarity, novel, disappearing

Lineage: Ecological similarity ranges between 0 and 1: the closer to zero, the greater the potential for compositional change in biodiversity. Each of the three ecological similarity measures were rescaled between 0 and 255 as integers to match the RGB colour scale, but the Potential degree of ecological change measure was inverted first (1-0 rescaled 0-255).

Using the Composite Bands tool in ArcGIS 10.2.2, a three-band raster was created with band1 = similarity, S; band 2 = novel, N; and band 3 = disappearing, D.

In ArcGIS mapping symbology, each of the three component measures are then assigned to a colour band: RED channel = BAND_3 (Disappearing) GREEN channel = BAND_1 (Similarity) BLUE channel = BAND_2 (Novel)

The gamma stretch legend scaling is not used and the min-max legend stretch is applied with statistics defined from the same custom settings for each band: minimum = 0; maximum= 255, mean = 126, std = 0.

These settings correctly reproduce the colours.

The composite ecological change index derives from the following three measures that are elsewhere described:

1. S, similarity: 9-second gridded continental Australia potential degree of ecological change for Reptiles 1990:2050 CanESM2 RCP 8.5 (CMIP5) (GDM: REP_R3_V2)
2. N, novel: 9-second gridded continental Australia novel ecological environments for Reptiles 1990:2050 CanESM2 RCP 8.5 (CMIP5) (GDM: REP_R3_V2)
3. D, dissimilarity: 9-second gridded continental Australia disappearing ecological environments for Reptiles 1990:2050 CanESM2 RCP 8.5 (CMIP5) (GDM: REP_R3_V2)

More detail of the calculations and methods used to derive the individual measures are given in the document “9sMethodsSummary.pdf” provided with the data download.

Each of these three measures use the GDM model that is elsewhere described: Generalised dissimilarity model of compositional turnover in reptile species for continental Australia at 9 second resolution using ANHAT data extracted 4 April 2013 (GDM: REP_R3_V2)

Climate data. Generalised dissimilarity models were built and projected using climate data that are elsewhere described: a) 9-second gridded climatology for continental Australia 1976-2005: Summary variables with elevation and radiative adjustment b) 9-second gridded climatology for continental Australia 2036-2065 CanESM2 RCP 8.5 (CMIP5): Summary variables with elevation and radiative adjustment

A brief summary of the climate downscaling method is given in the document “9sMethodsSummary.pdf” provided with the data download.

Further details about the CanESM2 global climate model: Chylek P, Li J, Dubey MK, Wang M and Lesins G (2011) ‘Observed and model simulated 20th century Arctic temperature variability: Canadian Earth System Model CanESM2’, ATMOSPHERIC CHEMISTRY and PHYSICS DISCUSSIONS 11, 22893—22907 doi:10.5194/acpd-11-22893-2011

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)

Wind Speed, LV Watershed, raster, 1/2000 to 12/2015 Reference Information and Units: GCS: EPSG:4326 (http://spatialreference.org/). Projection: Data has not been projected. Pixel Size: 0.125 degrees, approx. 14km at the equator. Units: m/s-1. At surface. Data values are monthly means of daily means. File Naming Convention: WS_Year_month Data Origin: ERA Interim, Monthly Means of Daily Means, and was developed by the European Centre for Medium-Range Weather Forecasts (ECMWF). http://apps.ecmwf.int/datasets/data/interim-full-moda/levtype=sfc/ Sensor: Various, "Reanalysis (as well as analysis) is a process by which model information and observations of many different sorts are combined in an optimal way to produce a consistent, global best estimate of the various atmospheric, wave and oceanographic parameters." Code: for %A in ("C:\temp*.nc") do gdal_translate -of GTiff -ot FLOAT32 -a_srs "+init=epsg:4326" -unscale -co "COMPRESS=PACKBITS" "%A" "%A.tif Data Development/Processing: Converted TIFF data was validated against the parent NetCDF file for correct cell size and pixel value. Output TIFFs were flipped. This was remedied via batch flipping in ArcGIS (Flip tool). The GCS was batch defined in ArcGIS as SR-ORG:14. Processed data was then batch clipped to Lake Victoria and the surrounding lakes and statistics were calculated.

The downloadable ZIP file contains a georeferenced TIF. This raster layer is a supervised maximum likelihood classification of bark beetle-caused tree mortality based on aggregated digital orthoimagery and LiDAR imagery from Central Idaho. The imagery was collected in August 2010. These data were contributed to INSIDE Idaho at the University of Idaho Library in 2011.

TIFF raster image format with TFW files. It represents the entire municipal territory divided into 116 sheets. The information content is on a scale of 1:2000. The Technical Map is made up of: 1) elements and entities of a geometric type: cartographic grid, quoted points. The altimetry, expressed in metres, relating to both the ground and the buildings, refers to the average sea level. The equidistance between the contour lines is 2 meters; for dashed curves it is 1 metre. Tolerance of the numerical dimensions = 0.60 m, of the contour lines = 0.90 m, MDL = 0.9996. The heights referred to the top of the buildings have decreased by 100 m; 2) constitutive elements of the anthropic landscape such as: buildings not covered with indication of destination for public buildings, technical artifacts, roads, railways, canals, trees and rows, etc; 3) constitutive elements of the natural landscape such as: hydrography, vegetation, etc.; 4) administrative limits; 5) toponymy. Available download service for portions of cartography at the address http://geodata.sitmilano.opendata.arcgis.com/ - Raster image TIFF file format with TFW file. It represents the entire territory of the Municipality of Milan divided into 116 tiles. The scale is 1:2000. The Municipal Base Map is composed by: 1) geometric elements and entities: metric grid, elevation points. The elevation points, in meters, are both of ground and buildings and are referred to the average sea level. The interval for contour lines is 2 meters and for auxiliary lines (dashed) is 1 meter. Tolerance of elevation points = 0.60 m, contour lines = 0.90 m, MDL = 0.9996. The elevation points of the top of the buildings must be increased by 100m; 2) elements of the anthropic landscape such as buildings (drawn without graphic pattern; function is specified for public buildings), technical structures, roads, railroads, canals, trees and rows of plants, etc.; 3) elements of the natural landscape such as hydrography, vegetation, etc.; 4) administrative boundaries; 5) toponyms. Available Clip and ship service of map areas at http://geodata.sitmilano.opendata.arcgis.com/

LiDAR (Light Detection and Ranging) is a remote sensing technology, i.e. the technology is not in direct contact with what is being measured. From satellite, aeroplane or helicopter, a LiDAR system sends a light pulse to the ground. This pulse hits the ground and returns back to a sensor on the system. The time is recorded to measure how long it takes for this light to return. Knowing this time measurement scientists are able to create topography maps.LiDAR data are collected as points (X,Y,Z (x & y coordinates) and z (height)). The data is then converted into gridded (GeoTIFF) data to create a Digital Terrain Model and Digital Surface Model of the earth. This LiDAR data was collected between June and October 2018.An ordnance datum (OD) is a vertical datum used as the basis for deriving heights on maps. This data is referenced to the Malin Head Vertical Datum which is the mean sea level of the tide gauge at Malin Head, County Donegal. It was adopted as the national datum in 1970 from readings taken between 1960 and 1969 and all heights on national grid maps are measured above this datum. Digital Terrain Models (DTM) are bare earth models (no trees or buildings) of the Earth’s surface.Digital Surface Models (DSM) are earth models in its current state. For example, a DSM includes elevations from buildings, tree canopy, electrical power lines and other features.Hillshading is a method which gives a 3D appearance to the terrain. It shows the shape of hills and mountains using shading (levels of grey) on a map, by the use of graded shadows that would be cast by high ground if light was shining from a chosen direction.This data shows the hillshade of the DTM.This data was collected by BlueSky and GeoAeroSpace and provided to the Geological Survey Ireland. All data formats are provided as GeoTIFF rasters but are at different resolutions. Data resolution is 1m.Both a DTM and DSM are raster data. Raster data is another name for gridded data. Raster data stores information in pixels (grid cells). Each raster grid makes up a matrix of cells (or pixels) organised into rows and columns. This data has a grid cell size of 1 meter by 1 meter. This means that each cell (pixel) represents an area of 1 meter squared.

TIFF raster image format with TFW files. It represents the entire municipal territory divided into 57 sheets. The information content is on a scale of 1:5000. The technical paper is made up of: 1) geometric elements. 2) constituent elements of the anthropic landscape such as: buildings, technical artifacts, roads, railways, canals, trees and rows, etc; 3) constitutive elements of the natural landscape such as: hydrography, vegetation, etc.; 4) administrative limits; 5) toponymy. WMS 1.3.0 service available at https://geoportale.comune.milano.it/arcgis/services/Cartografie_Raster/Milano_1930_TIF_CTC_GB/ImageServer/WMSServer and download service for portions of cartography at http://geodata.sitmilano .opendata.arcgis.com/ PLEASE NOTE: The map derives from the georeferencing of paper map sheets acquired via scanner. The paper support available at the time for the acquisition was found to be missing sheets 8,14,37 which are therefore not present in the digital format. - Raster image TIFF file format with TFW file. It represents the entire territory of the Municipality of Milan divided into 57 tiles. The scale is 1:5000. The Municipal Base Map is composed by: 1) geometric elements. 2) elements of the anthropic landscape such as buildings, technical structures, roads, railroads, canals, trees and rows of plants, etc.; 3) elements of the natural landscape such as hydrography, vegetation, etc.; 4) administrative boundaries; 5) toponyms. Available WMS service 1.3.0 at https://geoportale.comune.milano.it/arcgis/services/Cartografie_Raster/Milano_1930_TIF_CTC_GB/ImageServer/WMSServer and Clip and ship service of map areas at http://geodata.sitmilano.opendata.arcgis .com/ PLEASE NOTE: Digital map as result of scan process over a paper map. Original map had a lack of tiles number 8,14,37 during scan process, so that mentioned tiles are not available in digital copy.

BY USING THIS WEBSITE OR THE CONTENT THEREIN, YOU AGREE TO THE TERMS OF USE. The 1940 Historic Orthoimagery was derived from traditional orthophotography taken of Oakland County, Michigan. Original film negatives were cleaned, scanned, and raster digital files were created as TIF images. It is not known how the orthorectification, conversion or reprojection processes took place. This mosaic was created from a series of tiles stored in ERDAS Imagine format (IMG) in a Michigan State Plane South NAD83 International Feet projected coordinate system. Some tiles were missing from the IMG files used to create this mosaic, and one tile (XR-1A-109) did not have its respective world file. Therefore, some small gaps are present within the final dataset. The initial scale of the orthophotography is not known.

The zip files contain the following files:SLE_population_v1_0_gridded.tifThis geotiff raster (.tif), at a resolution of 3 arc (approximately 100m at the equator), contains estimated population counts per grid cell across Sierra Leone. The projection is Geographic Coordinate System, WGS84. ‘NoData’ values represent areas that were mapped as unsettled based on building footprints from “Digitize Africa data © 2020 Maxar Technologies, Ecopia.AI”. These data are stored as floating-point numbers rather than integers to avoid rounding errors in aggregated population totals for larger areas.SLE_population_v1_0_agesex.zipThis zip file contains 36 geotiff rasters (.tif) at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. Each raster contains estimated counts per grid cell for an age-sex group. The file names refer to the age-sex group represented by the raster. Age-sex group labels beginning with “f” are female populations and labels beginning with “m” are male populations. The age group labels refer to the first year of the age range. For example: “f_0” is females less than one year old. “f1” is females 1 to 4 years old. “f5” is females 5 to 9 years old. “f10” is females 10 to 14 years old. This pattern continues for each 5 year interval up to 80. “f80” is females greater than 80 years old. The labelling is the same for males: “m0”, “m1”, “m5”, “m10”, ... , “m80”.Data Citation: WorldPop and Statistics Sierra Leone. 2020. Census disaggregated gridded population estimates for Sierra Leone (2015), version 1.0. University of Southampton. doi:10.5258/SOTON/WP00668 CREDITS: Oliver Pannell (WorldPop) supported the generation of inputs for the application of the Random Forest (RF)- based dasymetric mapping approach developed by Stevens et al. (2015). The disaggregation was done by Maksym Bondarenko (WorldPop), using the Random Forests population modelling R scripts (Bondarenko et al., 2020), with oversight from Alessandro Sorichetta (WorldPop). Doug Leasure (WorldPop) prepared the mastergrid, tiles, and SQLite database. The sections-level administrative boundaries were provided by Jolynn Schmidt and Eniko Kelly-Voicu (CIESIN). The whole WorldPop group and GRID3 partners are acknowledged for overall project support.These data were produced by the WorldPop Research Group at the University of Southampton. This work is part of the GRID3 (Geo-Referenced Infrastructure and Demographic Data for Development) programme funded by the Bill & Melinda Gates Foundation (BMGF) and the United Kingdom’s Foreign, Commonwealth & Development Office. It is implemented by Columbia University’s Center for International Earth Science Information Network (CIESIN), the United Nations Population Fund (UNFPA), WorldPop at the University of Southampton, and the Flowminder Foundation.The downloadable Metadata provides more information about Source Data, Methods Overview, Assumptions & Limitations and Works and Data CitedContact release@worldpop.org for more information or go here.

This layer contains the number of livestock (pigs, sheep, goats, horses, buffalo, cattle, chickens, and ducks) in each country. The default symbology highlights most common livestock in each country, but with a few changes to the symbology, the map can also show the distribution of each livestock individually. The inspiration for this layer came from the FAO (Food and Agriculture Organization of the United Nations) site which is home to eight maps highlighting livestock distribution around the world. The source data, last updated in August 2018, contain the global distribution of each livestock in 2010 expressed in total number of livestock per pixel (5 min of arc) according to the Gridded Livestock of the World database (GLW 3). Click here to download the data from Harvard's Dataverse. This layer is derived from the tif file of the dasymetric product of the absolute number of animals per pixel (4,230 by 2,160 pixels of 0.083333 decimal degrees resolution).The following steps were taken to convert from a tif file to the country dataset:Download tifUse the "Int" tool to convert the pixel values to an integerRun the "Raster to Polygon" tool to convert the tif to a vectorImplement the "Intersect" tool to split vector pixels at the country boundariesFind the sum of each livestock in each country using the "Summary Statistics" toolJoin the "Summary Statistics" table output to the country layer with the "Join" toolPublish the country layer with the sum of each livestock to ArcGIS OnlineMap the most common livestock in each country with the Predominant Category drawing style in Smart MappingClick here to view the map of this layer.

Charlottesville participated in the 2021 NIHHIS-CAPA Urban Heat Island Mapping Campaign, a nationwide citizen-science based effort to collect local data on temperatures and humidity levels across the city. How urban environments and neighborhoods are built affects the amount of heat absorbed and retained, which can increase or reduce the impact of extreme heat events. Increases in extreme heat are one of the top projected impacts Charlottesville will experience from climate change.Data was collected by ~30 volunteers on August 24, 2021 along 7 pre-set routes (5 driving routes and 2 bicycle routes) during a heat wave on a day with high heat and low precipitation. The collected data was processed by CAPA Strategies and is now available through the City of Charlottesville's Open Data Portal. A PDF Report, including generated maps and explaining the data collection methodology, is also available.More information can be found on the City's Urban Heat Island Mapping Campaign webpage.File Notations:traverses (.shp, vector shapefile) = data collected by campaign participantsrasters (.tif, geotiff) = heat surface modelsam = morning; af = afternoon; pm = eveningt = temperature; hi = heat indexf = fahrenheite.g. pm_hi_f.tif = evening heat index geotiff raster with fahrenheit values.

The DEM of Cook County was developed from the DEM tiles that were delivered after the 2022 LiDAR acquisition. This DEM assembles all the tiles into one raster. It displays the bare earth returns of the LiDAR as a raster. There were no data values recorded in the Lidar for a small area of the Calumet Shores Gull Island wetland. The lidar is intended to return surface elevation for waterbodies; however in this instance there is a reflectance error. A corrected value for the surface elevation of 579.26 was calculated using a constant value raster. The original 3 tiffs are available (18508225.tif,18508250.tif, 18758250.tif).

These data show the magnetic field strength of different rock types across Ireland. The rock types can then be mapped. The data were collected between 2005 and 2021.Several surveys were merged to create this dataset. (1) Tellus Northern Ireland 2005-2006(2) Cavan-Monaghan, 2006(3) Tellus Border, 2011-2012(4) Tellus North Midlands, 2014-2015(5) Block A1, 2015(6) Block A2, 2016(7) Waterford, 2016(8) Block A3, 2017(9) Block A4, 2017(10) Block A5, 2018-2019(11) Block A6, 2018-2019(12) Block A7, 2019(13) Block A8 2020-2021(14) Block A9 2021The data were collected using an airplane. The airplane flies at 60 m flight height along lines that are 200 m apart. Magnetic data are recorded at around 6 m intervals along the flight lines. The magnetometer system mounted on the airplane records the magnetic field strength of the rocks. The magnetic field changes depending on the type of rock beneath the aircraft. Iron rich rocks (for example, basalt) are strongly magnetic and have a strong magnetic field, while rocks with low iron content (for example, limestone) are weakly magnetic.The data are collected as points in XYZ format. X and Y are the airplane coordinates. Z is the different recorded data, which include magnetic field strength and aircraft flight height. The XYZ data for each line contains thousands of points. The data from separate lines are merged to create a magnetic grid for each survey block. Individual survey blocks are then merged to create a final magnetic grid for Ireland.Colours are used to show magnetic field anomaly ranges. The values are defined in nanoTesla units. Pinks and reds show the highest values. Greens and blues show lower values.This is a raster dataset. Raster data stores information in a cell-based manner and consists of a matrix of cells (or pixels) organized into rows and columns. The format of the raster is an image (TIFF). The image has location information (GEOTIFF). The image cell size is 50 m by 50 m. This means that each cell (pixel) represents an area on the ground of 50 metres squared. Each cell has a value which is the average value of all the points located within that cell.The Tellus project is a national survey which collects geochemical and geophysical data across Ireland. It allows us to study the chemical and physical properties of our soil, rocks and water. It is managed by the Geological Survey Ireland.

The zip files contain the following files:SEN_population_v1_0_gridded.tifThis geotiff raster, at a spatial resolution of 3 arc-seconds (approximately 100m at the equator), contains estimates of the total population size per grid cell across Senegal. NA values represent areas that were mapped as unsettled based on gridded building patterns derived from building footprints (Dooley and Tatem, 2020). These data are stored as floating-point numbers rather than integers to avoid rounding errors in aggregated population totals for larger areas.SEN _population_v1_0_agesex.zipThis zip file contains the following two raster files:SEN_population_v1_0_gridded_female.tif This geotiff raster, at a spatial resolution of 3 arc-seconds (approximately 100m at the equator), contains estimates of the total female population size per grid cell across Senegal. NA values represent areas that were mapped as unsettled based on gridded building patterns derived from building footprints (Dooley and Tatem, 2020). These data are stored as floating-point numbers rather than integers to avoid rounding errors in aggregated population totals for larger areas.SEN_population_v1_0_gridded_male.tif This geotiff raster, at a spatial resolution of 3 arc-seconds (approximately 100m at the equator), contains estimates of the total male population size per grid cell across Senegal. NA values represent areas that were mapped as unsettled based on gridded building patterns derived from building footprints (Dooley and Tatem, 2020). These data are stored as floating-point numbers rather than integers to avoid rounding errors in aggregated population totals for larger areas.Note, these data are operational population estimates and are not official government statistics.The downloadable Metadata provides more information about Source Data, Methods Overview, Assumptions & Limitations and Works and Data Cited.Contact release@worldpop.org for more information or go here.Data Citation: Qader S. H., Abbott T., Boytinck, E., Kuepie, M., Lazar A. N., Tatem A. J. 2022. Census disaggregated gridded population estimates for Senegal (2020), version 1.0. University of Southampton. doi:10.5258/SOTON/WP00730These data were produced by the WorldPop Research Group at the University of Southampton. This work is part of the GRID3 (Geo-Referenced Infrastructure and Demographic Data for Development) programme funded by the Bill & Melinda Gates Foundation (BMGF) and the United Kingdom’s Foreign, Commonwealth & Development Office. It is implemented by Columbia University’s Center for International Earth Science Information Network (CIESIN), the United Nations Population Fund (UNFPA), WorldPop at the University of Southampton, and the Flowminder Foundation.