The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. We converted the photointerpreted data into a format usable in a geographic information system (GIS) by employing three fundamental processes: (1) orthorectify, (2) digitize, and (3) develop the geodatabase. All digital map automation was projected in Universal Transverse Mercator (UTM), Zone 16, using the North American Datum of 1983 (NAD83). Orthorectify: We orthorectified the interpreted overlays by using OrthoMapper, a softcopy photogrammetric software for GIS. One function of OrthoMapper is to create orthorectified imagery from scanned and unrectified imagery (Image Processing Software, Inc., 2002). The software features a method of visual orientation involving a point-and-click operation that uses existing orthorectified horizontal and vertical base maps. Of primary importance to us, OrthoMapper also has the capability to orthorectify the photointerpreted overlays of each photograph based on the reference information provided. Digitize: To produce a polygon vector layer for use in ArcGIS (Environmental Systems Research Institute [ESRI], Redlands, California), we converted each raster-based image mosaic of orthorectified overlays containing the photointerpreted data into a grid format by using ArcGIS. In ArcGIS, we used the ArcScan extension to trace the raster data and produce ESRI shapefiles. We digitally assigned map-attribute codes (both map-class codes and physiognomic modifier codes) to the polygons and checked the digital data against the photointerpreted overlays for line and attribute consistency. Ultimately, we merged the individual layers into a seamless layer. Geodatabase: At this stage, the map layer has only map-attribute codes assigned to each polygon. To assign meaningful information to each polygon (e.g., map-class names, physiognomic definitions, links to NVCS types), we produced a feature-class table, along with other supportive tables and subsequently related them together via an ArcGIS Geodatabase. This geodatabase also links the map to other feature-class layers produced from this project, including vegetation sample plots, accuracy assessment (AA) sites, aerial photo locations, and project boundary extent. A geodatabase provides access to a variety of interlocking data sets, is expandable, and equips resource managers and researchers with a powerful GIS tool.

The attached data are some large GIS raster files (GeoTIFFs) made with Natural Earth data. Natural Earth is a free vector and raster map data @ naturalearthdata.com. The data used for creating these large files was the "Cross Blended Hypso with Shaded Relief and Water". Data was concatenated to achieve larger and larger files. Internal pyramids were created, in order that the files can be opened easily in a GIS software such as QGIS or by a (future) GIS data visualisation module integrated in EnviDat. Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com

A 6-in resolution 8-class land cover dataset derived from the 2017 Light Detection and Ranging (LiDAR) data capture. This dataset was developed as part of an updated urban tree canopy assessment and therefore represents a ''top-down" mapping perspective in which tree canopy overhanging features is assigned to the tree canopy class. The eight land cover classes mapped were: (1) Tree Canopy, (2) Grass\Shrubs, (3) Bare Soil, (4) Water, (5) Buildings, (6) Roads, (7) Other Impervious, and (8) Railroads. The primary sources used to derive this land cover layer were 2017 LiDAR (1-ft post spacing) and 2016 4-band orthoimagery (0.5-ft resolution). Object based image analysis was used to automate land-cover features using LiDAR point clouds and derivatives, orthoimagery, and vector GIS datasets -- City Boundary (2017, NYC DoITT) Buildings (2017, NYC DoITT) Hydrography (2014, NYC DoITT) LiDAR Hydro Breaklines (2017, NYC DoITT) Transportation Structures (2014, NYC DoITT) Roadbed (2014, NYC DoITT) Road Centerlines (2014, NYC DoITT) Railroads (2014, NYC DoITT) Green Roofs (date unknown, NYC Parks) Parking Lots (2014, NYC DoITT) Parks (2016, NYC Parks) Sidewalks (2014, NYC DoITT) Synthetic Turf (2018, NYC Parks) Wetlands (2014, NYC Parks) Shoreline (2014, NYC DoITT) Plazas (2014, NYC DoITT) Utility Poles (2014, ConEdison via NYCEM) Athletic Facilities (2017, NYC Parks)

For the purposes of classification, only vegetation > 8 ft were classed as Tree Canopy. Vegetation below 8 ft was classed as Grass/Shrub.

To learn more about this dataset, visit the interactive "Understanding the 2017 New York City LiDAR Capture" Story Map -- https://maps.nyc.gov/lidar/2017/ Please see the following link for additional documentation on this dataset -- https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_LandCover.md

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.

Data were derived from 2014 6" resolution Island County lidar data using ArcGIS and Spatial Analyst Tools. The resulting raster was then converted to polygons. Polygons spanning elevation differences <10' were deleted.

High resolution land cover data set for New York City. This is the 3ft version of the high-resolution land cover dataset for New York City. Seven land cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads, and (7) other paved surfaces. The minimum mapping unit for the delineation of features was set at 3 square feet. The primary sources used to derive this land cover layer were the 2010 LiDAR and the 2008 4-band orthoimagery. Ancillary data sources included GIS data (city boundary, building footprints, water, parking lots, roads, railroads, railroad structures, ballfields) provided by New York City (all ancillary datasets except railroads); UVM Spatial Analysis Laboratory manually created railroad polygons from manual interpretation of 2008 4-band orthoimagery. The tree canopy class was considered current as of 2010; the remaining land-cover classes were considered current as of 2008. Object-Based Image Analysis (OBIA) techniques were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. More than 35,000 corrections were made to the classification. Overall accuracy was 96%. This dataset was developed as part of the Urban Tree Canopy (UTC) Assessment for New York City. As such, it represents a 'top down' mapping perspective in which tree canopy over hanging other features is assigned to the tree canopy class. At the time of its creation this dataset represents the most detailed and accurate land cover dataset for the area. This project was funded by National Urban and Community Forestry Advisory Council (NUCFAC) and the National Science Fundation (NSF), although it is not specifically endorsed by either agency. The methods used were developed by the University of Vermont Spatial Analysis Laboratory, in collaboration with the New York City Urban Field Station, with funding from the USDA Forest Service.

An index of TIFs received from the Rural Water Association. The TIFs are comparable to the Unconsolidated Aquifers and the Surficial Geologic Matetrials shapefiles. Data exists for the following towns: Ancram, Austerlitz, Chatham, Claverack, Copake, Germantown, Ghent, Hillsdale, Stuyvesant, and Taghkanic.

TIF Data current as of March 2016.

Geographic Information System (GIS) analyses are an essential part of natural resource management and research. Calculating and summarizing data within intersecting GIS layers is common practice for analysts and researchers. However, the various tools and steps required to complete this process are slow and tedious, requiring many tools iterating over hundreds, or even thousands of datasets. USGS scientists will combine a series of ArcGIS geoprocessing capabilities with custom scripts to create tools that will calculate, summarize, and organize large amounts of data that can span many temporal and spatial scales with minimal user input. The tools work with polygons, lines, points, and rasters to calculate relevant summary data and combine them into a single output table that can be easily incorporated into statistical analyses. These tools are useful for anyone interested in using an automated script to quickly compile summary information within all areas of interest in a GIS dataset.

Toolbox Use
License
Creative Commons-PDDC
Recommended Citation
Welty JL, Jeffries MI, Arkle RS, Pilliod DS, Kemp SK. 2021. GIS Clipping and Summarization Toolbox: U.S. Geological Survey Software Release. https://doi.org/10.5066/P99X8558

In this course, you will learn to work within the free and open-source R environment with a specific focus on working with and analyzing geospatial data. We will cover a wide variety of data and spatial data analytics topics, and you will learn how to code in R along the way. The Introduction module provides more background info about the course and course set up. This course is designed for someone with some prior GIS knowledge. For example, you should know the basics of working with maps, map projections, and vector and raster data. You should be able to perform common spatial analysis tasks and make map layouts. If you do not have a GIS background, we would recommend checking out the West Virginia View GIScience class. We do not assume that you have any prior experience with R or with coding. So, don't worry if you haven't developed these skill sets yet. That is a major goal in this course. Background material will be provided using code examples, videos, and presentations. We have provided assignments to offer hands-on learning opportunities. Data links for the lecture modules are provided within each module while data for the assignments are linked to the assignment buttons below. Please see the sequencing document for our suggested order in which to work through the material. After completing this course you will be able to: prepare, manipulate, query, and generally work with data in R. perform data summarization, comparisons, and statistical tests. create quality graphs, map layouts, and interactive web maps to visualize data and findings. present your research, methods, results, and code as web pages to foster reproducible research. work with spatial data in R. analyze vector and raster geospatial data to answer a question with a spatial component. make spatial models and predictions using regression and machine learning. code in the R language at an intermediate level.

GeoTIFF raster data with worldwide wind speed and wind power density potential. The GIS data stems from the Global Wind Atlas (http://globalwindatlas.info/). This link provides access to the following layers: (1) Wind speed (WS): at 3 heights (50m, 100m, and 200m) , stored as separate bands in the raster file (2) Power Density (PD): at 3 heights (50m, 100m, and 200m) , stored as separate bands in the raster file. (3) Elevation (ELEV): at ground level (4) Air Density (RHO): at ground level (5) Ruggedness Index (RIX): at ground level All layers have 250m resolution.

High resolution land cover dataset for City of Boston, MA. Seven land cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads, and (7) other paved surfaces. The primary sources used to derive this land cover layer were 2013 LiDAR data, 2014 Orthoimagery, and 2016 NAIP imagery. Ancillary data sources included GIS data provided by City of Boston, MA or created by the UVM Spatial Analysis Laboratory. Object-based image analysis techniques (OBIA) were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:2500 and all observable errors were corrected.

High resolution land cover dataset for City of Boston, MA. Seven land cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads, and (7) other paved surfaces. The primary sources used to derive this land cover layer were 2013 LiDAR data, 2014 Orthoimagery, and 2016 NAIP imagery. Ancillary data sources included GIS data provided by City of Boston, MA or created by the UVM Spatial Analysis Laboratory. Object-based image analysis techniques (OBIA) were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:2500 and all observable errors were corrected.

Credits: University of Vermont Spatial Analysis Laboratory in collaboration with the City of Boston, Trust for Public Lands, and City of Cambridge.

This dynamic image service provides access to the USGS 7.5 minute quadrangles for the Commonwealth of Kentucky. The source data is the Kentucky Raster Graphics (KRG) dataset created by the Commonwealth. The KRGs are a higher-resolution derivative of the USGS 7.5 minute topographic quadrangle data commonly known as DRGs, or Digital Raster Graphics.

The global geospatial data market is poised for significant expansion, projected to reach $3,788 million and grow at a Compound Annual Growth Rate (CAGR) of 6.1% during the forecast period of 2025-2033. This robust growth is propelled by an increasing demand for location-based intelligence across diverse industries. Key drivers include the proliferation of IoT devices generating vast amounts of location data, advancements in satellite imagery and remote sensing technologies, and the growing adoption of AI and machine learning for analyzing complex geospatial datasets. The enterprise sector is emerging as a dominant application segment, leveraging geospatial data for enhanced decision-making in areas such as logistics, urban planning, real estate, and natural resource management. Furthermore, government agencies are increasingly utilizing this data for public safety, infrastructure development, and environmental monitoring. The market is characterized by a bifurcated segmentation between vector data, representing discrete geographic features, and raster data, depicting continuous phenomena like elevation or temperature. Both segments are experiencing healthy growth, driven by specialized applications and analytical needs. Emerging trends include the rise of real-time geospatial data streams, the increasing importance of high-resolution imagery, and the integration of AI-powered analytics to extract deeper insights. However, challenges such as data privacy concerns, high infrastructure costs for data acquisition and processing, and the need for skilled professionals to interpret and utilize the data effectively may pose some restraints. Despite these hurdles, the overwhelming benefits of actionable location intelligence are expected to drive sustained market expansion, with North America and Europe currently leading in adoption, followed closely by the rapidly growing Asia Pacific region. This in-depth report delves into the dynamic and rapidly evolving Geospatial Data Provider market, offering a comprehensive analysis from the historical period of 2019-2024 through to a robust forecast extending to 2033. With the Base Year and Estimated Year set at 2025, the report provides an up-to-the-minute snapshot and a forward-looking perspective on this critical industry. The market size, valued in the millions, is meticulously dissected across various segments, companies, and industry developments.

The Raster Based GIS Coverage of Mexican Population is a gridded coverage (1 x 1 km) of Mexican population. The data were converted from vector into raster. The population figures were derived based on available point data (the population of known localities - 30,000 in all). Cell values were derived using a weighted moving average function (Burrough, 1986), and then calculated based on known population by state. The result from this conversion is a coverage whose population data is based on square grid cells rather than a series of vectors. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the Instituto Nacional de Estadistica Geografia e Informatica (INEGI).

The Cooperative Land Cover Map is a project to develop an improved statewide land cover map from existing sources and expert review of aerial photography. The project is directly tied to a goal of Florida's State Wildlife Action Plan (SWAP) to represent Florida's diverse habitats in a spatially-explicit manner. The Cooperative Land Cover Map integrates 3 primary data types: 1) 6 million acres are derived from local or site-specific data sources, primarily on existing conservation lands. Most of these sources have a ground-truth or local knowledge component. We collected land cover and vegetation data from 37 existing sources. Each dataset was evaluated for consistency and quality and assigned a confidence category that determined how it was integrated into the final land cover map. 2) 1.4 million acres are derived from areas that FNAI ecologists reviewed with high resolution aerial photography. These areas were reviewed because other data indicated some potential for the presence of a focal community: scrub, scrubby flatwoods, sandhill, dry prairie, pine rockland, rockland hammock, upland pine or mesic flatwoods. 3) 3.2 million acres are represented by Florida Land Use Land Cover data from the FL Department of Environmental Protection and Water Management Districts (FLUCCS). The Cooperative Land Cover Map integrates data from the following years: NWFWMD: 2006 - 07 SRWMD: 2005 - 08 SJRWMD: 2004 SFWMD: 2004 SWFWMD: 2008 All data were crosswalked into the Florida Land Cover Classification System. This project was funded by a grant from FWC/Florida's Wildlife Legacy Initiative (Project 08009) to Florida Natural Areas Inventory. The current dataset is provided in 10m raster grid format.Changes from Version 1.1 to Version 2.3:CLC v2.3 includes updated Florida Land Use Land Cover for four water management districts as described above: NWFWMD, SJRWMD, SFWMD, SWFWMDCLC v2.3 incorporates major revisions to natural coastal land cover and natural communities potentially affected by sea level rise. These revisions were undertaken by FNAI as part of two projects: Re-evaluating Florida's Ecological Conservation Priorities in the Face of Sea Level Rise (funded by the Yale Mapping Framework for Biodiversity Conservation and Climate Adaptation) and Predicting and Mitigating the Effects of Sea-Level Rise and Land Use Changes on Imperiled Species and Natural communities in Florida (funded by an FWC State Wildlife Grant and The Kresge Foundation). FNAI also opportunistically revised natural communities as needed in the course of species habitat mapping work funded by the Florida Department of Environmental Protection. CLC v2.3 also includes several new site specific data sources: New or revised FNAI natural community maps for 13 conservation lands and 9 Florida Forever proposals; new Florida Park Service maps for 10 parks; Sarasota County Preserves Habitat Maps (with FNAI review); Sarasota County HCP Florida Scrub-Jay Habitat (with FNAI Review); Southwest Florida Scrub Working Group scrub polygons. Several corrections to the crosswalk of FLUCCS to FLCS were made, including review and reclassification of interior sand beaches that were originally crosswalked to beach dune, and reclassification of upland hardwood forest south of Lake Okeechobee to mesic hammock. Representation of state waters was expanded to include the NOAA Submerged Lands Act data for Florida.Changes from Version 2.3 to 3.0: All land classes underwent revisions to correct boundaries, mislabeled classes, and hard edges between classes. Vector data was compared against high resolution Digital Ortho Quarter Quads (DOQQ) and Google Earth imagery. Individual land cover classes were converted to .KML format for use in Google Earth. Errors identified through visual review were manually corrected. Statewide medium resolution (spatial resolution of 10 m) SPOT 5 images were available for remote sensing classification with the following spectral bands: near infrared, red, green and short wave infrared. The acquisition dates of SPOT images ranged between October, 2005 and October, 2010. Remote sensing classification was performed in Idrisi Taiga and ERDAS Imagine. Supervised and unsupervised classifications of each SPOT image were performed with the corrected polygon data as a guide. Further visual inspections of classified areas were conducted for consistency, errors, and edge matching between image footprints. CLC v3.0 now includes state wide Florida NAVTEQ transportation data. CLC v3.0 incorporates extensive revisions to scrub, scrubby flatwoods, mesic flatwoods, and upland pine classes. An additional class, scrub mangrove – 5252, was added to the crosswalk. Mangrove swamp was reviewed and reclassified to include areas of scrub mangrove. CLC v3.0 also includes additional revisions to sand beach, riverine sand bar, and beach dune previously misclassified as high intensity urban or extractive. CLC v3.0 excludes the Dry Tortugas and does not include some of the small keys between Key West and Marquesas.Changes from Version 3.0 to Version 3.1: CLC v3.1 includes several new site specific data sources: Revised FNAI natural community maps for 31 WMAs, and 6 Florida Forever areas or proposals. This data was either extracted from v2.3, or from more recent mapping efforts. Domains have been removed from the attribute table, and a class name field has been added for SITE and STATE level classes. The Dry Tortugas have been reincorporated. The geographic extent has been revised for the Coastal Upland and Dry Prairie classes. Rural Open and the Extractive classes underwent a more thorough reviewChanges from Version 3.1 to Version 3.2:CLC v3.2 includes several new site specific data sources: Revised FNAI natural community maps for 43 Florida Park Service lands, and 9 Florida Forever areas or proposals. This data is from 2014 - 2016 mapping efforts. SITE level class review: Wet Coniferous plantation (2450) from v2.3 has been included in v3.2. Non-Vegetated Wetland (2300), Urban Open Land (18211), Cropland/Pasture (18331), and High Pine and Scrub (1200) have undergone thorough review and reclassification where appropriate. Other classification errors were opportunistically corrected as found or as reported by users to landcovermap@myfwc.com. Changes from Version 3.2.5 to Version 3.3: The CLC v3.3 includes several new site specific data sources: Revised FNAI natural community maps for 14 FWC managed or co-managed lands, including 7 WMA and 7 WEA, 1 State Forest, 3 Hillsboro County managed areas, and 1 Florida Forever proposal. This data is from the 2017 – 2018 mapping efforts. Select sites and classes were included from the 2016 – 2017 NWFWMD (FLUCCS) dataset. M.C. Davis Conservation areas, 18331x agricultural classes underwent a thorough review and reclassification where appropriate. Prairie Mesic Hammock (1122) was reclassified to Prairie Hydric Hammock (22322) in the Everglades. All SITE level Tree Plantations (18333) were reclassified to Coniferous Plantations (183332). The addition of FWC Oyster Bar (5230) features. Other classification errors were opportunistically corrected as found or as reported by users to landcovermap@myfwc.com, including classification corrections to sites in T.M. Goodwin and Ocala National Forest. CLC v3.3 utilizes the updated The Florida Land Cover Classification System (2018), altering the following class names and numbers: Irrigated Row Crops (1833111), Wet Coniferous Plantations (1833321) (formerly 2450), Major Springs (4131) (formerly 3118). Mixed Hardwood-Coniferous Swamps (2240) (formerly Other Wetland Forested Mixed).Changes from Version 3.4 to Version 3.5: The CLC v3.5 includes several new site specific data sources: Revised FNAI natural community maps for 16 managed areas, and 10 Florida Forever Board of Trustees Projects (FFBOT) sites. This data is from the 2019 – 2020 mapping efforts. Other classification errors were opportunistically corrected as found or as reported by users to landcovermap@myfwc.com. This version of the CLC is also the first to include land identified as Salt Flats (5241).Changes from Version 3.5 to 3.6: The CLC v3.6 includes several new site specific data sources: Revised FNAI natural community maps for 11 managed areas, and 24 Florida Forever Board of Trustees Projects (FFBOT) sites. This data is from the 2018 – 2022 mapping efforts. Other classification errors were opportunistically corrected as found or as reported by users to landcovermap@myfwc.com. Changes from Version 3.6 to 3.7: The CLC 3.7 includes several new site specific data sources: Revised FNAI natural community maps for 5 managed areas (2022-2023). Revised Palm Beach County Natural Areas data for Pine Glades Natural Area (2023). Other classification errors were opportunistically corrected as found or as reported by users to landcovermap@myfwc.com. In this version a few SITE level classifications are reclassified for the STATE level classification system. Mesic Flatwoods and Scrubby Flatwoods are classified as Dry Flatwoods at the STATE level. Upland Glade is classified as Barren, Sinkhole, and Outcrop Communities at the STATE level. Lastly Upland Pine is classified as High Pine and Scrub at the STATE level.Changes from Version 3.7 to 4.0: CLC 4.0 represents a major update to CLC performed cooperatively by FWC and FNAI via a State Wildlife Grant to address changes on the landscape such as conversion to development and to integrate other recent high quality land cover sources. CLC v4.0 includes FWC's comprehensive delineation of solar farms, FLUCCS updates based on aerial photos from 2017-2022, ground-truthed mapping from FNAI and Florida Park Service, a statewide update of Intensive Development from Google Dynamic World land cover as of 2024, and additional FNAI review and revisions of target classes including sand beach and beach dune. A complete description of updates is available in the Cooperative Land Cover version 4.0 report, available from FNAI

Developed by SOLARGIS and provided by the Global Solar Atlas (GSA), this data resource contains air temperature at 2m above ground level in °C covering the globe. Data is provided in a geographic spatial reference (EPSG:4326). The resolution (pixel size) of solar resource data (GHI, DIF, GTI, DNI) is 9 arcsec (nominally 250 m), PVOUT and TEMP 30 arcsec (nominally 1 km) and OPTA 2 arcmin (nominally 4 km). The data is hyperlinked under 'resources' with the following characteristics: TEMP GISdata (GeoTIFF) Data format: GEOTIFF File size : 121.03 MB There are two temporal representation of solar resource and PVOUT data available: • Longterm yearly/monthly average of daily totals (LTAym_AvgDailyTotals) • Longterm average of yearly/monthly totals (LTAym_YearlyMonthlyTotals) Both type of data are equivalent, you can select the summarization of your preference. The relation between datasets is described by simple equations: • LTAy_YearlyTotals = LTAy_DailyTotals * 365.25 • LTAy_MonthlyTotals = LTAy_DailyTotals * Number_of_Days_In_The_Month For individual country or regional data downloads please see: https://globalsolaratlas.info/download (use the drop-down menu to select country or region of interest) For data provided in AAIGrid please see: https://globalsolaratlas.info/download/world. For more information and terms of use, please, read metadata, provided in PDF and XML format for each data layer in a download file. For other data formats, resolution or time aggregation, please, visit Solargis website. Data can be used for visualization, further processing, and geo-analysis in all mainstream GIS software with raster data processing capabilities (such as open source QGIS, commercial ESRI ArcGIS products and others).

Wind Farms - follows on from the 'Dave' Data Download case study. View and symbolise OS raster and height data and Wind Farm location data. GIS vector data. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2014-04-10 and migrated to Edinburgh DataShare on 2017-02-22.

Polygon layer representing United States counties with name attributes.About Natural EarthNatural Earth is a convenient resource for creating custom maps. Unlike other map data intended for analysis or detailed government mapping, it is designed to meet the needs of cartographers and designers to make generalized maps. Maximum flexibility is a goal.Natural Earth is a public domain collection of map datasets available at 1:10 million (larger scale/more detailed), 1:50 million (medium scale/moderate detail), and 1:110 million (small scale/coarse detail) scales. It features tightly integrated vector and raster data to create a variety of visually pleasing, well-crafted maps with cartography or GIS software. Natural Earth data is made possible by many volunteers and supported by the North American Cartographic Information Society (NACIS).Convenience – Natural Earth solves a problem: finding suitable data for making small-scale maps. In a time when the web is awash in geospatial data, cartographers are forced to waste time sifting through confusing tangles of poorly attributed data to make clean, legible maps. Because your time is valuable, Natural Earth data comes ready to use.Neatness Counts–The carefully generalized linework maintains consistent, recognizable geographic shapes at 1:10m, 1:50m, and 1:110m scales. Natural Earth was built from the ground up, so you will find that all data layers align precisely with one another. For example, where rivers and country borders are one and the same, the lines are coincident.GIS Attributes – Natural Earth, however, is more than just a collection of pretty lines. The data attributes are equally important for mapmaking. Most data contain embedded feature names, which are ranked by relative importance. Other attributes facilitate faster map production, such as width attributes assigned to river segments for creating tapers. Intelligent dataThe attributes assigned to Natural Earth vectors make for efficient mapmaking. Most lines and areas contain embedded feature names, which are ranked by relative importance. Up to eight rankings per data theme allow easy custom map “mashups” to emphasize your subject while de-emphasizing reference features. Other attributes focus on map design. For example, width attributes assigned to rivers allow you to create tapered drainages. Assigning different colors to contiguous country polygons is another task made easier thanks to data attribution.Other key featuresVector features include name attributes and bounding box extents. Know that the Rocky Mountains are larger than the Ozarks.Large polygons are split for more efficient data handling—such as bathymetric layers.Projection-friendly vectors precisely match at 180 degrees longitude. Lines contain enough data points for smooth bending in conic projections, but not so many that computer processing speed suffers.Raster data includes grayscale-shaded relief and cross-blended hypsometric tints derived from the latest NASA SRTM Plus elevation data and tailored to register with Natural Earth Vector.Optimized for use in web mapping applications, with built-in scale attributes to assist features to be shown at different zoom levels.

This product set contains high-resolution Interferometric Synthetic Aperture Radar (IFSAR) imagery and geospatial data for the Barrow Peninsula (155.39 - 157.48 deg W, 70.86 - 71.47 deg N) and Barrow Triangle (156.13 - 157.08 deg W, 71.14 - 71.42 deg N), for use in Geographic Information Systems (GIS) and remote sensing software. The primary IFSAR data sets were acquired by Intermap Technologies from 27 to 29 July 2002, and consist of Orthorectified Radar Imagery (ORRI), a Digital Surface Model (DSM), and a Digital Terrain Model (DTM). Derived data layers include aspect, shaded relief, and slope-angle grids (floating-point binary and ArcInfo grid format), as well as a vector layer of contour lines (ESRI Shapefile format). Also available are accessory layers compiled from other sources: 1:250,000- and 1:63,360-scale USGS Digital Raster Graphic (DRG) mosaic images (GeoTIFF format); 1:250,000- and 1:63,360-scale USGS quadrangle index maps (ESRI Shapefile format); a quarter-quadrangle index map for the 26 IFSAR tiles (ESRI Shapefile format); and a simple polygon layer of the extent of the Barrow Peninsula (ESRI Shapefile format). Unmodified IFSAR data comprise 26 data tiles across UTM zones 4 and 5. The DSM and DTM tiles (5 m resolution) are provided in floating-point binary format with header and projection files. The ORRI tiles (1.25 m resolution) are available in GeoTIFF format. FGDC-compliant metadata for all data sets are provided in text, HTML, and XML formats, along with the Intermap License Agreement and product handbook. The baseline geospatial data support education, outreach, and multi-disciplinary research of environmental change in Barrow, which is an area of focused scientific interest. Data are provided on five DVDs, available through licensing only to National Science Foundation (NSF)-funded investigators. An NSF award number must be provided when ordering data.

Developed by SOLARGIS and provided by the Global Solar Atlas (GSA), this data resource contains diffuse horizontal irradiation (DIF) in kWh/m² covering the globe. Data is provided in a geographic spatial reference (EPSG:4326). The resolution (pixel size) of solar resource data (GHI, DIF, GTI, DNI) is 9 arcsec (nominally 250 m), PVOUT and TEMP 30 arcsec (nominally 1 km) and OPTA 2 arcmin (nominally 4 km). The data is hyperlinked under 'resources' with the following characeristics: DIF LTAy_AvgDailyTotals (GeoTIFF) Data format: GEOTIFF File size : 198.94 MB There are two temporal representation of solar resource and PVOUT data available: • Longterm yearly/monthly average of daily totals (LTAym_AvgDailyTotals) • Longterm average of yearly/monthly totals (LTAym_YearlyMonthlyTotals) Both type of data are equivalent, you can select the summarization of your preference. The relation between datasets is described by simple equations: • LTAy_YearlyTotals = LTAy_DailyTotals * 365.25 • LTAy_MonthlyTotals = LTAy_DailyTotals * Number_of_Days_In_The_Month For individual country or regional data downloads please see: https://globalsolaratlas.info/download (use the drop-down menu to select country or region of interest) For data provided in AAIGrid please see: https://globalsolaratlas.info/download/world. For more information and terms of use, please, read metadata, provided in PDF and XML format for each data layer in a download file. For other data formats, resolution or time aggregation, please, visit Solargis website. Data can be used for visualization, further processing, and geo-analysis in all mainstream GIS software with raster data processing capabilities (such as open source QGIS, commercial ESRI ArcGIS products and others).