In this course, you will explore a variety of open-source technologies for working with geosptial data, performing spatial analysis, and undertaking general data science. The first component of the class focuses on the use of QGIS and associated technologies (GDAL, PROJ, GRASS, SAGA, and Orfeo Toolbox). The second component of the class introduces Python and associated open-source libraries and modules (NumPy, Pandas, Matplotlib, Seaborn, GeoPandas, Rasterio, WhiteboxTools, and Scikit-Learn) used by geospatial scientists and data scientists. We also provide an introduction to Structured Query Language (SQL) for performing table and spatial queries. This course is designed for individuals that have a background in GIS, such as working in the ArcGIS environment, but no prior experience using open-source software and/or coding. You will be asked to work through a series of lecture modules and videos broken into several topic areas, as outlined below. Fourteen assignments and the required data have been provided as hands-on opportunites to work with data and the discussed technologies and methods. If you have any questions or suggestions, feel free to contact us. We hope to continue to update and improve this course. This course was produced by West Virginia View (http://www.wvview.org/) with support from AmericaView (https://americaview.org/). This material is based upon work supported by the U.S. Geological Survey under Grant/Cooperative Agreement No. G18AP00077. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the opinions or policies of the U.S. Geological Survey. Mention of trade names or commercial products does not constitute their endorsement by the U.S. Geological Survey. After completing this course you will be able to: apply QGIS to visualize, query, and analyze vector and raster spatial data. use available resources to further expand your knowledge of open-source technologies. describe and use a variety of open data formats. code in Python at an intermediate-level. read, summarize, visualize, and analyze data using open Python libraries. create spatial predictive models using Python and associated libraries. use SQL to perform table and spatial queries at an intermediate-level.

Have you ever wanted to create your own maps, or integrate and visualize spatial datasets to examine changes in trends between locations and over time? Follow along with these training tutorials on QGIS, an open source geographic information system (GIS) and learn key concepts, procedures and skills for performing common GIS tasks – such as creating maps, as well as joining, overlaying and visualizing spatial datasets. These tutorials are geared towards new GIS users. We’ll start with foundational concepts, and build towards more advanced topics throughout – demonstrating how with a few relatively easy steps you can get quite a lot out of GIS. You can then extend these skills to datasets of thematic relevance to you in addressing tasks faced in your day-to-day work.

This dataset was created to be used in my Capstone Project for the Google Data Analytics Professional Certificate. Data was web scraped from the state websites to combine the GIS information like FIPS, latitude, longitude, and County Codes by both number and Mailing Number.

RStudio was used for this web scrape and join. For details on how it was done you can go to the following link for my Github repository.

Feel free to follow my Github or LinkedIn profile to see what I end up doing with this Dataset.

Context

I made this dataset while performing Integrated Valuation of Ecosystem Services and Tradeoffss (InVEST) models of wetlands in India.

Content

This dataset is a collection of Geographic Information System (GIS) data sourced from various public domains. It includes shapefiles, image raster files, etc which can are primarily developed with the aim of using with GIS software such as ArcGIS Pro, QGIS, etc. Most of the datasets are global in nature with some, like the OpenStreetMap data pertaining to India only. The data is as described below:

The Protected Areas Database of the United States (PAD-US) is a geodatabase, managed by USGS GAP, that illustrates and describes public land ownership, management and other conservation lands, including voluntarily provided privately protected areas. The State, Regional and LCC geodatabases contain two feature classes. The PADUS1_3_FeeEasement feature class and the national MPA feature class. Legitimate and other protected area overlaps exist in the full inventory, with Easements loaded on top of Fee. Parcel data within a protected area are dissolved in this file that powers the PAD-US Viewer. As overlaps exist, GAP creates separate analytical layers to summarize area statistics for "GAP Status Code" and "Owner Name". Contact the PAD-US Coordinator for more information. The lands included in PAD-US are assigned conservation measures that qualify their intent to manage lands for the preservation of biological diversity and to other natural, recreational and cultural uses; managed for these purposes through legal or other effective means. The geodatabase includes: 1) Geographic boundaries of public land ownership and voluntarily provided private conservation lands (e.g., Nature Conservancy Preserves); 2) The combination land owner, land manager, management designation or type, parcel name, GIS Acres and source of geographic information of each mapped land unit 3) GAP Status Code conservation measure of each parcel based on USGS National Gap Analysis Program (GAP) protection level categories which provide a measurement of management intent for long-term biodiversity conservation 4) IUCN category for a protected area's inclusion into UNEP-World Conservation Monitoring Centre's World Database for Protected Areas. IUCN protected areas are defined as, "A clearly defined geographical space, recognized, dedicated and managed, through legal or other effective means, to achieve the long-term conservation of nature with associated ecosystem services and cultural values" and are categorized following a classification scheme available through USGS GAP; 5) World Database of Protected Areas (WDPA) Site Codes linking the multiple parcels of a single protected area in PAD-US and connecting them to the Global Community. As legitimate and other overlaps exist in the combined inventory GAP creates separate analytical layers to obtain area statistics for "GAP Status Code" and "Owner Name". PAD-US version 1.3 Combined updates include: 1) State, local government and private protected area updates delivered September 2011 from PAD-US State Data Stewards: CO (Colorado State University), FL (Florida Natural Areas Inventory), ID (Idaho Fish and Game), MA (The Commonwealth's Office of Geographic Information Systems, MassGIS), MO (University of Missouri, MoRAP), MT (Montana Natural Heritage Program), NM (Natural Heritage New Mexico), OR (Oregon Natural Heritage Program), VA (Department of Conservation and Recreation, Virginia Natural Heritage Program). 2) Select local government (i.e. county, city) protected areas (3,632) across the country (to complement the current PAD-US inventory) aggregated by the Trust for Public Land (TPL) for their Conservation Almanac that tracks the conservation finance movement across the country. 3) A new Date of Establishment field that identifies the year an area was designated or otherwise protected, attributed for 86% of GAP Status Code 1 and 2 protected areas. Additional dates will be provided in future updates. 4) A national wilderness area update from wilderness.net 5) The Access field that describes public access to protected areas as defined by data stewards or categorical assignment by Primary Designation Type. . The new Access Source field documents local vs. categorical assignments. See the PAD-US Standard Manual for more information: gapanalysis.usgs.gov/padus 6) The transfer of conservation measures (i.e. GAP Status Codes, IUCN Categories) and documentation (i.e. GAP Code Source, GAP Code Date) from PAD-US version 1.2 or categorical assignments (see PAD-US Standard) when not provided by data stewards 7) Integration of non-sensitive National Conservation Easement Database (NCED) easements from August 2011, July 2012 with PAD-US version 1.2 easements. Duplicates were removed, unless 'Stacked' = Y and multiple easements exist. 8) Unique ID's transferred from NCED or requested for new easements. NCED and PAD-US are linked via Source UID in the PAD-US version 1.3 Easement feature class. 9) Official (member and eligible) MPAs from the NOAA MPA Inventory (March 2011, www.mpa.gov) translated into the PAD-US schema with conservation measures transferred from PAD-US version 1.2 or categorically assigned to new protected areas. Contact the PAD-US Coordinator for documentation of categorical GAP Status Code assignments for MPAs. 10) Identified MPA records that overlap existing protected areas in the PAD-US Fee feature class (i.e. PADUS Overlap field in MPA feature class). For example, many National Wildlife Refuges and National Parks are also MPAs and are represented in the PAD-US MPA and Fee feature classes.

Dataset contains training material on using open source Geographic Information Systems (GIS) to improve protected area planning and management from workshops that were conducted on February 19-21 and October 6-7, 2020. Specifically, the dataset contains lectures on GIS fundamentals, QGIS 3.x, and global positioning system (GPS), as well as country-specific datasets and a workbook containing exercises for viewing data, editing/creating datasets, and creating map products in QGIS. Supplemental videos that narrate a step-by-step recap and overview of these processes are found in the Related Content section of this dataset.

Funding for this workshop and material was funded by the Biodiversity and Protected Areas Management (BIOPAMA) programme. The BIOPAMA programme is an initiative of the Organisation of African, Caribbean and Pacific (ACP) Group of States financed by the European Union's 11th European Development Fund. BIOPAMA is jointly implemented by the International Union for Conservation of Nature {IUCN) and the Joint Research Centre of the European Commission (EC-JRC). In the Pacific region, BIOPAMA is implemented by IUCN's Oceania Regional Office (IUCN ORO) in partnership with the Secretariat of the Pacific Regional Environment Programme (SPREP). The overall objective of the BIOPAMA programme is to contribute to improving the long-term conservation and sustainable use of biodiversity and natural resources in the Pacific ACP region in protected areas and surrounding communities through better use and monitoring of information and capacity development on management and governance.

The present file provides 1) comparison between black sky albedo at fixed solar zenith angle and local noon, and 2) dataset of black-sky albedo at fixed solar zenith angle at a 1-km spatial resolution, and 3) their links with forest structure using generalized additive models at 0.5-degree spatial resolution during peak growing season in 2005.

Active non-wholesale non-transmission Natural Gas Utility Service Areas as listed in the Regulatory Commission of Alaska (RCA) Certificate details for regulated utilities. Likely the most comprehensive collection of State of Alaska utility service areas - but not necessarily definitive for every utility. For complicated large city service areas such as water and sewer the GIS department that represents those cities might have the best representation of the service area. There are also utilities that may not be regulated by RCA which will not be in the data. Footprints in general were lifted from existing KML files created by a contractor in the years 2008-2017. Service area changes that have happened since 2008 may not yet be reflected in the footprints. In a few cases legal descriptions had typos which resulted in service areas miles from the community they intended to cover. In the case of the AsOfDate attribute in this dataset only reflects the date of the last syncing of master certificate metadata with RCA Library database - not the current polygon representation.Source: Regulatory Commission of AlaskaThis data has been visualized in a Geographic Information Systems (GIS) format and is provided as a service in the DCRA Information Portal by the Alaska Department of Commerce, Community, and Economic Development Division of Community and Regional Affairs (SOA DCCED DCRA), Research and Analysis section. SOA DCCED DCRA Research and Analysis is not the authoritative source for this data. For more information and for questions about this data, see: Regulatory Commission of Alaska Library

In Germany, orthopedic workforce planning relies on population-to-provider-ratios represented by the ‘official degree of care provision’. However, with geographic information systems (GIS), more sophisticated measurements are available. By utilizing GIS-based technologies we analyzed the current state of demand and supply of the orthopedic workforce in Germany (orthopedic accessibility) with the integrated Floating Catchment Area method. The analysis of n = 153,352,220 distances revealed significant geographical variations on national scale: 5,617,595 people (6.9% of total population) lived in an area with significant low orthopedic accessibility (average z-score = -4.0), whereas 31,748,161 people (39.0% of total population) lived in an area with significant high orthopedic accessibility (average z-score = 8.0). Accessibility was positively correlated with the degree of urbanization (r = 0.49; p

The data contains the geotif images of the maps derived from Landsat classifications in addition to the landscape metrics associated with the maps. We introduced seven LULC classes with the SVM algorithm following Baquero et al. (2004) and Homeier et al. (2008) land cover classification systems for the southern Ecuadorian Andes: 1) recent burned areas (BUR); 2) mixed development including residential, roads, and other infrastructure (MXD); 3) bare or exposed soils (BARE); 4) active pastures and agricultural areas (AGRP); 5) succession vegetation after human-induced disturbances (SUV) which may include areas with old pastures, native grasses, and/or bracken fern; 6) evergreen upper montane forest and humid shrub (UMV); and 7) herbaceous/shrub and dwarf bamboo páramo vegetation (PAR).

SIDI depicts the probability that any two pixels selected at random would be different patch types. To quantify evenness, we used Shannon’s evenness index (SHEI). SHEI is a modification of Shannon’s diversity index and provides information on area composition and richness relative to other LULC classes. It accounts for the number of different land cover types observed along a straight line and their relative abundances. We assessed LULC pattern using two elements of landscape composition: 1) percent of landscape occupancy (PLAND), and 2) largest class patch (LCP) (i.e., percentage of total landscape area comprised by the largest class patch); and one component of configuration: proportion of like adjacencies (PLADJ) (i.e., degree of aggregation of class patches). Both PLAND and LCP help characterize landscape change and class dominance trajectories based on the analysis of multi-temporal data. We measured LCP using the largest patch index (LPI) as an indicator of class dominance (Herold et al. 2002). PLADJ helps characterize habitat continuity or fragmentation (i.e., contagion/interspersion characteristics). Gillanders et al. (2008), for example, used interspersion metrics such as PLADJ to analyze forest/insect interactions in temperate ecosystems. A landscape containing greater aggregation of patch types (i.e., a smaller number of larger patches, thus a more continuous landscape) will contain a higher proportion of like adjacencies than a landscape containing disaggregated patch types (i.e., higher number of smaller patches, thus a more fragmented landscape) (McGarigal et al. 2012). We also evaluated LULC change trajectories by quantifying percent class change (PCC) (i.e., proportion of each class that transitioned from class i to class j and obtained directly from transition matrixes). PCC, specifically from forested land cover types to anthropogenic land uses, provides critical insights about the rate of habitat loss caused by human-induced disturbances, and to a certain extent, the state of habitat conservation in a given area (Cohen et al. 2016).

Forest Ecosystem Dynamics (FED) Project Spatial Data Archive: Elevation Contours for the Northern Experimental Forest

The Biospheric Sciences Branch (formerly Earth Resources Branch) within the Laboratory for Terrestrial Physics at NASA's Goddard Space Flight Center and associated University investigators are involved in a research program entitled Forest Ecosystem Dynamics (FED) which is fundamentally concerned with vegetation change of forest ecosystems at local to regional spatial scales (100 to 10,000 meters) and temporal scales ranging from monthly to decadal periods (10 to 100 years). The nature and extent of the impacts of these changes, as well as the feedbacks to global climate, may be addressed through modeling the interactions of the vegetation, soil, and energy components of the boreal ecosystem.

The Howland Forest research site lies within the Northern Experimental Forest of International Paper. The natural stands in this boreal-northern hardwood transitional forest consist of spruce-hemlock-fir, aspen-birch, and hemlock-hardwood mixtures. The topography of the region varies from flat to gently rolling, with a maximum elevation change of less than 68 m within 10 km. Due to the region's glacial history, soil drainage classes within a small area may vary widely, from well drained to poorly drained. Consequently, an elaborate patchwork of forest communities has developed, supporting exceptional local species diversity.

This data layer contains elevation contours for the 10 X 10 km area located within the Northern Experimental Forest. Contours and elevation benchmarks from the United States Geological Survey 7.5" Maine quadsheets for Howland and Lagrange were digitized, and elevation data in feet were added.

The data was revised by projecting it into NAD83 datum by L. Prihodko at NASA Goddard Space Flight Center. Although the data was received at GSFC with an undeclared datum, it was assumed to be in North American Datum of 1927 (NAD27) because the original map from which the data were digitized was in NAD27. Also, the data fit exactly within the bounds of the FED site grid (even Universal Transverse Mercator projections) in NAD27. After projecting the data into NAD83 it was checked to insure that the change was a linear translation of the coordinates.

This dataset was developed to provide a geologic map GIS of the Wallace 1x2 degree quadrangle for use in future spatial analysis by a variety of users.

This database is not meant to be used or displayed at any scale larger than 1:250,000 (e.g., 1:100,000 or 1:24,000)

This dataset was digitized by the U.S. Geological Survey EROS Data Center and U.S. Geological Survey Spokane Field Office for input into an Arc/Info geographic information system (GIS) The digital geologic map database can be queried in many ways to produce a variety of derivative geologic maps.

This GIS consists of two major and Arc/Info datasets: one line and polygon file (wal250k) containing geologic contacts and structures (lines) and geologic map rock units (polygons), and one point file (wal250bc) containing breccia outcrops.

We combined a detailed field study of canopy gap fraction with spectral mixture analysis of Landsat ETM+ satellite imagery to assess landscape and regional dynamics of canopy damage following selective logging in an eastern Amazon forest. Our field studies encompassed measurements of ground damage and canopy gap fractions along multitemporal sequences of post-harvest regrowth of 0.5-3.5 yr. Areas used to stage harvested logs prior to transport, called log decks, had the largest forest gap fractions, but their contribution to the landscape-level gap dynamics was minor. Tree falls were spatially the most extensive form of canopy damage following selective logging, but the canopy gap fractions resulting from them were small. Reduced-impact logging resulted in consistently less damage to the forest canopy than did conventional logging practices. This was true at the level of individual landscape strata such as roads, skids, and tree falls as well as at the area-integrated scale. A spectral mixture model was employed that utilizes bundles of field and image spectral reflectance measurements with a Monte Carlo analysis to estimate high spatial resolution (subpixel) cover of forest canopies, exposed nonphotosynthetic vegetation, and soils in the Landsat imagery. The method proved highly useful for quantifying forest canopy cover fraction in the log decks, roads, skids, tree fall, and intact forest areas, and it tracked caopy damage up to 3.5 yr post-harvest. Forest canopy cover fractions derived from satellite observations were highly and inversely correlated with field- and satellite-based measurements. A 450-km^2 study of gap fraction showed that approximately one-half of the canopy opening caused by logging is closed within one year of regrowth following timber harvests. This is the first regional-scale study utilizing field measurements, satellite observations, and models to quantify forest canopy damage and recovery following selective logging in the Amazon.

HCID is a global grid identification system offering users to refer the location and boundary of a grid cell, available at multiple spatial resolutions, using a single integer number. Instead of using the coordinates (latitude and longitude) of two corners of the grid cell bounding box (i.e., upper-left and lower-right), we assign each grid cell with a sequential integer number, or a grid cell ID, unique to each spatial resolution. This system was developed by HarvestChoice (http://harvestchoice.org) and is being widely used to facilitate analysis of spatial data layers, including the visualization, domain analysis, spatial aggregation/dis-aggregation, and general exchange of spatially-explicit data across disciplines - without needing to use a GIS software and spatial analysis skills. For the five arc-minute resolution of grids, we call the ID system as "CELL5M", whereas ones for 30 arc-second, 30-minute and 1 degree are called CELL30S, CELL30M and CELL1D, respectively. Assigning 0 starting at the upper-left corner (longitude: -180.0, latitude: 90.0) with a geographic projection, for example, CELL5M ranges up to 9,331,199 at the lower-right corner (longitude: 180.0, latitude: -90.0). The grid cell ID at a specific location can be easily computed mathematically, and this can be also easily converted to different resolutions.

Abbreviations: AWC (available water-holding capacity), GDD (growing degree-days), BEDD (biologically effective degree-days), FFD (frost-free days), PPT (growing season precipitation).

This publication consists of the online version of a CD-ROM publication, U.S. Geological Survey Digital Data Series DDS-43. The data for this publication total 175 MB on the CD-ROM and 167 MB for this online version. This online version does not include the Acrobat Search index files. It also has a link rather than files for the Adobe Acrobat Reader installer mentioned below.

The Sierra Nevada Ecosystem Project was requested by Congress in the Conference Report for Interior and Related Agencies 1993 Appropriation Act (H.R. 5503), which authorized funds for a "scientific review of the remaining old growth in the national forests of the Sierra Nevada in California, and for a study of the entire Sierra Nevada ecosystem by an independent panel of scientists, with expertise in diverse areas related to this issue."

This publication is a digital version of the set of reports titled Sierra Nevada Ecosystem Project, Final Report to Congress published in paper form by the Centers for Water and Wildland Resources of the University of California, Davis. The reports consist of Wildland Resources Center Report No. 39 (Summary), No. 36 (Vol. I - Assessment summaries and management strategies), No. 37 (Vol. II - Assessments and scientific basis for management options), No. 38 (Vol. III - Assessments, commissioned reports, and background information), and No. 40 (Addendum). Vol. IV is a computer-based catalogue of all public databases, maps, and other digitally stored information used in the project. Vol. IV materials are listed under the SNEP name and available on the Internet from the Alexandria Project at the University of California at Santa Barbara and the California Environmental Resource Evaluation System (CERES) project of the Resources Agency of the state of California (see links below).

[Summary provided by the USGS.]

This dataset provides attributed geospatial and tabular information for identifying and querying flight lines of interest for the Airborne Visible InfraRed Imaging Spectrometer-Classic (AVIRIS-C) and Airborne Visible InfraRed Imaging Spectrometer-Next Generation (AVIRIS-NG) Facility Instrument collections. It includes attributed shapefile and GeoJSON files containing polygon representation of individual flights lines for all years and separate KMZ files for each year. These files allow users to visualize and query flight line locations using Geographic Information System (GIS) software. Tables of AVIRIS-C and AVIRIS-NG flight lines with attributed information include dates, bounding coordinates, site names, investigators involved, flight attributes, associated campaigns, and corresponding file names for associated L1B (radiance) and L2 (reflectance) files in the AVIRIS-C and AVIRIS-NG Facility Instrument Collections. Tabular information is also provided in comma-separated values (CSV) format.

(See USGS Digital Data Series DDS-69-H) A geographic information system focusing on the Upper Cretaceous Taylor and Navarro Groups was developed for the U.S. Geological Survey's (USGS) 2003 assessment of undiscovered, technically recoverable oil and natural gas resources of the Gulf Coast Region. The USGS Energy Resources Science Center has developed map and metadata services to deliver the 2003 assessment results GIS data and services online. The Gulf Coast assessment is based on geologic elements of a total petroleum system (TPS) as described in Condon and Dyman (2005). The estimates of undiscovered oil and gas resources are within assessment units (AUs). The hydrocarbon assessment units include the assessment results as attributes within the AU polygon feature class (in geodatabase and shapefile format). Quarter-mile cells of the land surface that include single or multiple wells were created by the USGS to illustrate the degree of exploration and the type and distribution of production for each assessment unit. Other data that are available in the map documents and services include the TPS and USGS province boundaries. To easily distribute the Gulf Coast maps and GIS data, a web mapping application has been developed by the USGS, and customized ArcMap (by ESRI) projects are available for download at the Energy Resources Science Center Gulf Coast website. ArcGIS Publisher (by ESRI) was used to create a published map file (pmf) from each ArcMap document (.mxd). The basemap services being used in the GC map applications are from ArcGIS Online Services (by ESRI), and include the following layers: -- Satellite imagery -- Shaded relief -- Transportation -- States -- Counties -- Cities -- National Forests With the ESRI_StreetMap_World_2D service, detailed data, such as railroads and airports, appear as the user zooms in at larger scales.

Background and Data Limitations The Massachusetts 1830 map series represents a unique data source that depicts land cover and cultural features during the historical period of widespread land clearing for agricultural. To our knowledge, Massachusetts is the only state in the US where detailed land cover information was comprehensively mapped at such an early date. As a result, these maps provide unusual insight into land cover and cultural patterns in 19th century New England. However, as with any historical data, the limitations and appropriate uses of these data must be recognized: (1) These maps were originally developed by many different surveyors across the state, with varying levels of effort and accuracy. (2) It is apparent that original mapping did not follow consistent surveying or drafting protocols; for instance, no consistent minimum mapping unit was identified or used by different surveyors; as a result, whereas some maps depict only large forest blocks, others also depict small wooded areas, suggesting that numerous smaller woodlands may have gone unmapped in many towns. Surveyors also were apparently not consistent in what they mapped as ‘woodlands’: comparison with independently collected tax valuation data from the same time period indicates substantial lack of consistency among towns in the relative amounts of ‘woodlands’, ‘unimproved’ lands, and ‘unimproveable’ lands that were mapped as ‘woodlands’ on the 1830 maps. In some instances, the lack of consistent mapping protocols resulted in substantially different patterns of forest cover being depicted on maps from adjoining towns that may in fact have had relatively similar forest patterns or in woodlands that ‘end’ at a town boundary. (3) The degree to which these maps represent approximations of ‘primary’ woodlands (i.e., areas that were never cleared for agriculture during the historical period, but were generally logged for wood products) varies considerably from town to town, depending on whether agricultural land clearing peaked prior to, during, or substantially after 1830. (4) Despite our efforts to accurately geo-reference and digitize these maps, a variety of additional sources of error were introduced in converting the mapped information to electronic data files (see detailed methods below). Thus, we urge considerable caution in interpreting these maps. Despite these limitations, the 1830 maps present an incredible wealth of information about land cover patterns and cultural features during the early 19th century, a period that continues to exert strong influence on the natural and cultural landscapes of the region. For users without access to GIS software, the data are available for viewing at: http://harvardforest.fas.harvard.edu/research/1830instructions.html Acknowledgements Financial support for this project was provided by the BioMap Project of the Massachusetts Natural Heritage and Endangered Species Program, the National Science Foundation, and the Andrew Mellon Foundation. This project is a contribution of the Harvard Forest Long Term Ecological Research Program.