100+ datasets found
  1. Tropical deforestation - Environmental Science GeoInquiries

    • hub.arcgis.com
    • geoinquiries-education.hub.arcgis.com
    Updated May 10, 2016
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    Esri GIS Education (2016). Tropical deforestation - Environmental Science GeoInquiries [Dataset]. https://hub.arcgis.com/maps/da0653f60ebe4ee296ad06937bbabf27
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    Dataset updated
    May 10, 2016
    Dataset provided by
    Esrihttp://esri.com/
    Authors
    Esri GIS Education
    Area covered
    Description

    Proof web map for GeoInquiries Advanced Environmental Science lesson on Tropical Deforestation.THE ADVANCED ENVIRONMENTAL SCIENCE AND BIOLOGY GEOINQUIRY COLLECTIONhttp://www.esri.com/geoinquiriesTo support Esri’s involvement in the White House ConnectED Initiative, GeoInquiry instructional materials using ArcGIS Online for high school biology education are now freely available.The Advanced Environmental Science and Biology GeoInquiry collection contains 15 free, web-mapping activities that correspond and extend map-based concepts in leading elementary textbooks. The activities use a standard inquiry-based instructional model, require only 15 minutes for a teacher to deliver, and are device/laptop agnostic. The activities harmonize with the Next Generation Science Standards. Activity topics include:• Population dynamics • Megacities • Down to the last drop • Dead zones (water pollution) • The Beagle’s Path • Primary productivity • Tropical Deforestation • Marine debris • El Nino (and climate) • Slowing malaria • Altered biomes • Spinning up wind power • Resource consumption and wealthTeachers, GeoMentors, and administrators can learn more at http://www.esri.com/geoinquiries

  2. Socio-Environmental Science Investigations Using the Geospatial Curriculum...

    • icpsr.umich.edu
    Updated Oct 17, 2022
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    Bodzin, Alec M.; Anastasio, David J.; Hammond, Thomas C.; Popejoy, Kate; Holland, Breena (2022). Socio-Environmental Science Investigations Using the Geospatial Curriculum Approach with Web Geospatial Information Systems, Pennsylvania, 2016-2020 [Dataset]. http://doi.org/10.3886/ICPSR38181.v1
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    Dataset updated
    Oct 17, 2022
    Dataset provided by
    Inter-university Consortium for Political and Social Researchhttps://www.icpsr.umich.edu/web/pages/
    Authors
    Bodzin, Alec M.; Anastasio, David J.; Hammond, Thomas C.; Popejoy, Kate; Holland, Breena
    License

    https://www.icpsr.umich.edu/web/ICPSR/studies/38181/termshttps://www.icpsr.umich.edu/web/ICPSR/studies/38181/terms

    Time period covered
    Sep 1, 2016 - Aug 31, 2020
    Area covered
    Pennsylvania
    Description

    This Innovative Technology Experiences for Students and Teachers (ITEST) project has developed, implemented, and evaluated a series of innovative Socio-Environmental Science Investigations (SESI) using a geospatial curriculum approach. It is targeted for economically disadvantaged 9th grade high school students in Allentown, PA, and involves hands-on geospatial technology to help develop STEM-related skills. SESI focuses on societal issues related to environmental science. These issues are multi-disciplinary, involve decision-making that is based on the analysis of merged scientific and sociological data, and have direct implications for the social agency and equity milieu faced by these and other school students. This project employed a design partnership between Lehigh University natural science, social science, and education professors, high school science and social studies teachers, and STEM professionals in the local community to develop geospatial investigations with Web-based Geographic Information Systems (GIS). These were designed to provide students with geospatial skills, career awareness, and motivation to pursue appropriate education pathways for STEM-related occupations, in addition to building a more geographically and scientifically literate citizenry. The learning activities provide opportunities for students to collaborate, seek evidence, problem-solve, master technology, develop geospatial thinking and reasoning skills, and practice communication skills that are essential for the STEM workplace and beyond. Despite the accelerating growth in geospatial industries and congruence across STEM, few school-based programs integrate geospatial technology within their curricula, and even fewer are designed to promote interest and aspiration in the STEM-related occupations that will maintain American prominence in science and technology. The SESI project is based on a transformative curriculum approach for geospatial learning using Web GIS to develop STEM-related skills and promote STEM-related career interest in students who are traditionally underrepresented in STEM-related fields. This project attends to a significant challenge in STEM education: the recognized deficiency in quality locally-based and relevant high school curriculum for under-represented students that focuses on local social issues related to the environment. Environmental issues have great societal relevance, and because many environmental problems have a disproportionate impact on underrepresented and disadvantaged groups, they provide a compelling subject of study for students from these groups in developing STEM-related skills. Once piloted in the relatively challenging environment of an urban school with many unengaged learners, the results will be readily transferable to any school district to enhance geospatial reasoning skills nationally.

  3. GIS Data & Maps

    • figshare.com
    bin
    Updated Apr 24, 2023
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    JACKSON LORD (2023). GIS Data & Maps [Dataset]. http://doi.org/10.6084/m9.figshare.15152256.v2
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    binAvailable download formats
    Dataset updated
    Apr 24, 2023
    Dataset provided by
    Figsharehttp://figshare.com/
    Authors
    JACKSON LORD
    License

    https://www.apache.org/licenses/LICENSE-2.0.htmlhttps://www.apache.org/licenses/LICENSE-2.0.html

    Description

    Data for maps and figures in "Global Potential for Harvesting Drinking Water from Air using Solar Energy" in Nature.

  4. d

    Rose Swanson Mountain Data Collation and Citizen Science

    • search.dataone.org
    • borealisdata.ca
    Updated Dec 28, 2023
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    Sun, Xiaoqing (Sunny) (2023). Rose Swanson Mountain Data Collation and Citizen Science [Dataset]. http://doi.org/10.5683/SP3/FSTOUQ
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    Dataset updated
    Dec 28, 2023
    Dataset provided by
    Borealis
    Authors
    Sun, Xiaoqing (Sunny)
    Description

    This study focuses on the use of citizen science and GIS tools for collecting and analyzing data on Rose Swanson Mountain in British Columbia, Canada. While several organizations collect data on wildlife habitats, trail mapping, and fire documentation on the mountain, there are few studies conducted on the area and citizen science is not being addressed. The study aims to aggregate various data sources and involve citizens in the data collection process using ArcGIS Dashboard and ArcGIS Survey 123. These GIS tools allow for the integration and analysis of different kinds of data, as well as the creation of interactive maps and surveys that can facilitate citizen engagement and data collection. The data used in the dashboard was sourced from BC Data Catalogue, Explore the Map, and iNaturalist. Results show effective citizen participation, with 1073 wildlife observations and 3043 plant observations. The dashboard provides a user-friendly interface for citizens to tailor their map extent and layers, access surveys, and obtain information on each attribute included in the pop-up by clicking. Analysis on classification of fuel types, ecological communities, endangered wildlife species presence and critical habitat, and scope of human activities can be conducted based on the distribution of data. The dashboard can provide direction for researchers to develop research or contribute to other projects in progress, as well as advocate for natural resource managers to use citizen science data. The study demonstrates the potential for GIS and citizen science to contribute to meaningful discoveries and advancements in areas.

  5. T

    GIS dataset

    • dataverse-dev.tdl.org
    bin, tiff
    Updated Oct 18, 2016
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    Kristi Park; Kristi Park (2016). GIS dataset [Dataset]. https://dataverse-dev.tdl.org/dataset.xhtml?persistentId=doi:10.5072/FK2/AZCLXJ
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    tiff(13275561), bin(216), bin(387)Available download formats
    Dataset updated
    Oct 18, 2016
    Dataset provided by
    Texas Data Repository ***DEV*** Dataverse
    Authors
    Kristi Park; Kristi Park
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    GIS dataset with TIFFs and tsw files

  6. d

    Datasets for Computational Methods and GIS Applications in Social Science

    • search.dataone.org
    Updated Oct 29, 2025
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    Fahui Wang; Lingbo Liu (2025). Datasets for Computational Methods and GIS Applications in Social Science [Dataset]. http://doi.org/10.7910/DVN/4CM7V4
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    Dataset updated
    Oct 29, 2025
    Dataset provided by
    Harvard Dataverse
    Authors
    Fahui Wang; Lingbo Liu
    Description

    Dataset for the textbook Computational Methods and GIS Applications in Social Science (3rd Edition), 2023 Fahui Wang, Lingbo Liu Main Book Citation: Wang, F., & Liu, L. (2023). Computational Methods and GIS Applications in Social Science (3rd ed.). CRC Press. https://doi.org/10.1201/9781003292302 KNIME Lab Manual Citation: Liu, L., & Wang, F. (2023). Computational Methods and GIS Applications in Social Science - Lab Manual. CRC Press. https://doi.org/10.1201/9781003304357 KNIME Hub Dataset and Workflow for Computational Methods and GIS Applications in Social Science-Lab Manual Update Log If Python package not found in Package Management, use ArcGIS Pro's Python Command Prompt to install them, e.g., conda install -c conda-forge python-igraph leidenalg NetworkCommDetPro in CMGIS-V3-Tools was updated on July 10,2024 Add spatial adjacency table into Florida on June 29,2024 The dataset and tool for ABM Crime Simulation were updated on August 3, 2023, The toolkits in CMGIS-V3-Tools was updated on August 3rd,2023. Report Issues on GitHub https://github.com/UrbanGISer/Computational-Methods-and-GIS-Applications-in-Social-Science Following the website of Fahui Wang : http://faculty.lsu.edu/fahui Contents Chapter 1. Getting Started with ArcGIS: Data Management and Basic Spatial Analysis Tools Case Study 1: Mapping and Analyzing Population Density Pattern in Baton Rouge, Louisiana Chapter 2. Measuring Distance and Travel Time and Analyzing Distance Decay Behavior Case Study 2A: Estimating Drive Time and Transit Time in Baton Rouge, Louisiana Case Study 2B: Analyzing Distance Decay Behavior for Hospitalization in Florida Chapter 3. Spatial Smoothing and Spatial Interpolation Case Study 3A: Mapping Place Names in Guangxi, China Case Study 3B: Area-Based Interpolations of Population in Baton Rouge, Louisiana Case Study 3C: Detecting Spatiotemporal Crime Hotspots in Baton Rouge, Louisiana Chapter 4. Delineating Functional Regions and Applications in Health Geography Case Study 4A: Defining Service Areas of Acute Hospitals in Baton Rouge, Louisiana Case Study 4B: Automated Delineation of Hospital Service Areas in Florida Chapter 5. GIS-Based Measures of Spatial Accessibility and Application in Examining Healthcare Disparity Case Study 5: Measuring Accessibility of Primary Care Physicians in Baton Rouge Chapter 6. Function Fittings by Regressions and Application in Analyzing Urban Density Patterns Case Study 6: Analyzing Population Density Patterns in Chicago Urban Area >Chapter 7. Principal Components, Factor and Cluster Analyses and Application in Social Area Analysis Case Study 7: Social Area Analysis in Beijing Chapter 8. Spatial Statistics and Applications in Cultural and Crime Geography Case Study 8A: Spatial Distribution and Clusters of Place Names in Yunnan, China Case Study 8B: Detecting Colocation Between Crime Incidents and Facilities Case Study 8C: Spatial Cluster and Regression Analyses of Homicide Patterns in Chicago Chapter 9. Regionalization Methods and Application in Analysis of Cancer Data Case Study 9: Constructing Geographical Areas for Mapping Cancer Rates in Louisiana Chapter 10. System of Linear Equations and Application of Garin-Lowry in Simulating Urban Population and Employment Patterns Case Study 10: Simulating Population and Service Employment Distributions in a Hypothetical City Chapter 11. Linear and Quadratic Programming and Applications in Examining Wasteful Commuting and Allocating Healthcare Providers Case Study 11A: Measuring Wasteful Commuting in Columbus, Ohio Case Study 11B: Location-Allocation Analysis of Hospitals in Rural China Chapter 12. Monte Carlo Method and Applications in Urban Population and Traffic Simulations Case Study 12A. Examining Zonal Effect on Urban Population Density Functions in Chicago by Monte Carlo Simulation Case Study 12B: Monte Carlo-Based Traffic Simulation in Baton Rouge, Louisiana Chapter 13. Agent-Based Model and Application in Crime Simulation Case Study 13: Agent-Based Crime Simulation in Baton Rouge, Louisiana Chapter 14. Spatiotemporal Big Data Analytics and Application in Urban Studies Case Study 14A: Exploring Taxi Trajectory in ArcGIS Case Study 14B: Identifying High Traffic Corridors and Destinations in Shanghai Dataset File Structure 1 BatonRouge Census.gdb BR.gdb 2A BatonRouge BR_Road.gdb Hosp_Address.csv TransitNetworkTemplate.xml BR_GTFS Google API Pro.tbx 2B Florida FL_HSA.gdb R_ArcGIS_Tools.tbx (RegressionR) 3A China_GX GX.gdb 3B BatonRouge BR.gdb 3C BatonRouge BRcrime R_ArcGIS_Tools.tbx (STKDE) 4A BatonRouge BRRoad.gdb 4B Florida FL_HSA.gdb HSA Delineation Pro.tbx Huff Model Pro.tbx FLplgnAdjAppend.csv 5 BRMSA BRMSA.gdb Accessibility Pro.tbx 6 Chicago ChiUrArea.gdb R_ArcGIS_Tools.tbx (RegressionR) 7 Beijing BJSA.gdb bjattr.csv R_ArcGIS_Tools.tbx (PCAandFA, BasicClustering) 8A Yunnan YN.gdb R_ArcGIS_Tools.tbx (SaTScanR) 8B Jiangsu JS.gdb 8C Chicago ChiCity.gdb cityattr.csv ...

  7. STOTEN27464

    • figshare.com
    zip
    Updated Jun 27, 2018
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    Xinchen Lu (2018). STOTEN27464 [Dataset]. http://doi.org/10.6084/m9.figshare.6709490.v2
    Explore at:
    zipAvailable download formats
    Dataset updated
    Jun 27, 2018
    Dataset provided by
    figshare
    Figsharehttp://figshare.com/
    Authors
    Xinchen Lu
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This dataset is the supplementary data source for the manuscript "Seasonal patterns of canopy photosynthesis captured by remotely sensed sun-induced fluorescence and vegetation indexes in mid-to-high latitude forests: a cross-platform comparison" accepted for publication in Science of the Total Environment.The time series of all parameters presented in the manuscript is recorded in the Excel file of each site.Since the data policy of Fluxnet and European Data Flux Cluster prevents us from redistributing their data, We are unable to share with you the EC_GPP of each site.‘SIF’here in every Excel refers to GOME-2 SIF.If you have any questions regarding this data set, do not hesitate to contact Xinchen Lu (Stevenlvrs@foxmail.com)

  8. Dead zones - Environmental Science GeoInquiries

    • hub.arcgis.com
    • geoinquiries-education.hub.arcgis.com
    Updated Apr 27, 2016
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    Esri GIS Education (2016). Dead zones - Environmental Science GeoInquiries [Dataset]. https://hub.arcgis.com/maps/7e5120fadb2e44b1a3320e02a1ab457b
    Explore at:
    Dataset updated
    Apr 27, 2016
    Dataset provided by
    Esrihttp://esri.com/
    Authors
    Esri GIS Education
    Area covered
    Description

    Explore water pollution and its impact on marine life.THE ADVANCED ENVIRONMENTAL SCIENCE AND BIOLOGY GEOINQUIRY COLLECTIONhttp://www.esri.com/geoinquiriesTo support Esri’s involvement in the White House ConnectED Initiative, GeoInquiry instructional materials using ArcGIS Online for high school biology education are now freely available.The Advanced Environmental Science and Biology GeoInquiry collection contains 15 free, web-mapping activities that correspond and extend map-based concepts in leading elementary textbooks. The activities use a standard inquiry-based instructional model, require only 15 minutes for a teacher to deliver, and are device/laptop agnostic. The activities harmonize with the Next Generation Science Standards. Teachers, GeoMentors, and administrators can learn more at http://www.esri.com/geoinquiries

  9. H

    GIS Files

    • dataverse.harvard.edu
    Updated Aug 23, 2016
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    Stuart Hamilton (2016). GIS Files [Dataset]. http://doi.org/10.7910/DVN/VW2XKP
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Aug 23, 2016
    Dataset provided by
    Harvard Dataverse
    Authors
    Stuart Hamilton
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    The base polygons and points for the tables.

  10. e

    US Centric - GeoInquiries for Environmental Science by Esri

    • gisinschools.eagle.co.nz
    Updated May 3, 2016
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    GIS in Schools - Teaching Materials - New Zealand (2016). US Centric - GeoInquiries for Environmental Science by Esri [Dataset]. https://gisinschools.eagle.co.nz/documents/5052309fb9c6451792db687bd5293606
    Explore at:
    Dataset updated
    May 3, 2016
    Dataset authored and provided by
    GIS in Schools - Teaching Materials - New Zealand
    Description

    This collection supports the map-based concepts found in high school environmental science like speciation, pollution, population ecology, and energy.GeoInquiries are designed to be fast and easy-to-use instructional resources that incorporate advanced web mapping technology. Each 15-minute activity in a collection is intended to be presented by the instructor from a single computer/projector classroom arrangement. No installation, fees, or logins are necessary to use these materials and software.Find the student worksheets for these GeoInquiries here

  11. Rocky Mountain Research Station Air, Water, & Aquatic Environments Program

    • agdatacommons.nal.usda.gov
    • datasetcatalog.nlm.nih.gov
    bin
    Updated Nov 30, 2023
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    USDA Forest Service (2023). Rocky Mountain Research Station Air, Water, & Aquatic Environments Program [Dataset]. https://agdatacommons.nal.usda.gov/articles/dataset/Rocky_Mountain_Research_Station_Air_Water_Aquatic_Environments_Program/24661908
    Explore at:
    binAvailable download formats
    Dataset updated
    Nov 30, 2023
    Dataset provided by
    U.S. Department of Agriculture Forest Servicehttp://fs.fed.us/
    Authors
    USDA Forest Service
    License

    U.S. Government Workshttps://www.usa.gov/government-works
    License information was derived automatically

    Description

    The Air, Water, and Aquatic Environments (AWAE) research program is one of eight Science Program areas within the Rocky Mountain Research Station (RMRS). Our science develops core knowledge, methods, and technologies that enable effective watershed management in forests and grasslands, sustain biodiversity, and maintain healthy watershed conditions. We conduct basic and applied research on the effects of natural processes and human activities on watershed resources, including interactions between aquatic and terrestrial ecosystems. The knowledge we develop supports management, conservation, and restoration of terrestrial, riparian and aquatic ecosystems and provides for sustainable clean air and water quality in the Interior West. With capabilities in atmospheric sciences, soils, forest engineering, biogeochemistry, hydrology, plant physiology, aquatic ecology and limnology, conservation biology and fisheries, our scientists focus on two key research problems: Core watershed research quantifies the dynamics of hydrologic, geomorphic and biogeochemical processes in forests and rangelands at multiple scales and defines the biological processes and patterns that affect the distribution, resilience, and persistence of native aquatic, riparian and terrestrial species. Integrated, interdisciplinary research explores the effects of climate variability and climate change on forest, grassland and aquatic ecosystems. Resources in this dataset:Resource Title: Projects, Tools, and Data. File Name: Web Page, url: https://www.fs.fed.us/rm/boise/AWAE/projects.html Projects include Air Temperature Monitoring and Modeling, Biogeochemistry Lab in Colorado, Rangewide Bull Trout eDNA Project, Climate Shield Cold-Water Refuge Streams for Native Trout, Cutthroat trout-rainbow trout hybridization - data downloads and maps, Fire and Aquatic Ecosystems science, Fish and Cattle Grazing reports, Geomophic Road Analysis and Inventory Package (GRAIP) tool for erosion and sediment delivery to streams, GRAIP_Lite - Geomophic Road Analysis and Inventory Package (GRAIP) tool for erosion and sediment delivery to streams, IF3: Integrating Forests, Fish, and Fire, National forest climate change maps: Your guide to the future, National forest contributions to streamflow, The National Stream Internet network, people, data, GIS, analysis, techniques, NorWeST Stream Temperature Regional Database and Model, River Bathymetry Toolkit (RBT), Sediment Transport Data for Idaho, Nevada, Wyoming, Colorado, SnowEx, Stream Temperature Modeling and Monitoring, Spatial Statistical Modeling on Stream netowrks - tools and GIS downloads, Understanding Sculpin DNA - environmental DNA and morphological species differences, Understanding the diversity of Cottusin western North America, Valley Bottom Confinement GIS tools, Water Erosion Prediction Project (WEPP), Great Lakes WEPP Watershed Online GIS Interface, Western Division AFS - 2008 Bull Trout Symposium - Bull Trout and Climate Change, Western US Stream Flow Metric Dataset

  12. User’s and producer’s accuracies for the five main land cover types and for...

    • plos.figshare.com
    xls
    Updated Jun 1, 2023
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    Linda See; Alexis Comber; Carl Salk; Steffen Fritz; Marijn van der Velde; Christoph Perger; Christian Schill; Ian McCallum; Florian Kraxner; Michael Obersteiner (2023). User’s and producer’s accuracies for the five main land cover types and for different subsets of the data including confidence and expertise. [Dataset]. http://doi.org/10.1371/journal.pone.0069958.t010
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    xlsAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Linda See; Alexis Comber; Carl Salk; Steffen Fritz; Marijn van der Velde; Christoph Perger; Christian Schill; Ian McCallum; Florian Kraxner; Michael Obersteiner
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    1 =  Tree cover; 2 =  Shrub cover; 3 =  Herbaceous vegetation/Grassland; 4 =  Cultivated and managed; 5 =  Mosaic of cultivated and managed/natural vegetation.

  13. Marine debris - Environmental Science GeoInquiries

    • geoinquiries-education.hub.arcgis.com
    • hub.arcgis.com
    Updated Aug 18, 2016
    + more versions
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    Esri GIS Education (2016). Marine debris - Environmental Science GeoInquiries [Dataset]. https://geoinquiries-education.hub.arcgis.com/maps/8eb926b611f94a2a90744dbaf3121f56
    Explore at:
    Dataset updated
    Aug 18, 2016
    Dataset provided by
    Esrihttp://esri.com/
    Authors
    Esri GIS Education
    Area covered
    Description

    THE ADVANCED ENVIRONMENTAL SCIENCE AND BIOLOGY GEOINQUIRY COLLECTIONhttp://www.esri.com/geoinquiriesTo support Esri’s involvement in the White House ConnectED Initiative, GeoInquiry instructional materials using ArcGIS Online for high school biology education are now freely available.The Advanced Environmental Science and Biology GeoInquiry collection contains 15 free, web-mapping activities that correspond and extend map-based concepts in leading elementary textbooks. The activities use a standard inquiry-based instructional model, require only 15 minutes for a teacher to deliver, and are device/laptop agnostic. The activities harmonize with the Next Generation Science Standards. Activity topics include:• Population dynamics • Megacities • Down to the last drop • Dead zones (water pollution) • The Beagle’s Path • Primary productivity • Tropical Deforestation • Marine debris • El Nino (and climate) • Slowing malaria • Altered biomes • Spinning up wind power • Resource consumption and wealthTeachers, GeoMentors, and administrators can learn more at http://www.esri.com/geoinquiries

  14. fuzzy_habitat_modelling

    • figshare.com
    zip
    Updated Jun 9, 2023
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    Johannes Radinger (2023). fuzzy_habitat_modelling [Dataset]. http://doi.org/10.6084/m9.figshare.1221677.v1
    Explore at:
    zipAvailable download formats
    Dataset updated
    Jun 9, 2023
    Dataset provided by
    Figsharehttp://figshare.com/
    figshare
    Authors
    Johannes Radinger
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Fish habitat model and visualization of habitat niche using GRASS GIS r.fuzzy.system and R

  15. G

    DK-model2019 - Calibration statistics (GIS)

    • dataverse.geus.dk
    • search.dataone.org
    bin
    Updated Oct 13, 2023
    + more versions
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    Maria Ondracek; Maria Ondracek (2023). DK-model2019 - Calibration statistics (GIS) [Dataset]. http://doi.org/10.22008/FK2/0JRWGS
    Explore at:
    bin(2312873)Available download formats
    Dataset updated
    Oct 13, 2023
    Dataset provided by
    GEUS Dataverse
    Authors
    Maria Ondracek; Maria Ondracek
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This folder contains the calibrationstatistics from DK-model2019. Grid files and .shp files are assembled in ArcGIS Pro v.3.0.1. DKmodel2019 setup and calibration is described in GEUS report 2019/31.

  16. G

    DK-model2019 - Simulation results (GIS)

    • dataverse.geus.dk
    • search.dataone.org
    bin
    Updated Oct 13, 2023
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    Maria Ondracek; Maria Ondracek (2023). DK-model2019 - Simulation results (GIS) [Dataset]. http://doi.org/10.22008/FK2/P88XCU
    Explore at:
    bin(226415662)Available download formats
    Dataset updated
    Oct 13, 2023
    Dataset provided by
    GEUS Dataverse
    Authors
    Maria Ondracek; Maria Ondracek
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This folder contains contain various results simulated with DK-model2019. Grid files and .shp files are assembled in ArcGIS Pro v.3.0.1. For results regarding streamflow, seepage from groundwater to streams, depth to groundwater table, net precipitation, recharge and water velocity, refer to GEUS report 2019/32. For results regarding delineation of groundwater bodies, contact between groundwater bodies and surface water, abstractions linked to the groundwater bodies, refer to GEUS report 2020/1. For results, regarding changes in simulated groundwater head levels, change in depth to the groundwater table and change in stream runoff, refer again to GEUS report 2019/32. For a detailed description of the procedure, including the distribution of extracted amounts per water plant, connection to groundwater, etc., refer to GEUS report 2021/19 Annex 6.

  17. H

    Metadata for all GIS and Projection layers for FPHHM

    • dataverse.harvard.edu
    Updated Aug 12, 2015
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    Martin Main; Caitlin Jacobs (2015). Metadata for all GIS and Projection layers for FPHHM [Dataset]. http://doi.org/10.7910/DVN/DS9GCF
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Aug 12, 2015
    Dataset provided by
    Harvard Dataverse
    Authors
    Martin Main; Caitlin Jacobs
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    Metadata files for all GIS and Projection layers used in the Florida Panther Hunting Habitat Model.

  18. n

    Casey Station GIS Dataset update from various sources

    • access.earthdata.nasa.gov
    • cmr.earthdata.nasa.gov
    Updated Jun 4, 2018
    + more versions
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    (2018). Casey Station GIS Dataset update from various sources [Dataset]. https://access.earthdata.nasa.gov/collections/C1214313483-AU_AADC
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    Dataset updated
    Jun 4, 2018
    Time period covered
    Jan 1, 1999 - Present
    Area covered
    Description

    The Australian Antarctic Data Centre's Casey Station GIS data were originally mapped from Aerial photography (January 4 1994). Refer to the metadata record 'Casey Station GIS Dataset'. Since then various features have been added to these data as structures have been removed, moved or established. Some of these features have been surveyed. These surveys have metadata records from which the report describing the survey can be downloaded. However, the locations of other features have been obtained from a variety of sources. The data are included in the data available for download from the provided URLs. The data conforms to the SCAR Feature Catalogue which includes data quality information. See the provided URL. Data described by this metadata record has Dataset_id = 17. Each feature has a Qinfo number which, when entered at the 'Search datasets and quality' tab, provides data quality information for the feature.

  19. d

    Replication data for Calil et al. (2017): LAC Shapefile

    • dataone.org
    • dataverse.harvard.edu
    Updated Nov 22, 2023
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    Calil, Juliano (2023). Replication data for Calil et al. (2017): LAC Shapefile [Dataset]. http://doi.org/10.7910/DVN/OSNGFE
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    Dataset updated
    Nov 22, 2023
    Dataset provided by
    Harvard Dataverse
    Authors
    Calil, Juliano
    Description

    Shapefile used in the various maps in the study. Visit https://dataone.org/datasets/sha256%3A2fdaa83821076dc77d906d53f13fd8aaa6ecb2f8bf1e16082352037b5459f465 for complete metadata about this dataset.

  20. H

    Cattle Density GIS Data Layer

    • dataverse.harvard.edu
    Updated Aug 12, 2015
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    Martin Main (2015). Cattle Density GIS Data Layer [Dataset]. http://doi.org/10.7910/DVN/1C7PRQ
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Aug 12, 2015
    Dataset provided by
    Harvard Dataverse
    Authors
    Martin Main
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    GIS Layers used to create the hunting habitat model, which include Cattle density, Distance from edge, Dominant landcover, Forest edge density, Forest patch size, Improved pasture patch size, Landcover, and Percent forest cover. Area of analysis defined by Minimum Convex Polygons created from Florida panther GPS data.

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Esri GIS Education (2016). Tropical deforestation - Environmental Science GeoInquiries [Dataset]. https://hub.arcgis.com/maps/da0653f60ebe4ee296ad06937bbabf27
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Tropical deforestation - Environmental Science GeoInquiries

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Dataset updated
May 10, 2016
Dataset provided by
Esrihttp://esri.com/
Authors
Esri GIS Education
Area covered
Description

Proof web map for GeoInquiries Advanced Environmental Science lesson on Tropical Deforestation.THE ADVANCED ENVIRONMENTAL SCIENCE AND BIOLOGY GEOINQUIRY COLLECTIONhttp://www.esri.com/geoinquiriesTo support Esri’s involvement in the White House ConnectED Initiative, GeoInquiry instructional materials using ArcGIS Online for high school biology education are now freely available.The Advanced Environmental Science and Biology GeoInquiry collection contains 15 free, web-mapping activities that correspond and extend map-based concepts in leading elementary textbooks. The activities use a standard inquiry-based instructional model, require only 15 minutes for a teacher to deliver, and are device/laptop agnostic. The activities harmonize with the Next Generation Science Standards. Activity topics include:• Population dynamics • Megacities • Down to the last drop • Dead zones (water pollution) • The Beagle’s Path • Primary productivity • Tropical Deforestation • Marine debris • El Nino (and climate) • Slowing malaria • Altered biomes • Spinning up wind power • Resource consumption and wealthTeachers, GeoMentors, and administrators can learn more at http://www.esri.com/geoinquiries

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