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. The student sample is a convenience sample obtained via an urban school district local to the researchers, specifically three local high schools. Two traditional high schools provided responses forming a control group and a third, newly established high school served as the experimental test group. Datasets: DS0: Study-Level Files DS1: Y1 Data DS2: Y1 Feedback DS3: Y2 Data DS4: Y2 Scores DS5: Y3 Data DS6: Y3 Feedback Economically disadvantaged high school freshman in an urban school district. mixed mode; on-site questionnaire; web-based survey

Regression analysis for the degree of human impact.

Environmental pollution is a persistent problem in terrestrial ecosystems, including remote mountain areas. This study investigates the extent and patterns of littering on three popular hiking trails among mountaineers and tourists in the Dolomites range located in northeastern Italy. The data was collected adopting a citizen science approach with the participation of university students surveying the trails and recording the macroscopic waste items through a GPS-based offline platform. The waste items were categorized according to their material type, usage, and geographical location, and the sorted data was applied to Esri GIS ArcMapTM 10.8.1.

Consistency of response to degree of human impact.

Resilience—the keen ability of people to adapt to changing physical environments—is essential in today's world of unexpected changes.Resilient Communities across Geographies edited by Sheila Lakshmi Steinberg and Steven J. Steinberg focuses on how applying GIS to environmental and socio-economic challenges for analysis and planning helps make communities more resilient.A hybrid of theory and action, Resilient Communities across Geographies uses an interdisciplinary approach to explore resilience studied by experts in geography, social sciences, planning, landscape architecture, urban and rural sociology, economics, migration, community development, meteorology, oceanography, and other fields. Geographies covered include urban and rural, coastal and mountainous, indigenous areas in the United State and Australia, and more. Geographical Information Systems (GIS) is the unifying tool that helped researchers understand resilience.This book shows how GIS:integrates quantitative, qualitative, and spatial data to produce a holistic view of a need for resilience.serves as a valuable tool to capture and integrate knowledge of local people, places, and resources.allows us to visualize data clearly as portrayed in a real-time map or spatial dashboard, thus leading to opportunities to make decisions.lets us see patterns and communicate what the data means.helps us see what resources they have and where they are located.provides a big vision for action by layering valuable pieces of information together to see where gaps are located, where action is needed, or how policies can be instituted to manage and improve community resilience.Resilience is not only an ideal; it is something that people and communities can actively work to achieve through intelligent planning and assessment. The stories shared by the contributing authors in Resilient Communities across Geographies help readers to develop an expanded sense of the power of GIS to address the difficult problems we collectively face in an ever-changing world.AUDIENCEProfessional and scholarly. Higher education.AUTHOR BIOSSheila Lakshmi Steinberg is a professor of Social and Environmental Sciences at Brandman University and Chair of the GIS Committee, where she leads the university to incorporate GIS across the curriculum. Her research interests include interdisciplinary research methods, culture, community, environmental sociology, geospatial approaches, ethnicity, health policy, and teaching pedagogy.Steven J. Steinberg is the Geographic Information Officer for the County of Los Angeles, California. Throughout his career, he has taught GIS as a professor of geospatial sciences for the California State University and, since 2011, has worked as a geospatial scientist in the public sector, applying GIS across a wide range of both environmental and human contexts.Pub Date: Print: 11/24/2020 Digital: 10/27/2020ISBN: Print: 9781589484818 Digital: 9781589484825Price: Print: $49.99 USD Digital: $49.99 USDPages: 350 Trim: 7.5 x 9.25 in.Table of ContentsPrefaceChapter 1. Conceptualizing spatial resilience Dr. Sheila Steinberg and Dr Steven J. SteinbergChapter 2. Resilience in coastal regions: the case of Georgia, USAChapter 3. Building resilient regions: Spatial analysis as a tool for ecosystem-based climate adaptationChapter 4. The mouth of the Columbia River: USACE, GIS and resilience in a dynamic coastal systemChapter 5. Urban resilience: Neighborhood complexity and the importance of social connectivityChapter 6. Mapping Indigenous LAChapter 7. Indigenous Martu knowledge: Mapping place through song and storyChapter 8. Developing resiliency through place-based inquiry in CanadaChapter 9. Engaging Youth in Spatial Modes of Thought toward Social and Environmental ResilienceChapter 10. Health, Place, and Space: Public Participation GIS for Rural Community PowerChapter 11. Best Practices for Using Local KnowledgeContributorsIndex

This research explores the innovative use of a 3D gaming engine, Minetest, for visualizing changes in canopy cover change at the University of British Columbia (UBC) campus, addressing the pressing challenge of urban expansion on green spaces. We compared and visualized canopy height change for UBC campus in both 2D traditional environment and 3D gaming engine environment and we revealed a consistency between the spatial patterns of canopy cover change observed in both environments. Our findings indicate 3D environment provided multi-dimensional insights into canopy cover changes, offering decision-makers more straightforward and transparent insight than traditional maps can achieve in an immersive and interactive environment. We observed there is a significant change in canopy cover with 25 percent loss in total where Wesbrook community area experienced the most significant canopy cover loss in past 5 years due to rapid urban development. Our findings goes beyond merely presenting geographic maps and attributes from a 3D voxel game perspective. Instead, it will serve as a useful tool and references for UBC decision makers and planners to inform management plan on the pathway of building a green, well-planned community.

Regression analysis for the model Yi = a+bXi+εi, where Yi is the degree of human impact from the control data, Xi is the degree of human impact from the participants.

To address the global challenge of reducing greenhouse gas emissions contributing to climate change, it is essential to explore innovative, renewable, and sustainable energy solutions. Bioenergy, derived from biological sources, plays a vital role by providing renewable options for heat, electricity, and vehicle fuel. Biofuels from food crops like sugarcane and cassava demonstrate the potential of agricultural products for energy generation, while jatropha is cultivated primarily for oil. This learning activity focuses on land suitability mapping for these selected crops in Florida, incorporating criteria such as temperature, rainfall, soil type, soil pH, and topography. The analysis evaluates the land requirements of food and energy crops within the Food-Energy-Water (FEW) nexus framework, addressing potential land-use conflicts. Geographic Information Systems (GIS) are employed to identify optimal regions for energy crop cultivation, promoting sustainable practices that balance food security, water conservation, and renewable energy production. The modules are developed and designed for undergraduate students, particularly those enrolled in any of courses such as environmental science, GIS, natural resource management, agricultural science and remote sensing. Students will apply GIS and remote sensing techniques to analyze interactions among food, energy, and water resources, focusing on resilient crops. The activity incorporates the 4DEE framework – Core Ecological Concepts, Ecological Practices, Human-Environment Interactions, and Cross-Cutting Themes to enhance understanding of the FEW nexus. Through hands-on projects addressing real-world ecological challenges, students will develop critical skills in geospatial data analysis, data interpretation, and ethical considerations, preparing them for sustainable resource management. Likewise on part of the instructors, the activity is designed for those with intermediate to advanced GIS expertise, particularly in ArcGIS Pro and Google Earth Engine for spatial analysis and a basic understanding and application of the Food-Energy-Water (FEW) Nexus to guide students in making informed land-use decisions that support sustainable development goals.

Maps of California's Wildland Urban Interface (WUI) generated using the Time Step Moving Window (TSMW) method outlined in the paper "Remapping California's Wildland Urban Interface: A Property-Level Time-Space Framework, 2000-2020".

WUI maps were generated using Zillow ZTRAX parcel level attributes joined with FEMA USA Structures building footprints and the National Land Cover Database (NLCD).

All files are geotiff rasters with WUI areas mapped at a ~30m resolution. A raster value of null indicates not WUI, raster value of 1 indicates intermix WUI, and a raster value of 2 indicates interface WUI.

Three WUI maps were generated using structures built on of before the years indicated below:

2000 - "CA_WUI_2000.tif"

2010 - "CA_WUI_2010.tif"

2020 - "CA_WUI_2020.tif"

Acknowledgments -

We gratefully acknowledge access to the Zillow Transaction and Assessment Dataset (ZTRAX) through a data use agreement between the University of Colorado Boulder and Zillow Group, Inc. More information on accessing the data can be found at http://www.zillow.com/ztrax. We thank Johannes Uhl and Stefan Leyk for the incredible amount of work they dedicated to preparing this Zillow ZTRAX. Funding for our work has been provided by Arizona State University's Institute of Social Science Research (ISSR) Seed Grant Initiative. Additional funding was was provided through the Humans, Disasters, and the Built Environment program of the National Science Foundation, Award Number 1924670 to the University of Colorado Boulder, the Institute of Behavioral Science, Earth Lab, the Cooperative Institute for Research in Environmental Sciences, the Grand Challenge Initiative and the Innovative Seed Grant program at the University of Colorado Boulder as well as the Eunice Kennedy Shriver National Institute of Child Health & Human Development of the National Institutes of Health under Award Numbers R21 HD098717 01A1 and P2CHD066613.

Reinstating connectivity is seen as one way to ameliorate biodiversity loss resulting from agricultural activities. Natural resource management agencies require scientific knowledge to better inform revegetation programmes for increasing connectivity. Concepts of metapopulation theory and landscape ecology have been combined to produce spatially explicit outputs based on fragmentation-sensitive and poor-dispersing woodland species and which are designed to improve the occurrence and persistence of biodiversity. Selected outputs have been incorporated into the operations of a NRM revegetation programme. The results from the research provide alternative management options relevant to variegated and fragmented landscapes. Spatial data, spreadsheets, R scripts

In this course, you will explore the concepts, principles, and practices of acquiring, storing, analyzing, displaying, and using geospatial data. Additionally, you will investigate the science behind geographic information systems and the techniques and methods GIS scientists and professionals use to answer questions with a spatial component. In the lab section, you will become proficient with the ArcGIS Pro software package.

This course will prepare you to take more advanced geospatial science courses.

You will be asked to work through a series of modules that present information relating to a specific topic. You will also complete a series of lab exercises, assignments, and less guided challenges. Please see the sequencing document for our suggestions as to the order in which to work through the material. To aid in working through the lecture modules, we have provided PDF versions of the lectures with the slide notes included. This course makes use of the ArcGIS Pro software package from the Environmental Systems Research Institute (ESRI), and directions for installing the software have also been provided. If you are not a West Virginia University student, you can still complete the labs, but you will need to obtain access to the software on your own.

The regression analysis of predicting the degree of human impact by expert and non-expert groups, when the regression is split into 2 simultaneous models.

The aerial photographs, taken on the 6th of February 1975 at a scale 1: 50 000, were obtained from the Survey of Kenya and were used to generate my original data.

Co-sponsored by the Center for Geographic Analysis of Harvard University, RMDS Lab and Future Data Lab, the workflow-based data analysis project aims to provide new approach for efficient data analysis and replicable, reproducible and expandable research. This year-round webinar series is designed to help attendees advance in their career with research data, tools, and their applications.

The GIS-based Time model of Gothenburg aims to map the process of urban development in Gothenburg since 1960 and in particular to document the changes in the spatial form of the city - streets, buildings and plots - through time. Major steps have in recent decades been taken when it comes to understanding how cities work. Essential is the change from understanding cities as locations to understanding them as flows (Batty 2013)1. In principle this means that we need to understand locations (or places) as defined by flows (or different forms of traffic), rather than locations only served by flows. This implies that we need to understand the built form and spatial structure of cities as a system, that by shaping flows creates a series of places with very specific relations to all other places in the city, which also give them very specific performative potentials. It also implies the rather fascinating notion that what happens in one place is dependent on its relation to all other places (Hillier 1996)2. Hence, to understand the individual place, we need a model of the city as a whole. Extensive research in this direction has taken place in recent years, that has also spilled over to urban design practice, not least in Sweden, where the idea that to understand the part you need to understand the whole is starting to be established. With the GIS-based Time model for Gothenburg that we present here, we address the next challenge. Place is not only something defined by its spatial relation to all other places in its system, but also by its history, or its evolution over time. Since the built form of the city changes over time, often by cities growing but at times also by cities shrinking, the spatial relation between places changes over time. If cities tend to grow, and most often by extending their periphery, it means that most places get a more central location over time. If this is a general tendency, it does not mean that all places increase their centrality to an equal degree. Depending on the structure of the individual city’s spatial form, different places become more centrally located to different degrees as well as their relative distance to other places changes to different degrees. The even more fascinating notion then becomes apparent; places move over time! To capture, study and understand this, we need a "time model". The GIS-based time model of Gothenburg consists of: • 12 GIS-layers of the street network, from 1960 to 2015, in 5-year intervals • 12 GIS-layers of the buildings from 1960 to 2015, in 5-year intervals • 12 GIS- layers of the plots from1960 to 2015, in 5-year intervals In the GIS-based Time model, for every time-frame, the combination of the three fundamental components of spatial form, that is streets, plots and buildings, provides a consistent description of the built environment at that particular time. The evolution of three components can be studied individually, where one could for example analyze the changing patterns of street centrality over time by focusing on the street network; or, the densification processes by focusing on the buildings; or, the expansion of the city by way of occupying more buildable land, by focusing on plots. The combined snapshots of street centrality, density and land division can provide insightful observations about the spatial form of the city at each time-frame; for example, the patterns of spatial segregation, the distribution of urban density or the patterns of sprawl. The observation of how the interrelated layers of spatial form together evolved and transformed through time can provide a more complete image of the patterns of urban growth in the city. The Time model was created following the principles of the model of spatial form of the city, as developed by the Spatial Morphology Group (SMoG) at Chalmers University of Technology, within the three-year research project ‘International Spatial Morphology Lab (SMoL)’. The project is funded by Älvstranden Utveckling AB in the framework of a larger cooperation project called Fusion Point Gothenburg. The data is shared via SND to create a research infrastructure that is open to new study initiatives. 1. Batty, M. (2013), The New Science of Cities, Cambridge: MIT Press. 2. Hillier, B., (1996), Space Is the Machine. Cambridge: University of Cambridge

Open the Data Resource: https://gis.chesapeakebay.net/casestudies/ This database contains best management practice case studies in the Chesapeake Bay watershed. Case studies are searchable by location, best management practice (BMP), environmental benefit, and community and economic benefit. These case studies show how local governments, private companies and residents are installing BMPs to keep local waterways healthy, beautiful and economically viable. Local decision makers can use this database to recommend BMPs based on the benefits the BMP achieves and to view information about BMP costs, partners and funders. Listed benefits are representative of potential community, economic and environmental gains, but have not been quantified.

Urban green spaces are closely related to the abundance and biodiversity of birds by providing important habitats and together contribute to ecosystem health. This project aims to guide the University of British Columbia Botanical Garden to create Bird-friendly green spaces by using LiDAR data to analyze and map UBCBG's bird habitat suitability and create a 3D digital twin model of UBCBG in the open source game engine Minetest to increase 3D visualization and aid in landscape planning. By extracting the Canopy Height Model (CHM) using LiDAR data and performing individual tree segmentation, the derived metrics were used to identify trees with the highest bird habitat suitability index. The results showed that the suitability index ranges from -0.0016 to 0.5187, with a mean value of 0.2051. There are 68 trees with high suitability above the 0.4 intervals which have significance to bird populations and are worthy of being protected, accounting for only 3.38% of the total trees. They usually have a low vertical complexity index and foliage height diversity but are characterized by very tall trees with relatively large tree crowns. The Digital Elevation Model (DEM), Canopy Height Model (CHM) generated by LiDAR data were visualized in Minetest's UBCBG's 3D digital twin model using real terrain mod as topography and vegetation layers, while bird habitat suitability was used to symbolize the tree canopy layer. This study is highly relevant for landscape adaptation and planning in conjunction with other management considerations to support bird-friendly green spaces. The digital twin model can be used for educational and promotional purposes, and for landscape planning and aesthetic design with the consideration of bird conservation.

Since the start of the pandemic, some U.S. communities have faced record storms, fires, and floods. Communities have confronted the increased challenge of curbing the spread of COVID-19 amid evacuation orders and short-term displacement that result from hazards. This raises the question of whether disasters, evacuations, and displacements have resulted in above-average infection rates during the COVID-19 pandemic. This study investigates the relationship between disaster intensity, sheltering-in-place, evacuation-related mobility, and contagion following Hurricane Zeta in Southeastern Louisiana and The Wildfires in Napa and Sonoma Counties, California, known as the Glass Fire. We draw on data from the county subdivision level and mapped and aggregated tallies of Facebook user movement from the Facebook Data for Good program’s GeoInsights Portal. We test the effects of disasters, evacuation, and shelter-in-place behaviors on COVID-19 spread using panel data models, matched panel models, and synthetic control experiments. Our findings suggest associations between disaster intensity and higher rates of COVID-19 cases. We also find that while sheltering-in-place led to decreases in the spread of COVID-19, evacuation-related mobility did not result in our hypothesized surge of cases immediately after the disasters. The findings from this study aim to inform policymakers and scholars about how to better respond to disasters during multi-crisis events, such as offering hotel accommodations to evacuees instead of mass shelters and updating intake and accommodation procedures at shelters, such as administration temperature screenings, offering hand sanitizing stations, and providing isolated areas for ill evacuees.

The Global Land Cover Characterization Project was established to meet science data requirements identified by the International Geosphere and Biosphere Programme (IGBP), and the U. S. Global Change Research Program. The overall goal is to produce flexible large-area land cover databases to meet evolving requirements of the earth science research community.

The project was implemented by the United States Geological Survey/EROS Data Center (EDC), the University of Nebraska-Lincoln (UNL), and the Joint Research (JRC) of European Commission. This effort is part of the National Aeronautic's and Space Administration (NASA) Earth Observing System Pathfinder Program.

Funding for the project was provided by the USGS, NASA, the U.S. Environmental Protection Agency (EPA), National Oceanic and Atmospheric Administration (NOAA), U.S. Forest Service (USFS) , and the United Nations Environment Programme.

The data base has been adopted by the International Geosphere-Biosphere Programme Data and Information System office (IGBP-DIS) to fill its requirement for a global 1-km land cover data set.

[Summary provided by the USGS.]

This dataset is a collection of marine environmental data layers suitable for use in Southern Ocean species distribution modelling. All environmental layers have been generated at a spatial resolution of 0.1 degrees, covering the Southern Ocean extent (80 degrees S - 45 degrees S, -180 - 180 degrees). The layers include information relating to bathymetry, sea ice, ocean currents, primary production, particulate organic carbon, and other oceanographic data.

An example of reading and using these data layers in R can be found at https://australianantarcticdivision.github.io/blueant/articles/SO_SDM_data.html.

The following layers are provided:

1. Layer name: depth Description: Bathymetry. Downloaded from GEBCO 2014 (0.0083 degrees = 30sec arcmin resolution) and set at resolution 0.1 degrees. Then completed with the bathymetry layer manually corrected and provided in Fabri-Ruiz et al. (2017) Value range: -8038.722 - 0 Units: m

Source: This study. Derived from GEBCO URL: https://www.gebco.net/data_and_products/gridded_bathymetry_data/ Citation: Fabri-Ruiz S, Saucede T, Danis B and David B (2017). Southern Ocean Echinoids database_An updated version of Antarctic, Sub-Antarctic and cold temperate echinoid database. ZooKeys, (697), 1.

1. Layer name: geomorphology Description: Last update on biodiversity.aq portal. Derived from O'Brien et al. (2009) seafloor geomorphic feature dataset. Mapping based on GEBCO contours, ETOPO2, seismic lines). 27 categories Value range: 27 categories Units: categorical Source: This study. Derived from Australian Antarctic Data Centre URL: https://data.aad.gov.au/metadata/records/Polar_Environmental_Data Citation: O'Brien, P.E., Post, A.L., and Romeyn, R. (2009) Antarctic-wide geomorphology as an aid to habitat mapping and locating vulnerable marine ecosystems. CCAMLR VME Workshop 2009. Document WS-VME-09/10

2. Layer name: sediments Description: Sediment features Value range: 14 categories Units: categorical Source: Griffiths 2014 (unpublished) URL: http://share.biodiversity.aq/GIS/antarctic/

3. Layer name: slope Description: Seafloor slope derived from bathymetry with the terrain function of raster R package. Computation according to Horn (1981), ie option neighbor=8. The computation was done on the GEBCO bathymetry layer (0.0083 degrees resolution) and the resolution was then changed to 0.1 degrees. Unit set at degrees. Value range: 0.000252378 - 16.94809 Units: degrees Source: This study. Derived from GEBCO URL: https://www.gebco.net/data_and_products/gridded_bathymetry_data/ Citation: Horn, B.K.P., 1981. Hill shading and the reflectance map. Proceedings of the IEEE 69:14-47

4. Layer name: roughness Description: Seafloor roughness derived from bathymetry with the terrain function of raster R package. Roughness is the difference between the maximum and the minimum value of a cell and its 8 surrounding cells. The computation was done on the GEBCO bathymetry layer (0.0083 degrees resolution) and the resolution was then changed to 0.1 degrees. Value range: 0 - 5171.278 Units: unitless Source: This study. Derived from GEBCO URL: https://www.gebco.net/data_and_products/gridded_bathymetry_data/

5. Layer name: mixed layer depth Description: Summer mixed layer depth climatology from ARGOS data. Regridded from 2-degree grid using nearest neighbour interpolation Value range: 13.79615 - 461.5424 Units: m Source: https://data.aad.gov.au/metadata/records/Polar_Environmental_Data

6. Layer name: seasurface_current_speed Description: Current speed near the surface (2.5m depth), derived from the CAISOM model (Galton-Fenzi et al. 2012, based on ROMS model) Value range: 1.50E-04 - 1.7 Units: m/s Source: This study. Derived from Australian Antarctic Data Centre URL: https://data.aad.gov.au/metadata/records/Polar_Environmental_Data Citation: see Galton-Fenzi BK, Hunter JR, Coleman R, Marsland SJ, Warner RC (2012) Modeling the basal melting and marine ice accretion of the Amery Ice Shelf. Journal of Geophysical Research: Oceans, 117, C09031. http://dx.doi.org/10.1029/2012jc008214, https://data.aad.gov.au/metadata/records/polar_environmental_data

7. Layer name: seafloor_current_speed Description: Current speed near the sea floor, derived from the CAISOM model (Galton-Fenzi et al. 2012, based on ROMS) Value range: 3.40E-04 - 0.53 Units: m/s Source: This study. Derived from Australian Antarctic Data Centre URL: https://data.aad.gov.au/metadata/records/Polar_Environmental_Data Citation: see Galton-Fenzi BK, Hunter JR, Coleman R, Marsland SJ, Warner RC (2012) Modeling the basal melting and marine ice accretion of the Amery Ice Shelf. Journal of Geophysical Research: Oceans, 117, C09031. http://dx.doi.org/10.1029/2012jc008214, https://data.aad.gov.au/metadata/records/polar_environmental_data

8. Layer name: distance_antarctica Description: Distance to the nearest part of the Antarctic continent Value range: 0 - 3445 Units: km Source: https://data.aad.gov.au/metadata/records/Polar_Environmental_Data

9. Layer name: distance_canyon Description: Distance to the axis of the nearest canyon Value range: 0 - 3117 Units: km Source: https://data.aad.gov.au/metadata/records/Polar_Environmental_Data

10. Layer name: distance_max_ice_edge Description: Distance to the mean maximum winter sea ice extent (derived from daily estimates of sea ice concentration) Value range: -2614.008 - 2314.433 Units: km Source: https://data.aad.gov.au/metadata/records/Polar_Environmental_Data

11. Layer name: distance_shelf Description: Distance to nearest area of seafloor of depth 500m or shallower Value range: -1296 - 1750 Units: km Source: https://data.aad.gov.au/metadata/records/Polar_Environmental_Data

12. Layer name: ice_cover_max Description: Ice concentration fraction, maximum on [1957-2017] time period Value range: 0 - 1 Units: unitless Source: BioOracle accessed 24/04/2018, see Assis et al. (2018) URL: http://www.bio-oracle.org/ Citation: Assis J, Tyberghein L, Bosch S, Verbruggen H, Serrao EA and De Clerck O (2018). Bio_ORACLE v2. 0: Extending marine data layers for bioclimatic modelling. Global Ecology and Biogeography, 27(3), 277-284 , see also https://www.ecmwf.int/en/research/climate-reanalysis/ocean-reanalysis

13. Layer name: ice_cover_mean Description: Ice concentration fraction, mean on [1957-2017] time period Value range: 0 - 0.9708595 Units: unitless Source: BioOracle accessed 24/04/2018, see Assis et al. (2018) URL: http://www.bio-oracle.org/ Citation: Assis J, Tyberghein L, Bosch S, Verbruggen H, Serrao EA and De Clerck O (2018). Bio_ORACLE v2. 0: Extending marine data layers for bioclimatic modelling. Global Ecology and Biogeography, 27(3), 277-284 , see also https://www.ecmwf.int/en/research/climate-reanalysis/ocean-reanalysis

14. Layer name: ice_cover_min Description: Ice concentration fraction, minimum on [1957-2017] time period Value range: 0 - 0.8536261 Units: unitless Source: BioOracle accessed 24/04/2018, see Assis et al. (2018) URL: http://www.bio-oracle.org/ Citation: Assis J, Tyberghein L, Bosch S, Verbruggen H, Serrao EA and De Clerck O (2018). Bio_ORACLE v2. 0: Extending marine data layers for bioclimatic modelling. Global Ecology and Biogeography, 27(3), 277-284 , see also https://www.ecmwf.int/en/research/climate-reanalysis/ocean-reanalysis

15. Layer name: ice_cover_range Description: Ice concentration fraction, difference maximum-minimum on [1957-2017] time period Value range: 0 - 1 Units: unitless Source: BioOracle accessed 24/04/2018, see Assis et al. (2018) URL: http://www.bio-oracle.org/ Citation: Assis J, Tyberghein L, Bosch S, Verbruggen H, Serrao EA and De Clerck O (2018). Bio_ORACLE v2. 0: Extending marine data layers for bioclimatic modelling. Global Ecology and Biogeography, 27(3), 277-284 , see also https://www.ecmwf.int/en/research/climate-reanalysis/ocean-reanalysis

16. Layer name: ice_thickness_max Description: Ice thickness, maximum on [1957-2017] time period Value range: 0 - 3.471811 Units: m Source: BioOracle accessed 24/04/2018, see Assis et al. (2018) URL: http://www.bio-oracle.org/ Citation: Assis J, Tyberghein L, Bosch S, Verbruggen H, Serrao EA and De Clerck O (2018). Bio_ORACLE v2. 0: Extending marine data layers for bioclimatic modelling. Global Ecology and Biogeography, 27(3), 277-284 , see also https://www.ecmwf.int/en/research/climate-reanalysis/ocean-reanalysis

17. Layer name: ice_thickness_mean Description: Ice thickness, mean on [1957-2017] time period Value range: 0 - 1.614133 Units: m Source: BioOracle accessed 24/04/2018, see Assis et al. (2018) URL: http://www.bio-oracle.org/ Citation: Assis J, Tyberghein L, Bosch S, Verbruggen H, Serrao EA and De Clerck O (2018). Bio_ORACLE v2. 0: Extending marine data layers for bioclimatic modelling. Global Ecology and Biogeography, 27(3), 277-284 , see also https://www.ecmwf.int/en/research/climate-reanalysis/ocean-reanalysis

18. Layer name: ice_thickness_min Description: Ice thickness, minimum on [1957-2017] time period Value range: 0 - 0.7602701 Units: m Source: BioOracle accessed 24/04/2018, see Assis et al. (2018) URL: http://www.bio-oracle.org/ Citation: Assis J, Tyberghein L, Bosch S, Verbruggen H, Serrao EA and De Clerck O (2018). Bio_ORACLE v2. 0: Extending marine data layers for bioclimatic modelling. Global Ecology and Biogeography, 27(3), 277-284 , see also https://www.ecmwf.int/en/research/climate-reanalysis/ocean-reanalysis

19. Layer name: ice_thickness_range Description: Ice thickness, difference maximum-minimum on [1957-2017] time period Value range: 0 - 3.471811 Units: m Source: BioOracle accessed 24/04/2018, see Assis et al. (2018) URL: http://www.bio-oracle.org/ Citation: Assis J, Tyberghein L, Bosch S, Verbruggen H, Serrao EA and De Clerck O (2018). Bio_ORACLE v2. 0: Extending marine data layers for bioclimatic modelling. Global Ecology and Biogeography, 27(3), 277-284 , see also https://www.ecmwf.int/en/research/climate-reanalysis/ocean-reanalysis

20. Layer name: