Spatial Data and Python Code for 13 Development Potential Indices (part 03)
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- springernature.figshare.com
zipUpdated Mar 10, 2020
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EmailClick to copy linkLink copiedCiteJames R. Oakleaf (2020). Spatial Data and Python Code for 13 Development Potential Indices (part 03) [Dataset]. http://doi.org/10.6084/m9.figshare.7890932.v1zipAvailable download formatsUnique identifierhttps://doi.org/10.6084/m9.figshare.7890932.v1Dataset updatedMar 10, 2020AuthorsJames R. OakleafLicenseCC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automaticallyDescriptionThird of four zipfiles providing all data and Python code necessary to replicate any of the 13 development potential indexes (DPIs) described within Oakleaf et al. (2019), “Mapping global development potential for renewable energy, fossil fuels, mining and agriculture sectors”. A README.pdf guides users on setting up environment necessary to use data and run Python code.
To run Python code with accompanying spatial data, 64 GBs of disk space is required. Additionally ArcPY, a python module associated with ESRI’s ArcGIS Desktop, and an accompanying Spatial Analyst extension license are required to run Python code. All code was created by J.R. Oakleaf during 2018 and is licensed under Creative Commons Attribution-NonCommercial 4.0 International License http://creativecommons.org/licenses/by-nc/4.0/.
Spatial Data and Python Code for 13 Development Potential Indices (part 01)
- figshare.com
zipUpdated Jun 1, 2023ShareFacebookTwitterEmailClick to copy linkLink copiedCiteJames R. Oakleaf (2023). Spatial Data and Python Code for 13 Development Potential Indices (part 01) [Dataset]. http://doi.org/10.6084/m9.figshare.7890926.v1zipAvailable download formatsUnique identifierhttps://doi.org/10.6084/m9.figshare.7890926.v1Dataset updatedJun 1, 2023AuthorsJames R. OakleafLicenseCC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automaticallyDescriptionFirst of four zipfiles providing all data and Python code necessary to replicate any of the 13 development potential indexes (DPIs) described within Oakleaf et al. (2019), “Mapping global development potential for renewable energy, fossil fuels, mining and agriculture sectors”. A README.pdf guides users on setting up environment necessary to use data and run Python code.
To run Python code with accompanying spatial data, 64 GBs of disk space is required. Additionally ArcPY, a python module associated with ESRI’s ArcGIS Desktop, and an accompanying Spatial Analyst extension license are required to run Python code. All code was created by J.R. Oakleaf during 2018 and is licensed under Creative Commons Attribution-NonCommercial 4.0 International License http://creativecommons.org/licenses/by-nc/4.0/.
Spatial Data and Python Code for 13 Development Potential Indices (part 02)
- springernature.figshare.com
zipUpdated Jun 2, 2023ShareFacebookTwitterEmailClick to copy linkLink copiedCiteJames R. Oakleaf (2023). Spatial Data and Python Code for 13 Development Potential Indices (part 02) [Dataset]. http://doi.org/10.6084/m9.figshare.7890980.v1zipAvailable download formatsUnique identifierhttps://doi.org/10.6084/m9.figshare.7890980.v1Dataset updatedJun 2, 2023AuthorsJames R. OakleafLicenseCC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automaticallyDescriptionSecond of four zipfiles providing all data and Python code necessary to replicate any of the 13 development potential indexes (DPIs) described within Oakleaf et al. (2019), “Mapping global development potential for renewable energy, fossil fuels, mining and agriculture sectors”. A README.pdf guides users on setting up environment necessary to use data and run Python code.
To run Python code with accompanying spatial data, 64 GBs of disk space is required. Additionally ArcPY, a python module associated with ESRI’s ArcGIS Desktop, and an accompanying Spatial Analyst extension license are required to run Python code. All code was created by J.R. Oakleaf during 2018 and is licensed under Creative Commons Attribution-NonCommercial 4.0 International License http://creativecommons.org/licenses/by-nc/4.0/.
Spatial Data and Python Code for 13 Development Potential Indices (part 04)
- springernature.figshare.com
zipUpdated May 30, 2023ShareFacebookTwitterEmailClick to copy linkLink copiedCiteJames R. Oakleaf (2023). Spatial Data and Python Code for 13 Development Potential Indices (part 04) [Dataset]. http://doi.org/10.6084/m9.figshare.7890935.v1zipAvailable download formatsUnique identifierhttps://doi.org/10.6084/m9.figshare.7890935.v1Dataset updatedMay 30, 2023AuthorsJames R. OakleafLicenseCC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automaticallyDescriptionLast of four zipfiles providing all data and Python code necessary to replicate any of the 13 development potential indexes (DPIs) described within Oakleaf et al. (2019), “Mapping global development potential for renewable energy, fossil fuels, mining and agriculture sectors”. A README.pdf guides users on setting up environment necessary to use data and run Python code.
To run Python code with accompanying spatial data, 64 GBs of disk space is required. Additionally ArcPY, a python module associated with ESRI’s ArcGIS Desktop, and an accompanying Spatial Analyst extension license are required to run Python code. All code was created by J.R. Oakleaf during 2018 and is licensed under Creative Commons Attribution-NonCommercial 4.0 International License http://creativecommons.org/licenses/by-nc/4.0/.
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FacebookTwitterEmailCC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically
Third of four zipfiles providing all data and Python code necessary to replicate any of the 13 development potential indexes (DPIs) described within Oakleaf et al. (2019), “Mapping global development potential for renewable energy, fossil fuels, mining and agriculture sectors”. A README.pdf guides users on setting up environment necessary to use data and run Python code.
To run Python code with accompanying spatial data, 64 GBs of disk space is required. Additionally ArcPY, a python module associated with ESRI’s ArcGIS Desktop, and an accompanying Spatial Analyst extension license are required to run Python code. All code was created by J.R. Oakleaf during 2018 and is licensed under Creative Commons Attribution-NonCommercial 4.0 International License http://creativecommons.org/licenses/by-nc/4.0/.