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An open-data, open-source model to estimate urban green space and its temporal evolution globaly

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Replication code and data for: Tracking green space along streets of world cities

By Giacomo Falchetta and Ahmed T. Hammad

Manuscript under peer review, preprint: https://doi.org/10.21203/rs.3.rs-3916891/v1

To replicate the analysis:

  • Download input data from Zenodo: https://doi.org/10.5281/zenodo.13886667
  • Optional data extraction steps (processed output data are already available in the Zenodo repository):
    • Adjust your working directory
    • Run [lines 4-11] of workflow/sourcer.R
    • Run the Javascript scripts written by the string_generator_training.R and string_generator_prediction.R files in Google Earth Engine (https://code.earthengine.google.com) and complete the export to Drive tasks to generate the output .csv files
  • Run workflow/sourcer.R [lines 15-46] to train the ML model and make predictions (including figures and tables replication)

The analysis was implemented in the following environment:

  • RStudio 2023.03.0
  • R version 4.2.3 (2023-03-15 ucrt)
  • Platform: x86_64-w64-mingw32/x64 (64-bit)
  • Running under: Windows 10 x64 (build 22631)

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An open-data, open-source model to estimate urban green space and its temporal evolution globaly

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