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

# Replication code and data for: Tracking green space along streets of world citiesFalchetta, G., & Hammad, A. T. (2025). Tracking green space along streets of world cities. Environmental Research: Infrastructure and Sustainability. https://doi.org/10.1088/2634-4505/add9c4 The file "gvi_358cities_2016_2023_yearly_falchetta_hammad.csv" contains output data, reporting sampling-point level data on the yearly (2016-2023) values of the Green View Index for the 190 cities covered in the paper AND an additional number of world cities (for a total of 358 cities). The "README_gvi_358cities_2016_2023_yearly_falchetta_hammad.txt" file contains a dictionary of each column name and units. ____ To replicate the analysis, the results, and the figures of the paper: Download input data from this Zenodo repository and code from Github https://github.com/giacfalk/urban_green_space_mapping_and_tracking *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)