Estimates of the Green View Index (GVI) across 376 world cities and all Italian CAP units and municipalities

#######################Estimates of the Green View Index (GVI) across 376 world cities and all Italian CAP units and municipalitiesGiacomo Falchetta20th april 2026DOI: 10.5281/zenodo.19629672####################### README for:1. GVI_Italy_points_2016_2023.csv2. GVI_stats_cap_2016_2023.csv3. GVI_stats_comuni_2016_2023.csv4. gvi_376cities_2016_2023_yearly_falchetta_hammad.csv5. GVI_stats_376cities_2016_2023.csv OVERVIEW This repository contains Green View Index (GVI) data products for two complementarygeographic domains: - A sample of 376 cities around the world, with raw point-level data and yearly summary products at city level- Italy, with raw point-level data and yearly summary products at CAP and municipality level DATA PACKAGE STRUCTURE The five files are organized into two groups: GLOBAL CITY SAMPLE- gvi_376cities_2016_2023_yearly_falchetta_hammad.csv Raw yearly GVI observations for the international city sample- GVI_stats_376cities_2016_2023.csv Yearly GVI summary statistics aggregated by city-country combination ITALY- GVI_Italy_points_2016_2023.csv Raw Italy observations with year, CAP code, coordinates, and GVI values- GVI_stats_cap_2016_2023.csv Yearly GVI summary statistics aggregated by CAP code- GVI_stats_comuni_2016_2023.csv Yearly GVI summary statistics aggregated by municipality TEMPORAL COVERAGE 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023 FILE INVENTORY 1) gvi_376cities_2016_2023_yearly_falchetta_hammad.csv- Geographic scope: international city sample- Unit of observation: raw observation with city and country attribution- Number of rows: 13,038,336- Number of columns: 12- Columns: city, country, year, x, y, GVI_predicted, lcz_filter_v3, CTR_MN_ISO, GRGN_L1, GRGN_L2, Cls, Cls_short- Unique city-country combinations: 376- Unique city names: 358 2) GVI_stats_376cities_2016_2023.csv- Geographic scope: international city sample- Unit of observation: one city-country combination- Number of rows: 376- Number of columns: 42- Key fields: city, country 3) GVI_Italy_points_2016_2023.csv- Geographic scope: Italy- Unit of observation: raw observation with CAP attribution- Number of rows: 17,202,516- Number of columns: 5- Columns: year, CAP, x, y, GVI 4) GVI_stats_cap_2016_2023.csv- Geographic scope: Italy- Unit of observation: one CAP- Number of rows: 4,690- Number of columns: 41- Key field: CAP 5) GVI_stats_comuni_2016_2023.csv- Geographic scope: Italy- Unit of observation: one municipality- Number of rows: 7,901- Number of columns: 51- Recommended unique municipality identifiers: PRO_COM or PRO_COM_T COMMON YEARLY SUMMARY VARIABLES The three summary files:- GVI_stats_cap_2016_2023.csv- GVI_stats_comuni_2016_2023.csv- GVI_stats_376cities_2016_2023.csv share the same yearly summary structure. For each year YYYY in 2016-2023 they contain: - GVI_min_YYYY : minimum GVI observed within the spatial unit in year YYYY- GVI_mean_YYYY : mean GVI observed within the spatial unit in year YYYY- GVI_max_YYYY : maximum GVI observed within the spatial unit in year YYYY- GVI_sd_YYYY : standard deviation of GVI within the spatial unit in year YYYY- GVI_sum_YYYY : sum of GVI values within the spatial unit in year YYYY VARIABLE DICTIONARY RAW GLOBAL CITY FILE gvi_376cities_2016_2023_yearly_falchetta_hammad.csv contains: - city : city name- country : country code or country identifier used in the source file- year : calendar year- x : x coordinate of the observation- y : y coordinate of the observation- GVI_predicted : predicted Green View Index at the observation- lcz_filter_v3 : Local Climate Zone- CTR_MN_ISO : country ISO code- GRGN_L1 : broad world region- GRGN_L2 : sub-region- Cls : climate class- Cls_short : short climate class GLOBAL CITY SUMMARY FILE GVI_stats_376cities_2016_2023.csv contains: - city : city name- country : country code or country identifier used in the source file- GVI_min_2016 ... GVI_min_2023- GVI_mean_2016 ... GVI_mean_2023- GVI_max_2016 ... GVI_max_2023- GVI_sd_2016 ... GVI_sd_2023- GVI_sum_2016 ... GVI_sum_2023 RAW ITALY FILE GVI_Italy_points_2016_2023.csv contains: - year : calendar year- CAP : Italian postal code- x : x coordinate of the observation- y : y coordinate of the observation- GVI : Green View Index value at the observation ITALY CAP SUMMARY FILE GVI_stats_cap_2016_2023.csv contains: - CAP : 5-digit Italian postal code- GVI_min_2016 ... GVI_min_2023- GVI_mean_2016 ... GVI_mean_2023- GVI_max_2016 ... GVI_max_2023- GVI_sd_2016 ... GVI_sd_2023- GVI_sum_2016 ... GVI_sum_2023 ITALY MUNICIPALITY SUMMARY FILE GVI_stats_comuni_2016_2023.csv contains: - COMUNE : municipality name- GVI_min_2016 ... GVI_min_2023- GVI_mean_2016 ... GVI_mean_2023- GVI_max_2016 ... GVI_max_2023- GVI_sd_2016 ... GVI_sd_2023- GVI_sum_2016 ... GVI_sum_2023- COD_RIP : macro-area code- COD_REG : region code- COD_PROV : province code- COD_CM : administrative code from source municipality attributes- COD_UTS : territorial code from source municipality attributes- PRO_COM : municipality code- PRO_COM_T : zero-padded municipality code- COMUNE_A : alternate municipality name where available- CC_UTS : binary administrative flag from source municipality attributes- Shape_Leng: geometry-length attribute from the source municipality layer RELATIONSHIPS BETWEEN FILES - GVI_stats_376cities_2016_2023.csv is the yearly summary version of gvi_376cities_2016_2023_yearly_falchetta_hammad.csv aggregated by city and country.- GVI_stats_cap_2016_2023.csv is the yearly summary version of GVI_Italy_points_2016_2023.csv aggregated by CAP.- GVI_stats_comuni_2016_2023.csv is the yearly summary version of GVI_Italy_points_2016_2023.csv aggregated by COMUNE (municipality). NOTES FOR USE - Keep CAP and PRO_COM_T as text when importing to preserve leading zeros.- For the municipality file, do not use COMUNE alone as a unique join key because municipality names may repeat.- For the global city files, use city and country together as the key.- The global dataset contains 376 city-country combinations and 358 unique city names; some city names therefore occur in more than one country.- The three aggregated summary files are designed to be easy to compare across years because they share the same naming convention for yearly statistics. ORIGINAL BOUNDARY GEOMETRY FILES The following original boundary geometry files were used upstream to generate oraggregate the data products distributed in this repository. They are listed herefor provenance and reproducibility purposes. They are not necessarily redistributedwith the present Zenodo archive. - GHS-FUA R2019A ("GHS_FUA_UCDB2015_GLOBE_R2019A_54009_1K_V1_0.zip") Boundary geometry for the Global Human Settlement Functional Urban Areas dataset, used to define the international city sample and its corresponding urban footprints. - Official Italian municipal boundaries ("shape_comuni.zip") Municipality boundary geometry for Italy, used to aggregate the Italian point observations to municipality level and to assign the municipal identifiers reported in GVI_stats_comuni_2016_2023.csv. - Italian CAP shapefile This geometry is proprietary to Poste Italiane and is therefore not redistributed in this repository. ORIGNAL REFERENCE(s):Falchetta, G., & Hammad, A. T. (2025). Tracking green space along streets of world cities. Environ. Res.: Infrastruct. Sustain., 5(2), 025011. doi: 10.1088/2634-4505/add9c4 Falchetta, G., De Cian, E., & Lunghi, J. (2026). Street green space and electricity demand: Evidence from metered consumption data. Energy Econ., 158, 109311. doi: 10.1016/j.eneco.2026.109311