A Google Earth Engine code to analyze residential buildings' real estate values, summer surface thermal anomaly patterns and urban features: a Florence (Italy) case study

The layers included in the code were from the study conducted by the research group of CNR-IBE (Institute of BioEconomy of the National Research Council of Italy) and ISPRA (Italian National Institute for Environmental Protection and Research), published by the Sustainability journal (https://doi.org/10.3390/su14148412).

Link to the Google Earth Engine (GEE) code (link: https://code.earthengine.google.com/715aa44e13b3640b5f6370165edd3002)

You can analyze and visualize the following spatial layers by accessing the GEE link: 

1. Daytime summer land surface temperature (raster data, horizontal resolution 30 m, from Landsat-8 remote sensing data, years 2015-2019)
2. Surface thermal hot-spot (raster data, horizontal resolution 30 m) was obtained by using a statistical-spatial method based on the Getis-Ord Gi* approach through the ArcGIS Pro tool.
3. Surface albedo (raster data, horizontal resolution 10 m, Sentinel-2A remote sensing data, year 2017)
4. Impervious area (raster data, horizontal resolution 10 m, ISPRA data, year 2017)
5. Tree cover (raster data, horizontal resolution 10 m, ISPRA data, year 2018)
6. Grassland area (raster data, horizontal resolution 10 m, ISPRA data, year 2017)
7. Water bodies (raster data, horizontal resolution 2 m, Geoscopio Platform of Tuscany, year 2016)
8. Sky View Factor (raster data, horizontal resolution 1 m, lidar data from the OpenData platform of Florence, year 2016)
9. Buildings' units of Florence (shapefile from the OpenData platform of Florence) include data on the residential real estate value from the Real Estate Market Observatory (OMI) of the National Revenue Agency of Italy (source: https://www1.agenziaentrate.gov.it/servizi/Consultazione/ricerca.htm, accessed on 14 July 2022). Data on the characterization of the buffer area (50 m) surrounding the buildings are included in this shapefile [the names of table attributes are reported in the square brackets]: averaged values of the daytime summer land surface temperature [LST_media], thermal hot-spot pattern [Thermal_cl], mean values of sky view factor [SVF_medio], surface albedo [alb_medio], and average percentage areas of imperviousness [ImperArea%], tree cover [TreeArea%], grassland [GrassArea%] and water bodies [WaterArea%]. 

Here attached the .txt file of the GEE code. 

E-mail

Giulia Guerri, CNR-IBE, giulia.guerri@ibe.cnr.it

Marco Morabito, CNR-IBE, marco.morabito@cnr.it

Alfonso Crisci, CNR-IBE, alfonso.crisci@ibe.cnr.it