## 2 Materials and Methods
### GIS Database: Input Data Collection and Processing  
Building upon an ongoing research, a territorial database (DBT) was organized and implemented with the use of a GIS software processing remote sensing images, orthophotos, building characteristics, land cover data, local climate measurements, and energy consumption data. The main data content refers to:  
* Elevation models (raster data): The digital terrain model (DTM), with a precision of 10 m, describes the natural terrain. The digital elevation model (DEM), with a precision of 5 m, represents the bare-Earth surface, without natural or built features. The digital surface model (DSM), with a precision of 0.5 m and 5 m, represents the Earth's surface including trees and buildings. These kinds of data were used to assess shadows' effects on buildings and the surrounding's urban context to quantify the solar radiation, taking into account the sun and sky models, and to evaluate the building characteristics such as roof slope and orientation [10]. * Satellite images (raster data) from Landsat 8, the operational land imager (OLI) and the thermal infrared scanner (TIRS) with a precision of 30 m, were used to analyze the land cover types and to calculate the albedo of the outdoor spaces, the presence of vegetation with the use of the normalized difference vegetation index (_NDVI_), and the land-surface temperature (_LST_) [11]. * Orthophotos (raster data), with high spatial resolution of 0.1 m, red-green-blue (RGB) color model, and infrared (IR) spectral bands, were used to identify green areas and evaluate albedo values of outdoor urban spaces and buildings' roofs, as a function of color tones [12; 13]. * Municipal technical map of the city (polygonal vector data), updated to 2019, gave information on a building's footprint, type of users, number of buildings, number of floors or building height, period of construction, roof area, gross and net heated volume, net heated surface, and surface-to-volume (_S/V_) ratio. In addition, urban parameters were calculated with buildings' information at blocks of buildings scale [14]. Figure 1: Flowchart of materials, methods, and tools. * ISTAT (Italian National Institute of Statistics) census section data (polygonal vector data), updated to 2011, gave information at block-of-building scale on people occupancy, number of inhabitants, number of families and family members, percentage of foreigners, gender, age, income, employment rate, socio-economic data (income at 2009), central or autonomous heating systems, and type of fuels. * Urban parameters (polygonal vector data) at block-of-buildings scale were elaborated using Istat census database and municipal technical maps. The main variables were building density (_BD_), building height (_BH_), building coverage ratio (_BCR_), relative buildings' height (_H_/_H_\({}_{avg}\)), canyon effect (_H_/_W_ ratio), solar exposition, and main orientation of the streets (_MOS_) [15]. * Local climate data refers to weather stations' measurements (punctual vector data) located in the city. Available hourly data refer to temperature, relative humidity, vapor pressure, and wind velocity of the outdoor air. * Space heating and domestic hot water consumption data (punctual and polygonal vector data) were provided by the district heating IREN Company of the city. The annual, monthly, and hourly energy consumptions were processed and georeferenced. These data, used to design and validate urban-scale energy models, refer to three consecutive heating seasons: 2012-13, 2013-14, and 2014-15 [16; 17; 18]. * Energy performance certificates (EPCs) (punctual vector data) of the Piedmont Region gave information on residential buildings with 867,131 certificates in about 10 years. These data were used to evaluate the type of energy efficiency action and the impact retrofit interventions for the city of Turin [19].