Urban planning decisions increasingly depend on spatially granular, multi-dimensional evidence that open-data environments now make possible but that accessible decision-support tools have not yet operationalized at the neighbourhood scale for Italian cities. This thesis develops a geospatial Decision-Support System (DSS) for Turin (Torino), Italy, integrating two analytically complementary use cases built from open data using a reproducible Python geoprocessing stack. Use Case 1 operationalizes 24 indicators from the Sustainable Neighbourhood Tool for Mediterranean Cities (SNTool MED) framework across Turin’s 94 zone statistiche, applying Weighted Linear Combination (WLC) to produce a Composite Sustainability Index (CSI). Of the 24 indicators spanning five thematic domains (land use and biodiversity, transport and mobility, social equity, economy, and climate and energy), 18 meet the discriminatory power criterion for composite scoring.