The current study analyzes the spatial distribution of SDG 11 targets across neighborhoods in Cagliari, Italy. Using aggregated indicators at the different SDG 11 targets and spatial autocorrelation techniques, the research identifies clusters and outliers across key dimensions such as housing, transport, disaster resilience, and green space access. Results reveal strong spatial structuring for targets like SDG 11.1 and SDG 11.4, while others show weaker patterns. Correlation analysis uncovers both synergies and trade-offs, notably between SDG 11.4, SDG 11.5 and SDG 11.6. The proposed methodology emphasizes the importance of neighborhood-scale analysis for targeted interventions and offers a replicable methodology for localizing SDG monitoring in urban contexts and identifying spatial correlations.
Exploring Spatial Distribution and Interactions Toward SDG 11 Indicators at the Neighborhood Level: An Experimental Analysis
Francesco Piras
Primo
;Valeria saiuUltimo
2025-01-01
Abstract
The current study analyzes the spatial distribution of SDG 11 targets across neighborhoods in Cagliari, Italy. Using aggregated indicators at the different SDG 11 targets and spatial autocorrelation techniques, the research identifies clusters and outliers across key dimensions such as housing, transport, disaster resilience, and green space access. Results reveal strong spatial structuring for targets like SDG 11.1 and SDG 11.4, while others show weaker patterns. Correlation analysis uncovers both synergies and trade-offs, notably between SDG 11.4, SDG 11.5 and SDG 11.6. The proposed methodology emphasizes the importance of neighborhood-scale analysis for targeted interventions and offers a replicable methodology for localizing SDG monitoring in urban contexts and identifying spatial correlations.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.


