Big data and ‘healthy cities’: regeneration of urban contexts, green systems, and safe and healthy lifestyles

Rao, Priyanka (2025) Big data and ‘healthy cities’: regeneration of urban contexts, green systems, and safe and healthy lifestyles, [Dissertation thesis], Alma Mater Studiorum Università di Bologna. Dottorato di ricerca in Salute, sicurezza e sistemi del verde, 37 Ciclo.
Documenti full-text disponibili:
[thumbnail of PhD_Thesis_Priyanka_V1.pdf] Documento PDF (English) - Accesso riservato fino a 30 Aprile 2026 - Richiede un lettore di PDF come Xpdf o Adobe Acrobat Reader
Disponibile con Licenza: Salvo eventuali più ampie autorizzazioni dell'autore, la tesi può essere liberamente consultata e può essere effettuato il salvataggio e la stampa di una copia per fini strettamente personali di studio, di ricerca e di insegnamento, con espresso divieto di qualunque utilizzo direttamente o indirettamente commerciale. Ogni altro diritto sul materiale è riservato.
Download (21MB) | Contatta l'autore

Abstract

Urban Heat Island (UHI) is a prevalent global yet local environmental challenge characterized by increased surface and air temperatures in urban areas compared to adjacent rural regions. Impervious surfaces drive land surface temperature (LST) rise, while extreme heat endangers urban populations, causing deaths, health issues, economic losses, high energy use, and air pollution. Urban microclimate can be moderated by urban green spaces (UGS) as part of nature-based solutions (NBS), but their impact is complex due to various climatic and landscape factors. Satellite remote sensing, especially thermal infrared, has transformed urban climate research by enabling spatial and temporal analysis of surface temperatures, making it essential for assessing urban environmental quality. Motivated by aforesaid challenges and advances in thermal remote sensing, this thesis examines UGS to improve interventions and reduce heat stress. This thesis addresses key challenges in urban thermal climatology, including environmental interactions with temperature, landscape impacts on heat stress, urban morphology, and city-scale vegetation cooling variations. Using satellite remote sensing and novel machine learning (ML) methods, it advances the analysis of time-series open geo-datasets. In this work, the lesser-known details about UGS and their interplay with the climate are explored in the following ways: (i) developing an integrated framework designed based on multiple environmental and demographic factors influencing urban thermal stress; (ii) assessing the relative influence of land use type on surface thermal alterations to acknowledge the land use change impacts; and (iii) quantifying the contribution of urban morphology and landscape design towards LST at the neighborhood scale while analyzing the cooling benefits of UGS concerning local urban morphology and climate across multiple cities along a latitudinal gradient. These case studies contribute to state-of-the-art knowledge on approaches for analyzing UGS and its interactions with multiple thermal and climatic factors in diverse urban contexts, ultimately leading to sustainable green urban planning.

Abstract
Tipologia del documento
Tesi di dottorato
Autore
Rao, Priyanka
Supervisore
Co-supervisore
Dottorato di ricerca
Ciclo
37
Coordinatore
Settore disciplinare
Settore concorsuale
Parole chiave
Multiple-criteria Risk; Landscape-temperature nexus; Machine Learning; Remote Sensing; Urban Heat Island; Urban Green Spaces; Super-Resolution; Satellite Image Analysis
Data di discussione
4 Aprile 2025
URI

Altri metadati

Gestione del documento: Visualizza la tesi

^