Context-Specific Urban Optimisations Through Data-Driven Classification: A Perspective on Methods and Applications

Urban environments are increasingly challenged by rapid urbanisation and climate change, demanding strategic responses that are both adaptable and sensitive to local context. Typological classification offers a structured approach to understanding diverse urban contexts, enabling targeted interventions that support climate neutrality and livability. While global pressures are shared, their impacts differ widely across cities, highlighting the need for context-aware urban analytics to guide effective transformation. This paper presents a methodological perspective on a computational framework and workflow based on open source data, designed to support the classification and optimisation of urban environments across different urban contexts; it explores the framework’s potential and limitations, grounded in a review of relevant literature and available datasets. We propose a workflow encompassing four main steps: (1) classifying urban environments based on quantifiable characteristics, (2) identifying key performance indicators (KPIs) differentiated by urban typology, (3) proposing interventions to optimise urban environments according to underlying typological classification, and (4) validating the proposed solutions in simulated environments. The framework prioritises open data sources provided by public authorities as well as open science and citizen science initiatives. A more streamlined integration of data is proposed, facilitating both the classification and assessment of urban environments aligned with their primary typological designation.