A Quantitative Morphological Method for Mapping Local Climate Types

Abstract

Morphological characteristics of cities significantly influence urban heat island intensities and thermal responses to heat waves. Form attributes such as density, compactness, and vegetation cover are commonly used to analyse the impact of urban morphology on overheating processes. However, the use of abstract large-scale classifications hinders a full understanding of the thermal trade-off between single buildings and their immediate surrounding microclimate. Without analytical tools able to capture the complexity of cities with a high resolution, the microspatial dimension of urban climate phenomena cannot be properly addressed. Therefore, this study develops a new method for numerical identification of types, based on geometrical characteristics of buildings and climate-related form attributes of their surroundings in a 25m and 50m radius. The method, applied to the city of Rotterdam, combines quantitative descriptors of urban form, mapping GIS procedures, and clustering techniques. The resulting typo-morphological classification is assessed by modelling temperature, wind, and humidity during a hot summer period, in ENVI-met. Significant correlations are found between the morphotypes’ characteristics and local climate phenomena, highlighting the differences in performative potential between the classified urban patterns. The study suggests that the method can be used to provide insight into the systemic relations between buildings, their context, and the risk of overheating in different urban settings. Finally, the study highlights the relevance of advanced mapping and modelling tools to inform spatial planning and mitigation strategies to reduce the risk of urban overheating.

Building types in France

“…More recently, a large number of energy-related studies investigated the relationship between energy consumption (or other energy-related variables) and building morphological properties (Garbasevschi et al 2021, Evans et al 2019. Within this group of studies, building typologies are identified by expert-based classification (LSE Cities 2014) or through clustering approaches (Maiullari et al 2021). Nonetheless, building types identified by these works result in energy-oriented typologies rather than building types as defined in urban typo-morphology and architecture.…”

“…A high number of variables can benefit the algorithm accuracy (for instance, Hecht et al 2015 use between 72 and 87 features); yet the strong correlation among redundant features can arise issues of biases in data modeling and outcome interpretation. In this case, additional protocols of dimensionality reduction are required (such as PCA in Hecht et al 2015, Maiullari et al 2021, each one coming with a further cost of lower interpretability of the intermediary variables, and strong assumptions about the underlying data structure (for instance the absence of outliers and the linear relationship among variables required by the PCA protocol). Finally, the choice of the variables underlying building clustering/classification analysis, depends on the thematic and methodological goal of the work.…”

Análisis de componentes principales de la morfología urbano edilicia del Área Metropolitana de Mendoza, Argentina

“…Esto da lugar a la posibilidad de aplicación de métodos estadísticos entre el que se destaca el análisis de Componentes Principales CP (Johnson y Wichern, 1998). Metodológicamente, existen antecedentes de estudios espaciales que aplican análisis de Componentes Principales (CP) para evaluar las características de vecindarios e identificar diferencias territoriales en determinadas variables (Maiullari, Esch y Timmeren, 2021;Wu, Peng, Ma, Li y Rao, 2020) o para identificar la forma urbana y comprender las transformaciones inducidas por procesos de expansión (Lemoine-Rodríguez, Inostroza y Zepp, 2020). Mientras, otros antecedentes de análisis de CP vinculan la forma urbana a espacios vegetados y a servicios ecosistémicos (Grafius, Corstanje y Harris, 2018).…”