Urban morphometrics + earth observation: an integrated approach to rich/extra-large-scale taxonomies of urban form

Homogeneous fine-grained patterns of urban form represent identifiable areas in cities and allow their classification. Urban morphology uses the concepts of “morphological period,” “urban tissue,” or “character areas” to link fine-grained homogeneity of form to historical origins and the social and economic characters associated with them. However, identifying such fine-grained spatial patterns is a labor-intensive, specialist operation, thus limiting replicability and scalability. Therefore, comprehensive urban form classification has rarely been conducted at a very large scale, hindering our understanding of how form contributes to social, economic, and environmental urban dynamics. With expanding capacity in geo-computation, urban analytics, and Earth Observation (EO) technology, new numerical approaches to the large-scale and detailed description of urban form have recently emerged. However, limitations due to availability, quality, and consistency of data still apply. We present an integrated approach to extra-large-scale urban form analysis that combines a novel Urban MorphoMetrics (UMM) method for the generation of rich and unsupervised urban form taxonomies with advanced EO feature-extraction techniques. UMM utilizes extremely parsimonious input information to generate a comprehensive set of urban form characters for three morphometric elements (buildings, streets, and plots), over six categories (dimension, shape, spatial distribution, intensity, connectivity, and diversity) and three scales (small, medium, and large). All characters are measured at the building level and clustered into distinct homogeneous urban types, thus creating a comprehensive taxonomy of urban form. UMM is applicable across cases, allowing individual type profiling and cross-case comparison. We illustrate UMM outputs across a range of case studies covering formal and informal urban areas in sharply different geographical and cultural contexts worldwide. The results demonstrate an encouraging ability to map urban form in cities in ways that relate to historical origins, land uses, and other validating geographies.The method also shows a pathway to address varying degrees of availability, quality, and consistency of input data, which is commonly poor, for example, in informal settlements. Explorations of ways to resolve this issue include integrating UMM with EO. The latter offers a way to generate globally consistent input data from freely accessible repositories, hence ensuring full control of quality and consistency. Thus, we show that our first efforts to combine UMM and EO data into an integrated UMM+EO process are suitable for use at a global scale