Morphological 'Spatial' Clouds. Harnessing LIDAR Approaches as Measure in Volumetric and Spatial Complexity.

The scientific dependency on typological classification has historically been a useful tool to assess the compositional character urban form. More than the promulgation of mere spatial division, each analytic practice (traditional morphological analysis, configurative analysis, big data, environmental performative indexes analysis) has set in place measurable indicators, allowing for the wide comparison of functional components of the city. Contemporary dilemmas further arise when standardized metrics encounter complex and intricate settings. The environmental qualities of compressed and volumetric settings (Bruyns et al., 2021), including those specific to Asia, disarms formal features to assess space. In this framework, this paper aims to broaden the praxis of morphological analysis with the fusion of LIDAR technologies to assess layers levels of space and types. First, the paper will deliver a concise outline of the LIDAR metrics and their specific protocols. Thereafter reference will be made to key features of LIDAR technologies, coding as well as decoding environmental characteristics through the point cloud spatial data. Finally, as proof of case, the paper will discuss three examples extracted from the Hong Kong setting, each with their unique features and human scales. Main findings will expand on the challenges presented in capturing complex 3dimensional digital geometries, and their interpretation, their contributions in furthering other morphological toolsets, with specific reference to block and type scale orders. Conclusions will centre on the advantages of point cloud data, as the 'invisible' layer to the morphological praxis, whilst reflecting on the deeper understanding of such digital assets in multi-level base analysis.