Shadow Accrual Maps: Efficient Accumulation of City-Scale Shadows Over Time

Abstract

Large scale shadows from buildings in a city play an important role in determining the environmental quality of public spaces. They can be both beneficial, such as for pedestrians during summer, and detrimental, by impacting vegetation and by blocking direct sunlight. Determining the effects of shadows requires the accumulation of shadows over time across different periods in a year. In this paper, we propose a simple yet efficient class of approach that uses the properties of sun movement to track the changing position of shadows within a fixed time interval. We use this approach to extend two commonly used shadowing techniques, shadow maps and ray tracing, and demonstrate the efficiency of our approach. Our technique is used to develop an interactive visual analysis system, Shadow Profiler, targeted at city planners and architects that allows them to test the impact of shadows for different development scenarios. We validate the usefulness of this system through case studies set in Manhattan, a dense borough of New York City.

A Big Data Approach for Comprehensive Urban Shadow Analysis From Airborne Laser Scanning Point Clouds

“…In addition, the data flow can be iterated for a set of timesteps to cover a temporal extent (i.e. from sunrise to sunset) and generate what Miranda et al (2019) terms a shadow accrual map. Time-lapse video showing the shadow variation over a temporal extent can also be generated from the data resulting from the computations described in Section 3.…”

“…While shadow analysis is a well-studied subject supported by a large body of literature, computing shadows for large scenes, such as urban environments particularly when the computation is needed over a large temporal extent, has remained a challenge (Hinks et al, 2015;Miranda et al, 2019). In a recently published study on large-scale urban shadow analysis, to reduce the computational burden, Miranda et al (2019) proposed using a 3D vector model of New York City (NYC), in which the majority of structures are represented by CityGML's Level of Detail 1 (LoD 1) with several segments represented at LoD 2 (NYC Department of City Planning, 2018). The model was derived from a 2014 aerial survey by the NYC Department of Information Technology and Telecommunications.…”

“…As previously noted the main goal of the work by Miranda et al (2019) was to minimize computational time (see Section 2), which came at the expense of accuracy and visual fidelity, as only buildings were considered in the referenced maps and those building geometries were represented at LoD 1 (i.e. prismatic, block models with flat roofs) with 100 iconic buildings represented at LoD 2 (i.e.…”

“…Thus, the shadows that should be cast upon nearby buildings do not appear. Approaching the shadow computation from a wholly different perspective, this paper prioritizes the modeling accuracy and realism and takes minutes to hours (depending on the temporal resolution and the available computing resource) to compute a shadow accrual map for a day, as opposed to the seconds reported by Miranda et al (2019). Depending on the expectation (e.g.…”

“…So while shadow analysis is a well understood subject (Woo, Poulin, 2012), there has yet to be an efficient computational approach for capturing the full extent and complexity of a large-scale urban shadow analysis. As indicated by a recent review by Miranda et al (2019), most of the existing approaches today (e.g. NYC Department of City Planning, 2013; Prevision Design, 2016) are restricted to a small spatial scale and/or a limited temporal range, as described in Section 2.…”