Urbane: A 3D Framework to Support Data Driven Decision Making in Urban Development

In collaboration with The Visualization and Data Analytics (ViDA) lab at the NYU School of Computer Science and Engineering.

Nivan Ferreira, Marcos Lage, Harish Doraiswamy, Huy T. Vo, Luc Wilson, Heidi Werner, Muchan Park, Cláudio Silva

Abstract

Architects working with developers and city planners typically rely
on experience, precedent and data analyzed in isolation when making
decisions that impact the character of a city. These decisions
are critical in enabling vibrant, sustainable environments but must
also negotiate a range of complex political and social forces. This
requires those shaping the built environment to balance maximizing
the value of a new development with its impact on the character
of a neighborhood. As a result architects are focused on two issues
throughout the decision making process: a) what defines the character
of a neighborhood? and b) how will a new development change
its neighborhood? In the first, character can be influenced by a
variety of factors and understanding the interplay between diverse
data sets is crucial; including safety, transportation access, school
quality and access to entertainment. In the second, the impact of a
new development is measured, for example, by how it impacts the
view from the buildings that surround it. In this paper, we work
in collaboration with architects to design Urbane, a 3-dimensional multi-resolution framework that enables a data-driven approach for
decision making in the design of new urban development. This is
accomplished by integrating multiple data layers and impact analysis
techniques facilitating architects to explore and assess the effect
of these attributes on the character and value of a neighborhood.
Several of these data layers, as well as impact analysis, involve
working in 3-dimensions and operating in real time. Efficient computation
and visualization is accomplished through the use of techniques
from computer graphics. We demonstrate the effectiveness
of Urbane through a case study of development in Manhattan depicting
how a data-driven understanding of the value and impact of
speculative buildings can benefit the design-development process
between architects, planners and developers.