ABSTRACT
The concept of cities as complex networks has developed rapidly in the last decade as new approaches in complexity science (Neal, 2013; Newman, Barabási, & Watts, 2006; Pijanowski et al., 2014; Portugali, 2010; Tao, Corcoran, Mateo-Babiano, & Rohde, 2014). In The New Science of Cities (Batty, 2013), cities are interpreted as complex systems that are growing like biological organisms evolving in a Darwinian fitness landscape. Cities are networked spatial configurations over which different sets of actions, flows, interactions, and transactions are taking place. This type of complexity is better interpreted through urban models built on complexity theories and supported with big data that are being increasingly collected and available in both spatial and temporal dimensions (Szell, 2014). The purpose of this article is to develop a new approach to describe urban landscape as a hierarchically networked system by applying GIS and remote sensing techniques. This new approach integrates both statistical and geometric processes to construct a hierarchically networked system for describing the urban landscape from observed data derived from remote sensing. This research aims at developing a systematic method to measure spatial patterns of urban growth graphically and quantitatively across
Beijing, China
various regions so that a comparative analysis could be conducted to examine dynamic trajectories and spatial characteristics of urban growth at different stages and sizes.
