Computational Approaches for Urban Environments: An Editorial - Computational Approaches for Urban Environments - page 6

Jacobs' four generators of urban diversity - dwelling density, block length, mix
of building age, and mix of uses - and develop a new composite urban livability
index to measure social and economic characteristics of the built environment. The
capability of the index is tested for the District of Columbia (USA).
Part III deals with another component of urban environments, namely, Urban
Transportation and Mobility , and includes two chapters. In Chap. 8 , Godwin
Yeboah et al. propose a space-time analytical approach based on analyzing global
positioning system data for cyclists. In addition, they advance policy strategies in
Newcastle (UK) to improve cycling uptake as well as data processing methodologies
through gaining a profound gender-based understanding of cycling behaviors. In
contrast, Rashid Waraich et al. identify in Chap. 9 the challenges of utilizing
agent-based traffic simulation frameworks. Focusing on the MATSim software
environment, several methods to improve the simulation performance through a
combination of reducing disk access, decoupling computational tasks, and making
use of parallel computing are proposed. Additionally, an event-based model instead
of a fixed time increment approach for the traffic simulation is propagated.
Remote Sensing advances and applications are the focus of Part IV, comprising
of four chapters. In Chap. 10 , Konstantinos Karantzalos gives a comprehensive
review of the state of the art in the field of change detection to monitor the growth
trajectories of urban areas. Essential change detection components, unsupervised
and supervised classification methodologies, and object extraction, among others,
are discussed in this chapter. On the contrary, Chap. 11 by Christian Berger et al.
presents a data fusion technique in which airborne hyperspectral and light detection
and ranging (LiDAR) data are combined in order to derive an urban surface
material map required for a microclimate model. Two case studies underpin the
potential of data fusion to derive key input parameters in this research domain. In
Chap. 12 , Junmei Tang monitors the spatiotemporal urban expansion of Houston's
metropolitan area applying cellular automata models. It is concluded that the
incorporation of socioeconomic data improves the predictive accuracy to simulate
the growth of human-intervened landscapes.
Part V on Urban Sensing, Social Network, and Social Media contains three
chapters. To begin with, in Chap. 13 , Yaoli Wang et al. utilize mobile cell phone
data from a Chinese city to investigate how social networks are embedded in the
urban physical space. For instance, the authors find that higher degree users in the
telephone contact network tend to congregate in the central business district and
that the downtown area hosts many heterogeneous communities of social groups.
In Chap. 14 , Emily Schnebele et al. propose to integrate volunteered geographic
information and social media data with authoritative sources to fill data gaps
during environmental emergencies. Two applications are presented. While the first
study applies an artificial neural network to transportation infrastructure flooding,
the second study deals with the usage of mobile phone data during emergency
evacuations. In Chap. 15 , Sebastian Grauwin et al. look at the possibility of mapping
space-time human activities in urban environments in the metropolises of New York,
London, and Hong Kong based on the detection of mobile phone usage (i.e., number
of calls, SMSs, and data transfers). The authors provide insights into both the