Computational Approaches for Urban Environments: An Editorial - Computational Approaches for Urban Environments - page 5

weighted heavily on the decision whether or not a manuscript was accepted or
rejected. In cases where major revisions were requested by the reviewers and to
guarantee high scientific quality, a second round of review of the revised manuscript
by one of the original reviewers or an alternative reviewer was conducted. If the
reviews called for minor revisions, then a second round of reviews was not done.
Instead, the editors made the decision whether or not the revised manuscript was fit
for publication. In April 2014, 14 chapters were accepted and are now included in
the present topic.

1.3 Structure of the Topic

The topic integrates several areas of urban environments, each associated with
a main theme of the topic. The present volume has the following five sections:
(1) spatial planning and decision-making, (2) housing and real estate, (3) urban
transportation and mobility, (4) remote sensing, and (5) urban sensing, social
networks, and social media. However, this structure should not be understood as
fixed and definitive. Quite the contrary, the boundaries between these sections are
partly fuzzy and overlap each other to some extent.
Part I on Spatial Planning and Decision-Making includes three chapters. Chapter
2 by Pierre Frankhauser reviews fractal geometry to explore the spatial organization
of urban fabrics. He demonstrates how existing planning concepts can be enriched
through fractal analysis. In Chap. 3 , Martin Behnisch and Alfred Ultsch present
an approach coupling machine learning and data mining techniques for discov-
ering patterns in multidimensional building and land use data for urban districts
in Germany. It is demonstrated how these techniques may serve as hypothesis
generators for planning purposes. Closely related to this chapter is Chap. 4 by
Julian Hagenauer. He proposes a method for clustering spatial data by integrating
contextual neural gas and graph clustering. The efficiency of the method to derive
meaningful and theoretically sound regions is demonstrated on synthetic data and
a real-world case study dealing with demographic and socioeconomic data for
Philadelphia (USA).
Part II deals with Housing and Real Estate and comprises of three chapters. In
Chap. 5 , Alexander Razen et al. review recent developments in structured additive
regression models. Besides a multilevel structured additive regression for location
scale and shape, a Bayesian version of the quantile regression is proposed. Investi-
gating owner-occupied single family homes in Austrian urban areas, nonlinearities
in the hedonic price function and spatial heterogeneity, among others, are observed.
Next, in Chap. 6 , Shipeng Sun and Steven Manson highlight the importance of the
housing search in the context of intra-urban migration of domestic residents. In
order to simulate the complex location decision-making, an agent-based model is
formulated and validated against real-world housing vacancies for the Twin Cities of
Minnesota (USA). The case study demonstrates how realistic intra-urban migration
patterns emerge from rather simple behavioral rules of home searchers. Chapter 7
in Part II is written by Timothy Rosner and Kevin Curtin. The authors examine Jane