I contributed a chapter to the newly-released book, Big Data for Regional Science (eds. Laurie A. Schintler, Zhenhua Chen), on new methods for conducting urban visual preferences research. Here’s an excerpt from the introduction:
Aesthetic preferences for landscapes have been studied by researchers in many fields, given the importance of the issue to human well-being, ecosystem sustainability and public policy. […] The aim of this chapter is to demonstrate how utilizing new data sources and techniques in the big data era could transform this type of research. The utilizes images from Google Street View to obtain preference data through crowdsourcing, applies the Elo algorithm to transform the pairwise voting data into continuous beauty scores and then relates these scores to urban landscape indicators constructed from publicly available GIS data ( Figure 7.1 ). The remainder of the introduction reviews previous research, and describes how the method described here helps address four problems identified through a methodological review of landscape preference research.
The book also includes interesting chapters from a variety of leading scholars in urban research.