ABSTRACT
Research on public space behavior often involves primary data collection including observations, surveys, and interviews. These methods can be costly and time consuming as well as divorced from the way people behave in physical space. We define big data as enormous streams of images, geographical coordinates, videos, and audio recordings that are usually generated in real time by sensors. Big data has the potential to support public space research. This chapter discusses several challenges in public space research that may be addressed by using big data approaches, including: 1) limited objectivity, 2) high cost labor and time, 3) restricted scope to local orientation and small scale, 4) lack of temporal fluidity and separation of behavior from precise geographical location. Each challenge is discussed in the context of specific big data techniques that may help to overcome this limitation. Conclusions offer recommendations for the use of big data to inform public space design. Challenges for big data research on public spaces include questions of privacy and fairness. To advance the use of big data in public space research and design, researchers and practitioners should encourage multidisciplinary research and practice, spatial analysis into urban design and planning curricula.
