To extract data on built environment features from GSV images, we applied a deep learning model, DeepLabv3 + . We then used elas- tic net regression to test the relationship between the built environment empirically – distinguish- ing between car-related, walking-related and mixed-use land infrastructure – and the survival of neighbourhood organisations. This testing approach is novel, to our knowledge not yet having been applied in Urban Studies. Besides revealing the effects of built environment features on the social life between buildings, our study points to the value of easily applicable observational big data. Data captured by GSV and other recently developed methods offer researchers the oppor- tunity to conduct detailed yet relatively swift and inexpensive studies without resorting to overly coarse or common subjective measurements.
Source: Wang, M., & Vermeulen, F. 2020. Life between buildings from a street view: What do big data analytics reveal about neighborhood organizational vitality? Urban Studies Journal. DOI: 10.1177/0042098020957198