Using unmanned aerial systems (UAS) for remotely sensing physical disorder in neighborhoods

Abstract

Place and local milieu have always been important considerations in the study of human behavior. However, place is typically measured with secondary data in aggregate form, obfuscating crucial, hyper-local information on neighborhood ecological conditions contributing to larger social, criminological, and public health processes. Hyper-local information, which is rarely available via traditional neighborhood audits or secondary data, should include information on neighborhood aesthetics (e.g., architecture, trees, public art), physical disorder (e.g., litter, unkempt lots, building decay), pedestrian safety (e.g., lighting, curb cuts), and related street characteristics. When this information is absent, the ability to connect and interpret the underlying effects of place on social problems is severely compromised. Using two neighborhoods in Phoenix, Arizona as case studies, we employ a novel strategy to collect hyper-local ecological information on physical disorder using unmanned aerial systems (UAS). We compare the collected data to more widely available sources and methods, including systematic social observation, as well as the use of satellite and street imagery. Finally, we discuss the operational challenges, constraints and data quality issues that emerge from implementing a UAS-based approach.