Using Unmanned Aerial Vehicles (UAVs) for Remotely Sensing Neighborhood Disorder

• In Event: New Approaches to Measuring and Modeling Crime-Relevant Neighborhood Social Processes

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 likely to contribute 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), social disorder (e.g., loitering, panhandling), pedestrian safety (e.g., lighting), 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 the historical Coronado neighborhood in Phoenix, Arizona as our case study, we employ a novel strategy to collect hyper-local ecological information on physical disorder using unmanned aerial systems. We also discuss the operational challenges, constraints and data quality that emerge from piloting a new methodology.

Authors

• Tony Grubesic, Arizona State University

• Danielle Wallace, Arizona State University

• Alyssa Chamberlain, Arizona State University