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The physical characteristics of neighborhoods are important information for analyzing the effects of place on social problems. However, this information is usually unavailable or measured with aggregated secondary data that are only suitable for large‐scale investigations. Grubesic et al. (2018) used UAS to evaluate hyper‐local information on physical disorders like litter, unkempt lots, and building decay. They conducted two low‐altitude missions with an Ebee, a fix‐wing model from senseFly, at 65 m flying height, 60% lateral overlap, and 75% longitudinal overlap to survey two neighborhoods in Phoenix, Arizona. In each neighborhood, a total of 91.96 and 45.18 acres were covered with 382 and 217 RGB images, respectively. The resulting UAS imagery is much sharper than common medium‐resolution satellite imagery and makes it easier to discern small objects like air‐conditioning units. Compared to omnidirectional street imagery, UAS imagery is not influenced by temporal mismatches or blocked sight caused by depth perspective. The UAS approach also turned out to be more economical than the traditional way to collect hyper‐local data through systematic social observation (SSO) or neighborhood audits.