Background: Louisville ranks in the top 20 most challenging places to live with asthma in the US. Local asthma surveillance activities, which rely upon hospitalization reports and national survey prevalence data, do not provide real-time, spatially-explicit information that city leaders need to target the most effective interventions. Objective/Purpose: The study aimed to: 1) identify hotspots of asthma symptoms; 2) evaluate associations between asthma symptoms and environmental covariates in real-time and space; and 3) model the impact of municipal intervention scenarios. Methods: Participants recorded rescue inhaler use with a wireless, GPS-enabled sensor, which passively collected the date, time and location of inhaler use. We modeled normalized daily inhaler use counts and associations with space-time resolved environmental covariates using zero-truncated negative binomial models, and evaluated the potential of three strategic initiatives to reduce asthma inhaler use and cost. Results: Sensors recorded 5,660 inhaler use events in space and time for 140 participants from 06/13/2012 to 02/28/2014. We identified several environmental triggers positively associated with asthma inhaler use, including: AQI, PM10, weed pollen and mold (p< 0.01). Conversely, the spatial distribution of tree cover demonstrated a negative (protective) association with inhaler use (p< 0.01). We identified three interventions that could have the largest impact on asthma: reducing emissions by 20%, increasing tree canopy to 40%, and mitigating weed growth by 50%. By targeting these interventions within specific neighborhoods, 914,000 inhaler uses and $1.8 million of hospitalization costs could be avoided. Discussion/conclusion: Utilizing sensors to capture the spatiotemporal signal of asthma can complement existing surveillance, improve understanding of environmental drivers, and help cities target interventions in neighborhoods where they will have the most impact. This pilot study has expanded and enrolled over 500 participants, generating the largest citizen science asthma dataset ever collected.