·Urban flooding is an increasing threat to city and resident well-being. The Federal Emergency Management Agency (FEMA) typically reports losses attributed to flooding that results from a stream overtopping its banks, discounting impacts of flooding that occurs when precipitation intensity exceeds the capacity of a drainage system. This study uses municipal service requests reporting on street flooding in Denver, Colorado from 2000-2019 in coordination with Mile High Flood District rain gauge data and Census tract information to understand spatiotemporal drivers of urban flooding. A threshold analysis was conducted on storm characteristics with performance of best thresholds being close to random chance, indicating single storm characteristics were not able to effectively predict where and when flooding reports occurred, leading us to a combined spatial and temporal analysis. Topographic Wetness Index of locations of flooding reports were found higher than randomly selected points. A logistic regression describing the probability of a storm leading to a flood report showed the strongest predictors of urban flooding were, in descending order, maximum five-minute intensity, population density, storm depth, storm duration, median tract income, and stormwater pipe length per area. Maximum five-minute intensity and population density are nearly identical in prediction power for urban flood reports. The logistic regression revealed information about the comparative influence of spatial and temporal variables on flood reporting. A linear regression describing amounts of reports per area showed percent impervious as the only predictor. Our methodologies can be used to better inform urban flood awareness, response, and mitigation.