Pedestrian volume information is critical to study traffic safety as well as to plan for pedestrian friendly design. We present a model used to estimate the pedestrian volumes for street intersections in the city of San Francisco, California. Through regression analysis at multiple geographical scales, a set of socioeconomic variables and built-environment characteristics were examined. Three factors emerged as having strong explanatory power on the variances of pedestrian volume: population and job density, local transit access, and land use mix. It was found that the strongest area of influence on pedestrian traffic is around a one-block radius of an intersection. Multiple-scale analysis reveals that not all variables are significant at the same scale. In fact, the best model was obtained when a mix of scales was used. Because the analysis in this paper utilizes easy-to-access data and routine statistical analysis, it is easy to apply it in other cities, hence providing a valuable tool for geographers, public health professionals, urban planners, and transportation engineers.