Over the last decade, demand for active transportation modes such as walking and cycling has increased. While it is desirable to provide high levels of safety for these eco- friendly modes of travel, unfortunately, the overall percentage of pedestrian and bicycle fatalities, increased from 13% to 18% of total road crash fatalities in the last decade. In San Diego County, although the total number of pedestrian and cyclist fatalities decreased over the same period of time, a similar trend with a more drastic change is observed; the overall percentage of pedestrian and bicycle fatalities increased from 19.5% to 31.8%. Technological advancement in transportation has been creating new opportunities to explore and investigate new sources of data for the purpose of improving safety planning. This study aims to identify signalized intersections with the highest risk for walking and cycling within the City of San Diego, California, USA. Multiple data sources such as permanent pedestrian and bicycle counters, video cameras, and crash data were utilized. Data mining techniques and a new sampling strategy were adopted to demonstrate a holistic approach that can be applied to identify facilities with the highest need for improvement. Cluster analysis coupled with stratification was employed to select a representative sample of intersections for data collection and estimate annual average daily pedestrian and bicyclist (AADP and AADB). Additionally, the study quantified risk incorporating injury severity levels, the frequency of victims, distance crossed, and exposure into a single equation. It was found that not all intersections with the highest number of pedestrian and bicyclist victims were identified as high-risk after exposure and other factors such as crash severity were taken into account.