Thus predicting future actions of terrorist activities requires a methodical approach in providing accurate estimates for consideration in combating terrorism. This week’s readings mentioned a few methodical approaches in assisting predictive analysis utilized by the intelligence community. Schmidt and Willis introduced a predictive model to forecast terrorist attacks; whereas, Khalsa takes on a different methodology in forecasting terrorism. Schmidt’s model uses “up-to-date geospatial features, terrorist behaviors, and uncertainty and error in the input measurements and propagation of data” (Schmidt and Willis 2007, 217). His method essentially extracts behavioral indicators from associations from different sources. The propagate data does an excellent job all providing a potential location of the next attack but fails to identify the individual or group of individuals who will commit the attack. In contrast, Khalsa’s approach is a conglomerate of near-real-time information based off of intelligence reports. Her methodology is based on 68 indicators of terrorism and “consists of 23 tasks and 6 phases of warning analysis” (Khalsa n.d.). Taking on a more quantitative approach, her method has proven to be a systematic forecasting tool that “improves terrorism warning process, automates analytical techniques, fuses interagency intelligence into a meaningful picture, and provides a continuously updated analysis of potential-terrorist target” (Khalsa