Nov 7, 2013
As part of an exploration of ways to predict what determines the targets of suicide attacks, RAND conducted a proof-of-principle analysis of whether adding sociocultural, political, economic, and demographic factors would enhance the predictive ability of a methodology that focused on geospatial features. This test case focused on terrorist bombing incidents in Israel, but the findings indicate that the methodology merits further exploration.
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The Naval Research Laboratory (NRL) set out to develop ways to predict what determines the targets of suicide attacks. While the ultimate goal is to create a list of areas at risk for the U.S. environment, the first phase of development employed a data set from Israel. Initially, NRL focused on spatial attributes, creating its own risk index, but realized that this focus on the where ignored the broader social context, the why. The lab asked RAND to test, as a proof of principle, the ability of sociocultural, political, economic, and demographic factors to enhance the predictive ability of NRL's methodology. Again using Israel as a sample, RAND created a database that coded for these factors, then conducted both quantitative and qualitative analyses with an eye to determining what puts a given area at greater risk. The quantitative analysis established that these factors are related to the odds of attack within specific neighborhoods and that the relationships held even when controlling for geospatial factors, so they seem to confer risk for reasons beyond their association with geospatial features of neighborhoods. The specifics of the research are limited to the preferences of Palestinian suicide bombers in Israel; however, the methods used to assess target preferences in Israel could be transferred to the United States or other countries. Any results, if proven to be robust, could be used to develop recommendations for heightened public awareness in certain areas.
Introduction and Overview
Quantitative Data and Methods
Conclusions and Recommendations
Sociocultural Precipitant Database
Logistic Regression Output