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Autologistic models for benchmark risk or vulnerability assessment of urban terrorism outcomes
Author(s) -
Liu Jingyu,
Piegorsch Walter W.,
Grant Schissler A.,
Cutter Susan L.
Publication year - 2018
Publication title -
journal of the royal statistical society: series a (statistics in society)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.103
H-Index - 84
eISSN - 1467-985X
pISSN - 0964-1998
DOI - 10.1111/rssa.12323
Subject(s) - vulnerability (computing) , geospatial analysis , terrorism , benchmark (surveying) , computer science , risk assessment , vulnerability assessment , geography , computer security , cartography , psychology , social psychology , archaeology , psychological resilience
Summary We develop a quantitative methodology to characterize vulnerability among 132 US urban centres (‘cities’) to terrorist events, applying a place‐based vulnerability index to a database of terrorist incidents and related human casualties. A centred autologistic regression model is employed to relate urban vulnerability to terrorist outcomes and also to adjust for auto‐correlation in the geospatial data. Risk analytic ‘benchmark’ techniques are then incorporated in the modelling framework, wherein levels of high and low urban vulnerability to terrorism are identified. This new translational adaptation of the risk benchmark approach, including its ability to account for geospatial auto‐correlation, is seen to operate quite flexibly in this sociogeographic setting.