Modeling Postearthquake Fire Ignitions Using Generalized Linear (Mixed) Models

This paper presents a rigorous approach to statistical modeling of postearthquake fire ignitions and to data collection for such modeling and applies it to late 20th century California. Generalized linear and generalized linear mixed models are used for this application for the first time. The approach recognizes that ignition counts are discrete, examines many possible covariates, and uses a small unit of study to ensure homogeneity in variable values for each area unit. Two data sets were developed to explore the effect of missing ignition data, each with a different assumption about the missing data. For one data set, the recommended model includes instrumental intensity; percentage of land area that is commercial, industrial, or transportation; total building area; percentage of building area that is unreinforced masonry; and people per square kilometer. The other includes the same, except area of high-intensity residential development replaces total building area, and median year built over all housing units is also included. The models should be useful in estimating the number and locations of postearthquake ignitions in future earthquakes.

[1]  Rachel A. Davidson,et al.  Fire following Earthquake—Reviewing the State-of-the-Art of Modeling , 2008 .

[2]  A. Ren,et al.  A Spatial–Temporal Stochastic Simulation of Fire Outbreaks Following Earthquake Based on GIS , 2006 .

[3]  A. Ren,et al.  The Simulation of Post-Earthquake Fire-Prone Area Based on GIS , 2004 .

[4]  Jiang Jian-hua,et al.  Hazard analysis system of urban post-earthquake fire based on GIS , 2001 .

[5]  Pravin K. Trivedi,et al.  Regression Analysis of Count Data , 1998 .

[6]  Gilles Bureau,et al.  The Morgan Hill Earthquake of April 24, 1984—Fire-Related Aspects , 1985 .

[7]  Martina Mittlböck,et al.  Pseudo R-squared measures for Poisson regression models with over- or underdispersion , 2003, Comput. Stat. Data Anal..

[8]  R Kulmala,et al.  Measuring the contribution of randomness, exposure, weather, and daylight to the variation in road accident counts. , 1995, Accident; analysis and prevention.

[9]  F. Windmeijer,et al.  R-Squared Measures for Count Data Regression Models With Applications to Health-Care Utilization , 1996 .

[10]  S-P Miaou,et al.  MEASURING THE GOODNESS-OF-FIT OF ACCIDENT PREDICTION MODELS , 1996 .

[11]  H Lum,et al.  Modeling vehicle accidents and highway geometric design relationships. , 1993, Accident; analysis and prevention.

[12]  H. Akaike A new look at the statistical model identification , 1974 .

[13]  D. Pierce,et al.  Residuals in Generalized Linear Models , 1986 .

[14]  Thomas D. O'Rourke,et al.  The 1906 San Francisco Earthquake and Fire—Enduring Lessons for Fire Protection and Water Supply , 2006 .