Examining The Relationship Between Crime and Major Weather Events and Disasters in the 10 Largest Cities in Texas

This dissertation uses Social Disorganization Theory as a foundation to examine the relationship between yearly days with a major weather event or disaster and crime in the 10 largest cities in Texas. Multiple regressions and moderation analyses were conducted, with models that incorporated total Days with Disaster or major weather event, Social Disorganization, Population Density, and Disaster Consequences predicting all Index Crimes other than Arson. Days with Disaster significantly predicted Robbery, Murder, Rape, Burglary, and Auto Theft, but did not significantly predict Larceny or Assault. Additionally, Social Disorganization and Population Density were often found to predict crime. Moderation analysis also revealed that Social Disorganization increases the effect of Days with Disaster on Burglary rates. These findings indicate that, on average, large cities can expect increases to crime as days with major weather events and disasters increase, particularly when those increases are paired with high levels of Social Disorganization. These findings also suggest a need for police managers in disaster-prone communities to establish a disaster policing paradigm, with a focus on training for disaster-specific concerns, and preparation for crimes that increase during disasters, particularly in the more disorganized neighborhoods. This research also supports the establishment of disaster as a Social Disorganization Theory factor. A Unified Disaster Crime Theory is also proposed. Future disaster crime research should incorporate disaster phases timing, as well as compliance with disaster warnings