Automation of GIS-Based Population Data Collection for Transportation Risk Analysis

Details

• Creators:
  Mills, G. S. ; Neuhauser, K. S.
• Corporate Creators:
  Sandia National Laboratories
• Subject/TRT Terms:
  • Crashes
  • Disasters
  • Intelligent transportation systems
  • Risk analysis
  • Geographic information systems
  • Accidents
  • EDB/050900
  • EDB/054000
  • Failure Mode Analysis
  • Fires
  More +
• Publication/ Report Number:
  SAND99-2676
• Resource Type:
  Research Paper
• Geographical Coverage:
  United States
• Corporate Publisher:
  Sandia National Laboratories
• Abstract:
  Estimation of the potential radiological risks associated with highway transport of radioactive materials (RAM) requires input data describing population densities adjacent to all portions of the route to be traveled. Previously, aggregated risks for entire multi-state routes were adequately estimated from population data with low geographic resolution. Current demands for geographically-specific risk estimates require similar increases in resolution of population density adjacent to route segments. With the advent of commercial geographic information systems (GISs) and databases describing highways, U.S. Census Blocks, and other information that is geographically distributed, it became feasible to determine and tabulate population characteristics along transportation routes with l-kilometer resolution. This report describes an automated method of collecting population data adjacent to route segments (for calculation of incident-free doses) based on a commercial GIS. It also describes a statistical method of resolving remaining resolution issues, and an adaptation of the automation method to collection of data on population under a hypothetical plume of contamination resulting from a potential transportation accident.
• Format:
  PDF
• Funding:
  DE-AC04-94AL85000
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/4914/dot_4914_DS1.pdf
• File Type:
  [PDF - 878.87 KB]
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha256:e7080044b73c1ae9f9277f3d88c302e630809060387b44291df5b1e427d1f2a8