By Timothy Mahon

– Executive Summary –

Urban Search and Rescue (US&R) Task Forces operate in complex, resource-constrained environments, often during large-scale disasters (LSD). During LSDs, US&R Task Forces conduct wide area search (WAS) operations to rescue and provide aid to survivors.

The effectiveness of WAS operations relies on rapid and efficient segmentation of the area to be searched, and appropriate resource allocation to each of those search segments. Traditionally, search segments are hand-drawn with varying levels of efficacy. However, advances in the types and availability of geospatial data offer new opportunities to enhance US&R WAS operations.

This thesis explores how geospatial data can be utilized to draw and prioritize search segments for US&R operations and introduces the Segmented Emergency Analysis for Response using Census and Hazards (SEARCH) tool.

The research examines currently available geospatial data for different forecasts or observed event data that can be leveraged by US&R resources to identify areas of interest (AOI) that should be prioritized during WAS operations. This thesis also incorporates population and built environment data (roads and structures) related to the AOI. This combination of data ensures that the search segments consider where the people and structures are, not just where the impact of an event is most significant.

In looking at the nature of the US&R WAS operations, which emphasize house to house searches, it becomes clear that there is a commonality between WAS and delivery service logistics. Both tasks aim to visit structures in an area as efficiently as possible and rely on the existing road network to do so. In some cases, the US&R Task Forces are literally delivering aid to survivors during their searches. To exploit this similarity, WAS operations are treated as a last-mile delivery (LMD) problem. Existing geospatial tools for LMD problem solving were modified and customized to consider factors important to US&R operations and resources.

By integrating available geospatial data with customized LMD geospatial tools, this thesis creates the initial version of the SEARCH tool that can be used by Geographic Information Systems Specialists (GISS) to help draft search segments. The tool considers the road network as it relates to structures and divides an AOI into search segments. It then enhances search segments with underlying population, housing, and structure data and calculates the total area and road miles to be searched by US&R teams.

The SEARCH tool allows flexibility in defining the AOI that it will segment and what roads or structures to consider when performing its analysis. As proof of concept, this thesis also demonstrates how the SEARCH tool could have been applied to New York City during Hurricane Sandy. In this example, different sized search segments are created based on how many structures can be searched by available resources to show how the SEARCH tool can adapt to varying needs.

Beyond the SEARCH tool, this thesis also finds that additional research on the rate of search could help close a knowledge gap in predicting how long WAS operations should take. There are many variables to consider regarding the work rate, including the total area, degree and type of damage, structure type, etc. Understanding how different variables affect the search rate would help further inform how many resources are needed to conduct US&R WAS operations.

The SEARCH tool is in an initial operational state and ready to be utilized immediately in WAS operations. US&R GISS members will find the SEARCH tool in their incident template for future training and operations. Further development is recommended to fully realize its benefits and add additional functionality which is road-mapped within the thesis.