Spatial Analysis for Identifying Concentrations of Urban Damage

Abstract

Disasters resulting from earthquakes, hurricanes, fires, floods, and terrorist attacks can result in significant and highly concentrated damage to buildings and infrastructure within urban regions. Following such events, it is common to dispatch investigation teams to catalog and inventory damage locations. In recent years, these data gathering efforts have been aided by developments in high resolution satellite remote sensing technologies (e.g. Matsuoka & Yamazaki, 2005) and by advances in ground-based field data collection (e.g. Deaton & Frost, 2002). Damage inventories are typically presented as maps showing the location and damage state of structures in part or all of an effected region. In some cases information on the post-event condition of major infrastructure systems such as transportation, power, communications, and water networks is also included. Depending on the means used to acquire data, damage inventories may be developed in days (satellitebased data acquisition) or weeks-to-months (ground-based damage surveys) after an event. Once available, these inventories can be used for a range of purposes including guiding emergency rescues (short-term use), identification of neighborhoods requiring post-disaster financial assistance (intermediate-term use), and support of zoning, planning or urban policy studies (long-term use). An important task when analyzing these inventories is to identify and quantify damage concentrations or clusters, as this information is useful for prioritizing post-disaster recovery activities. Additionally, an understanding of damage concentrations can provide insight to the multiscale processes that govern an urban region's performance during an extreme event. In some cases spatial patterns and clusters can be inferred from damage inventories using simple, qualitative visual assessment techniques. While this may be a satisfactory approach in situations where there is a marked contrast in building performance, its effectiveness is limited when damage contrasts are subtle, and spatial patterns are less obvious. In these instances, more advanced spatial analysis tools such as point pattern analysis can be of benefit. Point pattern analysis (PPA) techniques are a group of quantitative methods that describe the pattern of point (or event) locations and determine if point locations are concentrated (or clustered) within a defined region of study. An early and often-cited example of a semi7