Integrated GIS- and network-based framework for assessing urban critical infrastructure accessibility and resilience: the case of Hurricane Michael

TL;DR

The paper develops an integrated GIS- and network-based framework to assess urban critical infrastructure accessibility and resilience under extreme events, addressing ex-ante preparedness and ex-post recovery. By applying this approach to Panama City during Hurricane Michael, it fuses open remote sensing, OpenStreetMap infrastructure data, and sociodemographic attributes within a three-state network model for roads and railways, and performs regression and probabilistic simulations to identify vulnerable populations and locations. Key findings show that individuals aged 65+—especially males in this group—face higher exposure to access losses; residential roads are the most susceptible, and targeted interventions like tree setbacks can substantially reduce global vulnerability (V from 0.56 to 0.32). The framework yields actionable insights for planners, demonstrates mixed resilience gains in the recovery phase, and offers a scalable method for evaluating and comparing urban resilience across contexts and disasters.

Abstract

This study presents a framework for assessing urban critical infrastructure resilience during extreme events, such as hurricanes. The approach combines GIS and network analysis with open remote sensing data of the aftermath, vector data on infrastructure, and socio-demographic attributes of populations in affected areas. Using Panama City as an example case study, this paper quantifies hurricane impacts on residents and identifies vulnerable locations for urban planners' attention. Simulations demonstrate how implementing measures at identified weak points can improve system resilience. Comparing pre-hurricane conditions with the aftermath and several years later allows observing network property changes and assessing overall resilience improvements. Findings indicate that individuals over 65 in the studied settlement are more susceptible to disasters, while males in this age category face higher risks.

Figures (13)

• Figure 1: Fallen trees and debris blocking the railways. Source: noaa2018.
• Figure 2: Missing digitization on the historical OSM data. In this example, yellow lines were missing in the 2018 historical vector data and were digitized manually to address the inaccuracies. Source:osmnoaa2018.
• Figure 3: Fallen trees blocking the free passage of vehicles. Source: noaa2018
• Figure 4: On the left: An excavator cleans the road from fallen trees. On the right: Emergency vehicles are passing through the road blockage caused by the fallen trees. Source: noaa2018.
• Figure 5: An example of the facility damage to which was considered minor. Intact parking area and present accessibility indicate that this facility is fully operation despite visible roof damage. Source: noaa2018.