Towards New Measures of Resilience: Leveraging Location Based Services Data for Evaluating Hazard-Induced Changes in Access to Essential Services and Community Recovery
Creators: name: Swanson, Tessa
Publisher: name: My University
Year: 2023
Subjects: subject: community resilience
subject: access
subject: risk analysis
subject: hazards
subject: data science
subject: location-based services
subject: Industrial and Operations Engineering
subject: Engineering

description: Access to essential services determines individuals’ ability to meet health, safety, and social needs that enable them to thrive in their daily lives. But such access is not evenly distributed across populations and disparities in access can be exacerbated when a community is faced with a disruption, like a natural hazard. How such access varies over populations, space, time, and in response to events is valuable for evaluating equity. Opportunistically collected location-based services (LBS) data available from cell phones offers new opportunities to evaluate access to essential services. LBS data reveals regular mobility patterns of cell phone users over time. These mobility patterns can reveal regular visits to home, workplaces, and essential services facilities like supermarkets and schools. Deviations from those patterns may indicate a disruption, and how long individuals experience that disruption is impacted by how long it takes to meet essential needs. Data science, risk analysis, and urban planning offer tools to quantify those deviations and evaluate the factors contributing to recovery. In this dissertation, I utilize LBS data with methods spanning data science, risk analysis, and urban planning to quantify the relationship between access and resilience in Southwestern Florida in the period surrounding Hurricane Irma in September 2017. In Chapter 2, I present a large-scale data-driven method to identify when facilities experience a change in visit patterns that may indicate the actual closure of the facility or other barriers to access such as lack of supplies or disruptions to the connecting transportation network. I demonstrate my approach by analyzing loss of access to supermarkets, schools, health care facilities, and home improvement stores in Southwest Florida both visually and using machine learning anomaly detection. Next, in Chapter 3 I present a novel Bayesian network based method for estimating recovery periods in LBS user’s home and work appearances to show differences in household and workplace recovery over space and time. Results show the proportion of users experiencing an anomalous period and the average length of recovery consistent with the storm’s path, with more users experiencing a workplace disruption but with home disruptions lasting longer on average. I validate these results against available survey results on evacuation. Finally, in Chapter 4 I statistically model the relationship between these estimated recovery periods and travel time to available essential services facilities, along with variables representing storm parameters, utility outages, and socioeconomic variables. Through highly accurate random forest models estimating the state of a user’s recovery on a given day, I show the importance of measures of access to essential services before and after an event for household and workplace recovery. Power, cell service, and school outages all rank highly in importance, followed by measures of the change in travel time to essential service facilities following Irma’s landfall. Importantly, I demonstrate in these models that measures of access rank as more important for estimating recovery status than Social Vulnerability Index measures, which are currently incorporated into measures of community resilience. This may be attributed to access measures capturing both social and infrastructure drivers of recovery, as compared to the static values represented in index measures. Together, the methods described in Chapters 2-4 of this dissertation form a framework for assessing access to essential services before, during, and after a disruptive event that can inform interventions for facilitating recovery and building more resilient communities.