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Many coastal communities around the world are threatened by a near-field (or local) tsunami that could inundate the low-lying areas in a matter of minutes after generation. The universal consensus amongst emergency agencies and academic researchers is that a safe evacuation requires an effective response, which is typically assessed by the evacuation time estimate (ETE). ETE is an integral component of community emergency evacuation planning, especially areas prone to tsunamis. This paper aims to investigate the ETE for pedestrian evacuation during a tsunami through two different approaches: (1) the deterministic Least-Cost Distance (LCD) model; and (2) the dynamic Agent-Based Model (ABM). Then, the comparison of the two models in their intrinsic characteristics, strengths and weaknesses, and its applicability was discussed based on methodology behind of the LCD model and ABM. The LCD model was conducted to generate a spatially distributed ETE map, visualizing vulnerable areas where the evacuation time would be insufficient for individuals to reach safety. The ABM investigated uncertainty during tsunami evacuations, such as population distribution, walking speed, and milling time. This paper provides insights into the differences between the LCD model and ABM in terms of methodology and application. It assists the academic researchers and emergency managers, evacuation planners, and decision makers to choose an appropriate method for modeling pedestrian evacuation during tsunami. 

Abstract Building community resilience has become a national imperative. Substantial uncertainties in dynamic environments of emergencies and crises require real‐time information collection and dissemination based on big data analytics. These, in turn, require networked communities and cross‐sector partnerships to build lasting resilience. This viewpoint article highlights an interdisciplinary approach to building community resilience through community‐engaged research and partnerships. This perspective leverages existing community partnerships and network resources, undertakes an all‐hazard and whole‐community approach, and evaluates the use of state‐of‐the‐art information communication technologies. In doing so, it reinforces the multifaceted intergovernmental and cross‐sector networks through which resilience can be developed and sustained. 

City governments incorporate ICTs into government services to improve citizen participation and access to those services. Too much dependence on technology, however, can lead to concerns about creating a digital divide between different groups of citizens. The potential for digital inequality is a critical issue that can be exacerbated by insufficient attention being paid to vulnerabilities across communities. Given that socio-economically vulnerable populations are the ones who need government services the most, especially during disaster events, it is critical to investigate the extent to which digital inequality is an issue for technology-based government services. With this in mind, this paper analyzes the use of different technology-enabled access options for a representative eGovernment service system, the New York City 311 service system, in the early stages of the COVID-19 pandemic. Two sets of socio-economically distinct locations in New York City are compared, using average income as a proxy for vulnerability, to draw conclusions about potential inequalities in such a system during a crisis. 

Abstract. Previous tsunami evacuation simulations have mostly been based on arbitrary assumptions or inputs adapted from non-emergency situations, but a few studies have used empirical behavior data. This study bridges this gap by integrating empirical decision data from surveys on local evacuation expectations and evacuation drills into an agent-based model of evacuation behavior for two Cascadia subduction zone (CSZ) communities that would be inundated within 20–40 min after a CSZ earthquake. The model also considers the impacts of liquefaction and landslides from the earthquake on tsunami evacuation. Furthermore, we integrate the slope-speed component from least-cost distance to build the simulation model that better represents the complex nature of evacuations. The simulation results indicate that milling time and the evacuation participation rate have significant nonlinear impacts on tsunami mortality estimates. When people walk faster than 1 m s−1, evacuation by foot is more effective because it avoids traffic congestion when driving. We also find that evacuation results are more sensitive to walking speed, milling time, evacuation participation, and choosing the closest safe location than to other behavioral variables. Minimum tsunami mortality results from maximizing the evacuation participation rate, minimizing milling time, and choosing the closest safe destination outside of the inundation zone. This study's comparison of the agent-based model and the beat-the-wave (BtW) model finds consistency between the two models' results. By integrating the natural system, built environment, and social system, this interdisciplinary model incorporates substantial aspects of the real world into the multi-hazard agent-based platform. This model provides a unique opportunity for local authorities to prioritize their resources for hazard education, community disaster preparedness, and resilience plans.