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Research papers, University of Canterbury Library

Based on a qualitative study of four organisations involving 47 respondents following the extensive 2010 – 2011 earthquakes in Christchurch, New Zealand, this paper presents some guidance for human resource practitioners dealing with post-disaster recovery. A key issue is the need for the human resource function to reframe its practices in a post-disaster context, developing a specific focus on understanding and addressing changing employee needs, and monitoring the leadership behaviour of supervisors. This article highlights the importance of flexible organisational responses based around a set of key principles concerning communication and employee perceptions of company support.

Research papers, University of Canterbury Library

Background This study examines the performance of site response analysis via nonlinear total-stress 1D wave-propagation for modelling site effects in physics-based ground motion simulations of the 2010-2011 Canterbury, New Zealand earthquake sequence. This approach allows for explicit modeling of 3D ground motion phenomena at the regional scale, as well as detailed nonlinear site effects at the local scale. The approach is compared to a more commonly used empirical VS30 (30 m time-averaged shear wave velocity)-based method for computing site amplification as proposed by Graves and Pitarka (2010, 2015), and to empirical ground motion prediction via a ground motion model (GMM).

Research papers, University of Canterbury Library

Overview of SeisFinder SeisFinder is an open-source web service developed by QuakeCoRE and the University of Canterbury, focused on enabling the extraction of output data from computationally intensive earthquake resilience calculations. Currently, SeisFinder allows users to select historical or future events and retrieve ground motion simulation outputs for requested geographical locations. This data can be used as input for other resilience calculations, such as dynamic response history analysis. SeisFinder was developed using Django, a high-level python web framework, and uses a postgreSQL database. Because our large-scale computationally-intensive numerical ground motion simulations produce big data, the actual data is stored in file systems, while the metadata is stored in the database. The basic SeisFinder architecture is shown in Figure 1.

Videos, UC QuakeStudies

A video of a presentation by Matthew Pratt during the Resilience and Response Stream of the 2016 People in Disasters Conference. The presentation is titled, "Investing in Connectedness: Building social capital to save lives and aid recovery".The abstract for this presentation reads as follows: Traditionally experts have developed plans to prepare communities for disasters. This presentation discusses the importance of relationship-building and social capital in building resilient communities that are both 'prepared' to respond to disaster events, and 'enabled' to lead their own recovery. As a member of the Canterbury Earthquake Recovery Authority's Community Resilience Team, I will present the work I undertook to catalyse community recovery. I will draw from case studies of initiatives that have built community connectedness, community capacity, and provided new opportunities for social cohesion and neighbourhood planning. I will compare three case studies that highlight how social capital can aid recovery. Investment in relationships is crucial to aid preparedness and recovery.