A PDF copy of a page on the EQ Recovery Learning site which linked to a YouTube video. This short video provides an insight into the design and location of the Canterbury Earthquake Memorial, due to be completed in February 2017. We meet its designer Grega Vezjak, who shares his vision for the Memorial
Geospatial liquefaction models aim to predict liquefaction using data that is free and readily-available. This data includes (i) common ground-motion intensity measures; and (ii) geospatial parameters (e.g., among many, distance to rivers, distance to coast, and Vs30 estimated from topography) which are used to infer characteristics of the subsurface without in-situ testing. Since their recent inception, such models have been used to predict geohazard impacts throughout New Zealand (e.g., in conjunction with regional ground-motion simulations). While past studies have demonstrated that geospatial liquefaction-models show great promise, the resolution and accuracy of the geospatial data underlying these models is notably poor. As an example, mapped rivers and coastlines often plot hundreds of meters from their actual locations. This stems from the fact that geospatial models aim to rapidly predict liquefaction anywhere in the world and thus utilize the lowest common denominator of available geospatial data, even though higher quality data is often available (e.g., in New Zealand). Accordingly, this study investigates whether the performance of geospatial models can be improved using higher-quality input data. This analysis is performed using (i) 15,101 liquefaction case studies compiled from the 2010-2016 Canterbury Earthquakes; and (ii) geospatial data readily available in New Zealand. In particular, we utilize alternative, higher-quality data to estimate: locations of rivers and streams; location of coastline; depth to ground water; Vs30; and PGV. Most notably, a region-specific Vs30 model improves performance (Figs. 3-4), while other data variants generally have little-to-no effect, even when the “standard” and “high-quality” values differ significantly (Fig. 2). This finding is consistent with the greater sensitivity of geospatial models to Vs30, relative to any other input (Fig. 5), and has implications for modeling in locales worldwide where high quality geospatial data is available.
This research aims to explore how business models of SMEs revolve in the face of a crisis to be resilient. The business model canvas was used as a tool to analyse business models of SMEs in Greater Christchurch. The purpose was to evaluate the changes SMEs brought in their business models after hit by a series of earthquake in 2010 and 2011. The idea was to conduct interviews of business owners and analyse using grounded theory methods. Because this method is iterative, a tentative theoretical framework was proposed, half way through the data collection. It was realised that owner specific characteristics were more prominent in the data than the elements business model. Although, SMEs in this study experienced several operational changes in their business models such as change of location and modification of payment terms. However, the suggested framework highlights how owner specific attributes influence the survival of a small business. Small businesses and their owners are extremely interrelated that the business models personify the owner specific characteristics. In other words, the adaptation of the business model reflects the extent to which the owner possess these attributes. These attributes are (a) Mindsets – the attitude and optimism of business owner; (b) Adaptive coping – the ability of business owner to take corrective actions; and (c) Social capital – the network of a business owner, including family, friends, neighbours and business partners.