The Canterbury Earthquake Sequence (CES), induced extensive damage in residential buildings and led to over NZ$40 billion in total economic losses. Due to the unique insurance setting in New Zealand, up to 80% of the financial losses were insured. Over the CES, the Earthquake Commission (EQC) received more than 412,000 insurance claims for residential buildings. The 4 September 2010 earthquake is the event for which most of the claims have been lodged with more than 138,000 residential claims for this event only. This research project uses EQC claim database to develop a seismic loss prediction model for residential buildings in Christchurch. It uses machine learning to create a procedure capable of highlighting critical features that affected the most buildings loss. A future study of those features enables the generation of insights that can be used by various stakeholders, for example, to better understand the influence of a structural system on the building loss or to select appropriate risk mitigation measures. Previous to the training of the machine learning model, the claim dataset was supplemented with additional data sourced from private and open access databases giving complementary information related to the building characteristics, seismic demand, liquefaction occurrence and soil conditions. This poster presents results of a machine learning model trained on a merged dataset using residential claims from the 4 September 2010.
Global biodiversity is threatened by human actions, including in urban areas. Urbanisation has removed and fragmented indigenous habitats. As one of the 25 biodiversity ’hot spots’, New Zealand is facing the problems of habitat loss and indigenous species extinction. In New Zealand cities, as a result of the land clearance and imported urban planning precepts, many urban areas have little or no original native forest remaining. Urbanisation has also been associated with the introduction of multitudes of species from around the world.
Two large earthquakes shook Christchurch in 2010 and 2011 and caused a lot of damage. Parts of the city suffered from soil liquefaction after the earthquakes. In the most damaged parts of Christchurch, particularly in the east, whole neighbourhoods were abandoned and later demolished except for larger trees.
Christchurch offers an excellent opportunity to study the biodiversity responses to an urban area with less intensive management, and to learn more about the conditions in urban environments that are most conducive to indigenous plant biodiversity.
This study focuses on natural woody plant regeneration of forested sites in Christchurch city, many of which were also surveyed prior to the earthquakes. By repeating the pre-earthquake surveys, I am able to describe the natural regeneration occurring in Christchurch forested areas. By combining this with the regeneration that has occurred in the Residential Red Zone, successional trajectories can be described under a range of management scenarios. Using a comprehensive tree map of the Residential Red Zone, I was also able to document minimum dispersal distances of a range of indigenous trees in Christchurch. This is important for planning reserve connectivity. Moreover, I expand and improve on a previous analysis of the habitat connectivity of Christchurch (made before the earthquakes) to incorporate the Residential Red Zone, to assess the importance for habitat connectivity of restoring the indigenous forest in this area. In combination, these data sets are used to provide patch scenarios and some management options for biodiversity restoration in the Ōtākaro-Avon Red Zone post-earthquake.
