A city’s planted trees, the great majority of which are in private gardens, play a fundamental role in shaping a city’s wild ecology, ecosystem functioning, and ecosystem services. However, studying tree diversity across a city’s many thousands of separate private gardens is logistically challenging. After the disastrous 2010–2011 earthquakes in Christchurch, New Zealand, over 7,000 homes were abandoned and a botanical survey of these gardens was contracted by the Government’s Canterbury Earthquake Recovery Authority (CERA) prior to buildings being demolished. This unprecedented access to private gardens across the 443.9 hectares ‘Residential Red Zone’ area of eastern Christchurch is a unique opportunity to explore the composition of trees in private gardens across a large area of a New Zealand city. We analysed these survey data to describe the effects of housing age, socio-economics, human population density, and general soil quality, on tree abundance, species richness, and the proportion of indigenous and exotic species. We found that while most of the tree species were exotic, about half of the individual trees were local native species. There is an increasing realisation of the native tree species values among Christchurch citizens and gardens in more recent areas of housing had a higher proportion of smaller/younger native trees. However, the same sites had proportionately more exotic trees, by species and individuals, amongst their larger planted trees than older areas of housing. The majority of the species, and individuals, of the larger (≥10 cm DBH) trees planted in gardens still tend to be exotic species. In newer suburbs, gardens in wealthy areas had more native trees than gardens from poorer areas, while in older suburbs, poorer areas had more native big trees than wealthy areas. In combination, these describe, in detail unparalleled for at least in New Zealand, how the tree infrastructure of the city varies in space and time. This lays the groundwork for better understanding of how wildlife distribution and abundance, wild plant regeneration, and ecosystem services, are affected by the city’s trees.
The Canterbury earthquake sequence of 2010-2011 wrought ruptures in not only the physical landscape of Canterbury and Christchurch’s material form, but also in its social, economic, and political fabrics and the lives of Christchurch inhabitants. In the years that followed, the widespread demolition of the CBD that followed the earthquakes produced a bleak landscape of grey rubble punctuated by damaged, abandoned buildings. It was into this post-earthquake landscape that Gap Filler and other ‘transitional’ organisations inserted playful, creative, experimental projects to bring life and energy back into the CBD. This thesis examines those interventions and the development of the ‘Transitional Movement’ between July 2013 and June 2015 via the methods of walking interviews and participant observation. This critical period in Christchurch’s recovery serves as an example of what happens when do-it-yourself (DIY) urbanism is done at scale across the CBD and what urban experimentation can offer city-making. Through an understanding of space as produced, informed by Lefebvre’s thinking, I explore how these creative urban interventions manifested a different temporality to orthodox planning and demonstrate how the ‘soft’ politics of these interventions contain the potential for gentrification and also a more radical politics of the city, by creating an opening space for difference.
This thesis presents the application of data science techniques, especially machine learning, for the development of seismic damage and loss prediction models for residential buildings. Current post-earthquake building damage evaluation forms are developed for a particular country in mind. The lack of consistency hinders the comparison of building damage between different regions. A new paper form has been developed to address the need for a global universal methodology for post-earthquake building damage assessment. The form was successfully trialled in the street ‘La Morena’ in Mexico City following the 2017 Puebla earthquake. Aside from developing a framework for better input data for performance based earthquake engineering, this project also extended current techniques to derive insights from post-earthquake observations. Machine learning (ML) was applied to seismic damage data of residential buildings in Mexico City following the 2017 Puebla earthquake and in Christchurch following the 2010-2011 Canterbury earthquake sequence (CES). The experience showcased that it is readily possible to develop empirical data only driven models that can successfully identify key damage drivers and hidden underlying correlations without prior engineering knowledge. With adequate maintenance, such models have the potential to be rapidly and easily updated to allow improved damage and loss prediction accuracy and greater ability for models to be generalised. For ML models developed for the key events of the CES, the model trained using data from the 22 February 2011 event generalised the best for loss prediction. This is thought to be because of the large number of instances available for this event and the relatively limited class imbalance between the categories of the target attribute. For the CES, ML highlighted the importance of peak ground acceleration (PGA), building age, building size, liquefaction occurrence, and soil conditions as main factors which affected the losses in residential buildings in Christchurch. ML also highlighted the influence of liquefaction on the buildings losses related to the 22 February 2011 event. Further to the ML model development, the application of post-hoc methodologies was shown to be an effective way to derive insights for ML algorithms that are not intrinsically interpretable. Overall, these provide a basis for the development of ‘greybox’ ML models.
