In 2010/11 Christchurch, New Zealand suffered a series of major earthquakes that resulted in significant damage to the physical and social environment. The majority of buildings suffered some type of damage, with an estimated 11% of homes requiring demolition. The total cost of rebuilding the city has been estimated at $31bn; equivalent to 17% of NZ’s annual GDP. The social impacts of the disaster are ongoing and difficult to estimate, with continuing social displacement throughout the city and metropolitan area. These impacts will continue to have a significant impact on community recovery and resilience for some time to come. This paper introduces the Greening the Greyfields research project, which aims to develop and implement of a number of tools to aid urban planning decision-making with an overt focus on community and stakeholder engagement in the post-disaster reconstruction of Christchurch. The research was initially developed in two Australian metropolitan areas (Perth and Melbourne) and has been extended to New Zealand, to help facilitate the reconstruction process in Christchurch. The project has developed a geospatial toolkit designed to help produce best reconstruction options, by identifying potential redevelopment precincts, and simulating different scenarios in a 3D visualisation environment. The implementation of the project in Christchurch includes direct feedback from different stakeholders, in order to get buy-in and make the reconstruction process more sustainable and community-inclusive. This paper will briefly outline the methodology comprising the tools, and how it encourages community and stakeholder involvement in the post-disaster reconstruction of Christchurch.
The development of Digital City technologies to manage and visualise spatial information has increasingly become a focus of the research community, and application by city authorities. Traditionally, the Geographic Information Systems (GIS) and Building Information Models (BIM) underlying Digital Cities have been used independently. However, integrating GIS and BIM into a single platform provides benefits for project and asset management, and is applicable to a range of issues. One of these benefits is the means to access and analyse large datasets describing the built environment, in order to characterise urban risk from and resilience to natural hazards. The aim of this thesis is to further explore methodologies of integration in two distinct areas. The first, integration through connectivity of heterogeneous datasets where GIS spatial infrastructure data is merged with 3D BIM building data to create a digital twin. Secondly, integration through analysis whereby data from the digital twin are extracted and integrated with computational models. To achieve this, a workflow was developed to identify the required datasets of a digital twin, and develop a process of integrating those datasets through a combination of; semi-autonomous conversion, translation and extension of data; and semantic web and services-based processes. Through use of a designed schema, the data were streamed in a homogenous format in a web-based platform. To demonstrate the value of this workflow with respect to urban risk and resilience, the process was applied to the Taiora: Queen Elizabeth II recreation and sports centre in eastern Christchurch, New Zealand. After integration of as-built GIS and BIM datasets, targeted data extraction was implemented, with outputs tailored for analysis in an infrastructure serviceability loss model, which assessed potable water network performance in the 22nd February 2011 Christchurch Earthquake. Using the same earthquake conditions as the serviceability loss model, performance of infrastructure assets in service at the time of the 22nd February 2011 Christchurch Earthquake was compared to new assets rebuilt at the site, post-earthquake. Due to improved potable water infrastructure resilience resulting from installation of ductile piles, a decrease of 35.5% in the probability of service loss was estimated in the serviceability loss model. To complete the workflow, the results from the external analysis were uploaded to the web-based platform. One of the more significant outcomes from the workflow was the identification of a lack of mandated metadata standards for fittings/valves connecting a building to private laterals. Whilst visually the GIS and BIM data show the building and pipes as connected, the semantic data does not include this connectivity relationship. This has no material impact on the current serviceability loss model as it is not one of the defined parameters. However, a proposed modification to the model would utilise the metadata to further assess the physical connection robustness, and increase the number of variables for estimating probability of service loss. This thesis has made a methodological contribution to urban resilience analysis by demonstrating how readily available up-to-date building and infrastructure data can be integrated, and with tailored extraction from a Digital City platform, be used for disaster impact analysis in an external computational engine, with results in turn imported and visualised in the Digital City platform. The workflow demonstrated that translation and integration of data would be more successful if a regional/national mandate was implemented for the submission of consent documentation in a specified standard BIM format. The results of this thesis have identified that the key to ensuring the success of an integrated tool lies in the initial workflow required to safeguard that all data can be either captured or translated in an interoperable format.
The development of Digital City technologies to manage and visualise spatial information has increasingly become a focus of the research community, and application by city authorities. Traditionally, the Geographic Information Systems (GIS) and Building Information Models (BIM) underlying Digital Cities have been used independently. However, integrating GIS and BIM into a single platform provides benefits for project and asset management, and is applicable to a range of issues. One of these benefits is the means to access and analyse large datasets describing the built environment, in order to characterise urban risk from and resilience to natural hazards. The aim of this thesis is to further explore methodologies of integration in two distinct areas. The first, integration through connectivity of heterogeneous datasets where GIS spatial infrastructure data is merged with 3D BIM building data to create a digital twin. Secondly, integration through analysis whereby data from the digital twin are extracted and integrated with computational models. To achieve this, a workflow was developed to identify the required datasets of a digital twin, and develop a process of integrating those datasets through a combination of; semi-autonomous conversion, translation and extension of data; and semantic web and services-based processes. Through use of a designed schema, the data were streamed in a homogenous format in a web-based platform. To demonstrate the value of this workflow with respect to urban risk and resilience, the process was applied to the Taiora: Queen Elizabeth II recreation and sports centre in eastern Christchurch, New Zealand. After integration of as-built GIS and BIM datasets, targeted data extraction was implemented, with outputs tailored for analysis in an infrastructure serviceability loss model, which assessed potable water network performance in the 22nd February 2011 Christchurch Earthquake. Using the same earthquake conditions as the serviceability loss model, performance of infrastructure assets in service at the time of the 22nd February 2011 Christchurch Earthquake was compared to new assets rebuilt at the site, post-earthquake. Due to improved potable water infrastructure resilience resulting from installation of ductile piles, a decrease of 35.5% in the probability of service loss was estimated in the serviceability loss model. To complete the workflow, the results from the external analysis were uploaded to the web-based platform. One of the more significant outcomes from the workflow was the identification of a lack of mandated metadata standards for fittings/valves connecting a building to private laterals. Whilst visually the GIS and BIM data show the building and pipes as connected, the semantic data does not include this connectivity relationship. This has no material impact on the current serviceability loss model as it is not one of the defined parameters. However, a proposed modification to the model would utilise the metadata to further assess the physical connection robustness, and increase the number of variables for estimating probability of service loss. This thesis has made a methodological contribution to urban resilience analysis by demonstrating how readily available up-to-date building and infrastructure data can be integrated, and with tailored extraction from a Digital City platform, be used for disaster impact analysis in an external computational engine, with results in turn imported and visualised in the Digital City platform. The workflow demonstrated that translation and integration of data would be more successful if a regional/national mandate was implemented for the submission of consent documentation in a specified standard BIM format. The results of this thesis have identified that the key to ensuring the success of an integrated tool lies in the initial workflow required to safeguard that all data can be either captured or translated in an interoperable format.
This research attempts to understand whether community resilience and perceived livability are influenced by housing typologies in Christchurch, New Zealand. Using recent resident surveys undertaken by the Christchurch City Council, two indexes were created to reflect livability and community resilience. Indicators used to create both indexes included (1) enjoyment living in neighbourhood (2) satisfaction with local facilities (3) safety walking and (4) safety using public transport, (5) sense of community (6) neighbour interactions, (7) home ownership and (8) civic engagement. Scores were attributed to 72 neighbourhoods across Christchurch –and each neighbourhood was classified in one of the following housing typologies; (1) earthquake damaged, (2) relatively undamaged, (3) medium density and (4) greenfield developments. Spatial analysis of index scores and housing classifications suggest housing typologies do influence resident’s perceived livability and community bonds to an extent. It was found that deprivation also had a considerable influence on these indexes as well as residential stability. These additional influences help explain why neighbourhoods within the same housing classification differ in their index scores. Based on these results, several recommendations have been made to the CCC in relation to future research, urban development strategies and suburb specific renewal projects. Of chief importance, medium density neighbourhoods and deprived neighbourhoods require conscious efforts to foster community resilience. Results indicate that community resilience might be more important than livability in having a positive influence on the lived experience of residents. While thoughtful design and planning are important, this research suggests geospatial research tools could enable better community engagement outcomes and planning outcomes, and this could be interwoven into proactive and inclusive planning approaches like placemaking.
Effective management of waste and debris generated by a disaster event is vital to ensure rapid and efficient response and recovery that supports disaster risk reduction (DRR). Disaster waste refers to any stream of debris that is created from a natural disaster that impacts the environment, infrastructure, and property. This waste can be problematic due to extensive volumes, environmental contamination and pollution, public health risks, and the disruption of response and recovery efforts. Due to the complexities in dealing with these diverse and voluminous materials, having disaster waste management (DWM) planning in place pre-event is crucial. In particular, coordinated, interagency plans that have been informed by estimates of waste volumes and types are vital to ensure management facilities, personnel, and recovery resources do not become overwhelmed. Globally, a priority when formulating DWM plans is the robust estimation of disaster waste stream types and volumes. This is a relatively under-researched area, despite the growing risk of natural disasters and increasingly inadequate waste management facilities. In Aotearoa New Zealand, a nation-wide DWM planning tool has been proposed for local government use, and waste amounts from events such as the Christchurch Earthquakes have been estimated. However, there has been little work undertaken to estimate waste types and volumes with a region-specific, multi-hazard focus, which is required to facilitate detailed regional DWM planning. This research provides estimates of potential disaster waste volumes and types in the Waitaha-Canterbury region of the South Island (Te Waipounamu) for three key hazard scenarios: a M8.0 Alpine Fault earthquake with a south-to-north rupture pattern, a far-sourced tsunami using a maximum credible event model for a Peru-sourced event, and major flooding using geospatial datasets taken from available local government modelling. Conducted in partnership with Environment Canterbury and Canterbury CDEM, this estimation work informed stakeholder engagement through multi-agency workshops at the district level. This research was comprised of two key parts. The first was enhancing and extending a disaster waste estimation model used in Wellington and applying it to the Canterbury region to quantify waste volumes and types. The second part was using this model and its estimates to inform engagement with stakeholders in multi-agency, district-level workshops in Kaikōura, Hurunui, and Waimakariri. In these workshops, the waste estimates were used to catalyse discussion around potential issues associated with the management of disaster waste. Regionally, model estimates showed that the earthquake scenario would generate the highest total volume of disaster waste (1.94 million m³), compared to the tsunami scenario (1.89 million m³) and the flood scenario (173,900 m³). Flood waste estimates are likely underrepresented due to limited flood modelling coverage, but still provide a valuable comparison. Whilst waste estimates differ significantly between districts, waste volumes were shown to be not solely dependent on building/population density. The district-level workshops showed that DWM challenges revolved around logistical constraints, public concerns, governance complexities, and environmental issues. Future work should further enhance this estimation model and apply it to other regions of Aotearoa New Zealand, to help develop a set of cohesive DWM plans for each region. The waste estimation model could also be adapted and applied internationally. The findings from this research provide a foundation for advancing DWM planning and stakeholder engagement in the Waitaha-Canterbury region. By offering region-specific waste estimates across multiple hazard scenarios, this work supports district councils and emergency managers in developing informed, proactive strategies for disaster preparedness and response. The insights gained from district-level workshops highlight key challenges that must be addressed in future planning. These outcomes contribute to a broader research agenda for DWM in Aotearoa New Zealand, and offer a framework adaptable to international contexts.
