Architecture, Building and Planning - Research Publications
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ItemDecision-making of municipal urban forest managers through the lens of governance(ELSEVIER SCI LTD, 2020-02)Awareness of the benefits of urban trees has led many cities to develop ambitious targets to increase tree numbers and canopy cover. Policy instruments that guide the planning of cities recognize the need for new governance arrangements to implement this agenda. Urban forests are greatly influenced by the decisions of municipal managers, but there is currently no clear understanding of how municipal managers find support to implement their decisions via new governance arrangements. To fill this knowledge gap, we collected empirical data through interviews with 23 urban forest municipal managers in 12 local governments in Greater Melbourne and regional Victoria, Australia, and analysed these data using qualitative interpretative methods through a governance lens. The goal of this was to understand the issues and challenges, stakeholders, resources, processes, and rules behind the decision-making of municipal managers. Municipal managers said that urban densification and expansion were making it difficult for them to implement their strategies to increase tree numbers and canopy cover. The coordination of stakeholders was more important for managers to find support to implement their decisions than having a bigger budget. The views of the public or wider community and a municipal government culture of risk aversion were also making it difficult for municipal managers to implement their strategies. Decision-making priorities and processes were not the same across urban centres. Lack of space to grow trees in new developments, excessive tree removal, and public consultation, were ideas more frequently raised in inner urban centres, while urban expansion, increased active use of greenspaces, and lack of data/information about tree assets were concerns for outer and regional centres. Nonetheless, inter-departmental coordination was a common theme shared among all cities. Strengthening coordination processes is an important way for local governments to overcome these barriers and effectively implement their urban forest strategies.
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ItemNo Preview AvailableIndigenising Practice: Inclusive Indigenous Community Housing(Architecture Media Australia Pty Ltd, 2022)No more than a door: Culturally appropriate housing need not be more expensive, but some basic steps in the design process go a long way to ensuring resident's satisfaction and comfort.
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ItemWho is to blame for crashes involving autonomous vehicles? Exploring blame attribution across the road transport system(TAYLOR & FRANCIS LTD, 2020-05-03)The introduction of fully autonomous vehicles is approaching. This warrants a re-consideration of road crash liability, given drivers will have diminished control. This study, underpinned by attribution theory, investigated blame attribution to different road transport system actors following crashes involving manually driven, semi-autonomous and fully autonomous vehicles. It also examined whether outcome severity alters blame ratings. 396 participants attributed blame to five actors (vehicle driver/user, pedestrian, vehicle, manufacturer, government) in vehicle-pedestrian crash scenarios. Different and unique patterns of blame were found across actors, according to the three vehicle types. In crashes involving fully autonomous vehicles, vehicle users received low blame, while vehicle manufacturers and government were highly blamed. There was no difference in the level of blame attributed between high and low severity crashes regarding vehicle type. However, the government received more blame in high severity crashes. The findings have implications for policy and legislation surrounding crash liability. Practitioner summary: Public views relating to blame and liability in transport accidents is a vital consideration for the introduction of new technologies such as autonomous vehicles. This study demonstrates how a systems ergonomics framework can assist to identify the implications of changing public opinion on blame for future road transport systems. Abbreviation: ANOVA: analysis of variance; DAT: defensive attribution theory; IV: independent variable.
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Item'Persistent' Migrant Kitchens: Spatial Analogies and the Politics of Sharing(ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD, 2022-04-03)Using the spatial analogy of the migrant kitchen this article makes an argument for diversifying Australian feminist architectural practice and disciplinary inquiry to anticipate other culturally plural framings and experiences of the built environment. Its parallel focus on four ethnographic vignettes offers insights into the ways in which migrants mobilise familial culinary traditions for building ontological security in new environments, examining how constituent parts of kitchen spaces migrate and are adapted by Lankan-Australians in Melbourne and Canberra. It argues that the ‘transmigration’ of kitchens and their hybrid reincarnation uncovers nuanced, temporal, socio-political dimensions of migrant origin and experience indecipherable to host communities that frequently reduce them to ethno-cultural traits. We discuss the assimilatory practices that migrant women of colour daily navigate as revealing the unavoidable complexities within normative constructions of the Australian home. We posit the migrant kitchen as a site of adaptation and persistence in the face of diffused processes of assimilation.
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ItemNo Preview AvailableRaising higher education access and success for care leavers under COVID-19(Engagement Australia, 2020)
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ItemThe need for a complex systems approach in rural health research(BMJ PUBLISHING GROUP, 2022-10)On a global scale, many major rural health issues have persisted for decades despite the introduction of new health interventions and public health policies. Although research efforts have generated valuable new knowledge about the aetiology of health, disease and health inequities in rural communities, rural health systems remain to be some of the most deprived and challenged in both the developing and developed world. While the reasons for this are many, a significant factor contributing to the current state of play is the pressing need for methodological innovation and relevant scientific approaches that have the capacity to support the translation of novel solutions into 'real world' rural contexts. Fortunately, complex systems approaches, which have seen an increase in popularity in the wider public health literature, could provide answers to some of the most resilient rural health problems in recent times. The purpose of this article is to promote the value and utility of a complex systems approach in rural health research. We explain the benefits of a complex systems approach and provide a background to the complexity sciences, including the main characteristics of complex systems. Two popular computational methods are described. The next step for rural health research involves exploring how a complex systems approach can help with the identification and evaluation of new and existing solutions to policy-resistant rural health issues. This includes generating awareness around the analytical trade-offs that occur between the use of traditional scientific methods and complex systems approaches.
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ItemEnlarged Interior Built Environment Scale Modulates High-Frequency EEG Oscillations.(Society for Neuroscience, 2022)There is currently no robust method to evaluate how built environment design affects our emotion. Understanding emotion is significant, as it influences cognitive processes, behavior, and wellbeing, and is linked to the functioning of physiological systems. As mental health problems are becoming more prevalent, and exposure to indoor environments is increasing, it is important we develop rigorous methods to understand whether design elements in our environment affect emotion. This study examines whether the scale of interior built environments modulate neural networks involved in emotion regulation. Using a Cave Automatic Virtual Environment (CAVE) and controlling for indoor environmental quality (IEQ), 66 adults (31 female, aged 18-55) were exposed to context-neutral enclosed indoor room scenes to understand whether built environment scale affected self-report, autonomic nervous system, and central nervous system correlates of emotion. Our results revealed enlarged scale increased electroencephalography (EEG) power in the β bandwidth. Frontal midline low-γ and high-γ power were also found to increase with enlarged scale, but contrary to our hypothesis, scale did not modulate frontal midline power or lateralization in the θ or α bandwidths. We did not detect an effect of scale on autonomic indicators or self-reported emotion. However, we did find increased range in skin conductance response (SCR) and heart rate variability (HRV) to the built environment conditions. This study provides a rigorous empirical framework for assessing the environmental impact of a design characteristic on human emotion and suggests that measures of high-frequency oscillations may provide a useful marker of the response to built environment.
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ItemC3-IoC: A Career Guidance System for Assessing Student Skills using Machine Learning and Network Visualisation.(Springer Science and Business Media LLC, 2022-12-01)UNLABELLED: Artificial Intelligence in Education (AIED) has witnessed significant growth over the last twenty-five years, providing a wide range of technologies to support academic, institutional, and administrative services. More recently, AIED applications have been developed to prepare students for the workforce, providing career guidance services for higher education. However, this remains challenging, especially concerning the rapidly changing labour market in the IT sector. In this paper, we introduce an AI-based solution named C3-IoC (https://c3-ioc.co.uk), which intends to help students explore career paths in IT according to their level of education, skills and prior experience. The C3-IoC presents a novel similarity metric method for relating existing job roles to a range of technical and non-technical skills. This also allows the visualisation of a job role network, placing the student within communities of job roles. Using a unique knowledge base, user skill profiling, job role matching, and visualisation modules, the C3-IoC supports students in self-evaluating their skills and understanding how they relate to emerging IT jobs. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40593-022-00317-y.
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ItemV2ReID: Vision-Outlooker-Based Vehicle Re-Identification(MDPI, 2022-11)With the increase of large camera networks around us, it is becoming more difficult to manually identify vehicles. Computer vision enables us to automate this task. More specifically, vehicle re-identification (ReID) aims to identify cars in a camera network with non-overlapping views. Images captured of vehicles can undergo intense variations of appearance due to illumination, pose, or viewpoint. Furthermore, due to small inter-class similarities and large intra-class differences, feature learning is often enhanced with non-visual cues, such as the topology of camera networks and temporal information. These are, however, not always available or can be resource intensive for the model. Following the success of Transformer baselines in ReID, we propose for the first time an outlook-attention-based vehicle ReID framework using the Vision Outlooker as its backbone, which is able to encode finer-level features. We show that, without embedding any additional side information and using only the visual cues, we can achieve an 80.31% mAP and 97.13% R-1 on the VeRi-776 dataset. Besides documenting our research, this paper also aims to provide a comprehensive walkthrough of vehicle ReID. We aim to provide a starting point for individuals and organisations, as it is difficult to navigate through the myriad of complex research in this field.
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ItemEdge-Computing Video Analytics Solution for Automated Plastic-Bag Contamination Detection: A Case from Remondis(MDPI, 2022-10)The increased global waste generation rates over the last few decades have made the waste management task a significant problem. One of the potential approaches adopted globally is to recycle a significant portion of generated waste. However, the contamination of recyclable waste has been a major problem in this context and causes almost 75% of recyclable waste to be unusable. For sustainable development, efficient management and recycling of waste are of huge importance. To reduce the waste contamination rates, conventionally, a manual bin-tagging approach is adopted; however, this is inefficient and requires huge labor effort. Within household waste contamination, plastic bags have been found to be one of the main contaminants. Towards automating the process of plastic-bag contamination detection, this paper proposes an edge-computing video analytics solution using the latest Artificial Intelligence (AI), Artificial Intelligence of Things (AIoT) and computer vision technologies. The proposed system is based on the idea of capturing video of waste from the truck hopper, processing it using edge-computing hardware to detect plastic-bag contamination and storing the contamination-related information for further analysis. Faster R-CNN and You Only Look Once version 4 (YOLOv4) deep learning model variants are trained using the Remondis Contamination Dataset (RCD) developed from Remondis manual tagging historical records. The overall system was evaluated in terms of software and hardware performance using standard evaluation measures (i.e., training performance, testing performance, Frames Per Second (FPS), system usage, power consumption). From the detailed analysis, YOLOv4 with CSPDarkNet_tiny was identified as a suitable candidate with a Mean Average Precision (mAP) of 63% and FPS of 24.8 with NVIDIA Jetson TX2 hardware. The data collected from the deployment of edge-computing hardware on waste collection trucks was used to retrain the models and improved performance in terms of mAP, False Positives (FPs), False Negatives (FNs) and True Positives (TPs) was achieved for the retrained YOLOv4 with CSPDarkNet_tiny backbone model. A detailed cost analysis of the proposed system is also provided for stakeholders and policy makers.