Strolling the coastline: criminology in everyday life : through ‘landscape’ from gaol to ‘badlands’

Part travelogue, part flight of fancy, this paper recounts a coastline stroll from Maroubra Beach to Bondi in Sydney’s eastern suburbs. The author as ‘travel guide’ points out features of potential interest to two visiting criminological colleagues as they ‘pass by’ scenery of great beauty shadowed by acts of spectacular violence. The everyday acts of walking and talking while passing through a ‘landscape’ serve to constitute a criminology of everyday life, illustrating the way in which a consciousness of crime, crime sites, analyses and theories permeates the ways a ‘tourist trail’ might be experienced and seen, myths made and histories forged. The walk starts with the unseen lines of penal force radiating from Long Bay Gaol, before skirting through surfing and its regulation; the ‘brotherhood’ of the BRA Boys; the Hines killing and the politics of self defence; the shark arm case, the Virgin Mary and the Bali bombing memorial at Coogee; zones of the beach and Jock Young’s Vertigo at Bronte and Tamarama; before finishing at the Marks Park ‘badlands’ at Bondi, scene of a series of mostly unsolved and unpunished homophobic killings, giving rise to reflections on ‘ungrievable lives’, memory, mourning and forgetting.

Branding Melbourne : re-imagining the landscape through Christos Tsiolkas’ The Slap (2008)

In this paper I am focusing on one aspect of what Henri Lefebvre first termed the espace vecu “the fully lived space” (Soja 1999); and that aspect of space which exists within the geographical imagination. Building upon Lefebvre’s ideas Edward Soja acknowledges that “lived cities are never completely knowable” (1999) although in this paper I argue that often a fictional city is much more complex and diverse, much more revealing of the practices of everyday life than the homogenised concept often put forward in public discourses. Recent Melbourne fiction opens out this complexity, destabilizing public policy-making to reveal a socially diverse suburban landscape occupying both planned and organic spaces. The text that will be analysed in relation to this year’s conference theme The Geographical Imagination is a fictional text situated in Melbourne called The Slap that was written by Christos Tsiolkas in 2008.

Recasting the theory of mosquito-borne pathogen transmission dynamics and control

Mosquito-borne diseases pose some of the greatest challenges in public health, especially in tropical and sub-tropical regions of theworld. Efforts to control these diseases have been underpinned by a theoretical framework developed for malaria by Ross and Macdonald, including models, metrics for measuring transmission, and theory of control that identifies key vulnerabilities in the transmission cycle. That framework, especially Macdonald’s formula for R0 and its entomological derivative, vectorial capacity, are nowused to study dynamics and design interventions for many mosquito-borne diseases. A systematic review of 388 models published between 1970 and 2010 found that the vast majority adopted the Ross–Macdonald assumption of homogeneous transmission in a well-mixed population. Studies comparing models and data question these assumptions and point to the capacity to model heterogeneous, focal transmission as the most important but relatively unexplored component in current theory. Fine-scale heterogeneity causes transmission dynamics to be nonlinear, and poses problems for modeling, epidemiology and measurement. Novel mathematical approaches show how heterogeneity arises from the biology and the landscape on which the processes of mosquito biting and pathogen transmission unfold. Emerging theory focuses attention on the ecological and social context formosquito blood feeding, themovement of both hosts and mosquitoes, and the relevant spatial scales for measuring transmission and for modeling dynamics and control.

Agricultural production and the ecological question

Environmental degradation is a worldwide phenomenon. It is manifested in the clearing of forests, polluted waterways, soil erosion, the loss of biodiversity, the presence of chemicals in the ecosystem and a host of other concerns. Modern agricultural practices have been implicated in much of this degradation. This chapter explores the connections between the form of agricultural production undertaken in advanced nations – so called ‘productivist’ or ‘high-tech’ farming – and environmental degradation. It is argued, first, that the entrenchment of productivist agriculture has placed considerable, and continuing, pressures on the environment and, second, that while there are both new options for a more sustainable agriculture and new policies being proposed to tackle the existing problem, the underlying basis of productivist agriculture remains largely unchallenged. The prediction is that environmental degradation will continue unabated until more dramatic (and possibly less palatable) measures are taken to alter the behaviour of producers and the trajectory of farming and grazing industries throughout the world.

Cell image classification using histograms, higher order statistics and adaboost

A cell classification algorithm that uses first, second and third order statistics of pixel intensity distributions over pre-defined regions is implemented and evaluated. A cell image is segmented into 6 regions extending from a boundary layer to an inner circle. First, second and third order statistical features are extracted from histograms of pixel intensities in these regions. Third order statistical features used are one-dimensional bispectral invariants. 108 features were considered as candidates for Adaboost based fusion. The best 10 stage fused classifier was selected for each class and a decision tree constructed for the 6-class problem. The classifier is robust, accurate and fast by design.

Real-time image classification for adaptive mission planning using an Autonomous Underwater Vehicle

Real-time image analysis and classification onboard robotic marine vehicles, such as AUVs, is a key step in the realisation of adaptive mission planning for large-scale habitat mapping in previously unexplored environments. This paper describes a novel technique to train, process, and classify images collected onboard an AUV used in relatively shallow waters with poor visibility and non-uniform lighting. The approach utilises Förstner feature detectors and Laws texture energy masks for image characterisation, and a bag of words approach for feature recognition. To improve classification performance we propose a usefulness gain to learn the importance of each histogram component for each class. Experimental results illustrate the performance of the system in characterisation of a variety of marine habitats and its ability to operate onboard an AUV's main processor suitable for real-time mission planning.

The impact of OCR accuracy on automated cancer classification of pathology reports

Objective To evaluate the effects of Optical Character Recognition (OCR) on the automatic cancer classification of pathology reports. Method Scanned images of pathology reports were converted to electronic free-text using a commercial OCR system. A state-of-the-art cancer classification system, the Medical Text Extraction (MEDTEX) system, was used to automatically classify the OCR reports. Classifications produced by MEDTEX on the OCR versions of the reports were compared with the classification from a human amended version of the OCR reports. Results The employed OCR system was found to recognise scanned pathology reports with up to 99.12% character accuracy and up to 98.95% word accuracy. Errors in the OCR processing were found to minimally impact on the automatic classification of scanned pathology reports into notifiable groups. However, the impact of OCR errors is not negligible when considering the extraction of cancer notification items, such as primary site, histological type, etc. Conclusions The automatic cancer classification system used in this work, MEDTEX, has proven to be robust to errors produced by the acquisition of freetext pathology reports from scanned images through OCR software. However, issues emerge when considering the extraction of cancer notification items.

Classification of pathology reports for cancer registry notifications

Objective: To develop a system for the automatic classification of pathology reports for Cancer Registry notifications. Method: A two pass approach is proposed to classify whether pathology reports are cancer notifiable or not. The first pass queries pathology HL7 messages for known report types that are received by the Queensland Cancer Registry (QCR), while the second pass aims to analyse the free text reports and identify those that are cancer notifiable. Cancer Registry business rules, natural language processing and symbolic reasoning using the SNOMED CT ontology were adopted in the system. Results: The system was developed on a corpus of 500 histology and cytology reports (with 47% notifiable reports) and evaluated on an independent set of 479 reports (with 52% notifiable reports). Results show that the system can reliably classify cancer notifiable reports with a sensitivity, specificity, and positive predicted value (PPV) of 0.99, 0.95, and 0.95, respectively for the development set, and 0.98, 0.96, and 0.96 for the evaluation set. High sensitivity can be achieved at a slight expense in specificity and PPV. Conclusion: The system demonstrates how medical free-text processing enables the classification of cancer notifiable pathology reports with high reliability for potential use by Cancer Registries and pathology laboratories.

Clinician-driven automated classification of limb fractures from free-text radiology reports

The aim of this research is to report initial experimental results and evaluation of a clinician-driven automated method that can address the issue of misdiagnosis from unstructured radiology reports. Timely diagnosis and reporting of patient symptoms in hospital emergency departments (ED) is a critical component of health services delivery. However, due to disperse information resources and vast amounts of manual processing of unstructured information, a point-of-care accurate diagnosis is often difficult. A rule-based method that considers the occurrence of clinician specified keywords related to radiological findings was developed to identify limb abnormalities, such as fractures. A dataset containing 99 narrative reports of radiological findings was sourced from a tertiary hospital. The rule-based method achieved an F-measure of 0.80 and an accuracy of 0.80. While our method achieves promising performance, a number of avenues for improvement were identified using advanced natural language processing (NLP) techniques.

Automatic classification of free-text radiology reports to identify limb fractures using machine learning and the SNOMED CT ontology

Objective To develop and evaluate machine learning techniques that identify limb fractures and other abnormalities (e.g. dislocations) from radiology reports. Materials and Methods 99 free-text reports of limb radiology examinations were acquired from an Australian public hospital. Two clinicians were employed to identify fractures and abnormalities from the reports; a third senior clinician resolved disagreements. These assessors found that, of the 99 reports, 48 referred to fractures or abnormalities of limb structures. Automated methods were then used to extract features from these reports that could be useful for their automatic classification. The Naive Bayes classification algorithm and two implementations of the support vector machine algorithm were formally evaluated using cross-fold validation over the 99 reports. Result Results show that the Naive Bayes classifier accurately identifies fractures and other abnormalities from the radiology reports. These results were achieved when extracting stemmed token bigram and negation features, as well as using these features in combination with SNOMED CT concepts related to abnormalities and disorders. The latter feature has not been used in previous works that attempted classifying free-text radiology reports. Discussion Automated classification methods have proven effective at identifying fractures and other abnormalities from radiology reports (F-Measure up to 92.31%). Key to the success of these techniques are features such as stemmed token bigrams, negations, and SNOMED CT concepts associated with morphologic abnormalities and disorders. Conclusion This investigation shows early promising results and future work will further validate and strengthen the proposed approaches.

Contributions towards a process for sustainable development in coastal Queensland, Australia

The measures by which major developments are officially approved for construction are - by common agreement - complex, time-consuming, and of questionable merit in terms of maintaining ecological viability.

A history of environmental management in the field in Queensland : episodes of activity

This paper reviews a wide range of literature on environmental management in the field in Queensland, and analyzes this by period and by author. An episodic pattern of activities since European settlement is evident. Periods of exploration (pre-1950) and inventory- compilation (ca. 1950-1970) were followed by two decades of media and non-government organization campaigning (ca. 1970-1990), then an era dominated by government regulatory action (ca. 1990-2010). These eras dominated public perception of what was happening in environmental practice. They were delineated by historic ‘interventions’ (summarily, the end of World War II, the 1971 inflationary crisis, and computerization respectively).

Regulatory compliance as a means to achieve environmental sustainability at some large coastal developments in Queensland

The growing public concern about the complexity, cost and uncertain efficacy of the statuary environmental impact assessment process applying to large-scale projects in Queensland is reviewed. This is based on field data gathered over the past six years sat large-scale marina developments that access major environmental reserves along the coast. An ecological design proposal to broaden the process consisted with both government aspirations and regional ecological parameters - termed Regional Landscape Strategies - would allow the existing Environmental Impact Asessment to be modified alone potentially more practicable and effective lines.

Landscape ecology of the lesser grain borer, Rhyzopertha dominica

The Lesser Grain Borer is a major pest of stored grain with a global distribution. This project has, for the first time recorded this pest throughout broad spatial areas, tens of kilometres from grain production or storage. Statistical analysis revealed that different factors such as ambient temperature and the availability of food resources affect R. dominica differently between different habitats. This suggests that, contrary to the prevailing view, this pest is not solely dependent on stored wheat and can continue to persist throughout a range of habitats. These findings have important management implications for Australia's wheat industry.