The impact of natural disasters on residential property markets

Over the past ten years various residential property markets throughout Australia in general and NSW in particular have been subject to substantial natural disasters. These occurrences have included floods, bushfires and hailstorms. In extreme cases the actual rectification costs have been up to AUD$1.5 billion, which occurred with the severe hailstorm in Sydney in April 1999 and cyclone Tracey in Darwin in 1974. Natural disasters such as severe storms and hailstorms have tended to be very indiscriminate in relation to frequency and the actual location of damage, whereas the nature of bushfire and flooding tends to be more defined. Although these extreme natural disasters tend to be infrequent, occurrences of floods and bushfires in residential property areas are more frequent, particularly as urban sprawl encroaches closer to national Parks, State recreation Parks and State forests. Considerable work has been carried out on flood effects on property markets by Bell (1999), Donnelly (1988), McClusky and Rausser (2001), Skrantz and Strickland (1987) in the US, and Chou and Shih (2001) in Taiwan. Fibbens (1994), Lambley and Cordery (1991) and Eves (1999, 2001, 2002) have carried out studies in relation to the effect of flooding on residential property values in the Sydney region, including the tracking of flood prone property values over time. However, no similar rigorous research has been carried out in relation to the impact of bushfires on residential property markets in the Sydney region.

Bayesian hybrid algorithms and models : implementation and associated issues

This thesis addresses computational challenges arising from Bayesian analysis of complex real-world problems. Many of the models and algorithms designed for such analysis are ‘hybrid’ in nature, in that they are a composition of components for which their individual properties may be easily described but the performance of the model or algorithm as a whole is less well understood. The aim of this research project is to after a better understanding of the performance of hybrid models and algorithms. The goal of this thesis is to analyse the computational aspects of hybrid models and hybrid algorithms in the Bayesian context. The first objective of the research focuses on computational aspects of hybrid models, notably a continuous finite mixture of t-distributions. In the mixture model, an inference of interest is the number of components, as this may relate to both the quality of model fit to data and the computational workload. The analysis of t-mixtures using Markov chain Monte Carlo (MCMC) is described and the model is compared to the Normal case based on the goodness of fit. Through simulation studies, it is demonstrated that the t-mixture model can be more flexible and more parsimonious in terms of number of components, particularly for skewed and heavytailed data. The study also reveals important computational issues associated with the use of t-mixtures, which have not been adequately considered in the literature. The second objective of the research focuses on computational aspects of hybrid algorithms for Bayesian analysis. Two approaches will be considered: a formal comparison of the performance of a range of hybrid algorithms and a theoretical investigation of the performance of one of these algorithms in high dimensions. For the first approach, the delayed rejection algorithm, the pinball sampler, the Metropolis adjusted Langevin algorithm, and the hybrid version of the population Monte Carlo (PMC) algorithm are selected as a set of examples of hybrid algorithms. Statistical literature shows how statistical efficiency is often the only criteria for an efficient algorithm. In this thesis the algorithms are also considered and compared from a more practical perspective. This extends to the study of how individual algorithms contribute to the overall efficiency of hybrid algorithms, and highlights weaknesses that may be introduced by the combination process of these components in a single algorithm. The second approach to considering computational aspects of hybrid algorithms involves an investigation of the performance of the PMC in high dimensions. It is well known that as a model becomes more complex, computation may become increasingly difficult in real time. In particular the importance sampling based algorithms, including the PMC, are known to be unstable in high dimensions. This thesis examines the PMC algorithm in a simplified setting, a single step of the general sampling, and explores a fundamental problem that occurs in applying importance sampling to a high-dimensional problem. The precision of the computed estimate from the simplified setting is measured by the asymptotic variance of the estimate under conditions on the importance function. Additionally, the exponential growth of the asymptotic variance with the dimension is demonstrated and we illustrates that the optimal covariance matrix for the importance function can be estimated in a special case.

Vulnerability factors for disaster-induced child post-traumatic stress disorder : the case for low family resilience and previous mental illness

Objective: The aim of the present study was to investigate whether parent report of family resilience predicted children’s disaster-induced post-traumatic stress disorder (PTSD) and general emotional symptoms, independent of a broad range of variables including event-related factors, previous child mental illness and social connectedness. ---------- Methods: A total of 568 children (mean age = 10.2 years, SD = 1.3) who attended public primary schools, were screened 3 months after Cyclone Larry devastated the Innisfail region of North Queensland. Measures included parent report on the Family Resilience Measure and Strengths and Difficulties Questionnaire (SDQ)–emotional subscale and child report on the PTSD Reaction Index, measures of event exposure and social connectedness. ---------- Results: Sixty-four students (11.3%) were in the severe–very severe PTSD category and 53 families (28.6%) scored in the poor family resilience range. A lower family resilience score was associated with child emotional problems on the SDQ and longer duration of previous child mental health difficulties, but not disaster-induced child PTSD or child threat perception on either bivariate analysis, or as a main or moderator variable on multivariate analysis (main effect: adjusted odds ratio (ORadj) = 0.57, 95% confidence interval (CI) = 0.13–2.44). Similarly, previous mental illness was not a significant predictor of child PTSD in the multivariate model (ORadj = 0.75, 95%CI = 0.16–3.61). ---------- Conclusion: In this post-disaster sample children with existing mental health problems and those of low-resilience families were not at elevated risk of PTSD. The possibility that the aetiological model of disaster-induced child PTSD may differ from usual child and adolescent conceptualizations is discussed.

On the nature of over-dispersion in motor vehicle crash prediction models

Statistical modeling of traffic crashes has been of interest to researchers for decades. Over the most recent decade many crash models have accounted for extra-variation in crash counts—variation over and above that accounted for by the Poisson density. The extra-variation – or dispersion – is theorized to capture unaccounted for variation in crashes across sites. The majority of studies have assumed fixed dispersion parameters in over-dispersed crash models—tantamount to assuming that unaccounted for variation is proportional to the expected crash count. Miaou and Lord [Miaou, S.P., Lord, D., 2003. Modeling traffic crash-flow relationships for intersections: dispersion parameter, functional form, and Bayes versus empirical Bayes methods. Transport. Res. Rec. 1840, 31–40] challenged the fixed dispersion parameter assumption, and examined various dispersion parameter relationships when modeling urban signalized intersection accidents in Toronto. They suggested that further work is needed to determine the appropriateness of the findings for rural as well as other intersection types, to corroborate their findings, and to explore alternative dispersion functions. This study builds upon the work of Miaou and Lord, with exploration of additional dispersion functions, the use of an independent data set, and presents an opportunity to corroborate their findings. Data from Georgia are used in this study. A Bayesian modeling approach with non-informative priors is adopted, using sampling-based estimation via Markov Chain Monte Carlo (MCMC) and the Gibbs sampler. A total of eight model specifications were developed; four of them employed traffic flows as explanatory factors in mean structure while the remainder of them included geometric factors in addition to major and minor road traffic flows. The models were compared and contrasted using the significance of coefficients, standard deviance, chi-square goodness-of-fit, and deviance information criteria (DIC) statistics. The findings indicate that the modeling of the dispersion parameter, which essentially explains the extra-variance structure, depends greatly on how the mean structure is modeled. In the presence of a well-defined mean function, the extra-variance structure generally becomes insignificant, i.e. the variance structure is a simple function of the mean. It appears that extra-variation is a function of covariates when the mean structure (expected crash count) is poorly specified and suffers from omitted variables. In contrast, when sufficient explanatory variables are used to model the mean (expected crash count), extra-Poisson variation is not significantly related to these variables. If these results are generalizable, they suggest that model specification may be improved by testing extra-variation functions for significance. They also suggest that known influences of expected crash counts are likely to be different than factors that might help to explain unaccounted for variation in crashes across sites

Bayesian multinomial logit: Theory and route choice example

Statisticians along with other scientists have made significant computational advances that enable the estimation of formerly complex statistical models. The Bayesian inference framework combined with Markov chain Monte Carlo estimation methods such as the Gibbs sampler enable the estimation of discrete choice models such as the multinomial logit (MNL) model. MNL models are frequently applied in transportation research to model choice outcomes such as mode, destination, or route choices or to model categorical outcomes such as crash outcomes. Recent developments allow for the modification of the potentially limiting assumptions of MNL such as the independence from irrelevant alternatives (IIA) property. However, relatively little transportation-related research has focused on Bayesian MNL models, the tractability of which is of great value to researchers and practitioners alike. This paper addresses MNL model specification issues in the Bayesian framework, such as the value of including prior information on parameters, allowing for nonlinear covariate effects, and extensions to random parameter models, so changing the usual limiting IIA assumption. This paper also provides an example that demonstrates, using route-choice data, the considerable potential of the Bayesian MNL approach with many transportation applications. This paper then concludes with a discussion of the pros and cons of this Bayesian approach and identifies when its application is worthwhile

Using a siphon to drain floodwaters

This article describes how to use a siphon to drain floodwaters. A siphon is a tube that conveys water to a lower level via point above the upper water level by gravity. A siphon can be set up to drain existing floodwaters more quickly than they would naturally and can also prevent flooding. Siphons are particularly useful in situations where no pump is available, and a drainage point exists lower than the level of the floodwaters. Some case studies are presented.

Systematics and biogeography of the Gondwanan Orthocladiinae (Diptera: Chironomidae)

Restrictions to effective dispersal and gene flow caused by the fragmentation of ancient supercontinents are considered to have driven diversification and speciation on disjunct landmasses globally. Investigating the role that these processes have played in the development of diversity within and among taxa is crucial to understanding the origins and evolution of regional biotas. Within the chironomid (non-biting midge) subfamily Orthocladiinae (Diptera: Chironomidae), a group of genera that are distributed across the austral continents (Australia, New Zealand, South America) have been proposed to represent a relict Gondwanan clade. We used a molecular approach to resolve relationships among taxa with the aim to determine the relative roles that vicariance and dispersal may have played in the evolution of this group. Continental biotas did not form monophyletic groups, in accordance with expectations given existing morphological evidence. Patterns of phylogenetic relationships among taxa did not accord with expected patterns based on the geological sequence of break-up of the Gondwanan supercontinent. Likewise, divergence time estimates, particularly for New Zealand taxa, largely post-dated continental fragmentation and implied instead that several transoceanic dispersal events may have occurred post-vicariance. Passive dispersal of gravid female chironomid adults is the most likely mechanism for transoceanic movement, potentially facilitated by West Wind Drift or anti-cyclone fronts. Estimated timings of divergence among Australian and South American Botryocladius, on the other hand, were congruent with the proposed ages of separation of the two continents from Antarctica. Taken together, these data suggest that a complex relationship between both vicariance and dispersal may explain the evolution of this group. The sampling regime we implemented here was the most intensive yet performed for austral members of the Orthocladiinae and unsurprisingly revealed several novel taxa that will require formal description.

A Python package for Bayesian estimation using Markov Chain Monte Carlo

Markov chain Monte Carlo (MCMC) estimation provides a solution to the complex integration problems that are faced in the Bayesian analysis of statistical problems. The implementation of MCMC algorithms is, however, code intensive and time consuming. We have developed a Python package, which is called PyMCMC, that aids in the construction of MCMC samplers and helps to substantially reduce the likelihood of coding error, as well as aid in the minimisation of repetitive code. PyMCMC contains classes for Gibbs, Metropolis Hastings, independent Metropolis Hastings, random walk Metropolis Hastings, orientational bias Monte Carlo and slice samplers as well as specific modules for common models such as a module for Bayesian regression analysis. PyMCMC is straightforward to optimise, taking advantage of the Python libraries Numpy and Scipy, as well as being readily extensible with C or Fortran.

The influence of maternal ethnic group and diet on breast milk fatty acid composition

INTRODUCTION: Breast milk fatty acids play a major role in infant development. However, no data have compared the breast milk composition of different ethnic groups living in the same environment. We aimed to (i) investigate breast milk fatty acid composition of three ethnic groups in Singapore and (ii) determine dietary fatty acid patterns in these groups and any association with breast milk fatty acid composition. MATERIALS AND METHODS: This was a prospective study conducted at a tertiary hospital in Singapore. Healthy pregnant women with the intention to breastfeed were recruited. Diet profile was studied using a standard validated 3-day food diary. Breast milk was collected from mothers at 1 to 2 weeks and 6 to 8 weeks postnatally. Agilent gas chromatograph (6870N) equipped with a mass spectrometer (5975) and an automatic liquid sampler (ALS) system with a split mode was used for analysis. RESULTS: Seventy-two breast milk samples were obtained from 52 subjects. Analysis showed that breast milk ETA (Eicosatetraenoic acid) and ETA:EA (Eicosatrienoic acid) ratio were significantly different among the races (P = 0.031 and P = 0.020), with ETA being the highest among Indians and the lowest among Malays. Docosahexaenoic acid was significantly higher among Chinese compared to Indians and Malays. No difference was demonstrated in n3 and n6 levels in the food diet analysis among the 3 ethnic groups. CONCLUSIONS: Differences exist in breast milk fatty acid composition in different ethnic groups in the same region, although no difference was demonstrated in the diet analysis. Factors other than maternal diet may play a role in breast milk fatty acid composition.

The Bayesian conditional independence model for measurement error: applications in ecology

The measurement error model is a well established statistical method for regression problems in medical sciences, although rarely used in ecological studies. While the situations in which it is appropriate may be less common in ecology, there are instances in which there may be benefits in its use for prediction and estimation of parameters of interest. We have chosen to explore this topic using a conditional independence model in a Bayesian framework using a Gibbs sampler, as this gives a great deal of flexibility, allowing us to analyse a number of different models without losing generality. Using simulations and two examples, we show how the conditional independence model can be used in ecology, and when it is appropriate.

Bouncing back : the role of design in facilitating students to understand and deal with adversity

After state-wide flooding and a category-5 tropical cyclone, three-quarters of the state of Queensland was declared a disaster zone in early 2011. This deluge of adversity had a significant impact on university students, a few weeks prior to the start of the academic semester. The purpose of this paper is to examine the role that design plays in facilitating students to understand and respond to, adversity. The participants of this study were second and fourth year architectural design students at a large Australian University, in Queensland. As a part of their core architectural design studies, students were required to provide architectural responses to the recent catastrophic events in Queensland. Qualitative data was obtained through student surveys, work design work submitted by students and a survey of guests who attending an exhibition of the student work. The results of this research showed that the students produced more than just the required set of architectural drawings, process journals and models, but also recognition of the important role that the affective dimension of the flooding event and the design process played in helping them to both understand and respond to, adversity. They held the ‘real world’ experience and practical aspect of the assessment in higher regard than their typical focus on aesthetics and the making of iconic design. Perhaps most importantly, the students recognised that this process allowed them to have a voice, and a means to respond to adversity through the powerful language of design.

Re-thinking how we plan to address technically complex problems : drawing on planning theory to guide practice in revision of land use plans in coastal high hazard zones

Queensland's new State Planning Policy for Coastal Protection, released in March and approved in April 2011 as part of the Queensland Coastal Plan, stipulates that local governments prepare and implement adaptation strategies for built up areas projected to be subject to coastal hazards between present day and 2100. Urban localities within the delineated coastal high hazard zone (as determined by models incorporating a 0.8 meter rise in sea level and a 10% increase in the maximum cyclone activity) will be required to re-evaluate their plans to accommodate growth, revising land use plans to minimise impacts of anticipated erosion and flooding on developed areas and infrastructure. While implementation of such strategies would aid in avoidance or minimisation of risk exposure, communities are likely to face significant challenges in such implementation, especially as development in Queensland is so intensely focussed upon its coasts with these new policies directing development away from highly desirable waterfront land. This paper examines models of planning theory to understand how we plan when faced with technically complex problems towards formulation of a framework for evaluating and improving practice.

Exploratory investigation of atmospheric total suspended particulate matter and heavy metals in urban air

Atmospheric concentration of total suspended particulate matter (TSP) and associated heavy metals are a great concern due to their adverse health impacts and contribution to stormwater pollution. This paper discusses the outcomes of a study which investigated the variation of atmospheric TSP and heavy metal concentrations with traffic and land use characteristics during weekdays and weekends. Data for this study was gathered from fifteen sites at the Gold Coast, Australia using a high volume air sampler. The study detected consistently high TSP concentrations during weekdays compared to weekends. This confirms the significant influence of traffic related sources on TSP loads during weekdays. Both traffic and land use related sources equally contribute to TSP during weekends. Almost all the measured heavy metals showed high concentration on weekdays compared to weekends indicating significant contributions from traffic related emissions. Among the heavy metals, Zn concentration was the highest followed by Pb. It is postulated that re-suspension of previously deposited reserves was the main Pb source. Soil related sources were the main contributors of Mn.