TMI for urban resilience : measuring and mapping long-term climate change effects on soil moisture

The effect of climate change on the shallow expansive foundation conditions of resident dwellings is costing several hundred billion dollars worldwide. The design and costs of constructing or repairing residential footings is greatly influenced by the degree of ground movement, which is driven by the magnitude of change in soil moisture. The impacts of climate change on urban infrastructure are expected to include accelerated degradation of materials and foundations of buildings and facilities, increased ground movement, changes in ground water affecting the chemical structure of foundations, and fatigue of structures from extreme storm events. Previous research found that residential houses that were built less than five years ago have suffered major cracks and other damage caused by slab movement after record rainfall. The Thornthwaite Moisture Index (TMI) categorises climate on the basis of rainfall, temperature, potential evapotranspiration and the water holding capacity of the soil. Originally TMI was mainly used to map soil moisture conditions for agriculture but soon became a method to predict pavement and foundation changes. Few researchers have developed TMI maps for Australia, but generally, their accuracy is low or unknown, and their use is limited. The aims of this paper are: (1) To produce accurate maps of TMI for the state of Victoria for 100 years (1913 to 2012) in 20 year periods using long-term historical climatic data and advanced spatial statistics methods in GIS, and (2) Analyse the spatial and temporal changes of TMI in Victoria. Preliminary results suggest that a better understanding of climate change through long-term TMI mapping can assist urban planning and guide construction regulations towards the development of cities which are more resilient.

Mapping WIL activities in the curriculum to develop graduate capabilities : a case study in accounting

Big business continues to request universities to produce graduates who possess both technical and generic skills. Although work-integrated learning (WIL) programs can be used to develop these skills, WIL placements in Australia are undertaken by a minority of students. Perceiving a gap, one Australian university undertook a major WIL revamp to expand WIL offerings embedded within its courses. This required major organizational change that impacted significantly on curriculum design. From a quality assurance perspective, this paper provides an overview of a revised WIL program in the accounting discipline, and discusses the issues and challenges associated with the revised WIL program four years after its implementation. The findings demonstrate that the discipline area has not yet fully met the revamped WIL program as defined by university policy. Recommendations are provided that form a valuable learning tool for educational institutions considering embedding broadly defined WIL within their courses.

An ontology-driven topic mapping approach to multi-level management of e-learning resources

An appropriate use of various pedagogical strategies is fundamental for the effective transfer of knowledge in a flourishing e-learning environment. The resultant information superfluity, however, needs to be tackled for developing sustainable e-learning. This necessitates an effective representation and intelligent access to learning resources. Topic maps address these problems of representation and retrieval of information in a distributed environment. The former aspect is particularly relevant where the subject domain is complex and the later aspect is important where the amount of resources is abundant but not easily accessible. Conversely, effective presentation of learning resources based on various pedagogical strategies along with global capturing and authentication of learning resources are an intrinsic part of effective management of learning resources. Towards fulfilling this objective, this paper proposes a multi-level ontology-driven topic mapping approach to facilitate an effective visualization, classification and global authoring of learning resources in e-learning.

Permeability mapping in porous media by magnetization prepared centric-scan SPRITE

The ability of porous media to transmit fluids is commonly referred to as permeability. The concept of permeability is central for hydrocarbon recovery from petroleum reservoirs and for studies of groundwater flow in aquifers. Spatially resolved measurements of permeability are of great significance for fluid dynamics studies. A convenient concept of local Darcy’s law is suggested for parallel flow systems. The product of porosity and mean velocity images in the plane across the average flow direction is directly proportional to permeability. Single Point Ramped Imaging with T 1 Enhancement (SPRITE) permits reliable quantification of local fluid content and flow in porous media. It is particularly advantageous for reservoir rocks characterized by fast magnetic relaxation of a saturating fluid. Velocity encoding using the Cotts pulsed field gradient scheme improves the accuracy of measured flow parameters. The method is illustrated through measurements of 2D permeability maps in a capillary bundle, glass bead packs and composite sandstone samples.

2Loud?: Community mapping of exposure to traffic noise with mobile phones

Abstract: Despite ample medical evidence of the adverse impacts of traffic noise on health, most policies for traffic noise management are arbitrary or incomplete, resulting in serious social and economic impacts. Surprisingly, there is limited information about citizen’s exposure to traffic noise worldwide. This paper presents the 2Loud? mobile phone application, developed and tested as a methodology to monitor, assess and map the level of exposure to traffic noise of citizens with focus on the night period and indoor locations, since sleep disturbance is one of the major triggers for ill health related to traffic noise. Based on a community participation experiment using the 2Loud? mobile phone application in a region close to freeways in Australia, the results of this research indicates a good level of accuracy for the noise monitoring by mobile phones and also demonstrates significant levels of indoor night exposure to traffic noise in the study area. The proposed methodology, through the data produced and the participatory process involved, can potentially assist in planning and management towards healthier urban environments.

Disease mapping and regression with count data in the presence of overdispersion and spatial autocorrelation: a Bayesian model averaging approach

This paper applies the generalised linear model for modelling geographical variation to esophageal cancer incidence data in the Caspian region of Iran. The data have a complex and hierarchical structure that makes them suitable for hierarchical analysis using Bayesian techniques, but with care required to deal with problems arising from counts of events observed in small geographical areas when overdispersion and residual spatial autocorrelation are present. These considerations lead to nine regression models derived from using three probability distributions for count data: Poisson, generalised Poisson and negative binomial, and three different autocorrelation structures. We employ the framework of Bayesian variable selection and a Gibbs sampling based technique to identify significant cancer risk factors. The framework deals with situations where the number of possible models based on different combinations of candidate explanatory variables is large enough such that calculation of posterior probabilities for all models is difficult or infeasible. The evidence from applying the modelling methodology suggests that modelling strategies based on the use of generalised Poisson and negative binomial with spatial autocorrelation work well and provide a robust basis for inference.

Integrating multibeam backscatter angular response, mosaic and bathymetry data for benthic habitat mapping

Multibeam echosounders (MBES) are increasingly becoming the tool of choice for marine habitat mapping applications. In turn, the rapid expansion of habitat mapping studies has resulted in a need for automated classification techniques to efficiently map benthic habitats, assess confidence in model outputs, and evaluate the importance of variables driving the patterns observed. The benthic habitat characterisation process often involves the analysis of MBES bathymetry, backscatter mosaic or angular response with observation data providing ground truth. However, studies that make use of the full range of MBES outputs within a single classification process are limited. We present an approach that integrates backscatter angular response with MBES bathymetry, backscatter mosaic and their derivatives in a classification process using a Random Forests (RF) machine-learning algorithm to predict the distribution of benthic biological habitats. This approach includes a method of deriving statistical features from backscatter angular response curves created from MBES data collated within homogeneous regions of a backscatter mosaic. Using the RF algorithm we assess the relative importance of each variable in order to optimise the classification process and simplify models applied. The results showed that the inclusion of the angular response features in the classification process improved the accuracy of the final habitat maps from 88.5% to 93.6%. The RF algorithm identified bathymetry and the angular response mean as the two most important predictors. However, the highest classification rates were only obtained after incorporating additional features derived from bathymetry and the backscatter mosaic. The angular response features were found to be more important to the classification process compared to the backscatter mosaic features. This analysis indicates that integrating angular response information with bathymetry and the backscatter mosaic, along with their derivatives, constitutes an important improvement for studying the distribution of benthic habitats, which is necessary for effective marine spatial planning and resource management.