Understanding causal relationships between the built and social environment, health and associated behaviours and risk factors

Introduction: Built environment interventions designed to reduce non-communicable diseases and health inequity, complement urban planning agendas focused on creating more ‘liveable’, compact, pedestrian-friendly, less automobile dependent and more socially inclusive cities.However, what constitutes a ‘liveable’ community is not well defined. Moreover, there appears to be a gap between the concept and delivery of ‘liveable’ communities. The recently funded NHMRC Centre of Research Excellence (CRE) in Healthy Liveable Communities established in early 2014, has defined ‘liveability’ from a social determinants of health perspective. Using purpose-designed multilevel longitudinal data sets, it addresses five themes that address key evidence-base gaps for building healthy and liveable communities. The CRE in Healthy Liveable Communities seeks to generate and exchange new knowledge about: 1) measurement of policy-relevant built environment features associated with leading non-communicable disease risk factors (physical activity, obesity) and health outcomes (cardiovascular disease, diabetes) and mental health; 2) causal relationships and thresholds for built environment interventions using data from longitudinal studies and natural experiments; 3) thresholds for built environment interventions; 4) economic benefits of built environment interventions designed to influence health and wellbeing outcomes; and 5) factors, tools, and interventions that facilitate the translation of research into policy and practice. This evidence is critical to inform future policy and practice in health, land use, and transport planning. Moreover, to ensure policy-relevance and facilitate research translation, the CRE in Healthy Liveable Communities builds upon ongoing, and has established new, multi-sector collaborations with national and state policy-makers and practitioners. The symposium will commence with a brief introduction to embed the research within an Australian health and urban planning context, as well as providing an overall outline of the CRE in Healthy Liveable Communities, its structure and team. Next, an overview of the five research themes will be presented. Following these presentations, the Discussant will consider the implications of the research and opportunities for translation and knowledge exchange. Theme 2 will establish whether and to what extent the neighbourhood environment (built and social) is causally related to physical and mental health and associated behaviours and risk factors. In particular, research conducted as part of this theme will use data from large-scale, longitudinal-multilevel studies (HABITAT, RESIDE, AusDiab) to examine relationships that meet causality criteria via statistical methods such as longitudinal mixed-effect and fixed-effect models, multilevel and structural equation models; analyse data on residential preferences to investigate confounding due to neighbourhood self-selection and to use measurement and analysis tools such as propensity score matching and ‘within-person’ change modelling to address confounding; analyse data about individual-level factors that might confound, mediate or modify relationships between the neighbourhood environment and health and well-being (e.g., psychosocial factors, knowledge, perceptions, attitudes, functional status), and; analyse data on both objective neighbourhood characteristics and residents’ perceptions of these objective features to more accurately assess the relative contribution of objective and perceptual factors to outcomes such as health and well-being, physical activity, active transport, obesity, and sedentary behaviour. At the completion of the Theme 2, we will have demonstrated and applied statistical methods appropriate for determining causality and generated evidence about causal relationships between the neighbourhood environment, health, and related outcomes. This will provide planners and policy makers with a more robust (valid and reliable) basis on which to design healthy communities.

A tag-based personalized item recommendation system using tensor modeling and topic model approaches

This research falls in the area of enhancing the quality of tag-based item recommendation systems. It aims to achieve this by employing a multi-dimensional user profile approach and by analyzing the semantic aspects of tags. Tag-based recommender systems have two characteristics that need to be carefully studied in order to build a reliable system. Firstly, the multi-dimensional correlation, called as tag assignment , should be appropriately modelled in order to create the user profiles [1]. Secondly, the semantics behind the tags should be considered properly as the flexibility with their design can cause semantic problems such as synonymy and polysemy [2]. This research proposes to address these two challenges for building a tag-based item recommendation system by employing tensor modeling as the multi-dimensional user profile approach, and the topic model as the semantic analysis approach. The first objective is to optimize the tensor model reconstruction and to improve the model performance in generating quality rec-ommendation. A novel Tensor-based Recommendation using Probabilistic Ranking (TRPR) method [3] has been developed. Results show this method to be scalable for large datasets and outperforming the benchmarking methods in terms of accuracy. The memory efficient loop implements the n-mode block-striped (matrix) product for tensor reconstruction as an approximation of the initial tensor. The probabilistic ranking calculates the probabil-ity of users to select candidate items using their tag preference list based on the entries generated from the reconstructed tensor. The second objective is to analyse the tag semantics and utilize the outcome in building the tensor model. This research proposes to investigate the problem using topic model approach to keep the tags nature as the “social vocabulary” [4]. For the tag assignment data, topics can be generated from the occurrences of tags given for an item. However there is only limited amount of tags availa-ble to represent items as collection of topics, since an item might have only been tagged by using several tags. Consequently, the generated topics might not able to represent the items appropriately. Furthermore, given that each tag can belong to any topics with various probability scores, the occurrence of tags cannot simply be mapped by the topics to build the tensor model. A standard weighting technique will not appropriately calculate the value of tagging activity since it will define the context of an item using a tag instead of a topic.

Challenges facing the application of sustainability to housing in Saudi Arabia

Modern Saudi housing supply is neither sustainable nor efficient in meeting the conservative Islamic culture of the community and the local environmental conditions. This thesis develops a model for successful development of sustainable housing in Saudi Arabia by incorporating multi stakeholder inputs on key barriers, critical success factors and best practice case studies. The model will help create public awareness and education tools for both private and public sectors on sustainable housing. It provides a framework that includes cultural and environmental needs of the conservative Saudi neighbourhood. It may assist the Saudi government to regulate new building codes and regulations.

Mapping mountains : a morphological study of traditional highland settlements in Chittagong Hill tracts of Bangladesh

This thesis is a morphological study of the settlement patterns of the diverse hill groups in Chittagong Hill Tracts – a mountainous borderland of Bangladesh in South Asia. It examines the settlement morphology of a hill town, using a combination of both quantitative and qualitative methods, and explains the recurrent neighbourhood types of the highland groups in relation to their urbanisation. The research findings related to the settlements of diverse cultural groups in a cross-border region of the Asian uplands are also relevant to similar contexts and enquiries. Furthermore, the developed methodological framework that facilitated the data collection process in CHT's culturally diverse regions is also applicable to the investigation of geographic areas with similar socio-cultural complexities. Finally, this research specifically contributes to the literature of cross-cultural studies of highland towns and vernacular settlements in the Asian context.

A new approach to spatial data interpolation using higher-order statistics

Interpolation techniques for spatial data have been applied frequently in various fields of geosciences. Although most conventional interpolation methods assume that it is sufficient to use first- and second-order statistics to characterize random fields, researchers have now realized that these methods cannot always provide reliable interpolation results, since geological and environmental phenomena tend to be very complex, presenting non-Gaussian distribution and/or non-linear inter-variable relationship. This paper proposes a new approach to the interpolation of spatial data, which can be applied with great flexibility. Suitable cross-variable higher-order spatial statistics are developed to measure the spatial relationship between the random variable at an unsampled location and those in its neighbourhood. Given the computed cross-variable higher-order spatial statistics, the conditional probability density function (CPDF) is approximated via polynomial expansions, which is then utilized to determine the interpolated value at the unsampled location as an expectation. In addition, the uncertainty associated with the interpolation is quantified by constructing prediction intervals of interpolated values. The proposed method is applied to a mineral deposit dataset, and the results demonstrate that it outperforms kriging methods in uncertainty quantification. The introduction of the cross-variable higher-order spatial statistics noticeably improves the quality of the interpolation since it enriches the information that can be extracted from the observed data, and this benefit is substantial when working with data that are sparse or have non-trivial dependence structures.

‘Healthy mobile check-ins’ study: using GIS in smartphones to track use of urban environment by survivors of endometrial cancer

Aims/Objectives Our study aims to test the capacity of a newly developed smartphone innovation to obtain data on social, structural, and spatial determinants of the daily health-related behaviours of women living in urban Brisbane neighbourhoods who have survived endometrial cancer. Methods The women used a mobile web app designed specifically for the project to record GIS/location data on every destination they visited within their local urban neighbourhoods over a two-week period. Additionally, we gathered textual data on the social context/reasons for travel, as well as mode of transport to reach these destinations. The data was transported to SPSS and Google Earth for statistical and spatial analysis. We then met with the women to discuss lifestyle interventions to maximise their use of their local neighbourhoods in ways that could increase their physical activity levels and improve their overall health and well-being. These interventions will be evaluated and translated into a large-scale national study if effective. Results Initial findings about patterns in the group’s use of the local urban environment will be displayed, including daily distances travelled, types of locations visited, walking levels, use of public transport, use of green spaces and use of health-related resources. Any socio-demograpahic differences found between the women will be reported. Qualitative, quantitative, and spatial/mapping data will be displayed Conclusion The benefits and limitations of the mobile website designed to collect a range of data types about human-neighbourhood interactions with implications for intervention design will be discussed.

A multidimensional collaborative filtering fusion approach with dimensionality reduction

Multidimensional data are getting increasing attention from researchers for creating better recommender systems in recent years. Additional metadata provides algorithms with more details for better understanding the interaction between users and items. While neighbourhood-based Collaborative Filtering (CF) approaches and latent factor models tackle this task in various ways effectively, they only utilize different partial structures of data. In this paper, we seek to delve into different types of relations in data and to understand the interaction between users and items more holistically. We propose a generic multidimensional CF fusion approach for top-N item recommendations. The proposed approach is capable of incorporating not only localized relations of user-user and item-item but also latent interaction between all dimensions of the data. Experimental results show significant improvements by the proposed approach in terms of recommendation accuracy.

How long does stigma impact property values

Purpose - The purpose of this paper is to determine the impact stigma has on property values and how long the stigma remains after the Not in My Back Yard (NIMBY) structure has been removed. Design/methodology/approach - A quantitative analysis was undertaken, using a high voltage overhead transmission line (HVOTL) case study, to determine the effect on property values prior and post removal of the NIMBY structure. A repeat sales index in conjunction with the regression analysis determined the length of time, the stigma remained after removal of the NIMBY structure. Findings - The results show that while the NIMBY is in place the impact on value is confined to those properties in close proximity. This is in contradiction to the findings, where on removal of the NIMBY the property values of the whole neighbourhood improve with the stigma remaining for 3 to 4 years. Research Implications - The implication of this research is that property Valuers need to change the way they take into account the presence of NIMBYs when valuing property with more emphasis, being placed on the neighbourhood rather than just the properties in close proximity. While the HVOTL was in place, only properties in close proximity were negatively affected, but on removal of the HVOTL the whole neighbourhood increased in value. Originality/value - Results expand on current knowledge by demonstrating the length of time the market takes to adjust to the removal of a NIMBY structure.

Topical pattern based document modelling and relevance ranking

For traditional information filtering (IF) models, it is often assumed that the documents in one collection are only related to one topic. However, in reality users’ interests can be diverse and the documents in the collection often involve multiple topics. Topic modelling was proposed to generate statistical models to represent multiple topics in a collection of documents, but in a topic model, topics are represented by distributions over words which are limited to distinctively represent the semantics of topics. Patterns are always thought to be more discriminative than single terms and are able to reveal the inner relations between words. This paper proposes a novel information filtering model, Significant matched Pattern-based Topic Model (SPBTM). The SPBTM represents user information needs in terms of multiple topics and each topic is represented by patterns. More importantly, the patterns are organized into groups based on their statistical and taxonomic features, from which the more representative patterns, called Significant Matched Patterns, can be identified and used to estimate the document relevance. Experiments on benchmark data sets demonstrate that the SPBTM significantly outperforms the state-of-the-art models.

Refining user and item profiles based on multidimensional data for top-n item recommendation

In recommender systems based on multidimensional data, additional metadata provides algorithms with more information for better understanding the interaction between users and items. However, most of the profiling approaches in neighbourhood-based recommendation approaches for multidimensional data merely split or project the dimensional data and lack the consideration of latent interaction between the dimensions of the data. In this paper, we propose a novel user/item profiling approach for Collaborative Filtering (CF) item recommendation on multidimensional data. We further present incremental profiling method for updating the profiles. For item recommendation, we seek to delve into different types of relations in data to understand the interaction between users and items more fully, and propose three multidimensional CF recommendation approaches for top-N item recommendations based on the proposed user/item profiles. The proposed multidimensional CF approaches are capable of incorporating not only localized relations of user-user and/or item-item neighbourhoods but also latent interaction between all dimensions of the data. Experimental results show significant improvements in terms of recommendation accuracy.

Theorizing why in digital learning: Opening frontiers for inquiry and innovation with technology

Asking why is an important foundation of inquiry and fundamental to the development of reasoning skills and learning. Despite this, and despite the relentless and often disruptive nature of innovations in information and communications technology (ICT), sophisticated tools that directly support this basic act of learning appear to be undeveloped, not yet recognized, or in the very early stages of development. Why is this so? To this question, there is no single factual answer. In response, however, plausible explanations and further questions arise, and such responses are shown to be typical consequences of why-questioning. A range of contemporary scenarios are presented to highlight the problem. Consideration of the various inputs into the evolution of digital learning is introduced to provide historical context and this serves to situate further discussion regarding innovation that supports inquiry-based learning. This theme is further contextualized by narratives on openness in education, in which openness is also shown to be an evolving construct. Explanatory and descriptive contents are differentiated in order to scope out the kinds of digital tools that might support inquiry instigated by why-questioning and which move beyond the search paradigm. Probing why from a linguistic perspective reveals versatile and ambiguous semantics. The why dimension—asking, learning, knowing, understanding, and explaining why—is introduced as a construct that highlights challenges and opportunities for ICT innovation. By linking reflective practice and dialogue with cognitive engagement, this chapter points to specific frontiers for the design and development of digital learning tools, frontiers in which inquiry may find new openings for support.

Normative requirements for regulatory compliance: An abstract formal framework

By definition, regulatory rules (in legal context called norms) intend to achieve specific behaviour from business processes, and might be relevant to the whole or part of a business process. They can impose conditions on different aspects of process models, e.g., control-flow, data and resources etc. Based on the rules sets, norms can be classified into various classes and sub-classes according to their effects. This paper presents an abstract framework consisting of a list of norms and a generic compliance checking approach on the idea of (possible) execution of processes. The proposed framework is independent of any existing formalism, and provides a conceptually rich and exhaustive ontology and semantics of norms needed for business process compliance checking. The possible uses of the proposed framework include to compare different compliance management frameworks (CMFs).

A probabilistic framework for analysing the compositionality of conceptual combinations

Conceptual combination performs a fundamental role in creating the broad range of compound phrases utilised in everyday language. While the systematicity and productivity of language provide a strong argument in favour of assuming compositionality, this very assumption is still regularly questioned in both cognitive science and philosophy. This article provides a novel probabilistic framework for assessing whether the semantics of conceptual combinations are compositional, and so can be considered as a function of the semantics of the constituent concepts, or not. Rather than adjudicating between different grades of compositionality, the framework presented here contributes formal methods for determining a clear dividing line between compositional and non-compositional semantics. Compositionality is equated with a joint probability distribution modelling how the constituent concepts in the combination are interpreted. Marginal selectivity is emphasised as a pivotal probabilistic constraint for the application of the Bell/CH and CHSH systems of inequalities (referred to collectively as Bell-type). Non-compositionality is then equated with either a failure of marginal selectivity, or, in the presence of marginal selectivity, with a violation of Bell-type inequalities. In both non-compositional scenarios, the conceptual combination cannot be modelled using a joint probability distribution with variables corresponding to the interpretation of the individual concepts. The framework is demonstrated by applying it to an empirical scenario of twenty-four non-lexicalised conceptual combinations.

Live and analogue : Theatre as a practice of social inclusion

This monograph argues for a repositioning of theatre as an antidote to the negative effects of the reduction of live social interaction among the current generation of young people. It argues for a repositioned valuing of the role that live performing arts can play in the development of social cohesion and well-being with young people. Highlighted therefore is an emphasis on developing ‘communitas’ or ‘collective joy’ (Turner, 2012) for young audiences. Using the lens of social acupuncture (O’Donnell, 2006) the monograph explores how two Australian live theatre or performance events – The Walking Neighbourhood and 지하 Underground – foreground inclusionary audience experiences. It concludes with a challenge to drama teachers, theatre makers, arts researchers, arts programmers and performance venue managers regarding how they might view their young audiences as more than just a marketing demographic and income stream, by providing new platforms for young people’s social cohesion and well-being.

Mirror neurons, the representation of word meaning, and the foot of the third left frontal convolution

Previous neuroimaging research has attempted to demonstrate a preferential involvement of the human mirror neuron system (MNS) in the comprehension of effector-related action word (verb) meanings. These studies have assumed that Broca's area (or Brodmann's area 44) is the homologue of a monkey premotor area (F5) containing mouth and hand mirror neurons, and that action word meanings are shared with the mirror system due to a proposed link between speech and gestural communication. In an fMRI experiment, we investigated whether Broca's area shows mirror activity solely for effectors implicated in the MNS. Next, we examined the responses of empirically determined mirror areas during a language perception task comprising effector-specific action words, unrelated words and nonwords. We found overlapping activity for observation and execution of actions with all effectors studied, i.e., including the foot, despite there being no evidence of foot mirror neurons in the monkey or human brain. These "mirror" areas showed equivalent responses for action words, unrelated words and nonwords, with all of these stimuli showing increased responses relative to visual character strings. Our results support alternative explanations attributing mirror activity in Broca's area to covert verbalisation or hierarchical linearisation, and provide no evidence that the MNS makes a preferential contribution to comprehending action word meanings.