A5055409326- Spatial and Panel Data Analysis
- Health disparities and outcomes
- Data-Driven Disease Surveillance
- demographic modeling and climate adaptation
- Food Security and Health in Diverse Populations
- Error Correcting Code Techniques
- Bayesian Methods and Mixture Models
- Coding theory and cryptography
- Species Distribution and Climate Change
- Economic and Environmental Valuation
- Animal Vocal Communication and Behavior
- Healthcare Policy and Management
- Cooperative Communication and Network Coding
- Bayesian Modeling and Causal Inference
- Global Cancer Incidence and Screening
- Gaussian Processes and Bayesian Inference
- Data Analysis with R
- Data Quality and Management
- Advanced Wireless Communication Techniques
- Child Nutrition and Water Access
- Immunotoxicology and immune responses
- Mobile Crowdsensing and Crowdsourcing
- DNA and Biological Computing
- Data Visualization and Analytics
- Early Childhood Education and Development
Queensland University of Technology
2021-2025
Murdoch University
2024
Cancer Council Queensland
2024
Background Spatial data are often aggregated by area to protect the confidentiality of individuals and aid calculation pertinent risks rates. However, analysis spatially is susceptible modifiable areal unit problem (MAUP), which arises when inference varies with boundary or aggregation changes. While impact MAUP has been examined previously, typically these studies have focused on well-populated areas. Understanding how behaves sparse particularly important for countries less populated...
Building on a strong foundation of philosophy, theory, methods and computation over the past three decades, Bayesian approaches are now an integral part toolkit for most statisticians data scientists. Whether they dedicated Bayesians or opportunistic users, applied professionals can reap many benefits afforded by paradigm. In this paper, we touch six modern opportunities challenges in statistics: intelligent collection, new sources, federated analysis, inference implicit models, model...
The health and development of children during their first year full time school is known to impact social, emotional, academic capabilities throughout beyond early education. Physical health, motor development, social emotional well-being, learning styles, language communication, cognitive skills, general knowledge are all considered be important aspects a child's development. It for many organisations governmental agencies continually improve understanding the factors which determine or...
Summary Crowdsourcing methods facilitate the production of scientific information by non‐experts. This form citizen science (CS) is becoming a key source complementary data in many fields to inform data‐driven decisions and study challenging problems. However, concerns about validity these often constrain their utility. In this paper, we focus on use addressing complex challenges environmental conservation. We consider issue from three perspectives. First, present literature scan papers that...
Demographic and educational factors are essential, influential of early childhood development. This study aimed to investigate spatial patterns in the association between attendance at preschool children’s developmental vulnerabilities one or more domain(s) their first year full-time school a small area level Queensland, Australia. was achieved by applying geographically weighted regression (GWR) followed K -means clustering coefficients. Three distinct geographical clusters were found...
This study aimed to better understand the vulnerability of children in their first year school, aged between 5 years months and 6 months, based on five health development domains. Identification subgroups within these domains can lead more targeted policies reduce vulnerabilities. The focus this was determine clusters geographical regions with high low proportions vulnerable Queensland, Australia. achieved by carrying out a K-means analysis data from Australian Early Development Census...
Abstract Background Spatial data are often aggregated by area to protect the confidentiality of individuals and aid calculation pertinent risks rates. However, analysis spatially is susceptible modifiable areal unit problem (MAUP), which arises when inference varies with boundary or aggregation changes. While impact MAUP has been examined previously, typically these studies have focused on well-populated areas. Understanding how behaves sparse particularly important for countries less...
Globally, there is an increased need for guidelines to produce high-quality data outputs analysis. There no framework currently exists providing a comprehensive approach in producing analysis ready (ARD). Through critically reviewing and summarising current literature, this paper proposes such the creation of ARD. The proposed inform ten steps generation ARD: ethics, project documentation, governance, management, storage, discovery collection, cleaning, quality assurance, metadata,...
Extreme natural hazards are increasing in frequency and intensity. These changes our environment, combined with man-made pollution, have substantial economic, social health impacts globally. The impact of the environment on human (environmental health) is becoming well understood international research literature. However, there significant barriers to understanding key characteristics this impact, related data volumes, access rights time required compile compare over regions time. This...
Abstract In the field of population health research, understanding similarities between geographical areas and quantifying their shared effects on outcomes is crucial. this paper, we synthesise a number existing methods to create new approach that specifically addresses goal. The called Bayesian spatial Dirichlet process clustered heterogeneous regression model. This non-parametric framework allows for inference clusters clustering configurations, while simultaneously estimating parameters...
Spatial statistical models are commonly used in geographical scenarios to ensure spatial variation is captured effectively. However, and cluster algorithms can be complicated expensive. One of these geographically weighted regression (GWR) which was proposed the geography literature allow relationships a model vary over space. In contrast traditional linear models, have constant coefficients space, estimated locally at spatially referenced data points with GWR. The motivation for adaption...
LDPC codes are used in many applications, however, their error correcting capabilities limited by the presence of stopping sets and trapping sets. Trapping occur when specific low-wiehgt patterns cause a decoder to fail. were first discovered with investigation floor Margulis code. Possible solutions constructions which avoid creating sets, such as progressive edge growth (PEG), or methods remove from existing constructions, graph covers. This survey examines over channels BSC, BEC AWGNC.
Crowdsourcing methods facilitate the production of scientific information by non-experts. This form citizen science (CS) is becoming a key source complementary data in many fields to inform data-driven decisions and study challenging problems. However, concerns about validity these often constrain their utility. In this paper, we focus on use addressing complex challenges environmental conservation. We consider issue from three perspectives. First, present literature scan papers that have...
The research explores the influence of preschool attendance (one year before full-time school) on development children during their first school. Using data collected by Australian Early Development Census, findings show that areas with high proportions tended to have lower at least one developmental vulnerability. Developmental vulnerablities include not being able cope school day (tired, hungry, low energy), unable get along others or aggressive behaviour, trouble reading/writing numbers....
Spatial statistical models are commonly used in geographical scenarios to ensure spatial variation is captured effectively. However, and cluster algorithms can be complicated expensive. This paper pursues three main objectives. First, it introduces covariate effect clustering by integrating a Bayesian Geographically Weighted Regression (BGWR) with Gaussian mixture model the Dirichlet process model. Second, this examines situations which particular holds significant importance one region but...
In the field of population health research, understanding similarities between geographical areas and quantifying their shared effects on outcomes is crucial. this paper, we synthesise a number existing methods to create new approach that specifically addresses goal. The called Bayesian spatial Dirichlet process clustered heterogeneous regression model. This non-parametric framework allows for inference clusters clustering configurations, while simultaneously estimating parameters each...
plan treatment accordingly.While this pragmatic approach has its merits, advances in our understanding of group diseases and the evaluation