A magnetometer survey with electronic positioning control and calculator-plotter system

Historical Archaeology - This paper describes the instruments, methodology, and results of a marine magnetometer survey of twenty-five miles of the South Texas coast conducted by the Underwater...     

Towards a nuanced understanding of health‐supportive place using composite research methods

Chronic non‐communicable diseases comprise a major challenge for health in contemporary Australia and across the globe and, while various factors are at play, personal choices in behaviour and lifestyle are significant. The physical make‐up and ongoing management of the places where we live positively and negatively influences these lifestyle choices and subsequent health outcomes. However, responses—in research, policy, built environment design, construction, and management—frequently fail to recognise the intricacies of this people‐place‐behaviour nexus largely because those responses are highly focused and empirical. Rather, the health‐supportive environments needed to address the current chronic disease epidemic also require additional—networked, nuanced, and intuitive—understandings. Responding to that need, the study described in this paper took a comprehensive view of what constitutes a health‐supportive built environment. We detail the composite methods employed—built environment audits, interviews, and focus groups—all of which are available for use by others in similar situations. Then, using two case study examples, we reflect upon how our methodology revealed otherwise hidden aspects of the extent to which the study sites supported or hindered health‐supportive behaviours and responses. These results suggest intervention actions for policy makers and practitioners that will help them respond to the complex needs of communities in creating a health‐supportive environment.     

Nonlinear Multivariate Spatial Modeling Using NLPCA and Pair‐Copulas

A novel geostatistical modeling approach is developed to model nonlinear multivariate spatial dependence using nonlinear principal component analysis (NLPCA) and pair‐copulas. In spatial studies, multivariate measurements are frequently collected at each location. The dependence between such measurements can be complex. In this article, a multivariate geostatistical model is developed that can capture both nonlinear spatial dependence across locations and nonlinear dependence between measurements at a particular location. Nonlinear multivariate dependence between spatial variables is removed using NLPCA. Subsequently, a pair‐copula based model is fitted to each transformed variable to model the univariate nonlinear spatial dependencies. NLPCA and pair‐copulas, within the proposed model, are compared with stepwise conditional transformation (SCT) and conventional kriging. The results show that, for the two case studies presented, the proposed model that utilizes NLPCA and pair‐copulas reproduces nonlinear multivariate structures and univariate distributions better than existing methods based on SCT and kriging.