LONE WOLVES? ISOLATION AND SOLITUDE WITHIN THE FRONTIER TRAVEL EXPERIENCE

The frontier traveller, venturing out into the peripheral parts of our world such as the poles, the peaks of mountains or the great deserts, must deal with the remoteness and riskiness of the setting. The traveller is mentally apart from friends and family, and immediate rescue may be difficult, if not impossible. This article explores the attraction of remoteness, isolation and solitude for the frontier traveller, using qualitative interview and biographical data collected in a study of frontier travel experiences. Some participants referred to intense, spiritual experiences at the frontier, with the remoteness and silence of their journeys facilitating reflection, both internal and external. Isolation and solitude appear to engender a sense of freedom and escape from the cares of everyday life in the frontier traveller, while the study also highlights the attraction of self‐sufficiency in remote settings, particularly during the solo frontier travel experience, where the individual is forced to make decisions and manage situations, without recourse to another's advice, skills or experience. The resultant heightened challenge and risk was perceived as a form of authenticity by some participants. The links between isolation and opportunities for self‐actualization are also noted in this article. The implications of these findings for marketing tourism experiences are examined, given the potential for the peripheries to become more accessible to tourists in the future, as well as the role played by culture and privilege in these types of adventurous experiences in far‐flung locations.     

Entropy maximization and input–output analysis

ABSTRACT

Wilson’s original introduction of the concept of entropy to the modelling of input–output and commodity flow systems provided a more flexible alternative to the prevailing state of the art. Following a brief review of prior approaches, the modified Wilson approach is presented and illustrated with an application to Korea. Thereafter, recent developments in entropy econometrics are explored to return to some of the initial concerns, namely the estimation and updating of input–output systems.     

Exploring scale-dependent correlations between cancer mortality rates using factorial kriging and population-weighted semivariograms

This article presents a geostatistical methodology that accounts for spatially varying population size in the processing of cancer mortality data. The approach proceeds in two steps: (1) spatial patterns are first described and modeled using population-weighted semivariogram estimators, (2) spatial components corresponding to nested structures identified on semivariograms are then estimated and mapped using a variant of factorial kriging. The main benefit over traditional spatial smoothers is that the pattern of spatial variability (i.e., direction-dependent variability, range of correlation, presence of nested scales of variability) is directly incorporated into the computation of weights assigned to surrounding observations. Moreover, besides filtering the noise in the data, the procedure allows the decomposition of the structured component into several spatial components (i.e., local versus regional variability) on the basis of semivariogram models. A simulation study demonstrates that maps of spatial components are closer to the underlying risk maps in terms of prediction errors and provide a better visualization of regional patterns than the original maps of mortality rates or the maps smoothed using weighted linear averages. The proposed approach also attenuates the underestimation of the magnitude of the correlation between various cancer rates resulting from noise attached to the data. This methodology has great potential to explore scale-dependent correlation between risks of developing cancers and to detect clusters at various spatial scales, which should lead to a more accurate representation of geographic variation in cancer risk, and ultimately to a better understanding of causative relationships.