The present work explores the link between navigational processes and the experience of place by considering the case of Evenki reindeer herders and hunters. Our analysis shows how the idiosyncratic wayfinding methods of the Evenki result in a unique experience of place – a case that elucidates the important question of the impact of navigational processes on environmental experience, and that advances the debate between mental map theory and practical mastery theory in anthropology. We defend that their wayfinding methods – involving a particular gait, path networks, and vast hydrological and toponymical knowledge – allow the Evenki to navigate without a need for integrating egocentric and allocentric frames of reference. As a result, the Evenki experience themselves as free individuals moving through an environment that is alive and rife with possibility. This analysis reveals the ways in which wayfinding processes relying predominantly on route knowledge – as opposed to survey knowledge – affect environmental experience. Alternative methods of wayfinding can be seen as a form of resistance to the uniformisation of landscapes, and as a way of embracing the heterogeneity of space.

Electronic navigational charts (ENCs) can be compiled using existing paper charts to improve their coverage of the world's oceans. However, in the process of assigning symbols on ENCs, in some cases the software uses the same symbol for different paper chart symbols. This could ultimately compromise maritime safety. Addressing this issue, this paper describes a methodology for developing a new tool that complements the efficient production of ENCs using paper charts. First, the ENC product was produced utilising CARIS S-57 Composer. After considering the difficulties in assigning symbols through the compilation process, a new web application named SYMO EXPERT was introduced. It was developed using Firebase Realtime Database and React app. A questionnaire was prepared to collect data about the time factor and accuracy of using SYMO EXPERT. Results showed that it supports the users in selecting relevant symbols efficiently with an accuracy of up to 98%.