Spatio-temporal visualisation and data exploration of traditional ecological knowledge/indigenous knowledge

Kierin Mackenzie (1) , Willington Siabato (2) , Femke Reitsma (1) , Christophe Claramunt (3)

1 University of Canterbury [Christchurch]
2 UPM - Universidad Politécnica de Madrid
3 IRENAV - Institut de Recherche de l'Ecole Navale

Traditional Ecological Knowledge (TEK) has been at the centre of mapping efforts for decades. Indigenous knowledge (IK) is a critical subset of TEK, and Indigenous peoples utilize a wide variety of techniques for keeping track of time. Although techniques for mapping and visualizing the temporal aspects of TEK/IK have been utilized, the spatio-temporal dimensions of TEK are not well explored visually outside of seasonal data and narrative approaches. Existing spatio-temporal models can add new visualization approaches for TEK but are limited by ontological constraints regarding time, particularly the poor support for multi-cyclical data and localized timing. For TEK to be well represented, flexible systems are needed for modelling and mapping time that correspond well with traditional conceptions of time being supported. These approaches can take cues from previous spatio-temporal visualization work in the GIS community, and from temporal depictions extant in existing cultural traditions.

Mots clés: Visualization; Spatio-temporal data; Traditional ecological knowledge (TEK); Indigenous knowledge (IK); Cyclical time; Modelling; Data exploration

Domaines: Intelligence artificielle [cs.AI]; Modélisation et simulation; Environnement et Société; Sciences de l'information et de la communication

Kierin Mackenzie, Willington Siabato, Femke Reitsma, Christophe Claramunt. Spatio-temporal visualisation and data exploration of traditional ecological knowledge/indigenous knowledge. Conservation and Society, 2017, 15 (1), pp.41-58. ⟨10.4103/0972-4923.201391⟩. ⟨hal-01527576⟩