A new GIS data model for depicting traditional knowledge of spatio-temporal patterns via motifs, folk thesauri, narratives and accompanying visualisations

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Doctor of Philosophy
University of Canterbury
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Mackenzie, Kierin Jeffrey

The current standard GIS data models have some limitations, particularly when dealing with interconnected data. These models, such as raster and vector data models, rely on features and layers that by nature are divorced from one another, other than via various queries and transformations desired by the user. Time is linear if utilised, and space is seen in terms of co-ordinates linked to some given set of semantic content or values at a given set of co-ordinates. The model is flexible, but lacks holism. It is the logic of the spreadsheet. Traditional knowledge systems tend to have the interconnected nature of the spoken word or living systems, and we have developed a data model and knowledge model to capture this.

Motifs act as containers for an action, a set of actions, a state, or a set of states that are contextualised via components like actors, location, time period and the like. They form the basic component of the model. They can function much in the same way as the current standard GIS vector data model that ties a single vector object to a tuple or range of markers in a database, but can also operate more abstractly than the current GIS model by allowing for grouping and interconnection of features in ways that would otherwise be difficult, and by focussing on process and action beyond just existence values.

Another drawback to current GIS approaches is the need for strong a priori schema and ontologies. These require in depth planning and often reflect western understandings of how the world should be classified. They also tend to be tree-like instead of lattice like. We propose a Folk Thesaurus, which collects Motifs via the semantic relationships between the components of the facets of a motif, thereby allowing for the querying of a motif database via queries that are themselves motifs. This improves querying by making it more like thought or natural language. By pairing motifs with a folk thesaurus, we allow for those classifying the motifs to be able to utilise equivalence, hierarchical and associative relationships to draw connections to motifs, and to group more specific motifs properly under their more generalised equivalents. Folk thesauri also allow for traditional concepts of time, space, actors and actions to be utilised and added upon as data is compiled, rather than having a solid schema beforehand, and allows for semantic and multifaceted depictions of time and space along with the linear and crisp notions that prevail in GIS. We have also built a graphical user interface to keep track of things like multi-cyclical time, and the right time and wrong time for activities.

We then use narratives to string simple motifs into more complex motifs, thereby allowing for traditional knowledge structures in the forms of narrative to be represented directly, as well as providing a data structure for modelling processes. We utilise syntagmatic, paradigmatic, antithetic and meronymic relations to tie motifs together, and utilise modal logic to shape the contours of those narratives by tagging the submotifs to the narrative, allowing for the motif model to provide the starting point for analogical reasoning via other models.

The Motif, Folk Thesaurus and Narrative, when combined, provide the schema or a future GIS database that goes beyond the latest graph databases, via the multiple levels of interrelation and the support for multiple understandings of the world and its workings, making it more suitable for recording systems relations and providing a solid base for analogical reasoning.

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