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Enregistrement W2962094809 · doi:10.5194/ica-abs-1-76-2019

From Spatial to Platial Information Systems: For a Better Representation of the Sense of Place

2019· article· en· W2962094809 sur OpenAlexaff
Yaïves Ferland, Mir Abolfazl Mostafavi

Notice bibliographique

RevueAbstracts of the ICA · 2019
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueGeographic Information Systems Studies
Établissements canadiensUniversité Laval
Organismes subventionnairesnon disponible
Mots-clésToponymyRepresentation (politics)Meaning (existential)Perspective (graphical)IdentifierIdentity (music)Sense of placeEtymologySpace (punctuation)Table (database)Point (geometry)Set (abstract data type)Computer scienceGeographyLinguisticsEpistemologyArtificial intelligenceMathematicsArchaeologyData miningAestheticsLaw

Résumé

récupéré en direct d'OpenAlex

Abstract. In a traditional Geographical Information System (GIS), one usually characterizes spatial entities by their individual placename (or toponym) and a set of defined attributes. These entities and their attributes are structured in table frames with functional relations, plus coordinates of their geometric primitives for location in a quantified space. In such a case, one considers the geographical names of these entities as unique identifiers under the standardized ‘generic-specific’ binomial, in one-to-one relationships with features of interest on the ground. This technical perspective convenes quite well to administrators of land-based public mandates needing univocal references to particular places for planning as well as daily applications (e.g., municipal, postal, emergency). But, from a linguistic point of view, the exact location and dimensions of places are among other attributes of the toponym itself, since the study of placenames looks mainly to its meaning, etymology, and evolution in particular language(s) through the epochs of naming practices, forming kind of toponymic “strata”. So, that is investigatory, documented, critical, often anecdotal, sometimes even searching for identity, but devotes few considerations to the geographical place per se, its landscape nor its limits. On the other hand, the geographical perspective to placenames comes more concerned by a real or identifiable place within its vicinity and the dynamics of situations occurring in that location at multiple scales in the same period, whatever the names covering part or all the area. Thus, looking for a place based information system to structure perceived or unofficial places of specified interest require the design and development of tools and functionalities able to support analysis of such information for particular purposes (personal, commercial, military, transit, indigenous, participatory, tourism, etc.). In such a context, definition of their substantial components requires other technical means than their descriptive attributes and geometric primitives. Over the rational spatial databases as a base map, the user of a platial information system would design place-entities on an autonomous fashion, more subjective and colloquial, while referring to landmarks for identity or distinction with respect to different people, being inhabitant, local worker or just visitor. The place-entity comes to the mind like a shapeless mass with a core and some peripheries, where it shows an oriented front or façade, or a force line as a trend, and more fuzzy and moveable limits on the other sides. For instance, downtown and the central business district may not correspond to each other as platial synonyms. For this purpose, the necessary data structures remain to be developed. To do so, Voronoi tessellation, with its flexible spatial proximity definition and its topological (instead of geometrical) properties, represents an interesting alternative model for further researches. For short, a Voronoi diagram partitions the space into regions such that any location is associated with its nearest Voronoi generators (centroids representing places). This allows an adaptive discretization of the space, and provides a simple and intuitive basis for the definition of adjacency relations between the generation points. Depending on the distribution of generating points, Voronoi cells can approximate place extensions close to human perception of those places with flexibility and still keep the fundamental qualitative relations between places, thanks to topological properties of this model. Irregularity of such Voronoi model also has a significant advantage that allow to better approximate places and their variable distribution in the geographical space. Here in this paper, we also consider scale as an important factor for the representation and analysis of the places approximated with Voronoi cells. Indeed, in higher level, a place may be constituted either by the aggregation of several places in fine scales or by their parts. Hierarchical Voronoi diagram can also be considered to model such relations between places and their vertical relations, for instance where a same particular placename identifies different features or entities that overlap, evolve, or have various limits or meanings. In such a model, moving between different scales or data “levels” is done without being worried about exact aggregation of lower geometries in a high-level place representation. In order to structure Voronoi hierarchies for a set of points representing centroids of places, one must start by constructing Voronoi cells for the finest level and then creating pointers that relate higher level places to the lower ones. Based on its unique properties, Voronoi tessellation, among strong solutions for platial information systems, can provide a firm base for representing the complexity of places, either as entities (or features) and toponyms that identify or refer to them. Paralleling relational GIS data frames, it would allow to adapt to unusual or fuzzy places, to represent their geographical evolution, to preserve their name and spatial extension, and to take good notes of local or ancient variants and even of exonyms applied to such places from abroad. Thus, it presents an opportunity to map the sense, if not the spirit, of place.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,006
score de la tête « metaresearch » (Gemma)0,014
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Théorique ou conceptuel · Signal consensuel: Théorique ou conceptuel
GenreSignal candidat: Méthodes · Signal consensuel: Méthodes
Score de désaccord entre enseignants0,016
Score d'incertitude au seuil0,051

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0060,014
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0050,009
Études des sciences et des technologies0,0030,024
Communication savante0,0160,046
Science ouverte0,0020,011
Intégrité de la recherche0,0040,008
Charge utile insuffisante (le modèle a refusé de juger)0,0150,003

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,013
Tête enseignante GPT0,270
Écart entre enseignants0,256 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeThéorique ou conceptuel
Domainenon disponible
GenreMéthodes

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2019
Routes d'admission1
Résumé présentoui

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