Lo imaginario espacial y la geografía. En defensa de la geografía de las representaciones.

Sin resume

La différenciation de l’espace urbain de Québec : une application de l’analyse discriminante en géographie urbaine

Puisqu'il n'est pas de connaissance géographique sans différenciation des paysages, la géographie quantitative ne peut se contenter des seules méthodes factorielles. Avec l'analyse discriminante, les poids locaux ne constituent plus l'étape finale, mais le point de départ de l'analyse spatiale. Ainsi procédons-nous à l'étude des espaces socio-économiques et familiaux pour la région métropolitaine de Québec. Loin d'être conforme aux modèles nord-américains, cette ville présente un double aspect: l'un culturel et symbolique, favorisant la conservation de plusieurs secteurs centraux, l'autre économique, entraînant invasions et successions dans les aires périphériques. Même si l'analyse discriminante pose plusieurs problèmes méthodologiques, elle offre au géographe un instrument qui peut servir de support logique pour bâtir une théorie de la ville.Since geography implies the study of space and its influence on human behavior, quantitative geography must encompass more than standard factorial methods. The interpretation of factor loadings and the subsequent description of factor score patterns should thus not constitute the final stage of any spatial analysis but rather the starting point. We study the socio-economic and family « spaces » of the Québec city urban region. We find that the Québec model tends to differ from the classic North American model; symbolic and cultural factors make for a much more coherent and dense centre city while economic factors, as elsewhere on the continent, result in a pattern of concentric zones around the C.B.D. Even though the use of discriminant analysis poses certain methodological problems, we nonetheless found it to be a useful technique for understanding urban social space

Clustering with shallow trees

We propose a new method for hierarchical clustering based on the optimisation of a cost function over trees of limited depth, and we derive a message--passing method that allows to solve it efficiently. The method and algorithm can be interpreted as a natural interpolation between two well-known approaches, namely single linkage and the recently presented Affinity Propagation. We analyze with this general scheme three biological/medical structured datasets (human population based on genetic information, proteins based on sequences and verbal autopsies) and show that the interpolation technique provides new insight.Comment: 11 pages, 7 figure

Social behavior modeling based on Incremental Discrete Hidden Markov Models

12 pagesInternational audienceModeling multimodal face-to-face interaction is a crucial step in the process of building social robots or users-aware Embodied Conversational Agents (ECA). In this context, we present a novel approach for human behavior analysis and generation based on what we called "Incremental Discrete Hidden Markov Model" (IDHMM). Joint multimodal activities of interlocutors are first modeled by a set of DHMMs that are specific to supposed joint cognitive states of the interlocutors. Respecting a task-specific syntax, the IDHMM is then built from these DHMMs and split into i) a recognition model that will determine the most likely sequence of cognitive states given the multimodal activity of the in- terlocutor, and ii) a generative model that will compute the most likely activity of the speaker given this estimated sequence of cognitive states. Short-Term Viterbi (STV) decoding is used to incrementally recognize and generate behav- ior. The proposed model is applied to parallel speech and gaze data of interact- ing dyads