Analysis, modelling and visualisation of spatiotemporal data for urban studies

The proportion of population living in or around cites is more important than ever. Urban sprawl and car dependence have taken over the pedestrian-friendly compact city. Environmental problems like air pollution, land waste or noise, and health problems are the result of this still continuing process. The urban planners have to find solutions to these complex problems, and at the same time insure the economic performance of the city and its surroundings. At the same time, an increasing quantity of socio-economic and environmental data is acquired. In order to get a better understanding of the processes and phenomena taking place in the complex urban environment, these data should be analysed. Numerous methods for modelling and simulating such a system exist and are still under development and can be exploited by the urban geographers for improving our understanding of the urban metabolism. Modern and innovative visualisation techniques help in communicating the results of such models and simulations. This thesis covers several methods for analysis, modelling, simulation and visualisation of problems related to urban geography. The analysis of high dimensional socio-economic data using artificial neural network techniques, especially self-organising maps, is showed using two examples at different scales. The problem of spatiotemporal modelling and data representation is treated and some possible solutions are shown. The simulation of urban dynamics and more specifically the traffic due to commuting to work is illustrated using multi-agent micro-simulation techniques. A section on visualisation methods presents cartograms for transforming the geographic space into a feature space, and the distance circle map, a centre-based map representation particularly useful for urban agglomerations. Some issues on the importance of scale in urban analysis and clustering of urban phenomena are exposed. A new approach on how to define urban areas at different scales is developed, and the link with percolation theory established. Fractal statistics, especially the lacunarity measure, and scale laws are used for characterising urban clusters. In a last section, the population evolution is modelled using a model close to the well-established gravity model. The work covers quite a wide range of methods useful in urban geography. Methods should still be developed further and at the same time find their way into the daily work and decision process of urban planners. La part de personnes vivant dans une région urbaine est plus élevé que jamais et continue à croître. L'étalement urbain et la dépendance automobile ont supplanté la ville compacte adaptée aux piétons. La pollution de l'air, le gaspillage du sol, le bruit, et des problèmes de santé pour les habitants en sont la conséquence. Les urbanistes doivent trouver, ensemble avec toute la société, des solutions à ces problèmes complexes. En même temps, il faut assurer la performance économique de la ville et de sa région. Actuellement, une quantité grandissante de données socio-économiques et environnementales est récoltée. Pour mieux comprendre les processus et phénomènes du système complexe "ville", ces données doivent être traitées et analysées. Des nombreuses méthodes pour modéliser et simuler un tel système existent et sont continuellement en développement. Elles peuvent être exploitées par le géographe urbain pour améliorer sa connaissance du métabolisme urbain. Des techniques modernes et innovatrices de visualisation aident dans la communication des résultats de tels modèles et simulations. Cette thèse décrit plusieurs méthodes permettant d'analyser, de modéliser, de simuler et de visualiser des phénomènes urbains. L'analyse de données socio-économiques à très haute dimension à l'aide de réseaux de neurones artificiels, notamment des cartes auto-organisatrices, est montré à travers deux exemples aux échelles différentes. Le problème de modélisation spatio-temporelle et de représentation des données est discuté et quelques ébauches de solutions esquissées. La simulation de la dynamique urbaine, et plus spécifiquement du trafic automobile engendré par les pendulaires est illustrée à l'aide d'une simulation multi-agents. Une section sur les méthodes de visualisation montre des cartes en anamorphoses permettant de transformer l'espace géographique en espace fonctionnel. Un autre type de carte, les cartes circulaires, est présenté. Ce type de carte est particulièrement utile pour les agglomérations urbaines. Quelques questions liées à l'importance de l'échelle dans l'analyse urbaine sont également discutées. Une nouvelle approche pour définir des clusters urbains à des échelles différentes est développée, et le lien avec la théorie de la percolation est établi. Des statistiques fractales, notamment la lacunarité, sont utilisées pour caractériser ces clusters urbains. L'évolution de la population est modélisée à l'aide d'un modèle proche du modèle gravitaire bien connu. Le travail couvre une large panoplie de méthodes utiles en géographie urbaine. Toutefois, il est toujours nécessaire de développer plus loin ces méthodes et en même temps, elles doivent trouver leur chemin dans la vie quotidienne des urbanistes et planificateurs.

Projecting the self to past and future in mental time

Introduction: Human experience takes place in the line of mental-time (MT) created through imagination of oneself in different time-points in past or future (self-projection in time). Here we manipulated self-projection in MT not only with respect to one's life-events but also with respect to one's faces from different past and future time-points. Methods: We here compared MTT with respect to one's facial images from different time points in past and future (study 1: MT-faces) as well as with respect to different past and future life events (study 2: MT-events). Participants were asked to make judgments about past and future face images and past and future events from three different time-points: the present (Now), eight years earlier (Past) or eight years later (Future). In addition, as a control task participants were asked to make recognition judgments with respect to faces and memory-related judgments with respect to events without changing their habitual self-location in time. Behavioral measures and functional magnetic resonance imaging (fMRI) activity after subtraction of recognition and memory related activities show both absolute MT and relative MT effects for faces and events, signifying a fundamental brain mechanism of MT, disentangled from episodic memory functions. Results: Behavioural and event-related fMRI activity showed three independent effects characterized by (1) similarity between past recollection and future imagination, (2) facilitation of judgments related to the future as compared to the past, and (3) facilitation of judgments related to time-points distant from the present. These effects were found with respect to faces and events suggesting that the brain mechanisms of MT are independent of whether actual life episodes have to be re-/pre-experienced and recruited a common cerebral network including the medial-temporal, precuneus, inferior-frontal, temporo-parietal, and insular cortices. Conclusions: These behavioural and neural data suggest that self-projection in time is a crucial aspect of MT, relying on neural structures encoding memory, mental imagery, and self. Furthermore our results emphasize the idea that mental temporal processing is more strongly directed to future prediction than to past recollection.

Multitask learning of environmental spatial data

The present research deals with an application of artificial neural networks for multitask learning from spatial environmental data. The real case study (sediments contamination of Geneva Lake) consists of 8 pollutants. There are different relationships between these variables, from linear correlations to strong nonlinear dependencies. The main idea is to construct a subsets of pollutants which can be efficiently modeled together within the multitask framework. The proposed two-step approach is based on: 1) the criterion of nonlinear predictability of each variable ?k? by analyzing all possible models composed from the rest of the variables by using a General Regression Neural Network (GRNN) as a model; 2) a multitask learning of the best model using multilayer perceptron and spatial predictions. The results of the study are analyzed using both machine learning and geostatistical tools.

Data-driven clustering : new methods and applications

Abstract : This work is concerned with the development and application of novel unsupervised learning methods, having in mind two target applications: the analysis of forensic case data and the classification of remote sensing images. First, a method based on a symbolic optimization of the inter-sample distance measure is proposed to improve the flexibility of spectral clustering algorithms, and applied to the problem of forensic case data. This distance is optimized using a loss function related to the preservation of neighborhood structure between the input space and the space of principal components, and solutions are found using genetic programming. Results are compared to a variety of state-of--the-art clustering algorithms. Subsequently, a new large-scale clustering method based on a joint optimization of feature extraction and classification is proposed and applied to various databases, including two hyperspectral remote sensing images. The algorithm makes uses of a functional model (e.g., a neural network) for clustering which is trained by stochastic gradient descent. Results indicate that such a technique can easily scale to huge databases, can avoid the so-called out-of-sample problem, and can compete with or even outperform existing clustering algorithms on both artificial data and real remote sensing images. This is verified on small databases as well as very large problems. Résumé : Ce travail de recherche porte sur le développement et l'application de méthodes d'apprentissage dites non supervisées. Les applications visées par ces méthodes sont l'analyse de données forensiques et la classification d'images hyperspectrales en télédétection. Dans un premier temps, une méthodologie de classification non supervisée fondée sur l'optimisation symbolique d'une mesure de distance inter-échantillons est proposée. Cette mesure est obtenue en optimisant une fonction de coût reliée à la préservation de la structure de voisinage d'un point entre l'espace des variables initiales et l'espace des composantes principales. Cette méthode est appliquée à l'analyse de données forensiques et comparée à un éventail de méthodes déjà existantes. En second lieu, une méthode fondée sur une optimisation conjointe des tâches de sélection de variables et de classification est implémentée dans un réseau de neurones et appliquée à diverses bases de données, dont deux images hyperspectrales. Le réseau de neurones est entraîné à l'aide d'un algorithme de gradient stochastique, ce qui rend cette technique applicable à des images de très haute résolution. Les résultats de l'application de cette dernière montrent que l'utilisation d'une telle technique permet de classifier de très grandes bases de données sans difficulté et donne des résultats avantageusement comparables aux méthodes existantes.

Resting-State Functional Connectivity Emerges from Structurally and Dynamically Shaped Slow Linear Fluctuations.

Brain fluctuations at rest are not random but are structured in spatial patterns of correlated activity across different brain areas. The question of how resting-state functional connectivity (FC) emerges from the brain's anatomical connections has motivated several experimental and computational studies to understand structure-function relationships. However, the mechanistic origin of resting state is obscured by large-scale models' complexity, and a close structure-function relation is still an open problem. Thus, a realistic but simple enough description of relevant brain dynamics is needed. Here, we derived a dynamic mean field model that consistently summarizes the realistic dynamics of a detailed spiking and conductance-based synaptic large-scale network, in which connectivity is constrained by diffusion imaging data from human subjects. The dynamic mean field approximates the ensemble dynamics, whose temporal evolution is dominated by the longest time scale of the system. With this reduction, we demonstrated that FC emerges as structured linear fluctuations around a stable low firing activity state close to destabilization. Moreover, the model can be further and crucially simplified into a set of motion equations for statistical moments, providing a direct analytical link between anatomical structure, neural network dynamics, and FC. Our study suggests that FC arises from noise propagation and dynamical slowing down of fluctuations in an anatomically constrained dynamical system. Altogether, the reduction from spiking models to statistical moments presented here provides a new framework to explicitly understand the building up of FC through neuronal dynamics underpinned by anatomical connections and to drive hypotheses in task-evoked studies and for clinical applications.

Prediction of cross-recognition of peptide-HLA A2 by Melan-A-specific cytotoxic T lymphocytes using three-dimensional quantitative structure-activity relationships.

The cross-recognition of peptides by cytotoxic T lymphocytes is a key element in immunology and in particular in peptide based immunotherapy. Here we develop three-dimensional (3D) quantitative structure-activity relationships (QSARs) to predict cross-recognition by Melan-A-specific cytotoxic T lymphocytes of peptides bound to HLA A*0201 (hereafter referred to as HLA A2). First, we predict the structure of a set of self- and pathogen-derived peptides bound to HLA A2 using a previously developed ab initio structure prediction approach [Fagerberg et al., J. Mol. Biol., 521-46 (2006)]. Second, shape and electrostatic energy calculations are performed on a 3D grid to produce similarity matrices which are combined with a genetic neural network method [So et al., J. Med. Chem., 4347-59 (1997)] to generate 3D-QSAR models. The models are extensively validated using several different approaches. During the model generation, the leave-one-out cross-validated correlation coefficient (q (2)) is used as the fitness criterion and all obtained models are evaluated based on their q (2) values. Moreover, the best model obtained for a partitioned data set is evaluated by its correlation coefficient (r = 0.92 for the external test set). The physical relevance of all models is tested using a functional dependence analysis and the robustness of the models obtained for the entire data set is confirmed using y-randomization. Finally, the validated models are tested for their utility in the setting of rational peptide design: their ability to discriminate between peptides that only contain side chain substitutions in a single secondary anchor position is evaluated. In addition, the predicted cross-recognition of the mono-substituted peptides is confirmed experimentally in chromium-release assays. These results underline the utility of 3D-QSARs in peptide mimetic design and suggest that the properties of the unbound epitope are sufficient to capture most of the information to determine the cross-recognition.

Subjective mental time: the functional architecture of projecting the self to past and future.

Abstract Human experience takes place in the line of mental time (MT) created through 'self-projection' of oneself to different time-points in the past or future. Here we manipulated self-projection in MT not only with respect to one's life events but also with respect to one's faces from different past and future time-points. Behavioural and event-related functional magnetic resonance imaging activity showed three independent effects characterized by (i) similarity between past recollection and future imagination, (ii) facilitation of judgements related to the future as compared with the past, and (iii) facilitation of judgements related to time-points distant from the present. These effects were found with respect to faces and events, and also suggest that brain mechanisms of MT are independent of whether actual life episodes have to be re-experienced or pre-experienced, recruiting a common cerebral network including the anteromedial temporal, posterior parietal, inferior frontal, temporo-parietal and insular cortices. These behavioural and neural data suggest that self-projection in time is a fundamental aspect of MT, relying on neural structures encoding memory, mental imagery and self.

Statistical methods for river runoff prediction

Methods used to analyze one type of nonstationary stochastic processes?the periodically correlated process?are considered. Two methods of one-step-forward prediction of periodically correlated time series are examined. One-step-forward predictions made in accordance with an autoregression model and a model of an artificial neural network with one latent neuron layer and with an adaptation mechanism of network parameters in a moving time window were compared in terms of efficiency. The comparison showed that, in the case of prediction for one time step for time series of mean monthly water discharge, the simpler autoregression model is more efficient.

Automatic Decision-Oriented Mapping of Pollution Data

The paper deals with the development and application of the methodology for automatic mapping of pollution/contamination data. General Regression Neural Network (GRNN) is considered in detail and is proposed as an efficient tool to solve this problem. The automatic tuning of isotropic and an anisotropic GRNN model using cross-validation procedure is presented. Results are compared with k-nearest-neighbours interpolation algorithm using independent validation data set. Quality of mapping is controlled by the analysis of raw data and the residuals using variography. Maps of probabilities of exceeding a given decision level and ?thick? isoline visualization of the uncertainties are presented as examples of decision-oriented mapping. Real case study is based on mapping of radioactively contaminated territories.

A spatial modelling framework for assessing climate change impacts on freshwater ecosystems: Response of brown trout (Salmo trutta L.) biomass to warming water temperature

Mountain regions worldwide are particularly sensitive to on-going climate change. Specifically in the Alps in Switzerland, the temperature has increased twice as fast than in the rest of the Northern hemisphere. Water temperature closely follows the annual air temperature cycle, severely impacting streams and freshwater ecosystems. In the last 20 years, brown trout (Salmo trutta L) catch has declined by approximately 40-50% in many rivers in Switzerland. Increasing water temperature has been suggested as one of the most likely cause of this decline. Temperature has a direct effect on trout population dynamics through developmental and disease control but can also indirectly impact dynamics via food-web interactions such as resource availability. We developed a spatially explicit modelling framework that allows spatial and temporal projections of trout biomass using the Aare river catchment as a model system, in order to assess the spatial and seasonal patterns of trout biomass variation. Given that biomass has a seasonal variation depending on trout life history stage, we developed seasonal biomass variation models for three periods of the year (Autumn-Winter, Spring and Summer). Because stream water temperature is a critical parameter for brown trout development, we first calibrated a model to predict water temperature as a function of air temperature to be able to further apply climate change scenarios. We then built a model of trout biomass variation by linking water temperature to trout biomass measurements collected by electro-fishing in 21 stations from 2009 to 2011. The different modelling components of our framework had overall a good predictive ability and we could show a seasonal effect of water temperature affecting trout biomass variation. Our statistical framework uses a minimum set of input variables that make it easily transferable to other study areas or fish species but could be improved by including effects of the biotic environment and the evolution of demographical parameters over time. However, our framework still remains informative to spatially highlight where potential changes of water temperature could affect trout biomass. (C) 2015 Elsevier B.V. All rights reserved.-

Fluctuation-Driven Neural Dynamics Reproduce Drosophila Locomotor Patterns.

The neural mechanisms determining the timing of even simple actions, such as when to walk or rest, are largely mysterious. One intriguing, but untested, hypothesis posits a role for ongoing activity fluctuations in neurons of central action selection circuits that drive animal behavior from moment to moment. To examine how fluctuating activity can contribute to action timing, we paired high-resolution measurements of freely walking Drosophila melanogaster with data-driven neural network modeling and dynamical systems analysis. We generated fluctuation-driven network models whose outputs-locomotor bouts-matched those measured from sensory-deprived Drosophila. From these models, we identified those that could also reproduce a second, unrelated dataset: the complex time-course of odor-evoked walking for genetically diverse Drosophila strains. Dynamical models that best reproduced both Drosophila basal and odor-evoked locomotor patterns exhibited specific characteristics. First, ongoing fluctuations were required. In a stochastic resonance-like manner, these fluctuations allowed neural activity to escape stable equilibria and to exceed a threshold for locomotion. Second, odor-induced shifts of equilibria in these models caused a depression in locomotor frequency following olfactory stimulation. Our models predict that activity fluctuations in action selection circuits cause behavioral output to more closely match sensory drive and may therefore enhance navigation in complex sensory environments. Together these data reveal how simple neural dynamics, when coupled with activity fluctuations, can give rise to complex patterns of animal behavior.

Sleep in vitro : Erk pathway regulates sleep duration and sleep related genes

To date, for most biological and physiological phenomena, the scientific community has reach a consensus on their related function, except for sleep, which has an undetermined, albeit mystery, function. To further our understanding of sleep function(s), we first focused on the level of complexity at which sleep-like phenomenon can be observed. This lead to the development of an in vitro model. The second approach was to understand the molecular and cellular pathways regulating sleep and wakefulness, using both our in vitro and in vivo models. The third approach (ongoing) is to look across evolution when sleep or wakefulness appears. (1) To address the question as to whether sleep is a cellular property and how this is linked to the entire brain functioning, we developed a model of sleep in vitro by using dissociated primary cortical cultures. We aimed at simulating the major characteristics of sleep and wakefulness in vitro. We have shown that mature cortical cultures display a spontaneous electrical activity similar to sleep. When these cultures are stimulated by waking neurotransmitters, they show a tonic firing activity, similar to wakefulness, but return spontaneously to the "sleep-like" state 24h after stimulation. We have also shown that transcriptional, electrophysiological, and metabolic correlates of sleep and wakefulness can be reliably detected in dissociated cortical cultures. (2) To further understand at which molecular and cellular levels changes between sleep and wakefulness occur, we have used a pharmacological and systematic gene transcription approach in vitro and discovered a major role played by the Erk pathway. Indeed, pharmacological inhibition of this pathway in living animals decreased sleep by 2 hours per day and consolidated both sleep and wakefulness by reducing their fragmentation. (3) Finally, we tried to evaluate the presence of sleep in one of the most primitive species with a neural network. We set up Hydra as a model organism. We hypothesized that sleep as a cellular (neuronal) property may occur with the appearance of the most primitive nervous system. We were able to show that Hydra have periodic rest phases amounting to up to 5 hours per day. In conclusion, our work established an in vitro model to study sleep, discovered one of the major signaling pathways regulating vigilance states, and strongly suggests that sleep is a cellular property highly conserved at the molecular level during evolution. -- Jusqu'à ce jour, la communauté scientifique s'est mise d'accord sur la fonction d'une majorité des processus physiologiques, excepté pour le sommeil. En effet, la fonction du sommeil reste un mystère, et aucun consensus n'est atteint le concernant. Pour mieux comprendre la ou les fonctions du sommeil, (1) nous nous sommes d'abord concentré sur le niveau de complexité auquel un état ressemblant au sommeil peut être observé. Nous avons ainsi développé un modèle du sommeil in vitro, (2) nous avons disséqué les mécanismes moléculaires et cellulaires qui pourraient réguler le sommeil, (3) nous avons cherché à savoir si un état de sommeil peut être trouvé dans l'hydre, l'animal le plus primitif avec un système nerveux. (1) Pour répondre à la question de savoir à quel niveau de complexité apparaît un état de sommeil ou d'éveil, nous avons développé un modèle du sommeil, en utilisant des cellules dissociées de cortex. Nous avons essayé de reproduire les corrélats du sommeil et de l'éveil in vitro. Pour ce faire, nous avons développé des cultures qui montrent les signes électrophysiologiques du sommeil, puis quand stimulées chimiquement passent à un état proche de l'éveil et retournent dans un état de sommeil 24 heures après la stimulation. Notre modèle n'est pas parfait, mais nous avons montré que nous pouvions obtenir les corrélats électrophysiologiques, transcriptionnels et métaboliques du sommeil dans des cellules corticales dissociées. (2) Pour mieux comprendre ce qui se passe au niveau moléculaire et cellulaire durant les différents états de vigilance, nous avons utilisé ce modèle in vitro pour disséquer les différentes voies de signalisation moléculaire. Nous avons donc bloqué pharmacologiquement les voies majeures. Nous avons mis en évidence la voie Erkl/2 qui joue un rôle majeur dans la régulation du sommeil et dans la transcription des gènes qui corrèlent avec le cycle veille-sommeil. En effet, l'inhibition pharmacologique de cette voie chez la souris diminue de 2 heures la quantité du sommeil journalier et consolide l'éveil et le sommeil en diminuant leur fragmentation. (3) Finalement, nous avons cherché la présence du sommeil chez l'Hydre. Pour cela, nous avons étudié le comportement de l'Hydre pendant 24-48h et montrons que des périodes d'inactivité, semblable au sommeil, sont présentes dans cette espèce primitive. L'ensemble de ces travaux indique que le sommeil est une propriété cellulaire, présent chez tout animal avec un système nerveux et régulé par une voie de signalisation phylogénétiquement conservée.