Juste Raimbault

Bio: Juste Raimbault is an academic researcher from École Polytechnique. The author has contributed to research in topics: Population & Context (language use). The author has an hindex of 13, co-authored 105 publications receiving 575 citations. Previous affiliations of Juste Raimbault include IFSTTAR & École des ponts ParisTech.

Caractérisation et modélisation de la co-évolution des réseaux de transport et des territoires

Abstract: L’identification d’effets structurants des infrastructures de transports sur la dynamique des territoires reste un defi scientifique ouvert. Cette question est une des facettes de recherches sur la complexite des dynamiques territoriales, au sein desquelles territoires et reseaux de transport seraient en co-evolution. L’objectif de cette these est de mettre a l’epreuve cette vision des interactions entre reseaux et territoires, autant sur le plan conceptuel que sur le plan empirique, en les integrant au sein de modeles de simulation des systemes territoriaux. La nature intrinsequement pluri-disciplinaire de la question nous conduit a mener un travail d’epistemologie quantitative, qui permet de dresser une carte du paysage scientifique et une description des elements communs et des specificites des modeles traitant la co-evolution entre reseaux et territoires dans chaque discipline. Nous proposons ensuite une definition de la co-evolution, ainsi qu’une methode de caracterisation empirique, basee sur une analyse de correlations spatio-temporelles. Deux pistes complementaires de modelisation, correspondant a des ontologies et des echelles differentes sont alors explorees. A l’echelle macroscopique, nous construisons une famille de modeles dans la lignee des modeles d’interaction au sein des systemes de villes developpes par la Theorie Evolutive des Villes (Pumain, 1997). Leur exploration montre qu’ils capturent effectivement des dynamiques de co-evolution, et leur calibration sur des donnees demographiques pour le systeme de villes francais (1830-1999) quantifie l’evolution des processus d’interaction comme l’effet tunnel ou le role de la centralite. A l’echelle mesoscopique, un modele de morphogenese capture la co-evolution de la forme urbaine et de la topologie du reseau. Il est calibre sur les indicateurs correspondants pour la forme et la topologie locales calcules pour l’ensemble de l’Europe. De multiples processus d’evolution du reseau s’averent etre complementaires pour reproduire la grande variete des configurations observees, au niveau des indicateurs ainsi que des interactions entre indicateurs. Ces resultats suggerent de nouvelles pistes d’exploration des modeles urbains integrant les dynamiques co-evolutives dans une perspective multi-echelles.

Classifying patents based on their semantic content

Abstract: In this paper, we extend some usual techniques of classification resulting from a large-scale data-mining and network approach. This new technology, which in particular is designed to be suitable to big data, is used to construct an open consolidated database from raw data on 4 million patents taken from the US patent office from 1976 onward. To build the pattern network, not only do we look at each patent title, but we also examine their full abstract and extract the relevant keywords accordingly. We refer to this classification as semantic approach in contrast with the more common technological approach which consists in taking the topology when considering US Patent office technological classes. Moreover, we document that both approaches have highly different topological measures and strong statistical evidence that they feature a different model. This suggests that our method is a useful tool to extract endogenous information.

Calibration of a density-based model of urban morphogenesis.

Abstract: We study a stochastic model of urban growth generating spatial distributions of population densities at an intermediate mesoscopic scale. The model is based on the antagonist interplay between the two opposite abstract processes of aggregation (preferential attachment) and diffusion (urban sprawl). Introducing indicators to quantify urban form, the model is first statistically validated and intensively explored to understand its complex behavior across the parameter space. We then compute real morphological indicators on local areas of size 50km covering all European Union, and show that the model can reproduce most of these existing urban morphologies. It implies that the morphological dimension of urban growth processes at this scale are sufficiently captured by the two abstract processes of aggregation and diffusion.

Space Matters: Extending Sensitivity Analysis to Initial Spatial Conditions in Geosimulation Models

Abstract: Although simulation models of socio-spatial systems in general and agent-based models in particular represent a fantastic opportunity to explore socio-spatial behaviours and to test a variety of scenarios for public policy, the validity of generative models is uncertain unless their results are proven robust and representative of 'real-world' conditions. Sensitivity analysis usually includes the analysis of the effect of stochasticity on the variability of results, as well as the effects of small parameter changes. However, initial spatial conditions are usually not modified systematically in socio-spatial models, thus leaving unexplored the effect of initial spatial arrangements on the interactions of agents with one another as well as with their environment. In this article, we present a method to assess the effect of variation of some initial spatial conditions on simulation models, using a systematic geometric structures generator in order to create density grids with which socio-spatial simulation models are initialised. We show, with the example of two classical agent-based models (Schelling's model of segregation and Sugarscape's model of unequal societies) and a straightforward open-source workflow using high performance computing, that the effect of initial spatial arrangements is significant on the two models. We wish to illustrate the potential interest of adding spatial sensitivity analysis during the exploration of models for both modellers and thematic specialists.

Indirect Evidence of Network Effects in a System of Cities

Abstract: We describe a simple spatial model of urban growth for systems of cities at the macroscopic scale, which combines direct interaction between cities and an indirect effect of physical network flows as population growth drivers. The model is parametrized on population data for the French system of cities between 1831 and 1999, which strong non-stationarity in correlation patterns suggest to apply the model on local time windows. The corresponding calibration of the model using genetic algorithms provide the evolution of interaction processes and network effects in time. Furthermore, the fit improvement when adding network module appears effective when controlling for additional parameters, what confirms the ability of the model to unveil network effects in the system of cities.

IEEE transactions on pattern analysis and machine intelligence

Abstract: In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to: • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information. Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.

Geographically Weighted Regression

Abstract: Being newly immersed in the upstream part of the oil business, I just recently had my first work session with data in ARC–GIS®. The project involves subsurface geographical modeling. Obviously I had considerable interest in discovering if the methodology in this book would enhance my modeling capabilities. The book is for social scientists, but I had no difficulty imagining my own important oil exploration application within the framework of geographically weighted regression (GWR). The first chapter nicely explains what is unique in this book. A standard regression model using geographically oriented data (the example is housing prices across all of England) is a global representation of a spatial relationship, an average that does not account for any local differences. In y = f (x), imagine a whole family of f ’s that are indexed by spatial location. That is the focus of this book. It is about one form of local spatial modeling, which is GWR. A more general resource for this topic is the earlier book by Fotheringham and Wegener (2000), which escaped the notice of Technometrics. Imagine a display of model parameters in a geographical information system (GIS) and you will understand the focus for this book. The authors note, “only where there is no significant spatial variation in a measured relationship can global models be accepted” (p. 10). The second chapter develops the basis of GWR. It analyzes the housing sales prices versus the 33 boroughs in London and begins by fitting a conventional multiple regression model versus housing characteristics. The GWR is motivated by differences in the regression models fitted separately by borough. The GWR is a spatial moving-window approach with all data distances weighted versus a specific data point using a weighting function and a bandwidth. A GIS can then be used to evaluate the spatial dependency of the parameters. As in kriging, local standard errors also are calculated. The chapter also provides all the math. Chapter 3 comprises several further considerations: parameters that are globally constant, outliers, and spatial heteroscedasticity. The first issue leads to hypothesis tests for model comparison using an Akaike information criterion (AIC). Local outliers are hard to detect. Studentized (deletion) residuals are recommended. The outliers can be plotted geographically. Robust regression is suggested as a less computationally intensive alternative. Hetereoscedasticity is harder to handle. Chapter 4 adds statistical inference to the capabilities of GWR: both a confidence interval approach using local likelihood and an AIC method. Four additional methodology chapters present various extensions of GWR. Chapter 5 considers the relationship between GWR and spatial autocorrelation, and includes a combined version of GWR and spatial regression using some complex hybrid models. Chapter 6 examines the relationship of scale and zoning problems in spatial analysis to GWR. Chapter 7 introduces the use of initial exploratory data analysis using geographically weighted statistics, which are based on the idea of using a kernel around each data point to create weights. Univariate statistics and correlation coefficients are defined for exploring local patterns in data. A final set of extensions in Chapter 8 discusses regression models with non-Gaussian errors, logistic regression, local principal components analysis, and local probability density estimation. The methods all use some kind of distributional model. The million-dollar question for me is always, “What about software?” The authors have a stand-alone program, GWR 3, available in CD–ROM by contacting the authors. Basically the drill with GWR 3 is to gather your data, use Excel to transform and reformat the data for GWR 3, use GWR 3 to produce a set of coefficients, and feed those coefficients to your favorite GIS to produce your maps. Forty pages of discussion about using the software are provided. A final epilogue chapter also discusses embedding GWR in R or Matlab and includes some references to people who have done that type of work. I probably would not have read this book if I had not happened to have had it in my briefcase on a visit with the exploration technologists. Though inclusive of appropriate mathematical development, this material is readily approachable because of the many illustrations and the pages and pages of GIS displays. The authors unabashedly present much of the material as their developmental work, so GWR offers a lot of opportunity for research and further development through novel applications and extensions.

Handbook of regional and urban economics

Abstract: Developments in methodologies, agglomeration, and a range of applied issues have characterized recent advances in regional and urban studies. Volume 5 concentrates on these developments while treating traditional subjects such as housing, the costs and benefits of cities, and policy issues beyond regional inequalities. Contributors make a habit of combining theory and empirics in each chapter, guiding research amid a trend in applied economics towards structural and quasi-experimental approaches. Clearly distinguished from the New Economic Geography covered by Volume 4, these articles feature an international approach that positions recent advances within the discipline of economics and society at large. * Editors are recognized as leaders and can attract an international list of contributors* Regional and urban studies interest economists in many subdisciplines, such as labor, development, and public economics* Table of contents combines theoretical and applied subjects, ensuring broad appeal to readers