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This paper studies the determinants of urban sprawl in France using panel datasets for the four largest metropolitan areas (Paris, Lyon, Marseille, Lille) over the period 1985–98. A measure of urban sprawl is proposed at municipality level. Due to the huge heterogeneity of the panels, it seems difficult to make the fundamental homogeneity assumption underlying pooled models. Thus, random coefficient models under heteroscedasticity of the disturbances are estimated for each metropolitan area using a hierarchical Bayes approach based on the Markov chain Monte Carlo simulation method. It is found that urban sprawl is positively related to the income growth of the tax payers’ fiscal households for the period of rapid growth in the late 1980s. At the opposite extreme, the income effects are negative for non-tax payers. During the recession, income effects are significant neither for tax payers’ nor for non-tax payers’ fiscal households and are significantly positive for tax payers and negative for non-tax payers over the recovery. Finally, on average, the inequality index—difference of average net income between tax payers and exempted fiscal households—has a lower impact on urban sprawl than the income effect. [ABSTRACT FROM PUBLISHER]

This work presents a new perspective on characterizing the similarity between elements of a database or, more generally, nodes of a weighted and undirected graph. It is based on a Markov-chain model of random walk through the database. More precisely, we compute quantities (the average commute time, the pseudoinverse of the Laplacian matrix of the graph, etc.) that provide similarities between any pair of nodes, having the nice property of increasing when the number of paths connecting those elements increases and *en the 'length" of paths decreases. It turns out that the square root of the average commute time is a Euclidean distance and that the pseudoinverse of the Laplacian matrix is a kernel matrix (its elements are inner products closely related to commute times). A principal component analysis (PCA) of the graph is introduced for computing the subspace projection of the node vegors in a manner that preserves as much variance as possible in terms of the Euclidean commute-time distance. This graph PCA provides a nice interpretation to the 'Fiedler vector." widely used for graph partitioning. The model is evaluated on a collaborative recommendation task where suggestions are made about which movies people should watch based upon what they watched in the pest. Experimental results on the MovieLens database show that the Laplacian-based similarities perform well in comparison with other methods. The model, which nicely fits into the so-called "statistical relational learning" framework, could also be used to compute document or word similarities, and, more generally, could be applied to machine-learning and pattern-recognition tasks involving a relational database. [ABSTRACT FROM AUTHOR]

The role generic relationship for conceptual modeling relates a class of objects (e.g., persons) and classes of roles (e.g., students, employees) for those objects. The role relationship is meant to capture dynamic aspects of real-world objects while the usual generalization relationship deals with their more static aspects. Therefore, to take into account both static and dynamic aspects, object languages and systems must somehow support both relationships. This paper presents a generic role model where the semantics of roles is defined at both the class and the instance levels. It discusses the interaction between the role relationship and generalization, and it attempts to clarify some of their similarities and differences. The introduction of roles as an abstraction mechanism in the overall software development lifecycle is reviewed. The paper then proposes a comprehensive implementation for the role relationship, with the help of a metaclass mechanism. Our implementation is illustrated along the lines of the VODAK modeling language. Thus, the semantics of our role model is implemented in a metaclass that is a template to be instantiated in applications. Application classes are then created as instances of the metaclass and they are thereby endowed with structure and behavior consistent with the semantics of roles. [Copyright &y& Elsevier]

Materialization is a powerful and ubiquitous abstraction pattern for conceptual modeling that relates a class of categories (e.g., models of cars) and a class of more concrete objects (e.g., individual cars). This paper presents materialization as a generic relationship between two classes of objects and describes an abstract implementation of it. The presentation is abstract in that it is not targeted at a specific object system. The target system is supposed to provide: 1) basic object-modeling facilities, supplemented with an explicit metaclass concept and 2) operations for dynamic schema evolution like creation or deletion of a subclass of a given class and modification of the type of an attribute of a class. The presentation is generic in that the semantics of materialization is implemented in a metaclass, which is a template to be instantiated in applications. Application classes are created as instances of the metaclass and they are thereby endowed with structure and behavior consistent with the generic semantics of materialization. [ABSTRACT FROM AUTHOR]

This paper adopts a dynamic general nesting spatial panel data model with common factors to explore the effect of population density, real household income per capita, car fleet per capita, and real price of gasoline on departmental traffic per light vehicle in France over the period 1990–2009. Spatial heterogeneity is modeled by a translog function in the first three explanatory variables, which are dominated by variation in the cross-sectional domain, while the real price of gasoline, which is dominated by variation in the time domain, is treated as an observable common factor. Additional unobservable common factors are controlled for by principal components with heterogenous coefficients, building on previous work of Shi and Lee (2017a), thereby, generalizing the dynamic spatial panel data model with spatial and time period fixed applied in recent studies. It is found that the spatial lag in the dependent variable becomes insignificant due to these extensions. This paper explains the wider implications of this finding for spatial econometric modeling of cross-sectional dependence. In addition, the elasticities of the first three explanatory variables are shown to vary across space and time and to follow a plausible structure. Among other, an important result is that the long run income elasticity of car traffic diminished from 1.0 in 1990 to 0.4 in 2003, and then remained almost constant until the end of our sample period in 2009, i.e., during the peak-car traffic period. [ABSTRACT FROM AUTHOR]

ABSTRACT: This paper focuses on panel data models combining spatial dependence with a nested (hierarchical) structure. We use a generalized moments estimator to estimate the spatial autoregressive parameter and the variance components of the disturbance process. A spatial counterpart of the Cochrane‐Orcutt transformation leads to a feasible generalized least squares procedure to estimate the regression parameters. Monte Carlo simulations show that our estimators perform well in terms of root mean square error compared to the maximum likelihood estimator. The approach is applied to English house price data for districts nested within counties. [ABSTRACT FROM AUTHOR]

This paper analyzes the demand of French farmers for pesticides by disaggregating among the three main categories of pesticides. We estimate the demand elasticities of herbicides, insecticides, and fungicides with respect to pesticide expenditure, and also consider crop differentiation. We retain a Linear Approximated Almost Ideal Demand System specification. A Full-Information Maximum Likelihood estimation procedure is used to deal with corner solution problems and censored data. The estimation is based on two cross-sections covering pesticide use of three major crops cultivated in France in 2001 and 2006. Our results show that farmers' response to price variation is very low, especially for 2001. Furthermore, elasticities of pesticide expenditure are significantly different across categories: the highest levels are obtained for fungicides and the lowest ones for insecticides. Finally, we find higher own-price elasticities for herbicides and fungicides than for insecticides, which are less frequently used. [ABSTRACT FROM AUTHOR]

This paper checks the sensitivity of two adaptive heteroskedastic estimators suggested by Li and Stengos (1994) and Roy (2002) for an error component regression model to misspecification of the form of heteroskedasticity. In particular, we run Monte Carlo experiments using the heteroskedasticity setup by Li and Stengos (1994) to see how the misspecified Roy (2002) estimator performs. Next, we use the heteroskedasticity setup by Roy (2002) to see how the misspecified Li and Stengos (1994) estimator performs. We also check the sensitivity of these results to the choice of the smoothing parameters, the sample size, and the degree of heteroskedasticity. We find that the Li and Stengos (1994) estimator performs better under this type of misspecification than the corresponding estimator of Roy (2002). However, the former estimator is sensitive to the choice of the bandwidth. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a survey as well as an empirical comparison and evaluation of seven kernels on graphs and two related similarity matrices, that we globally refer to as “kernels on graphs” for simplicity. They are the exponential diffusion kernel, the Laplacian exponential diffusion kernel, the von Neumann diffusion kernel, the regularized Laplacian kernel, the commute-time (or resistance-distance) kernel, the random-walk-with-restart similarity matrix, and finally, a kernel first introduced in this paper (the regularized commute-time kernel) and two kernels defined in some of our previous work and further investigated in this paper (the Markov diffusion kernel and the relative-entropy diffusion matrix). The kernel-on-graphs approach is simple and intuitive. It is illustrated by applying the nine kernels to a collaborative-recommendation task, viewed as a link prediction problem, and to a semisupervised classification task, both on several databases. The methods compute proximity measures between nodes that help study the structure of the graph. Our comparisons suggest that the regularized commute-time and the Markov diffusion kernels perform best on the investigated tasks, closely followed by the regularized Laplacian kernel. [Copyright &y& Elsevier]

Abstract: This paper presents a model allowing to tune continual exploration in an optimal way by integrating exploration and exploitation in a common framework. It first quantifies exploration by defining the degree of exploration of a state as the entropy of the probability distribution for choosing an admissible action in that state. Then, the exploration/exploitation tradeoff is formulated as a global optimization problem: find the exploration strategy that minimizes the expected cumulated cost, while maintaining fixed degrees of exploration at the states. In other words, maximize exploitation for constant exploration. This formulation leads to a set of nonlinear iterative equations reminiscent of the value-iteration algorithm and demonstrates that the Boltzmann strategy based on the Q-value is optimal in this sense. Convergence of those equations to a local minimum is proved for a stationary environment. Interestingly, in the deterministic case, when there is no exploration, these equations reduce to the Bellman equations for finding the shortest path. Furthermore, if the graph of states is directed and acyclic, the nonlinear equations can easily be solved by a single backward pass from the destination state. Stochastic shortest-path problems and discounted problems are also studied, and links between our algorithm and the SARSA algorithm are examined. The theoretical results are confirmed by simple simulations showing that the proposed exploration strategy outperforms the -greedy strategy. [Copyright &y& Elsevier]

This paper presents a panel data analysis of annual time series from 1975 to 1995 for 62 urban areas in France. It compares the results obtained from a conventional fixed-effects (FE) model with a Bayesian approach (shrinkage estimators), which allows the computation of elasticities for each urban area. First, considering only three economic determinants (vehicle km, income and price), we show the sensitivity of the estimates to the time period used for the estimation. On the basis of these models, public transport appears as an “inferior good” (i.e. its income elasticity is negative). By combining economic determinants with structural determinants (i.e. population ageing, urban sprawl and growing car ownership) synthesized in a single indicator, we show that this ‘income effect’ is in fact mainly a “motorization effect”. The competition with the automobile also appears through the cross-elasticity to the fuel price. Finally, the impact of supply is decomposed into a dominant effect of quantity (seats kilometres), and the weaker effects of quality (frequency and density of network). The major conclusion is that the downward trend in public transport patronage is mainly due to increasing car ownership, and that this effect will be less and less important over time since the growth of the car stock is decelerating. In addition, the use of public transport is quite sensitive to the volume supplied and to its price, which makes the financial equilibrium of this industry problematic. [Copyright &y& Elsevier]

This study analyses the impacts of changes in fares, service supply, income and other factors on the demand for public transport on the basis of panels of English counties and French urban areas. The analysis is based on dynamic econometric models, so that both short- and long-run elasticities are estimated. Conventional approaches (i.e. fixed- and random-effect models) rely on the hypothesis that elasticities are the same for all areas. Having shown that this hypothesis is not valid for these data sets, the heterogeneity amongst areas is accounted for using a random-coefficients approach, and Bayesian shrinkage estimators.Estimated elasticities for France and England are compared, by using a common set of variables, similar time period and a common methodology. The results show a considerable variation in elasticities among areas within each country. The major conclusion is that public transport demand is relatively sensitive to fare changes, so that policy measures aimed at fare reduction (subsidisation) can play a substantial role in encouraging the use of public transport, thus reducing the use of private cars. [Copyright &y& Elsevier]