Orchestrating the spatial planning process: from Business Process Management to 2nd generation Planning Support Systems

Metaplanning can be considered as a necessary step for improving collaboration, transparency and accountability in sustainable and democratic spatial decision-making process. This paper reports current findings on the operational implementation of the metaplanning concept developed by the authors relying on Business Process Management methods and techniques. Two solutions are presented which implement spatial planning process workflows thanks to the development of original spatial data and processing services connectors to a Business Process Management suite. These results can be considered as a first step towards the development of 2nd generation Planning Support Systems

State reconstruction for composite systems of two spatial qubits

Pure entangled states of two spatial qudits have been produced by using the momentum transverse correlation of the parametric down-converted photons [Phys. Rev. Lett. \textbf{94} 100501]. Here we show a generalization of this process to enable the creation of mixed states of spatial qudits and by using the technique proposed we generate mixed states of spatial qubits. We also report how the process of quantum tomography is experimentally implemented to characterize these states. This tomographic reconstruction is based on the free evolution of spatial qubits, coincidence detection and a filtering process. The reconstruction method can be generalized for the case of two spatial qudits.Comment: 3 Figure

Modelling of Deceleration Process of Spatial Movements

Solution of breaking problem of moving mechanical object is discussed. Five boundary values are used for the problem. Mathematical model of the process has been obtained. Also all dynamic functions and characteristics are represented. They shoe the accuracy of terminal positioning of deceleration.Terminal control, moving objects, boundary problems, adaptive control, reduction, acceleration, transient process, deceleration, positioning of deceleration

Refining Coarse-grained Spatial Data using Auxiliary Spatial Data Sets with Various Granularities

We propose a probabilistic model for refining coarse-grained spatial data by utilizing auxiliary spatial data sets. Existing methods require that the spatial granularities of the auxiliary data sets are the same as the desired granularity of target data. The proposed model can effectively make use of auxiliary data sets with various granularities by hierarchically incorporating Gaussian processes. With the proposed model, a distribution for each auxiliary data set on the continuous space is modeled using a Gaussian process, where the representation of uncertainty considers the levels of granularity. The fine-grained target data are modeled by another Gaussian process that considers both the spatial correlation and the auxiliary data sets with their uncertainty. We integrate the Gaussian process with a spatial aggregation process that transforms the fine-grained target data into the coarse-grained target data, by which we can infer the fine-grained target Gaussian process from the coarse-grained data. Our model is designed such that the inference of model parameters based on the exact marginal likelihood is possible, in which the variables of fine-grained target and auxiliary data are analytically integrated out. Our experiments on real-world spatial data sets demonstrate the effectiveness of the proposed model.Comment: Appears in Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI 2019

A tutorial on estimator averaging in spatial point process models

Assume that several competing methods are available to estimate a parameter in a given statistical model. The aim of estimator averaging is to provide a new estimator, built as a linear combination of the initial estimators, that achieves better properties, under the quadratic loss, than each individual initial estimator. This contribution provides an accessible and clear overview of the method, and investigates its performances on standard spatial point process models. It is demonstrated that the average estimator clearly improves on standard procedures for the considered models. For each example, the code to implement the method with the R software (which only consists of few lines) is provided

Convergence, Patenting Activity and Geographic Spillovers: A Spatial Econometric Analysis for European Regions

In this paper, we investigate the impact of geographical spillovers in the patenting activity and convergence process for a sample of 131 European regions over the 1981-2001 period. Using spatial econometrics methods (Anselin, 1988, 2001), we detect spatial autocorrelation and heterogeneity in the regional distribution of patent applications to the European Patent Office. Then, we include successively these spatial effects in a convergence analysis. A first specifcation taking into account the spatial dependence reveals a global convergence process between European regions as also a positive effect of geographical spillovers on this convergence process. Secondly, the spatial heterogeneity is taking into account by a specification with two spatial regimes, a "Core-Periphery" type. Finally, ours results show that the global convergence process is hiding disparities and different convergence processes for the two regimes. Only regions that belong to the "Core" of the EU are converging.