Modelling of Dynamic Spatial Processes

The paper is concerned with econometric modeling of the dynamic spatial processes on the example of the GDP per capita in selected European countries. The considerations of the paper are focused on investigations of the structure of components of the spatio-temporal process. As a result of the analysis some specifications of the dynamic spatial models have been obtained. Next the issues of the estimation and verification of the models are presented. The main conclusion from the analysis is that the econometric models of the spatio-temporal processes ought to be of the dynamic character, e.g. considering the spatial and spatio-temporal trends and spatial, temporal and spatio-temporal autodependence as well.spatio-temporal trend, autocorrelation, spatial lag model, dynamic spatial model.

Spatial and Temporal Modeling of Community Non-Timber Forest Extraction

This paper examines the interaction of spatial and dynamic aspects of resource extraction from forests by local people. Highly cyclical and varied across both space and time, the patterns of resource extraction resulting from the spatial-temporal model bear little resemblance to the patterns drawn from focusing either on spatial or temporal aspects of extraction, as is typical in both the modeling and empirical literature to date. Combining the spatial-temporal model with a measure of success in community forest management.the ability to avoid open-access resource degradation.characterizes the impact of incomplete property rights on patterns of resource extraction and stocks. Key words: Spatial and temporal modeling; renewable resources; non-timber forest products; common property resources

Structured Sequence Modeling with Graph Convolutional Recurrent Networks

This paper introduces Graph Convolutional Recurrent Network (GCRN), a deep learning model able to predict structured sequences of data. Precisely, GCRN is a generalization of classical recurrent neural networks (RNN) to data structured by an arbitrary graph. Such structured sequences can represent series of frames in videos, spatio-temporal measurements on a network of sensors, or random walks on a vocabulary graph for natural language modeling. The proposed model combines convolutional neural networks (CNN) on graphs to identify spatial structures and RNN to find dynamic patterns. We study two possible architectures of GCRN, and apply the models to two practical problems: predicting moving MNIST data, and modeling natural language with the Penn Treebank dataset. Experiments show that exploiting simultaneously graph spatial and dynamic information about data can improve both precision and learning speed

A Review of 21st-Century Studies

PM10 prediction has attracted special legislative and scientific attention due to its harmful effects on human health. Statistical techniques have the potential for high-accuracy PM10 prediction and accordingly, previous studies on statistical methods for temporal, spatial and spatio-temporal prediction of PM10 are reviewed and discussed in this paper. A review of previous studies demonstrates that Support Vector Machines, Artificial Neural Networks and hybrid techniques show promise for suitable temporal PM10 prediction. A review of the spatial predictions of PM10 shows that the LUR (Land Use Regression) approach has been successfully utilized for spatial prediction of PM10 in urban areas. Of the six introduced approaches for spatio-temporal prediction of PM10, only one approach is suitable for high-resolved prediction (Spatial resolution < 100 m; Temporal resolution ¤ 24 h). In this approach, based upon the LUR modeling method, short-term dynamic input variables are employed as explanatory variables alongside typical non-dynamic input variables in a non- linear modeling procedure

Testing the spatial scale and the dynamic structure in regional models (a contribution to spatial econometric specification analysis)

This article addresses the problem of specification uncertainty in modeling spatial economic theories in stochastic form. It is ascertained that the traditional approach to spatial econometric modeling does not adequately deal with the type and extent of specification uncertainty commonly encountered in spatial economic analyses. Two alternative spatial econometric modeling procedures proposed in the literature are reviewed and shown to be suitable for analyzing systematically two sources of specification uncertainty, viz., the level of aggregation and the spatio-temporal dynamic structure in multiregional econometric models. The usefulness of one of these specification procedures is illustrated by the construction of a simple multiregional model for The Netherlands

Describing Videos by Exploiting Temporal Structure

Recent progress in using recurrent neural networks (RNNs) for image description has motivated the exploration of their application for video description. However, while images are static, working with videos requires modeling their dynamic temporal structure and then properly integrating that information into a natural language description. In this context, we propose an approach that successfully takes into account both the local and global temporal structure of videos to produce descriptions. First, our approach incorporates a spatial temporal 3-D convolutional neural network (3-D CNN) representation of the short temporal dynamics. The 3-D CNN representation is trained on video action recognition tasks, so as to produce a representation that is tuned to human motion and behavior. Second we propose a temporal attention mechanism that allows to go beyond local temporal modeling and learns to automatically select the most relevant temporal segments given the text-generating RNN. Our approach exceeds the current state-of-art for both BLEU and METEOR metrics on the Youtube2Text dataset. We also present results on a new, larger and more challenging dataset of paired video and natural language descriptions.Comment: Accepted to ICCV15. This version comes with code release and supplementary materia

HIERARCHICAL BAYESIAN MODELING FOR SPATIAL TIME SERIES: AN ALTERNATIVE APPROACH TO SPATIAL SUR

Despite the fact that the amount of datasets containing long economic time series with a spatial reference has significantly increased during the years, the presence of integrated techniques that aim to describe the temporal evolution of the series while accounting for the location of the measurements and their neighboring relations is very sparse in the econometric literature. This paper shows how the Hierarchical Bayesian Space Time model presented by Wikle, Berliner and Cressie (Environmental and Ecological Statistics, l998) for temperature modeling, can be tailored to model relationships between variables that have both a spatial and a temporal reference. The first stage of the hierarchical model includes a set of regression equations (each one corresponding to a different location) coupled with a dynamic space-time process that accounts for the unexplained variation. At the second stage, the regression parameters are endowed with priors that reflect the neighboring relations of the locations under study; moreover, the spatio-temporal dependencies in the dynamic process for the unexplained variation are being established. Putting hyperpriors on previous stages’ parameters completes the Bayesian formulation, which can be implemented in a Markov Chain Monte Carlo framework. The proposed modeling strategy is useful in quantifying the temporal evolution in relations between economic variables and this quantification may serve for excess forecasting accuracy.space-time models