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

Optimal Preservation of Agricultural and Environmental Land within a Municipality Under Irreversibility and Uncertainty

Replaced with revised version of paper 07/22/11.Uncertainty, Irreversibility, Spatial-temporal modeling, Value of information, Policy design, Climate change, Agricultural preservation, Environmental conservation, Environmental Economics and Policy, Land Economics/Use, Political Economy, Risk and Uncertainty,

Modeling spatial-temporal change of Poyang Lake marshland based on an uncertainty theory - random sets

AbstractUncertainty modeling now engages the attention of researchers in spatial temporal change analysis in remote sensing. Some studies proposed to use random sets for modeling the spatial uncertainty of image objects with uncertain boundaries, but none have considered the parameter determination problem for large datasets. In this paper we refined the random set models for monitoring monthly changes in wetland vegetation areas from series of images. Twelve cloud-free HJ-1A/1B images from April 2009 to March 2010 were used for monitoring spatial-temporal changes of Poyang Lake wetlands. We applied random sets to represent spatial uncertainty of wetland vegetation that were extracted from normalized difference vegetation index (NDVI) maps. Time series of random sets reflect the seasonal differences of location and extents of the wetlands, whereas degree of uncertainties indicated by SD and CV indices reflect the gradual change of the wetland vegetation in space. Results show that the uncertain extents of wetland vegetation change through the year, achieving the largest range and uncertainty degree in autumn. This coincides with the highly heterogeneous vegetation status in autumn, since the wetland recovers gradually after flooding and young vegetation emerges at gradually changing densities, thus providing forage in different ecological zones for different types of migratory birds. We conclude that the random set model enriches spatial-temporal modeling of phenomena which are uncertain in space and dynamic in time

A Deep Spatio-Temporal Fuzzy Neural Network for Passenger Demand Prediction

In spite of its importance, passenger demand prediction is a highly challenging problem, because the demand is simultaneously influenced by the complex interactions among many spatial and temporal factors and other external factors such as weather. To address this problem, we propose a Spatio-TEmporal Fuzzy neural Network (STEF-Net) to accurately predict passenger demands incorporating the complex interactions of all known important factors. We design an end-to-end learning framework with different neural networks modeling different factors. Specifically, we propose to capture spatio-temporal feature interactions via a convolutional long short-term memory network and model external factors via a fuzzy neural network that handles data uncertainty significantly better than deterministic methods. To keep the temporal relations when fusing two networks and emphasize discriminative spatio-temporal feature interactions, we employ a novel feature fusion method with a convolution operation and an attention layer. As far as we know, our work is the first to fuse a deep recurrent neural network and a fuzzy neural network to model complex spatial-temporal feature interactions with additional uncertain input features for predictive learning. Experiments on a large-scale real-world dataset show that our model achieves more than 10% improvement over the state-of-the-art approaches.Comment: https://epubs.siam.org/doi/abs/10.1137/1.9781611975673.1

Optimal integration of auditory and vibrotactile information for judgments of temporal order

Recent research that assessed spatial judgments about multisensory stimuli suggests that humans integrate multisensory inputs in a statistically optimal manner by weighting each input by its normalized reciprocal variance. Is integration similarly optimal When humans judge the temporal properties of bimodal stimuli? Twenty-four participants performed temporal order judgments (TOJs,) about 2 spatially separated stimuli. Stimuli were auditory, vibrotactile, or both. The temporal profiles of vibrotactile stimuli were manipulated to produce 3 levels of precision for TOJs. In bimodal conditions, the asynchrony between the 2 unimodal stimuli that comprised it bimodal Stimulus was manipulated to determine the weight given to touch. Bimodal performance on 2 measures-judgment uncertainty and tactile weight-was predicted With unimodal data. A model relying exclusively on audition wits rejected on the basis of both measures. A second model that selected the best input on each trial did not predict the reduced judgment uncertainty observed in bimodal trials. Only the optimal Maximum-likelihood-estimation model predicted both judgment uncertainties and weights the model's validity is extended to TOJs. Alternatives for modeling the process of event sequencing based on integrated multisensory inputs are discussed

Spatio-Temporal Modeling of Southern Pine Beetle Outbreaks with a Block Bootstrapping Approach

Our study focuses on modeling southern pine beetle (SPB) outbreaks in the southern area. The approach is to evaluate SPB outbreak frequency in a spatio-temporal framework. A block bootstrapping method with zero-inflated estimation has been proposed to construct a statistical model accounting for explanatory variables while adjusting for spatial and temporal autocorrelation. Although the bootstrap (Efron 1979) method can handle independent observations well, the strong autocorrelation of SPB outbreaks brings about a major challenge. Motivated by bootstrapping overlapping blocks method in autoregressive time series scenario (Kunsch 1989) and block bootstrapping method of dependent data from a spatial map (Hall 1985), we have developed a method to bootstrap overlapping spatio-temporal blocks. By selecting an appropriate block size, the spatial-temporal correlation can be eliminated. The second challenge arises from the fact that the SPB spots distribution has a heavy weight on 0. To accommodate this issue, the zero-inflated models are adopted in the estimation stage. With our saptio-temporal block bootstrapping approach, impacts of environmental factors on SPB outbreaks and implications of pine forest management are assessed. Almost all the explanatory variables, including drought, temperature, forest ecosystem and hurricane, have been detected to have significant impacts. Forestland size and government share of forestland would positively contribute to SPB outbreaks significantly. Meanwhile, our method offers a way to forecast the frequency of future SPB outbreaks, given the current environmental information of a county.Southern Pinebeetle, Block Bootstrapping, Risk and Uncertainty,

Representing uncertainty in continental-scale gridded precipitation fields for agrometeorological modeling

This work proposes a relatively simple methodology for creating ensembles of precipitation inputs that are consistent with the spatial and temporal scale necessary for regional crop modeling. A high-quality reference precipitation dataset [the European Land Data Assimilation System (ELDAS)] was used as a basis to define the uncertainty in an operational precipitation database [the Crop Growth Monitoring System (CGMS)]. The distributions of precipitation residuals (CGMS ¿ ELDAS) were determined for classes of CGMS precipitation and transformed to a Gaussian distribution using normal score transformations. In cases of zero CGMS precipitation, the occurrence of rainfall was controlled by an indicator variable. The resulting normal-score-transformed precipitation residuals appeared to be approximately multivariate Gaussian and exhibited strong spatial correlation; however, temporal correlation was very weak. An ensemble of 100 precipitation realizations was created based on back-transformed spatially correlated Gaussian residuals and indicator realizations. Quantile¿quantile plots of 100 realizations against the ELDAS reference data for selected sites revealed similar distributions (except for the 100th percentile, owing to some large residuals in the realizations). The semivariograms of realizations for sampled days showed considerable variability in the overall variance; the range of the spatial correlation was similar to that of the ELDAS reference dataset. The intermittency characteristics of wet and dry periods were reproduced well for most of the selected sites, but the method failed to reproduce the dry period statistics in semiarid areas (e.g., southern Spain). Finally, a case study demonstrates how rainfall ensembles can be used in operational crop modeling and crop yield forecasting

Score Matching-based Pseudolikelihood Estimation of Neural Marked Spatio-Temporal Point Process with Uncertainty Quantification

Spatio-temporal point processes (STPPs) are potent mathematical tools for modeling and predicting events with both temporal and spatial features. Despite their versatility, most existing methods for learning STPPs either assume a restricted form of the spatio-temporal distribution, or suffer from inaccurate approximations of the intractable integral in the likelihood training objective. These issues typically arise from the normalization term of the probability density function. Moreover, current techniques fail to provide uncertainty quantification for model predictions, such as confidence intervals for the predicted event's arrival time and confidence regions for the event's location, which is crucial given the considerable randomness of the data. To tackle these challenges, we introduce SMASH: a Score MAtching-based pSeudolikeliHood estimator for learning marked STPPs with uncertainty quantification. Specifically, our framework adopts a normalization-free objective by estimating the pseudolikelihood of marked STPPs through score-matching and offers uncertainty quantification for the predicted event time, location and mark by computing confidence regions over the generated samples. The superior performance of our proposed framework is demonstrated through extensive experiments in both event prediction and uncertainty quantification