James P. LeSage

TL;DR: In this article, an introduction to spatial econometric models and methods is provided that discusses spatial autoregressive processes that can be used to extend conventional regression models and an applied example that examines the relationship between commuting to work times and transportation mode choice for a sample of 3,110 US counties in the year 2000.

TL;DR: In this paper, the authors propose spatial weight structures that model dependence among the N OD pairs in a fashion consistent with standard spatial autoregressive models, which is an extension of the spatial regression models described in Anselin (1988).

TL;DR: This text provides an introduction to spatial econometric theory along with numerous applied illustrations of the models and methods described, and describes the implementation details that greatly enhance understanding and allow users to make intelligent use of these methods in applied settings.

TL;DR: The overall results of this study demonstrated that language f MRI could not be used to make critical surgical decisions in the absence of direct brain mapping, and other acquisition protocols are required for evaluation of the potential role of language fMRI in the accurate detection of essential cortical language areas.

TL;DR: The authors used Bayesian model comparison methods to simultaneously specify both the spatial weight structure and explanatory variables for a spatial growth regression involving 255 NUTS 2 regions across 25 European countries and found that incorporating model uncertainty in conjunction with appropriate parameter interpretation decreased the importance of explanatory variables traditionally thought to exert an important influence on regional income growth rates.