Population segregation measurement is a topic of broad interest in the social sciences. In this paper we draw from recent advances in the spatial analysis literature to derive individualized measures of clustering and exposure. Recent research on accessibility has seen a shift from place-based...

Large variability and correlations among the coefficients obtained from the method of geographically weighted regression (GWR) have been identified in previous research. This is an issue that poses a serious challenge for the utility of the method as a tool to investigate multivariate...

Geographically weighted regression (GWR) has been proposed as a technique to explore spatial parametric nonstationarity. The method has been developed mainly along the lines of local regression and smoothing techniques, a strategy that has led to a number of difficult questions about the...

This paper addresses interagent interactions, an issue that has received limited attention in travel behavior research. Drawing upon the theory of externalities and the sociological notion of social networks, we develop a discrete choice model that incorporates elements of social influence in...

Spatial association effects, perhaps the most important concern in the analysis of spatial data, have been amply studied from a global perspective in the exploratory and modeling domains, and more recently also from a local perspective in the realm of exploratory data analysis. In a local...

Abstract. This paper contributes to the telework literature by investigating empirically the impact of social influence on the decision to telework. An innovative web-based telework survey was developed and used to obtain data from a sample of employees of a large postsecondary institution in...

Identification of suitable instruments is a critical step for the implementation of instrumental variable (IV) estimation. A challenge is that, as the level of correlation between the instruments and the endogenous variable increases, so also do the chances that the instruments themselves will...