Browsing by Subject "Spatial analysis (Statistics)"
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Item Information heterogeneity and voter uncertainty in spatial voting: the U.S. presidential elections, 1992-2004(2007-12) Lee, So Young; Hinich, Melvin J.; Lin, Tse-minThis dissertation addresses voters' information heterogeneity and its effect on spatial voting. While most spatial voting models simply assume that voter uncertainty about candidate preferences is homogeneous across voters despite Downs' early use of uncertainty scale to classify the electorate, information studies have discovered that well and poorly informed citizens have sizeable and consistent differences in issue conceptualization, perception, political opinion and behavior. Built upon the spatial theory's early insights on uncertainty and the findings of information literature, this dissertation claims that information effects should be incorporated into the spatial voting model. By this incorporation, I seek to unify the different scholarly traditions of the spatial theory of voting and the study of political information. I hypothesize that uncertainty is not homogeneous, but varies with the level of information, which are approximated by political activism as well as information on candidate policy positions. To test this hypothesis, I employ heteroskedastic probit models that specify heterogeneity of voter uncertainty in probabilistic models of spatial voting. The models are applied to the U.S. presidential elections in 1992-2004. The empirical results of the analysis strongly support the expectation. They reveal that voter uncertainty is heterogeneous as a result of uneven distributions of information and political activism even when various voting cues are available. This dissertation also discovers that this heterogeneity in voter uncertainty has a significant effect on electoral outcomes. It finds that the more uncertain a voter is about the candidates, the more likely he or she is to vote for the incumbent or a better-known candidate. This clearly reflects voters' risk-averse attitudes that reward the candidate with greater certainty, all other things held constant. Heterogeneity in voter uncertainty and its electoral consequences, therefore, have important implications for candidates' strategies. The findings suggest that the voter heterogeneity leads candidates' equilibrium strategies and campaign tactics to be inconsistent with those that spatial analysts have normally proposed.Item Spatial modelling and analysis of wireless ad-hoc and sensor networks: an energy perspective(2006) Baek, Seung Jun; De Veciana, GustavoThis dissertation focuses on modelling and analyzing the spatial characteristics of traffic in these networks so as to extend network lifetime for various application/traffic scenarios. In the first part of the dissertation we consider large-scale sensor networks that systematically sample a spatio-temporal field. Firstly we formulate a distributed compression problem subject to aggregation costs to a single sink. We show that the optimal solution is based on ordering sensors according to aggregation costs. Next we consider a hierarchical model for a sensor network including sinks, compressors and sensors. We show that the optimal organization is associated with the Johnson-Mehl tessellation induced by nodes’ locations. Our analysis and simulations show the proposed scheme can yield 8-28% energy savings depending on the compression ratio. In the second part of the dissertation we investigate the use of proactive multipath routing in ad hoc wireless networks. The focus is on optimizing tradeoffs between the increased energy cost associated with spreading traffic and the improved spatial balance of energy burdens. We show how its optimization depends on the relative values of the energy reserves/storage, replenishing rates, and network load characteristics. In particular, we show that the degree of spreading should roughly scale as the square root of the bits-meters load offered by a session. Simulation v results confirm that proactive multipath routing decreases the probability of energy depletion by orders of magnitude versus that of a shortest path routing scheme when the initial energy reserve is high. In the third part of the dissertation we consider a large sensor network with mobile sinks. The network makes use of aggregation nodes (AGNs), for compression and/or data fusion of locally sensed data. Since the aggregated data may cause a concentration of energy burdens when routed to sinks, we use proactive multipath routing between AGNs to mobile sinks. We show that the scale of aggregation and degree of spreading can be optimized. Particularly if the sensed data is bursty in space and time, then one can reap substantial benefits from aggregation and balancing.Item Toward a comprehensive, unified, framework for analyzing spatial location choice(2005) Sivakumar, Aruna; Bhat, Chandra R. (Chandrasekhar R.), 1964-In today’s world of increasing congestion and insufficient scope for infrastructural expansions, urban and transportation planners rely on the accuracy and behavioral realism of travel demand models to make informed policy decisions. The development of accurate and behaviorally realistic travel demand models requires a good understanding of individual travel behavior, and an important step toward this has been the development of the activity-based paradigm, which states that travel is a result of the desire to participate in activities at spatially scattered locations. Activity-based travel demand modeling systems essentially model the activity-travel patterns of individuals, which are characterized by several attributes such as activity purpose, location of activity participation and choice of mode. Of all these attributes, the choice of location of activity participation is one that has received relatively inadequate attention in the literature. On the other hand, the location of activity participation spatially pegs the daily activity-travel patterns of individuals. Accurate predictions of activity location are, therefore, key to effective travel demand management and air quality control strategies. Moreover, an understanding of the factors that influence the choice of location can contribute to more effective land-use and zoning policies. The broad objectives of this dissertation research are two-fold. The first objective is to develop a comprehensive econometric model of location choice for non-work activities that incorporates accuracy and behavioral realism in capturing different choice behaviors. This was achieved through the comprehensive introduction of heterogeneity in choice behavior, including observed and unobserved sources of inter- and intra-personal heterogeneity, spatial correlation, variety seeking and loyalty/inertial behavior, and spatial cognition. The estimation of such a flexible model typically requires the use of simulated maximum likelihood estimation (SMLE). The second broad objective of this research is to contribute toward improving the efficiency of the SMLE by comparing the performance of various quasi-Monte Carlo (QMC) sequences and their scrambled versions. Numerical experiments were designed and the Random Linear and Random Digit Scrambled Faure sequences are identified as the most efficient. Finally, all these research efforts contribute to the empirical estimation of non-maintenance shopping location choice models using panel data from the Mobidrive survey.