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- Genre: Academic theses
Description
With the rapid development of mobile sensing technologies like GPS, RFID, sensors in smartphones, etc., capturing position data in the form of trajectories has become easy. Moving object trajectory analysis is a growing area of interest these days owing to its applications in various domains such as marketing, security, traffic monitoring and management, etc. To better understand movement behaviors from the raw mobility data, this doctoral work provides analytic models for analyzing trajectory data. As a first contribution, a model is developed to detect changes in trajectories with time. If the taxis moving in a city are viewed as sensors that provide real time information of the traffic in the city, a change in these trajectories with time can reveal that the road network has changed. To detect changes, trajectories are modeled with a Hidden Markov Model (HMM). A modified training algorithm, for parameter estimation in HMM, called m-BaumWelch, is used to develop likelihood estimates under assumed changes and used to detect changes in trajectory data with time. Data from vehicles are used to test the method for change detection. Secondly, sequential pattern mining is used to develop a model to detect changes in frequent patterns occurring in trajectory data. The aim is to answer two questions: Are the frequent patterns still frequent in the new data? If they are frequent, has the time interval distribution in the pattern changed? Two different approaches are considered for change detection, frequency-based approach and distribution-based approach. The methods are illustrated with vehicle trajectory data. Finally, a model is developed for clustering and outlier detection in semantic trajectories. A challenge with clustering semantic trajectories is that both numeric and categorical attributes are present. Another problem to be addressed while clustering is that trajectories can be of different lengths and also have missing values. A tree-based ensemble is used to address these problems. The approach is extended to outlier detection in semantic trajectories.
ContributorsKondaveeti, Anirudh (Author) / Runger, George C. (Thesis advisor) / Mirchandani, Pitu (Committee member) / Pan, Rong (Committee member) / Maciejewski, Ross (Committee member) / Arizona State University (Publisher)
Created2012
Description
For some children, peer victimization stops rather quickly, whereas for others it marks the beginning of a long trajectory of peer abuse (Kochenderfer-Ladd & Wardrop, 2001). Unfortunately, we know little about these trajectories and what factors may influence membership in increasing or decreasing victimization over time. To address this question, I identified children's developmental patterns of victimization in early elementary school and examined which child-level factors influenced children's membership in victimization trajectories using latent growth mixture modeling. Results showed that boys and girls demonstrated differential victimization patterns over time that also varied by victimization type. For example, boys experienced more physical victimization than girls and increased victimization over time was predicted by boys who display high levels of negative emotion (e.g., anger) towards peers and low levels of effortful control (e.g., gets frustrated easily). Conversely, girls exhibited multiple trajectories of increasing relational victimization (i.e., talking about others behind their back) over time, whereas most boys experienced low levels or only slightly increasing relational victimization over time. For girls, withdrawn behavior lack of positive emotion, and displaying of negative emotions was predictive of experiencing high levels of victimization over time.
ContributorsClary, Laura K (Author) / Ladd, Becky (Thesis advisor) / Updegraff, Kimberly (Committee member) / Valiente, Carlos (Committee member) / Ladd, Gary (Committee member) / Arizona State University (Publisher)
Created2015