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- Creators: Prokofiev, Sergey, 1891-1953
Description
This dissertation addresses the research challenge of developing efficient new methods for discovering useful patterns and knowledge in large volumes of electronically collected spatiotemporal activity data. I propose to analyze three types of such spatiotemporal activity data in a methodological framework that integrates spatial analysis, data mining, machine learning, and geovisualization techniques. Three different types of spatiotemporal activity data were collected through different data collection approaches: (1) crowd sourced geo-tagged digital photos, representing people's travel activity, were retrieved from the website Panoramio.com through information retrieval techniques; (2) the same techniques were used to crawl crowd sourced GPS trajectory data and related metadata of their daily activities from the website OpenStreetMap.org; and finally (3) preschool children's daily activities and interactions tagged with time and geographical location were collected with a novel TabletPC-based behavioral coding system. The proposed methodology is applied to these data to (1) automatically recommend optimal multi-day and multi-stay travel itineraries for travelers based on discovered attractions from geo-tagged photos, (2) automatically detect movement types of unknown moving objects from GPS trajectories, and (3) explore dynamic social and socio-spatial patterns of preschool children's behavior from both geographic and social perspectives.
ContributorsLi, Xun (Author) / Anselin, Luc (Thesis advisor) / Koschinsky, Julia (Committee member) / Maciejewski, Ross (Committee member) / Rey, Sergio (Committee member) / Griffin, William (Committee member) / Arizona State University (Publisher)
Created2012
Description
Adopting smart city tactics is important because it allows cities to develop sustainable communities through efficient policy initiatives. This study exemplifies how data analytics enables planners within smart cities to gain a better understanding of their population, and can make more informed choices based on these consumer choices. As a rising share of the millennial generation enters the workforce, cities across the world are developing policy initiatives in the hopes of attracting these highly educated individuals. Due to this generation's strength in driving regional economic vitality directly and indirectly, it is in the best interests of city planners to understand the preferences of millennials so this information can be used to improve the attractiveness of communities for this high-purchasing power, productive segment of the population. Past research has revealed a tendency within this demographic to make location decisions based on the degree of ‘livability’ in an area. This degree represents a holistic approach at defining quality of life through the interconnectedness of both the built and social environments in cities.
Due to the importance of millennials to cities around the globe, this study uses 2010 ZIP code area data and the Phoenix metropolitan area as a case study to test the relationships between thirteen parameters of livability and the presence of millennials after controlling for other correlates of millennial preference.
The results of a multiple regression model indicated a positive linear association between livability parameters within smart cities and the presence of millennials. Therefore, the selected parameters of livability within smart cities are significant measures in influencing location decisions made by millennials. Urban planners can consequently increase the likelihood in which millennials will choose to live in a given area by improving livability across the parameters exemplified in this study. This mutually beneficial relationship provides added support to the notion that planners should develop solutions to improve livability within smart cities.
Due to the importance of millennials to cities around the globe, this study uses 2010 ZIP code area data and the Phoenix metropolitan area as a case study to test the relationships between thirteen parameters of livability and the presence of millennials after controlling for other correlates of millennial preference.
The results of a multiple regression model indicated a positive linear association between livability parameters within smart cities and the presence of millennials. Therefore, the selected parameters of livability within smart cities are significant measures in influencing location decisions made by millennials. Urban planners can consequently increase the likelihood in which millennials will choose to live in a given area by improving livability across the parameters exemplified in this study. This mutually beneficial relationship provides added support to the notion that planners should develop solutions to improve livability within smart cities.
ContributorsBobman, Mitchell Cole (Author) / Mack, Elizabeth (Thesis director) / Koschinsky, Julia (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / School of Geographical Sciences and Urban Planning (Contributor)
Created2015-05
Description
A major challenge in health-related policy and program evaluation research is attributing underlying causal relationships where complicated processes may exist in natural or quasi-experimental settings. Spatial interaction and heterogeneity between units at individual or group levels can violate both components of the Stable-Unit-Treatment-Value-Assumption (SUTVA) that are core to the counterfactual framework, making treatment effects difficult to assess. New approaches are needed in health studies to develop spatially dynamic causal modeling methods to both derive insights from data that are sensitive to spatial differences and dependencies, and also be able to rely on a more robust, dynamic technical infrastructure needed for decision-making. To address this gap with a focus on causal applications theoretically, methodologically and technologically, I (1) develop a theoretical spatial framework (within single-level panel econometric methodology) that extends existing theories and methods of causal inference, which tend to ignore spatial dynamics; (2) demonstrate how this spatial framework can be applied in empirical research; and (3) implement a new spatial infrastructure framework that integrates and manages the required data for health systems evaluation.
The new spatially explicit counterfactual framework considers how spatial effects impact treatment choice, treatment variation, and treatment effects. To illustrate this new methodological framework, I first replicate a classic quasi-experimental study that evaluates the effect of drinking age policy on mortality in the United States from 1970 to 1984, and further extend it with a spatial perspective. In another example, I evaluate food access dynamics in Chicago from 2007 to 2014 by implementing advanced spatial analytics that better account for the complex patterns of food access, and quasi-experimental research design to distill the impact of the Great Recession on the foodscape. Inference interpretation is sensitive to both research design framing and underlying processes that drive geographically distributed relationships. Finally, I advance a new Spatial Data Science Infrastructure to integrate and manage data in dynamic, open environments for public health systems research and decision- making. I demonstrate an infrastructure prototype in a final case study, developed in collaboration with health department officials and community organizations.
The new spatially explicit counterfactual framework considers how spatial effects impact treatment choice, treatment variation, and treatment effects. To illustrate this new methodological framework, I first replicate a classic quasi-experimental study that evaluates the effect of drinking age policy on mortality in the United States from 1970 to 1984, and further extend it with a spatial perspective. In another example, I evaluate food access dynamics in Chicago from 2007 to 2014 by implementing advanced spatial analytics that better account for the complex patterns of food access, and quasi-experimental research design to distill the impact of the Great Recession on the foodscape. Inference interpretation is sensitive to both research design framing and underlying processes that drive geographically distributed relationships. Finally, I advance a new Spatial Data Science Infrastructure to integrate and manage data in dynamic, open environments for public health systems research and decision- making. I demonstrate an infrastructure prototype in a final case study, developed in collaboration with health department officials and community organizations.
ContributorsKolak, Marynia Aniela (Author) / Anselin, Luc (Thesis advisor) / Rey, Sergio (Committee member) / Koschinsky, Julia (Committee member) / Maciejewski, Ross (Committee member) / Arizona State University (Publisher)
Created2017
Spatial-Temporal Analysis of Barrett Freshmen 2007-2012: Source Area Analysis and Poisson Regression
Description
In order to help enhance admissions and recruiting efforts, this longitudinal study analyzed the geographic distribution of matriculated Barrett freshmen from 2007-2012 and sought to explore hot and cold spot locations of Barrett enrollment numbers using geographic information science (GIS) methods. One strategy involved   weighted mean center and standard distance analyses for each year of data for non-resident (out-of-state) freshmen home zip codes. Another strategy, a Poisson regression model, revealed recruitment "hot and cold spots" across the U.S. to project the expected counts of Barrett freshmen by zip code. This projected count served as a comparison for the actual admissions data, where zip codes with over and under predictions represented cold and hot spots, respectively. The mean center analysis revealed a westward shift from 2007 to 2012 with similar distance dispersions. The Poisson model projected zero-student zip codes with 99.2% accuracy and non-zero zip codes with 73.8% accuracy. Norwalk, CA (90650) and New York, NY (10021) represented the top out-of-state cold spot zip codes, while the model indicated that Chandler, AZ (85249) and Queen Creek, AZ (85242) had the most in-state potential for recruitment. The model indicated that more students have come from Albuquerque, NM (87122) and Aurora, CO (80015) than anticipated, while Phoenix, AZ (85048) and Tempe, AZ (85284) represent in-state locations with higher correlations between the variables included, especially regarding distance decay, and the than expected numbers of freshmen. The regression also indicated the existence of strong likelihood of attracting Barrett students.
ContributorsKostanick, Megan Elizabeth (Author) / Rey, Sergio (Thesis director) / Dorn, Ron (Committee member) / Koschinsky, Julia (Committee member) / Barrett, The Honors College (Contributor) / School of Geographical Sciences and Urban Planning (Contributor) / School of Politics and Global Studies (Contributor)
Created2013-05
ContributorsProkofiev, Sergey, 1891-1953 (Composer)
ContributorsProkofiev, Sergey, 1891-1953 (Composer)
ContributorsProkofiev, Sergey, 1891-1953 (Composer)
ContributorsProkofiev, Sergey, 1891-1953 (Composer)
ContributorsProkofiev, Sergey, 1891-1953 (Composer)
ContributorsProkofiev, Sergey, 1891-1953 (Composer)