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Spatial-Temporal Analysis of Barrett Freshmen 2007-2012: Source Area Analysis and Poisson Regression

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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)

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.

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2013-05

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Policy and Place: A Spatial Data Science Framework for Research and Decision-Making

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

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.

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Date Created
2017