Matching Items (6)

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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

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.

Contributors

Agent

Created

Date Created
  • 2013-05

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Effects of Smart City Infrastructure on Millennials

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

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.

Contributors

Created

Date Created
  • 2015-05

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Open Geospatial Analytics with PySAL

Description

This article reviews the range of delivery platforms that have been developed for the PySAL open source Python library for spatial analysis. This includes traditional desktop software (with a graphical

This article reviews the range of delivery platforms that have been developed for the PySAL open source Python library for spatial analysis. This includes traditional desktop software (with a graphical user interface, command line or embedded in a computational notebook), open spatial analytics middleware, and web, cloud and distributed open geospatial analytics for decision support. A common thread throughout the discussion is the emphasis on openness, interoperability, and provenance management in a scientific workflow. The code base of the PySAL library provides the common computing framework underlying all delivery mechanisms.

Contributors

Created

Date Created
  • 2015-06-01

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Does context matter for the relationship between deprivation and all-cause mortality? The West vs. the rest of Scotland

Description

Background
A growing body of research emphasizes the importance of contextual factors on health outcomes. Using postcode sector data for Scotland (UK), this study tests the hypothesis of spatial heterogeneity

Background
A growing body of research emphasizes the importance of contextual factors on health outcomes. Using postcode sector data for Scotland (UK), this study tests the hypothesis of spatial heterogeneity in the relationship between area-level deprivation and mortality to determine if contextual differences in the West vs. the rest of Scotland influence this relationship. Research into health inequalities frequently fails to recognise spatial heterogeneity in the deprivation-health relationship, assuming that global relationships apply uniformly across geographical areas. In this study, exploratory spatial data analysis methods are used to assess local patterns in deprivation and mortality. Spatial regression models are then implemented to examine the relationship between deprivation and mortality more formally.
Results
The initial exploratory spatial data analysis reveals concentrations of high standardized mortality ratios (SMR) and deprivation (hotspots) in the West of Scotland and concentrations of low values (coldspots) for both variables in the rest of the country. The main spatial regression result is that deprivation is the only variable that is highly significantly correlated with all-cause mortality in all models. However, in contrast to the expected spatial heterogeneity in the deprivation-mortality relationship, this relation does not vary between regions in any of the models. This result is robust to a number of specifications, including weighting for population size, controlling for spatial autocorrelation and heteroskedasticity, assuming a non-linear relationship between mortality and socio-economic deprivation, separating the dependent variable into male and female SMRs, and distinguishing between West, North and Southeast regions. The rejection of the hypothesis of spatial heterogeneity in the relationship between socio-economic deprivation and mortality complements prior research on the stability of the deprivation-mortality relationship over time.
Conclusions
The homogeneity we found in the deprivation-mortality relationship across the regions of Scotland and the absence of a contextualized effect of region highlights the importance of taking a broader strategic policy that can combat the toxic impacts of socio-economic deprivation on health. Focusing on a few specific places (e.g. 15% of the poorest areas) to concentrate resources might be a good start but the impact of socio-economic deprivation on mortality is not restricted to a few places. A comprehensive strategy that can be sustained over time might be needed to interrupt the linkages between poverty and mortality.

Contributors

Created

Date Created
  • 2011-05-12

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Spatiotemporal data mining, analysis, and visualization of human activity data

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

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.

Contributors

Agent

Created

Date Created
  • 2012

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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

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.

Contributors

Agent

Created

Date Created
  • 2017