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

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Policy and Place :/
其他題名:	A Spatial Data Science Framework for Research and Decision-Making.
作者:	Kolak, Marynia Aniela.
面頁冊數:	1 online resource (204 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-01(E), Section: A.
Contained By:	Dissertation Abstracts International79-01A(E).
標題:	Geography. -
電子資源:	click for full text (PQDT)
ISBN:	9780355159899

Policy and Place : = A Spatial Data Science Framework for Research and Decision-Making.
Kolak, Marynia Aniela.

Policy and Place :A Spatial Data Science Framework for Research and Decision-Making. - 1 online resource (204 pages)

Source: Dissertation Abstracts International, Volume: 79-01(E), Section: A.

Thesis (Ph.D.)

Includes bibliographical references

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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355159899Subjects--Topical Terms:

654331
Geography.
Index Terms--Genre/Form:

554714
Electronic books.

Policy and Place : = A Spatial Data Science Framework for Research and Decision-Making.
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504 $a Includes bibliographical references
520 $a 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.
520 $a 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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