Framework For Spatiotemporal Visualization of Community Health In a Smart And Connected Community (SCC)

Smart and Connected Community (SCC) will use health data of the community members for knowledge generation beyond mobile health (mHealth). Current mHealth only assists individual users to monitor their health status, but do not allow integration and interpretation of collective health data. The objective of this thesis is to exhibit the continuous health status of the community members through a framework of visualization including spatial and temporal plots, such as anonymous user health severity graph, severity flow plot, a severity map view, the cumulative and segmented animation. The framework composes of physiological data collection with smartphones and sharing of anonymous data to SCC health server. Physiological data is sent from the smartphone app in JSON (JavaScript Object Notation) format and stored in the server database. Temporal visualization is presented as graph and flow, whereas spatial visualization utilizes Google Map overlay to display the severity distribution through the color code of severity. Furthermore, an animation mode is developed that displays combined spatiotemporal data over the selected duration in either cumulative or segmented at specified intervals. To implement this, a web-based dynamic server is used. The front end of the server is built with JavaScript JQuery and Ajax, whereas the backend of the server is managed by Hypertext Preprocessor, i.e. PHP, a server-side scripting language. The phpMyAdmin (administration tool for MySQL) stores the JSON data that comes from the smartphone app. To assess the framework, we utilized the MIT-BIH database with pre-recorded data from Arrhythmia patients. We assume each dataset record as a community member (subject). From these records, we classified arrhythmia and measure severity ranging from 0 to 100 considering various severity of arrhythmia (e.g. ventricular tachycardia is the most severe). These data are then randomized to a different location and fed to the visualization tool for functionally verify and assess the performance of the visualization tool. Furthermore, a survey was conducted to collect feedback about the visualization tool that shows that 81.4% participants in pre-session and 84.75% in post-session provided positive feedback about the visualization of health data. By using this framework, community members can generate collective knowledge that might assist community stakeholders such as the Health Department to improve community health by identifying health issues, developing strategies, and resource allocation

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

abstract: 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.Dissertation/ThesisDoctoral Dissertation Geography 201

A spatial data analysis infrastructure for environmental health research

none6noIn spatial health research, it is necessary to consider not only the spatial-temporal patterns of diseases, but also external environmental factors, such as the effects of climate change on air quality, that may influence the insurgence or progression of diseases (e.g. respiratory and cardiovascular diseases, cancer, male human infertility, etc.). In this paper, we propose a Spatial Data analysis Infrastructure (SDI) for the analysis of health pathologies related to environmental factors and, more specifically, to climate change. The main goal is the development of a new methodology to predict and manage health risks, finding correlations between diseases and air pollution due to climatic factors. The presented SDI consists of different modules. A gynecological-obstetrical clinical folder application has been developed to collect and manage clinical data. Anonymous and geo-referenced patients data are extracted from the clinical folder application and data mining techniques, such as a hot spot analysis based on the Getis-Ord Gi∗ statistics, have been applied to the gathered data by exploiting the Hadoop framework. The results of the analysis are displayed in a web application that provides data visualization through geographical maps, using Geographical Information Systems (GIS) technology. This prototype, combining big data, data mining techniques, and GIS technology, represents an innovative approach to find correlations between spatial environmental factors and the insurgence of health diseases.Mirto, Maria; Fiore, Sandro; Conte, Laura; Bruno, Luisa, Vittoria; Aloisio, GiovanniMirto, Maria; Fiore, Sandro; Conte, Laura; Bruno, Luisa; Vittoria, ; Aloisio, Giovann