Applied CyberGIS in the Age of Complex Spatial Health Data

Abstract

GIScience 2016 Short Paper Proceedings Applied CyberGIS in the Age of Complex Spatial Health Data M. M. Jankowska 1 , J. Schipperijn 2 , J. Kerr 1 , I. Altintas 1 University of California San Diego, 9500 Gilman Drive La Jolla CA 92093-0811 USA Email: {majankowska; jkerr}@ucsd.edu; altintas@sdsc.edu University of Southern Denmark, Campusvej 55 Odense 5230 Denmark Email: jschipperijn@health.sdu.dk Abstract Advances in data acquisition in a number of fields throughout the health spectrum are resulting in large, complex, and diverse data sets. With proliferation of sensor and spatial data acquisition, the analysis and processing of complex spatial health data analytics will become a pressing problem. CyberGIS can offer solutions in this realm, however no systems have been developed that cater to the specific challenges associated with complex spatial health data such as privacy, real-time analytics, data standardization, data integration, workflow provenance, and a front end interface that is accessible to individuals in the public health realm. In this paper we present SPACES, an in-development CyberGIS to address some of these challenges. We discuss the architecture, and present two test case examples of utilizing SPACES for understanding environmental influences on physical activity. 1. Introduction Public health, health care, and medical advances are increasingly looking to the collection of voluminous data sets. Movements such as Quantified Self, where individuals engage in self- tracking of biological, physical, behavioural, and environmental information (Fawcett 2015), or the Precision Medicine Initiative Cohort announced by President Obama in 2015, which will include extensive data collection and tracking of over one million U.S. participants (Ashley 2015), are driving the need to develop cyberinfrastructures and methodologies for big and complex spatial health data integration, processing, and analysis. Complex spatial health data may include temporally-linked objective measures of behavior and health, molecular data such as genomics, proteomics, or metabolomics, life course data including movement trajectories, self-reported survey and demographic data, environmental data assessing exposures, and finely resolved spatial data. CyberGIS may assist in addressing the challenges of complex spatial health dat. It represents a new-generation GIS based on the synthesis of advanced cyberinfrastructure, geographic information science, and spatial analysis and modeling (Wang 2010). The field of CyberGIS is rapidly developing and the US National Science Foundation funded a 5-year major initiative titled ‘CyberGIS Software Integration for Sustained Geospatial Innovation’ (http://cybergis.org). However, advances in the application of CyberGIS to health specific problems are lacking. Goldberg et al. (2013) presented a comprehensive vision of a Spatial- Health CyberGIS Marketplace, which tackled many of the opportunities as well as challenges of a health focused CyberGIS including confidentiality and privacy protections, real-time analytic methods, data standardization, and a comprehensive end-to-end ecosystem architecture. We would add to this list the need for shareable workflows to promote inter- field collaboration, diverse data type integration, and replicability of analytic processes. Recently, the NSF funded a platform called DELPHI (Data E-platform for personalized population health) (Katsis et al. 2013), one of the first cyberinfrastructures in development specifically catered toward meeting the goals of integrating complex and diverse health data