Investigating Health Context: Using Geospatial Big Data Ecosystem (Preprint)

2021 ◽  
Author(s):  
Timothy Haithcoat ◽  
Chi-Ren Shyu ◽  
Tiffany Young ◽  
Danlu Liu
Keyword(s):  
Big Data ◽  
Population Health ◽  
Spatial Data ◽  
Wide Spectrum ◽  
Digital Health ◽  
Analytical Framework ◽  
Digital Data ◽  
Contextual Knowledge ◽  
Data Ecosystem ◽  
Social Environmental

BACKGROUND Enabling the use of spatial context is vital to understanding today’s digital health problems. Any given location is associated with many different contexts. The strategic transformation of population health, epidemiology, and eHealth studies require vast amounts of integrated digital data. Needed is a novel analytical framework designed to leverage location to create new contextual knowledge. GeoARK, a research resource has the robust, locationally integrated, social, environmental, and infrastructural information to address today’s complex questions, investigate context and to spatially-enable health investigations. GeoARK is different from other GIS resources in that it has taken the layered world of GIS and flattened it into a Big Data table that ties all the data and information together using location and developing its context. OBJECTIVE It is paramount to build a robust spatial data analytics framework that integrates social, environmental, and infrastructural knowledge base to empower health researchers’ use of geospatial context to timely answer population health issues. The goal is two-fold in that it embodies an innovative technological approach and serves to ease the educational burden for health researchers to think spatially about their problems. METHODS A unique analytical tool using location as the key is developed. It allows integration across source, geography, and time to create a geospatial big table with over 162 million individual locations (X-Y points that serve as rows) and 5549 attributes (represented as columns). The concept of context (adjacency, proximity, distance, etc.) has been quantified through geo-analytics and captured as new distance, density, or neighbor attributes within the system. Development of geospatial analytics permit contextual extraction and investigator-initiated eHealth and mHealth analysis across multiple attributes. RESULTS We built a unique geospatial big data ecosystem called Geospatial Analytical Research Knowledgebase (GeoARK). Analytics on this big table occur across resolution groups, sources, and geographies for extraction and analysis of information to gain new insights. Case studies, including telehealth assessment, income inequality and health outcomes disparity, and COVID-19 risk assessment, demonstrate the capability to support robust and efficient geospatial understanding of a wide spectrum of population health questions. CONCLUSIONS This research has identified, compiled, transformed, standardized, and integrated the multifaceted data required to better understand the context of health events within a large location-enabled database. The GeoARK system empowers health professionals to engage more complex research where the synergisms of health and geospatial information will be robustly studied beyond what could be accomplished today. No longer is the need to know how to do geospatial processing an impediment to the health researcher, but rather the development of how to think spatially becomes the greater challenge.

Using informatics to improve healthcare quality

2019 ◽  
Vol 32 (2) ◽  
pp. 425-430
Author(s):  
Ahmed Otokiti
Keyword(s):  
Big Data ◽  
Quality Improvement ◽  
Population Health ◽  
Clinical Decision Making ◽  
Predictive Analytics ◽  
Wide Spectrum ◽  
Healthcare Quality ◽  
Improve Population Health ◽  
Content Type ◽  
Healthcare Quality Improvement

Purpose The purpose of this paper is to provide insights into contemporary challenges associated with applying informatics and big data to healthcare quality improvement. Design/methodology/approach This paper is a narrative literature review. Findings Informatics serve as a bridge between big data and its applications, which include artificial intelligence, predictive analytics and point-of-care clinical decision making. Healthcare investment returns, measured by overall population health, healthcare operation efficiency and quality, are currently considered to be suboptimal. The challenges posed by informatics/big data span a wide spectrum from individual patients to government/regulatory agencies and healthcare providers. Practical implications The paper utilizes informatics and big data to improve population health and healthcare quality improvement. Originality/value Informatics and big data utilization have the potential to improve population health and service quality. This paper discusses the challenges posed by these methods as the author strives to achieve the aims.

scholarly journals Solving Big GIS Projects on Desktop Computers

2019 ◽  
Vol 18 (32) ◽  
pp. 44-62
Author(s):  
Dalibor Bartoněk
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Spatial Data ◽  
Geographical Information Systems ◽  
Information Technologies ◽  
Digital Data ◽  
Geographical Information ◽  
Digital Information ◽  
Big Data Processing ◽  
Data Volume

We are witnessing great developments in digital information technologies. The situation encroaches on spatial data, which contain both attributive and localization features, and this determines their position unequally within an obligatory coordinate system. These changes have resulted in the rapid growth of digital data, significantly supported by technical advances regarding the devices which produce them. As technology for making spatial data advances, methods and software for big data processing are falling behind. Paradoxically, only about 2% of the total volume of data is actually used. Big data processing often requires high computation performance hardware and software. Only a few users possess the appropriate information infrastructure. The proportion of processed data would improve if big data could be processed by ordinary users. In geographical information systems (GIS), these problems arise when solving projects related to extensive territory or considerable secondary complexity, which require big data processing. This paper focuses on the creation and verification of methods by which it would be possible to process effectively extensive projects in GIS supported by desktop hardware and software. It is a project regarding new quick methods for the functional reduction of the data volume, optimization of processing, edge detection in 3D and automated vectorization.

Big Data and Communication Research

2016 ◽  
Author(s):  
Ralph Schroeder
Keyword(s):  
Big Data ◽  
Digital Media ◽  
Interpersonal Communication ◽  
Political Communication ◽  
Mobile Communication ◽  
Analytical Framework ◽  
Digital Data ◽  
Communication Research ◽  
Or Groups ◽  
Filter Bubble

Communication research has recently had an influx of groundbreaking findings based on big data. Examples include not only analyses of Twitter, Wikipedia, and Facebook, but also of search engine and smartphone uses. These can be put together under the label “digital media.” This article reviews some of the main findings of this research, emphasizing how big data findings contribute to existing theories and findings in communication research, which have so far been lacking. To do this, an analytical framework will be developed concerning the sources of digital data and how they relate to the pertinent media. This framework shows how data sources support making statements about the relation between digital media and social change. It is also possible to distinguish between a number of subfields that big data studies contribute to, including political communication, social network analysis, and mobile communication. One of the major challenges is that most of this research does not fall into the two main traditions in the study of communication, mass and interpersonal communication. This is readily apparent for media like Twitter and Facebook, where messages are often distributed in groups rather than broadcast or shared between only two people. This challenge also applies, for example, to the use of search engines, where the technology can tailor results to particular users or groups (this has been labeled the “filter bubble” effect). The framework is used to locate and integrate big data findings in the landscape of communication research, and thus to provide a guide to this emerging area.

An Optimal Framework for Spatial Query Optimization Using Hadoop in Big Data Analytics

2019 ◽  
Vol 12 ◽  
Author(s):  
Pankaj Dadheech ◽  
Dinesh Goyal ◽  
Sumit Srivastava ◽  
Ankit Kumar
Keyword(s):  
Big Data ◽  
Query Optimization ◽  
Spatial Data ◽  
Spatial Information ◽  
Big Data Analytics ◽  
Spatial Query ◽  
Data Process ◽  
Boolean Queries ◽  
Spatial Query Optimization ◽  
Hadoop System

Spatial queries frequently used in Hadoop for significant data process. However, vast and massive size of spatial information makes it difficult to process the spatial inquiries proficiently, so they utilized the Hadoop system for process Big Data. We have used Boolean Queries & Geometry Boolean Spatial Data for Query Optimization using Hadoop System. In this paper, we show a lightweight and adaptable spatial data index for big data which will process in Hadoop frameworks. Results demonstrate the proficiency and adequacy of our spatial ordering system for various spatial inquiries.

scholarly journals 4.C. Round table: Joining forces: frameworks for international and multi-sectoral collaborations in health information

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
◽  
Keyword(s):  
Public Health ◽  
Big Data ◽  
Health Information ◽  
Population Health ◽  
Round Table ◽  
Secondary Use ◽  
Use Of Data ◽  
National Initiatives ◽  
Information Research

Abstract Countries have a wide range of lifestyles, environmental exposures and different health(care) systems providing a large natural experiment to be investigated. Through pan-European comparative studies, underlying determinants of population health can be explored and provide rich new insights into the dynamics of population health and care such as the safety, quality, effectiveness and costs of interventions. Additionally, in the big data era, secondary use of data has become one of the major cornerstones of digital transformation for health systems improvement. Several countries are reviewing governance models and regulatory framework for data reuse. Precision medicine and public health intelligence share the same population-based approach, as such, aligning secondary use of data initiatives will increase cost-efficiency of the data conversion value chain by ensuring that different stakeholders needs are accounted for since the beginning. At EU level, the European Commission has been raising awareness of the need to create adequate data ecosystems for innovative use of big data for health, specially ensuring responsible development and deployment of data science and artificial intelligence technologies in the medical and public health sectors. To this end, the Joint Action on Health Information (InfAct) is setting up the Distributed Infrastructure on Population Health (DIPoH). DIPoH provides a framework for international and multi-sectoral collaborations in health information. More specifically, DIPoH facilitates the sharing of research methods, data and results through participation of countries and already existing research networks. DIPoH's efforts include harmonization and interoperability, strengthening of the research capacity in MSs and providing European and worldwide perspectives to national data. In order to be embedded in the health information landscape, DIPoH aims to interact with existing (inter)national initiatives to identify common interfaces, to avoid duplication of the work and establish a sustainable long-term health information research infrastructure. In this workshop, InfAct lays down DIPoH's core elements in coherence with national and European initiatives and actors i.e. To-Reach, eHAction, the French Health Data Hub and ECHO. Pitch presentations on DIPoH and its national nodes will set the scene. In the format of a round table, possible collaborations with existing initiatives at (inter)national level will be debated with the audience. Synergies will be sought, reflections on community needs will be made and expectations on services will be discussed. The workshop will increase the knowledge of delegates around the latest health information infrastructure and initiatives that strive for better public health and health systems in countries. The workshop also serves as a capacity building activity to promote cooperation between initiatives and actors in the field. Key messages DIPoH an infrastructure aiming to interact with existing (inter)national initiatives to identify common interfaces, avoid duplication and enable a long-term health information research infrastructure. National nodes can improve coordination, communication and cooperation between health information stakeholders in a country, potentially reducing overlap and duplication of research and field-work.

scholarly journals DaLiF: a data lifecycle framework for data-driven governments

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Syed Iftikhar Hussain Shah ◽  
Vassilios Peristeras ◽  
Ioannis Magnisalis
Keyword(s):  
Big Data ◽  
Data Management ◽  
Good Governance ◽  
Business Community ◽  
Data Driven ◽  
Governmental Organizations ◽  
Data Lifecycle ◽  
The Government ◽  
Data Ecosystem ◽  
Governance Transparency

AbstractThe public sector, private firms, business community, and civil society are generating data that is high in volume, veracity, velocity and comes from a diversity of sources. This kind of data is known as big data. Public Administrations (PAs) pursue big data as “new oil” and implement data-centric policies to transform data into knowledge, to promote good governance, transparency, innovative digital services, and citizens’ engagement in public policy. From the above, the Government Big Data Ecosystem (GBDE) emerges. Managing big data throughout its lifecycle becomes a challenging task for governmental organizations. Despite the vast interest in this ecosystem, appropriate big data management is still a challenge. This study intends to fill the above-mentioned gap by proposing a data lifecycle framework for data-driven governments. Through a Systematic Literature Review, we identified and analysed 76 data lifecycles models to propose a data lifecycle framework for data-driven governments (DaliF). In this way, we contribute to the ongoing discussion around big data management, which attracts researchers’ and practitioners’ interest.

scholarly journals Assessing and piloting interoperability for population health research

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
F Estupiñán-Romero ◽  
J Gonzalez-García ◽  
E Bernal-Delgado
Keyword(s):  
Case Studies ◽  
Population Health ◽  
Health Research ◽  
Network Architecture ◽  
Digital Health ◽  
Data Extraction ◽  
Health Data ◽  
Research Network ◽  
Technical Solution ◽  
Privacy And Security

Abstract Issue/problem Interoperability is paramount when reusing health data from multiple data sources and becomes vital when the scope is cross-national. We aimed at piloting interoperability solutions building on three case studies relevant to population health research. Interoperability lies on four pillars; so: a) Legal frame (i.e., compliance with the GDPR, privacy- and security-by-design, and ethical standards); b) Organizational structure (e.g., availability and access to digital health data and governance of health information systems); c) Semantic developments (e.g., existence of metadata, availability of standards, data quality issues, coherence between data models and research purposes); and, d) Technical environment (e.g., how well documented are data processes, which are the dependencies linked to software components or alignment to standards). Results We have developed a federated research network architecture with 10 hubs each from a different country. This architecture has implied: a) the design of the data model that address the research questions; b) developing, distributing and deploying scripts for data extraction, transformation and analysis; and, c) retrieving the shared results for comparison or pooled meta-analysis. Lessons The development of a federated architecture for population health research is a technical solution that allows full compliance with interoperability pillars. The deployment of this type of solution where data remain in house under the governance and legal requirements of the data owners, and scripts for data extraction and analysis are shared across hubs, requires the implementation of capacity building measures. Key messages Population health research will benefit from the development of federated architectures that provide solutions to interoperability challenges. Case studies conducted within InfAct are providing valuable lessons to advance the design of a future pan-European research infrastructure.

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