scholarly journals Perils of data-driven equity: Safety-net care and big data’s elusive grasp on health inequality

2020 ◽  
Vol 7 (1) ◽  
pp. 205395172092809
Author(s):  
Taylor M Cruz
Keyword(s):  
Big Data ◽  
Data Collection ◽  
Education Reform ◽  
Large Scale ◽  
Time Window ◽  
Empirical Work ◽  
Safety Net ◽  
Data Driven ◽  
Data Systems ◽  
Social Significance

Large-scale data systems are increasingly envisioned as tools for justice, with big data analytics offering a key opportunity to advance health equity. Health systems face growing public pressure to collect data on patient “social factors,” and advocates and public officials seek to leverage such data sources as a means of system transformation. Despite the promise of this “data-driven” strategy, there is little empirical work that examines big data in action directly within the sites of care expected to transform. In this article, I present a case study on one such initiative, focusing on a large public safety-net health system’s initiation of sexual orientation and gender identity (SOGI) data collection within the clinical setting. Drawing from ethnographic fieldwork and in-depth interviews with providers, staff, and administrators, I highlight three main challenges that elude big data’s grasp on inequality: (1) provider and staff’s limited understanding of the social significance of data collection; (2) patient perception of the cultural insensitivity of data items; and (3) clinic need to balance data requests with competing priorities within a constrained time window. These issues reflect structural challenges within safety-net care that big data alone are unable to address in advancing social justice. I discuss these findings by considering the present data-driven strategy alongside two complementary courses of action: diversifying the health professions workforce and clinical education reform. To truly advance justice, we need more than “just data”: we need to confront the fundamental conditions of social inequality.

The Use of Social Media for Urban Planning

2019 ◽  
pp. 1049-1070
Author(s):  
Fabian Neuhaus
Keyword(s):  
Social Media ◽  
Big Data ◽  
Urban Areas ◽  
Large Scale ◽  
Urban Environments ◽  
Data Systems ◽  
User Management ◽  
Social Nature ◽  
User Data ◽  
Use Of Social Media

User data created in the digital context has increasingly been of interest to analysis and spatial analysis in particular. Large scale computer user management systems such as digital ticketing and social networking are creating vast amount of data. Such data systems can contain information generated by potentially millions of individuals. This kind of data has been termed big data. The analysis of big data can in its spatial but also in a temporal and social nature be of much interest for analysis in the context of cities and urban areas. This chapter discusses this potential along with a selection of sample work and an in-depth case study. Hereby the focus is mainly on the use and employment of insight gained from social media data, especially the Twitter platform, in regards to cities and urban environments. The first part of the chapter discusses a range of examples that make use of big data and the mapping of digital social network data. The second part discusses the way the data is collected and processed. An important section is dedicated to the aspects of ethical considerations. A summary and an outlook are discussed at the end.

Effective Statistical Methods for Big Data Analytics

2017 ◽  
pp. 280-299 ◽  
Author(s):  
Cheng Meng ◽  
Ye Wang ◽  
Xinlian Zhang ◽  
Abhyuday Mandal ◽  
Wenxuan Zhong ◽  
...  
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Knowledge Discovery ◽  
Statistical Methods ◽  
Large Scale ◽  
Big Data Analytics ◽  
Divide And Conquer ◽  
Data Driven ◽  
The Past ◽  
Large Scale Dataset

With advances in technologies in the past decade, the amount of data generated and recorded has grown enormously in virtually all fields of industry and science. This extraordinary amount of data provides unprecedented opportunities for data-driven decision-making and knowledge discovery. However, the task of analyzing such large-scale dataset poses significant challenges and calls for innovative statistical methods specifically designed for faster speed and higher efficiency. In this chapter, we review currently available methods for big data, with a focus on the subsampling methods using statistical leveraging and divide and conquer methods.

scholarly journals Efficient Data Collection using Mobile Sink Scheme in IoT

2019 ◽  
Vol 9 (1) ◽  
pp. 1360-1371
Keyword(s):  
Big Data ◽  
Energy Consumption ◽  
Data Collection ◽  
Large Scale ◽  
Performance Metrics ◽  
Route Planning ◽  
Mobile Sink ◽  
Sensor Nodes ◽  
Delivery Ratio ◽  
Energy Hole

Wireless sensor networks (WSNs) have become increasingly important in the informative development of communication technology. The growth of Internet of Things (IoT) has increased the use of WSNs in association with large scale industrial applications. The integration of WSNs with IoT is the pillar for the creation of an inescapable smart environment. A huge volume of data is being generated every day by the deployment of WSNs in smart infrastructure. The collaboration is applicable to environmental surveillance, health surveillance, transportation surveillance and many more other fields. A huge quantity of data which is obtained in various formats from varied applications is called big data. The Energy efficient big data collection requires new techniques to gather sensor-based data which is widely and densely distributed in WSNs and spread over wider geographical areas. In view of the limited range of communication and low powered sensor nodes, data gathering in WSN is a tedious task. The energy hole is another considerable issue that requires attention for efficient handling in WSN. The concept of mobile sink has been widely accepted and exploited, since it is able to effectively alleviate the energy hole problem. Scheduling a mobile sink with energy efficiency is still a challenge in WSNs time constraint implementation due to the slow speed of the mobile sink. The paper addresses the above issues and the proposal contains four-phase data collection model; the first phase is the identification of network subgroups, which are formed due to a restricted range of communication in sensor nodes in a wide network, second is clustering which is addressed on each identified subgroup for reducing energy consumption, third is efficient route planning and fourth is based on data collection. The two time-sensitive route planning schemes are presented to build a set of trajectories which satisfy the deadline constraint and minimize the overall delay. We have evaluated the performance of our schemes through simulation and compared them with the generic enhanced expectation-maximization (EEM) mobility based scenario of data collection. Simulation results reveal that our proposed schemes give much better results as compared to the generic EEM mobility approach in terms of selected performance metrics such as energy consumption, delay, network lifetime and packet delivery ratio.

scholarly journals A Mixed Methods Systematic Scoping Review of the Use of Big Data in Early Intervention Research: Ethical and Practical Implications

2018 ◽  
Author(s):  
Sondra M Stegenga ◽  
Kelley Munger ◽  
Jane Squires ◽  
Daniel Anderson
Keyword(s):  
Big Data ◽  
Mixed Methods ◽  
Early Intervention ◽  
Scoping Review ◽  
Large Scale ◽  
Research Evaluation ◽  
Developmentally Appropriate ◽  
Data Systems ◽  
Ethical Practices ◽  
Department Of Health

Big data holds immense potential for innovation and new understanding in research, evaluation, practice, and policy related to young children and their families. Although big data is a relatively new concept, particularly in early intervention systems (EI), recent pushes for data systems alignment in EI and education have propelled the use of large-scale integrated data systems in recent years (U.S. Department of Health and Human Services & the U.S. Department of Education, 2016). This combined with a plethora of new and rapidly increasing data sources has created a new data world. In response, research methodology, ethics, and tools need to be examined to ensure developmentally appropriate and ethical practices in research. A mixed methods systematic scoping review was conducted to gain a foundational understanding of the literature on big data use in EI settings. Strengths, challenges, systems-level needs, and implications for researchers, administrators, and policy makers are included.

The Use of Social Media for Urban Planning

2014 ◽  
pp. 113-134 ◽  
Author(s):  
Fabian Neuhaus
Keyword(s):  
Social Media ◽  
Big Data ◽  
Urban Areas ◽  
Large Scale ◽  
Urban Environments ◽  
Data Systems ◽  
User Management ◽  
Social Nature ◽  
User Data ◽  
Use Of Social Media

User data created in the digital context has increasingly been of interest to analysis and spatial analysis in particular. Large scale computer user management systems such as digital ticketing and social networking are creating vast amount of data. Such data systems can contain information generated by potentially millions of individuals. This kind of data has been termed big data. The analysis of big data can in its spatial but also in a temporal and social nature be of much interest for analysis in the context of cities and urban areas. This chapter discusses this potential along with a selection of sample work and an in-depth case study. Hereby the focus is mainly on the use and employment of insight gained from social media data, especially the Twitter platform, in regards to cities and urban environments. The first part of the chapter discusses a range of examples that make use of big data and the mapping of digital social network data. The second part discusses the way the data is collected and processed. An important section is dedicated to the aspects of ethical considerations. A summary and an outlook are discussed at the end.

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