Population informatics

The field of population informatics is the systematic study of populations via secondary analysis of massive data collections (termed "big data") about people. Scientists in the field refer to this massive data collection as the social genome, denoting the collective digital footprint of our society. Population informatics applies data science to social genome data to answer fundamental questions about human society and population health much like bioinformatics applies data science to human genome data to answer questions about individual health. It is an emerging research area at the intersection of SBEH (Social, Behavioral, Economic, & Health) sciences, computer science, and statistics in which quantitative methods and computational tools are used to answer fundamental questions about our society.

The term was first used in August 2012 when the Population Informatics Research Group was founded at the University of North Carolina at Chapel Hill. The term was first defined in a peer reviewed article in 2013 and further elaborated on in another article in 2014. The first Workshop on Population Informatics for Big Data was held at the ACM SIGKDD conference in Sydney, Australia, in August 2015.

To study social, behavioral, economic, and health sciences using the massive data collections, aka social genome data, about people. The primary goal of population informatics is to increase the understanding of social processes by developing and applying computationally intensive techniques to the social genome data.

Some of the important sub-disciplines are :

Record Linkage, the task of finding records in a dataset that refer to the same entity across different data sources, is a major activity in the population informatics field because most of the digital traces about people are fragmented in many heterogeneous databases that need to be linked before analysis can be done.

Once relevant datasets are linked, the next task is usually to develop valid meaningful measures to answer the research question. Often developing measures involves iterating between inductive and deductive approaches with the data and research question until usable measures are developed because the data were collected for other purposes with no intended use to answer the question at hand. Developing meaningful and useful measures from existing data is a major challenge in many research projects. In computation fields, these measures are often called features.

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