Key Note Address at Institution of Engineers, Hyderabad
Open Innovation for Exploitation of Power of Geospatial Tools and Big data Analytics -A case of Design to Public Health Data Management.
I V MURALIKRISHNA PhD(IISc) Fellow-Institution of Engineers, Fellow-Institution of Surveyors Dr Raja Ramanna Distinguished DRDO Fellow Coordinator, National Geo Health Networking Project ivm@ieee.org +91 9848049624
EXECUTIVE SUMMARY
Geospatial Tools Public health, being a geographic issue, finds many solutions in geospatial technology and epidemiology. There is great need to develop Geospatial technological tools to address various aspects related to public health care.
Geospatial technology tools basically deal with information systems for capturing, storing, analyzing, managing and presenting spatially referenced data (linked to location). Public health being a geographic issue and related to geospatial analyses finds many solutions in epidemiology. Geospatial epidemiology is defined as “the description and analysis of geographic variations in disease with respect to demographic, environmental, behavioral, socioeconomic, genetic, and infectious risk factors”. Thus there are quite a few issues that could be analyzed using the tools of information technology, in general and geospatial technology in particular, which would facilitate public health policy formulation and planning, implementation and monitoring of appropriate interventions by regulatory authorities
It is further envisaged that the integration of Geospatial analysis and modeling would strengthen the Health Management Information Systems. The Health-Data Management Systems networking program approved by Government of India comprises of pilot studies in different parts of the country addressing the following issues:
• Methods for disease and Risk mapping • Spatial patterns of diseases • Hotspot detection of diseases • Spatial diffusion of disease outbreak • Road map for Spatial Epidemiological Model • Geospatial analysis and visualization • Geospatial public health Interoperability • Location based hazard vulnerability assessment.
Geo Analytics and Big Data Geography and spatial relationship is a significant tool in terms of outcomes research, comparative clinical effectiveness, and evidence-based medicine. As early as the 1840s, Dr. John Snow used a map to track cholera deaths in the Soho district of London from a contaminated water source – the now infamous Broad Street pump. Dr. Snow is widely considered to be the father of modern epidemiology. There is much potential for using maps and geo-analytics as a tool for healthcare predictive analysis. The term Big data seems to be everywhere. Bloggers write about it, vendors describe their support for it, and many companies talk about what they have done with it. It seems like everyone has something to say about it, and it certainly is a trendy topic for discussion in the technology world. In 2001, Doug Laney authored a report about data management and described three characteristics of data that push traditional data related techniques and capabilities to their limits: volume, velocity, and variety. In addition to high volume, high velocity, and high variety, big data can also mean deeper examination and analysis of the data you already have.
We need to examine each one of these facets of big data, and how they apply in healthcare. Historically, hardware speed and capacity have grown quickly while simultaneously dropping in price, a trend expected to continue. Modern operating systems and databases have removed many technical limits such as maximum database size, and the remaining hard boundaries are so large that most organizations will never approach the maximums. Hadoop, a leading big data engine, is open source and has no licensing costs. It is designed to run on commodity hardware, which reduces the initial capital expense of deploying a system. Careful consideration should be given to the capacity, technology, staffing, and cost tradeoffs between traditional database engines and big data tools. In near future, the predictive geospatial analysis is going to become the essential for many applications including public health. Big data will really become valuable to healthcare in what’s known as the Internet of Things which is a growing network of everyday objects from industrial machines to consumer goods that can share information and complete tasks while the public are busy with other activities, like work, sleep, or exercise. Soon, our cars, our homes, our major appliances, and even our city streets will be connected to the Internet–creating this network of objects that is called the Internet of Things, or IoT for short. Made up of millions of sensors and devices that generate incessant streams of data, the IoT can be used to improve our lives and our businesses in many ways. By giving the user an access to an excessive amount of interactive geospatial big data as per user needs, it allows the people take more informed and hence, smarter decisions. Predictive analysis, in the case of Geospatial Information systems, includes the analysis of spatial patterns and trends as per location. By developing the predictive models, it can be turned into a fairly impressive decision-making tool.
The data used for predictive analysis is generally demographics, location data, infrastructure and environmental conditions and addresses some of the following pertinent issues:
§ Predicting the paths, locations, feasible travel routes and more, as per your requirements. § Allows faster deployment or iterative analysis. § Automated analysis on the basis of already-available old data. § Using the right tools, sharing the workflows and reusing them will be easily achievable. § Quick and easy integration with your industry practices.
The process of predictive analysis starts with loading, saving or creating the queries.Through these queries, you will be able to find out the locations and their related analytics. The results of these queries are used in predicting the usefulness of future events or activities. Several challenges with big data have yet to be addressed in the current big data distributions. Two roadblocks to the general use of big data in healthcare are the technical expertise required to use it and a lack of robust, integrated security surrounding it. The value for big data in healthcare today is largely limited to research because using big data requires a very specialized skill set. In fact, most organizations need geospatial big data scientists to manipulate and get information out of a big data environment.
References Institute for Healthcare Improvement Triple Aim. Retrieved from http://www.ihi.org, 2013. Institute of Medicine. (1988). The future of public health. Washington, D.C: National Academy Press. Kindig, David & Stoddard, Greg