As an IT-based GIS analyst for the final 30 years, I’ve spent a number of time working with and excited about information — how we gather information, how we create context by turning information into data, after which how we flip that data into information by analyzing and decoding it by way of GIS. In the final 12 months or so, I’ve transitioned right into a primarily information governance function in public well being. Along with with the ability to depart my generalist methods behind, I’ve needed to get with the occasions and develop into extra educated about large information and GIS, and the way they affect public well being.
The Three Vs
Big data are advanced datasets so large that the conventional programs and software program many people utilized in the previous are much less and fewer related. As an alternative of databases, we speak about “data lakes” and “data lakehouses,” pure language processing as an alternative of machine studying algorithms. Databases and machine studying are nonetheless — and can proceed to be — a part of the IT lexicon, however these of us who began in the early days of GIS should assume exponentially bigger! There are extra sorts of knowledge (selection), arriving in rising quantities (volumes), a lot sooner than ever earlier than, typically in actual time (velocity).
Syndromic Data is Big Data.
Documenting and mapping sickness clusters has been round since a minimum of the 1850s. In the mid-Nineteen Nineties, there was a push in public well being to determine sickness clusters earlier than they turned bigger outbreaks. As the menace of bioterrorism turned extra prevalent in the United States and round the world, public well being professionals had been searching for strategies to collect information in actual time or close to actual time in order that analysts and investigators would be capable of anticipate occasions, and first responders may put together for giant scale occasions as an alternative of simply reacting.
Syndromic surveillance is the use of nontraditional information sources, like over-the-counter drug gross sales and college absenteeism charges, with extra conventional information sources, like laboratory check outcomes and doctor diagnoses, to know rising public well being occasions. Syndromic surveillance employs numerous analyses, together with information projections and modeling of real-time information, to offer quick data to the epidemiologists and analysts investigating and following up on potential outbreaks.
By the early 2000s, social media had develop into a big a part of human communication, and new sorts of knowledge had been being generated. As an alternative of simply structured information in databases and different information administration instruments, we started to see extra unstructured information in the type of text- and image-based posts, audio information, pdf paperwork, and different qualitative information.
At the similar time, sources of distant sensing information like satellite tv for pc imagery and aerial pictures had been beginning to develop into extra broadly obtainable and might be integrated into syndromic surveillance to know the affect of local weather on well being variables equivalent to vector-borne illness.
What Have Maps Bought to Do With It?
Michael Goodchild, Professor of Geography Emeritus at the College of California, Santa Barbara, mentioned: “… Big data has a role to play in what we might term spatial prediction, or the prediction of where rather than when.”
GIS helps public well being professionals combine information like:
- medical surveillance information from programs like ESSENCE, GeoMedStat, and COVIDcast,
- structured information from hospital emergency departments and pressing care facilities,
- unstructured information from social media, sensor information (all the things from satellite tv for pc imagery to video digicam recordings and public transit), and extra,
whereas languages/environments like R and SAS help map improvement via information cleaning, geocoding, and visualization.
Once you add in the means to watch local weather variability, environmental situations, and their impacts on the dynamics of infectious illnesses, it’s simple to see how GIS and associated applied sciences can have a huge effect on predicting vector-borne illnesses, like malaria and dengue fever, and why it’s important to understanding how these illnesses could affect the public.
GIS provides analysts and epidemiologists the means to create context in public well being information, turning data into not simply information however actionable information, giving public well being analysts the means to make fully-formed, real-time selections that may considerably enhance outcomes for the individuals they serve.