Luc Anselin, a Fellow of the University Consortium for Geographic Information Science, recently remarked that "GIScience [is] morphing into spatial data science” (Anselin 1027).
Is it really?
Depending on the origin stories you choose, both GIScience and Data Science began to take shape in the 1960s and 70s. Stanford professor David Donoho traces the origins of Data Science to the work of the maverick statistician John Tukey, then Donoho’s undergraduate thesis adviser at Princeton (and one of my own scholar-heroes; hence my choice of stories).
Donoho’s definition of data science as “a superset of the fields of statistics and machine learning which adds some technology for ‘scaling up’ to ‘big data’” belies his skepticism about the hype that surrounds the “contemplated field.” Indeed, Gartner reports that data science and machine learning began reaching the peak of their “hype cycle” in the past year.
Beyond the academy, there is evidence of convergence in the occupations as well. A search on “data scientist” at O*NET Online – the U.S. Department of Labor’s database of occupations – produces "Geospatial Information Scientists and Technologists" and "Remote Sensing Scientists and Technologists" among its top ten search results.
Should GIScience converge with Data Science?