GIS is bigger than Big Data: The battle of the buzzwords

Have you heard of hyperlocal, cloud computing, and the gig economy? Arguably, these are buzzwords used to describe things that have existed for a long time in a new, exciting way (e.g. services aimed at local markets, outsourcing computational tasks to data centres, and poorly paid, insecure jobs). Wikipedia has a nice, up-to-date list of buzzwords and there is a Marketing Gibberish Generator that shows buzzwords being used randomly in corporate talk. Indeed, buzzwords have the tendency to become over-used, shallow, and ultimately meaningless – blockchain has recently become a proper buzzword when it was weirdly presented as a solution to the migration crisis.

As I argued before re. the so-called Digital Earth, buzzwords are not all bad, as they provide interesting ways to frame problems and technologies as horizons of research. Terms like “big data” help marketers, engineers, journalists, scientists, entrepreneurs, and politicians foster interest (and attract capital) around a rather obvious fact – namely, that nowadays we produce and consume a lot of data in digital forms, and that analyzing this data might reveal something interesting about us. This might come as a surprise to students, but academic research follows fads as much as any other sector, including politics, design, and fashion.

Buzzwords in GIS and geographic data science

In this post, inspired by recent work we have done with Google Trends, I want to take a quantitative look at recent terms that are used to characterise work in GIS and geographic data science. Understanding how popular these terms are can support academic marketing, for example choosing appropriate names for programmes and modules, as well as research. Popular terms can make articles and books more visible in Google Scholar and other search engines.

Google Trends kindly provides data about the relative popularity of search terms and concepts over time and space, allowing us to gather empirical observations about the collective behaviour of billions of search engine users. It is possible to see, for instance, a comparison of terms GIS, Geographic Information Science, and GIScience.

gis, big data, data science, data analytics, geographic data science
Google Trends index (rescaled) for period Dec 2017 – Dec 2018, extracted with Google Trends comparator

Using our R tool Google Trends comparator, I collected search statistics for some terms in our field over the last year (December 2017–December 2018) (see Table 1). While all terms appear to be unambiguous, one should always bear in mind that this data only captures search behaviour, which is only one channel to express interest in a topic. Many interactions will not be visible in searches. For example, I work predominantly in GIScience, but I don’t necessarily google “GIScience” very often.

And the winner is GIS

Surprisingly, “GIS” is still the most popular term by far, outperforming even the more fashionable “big data” and “data science. Despite many efforts by Mike Goodchild et al. to state that there is a geographic information science behind the systems, “geographic information science” and “GIScience” remain niche terms, used only in academic circles–and not popular in search data, as is possible to see in the table. Sadly, the difference between research that uses GIS and research in GIS still escapes many academics.

Other terms, such as “spatial data science”, are slowly emerging and is too early to see which one will (if ever) prevail over “GIScience” in the 21st century. This data shows that, after all, people online still need to know about good-old plain GIS more than other fancy (or clumsy) concoctions that we come up with to promote our field in a crowded attention economy.

EDIT: A colleague suggested that the “gis” searches might contain searches for ArcGIS tools and technical pages. For example, “gis buffer” might be a short for “arcgis buffer”. However, my understanding is that the Google Trends index for “gis” does not include “gis buffer” and other more specific queries.