Geography and computers: Past, present, and future

Abstract

The discipline of Geography has long been intertwined with the use of computers. This close interaction is likely to increase with the embeddedness of computers and concomitant growth of spatially referenced data. To better understand the current situation, and to be able to better speculate about the future, this article provides two parallel perspectives: first, we offer an historical perspective on the relationship between Geography and computers; second, we document developments—in particular the nascent field of data science—that are currently taking place outside of Geography and to which we argue the discipline should be paying close attention. Combining both perspectives, we identify the benefits of tighter integration between Geography and Data Science and argue for the establishment of a new space—that we term Geographic Data Science—in which cross‐pollination could occur to the benefit of both Geography and the larger data community.

Geographical Analysis: Reflections of a Recovering Editor

“…As they seek to conquer the world, some newly minted data scientists are ignoring the hard‐learned lessons about the unique nature of geographic data that academic quantitative geographers have to offer. As a result, their data science is repeating mistakes we made decades ago Arribas‐Bel and Reades (2018).…”

The right tools for the job: The case for spatial science tool‐building

“…Despite urban science's recent bold claims to a "new kind of science," urban geographers, sociologists, and planners have of course long investigated cities' patterns and processes through spatial data, mathematical models, and the scientific method (Batty, 1971(Batty, , 1980Behrend & Levin-Keitel, 2020;Bertuglia, Bianchi, & Mela, 1998;Branch, 1966; Burgess, 1925;Derudder & van Meeteren, 2019;Hoyt, 1951;Johannesen, Olaisen, & Olsen, 1998;Lee, 1973Lee, , 1994O'Sullivan & Manson, 2015). Computational geography itself now has a long history, yet, too often, geographic science and domain theory fail to fully permeate our computational tools (Arribas-Bel & Reades, 2018;Gahegan, 1999Gahegan, , 2018Gahegan, , 2020Harris et al, 2017;Singleton & Arribas-Bel, 2019).…”

An Online Geographic Data Visualization Tool to Relate Preterm Births to Environmental Factors

“…Being able to explore and visualize multilevel and multifactor risks for preterm birth may lead to new mechanistic hypotheses (14), and allow researchers, clinicians, and community health organizations to work with patients in the context of population-level patterns (15). For such a visualization tool to be useful it must be easy to use and immediately accessible, preferably through an online platform; it must leverage ESDA and geographic data science exploratory tools (16) and must have an integrated data structure that includes medical, behavioral, social, and environmental factors.…”