Searching maps by words: how machine learning changes the way we explore map collections

Abstract

Large numbers of maps have always been difficult to examine in detail, even now that they are being digitized around the world. But imagine searching digitised map collections by their text content: moving beyond titles or other catalogue fields, you could search every single word that appears on map sheets, as if they were book pages in any of the well-known, full-text-search enabled collections. This experience is now a reality. This piece is a data-driven journey across such experimental “text on maps” searching in the online interface for one of the largest and best-known digital libraries of maps, the David Rumsey Map Collection. Starting from the search for a single placename, the author discusses potential, as well as the limitations, of this approach, and suggests ways in which this new interface, which brings together the power of machine learning, the beauty of data visualisation, and the interactivity of annotation, can fuel scientific curiosity as well as playful exploration.