A place for Big Data: Close and distant readings of accessions data from the Arnold Arboretum. Issue 2 (August 2016)

• Record Type: Journal Article
• Authors: Loukissas, Yanni Alexander
• Is Part Of: Big data & society. Volume 3:Issue 2(2016)
• Publication Date: 2016-08
• Subjects: Environmental data -- place -- situated knowledge -- close reading -- distant reading; Big data -- Social aspects -- Periodicals; Social sciences -- Research -- Data processing -- Periodicals; Social sciences -- Research -- Methodology -- Periodicals; Data mining -- Periodicals; 300.28557
• DOI: 10.1177/2053951716661365
• Languages: English
• ISSNs: 2053-9517

Abstract

Place is a key concept in environmental studies and criticism. However, it is often overlooked as a dimension of situatedness in social studies of information. Rather, situatedness has been defined primarily as embodiment or social context. This paper explores place attachments in Big Data by adapting close and distant approaches for reading texts to examine the accessions data of the Arnold Arboretum, a living collection of trees, vines and shrubs established by Harvard University in 1872 (The original interactive data visualizations can be found online:http://www.lifeanddeathofdata.org ). Although it is an early and unconventional example of the phenomenon, there are several reasons that the Arboretum is a useful site for investigating the relationship between Big Data and place. First, the category of place is embedded in a range of data fields used in the Arboretum's records. Second, the Arboretum has long sought to be a place in which scientists and citizens alike can encounter large collections of data firsthand. Third, the place has shaped fluctuations in the daily production of data over the course of the Arboretum's 144 year history. Furthermore, Arboretum data can help us see place in ways not necessarily tied to geolocation. Each of these place attachments suggests a different way in which data can be environmental: by being about, in, from, or generative of place. Taken together, these attachments offer a model for examining other data in relation to their environments. Moreover, the paper contends that rather than being detached from place, as prevailing discourses suggest, Big Data bring together more and further reaching place attachments than data sets of smaller sizes.