The ongoing digitalisation of our everyday lives is leading to a steady increase in so-called Big Data. These data make our lives increasingly traceable and (often unintentionally) publicly discernible. A lot of Big Data is spatial in nature using GPS, spatial language, or checking in at places, making them accessible to spatial analysis. Unsurprisingly, a substantial number of scholarly articles have been published that make use of these novel data, providing a variety of interesting insights into aspects of everyday life that were previously difficult to access on a large scale. However, datasets such as tweets, geotagged photos, and check-ins differ from traditional, scientifically collected counterparts in that they come from platforms designed for mundane use. Main drivers of Big Data generation include communication with peers and friends, curation of digital alter egos, and enhancement of life comfort. The resulting often unscientific nature of said spatial data has implications when it comes to supposedly rigorous, quantitative analyses. Quantitative methods attempt to uncover structures in a nomothetic way. They assume that the information analysed is representative of joint underlying processes, which show uncertainties only due to varying contextual conditions or measurement error. However, Big Data is not collected via calibrated devices, as would be the case with questionnaires or with measuring devices. Rather, these data often reflect subjective mental geographies and thus information that is much closer to the human-geographical concept of intimate place than to the concept of abstract space.
